From 860176e62319eebaeca0a70f717e26e0c9e93aee Mon Sep 17 00:00:00 2001 From: Bharath Ramaswamy Date: Tue, 14 Jan 2025 02:37:58 +0000 Subject: [PATCH] Added documentation for release 2.0.0 Signed-off-by: Bharath Ramaswamy --- releases/2.0.0/_images/accuracy_list.png | Bin 0 -> 242664 bytes releases/2.0.0/_images/adaround.png | Bin 0 -> 11474 bytes releases/2.0.0/_images/channel_pruning_1.png | Bin 0 -> 27278 bytes releases/2.0.0/_images/compression_flow.png | Bin 0 -> 55762 bytes .../2.0.0/_images/compression_use_case.PNG | Bin 0 -> 46589 bytes releases/2.0.0/_images/cp_2.png | Bin 0 -> 58390 bytes releases/2.0.0/_images/cp_3.jpg | Bin 0 -> 68184 bytes releases/2.0.0/_images/cp_4.jpg | Bin 0 -> 70953 bytes .../2.0.0/_images/cross_layer_scaling.png | Bin 0 -> 88402 bytes releases/2.0.0/_images/greedy_2.png | Bin 0 -> 17884 bytes releases/2.0.0/_images/greedy_3.png | Bin 0 -> 8253 bytes releases/2.0.0/_images/greedy_4.jpg | Bin 0 -> 50524 bytes releases/2.0.0/_images/greedy_5.jpg | Bin 0 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+ + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

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    AIMET
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    Source code for aimet_common.defs

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2019-2023, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+
+""" Common type definitions that are used across aimet """
+
+import io
+from enum import Enum
+from typing import Union, Callable, Any, Optional, Dict, List
+from decimal import Decimal
+
+from aimet_common.layer_database import Layer
+import aimet_common.libpymo as libpymo
+
+
+# supported quantization schemes
+
    
+[docs]
+class QuantScheme(Enum):
+    """ Enumeration of Quant schemes"""
+
+    post_training_tf = 1
+    """ For a Tensor, the absolute minimum and maximum value of the Tensor are used to compute the Quantization
+    encodings. """
+    post_training_tf_enhanced = 2
+    """ For a Tensor, searches and selects the optimal minimum and maximum value that minimizes the Quantization Noise.
+    The Quantization encodings are calculated using the selected minimum and maximum value. """
+    training_range_learning_with_tf_init = 3
+    """ For a Tensor, the encoding values are initialized with the post_training_tf scheme. Then, the encodings are
+    learned during training. """
+    training_range_learning_with_tf_enhanced_init = 4
+    """ For a Tensor, the encoding values are initialized with the post_training_tf_enhanced scheme. Then, the encodings
+    are learned during training. """
+    training_range_learning = 5
+    post_training_percentile = 6
+    """ For a Tensor, adjusted minimum and maximum values are selected based on the percentile value passed.
+    The Quantization encodings are calculated using the adjusted minimum and maximum value."""
    
+
+
+MAP_QUANT_SCHEME_TO_PYMO = {QuantScheme.post_training_tf: libpymo.QuantizationMode.QUANTIZATION_TF,
+                            QuantScheme.post_training_tf_enhanced:
+                                libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED,
+                            QuantScheme.training_range_learning_with_tf_init:
+                                libpymo.QuantizationMode.QUANTIZATION_TF,
+                            QuantScheme.training_range_learning_with_tf_enhanced_init:
+                                libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED,
+                            QuantScheme.post_training_percentile:
+                                libpymo.QuantizationMode.QUANTIZATION_PERCENTILE}
+MAP_ROUND_MODE_TO_PYMO = {'nearest': libpymo.RoundingMode.ROUND_NEAREST,
+                          'stochastic': libpymo.RoundingMode.ROUND_STOCHASTIC}
+
+RANGE_LEARNING_SCHEMES = {QuantScheme.training_range_learning_with_tf_init,
+                          QuantScheme.training_range_learning_with_tf_enhanced_init}
+
+
+class ActivationType(Enum):
+    """ Enums to identify activation type"""
+    no_activation = 0
+    """ No activation """
+
+    relu = 1
+    """ ReLU activation """
+
+    relu6 = 2
+    """ ReLU6 activation """
+
+    def __eq__(self, other: "ActivationType"):
+        return self.value == other.value and self.name == other.name # pylint: disable=comparison-with-callable
+
+
+
    
+[docs]
+class CostMetric(Enum):
+    """ Enumeration of metrics to measure cost of a model/layer """
+
+    mac = 1
+    """ MAC: Cost modeled for compute requirements """
+
+    memory = 2
+    """ Memory: Cost modeled for space requirements """
    
+
+
+
+
    
+[docs]
+class CompressionScheme(Enum):
+    """ Enumeration of compression schemes supported in aimet """
+
+    weight_svd = 1
+    """ Weight SVD """
+
+    spatial_svd = 2
+    """ Spatial SVD """
+
+    channel_pruning = 3
+    """ Channel Pruning """
    
+
+
+
+class RankSelectScheme(Enum):
+    """ Enumeration of rank selection schemes supported in aimet """
+
+    greedy = 1
+    """ Greedy scheme"""
+
+
+class LayerCompRatioPair:
+    """
+    Models a pair of (layer: nn.Module, CompRatio: Decimal)
+    """
+
+    def __init__(self, layer: Layer, comp_ratio: Union[Decimal, None]):
+        """
+        Constructor
+        :param layer: Reference to layer
+        :param comp_ratio: Comp-ratio as a floating point number between 0 and 1
+        """
+        self.layer = layer
+        self.comp_ratio = comp_ratio
+
+    def __str__(self):
+        return 'LayerCompRatioPair: layer={}, comp-ratio={}'.format(self.layer.name, self.comp_ratio)
+
+
+class LayerCompRatioEvalScore:
+    """
+    Models data element with (layer: nn.Module, CompRatio: Decimal, EvalScore: Decimal) attributes
+    """
+
+    def __init__(self, layer: Layer, comp_ratio: Union[Decimal, None], eval_score: Optional[Union[Decimal, None]]):
+        """
+        Constructor
+        :param layer: Reference to layer
+        :param comp_ratio: Comp-ratio as a floating point number between 0 and 1
+        :param eval_score: Eval score as floating point number
+        """
+        self.layer = layer
+        self.comp_ratio = comp_ratio
+        self.eval_score = eval_score
+
+    def __str__(self):
+        return 'LayerCompRatioEvalScore: layer={}, comp-ratio={}, eval_score={}'. \
+            format(self.layer.name, self.comp_ratio, self.eval_score)
+
+
+EvalFunction = Callable[[Any, Optional[int], bool], float]
+
+
+
    
+[docs]
+class GreedySelectionParameters:
+    """
+    Configuration parameters for the Greedy compression-ratio selection algorithm
+
+    :ivar target_comp_ratio: Target compression ratio. Expressed as value between 0 and 1.
+            Compression ratio is the ratio of cost of compressed model to cost of the original model.
+    :ivar num_comp_ratio_candidates: Number of comp-ratio candidates to analyze per-layer
+            More candidates allows more granular distribution of compression at the cost
+            of increased run-time during analysis. Default value=10. Value should be greater than 1.
+    :ivar use_monotonic_fit: If True, eval scores in the eval dictionary are fitted to a monotonically increasing
+            function. This is useful if you see the eval dict scores for some layers are not monotonically increasing.
+            By default, this option is set to False.
+    :ivar saved_eval_scores_dict: Path to the eval_scores dictionary pickle file that was
+            saved in a previous run. This is useful to speed-up experiments when trying
+            different target compression-ratios for example. aimet will save eval_scores
+            dictionary pickle file automatically in a ./data directory relative to the
+            current path. num_comp_ratio_candidates parameter will be ignored when this option is used.
+    """
+
+    def __init__(self,
+                 target_comp_ratio: float,
+                 num_comp_ratio_candidates: int = 10,
+                 use_monotonic_fit: bool = False,
+                 saved_eval_scores_dict: Optional[str] = None):
+
+        self.target_comp_ratio = target_comp_ratio
+
+        # Sanity check
+        if num_comp_ratio_candidates < 2:
+            raise ValueError("Error: num_comp_ratio_candidates={}. Need more than 1 candidate for "
+                             "Greedy compression-ratio selection".format(num_comp_ratio_candidates))
+
+        self.num_comp_ratio_candidates = num_comp_ratio_candidates
+        self.use_monotonic_fit = use_monotonic_fit
+        self.saved_eval_scores_dict = saved_eval_scores_dict
    
+
+
+
+class GreedyCompressionRatioSelectionStats:
+    """ Statistics for the greedy compression-ratio selection algorithm """
+
+    def __init__(self, eval_scores_dict: Dict[str, Dict[Decimal, float]]):
+        """
+        Constructor
+        :param eval_scores_dict: Dictionary of {layer_name: {compression_ratio: eval_score}}
+        """
+        self.eval_scores_dictionary = eval_scores_dict
+
+    def __str__(self):
+        stream = io.StringIO(newline='\n')
+        stream.write('\nGreedy Eval Dict\n')
+        layer_dict = self.eval_scores_dictionary
+        for layer in layer_dict:
+            stream.write('    Layer: {}\n'.format(layer))
+
+            for ratio in sorted(layer_dict[layer]):
+                stream.write('        Ratio={}, Eval score={}\n'.format(ratio, layer_dict[layer][ratio]))
+
+        return stream.getvalue()
+
+
+class TarCompressionRatioSelectionStats:
+    """ Statistics for the TAR compression-ratio selection algorithm """
+
+    def __init__(self, layers_comp_ratio_eval_score_per_rank_index):
+        """
+        Constructor
+        :param  layers_comp_ratio_eval_score_per_rank_index: List of [layer_name: compression_ratio: eval_score] params
+        """
+        self.layers_comp_ratio_eval_score_per_rank_index = layers_comp_ratio_eval_score_per_rank_index
+
+    def __str__(self):
+        stream = io.StringIO(newline='\n')
+        stream.write('\nTar Eval table\n')
+        for data_to_print in self.layers_comp_ratio_eval_score_per_rank_index:
+            stream.write('    Layer: {}\n'.format(data_to_print.layer))
+            stream.write('        Ratio={}, Eval score={}\n'.format((data_to_print.comp_ratio),
+                                                                    (data_to_print.eval_score)))
+
+        return stream.getvalue()
+
+
+class CompressionStats:
+    """ Statistics generated during model compression """
+
+    class LayerStats:
+        """ Statistics for every layer in the model that was compressed """
+
+        def __init__(self, name: str, comp_ratio: Decimal):
+            self.name = name
+            self.compression_ratio = comp_ratio
+
+    def __init__(self, base_accuracy: float, comp_accuracy: float,
+                 mem_comp_ratio: Decimal, mac_comp_ratio: Decimal,
+                 per_layer_stats: List[LayerStats],
+                 comp_ratio_select_stats: Union[GreedyCompressionRatioSelectionStats, None]):
+
+        self.baseline_model_accuracy = format(base_accuracy, '.6f')
+        self.compressed_model_accuracy = format(comp_accuracy, '.6f')
+        self.memory_compression_ratio = format(mem_comp_ratio, '.6f')
+        self.mac_compression_ratio = format(mac_comp_ratio, '.6f')
+        self.per_layer_stats = per_layer_stats
+        self.compression_ratio_selection_stats = comp_ratio_select_stats
+
+    def __str__(self):
+
+        stream = io.StringIO(newline='\n')
+        stream.write('**********************************************************************************************\n')
+        stream.write('Compressed Model Statistics\n')
+        stream.write('Baseline model accuracy: {}, Compressed model accuracy: {}\n'
+                     .format(self.baseline_model_accuracy,
+                             self.compressed_model_accuracy))
+        stream.write('Compression ratio for memory={}, mac={}\n'.format(self.memory_compression_ratio,
+                                                                        self.mac_compression_ratio))
+        stream.write('\n')
+        stream.write('**********************************************************************************************\n')
+
+        stream.write('\nPer-layer Stats\n')
+        for layer in self.per_layer_stats:
+            stream.write('    Name:{}, compression-ratio: {}\n'.format(layer.name,
+                                                                       layer.compression_ratio))
+        stream.write('\n')
+        stream.write('**********************************************************************************************\n')
+
+        stream.write('{}\n'.format(self.compression_ratio_selection_stats))
+        stream.write('**********************************************************************************************\n')
+
+        return stream.getvalue()
+
+
+class AdaroundConstants:
+    """ Constants used for Adarounding """
+
+    GAMMA = -0.1
+    ZETA = 1.1
+
+
+class QuantizationDataType(Enum):
+    """ Enumeration of tensor quantizer data types supported """
+    undefined = 0
+    int = 1
+    float = 2
+
+class SupportedKernelsAction(Enum):
+    """ Enumeration to specify the action to apply during supported_kernels validation"""
+    allow_error = 1
+    warn_on_error = 2
+    assert_on_error = 3
+
+
+class QuantDtypeBwInfo:
+    """
+    QuantDtypeBwInfo holds activation dtype/bw and param dtype/bw
+    """
+
+
+    def __init__(self, act_dtype: QuantizationDataType, act_bw: int,
+                 param_dtype: QuantizationDataType = QuantizationDataType.undefined, param_bw: int = 0):
+        """
+        Data class to hold dtype and bw info
+        :param act_dtype: Activation datatype of type QuantizationDataType
+        :param act_bw: Activation bitwidth of type int
+        :param param_dtype: Param datatype of type QuantizationDataType
+        :param param_bw: Param bitwidth of type int
+        """
+        self.act_dtype = act_dtype
+        self.act_bw = act_bw
+        self.param_dtype = param_dtype
+        self.param_bw = param_bw
+        self._validate_inputs()
+
+    def __repr__(self):
+        return f'(activation:({self.act_dtype}, {self.act_bw}) param:({self.param_dtype}, {self.param_bw})'
+
+    def __str__(self):
+        return f'activation:({self.act_dtype}, {self.act_bw}) param:({self.param_dtype}, {self.param_bw})'
+
+    def __eq__(self, other):
+        return self.act_dtype == other.act_dtype and self.act_bw == other.act_bw and \
+               self.param_dtype == other.param_dtype and self.param_bw == other.param_bw
+
+    def _validate_inputs(self):
+        """
+        Validate inputs
+        """
+        if self.param_dtype and self.param_bw:
+            if self.param_dtype == QuantizationDataType.float and self.param_bw not in [16, 32]:
+                raise ValueError(
+                    'float param_dtype can only be used when param_bw is set to 16, not ' + str(self.param_bw))
+
+        if self.act_dtype == QuantizationDataType.float and self.act_bw not in [16, 32]:
+            raise ValueError(
+                'float act_dtype can only be used when act_bw is set to 16, not ' + str(self.act_bw))
+
+    def is_same_activation(self, dtype: QuantizationDataType, bw: int):
+        """
+        helper function to check if activation of the object is same as input
+        :param bw: bitwidth to verify against
+        :param dtype: dtype to verify against
+        """
+        return bw == self.act_bw and dtype == self.act_dtype
+
+    def is_same_param(self, dtype: QuantizationDataType, bw: int):
+        """
+        helper function to check if param of the object is same as input
+        :param bw: bitwidth to verify against
+        :param dtype: dtype to verify against
+        """
+        return bw == self.param_bw and dtype == self.param_dtype
+
+    def get_activation(self) -> tuple:
+        """ getter method for activation candidate"""
+        return self.act_dtype, self.act_bw
+
+    def get_param(self) -> tuple:
+        """ getter method for param candidate"""
+        return self.param_dtype, self.param_bw
+
+
+
    
+[docs]
+class CallbackFunc:
+    """
+    Class encapsulating call back function and it's arguments
+    """
+    def __init__(self, func: Callable, func_callback_args=None):
+        """
+        :param func: Callable Function
+        :param func_callback_args: Arguments passed to the callable function
+        """
+        self.func = func
+        self.args = func_callback_args
    
+
+
+class EncodingType(Enum):
+    """ Encoding type """
+    PER_TENSOR = 0
+    PER_CHANNEL = 1
+    PER_BLOCK = 2
+    LPBQ = 3
+    VECTOR = 4
+
    +
    +
    +
    + +
    + + +
    +
    +
    +
    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
    + Made with Sphinx and @pradyunsg's + + Furo + +
    +
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    + +
    +
    + +
    +
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    +

    Source code for aimet_onnx.adaround.adaround_weight

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2023, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+# pylint: skip-file
+
+""" Top level API for Adaptive Rounding - Post-Training Quantization (PTQ) """
+import copy
+import os
+import tempfile
+import json
+from typing import Tuple, Dict, List, Callable
+from onnx import onnx_pb
+from onnxruntime.quantization.onnx_quantizer import ONNXModel
+from tqdm import tqdm
+
+# Import AIMET specific modules
+from aimet_common import quantsim
+from aimet_common.utils import AimetLogger
+from aimet_common.defs import QuantScheme, QuantizationDataType
+
+from aimet_onnx.adaround.adaround_loss import AdaroundHyperParameters
+from aimet_onnx.adaround.adaround_tensor_quantizer import AdaroundTensorQuantizer
+from aimet_onnx.quantsim import QuantizationSimModel
+from aimet_onnx.qc_quantize_op import OpMode
+from aimet_onnx.meta.utils import get_module_act_func_pair, get_ordered_ops
+from aimet_onnx import utils
+from aimet_onnx.adaround.adaround_optimizer import AdaroundOptimizer
+from aimet_onnx.adaround.utils import ModelData, ModuleInfo
+
+
+logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.Quant)
+
+# The following modules with weights are supported by Adaround
+AdaroundSupportedModules = ['Conv', 'ConvTranspose', 'MatMul', 'Gemm']
+
+
+
    
+[docs]
+class AdaroundParameters:
+    """
+    Configuration parameters for Adaround
+    """
+    def __init__(self, data_loader, num_batches: int,
+                 default_num_iterations: int = None, default_reg_param: float = 0.01,
+                 default_beta_range: Tuple = (20, 2), default_warm_start: float = 0.2,
+                 forward_fn: Callable = None, forward_pass_callback_args = None):
+        """
+        :param data_loader: Data loader
+        :param num_batches: Number of batches to be used for Adaround.
+         A commonly recommended value for this parameter is the smaller value among (1) len(data_loader) and (2) ceil(2000/batch_size)
+        :param default_num_iterations: Number of iterations to adaround each layer.
+         The default value is 10K for models with 8- or higher bit weights, and 15K for models with lower than 8 bit weights.
+        :param default_reg_param: Regularization parameter, trading off between rounding loss vs reconstruction loss.
+         Default 0.01
+        :param default_beta_range: Start and stop beta parameter for annealing of rounding loss (start_beta, end_beta).
+         Default (20, 2)
+        :param default_warm_start: warm up period, during which rounding loss has zero effect. Default 20% (0.2)
+        :param forward_fn: Function to compute encodings for sim
+        :param forward_pass_callback_args: These argument(s) are passed to the forward_pass_callback as-is. Up to
+            the user to determine the type of this parameter. E.g. could be simply an integer representing the number
+            of data samples to use. Or could be a tuple of parameters or an object representing something more complex.
+            If set to None, forward_pass_callback will be invoked with no parameters.
+        """
+        if len(data_loader) < num_batches:
+            raise ValueError(f'Can not fetch {num_batches} batches from '
+                             f'a data loader of length {len(data_loader)}.')
+
+        self.data_loader = data_loader
+        self.num_batches = num_batches
+        self.num_iterations = default_num_iterations
+        self.reg_param = default_reg_param
+        self.beta_range = default_beta_range
+        self.warm_start = default_warm_start
+        self.forward_fn = forward_fn
+        self.forward_pass_callback_args = forward_pass_callback_args
    
+
+
+
+class Adaround:
+    """
+    Weight-rounding mechanism for Post Training Quantization (PTQ)
+    """
+    @classmethod
+    def apply_adaround(cls, model: onnx_pb.ModelProto, params: AdaroundParameters,
+                       path: str, filename_prefix: str, default_param_bw: int = 4,
+                       param_bw_override_list: List[Tuple[str, int]] = None,
+                       ignore_quant_ops_list: List[str] = None,
+                       default_quant_scheme: QuantScheme = QuantScheme.post_training_tf_enhanced,
+                       default_config_file: str = None, use_cuda: bool = True, device: int = 0,
+                       user_onnx_libs: List[str] = None) -> onnx_pb.ModelProto:
+        """
+        Returns model with optimized weight rounding of every module (Conv and Linear) and also saves the
+        corresponding quantization encodings to a separate JSON-formatted file that can then be imported by
+        QuantSim for inference or QAT
+
+        :param model: Model to Adaround
+        :param params: Parameters for Adaround
+        :param path: path where to store parameter encodings
+        :param filename_prefix: Prefix to use for filename of the encodings file
+        :param default_param_bw: Default bitwidth (4-31) to use for quantizing layer parameters
+        :param param_bw_override_list: List of Tuples. Each Tuple is a param name and the corresponding parameter bitwidth
+                                       to be used for that param.
+        :param ignore_quant_ops_list: Ops listed here are skipped during quantization needed for AdaRounding. Do not
+                                      specify Conv and Linear modules in this list. Doing so, will affect accuracy.
+        :param default_quant_scheme: Quantization scheme. Supported options are using Quant Scheme Enum
+                                    QuantScheme.post_training_tf or QuantScheme.post_training_tf_enhanced
+        :param default_config_file: Default configuration file for model quantizers
+        :param use_cuda: If we should use cuda
+        :param device: CUDA device ID
+        :param user_onnx_libs: List of paths to all compiled ONNX custom ops libraries
+        :return: Model with Adarounded weights and saves corresponding parameter encodings JSON file at provided path
+        """
+        # pylint: disable=too-many-arguments
+        # Create Quant sim with given parameters
+        if not isinstance(model, ONNXModel):
+            model = ONNXModel(model)
+
+        quant_sim = QuantizationSimModel(copy.deepcopy(model), quant_scheme=default_quant_scheme,
+                                         default_param_bw=default_param_bw,
+                                         config_file=default_config_file,
+                                         user_onnx_libs=user_onnx_libs,
+                                         use_cuda=use_cuda)
+
+        # For the params in the param_bw_override_list, override the default parameter bitwidths in the QuantSim
+        if param_bw_override_list:
+            cls._override_param_bitwidth(quant_sim, param_bw_override_list)
+
+        if ignore_quant_ops_list:
+            cls._exclude_modules(quant_sim, ignore_quant_ops_list)
+
+        # Compute only param encodings
+        cls._compute_param_encodings(quant_sim, params)
+
+        return cls._apply_adaround(quant_sim, model, params, path, filename_prefix, use_cuda, device, user_onnx_libs)
+
+    @classmethod
+    def _apply_adaround(cls, quant_sim: QuantizationSimModel, model: onnx_pb.ModelProto, params: AdaroundParameters,
+                        path: str, filename_prefix: str, use_cuda: bool = True, device: int = 0,
+                        user_onnx_libs: List[str] = None) -> onnx_pb.ModelProto:
+        """
+        Returns model with optimized weight rounding of every module (Conv and Linear) and also saves the
+        corresponding quantization encodings to a separate JSON-formatted file that can then be imported by
+        QuantSim for inference or QAT
+
+        :param quant_sim: QuantizationSimModel object to optimize weight rounding.
+                          The activation quantizers are expected to have been disabled.
+        :param model: Original fp32 model from which quant_sim was created.
+        :param params: Parameters for Adaround
+        :param path: path where to store parameter encodings
+        :param filename_prefix: Prefix to use for filename of the encodings file
+        :param use_cuda: If we should use cuda
+        :param device: CUDA device ID
+        :param user_onnx_libs: List of paths to all compiled ONNX custom ops libraries
+        :return: Model with Adarounded weights and saves corresponding parameter encodings JSON file at provided path
+        """
+
+        # Sanity check: All the input/output quantizers should be disabled
+        for quantizer_name in quant_sim.activation_names:
+            assert not quant_sim.qc_quantize_op_dict[quantizer_name].enabled
+
+        # Get the module - activation function pair using ConnectedGraph
+        module_act_func_pair = get_module_act_func_pair(model)
+
+        cls._adaround_model(model, quant_sim, module_act_func_pair, params, use_cuda, device, user_onnx_libs)
+
+        # Export quantization encodings to JSON-formatted file
+        cls._export_encodings_to_json(path, filename_prefix, quant_sim)
+
+        quant_sim.remove_quantization_nodes()
+        logger.info('Completed Adarounding Model')
+        return quant_sim.model
+
+    @classmethod
+    def _adaround_model(cls, model: onnx_pb.ModelProto, quant_sim: QuantizationSimModel, module_act_func_pair: Dict,
+                        params: AdaroundParameters, use_cuda: bool = True, device: int = 0, user_onnx_libs: List[str] = None):
+        """
+        Optimize weight rounding of every module (AdaroundSupportedModules) of model in sequential manner
+        based on occurrence
+
+        :param model: Original fp32 model from which quant_sim was created.
+        :param quant_sim: QuantizationSimModel object to optimize weight rounding.
+                          The activation quantizers are expected to have been disabled.
+        :param module_act_func_pair: Dictionary of module to immediate following activation function
+        :param params: Adaround parameters
+        :param use_cuda: If we should use cuda
+        :param device: CUDA device ID
+        :param user_onnx_libs: List of paths to all compiled ONNX custom ops libraries
+        """
+        # pylint: disable=too-many-locals, protected-access
+
+        num_iterations = params.num_iterations
+
+        if num_iterations is None:
+            lowest_weight_bw = 32
+            for param_name in quant_sim.param_names:
+                quantizer = quant_sim.qc_quantize_op_dict[param_name]
+                if quantizer.enabled and quantizer.data_type == QuantizationDataType.int:
+                    lowest_weight_bw = min(lowest_weight_bw, quantizer.bitwidth)
+            # If the lowest wegith bitwidth is < 8, then set num_iterations to 15K by default
+            if lowest_weight_bw < 8:
+                num_iterations = 15000
+            else:
+                num_iterations = 10000
+
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            # Cache model input data to temporary directory
+            cached_dataset = utils.CachedDataset(params.data_loader, params.num_batches, tmp_dir)
+
+            # Optimization Hyper parameters
+            opt_params = AdaroundHyperParameters(num_iterations, params.reg_param, params.beta_range,
+                                                 params.warm_start)
+            param_to_tensor_quantizer_dict = Adaround._create_param_to_tensor_quantizer_dict(quant_sim)
+            model_data = ModelData(model.model)
+            quantized_layer_to_input_tensor_name = Adaround._get_quantized_layer_input_tensor_name(quant_sim)
+            # AdaRound must be applied to modules in the order of occurrence
+            modules = get_ordered_ops(model)
+            for module in tqdm(modules):
+                name = module.name
+                if cls._is_supported_layer_type(model_data.module_to_info[name]):
+                    # Get module's next following activation function
+                    act_func = module_act_func_pair[name]
+                    quantized_input_name = quantized_layer_to_input_tensor_name[name]
+                    logger.info("Started Optimizing weight rounding of module: %s", name)
+                    AdaroundOptimizer.adaround_module(model_data.module_to_info[name], quantized_input_name,
+                                                      model, quant_sim.model, act_func,
+                                                      cached_dataset, opt_params, param_to_tensor_quantizer_dict,
+                                                      use_cuda, device, user_onnx_libs)
+
+    @classmethod
+    def _is_supported_layer_type(cls, module_info: ModuleInfo):
+        if not module_info.type in AdaroundSupportedModules:
+            return False
+
+        if not "weight" in module_info.params:
+            return False
+
+        if module_info.type in ("Conv", "ConvTranspose"):
+            # Only 2d conv/convtranspose is supported
+            return len(module_info.params["weight"].shape) == 4
+
+        return True
+
+    @staticmethod
+    def _compute_param_encodings(quant_sim: QuantizationSimModel, params: AdaroundParameters):
+        """
+        Compute encodings for parameters, needed for initializing Adaround quantizers
+
+        :param quant_sim: Quant sim
+        :param params: Adaround params
+        """
+        for op_name, qc_op in quant_sim.qc_quantize_op_dict.items():
+            if op_name in quant_sim.activation_names:
+                qc_op.enabled = False
+            else:
+                qc_op.op_mode = OpMode.oneShotQuantizeDequantize
+
+        params.forward_fn(quant_sim.session, params.forward_pass_callback_args)
+        for op_name, qc_op in quant_sim.qc_quantize_op_dict.items():
+            if op_name in quant_sim.param_names:
+                qc_op.compute_encodings()
+                qc_op.op_mode = OpMode.quantizeDequantize
+
+    @staticmethod
+    def _create_param_to_tensor_quantizer_dict(quant_sim: QuantizationSimModel) -> Dict[str, AdaroundTensorQuantizer]:
+        """
+        Create Adaround tensor quantizers for weight tensor
+
+        :param quant_sim: Quant sim
+        :return: Dict of param name to AdaroundTensorQuantizer
+        """
+        param_to_tq_dict = {}
+        for param_name in quant_sim.param_names:
+            quantizer = quant_sim.qc_quantize_op_dict[param_name]
+            ch_axis = -1
+            if quantizer.quant_info.usePerChannelMode:
+                ch_axis = quantizer.quant_info.channelAxis
+            adaround_quantizer = AdaroundTensorQuantizer(quantizer.bitwidth, 'Adaptive', quantizer.quant_scheme,
+                                                         quantizer.use_symmetric_encodings, quantizer.enabled, ch_axis)
+
+            adaround_quantizer.use_strict_symmetric = quantizer.use_strict_symmetric
+            adaround_quantizer.use_unsigned_symmetric = quantizer.use_unsigned_symmetric
+
+            # Set the encodings and replace by Adaround tensor quantizer
+            adaround_quantizer.encoding = quantizer.encodings
+            param_to_tq_dict[param_name] = adaround_quantizer
+
+        return param_to_tq_dict
+
+    @classmethod
+    def _export_encodings_to_json(cls, path: str, filename_prefix: str, quant_sim: QuantizationSimModel):
+        """
+        Save Adadrounded module's parameter encodings to JSON file
+
+        :param path: path where to store param encodings
+        :param filename_prefix: filename to store exported weight encodings in JSON format
+        :param quant_sim: QunatSim that contains the model and Adaround tensor quantizers
+        """
+        # pylint: disable=protected-access
+        param_encodings = quant_sim._get_encodings(quant_sim.param_names, quantsim.encoding_version)
+
+        # export encodings to JSON file
+        os.makedirs(os.path.abspath(path), exist_ok=True)
+        encoding_file_path = os.path.join(path, filename_prefix + '.encodings')
+        with open(encoding_file_path, 'w') as encoding_fp:
+            json.dump(param_encodings, encoding_fp, sort_keys=True, indent=4)
+
+    @staticmethod
+    def _override_param_bitwidth(quant_sim: QuantizationSimModel,
+                                 param_bw_override_list: List[Tuple[str, int]]):
+        """
+        For the QuantSim, for the list of modules in the param_bw_override_list,
+        overrides the default parameter bitwidths with the provided bitwidth.
+
+        :param quant_sim: The QuantSim that was created using a deepcopy of the original model.
+        :param param_bw_override_list: List of Tuples. Each Tuple is a param name and the corresponding parameter bitwidth
+                                       to be used for that param.
+        """
+        # For the params specified in the param_bw_override_list, set the weight quantizer bitwidth
+        for (param_name, bw) in param_bw_override_list:
+            quant_sim.qc_quantize_op_dict[param_name] = bw
+
+    @classmethod
+    def _exclude_modules(cls, quant_sim: QuantizationSimModel,
+                         ignore_quant_ops_list: List[str]):
+        """
+        For the modules mentioned in the ignore_quant_ops_list, remove the corresponding quant wrappers from the
+        quantSim and excludes modules from adaround optimization.
+
+        :param model: The original model
+        :param quant_sim: The QuantSim that was created using a deepcopy of the original model.
+        :param ignore_quant_ops_list: The list of quantizers for which the Quantization wrappers are removed from the
+                                      QuantSim object.
+        """
+
+    @staticmethod
+    def _get_quantized_layer_input_tensor_name(sim):
+        quantized_layer_to_input_tensor_name = {}
+        for node in sim.model.model.graph.node:
+            if node.op_type in AdaroundSupportedModules:
+                quantized_layer_to_input_tensor_name[node.name] = node.input[0]
+        return quantized_layer_to_input_tensor_name
+
    +
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    +

    Source code for aimet_onnx.amp.mixed_precision_algo

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2024, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+""" Evaluator class for mixed precision """
+
+import contextlib
+import os
+from collections import defaultdict, OrderedDict
+import pickle
+import functools
+from typing import Any, Callable, Tuple, List, Dict
+import json
+import numpy as np
+import onnxruntime
+
+from aimet_common.utils import AimetLogger, save_json_yaml
+from aimet_common.defs import CallbackFunc
+from aimet_common.amp.mixed_precision_algo import GreedyMixedPrecisionAlgo as MixedPrecisionAlgo
+from aimet_common.amp.quantizer_groups import reformat_supported_kernels
+from aimet_common.amp.utils import (
+    sort_accuracy_list,
+    CANDIDATE_WITH_DTYPE,
+    ACCURACY_LIST,
+    disable_quantizers,
+    enable_quantizers,
+)
+
+from aimet_onnx.amp import utils as mixed_precision_utils
+from aimet_onnx.amp.quantizer_groups import find_quantizer_group, QuantizerGroup, find_supported_candidates
+from aimet_onnx.quantsim import QuantizationSimModel, load_encodings_to_sim
+from aimet_onnx.qc_quantize_op import QcQuantizeOp
+from aimet_onnx.defs import DataLoader
+
+logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.MixedPrecision)
+
+
+@contextlib.contextmanager
+def _disable_all_quantizers(sim: QuantizationSimModel):
+    """
+    Temporarily disable all quantizers in the model within with-as block.
+
+    :param sim: Quantized sim model
+    """
+    active_quantizers = set(quantizer for quantizer in sim.qc_quantize_op_dict.values() if quantizer.enabled)
+
+    try:
+        for quantizer in active_quantizers:
+            quantizer.enabled = False
+        yield
+    finally:
+        for quantizer in active_quantizers:
+            quantizer.enabled = True
+
+
+
    
+[docs]
+class EvalCallbackFactory:
+    """
+    Factory class for various built-in eval callbacks
+    """
+    def __init__(self,
+                 data_loader: DataLoader,
+                 forward_fn: Callable = None):
+        """
+        :param data_loader: Data loader to be used for evaluation
+        :param forward_fn: Function that runs forward pass and returns the output tensor.
+                           This function is expected to take 1) a model 2) List of starting op names
+                           3) List of output op names and 4) batch yielded from the data set, and
+                           return a single tf.Tensor (or np.ndarray) object which represents the output of the model.
+        """
+        self._data_loader = data_loader
+        self._forward_fn = forward_fn or _default_forward_fn
+
+        # storing batchwise fp32 outputs in the list
+        self._batchwise_fp32_outputs_list = []
+
+    def _forward_fn_wrapper(self, *args, **kwargs):
+        output = self._forward_fn(*args, **kwargs)
+        if not isinstance(output, np.ndarray):
+            raise RuntimeError(
+                "Forward pass was expected to return a numpy.ndarray, "
+                f"but returned an object of type {type(output)}. "
+                "Try specifying `forward_fn` to adapt the output."
+            )
+        return output
+
+    _DEFAULT_SQNR_NUM_SAMPLES = 128
+
+
    
+[docs]
+    def sqnr(self, sim: QuantizationSimModel, num_samples: int = _DEFAULT_SQNR_NUM_SAMPLES) -> CallbackFunc:
+        """
+        Returns SQNR eval callback.
+        NOTE: sim object is required to enable/disable quantizer_info objects associated with quant ops.
+
+        :param sim: Quantized sim model
+        :param num_samples: Number of samples used for evaluation
+        :return: A callback function that evaluates model SQNR between fp32_outputs and quantized outputs.
+        """
+        evaluate_sqnr = functools.partial(_evaluate_sqnr,
+                                          sim=sim,
+                                          data_loader=self._data_loader,
+                                          forward_fn=self._forward_fn_wrapper,
+                                          num_samples=num_samples,
+                                          batchwise_fp32_outputs_list=self._batchwise_fp32_outputs_list)
+        return CallbackFunc(evaluate_sqnr)
    
+
    
+
+
+
+def _default_forward_fn(sess, inputs):
+    output_tensors = sess.run(None, {'input': inputs})[0]
+    return output_tensors
+
+
+def _evaluate_sqnr(session: onnxruntime.InferenceSession, _: Any,
+                   sim: QuantizationSimModel,
+                   data_loader: DataLoader,
+                   forward_fn: Callable,
+                   num_samples: int,
+                   batchwise_fp32_outputs_list: list) -> float:
+    """
+    Compute SQNR given a model and a data loader.
+
+    :param session: sim session
+    :param _: Placeholder for CallbackFunc
+    :param sim: Quantization sim model
+    :param data_loader: Data loader to evaluate SQNR from
+    :param forward_fn: Function that runs forward pass and returns the output tensor.
+    :param num_samples: Number of samples used for evaluation
+    :return: SQNR in dB scale
+    """
+    assert sim.session == session, "session associated with sim and session passed to this callback should be same."
+    capture_fp32_output_only_once = False
+    if not batchwise_fp32_outputs_list:
+        capture_fp32_output_only_once = True
+
+    sqnr = 0.0
+    batch_size = data_loader.batch_size or 1
+    for i, x in enumerate(data_loader):
+        if i * batch_size < num_samples:
+            if capture_fp32_output_only_once:
+                with _disable_all_quantizers(sim):
+                    fp32_output = forward_fn(session, x)
+                batchwise_fp32_outputs_list.append(fp32_output)
+            else:
+                fp32_output = batchwise_fp32_outputs_list[i]
+
+            quantized_output = forward_fn(session, x)
+            # Accumulate signal by noise ratio
+            sqnr += _compute_sqnr(fp32_output, quantized_output)
+        else:
+            break
+
+    # Convert SQNR into dB scale
+    sqnr_db = 10 * np.log10(sqnr / num_samples)
+    return sqnr_db
+
+
+def _compute_sqnr(orig_tensor: np.ndarray, noisy_tensor: np.ndarray) -> float:
+    """
+    Compute SQNR between two tensors.
+
+    :param orig_tensor: Original tensor
+    :param noisy_tensor: Noisy tensor
+    :return: SQNR
+    """
+    assert orig_tensor.shape == noisy_tensor.shape
+
+    # SQNR := E[signal**2] / E[noise**2]
+    signal = orig_tensor
+    noise = orig_tensor - noisy_tensor
+    sqnr = (np.power(signal, 2).mean()) / ((np.power(noise, 2).mean()) + 0.0001)
+    return float(sqnr)
+
+
+class GreedyMixedPrecisionAlgo(MixedPrecisionAlgo):
+    """ Naive Greedy MixedPrecisionAlgo class """
+    # pylint: disable=too-many-arguments
+    def __init__(self, sim: QuantizationSimModel,
+                 candidates: List[CANDIDATE_WITH_DTYPE],
+                 eval_callback_for_phase1: CallbackFunc,
+                 eval_callback_for_phase2: CallbackFunc,
+                 results_dir: str, clean_start: bool,
+                 forward_pass_callback: CallbackFunc,
+                 use_all_amp_candidates: bool = False,
+                 phase1_optimize: bool = False):
+        """
+        :param sim: Quantized sim model
+        :param candidates: List of Tuple of all possible [bitwidth, QuantizationDataType] values to quantize to
+        :param eval_callback_for_phase1: An object of CallbackFunc class which takes in Eval function (callable) and eval
+                                     function parameters. This evaluation callback used to measure sensitivity of each
+                                     quantizer group during phase 1. The phase 1 involves finding accuracy list/sensitivity of each
+                                     module. Therefore, a user might want to run the phase 1 with a smaller dataset
+        :param eval_callback_for_phase2: An object of CallbackFunc class which takes in Eval function (callable) and eval
+                                     function parameters. Evaluation callback used to get accuracy of quantized model
+                                     for phase 2 calculations. The phase 2 involves finding pareto front curve
+        :param results_dir: Path to save results and cache intermediate results
+        :param clean_start: If true, any cached information from previous runs will be deleted prior to starting the
+                            mixed-precision analysis. If false, prior cached information will be used if applicable. Note
+                            it is the user's responsibility to set this flag to true if anything in the model or
+                            quantization parameters changes compared to the previous run.
+        :param forward_pass_callback: An object of CallbackFunc class which takes in Forward pass function (callable) and its
+                                  function parameters. Forward pass callback used to compute quantization encodings
+        :param use_all_amp_candidates: Using the “supported_kernels” field in the config file (under defaults
+                    and op_type sections), a list of supported candidates can be specified. All the AMP candidates
+                    which are passed through the “candidates” field may not be supported based on the data passed
+                    through “supported_kernels”. When the field “use_all_amp_candidates” is set to True, the AMP algo
+                    will ignore the "supported_kernels" in the config file and will continue to use all the candidates.
+        :phase1_optimize: If user set this parameter to true then phase1 optimized logic will be executed else default code will be executed
+        """
+        mac_dict = mixed_precision_utils.create_mac_dict(sim)
+        self.phase1_optimize = phase1_optimize
+
+        super().__init__(sim, candidates, eval_callback_for_phase1, eval_callback_for_phase2, forward_pass_callback,
+                         mac_dict, results_dir, clean_start)
+        self._param_name_to_op_name_dict = \
+            mixed_precision_utils.find_param_name_to_parent_name_dict(sim.connected_graph)
+
+        supported_kernels = reformat_supported_kernels(sim.get_supported_kernels())
+
+        self._supported_candidates_per_quantizer_group, self._baseline_candidate_options = find_supported_candidates(
+            self.quantizer_groups,
+            candidates,
+            supported_kernels,
+            mixed_precision_utils.get_quantizer_to_op_type_dict(sim),
+            use_all_amp_candidates)
+
+    def _create_and_save_accuracy_list_optimized(self, baseline_candidate) -> ACCURACY_LIST:
+        """
+        Create a list of tuples of (quantizer_group, bitwidth, accuracy score)
+
+        :param baseline_candidate: Candidate [bitwidth, dtype] which yields max accuracy
+        :return: Sorted accuracy list containing tuples of (quantizer, candidate, accuracy score, bit ops reduction)
+        """
+        # pylint: disable=too-many-locals, too-many-branches, too-many-statements
+        index_of_quantizer_group = {}
+        for index, quantizer_group in enumerate(self.quantizer_groups):
+            index_of_quantizer_group[quantizer_group] = index
+
+        accuracy_list: ACCURACY_LIST = []
+
+        file = os.path.join(self._results_dir, '.cache', 'accuracy_list.pkl')
+        combinations_already_computed = set()
+
+        if os.path.isfile(file):
+            if self._clean_start:
+                os.remove(file)
+                logger.info("Removed old cached files and restarting computation")
+            else:
+                with open(file, 'rb') as f:
+                    accuracy_list = pickle.load(f)
+
+                combinations_already_computed.update(
+                    (quantizer_group, candidate)
+                    for quantizer_group, candidate, _, _ in accuracy_list
+                )
+
+        disabled_quantizers = OrderedDict()
+
+        try:
+            # Disable all quantizers
+            for quantizer_group in self.quantizer_groups:
+                quantizers = quantizer_group.get_active_quantizers(self._module_name_dict)
+                disable_quantizers(quantizers)
+                disabled_quantizers[quantizer_group] = quantizers
+
+            # quantizer_groups_per_candidate = {"candidate1":[quantizer_group1,quantizer_group2,...]}
+            # quantizer_groups_per_candidate is the dictionary with keys as candidates and values as quantizer groups that supports the corresponding candidate
+            # quantizer_groups_per_candidate is like reverse mapping to self._supported_candidates_per_quantizer_group
+            quantizer_groups_per_candidate = defaultdict(list)
+            for quantizer_group, candidates in self._supported_candidates_per_quantizer_group.items():
+                for candidate in candidates:
+                    quantizer_groups_per_candidate[candidate].append(quantizer_group)
+
+            # Loop through all possible bitwidths(candidates). Set all the quantizer groups to the corresponding bitwidth(candidate)
+            # Compute encodings by disabling the parameters and  reuse the encodings
+            for candidate, quantizer_groups in quantizer_groups_per_candidate.items():
+                if candidate == baseline_candidate:
+                    continue
+
+                 # configure the sim model with the candidate by enabling the quantizers and set quantizers to corresponding candidate
+                for quantizer_group in quantizer_groups:
+                    quantizers = disabled_quantizers[quantizer_group]
+                    try:
+                        enable_quantizers(quantizers)
+                        # Set quantizer bitwidth to candidate (bitwidth)
+                        quantizer_group.set_quantizers_to_candidate(self._module_name_dict, candidate)
+                    except RuntimeError as e:
+                        logger.info("Exception occured while setting Quantizers to Candidate: %s", e)
+
+                # list to store all the param quantizers
+                param_quantizers_qgp = []
+
+                for quantizer_group in quantizer_groups:
+                    for quantizer in quantizer_group.get_param_quantizers(self._module_name_dict):
+                        if quantizer.enabled:
+                            param_quantizers_qgp.append(quantizer)
+
+                # compute encodings
+                self._sim.compute_encodings(self.algo_params.forward_pass_callback,
+                                            self.algo_params.forward_pass_callback_args)
+                # export encodings
+                self._export_encodings(self._results_dir)
+
+                # disable the parameter quantization
+                disable_quantizers(param_quantizers_qgp)
+
+                # compute encodings with out parameter quantization
+                self._sim.compute_encodings(self.algo_params.forward_pass_callback,
+                                            self.algo_params.forward_pass_callback_args)
+
+                # export activation encodings
+                self._export_activation_encodings(self._results_dir)
+                # enable the parameter quantization
+                enable_quantizers(param_quantizers_qgp)
+                self._load_param_encodings(self._results_dir)
+
+                # Disable all the quantizers
+                for quantizer_group in quantizer_groups:
+                    quantizers = quantizer_group.get_active_quantizers(self._module_name_dict)
+                    disable_quantizers(quantizers)
+                    disabled_quantizers[quantizer_group] = quantizers
+
+                # Loop over all the quantizer groups and enable one at a time and calculate resulting model accuracy and disable the enabled quantizer
+                # Accuracy list will contain tuples of the quantizer, bitwidth, and accuracy score
+                for quantizer_group in quantizer_groups:
+                    quantizers = disabled_quantizers[quantizer_group]
+                    try:
+                        enable_quantizers(quantizers)
+
+                        # If starting the computation from an already existing state, then check if that combination
+                        # has already been executed
+                        if (quantizer_group, candidate) in combinations_already_computed:
+                            continue
+                        # Compute accuracy of model with new candidate (bitwidth)
+                        eval_score = self.evaluate_model(self.algo_params.eval_callback_for_phase1)
+
+                        bit_ops_reduction = self._find_bit_ops_reduction_for_acc_list(quantizer_group,
+                                                                                      baseline_candidate,
+                                                                                      candidate)
+                        accuracy_list.append((quantizer_group, candidate, eval_score, bit_ops_reduction))
+                        # Sort accuracy list, first by descending accuracy score, then by descending order of addition of bitwidths if accuracy
+                        # scores are identical, if that is also identical we sort by relative bit ops change in descending order
+                        # If bit ops reduction is also the same, then we sort in ascending order based on occurence of
+                        # quantizer group in the model
+                        accuracy_list = sort_accuracy_list(accuracy_list, index_of_quantizer_group)
+                        self._export_accuracy_list(accuracy_list, self._results_dir)
+                        logger.info('\n Quantizer: %s candidate: %s eval_score: %f \n', quantizer_group,
+                                    candidate, eval_score)
+                    finally:
+                        # Disable the quantizer
+                        disable_quantizers(quantizers)
+        finally:
+            # set all quantizers to baseline candidate
+            for quantizer_group in self.quantizer_groups:
+                quantizers = disabled_quantizers[quantizer_group]
+                try:
+                    # Enable the disabled quantizers
+                    enable_quantizers(quantizers)
+                    quantizer_group.set_quantizers_to_candidate(self._module_name_dict, baseline_candidate)
+                except RuntimeError as e:
+                    logger.info("Exception occured while setting Quantizers to Candidate: %s", e)
+
+        logger.info('Completed Accuracy list computation')
+        # Recompute encodings after quantizer's bitwidth is set back to self._max_bitwidth
+        self._sim.compute_encodings(self.algo_params.forward_pass_callback, self.algo_params.forward_pass_callback_args)
+        return accuracy_list
+
+    def _export_encodings(self, path: str):
+        """
+        Export encodings of the sim model to the given path
+
+        :param path: Encodings will store in the given path/.cache folder
+        """
+        results_dir = os.path.join(path, '.cache')
+        if not os.path.exists(results_dir):
+            os.makedirs(results_dir)
+        path = os.path.join(results_dir, 'encodings_with_param.encodings')
+        # pylint: disable=protected-access
+        self._sim._export_encodings(path)
+
+    def _export_activation_encodings(self, path: str):
+        """
+        Export encodings of the sim model to the given path
+
+        :param path: Encodings will store in the given path/.cache folder
+        """
+        results_dir = os.path.join(path, '.cache')
+        if not os.path.exists(results_dir):
+            os.makedirs(results_dir)
+        path = os.path.join(results_dir, 'encodings_with_act.encodings')
+        # pylint: disable=protected-access
+        self._sim._export_encodings(path)
+
+    def _load_param_encodings(self, path: str):
+        """
+        Loads parameter encodings to the sim model
+
+        :param path: Folder Path where encodings file is present
+        """
+        param_path = os.path.join(path, '.cache/encodings_with_param.encodings')
+
+        # Load encodings file
+        with open(param_path) as json_file:
+            param_encodings = json.load(json_file)
+
+        # Loading activation encodings also along with param encodings to get compatible with load_encodings_to_sim function
+        # These activation encodings are already present in quantsim model
+        act_path = os.path.join(path, '.cache/encodings_with_act.encodings')
+        with open(act_path) as json_file:
+            act_encodings = json.load(json_file)
+
+        param_encodings['activation_encodings'] = act_encodings['activation_encodings']
+
+        # Save the updated encodings to the file
+        save_json_yaml(param_path, param_encodings)
+        #load encodings
+        load_encodings_to_sim(self._sim, param_path, strict=True)
+
+        # Removing the files created by _export_encodings function
+        os.remove(param_path)
+        os.remove(act_path)
+
+    def _evaluate_model(self, eval_callback: CallbackFunc) -> float:
+        """
+        Evaluates a model
+
+        :param eval_callback: Callback function that contains eval function and eval args
+        :return: Eval score
+        """
+        return eval_callback.func(self._sim.session, eval_callback.args)
+
+    def _find_quantizer_group(self, sim: QuantizationSimModel) -> Tuple[Dict[str, QcQuantizeOp], List[QuantizerGroup]]:
+        """
+        Finds quantizer groups in a quantization sim
+        :param sim: Quantization sim
+        :return: Dictionary mapping quantized op name to quantizer,
+            and a List of quantizer groups
+        """
+        return find_quantizer_group(sim)
+
+    @property
+    def baseline_candidate_options(self) -> List[CANDIDATE_WITH_DTYPE]:
+        """
+        Returns the _baseline_candidate_options which is the intersection of amp candidates and candidates supported by
+        all the quantizer groups
+        """
+        return self._baseline_candidate_options
+
+    def _find_bit_ops_reduction_for_acc_list(
+            self,
+            quantizer_group: QuantizerGroup,
+            max_candidate: CANDIDATE_WITH_DTYPE,
+            candidate: CANDIDATE_WITH_DTYPE,
+    ) -> int:
+        """
+        Finds reduction in bit ops from max candidate to new candidate
+
+        :param quantizer_group: Quantizer group
+        :param max_candidate: Maximum bitwidth and data type for the TensorQuantizer
+        :param candidate: Activation bitwidth, parameter bitwidth
+        :return: Bit ops reduction
+        """
+        return mixed_precision_utils.find_bit_ops_reduction(quantizer_group, self._mac_dict,
+                                                            self._param_name_to_op_name_dict,
+                                                            max_candidate, candidate)
+
+    def calculate_running_bit_ops(
+            self,
+            quantizer_group: QuantizerGroup,
+            module_bitwidth_dict: Dict[str, int],
+            max_candidate: CANDIDATE_WITH_DTYPE,
+            candidate: CANDIDATE_WITH_DTYPE,
+            running_bit_ops: int,
+    ) -> int:
+        """
+        Calculates running bit ops value for every quantizer group
+
+        :param quantizer_group: A group of activation & parameter quantizers
+        :param module_bitwidth_dict: Dict; Key: Module name value: Activation, parameter bitwidth of module
+        :param max_candidate: Maximum bitwidth and data type for the TensorQuantizer
+        :param candidate: candidate to change the quantizer group to
+        :param running_bit_ops: Running bit ops value calculated uptil the quantizer group
+        :return: Running bit ops value
+        """
+        running_bit_ops = mixed_precision_utils.calculate_running_bit_ops(self._mac_dict, quantizer_group,
+                                                                          self._param_name_to_op_name_dict,
+                                                                          module_bitwidth_dict,
+                                                                          max_candidate,
+                                                                          candidate,
+                                                                          running_bit_ops)
+        return running_bit_ops
+
+    def _create_and_save_accuracy_list(self, baseline_candidate):
+        try:
+            if self.phase1_optimize:
+                return self._create_and_save_accuracy_list_optimized(baseline_candidate)
+            return super()._create_and_save_accuracy_list(baseline_candidate)
+        finally:
+            pass
+
+    def _create_op_graph(self, sim):
+        """
+        Creates op graph
+
+        :param sim: QuantizationSimModel object
+        """
+        return None
+
+    def _optimize_mp_profile_and_evaluate_model(self):
+        """
+        Uses OpGraph if available to optimize the mixed precision profile in the sim object
+        """
+        # Recompute quantizer encodings
+        self._sim.compute_encodings(self.algo_params.forward_pass_callback,
+                                    self.algo_params.forward_pass_callback_args)
+        # Compute new accuracy score
+        eval_score = self.evaluate_model(self.algo_params.eval_callback_for_phase2)
+        return eval_score
+
+    def _reduce_mp_convert_ops(self):
+        """
+        Reduce mixed precision convert ops if enabled and supported
+        """
+
    +
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    +

    Source code for aimet_onnx.amp.quantizer_groups

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2024, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+
+""" Find quantizer groups in a model """
+import itertools
+from typing import Dict, Tuple, List
+from collections import defaultdict
+from dataclasses import dataclass, field
+
+from aimet_common.connected_graph.operation import Op
+from aimet_common.connected_graph.connectedgraph_utils import get_all_input_ops, get_all_output_ops
+
+from aimet_common.amp.utils import CANDIDATE_WITH_DTYPE
+
+from aimet_common.connected_graph.connectedgraph import get_ordered_ops
+from aimet_common.amp.quantizer_groups import QuantizerGroupBase, get_supported_candidates_for_quantizers, \
+    compute_baseline_candidate_options, find_valid_ops
+from aimet_common.utils import AimetLogger
+
+from aimet_onnx.meta.connectedgraph import ConnectedGraph
+from aimet_onnx.quantsim import QuantizationSimModel
+from aimet_onnx.qc_quantize_op import QcQuantizeOp
+
+logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.MixedPrecision)
+
+
+
    
+[docs]
+@dataclass(frozen=True)
+class QuantizerGroup(QuantizerGroupBase):
+    """
+    Group of modules and quantizers
+    """
+    parameter_quantizers: Tuple[str, ...] = field(default_factory=tuple)
+    activation_quantizers: Tuple[str, ...] = field(default_factory=tuple)
+
+
    
+[docs]
+    def get_candidate(self, name_to_quantizer_dict: Dict) -> CANDIDATE_WITH_DTYPE:
+        """
+        Gets Activation & parameter bitwidth
+
+        :param name_to_quantizer_dict: Gets module from module name
+        :return: Tuple of Activation, parameter bitwidth and data type
+        """
+        activation_bw, parameter_bw = None, None
+        activation_dtype, param_dtype = None, None
+
+        for quantizer in self.get_activation_quantizers(name_to_quantizer_dict):
+            activation_bw = quantizer.bitwidth
+            activation_dtype = quantizer.data_type
+            break
+
+        for quantizer in self.get_param_quantizers(name_to_quantizer_dict):
+            if quantizer.enabled:
+                parameter_bw = quantizer.bitwidth
+                param_dtype = quantizer.data_type
+                break
+
+        return (activation_bw, activation_dtype), (parameter_bw, param_dtype)
    
+
+
+
    
+[docs]
+    def set_quantizers_to_candidate(self,
+                                    name_to_quantizer_dict: Dict,
+                                    candidate: CANDIDATE_WITH_DTYPE):
+        """
+        Sets a quantizer group to a given candidate bitwidth
+
+        :param name_to_quantizer_dict: Gets module from module name
+        :param candidate: candidate with act and param bw and data types
+        """
+        (activation_bw, activation_dtype), (param_bw, param_dtype) = candidate
+
+        for quantizer in self.get_activation_quantizers(name_to_quantizer_dict):
+            quantizer.bitwidth = activation_bw
+            quantizer.data_type = activation_dtype
+
+        for quantizer in self.get_param_quantizers(name_to_quantizer_dict):
+            quantizer.bitwidth = param_bw
+            quantizer.data_type = param_dtype
    
+
+
+
    
+[docs]
+    def to_list(self) -> List[Tuple[str, str]]:
+        """
+        Converts quantizer group to a list
+
+        :return: List containing input/output quantizers & weight quantizers
+        """
+        return list(itertools.chain(
+            (("activation", module_name) for module_name in self.activation_quantizers),
+            (("weight", module_name) for module_name in self.parameter_quantizers),
+        ))
    
+
+
+
    
+[docs]
+    def get_active_quantizers(self, name_to_quantizer_dict) -> List[QcQuantizeOp]:
+        """
+        Find all active tensor quantizers associated with this quantizer group
+
+        :param name_to_quantizer_dict: Gets module from module name
+        :return: List of active quantizers
+        """
+        quantizers = self.get_activation_quantizers(name_to_quantizer_dict) + \
+                     self.get_param_quantizers(name_to_quantizer_dict)
+        return [quantizer for quantizer in quantizers if quantizer.enabled]
    
+
+
+
    
+[docs]
+    def get_activation_quantizers(self, name_to_quantizer_dict):
+        """
+        Gets activation quantizers
+
+        :param name_to_quantizer_dict: Gets module from module name
+
+        :return List of activation quantizers
+        """
+        result = []
+        for module_name in self.activation_quantizers:
+            quantizer = name_to_quantizer_dict[module_name]
+            result.append(quantizer)
+        return result
    
+
+
+
    
+[docs]
+    def get_param_quantizers(self, name_to_quantizer_dict):
+        """
+        Gets parameter quantizers
+
+        :param name_to_quantizer_dict: Gets module from module name
+
+        :return List of parameter quantizers
+        """
+        result = []
+        for module_name in self.parameter_quantizers:
+            quantizer = name_to_quantizer_dict[module_name]
+            result.append(quantizer)
+        return result
    
+
    
+
+
+
+op_types_to_ignore = ['Reshape', 'branch', 'Gather', 'Unsqueeze', 'Pad']
+ops_not_to_traverse = ['Shape']
+
+
+def find_op_groups(connected_graph: ConnectedGraph) -> Dict:
+    """
+    Finds parent child groups based on following rules.
+    1) If there is a direct connection between two ops, op1 and op2, then op1 is parent of op2 and they form a group
+    2) If the input to an op (op1) is shared with another op (op2), the op producing the input (op0) is the parent,
+    and op1 and op2 are the children
+
+    :param connected_graph: Connected graph
+    :return: Dict of parent (key) and children (value) groups
+    """
+    # Get ordered ops in Connected graph
+    ordered_ops = get_ordered_ops(connected_graph.starting_ops)
+    valid_ops = find_valid_ops(connected_graph, ops_not_to_traverse)
+
+    parent_child_op_groups = defaultdict(list)
+    map_for_skipped_ops = {}
+
+    for op in ordered_ops:
+        if op.dotted_name not in valid_ops or op.type in op_types_to_ignore:
+            continue
+        _find_parent_child_op_groups(op, parent_child_op_groups, map_for_skipped_ops)
+
+    return parent_child_op_groups
+
+
+def _find_parent_child_op_groups(op: Op, parent_child_op_groups: Dict, map_for_skipped_ops: Dict):
+    """
+    Finds op groups along the parent to child flow
+    :param op: Op
+    :param parent_child_op_groups: parent child op groups dict
+    :param map_for_skipped_ops: map to find first skipped parents of skipped ops
+    """
+    output = op.output
+
+    if output:
+        consumers = output.consumers
+        for consumer in consumers:
+            dotted_name = op.dotted_name
+            if consumer.type in ops_not_to_traverse:
+                continue
+            if op.dotted_name in map_for_skipped_ops:
+                dotted_name = map_for_skipped_ops[op.dotted_name]
+
+            if consumer.type in op_types_to_ignore:
+                map_for_skipped_ops[consumer.dotted_name] = dotted_name
+                _find_parent_child_op_groups(consumer, parent_child_op_groups, map_for_skipped_ops)
+            else:
+                if consumer.dotted_name not in parent_child_op_groups[dotted_name]:
+                    parent_child_op_groups[dotted_name].append(consumer.dotted_name)
+        if not consumers and op.dotted_name in map_for_skipped_ops:
+            parent_child_op_groups[map_for_skipped_ops[op.dotted_name]] = []
+    else:
+        dotted_name = op.dotted_name
+        parent_child_op_groups[dotted_name].append(None)
+
+
+def find_quantizer_group(sim: QuantizationSimModel) -> Tuple[Dict, List[QuantizerGroup]]:
+    """
+    Finds quantizer groups in a quantization sim
+    :param sim: Quantization sim
+    :return: Dictionary of quantized op name to sim.quantizer_config object, List of quantizer groups
+    """
+    # Get connected graph from quantsim
+    connected_graph = sim.connected_graph
+
+    if connected_graph is None:
+        raise AssertionError('Aborting Auto Mixed Precision, connected graph needs to exist for Auto Mixed precision')
+
+    # Find parent to children mapping for connected graph ops
+    parent_child_op_groups = find_op_groups(connected_graph)
+
+    # Find mapping of quantized op name to quantizer info
+    op_name_to_quantizer_dict = _get_op_name_to_act_quantizer_name_dicts(sim)
+    op_to_param_dict = _get_op_to_param_name_dict(sim)
+
+    quantizer_groups = []
+    _add_input_quantizer_group(op_to_param_dict, sim, quantizer_groups)
+    for parent, children in parent_child_op_groups.items():
+        activation_quantizers = []
+        if parent in op_name_to_quantizer_dict:
+            activation_quantizers.append(op_name_to_quantizer_dict[parent])
+        parameter_quantizers = []
+        for child in children:
+            if child and child in op_to_param_dict:
+                parameter_quantizers.append(op_to_param_dict[child])
+
+        if activation_quantizers or parameter_quantizers:
+            _add_quantizer_group(quantizer_groups, tuple(activation_quantizers), tuple(parameter_quantizers))
+
+    _add_output_quantizer_group(op_name_to_quantizer_dict, sim, quantizer_groups)
+
+    return sim.qc_quantize_op_dict, quantizer_groups
+
+
+def _add_quantizer_group(quantizer_groups: List[QuantizerGroup], activation_quantizers: Tuple,
+                         parameter_quantizers: Tuple):
+    """
+    Adds quantizer group to the quantizer groups list
+    :param quantizer_groups: List of Quantizer groups
+    :param activation_quantizers: Tuple of activation quantizers
+    :param parameter_quantizers: Tuple of parameter quantizers
+    """
+    quantizer_group = QuantizerGroup(parameter_quantizers=parameter_quantizers,
+                                     activation_quantizers=activation_quantizers)
+    quantizer_groups.append(quantizer_group)
+    logger.info('Quantizer Group added: %s', quantizer_group)
+
+
+def _add_input_quantizer_group(op_to_param_dict: Dict, sim: QuantizationSimModel, quantizer_groups: List):
+    """
+    Adds input's (of the model) quantizer group
+    :param op_to_param_dict: Key: op_name Value: Weight name associated
+    :param sim: Quantization Sim
+    :param quantizer_groups: Quantizer Groups List
+    """
+    conn_graph_ops = get_all_input_ops(sim.connected_graph)
+    for input_op in conn_graph_ops:
+        parameter_quantizers = []
+        activation_quantizers = []
+        if input_op.dotted_name in op_to_param_dict:
+            parameter_quantizers.append(op_to_param_dict[input_op.dotted_name])
+        for input_product in input_op.inputs:
+            activation_quantizer = input_product.tensor_dict[input_op]
+            if isinstance(activation_quantizer, str) and \
+                    activation_quantizer in sim.activation_names and \
+                    sim.qc_quantize_op_dict[activation_quantizer].enabled:
+                activation_quantizers.append(input_product.tensor_dict[input_op])
+        if activation_quantizers or parameter_quantizers:
+            _add_quantizer_group(quantizer_groups, tuple(activation_quantizers), tuple(parameter_quantizers))
+
+
+def _add_output_quantizer_group(op_name_to_quantizer_dict: Dict, sim: QuantizationSimModel, quantizer_groups: List):
+    """
+    Adds output's (of the model) quantizer group
+    :param op_name_to_quantizer_dict: Key: op_name Value: quantizer associated with op name
+    :param sim: Quantization Sim
+    :param quantizer_groups: Quantizer Groups List
+    """
+    conn_graph_ops = get_all_output_ops(sim.connected_graph)
+    for output_op in conn_graph_ops:
+        activation_quantizers = []
+        if output_op.dotted_name in op_name_to_quantizer_dict:
+            activation_quantizers.append(op_name_to_quantizer_dict[output_op.dotted_name])
+        if activation_quantizers:
+            _add_quantizer_group(quantizer_groups, tuple(activation_quantizers), ())
+
+
+def _get_op_to_param_name_dict(sim: QuantizationSimModel) -> Dict:
+    """
+    Creates the dict where param name (weight) is mapped to op's name
+    :param sim: Quantization Sim
+    """
+    op_to_param_dict = {}
+    conn_graph_ops = sim.connected_graph.get_all_ops()
+    for op in conn_graph_ops.values():
+        for param_name in op.parameters:
+            _, param_type = op.parameters[param_name]
+            if param_type == 'weight' and sim.qc_quantize_op_dict[param_name].enabled:
+                op_to_param_dict[op.dotted_name] = param_name
+
+    return op_to_param_dict
+
+
+def _get_op_name_to_act_quantizer_name_dicts(sim: QuantizationSimModel) -> Dict:
+    """
+    Creates the dict where param quantizers if enabled are mapped to their param_names and activation
+    quantizer if enabled is mapped to it's inputs name
+    :param sim: Quantization Sim
+    :return op_name_to_activation_quantizer_name_dict
+    """
+    op_name_to_activation_quantizer_name_dict = {}
+    for node in sim.model.model.graph.node:
+        if 'QcQuantizeOp' in node.name:
+            continue
+        for output_product in node.output:
+            if output_product in sim.activation_names:
+                activation_quantizer_op = sim.qc_quantize_op_dict[output_product]
+                if activation_quantizer_op.enabled:
+                    op_name_to_activation_quantizer_name_dict[node.name] = output_product
+    return op_name_to_activation_quantizer_name_dict
+
+
+def find_supported_candidates(quantizer_groups: List[QuantizerGroup],
+                              amp_candidates: List[CANDIDATE_WITH_DTYPE],
+                              supported_kernels: Dict,
+                              quantizer_to_op_type: Dict,
+                              use_all_amp_candidates: bool) -> Tuple[Dict, List]:
+    """
+    Computes 1. a list of supported candidates per Quantizer and 2. List of candidate options for max_candidate
+    :param quantizer_groups: List of quantizer groups computed for the given model
+    :param amp_candidates: List of candidates specified by the user to be used for the AMP algorithm
+    :param supported_kernels: Dict of supported kernels for a given op/defaults specified in the config file
+    :param quantizer_to_op_type: Dict of quantizers to onnx op type
+    :param use_all_amp_candidates: Boolean value representing whether the unsupported candidates in the
+    "candidates" list need to be considered for creating the output lists. If set to True, all the AMP candidates are
+    directly used for all the Quantizers, else the candidates per Quantizers are computed.
+    """
+
+    quantizers_with_supported_candidates = defaultdict(list)
+
+    for quantizer_group in quantizer_groups:
+        quantizers = sorted(set(itertools.chain(quantizer_group.activation_quantizers,
+                                                quantizer_group.parameter_quantizers)))
+
+        supported_kernels_for_quantizers = get_supported_candidates_for_quantizers(quantizers,
+                                                                                   quantizer_to_op_type,
+                                                                                   supported_kernels,
+                                                                                   amp_candidates,
+                                                                                   use_all_amp_candidates)
+
+        quantizers_with_supported_candidates[quantizer_group] = supported_kernels_for_quantizers.copy()
+
+    max_candidate_options = compute_baseline_candidate_options(quantizers_with_supported_candidates, amp_candidates,
+                                                               use_all_amp_candidates)
+
+    return quantizers_with_supported_candidates, max_candidate_options
+
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    +

    Source code for aimet_onnx.auto_quant_v2

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2023, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+# pylint: disable=too-many-lines, protected-access
+
+"""Automatic Post-Training Quantization V2"""
+
+import copy
+from collections import OrderedDict, defaultdict
+from dataclasses import dataclass
+import functools
+import math
+import traceback
+import os
+import sys
+import io
+from unittest.mock import patch
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union, Mapping, Iterable
+import shutil
+import pickle
+from uuid import uuid4
+from tqdm import tqdm
+import jinja2
+import bokeh.plotting
+from bokeh.resources import CDN
+
+import onnx
+import onnxruntime as ort
+from onnxruntime.quantization.onnx_quantizer import ONNXModel
+import numpy as np
+
+from aimet_onnx import utils
+from aimet_onnx.adaround.adaround_weight import Adaround, AdaroundParameters
+from aimet_onnx.cross_layer_equalization import equalize_model
+from aimet_onnx.batch_norm_fold import fold_all_batch_norms_to_weight
+from aimet_onnx.quantsim import QuantizationSimModel
+from aimet_onnx.amp.mixed_precision_algo import GreedyMixedPrecisionAlgo, EvalCallbackFactory, _default_forward_fn
+from aimet_onnx.amp.quantizer_groups import QuantizerGroup
+from aimet_onnx.defs import DataLoader
+
+from aimet_common.auto_quant import Diagnostics
+from aimet_common.cache import Cache
+from aimet_common.defs import QuantScheme, QuantizationDataType, CallbackFunc
+from aimet_common.utils import AimetLogger, Spinner
+from aimet_common.quantsim import validate_quantsim_inputs
+from aimet_common.amp.utils import (
+    create_sensitivity_plot,
+    create_pareto_curve,
+    CANDIDATE_WITH_DTYPE,
+    AmpCandidate,
+)
+
+
+_logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.AutoQuant)
+
+cache = Cache()
+
+# The number of samples to be used for performance evaluation.
+# NOTE: None means "all".
+NUM_SAMPLES_FOR_PERFORMANCE_EVALUATION = None
+
+
+@dataclass(frozen=True)
+class _QuantSchemePair:
+    param_quant_scheme: QuantScheme
+    output_quant_scheme: QuantScheme
+    param_percentile: Optional[float] = None
+    output_percentile: Optional[float] = None
+
+    def __str__(self):
+        def scheme_to_str(quant_scheme, percentile):
+            if quant_scheme == QuantScheme.post_training_percentile:
+                return f"{percentile}%ile"
+            if quant_scheme in (QuantScheme.post_training_tf,
+                                QuantScheme.training_range_learning_with_tf_init):
+                return "tf"
+            if quant_scheme in (QuantScheme.post_training_tf_enhanced,
+                                QuantScheme.training_range_learning_with_tf_enhanced_init):
+                return "tf-enhanced"
+            raise ValueError
+
+        param_str = scheme_to_str(self.param_quant_scheme, self.param_percentile)
+        output_str = scheme_to_str(self.output_quant_scheme, self.output_percentile)
+        return f"W@{param_str} / A@{output_str}"
+
+
+_QUANT_SCHEME_CANDIDATES = (
+    # Weight:     tf
+    # Activation: tf
+    _QuantSchemePair(QuantScheme.post_training_tf,
+                     QuantScheme.post_training_tf),
+
+    # Weight:     tf_enhanced
+    # Activation: tf
+    _QuantSchemePair(QuantScheme.post_training_tf_enhanced,
+                     QuantScheme.post_training_tf),
+
+    # Weight:     tf_enhanced
+    # Activation: tf_enhanced
+    _QuantSchemePair(QuantScheme.post_training_tf_enhanced,
+                     QuantScheme.post_training_tf_enhanced),
+
+    # TODO: Enable below candidates once we figure out how to set percentile value in QcQuantizeOp's Tensor Quantizer
+
+    # Weight:     tf_enhanced
+    # Activation: percentile(99.9)
+    # _QuantSchemePair(QuantScheme.post_training_tf_enhanced,
+    #                  QuantScheme.post_training_percentile,
+    #                  output_percentile=99.9),
+
+    # Weight:     tf_enhanced
+    # Activation: percentile(99.99)
+    # _QuantSchemePair(QuantScheme.post_training_tf_enhanced,
+    #                  QuantScheme.post_training_percentile,
+    #                  output_percentile=99.99),
+)
+
+
+def _validate_inputs(model: Union[onnx.ModelProto, ONNXModel], # pylint: disable=too-many-arguments
+                     data_loader: Iterable[Union[np.ndarray, List[np.ndarray]]],
+                     eval_callback: Callable[[ort.InferenceSession], float],
+                     dummy_input: Dict[str, np.ndarray],
+                     results_dir: str,
+                     strict_validation: bool,
+                     quant_scheme: QuantScheme,
+                     param_bw: int,
+                     output_bw: int,
+                     rounding_mode: str):
+    """
+    Confirms inputs are of the correct type
+    :param model: Model to be quantized
+    :param data_loader: A collection that iterates over an unlabeled dataset, used for computing encodings
+    :param eval_callback: Function that calculates the evaluation score
+    :param dummy_input: Dummy input for the model
+    :param results_dir: Directory to save the results of PTQ techniques
+    :param strict_validation: Flag set to True by default. When False, AutoQuant will proceed with execution and try to handle errors internally if possible. This may produce unideal or unintuitive results.
+    :param quant_scheme: Quantization scheme
+    :param param_bw: Parameter bitwidth
+    :param output_bw: Output bitwidth
+    :param rounding_mode: Rounding mode
+    """
+    if not isinstance(model, (onnx.ModelProto, ONNXModel)):
+        raise ValueError('Model must be of type onnx.ModelProto or ONNXModel, not ' + str(type(model).__name__))
+
+    if not isinstance(data_loader, Iterable):
+        raise ValueError('data_loader must be of type Iterable, not ' + str(
+            type(data_loader).__name__))
+
+    if not isinstance(eval_callback, Callable):  # pylint: disable=isinstance-second-argument-not-valid-type
+        raise ValueError('eval_callback must be of type Callable, not ' + str(type(eval_callback).__name__))
+
+    if not isinstance(dummy_input, Dict):
+        raise ValueError(
+            'dummy_input must be of type Dict, not ' + str(type(dummy_input).__name__))
+
+    if not isinstance(results_dir, str):
+        raise ValueError('results_dir must be of type str, not ' + str(type(results_dir).__name__))
+
+    results_dir = os.path.abspath(results_dir)
+    os.makedirs(results_dir, exist_ok=True)
+
+    if not isinstance(strict_validation, bool):
+        raise ValueError('strict_validation must be of type bool, not ' + str(type(strict_validation).__name__))
+
+    validate_quantsim_inputs(quant_scheme, rounding_mode, output_bw, param_bw)
+
+
+class AutoQuant: # pylint: disable=too-many-instance-attributes
+    """
+    Integrate and apply post-training quantization techniques.
+
+    AutoQuant includes 1) batchnorm folding, 2) cross-layer equalization,
+    and 3) Adaround.
+    These techniques will be applied in a best-effort manner until the model
+    meets the evaluation goal given as allowed_accuracy_drop.
+    """
+
+    def __init__( # pylint: disable=too-many-arguments, too-many-locals
+            self,
+            model: Union[onnx.ModelProto, ONNXModel],
+            dummy_input: Dict[str, np.ndarray],
+            data_loader: Iterable[Union[np.ndarray, List[np.ndarray], Tuple[np.ndarray]]],
+            eval_callback: Callable[[ort.InferenceSession, int], float],
+            param_bw: int = 8,
+            output_bw: int = 8,
+            quant_scheme: QuantScheme = QuantScheme.post_training_tf_enhanced,
+            rounding_mode: str = 'nearest',
+            use_cuda: bool = True,
+            device: int = 0,
+            config_file: str = None,
+            results_dir: str = "/tmp",
+            cache_id: str = None,
+            strict_validation: bool = True) -> None:
+        '''
+        :param model: Model to be quantized.
+        :param dummy_input: Dummy input dict for the model.
+        :param data_loader: A collection that iterates over an unlabeled dataset, used for computing encodings
+        :param eval_callback: Function that calculates the evaluation score given the model session
+        :param param_bw: Parameter bitwidth
+        :param output_bw: Output bitwidth
+        :param quant_scheme: Quantization scheme
+        :param rounding_mode: Rounding mode
+        :param use_cuda: True if using CUDA to run quantization op. False otherwise.
+        :param config_file: Path to configuration file for model quantizers
+        :param results_dir: Directory to save the results of PTQ techniques
+        :param cache_id: ID associated with cache results
+        :param strict_validation: Flag set to True by default. When False, AutoQuant will proceed with execution and handle errors internally if possible. This may produce unideal or unintuitive results.
+        '''
+
+        _validate_inputs(model, data_loader, eval_callback, dummy_input, results_dir,
+                         strict_validation, quant_scheme, param_bw, output_bw, rounding_mode)
+
+        if not isinstance(model, ONNXModel):
+            model = ONNXModel(model)
+
+        self.fp32_model = model
+        self.dummy_input = dummy_input
+        self.data_loader = data_loader
+        self.eval_callback = eval_callback
+
+        self._quantsim_params = dict(
+            param_bw=param_bw,
+            output_bw=output_bw,
+            quant_scheme=_QuantSchemePair(quant_scheme, quant_scheme),
+            rounding_mode=rounding_mode,
+            config_file=config_file,
+            use_cuda=use_cuda,
+            device=device
+        )
+
+        self.results_dir = results_dir
+        self.cache_dir = None
+        if cache_id:
+            self.cache_dir = os.path.join(results_dir, ".auto_quant_cache", cache_id)
+
+        def forward_pass_callback(session, _: Any = None):
+            for input_data in tqdm(data_loader):
+                input_data_dict = utils.create_input_dict(model.model, input_data)
+                _ = session.run(None, input_data_dict)
+
+        self.forward_pass_callback = forward_pass_callback
+
+        # Use at most 2000 samples for AdaRound.
+        input_instance = next(iter(self.data_loader))
+        batch_size = len(input_instance[0]) if isinstance(input_instance, (List, Tuple)) else len(input_instance)
+        num_batches = 0
+        for _ in self.data_loader:
+            num_batches += 1
+        num_samples = min(num_batches * batch_size, 2000)
+        num_batches = math.ceil(num_samples / batch_size)
+        self.adaround_params = AdaroundParameters(self.data_loader, num_batches)
+
+        self.eval_manager = _EvalManager(
+            quantsim_factory=self._create_quantsim_and_encodings,
+            eval_func=self._evaluate_model_performance,
+            results_dir=self.results_dir,
+            strict_validation=strict_validation)
+
+        self._quant_scheme_candidates = _QUANT_SCHEME_CANDIDATES
+        self._fp32_acc = None
+
+    def _evaluate_model_performance(self, session) -> float:
+        """
+        Evaluate the model performance.
+        """
+        return self.eval_callback(session, NUM_SAMPLES_FOR_PERFORMANCE_EVALUATION)
+
+    def run_inference(self) -> Tuple[QuantizationSimModel, float]:
+        '''
+        Creates a quantization model and performs inference
+
+        :return: QuantizationSimModel, model accuracy as float
+        '''
+        model = self.fp32_model
+
+        # Batchnorm Folding
+        with self.eval_manager.session("Batchnorm Folding - Inference Run") as sess:
+            model, _ = sess.wrap(self._apply_batchnorm_folding)(model)
+            if sess.ptq_result is None:
+                sess.set_ptq_result(model=model,
+                                    applied_techniques=["batchnorm_folding"])
+
+        sim = self._create_quantsim_and_encodings(model)
+
+        if sess.ptq_result is None:
+            # BN folding failed. Need to measure the eval score
+            acc = self._evaluate_model_performance(sim.session)
+        else:
+            # BN folding success. No need to measure the eval score again
+            acc = sess.ptq_result.accuracy
+
+        return sim, acc
+
+    def optimize(self, allowed_accuracy_drop: float = 0.0) -> Tuple[ONNXModel, float, str]:
+        """
+        Integrate and apply post-training quantization techniques.
+
+        :param allowed_accuracy_drop: Maximum allowed accuracy drop
+        :return: Tuple of (best model, eval score, encoding path)
+        """
+        result = self._optimize_helper(self._optimize_main, allowed_accuracy_drop)
+        return result["model"],\
+               result["accuracy"],\
+               result["encoding_path"]
+
+    def set_adaround_params(self, adaround_params: AdaroundParameters) -> None:
+        """
+        Set Adaround parameters.
+        If this method is not called explicitly by the user, AutoQuant will use
+        `data_loader` (passed to `__init__`) for Adaround.
+
+        :param adaround_params: Adaround parameters.
+        """
+        self.adaround_params = adaround_params
+
+    def _create_quantsim_and_encodings( # pylint: disable=too-many-arguments, too-many-locals, too-many-branches
+            self,
+            fp32_model: ONNXModel,
+            rounding_mode: str = None,
+            output_bw: int = None,
+            output_quant_scheme: QuantScheme = None,
+            output_percentile: float = None,
+            param_bw: int = None,
+            param_quant_scheme: QuantScheme = None,
+            param_percentile: float = None,
+            config_file: str = None,
+            encoding_path: str = None,
+    ) -> QuantizationSimModel:
+        """
+        Create a QuantizationSimModel and compute encoding. If `encoding_path` is not None,
+        it is prioritized over other arguments (`output_bw`, `param_bw`, ...).
+
+        :param fp32_model: Model to quantize.
+        :param rounding_mode: Rounding mode. Defaults to self._quantsim_params["rounding_mode"].
+        :param output_bw: Default bitwidth (4-31) to use for quantizing layer inputs andoutputs.
+            Defaults to self._quantsim_params["output_bw"].
+        :param output_quant_scheme: Quantization scheme for output quantizers.
+            Defaults to self._quantsim_params["quant_scheme"].output_quant_scheme.
+        :param output_percentile: Percentile value for outputs.
+            Only valid if output quant scheme is percentile scheme.
+        :param param_bw: Default bitwidth (4-31) to use for quantizing layer parameters.
+            Defaults to self._quantsim_params["param_bw"].
+        :param param_quant_scheme: Quantization scheme for param quantizers.
+            Defaults to self._quantsim_params["quant_scheme"].param_quant_scheme.
+        :param param_percentile: Percentile value for parameters.
+            Only valid if param quant scheme is percentile scheme.
+        :param config_file: Path to configuration file for model quantizers.
+                            Defaults to self._quantsim_params["config_file"].
+        :param encoding_path: Path to parameter encodings file.
+        :return: Quantsim model.
+        """
+        if output_bw is not None:
+            assert output_bw <= 32
+
+        if param_bw is not None:
+            assert param_bw <= 32
+
+        if output_quant_scheme is None or param_quant_scheme is None:
+            assert self._quantsim_params["quant_scheme"] is not None
+
+        model = copy.deepcopy(fp32_model)
+        kwargs = dict(
+            rounding_mode=(rounding_mode or self._quantsim_params["rounding_mode"]),
+            default_activation_bw=(output_bw or self._quantsim_params["output_bw"]),
+            default_param_bw=(param_bw or self._quantsim_params["param_bw"]),
+            config_file=(config_file or self._quantsim_params["config_file"]),
+            use_cuda=self._quantsim_params['use_cuda'],
+            device=self._quantsim_params['device']
+        )
+        sim = QuantizationSimModel(model, self.dummy_input, **kwargs)
+
+        param_quantizers, activation_quantizers = sim.get_all_quantizers()
+
+        default_quant_scheme = self._quantsim_params.get("quant_scheme")
+
+        output_quant_scheme = output_quant_scheme or\
+                              default_quant_scheme.output_quant_scheme
+        output_percentile = output_percentile or default_quant_scheme.output_percentile
+        param_quant_scheme = param_quant_scheme or\
+                             default_quant_scheme.param_quant_scheme
+        param_percentile = param_percentile or default_quant_scheme.param_percentile
+
+        # Set activation quantizers' quant schemes
+        for quantizer in activation_quantizers:
+            quantizer.set_quant_scheme(output_quant_scheme)
+            # TODO: Enable once we figure out how to set percentile value in QcQuantizeOp's Tensor Quantizer
+            # if quantizer.quant_scheme == QuantScheme.post_training_percentile and\
+            #         output_percentile is not None:
+            #     quantizer.set_percentile_value(output_percentile)
+
+        # Set param quantizers' quant schemes
+        for quantizer in param_quantizers:
+            quantizer.set_quant_scheme(param_quant_scheme)
+            # TODO: Enable once we figure out how to set percentile value in QcQuantizeOp's Tensor Quantizer
+            # if quantizer.quant_scheme == QuantScheme.post_training_percentile and\
+            #         param_percentile is not None:
+            #     quantizer.set_percentile_value(param_percentile)
+
+        if encoding_path:
+            sim.set_and_freeze_param_encodings(encoding_path)
+
+        # Disable activation quantizers, using fp32 to simulate int32.
+        if output_bw == 32:
+            for quantizer in activation_quantizers:
+                quantizer.enabled = False
+
+        # Disable param quantizers, using fp32 to simulate int32.
+        if param_bw == 32:
+            for quantizer in param_quantizers:
+                quantizer.enabled = False
+
+        # Skip encoding computation if none of the quantizers are enabled
+        if any(quantizer.enabled for quantizer in param_quantizers +\
+                                                  activation_quantizers):
+            sim.compute_encodings(self.forward_pass_callback, None)
+
+        return sim
+
+    @staticmethod
+    @cache.mark("batchnorm_folding")
+    def _apply_batchnorm_folding(model: ONNXModel)\
+            -> Tuple[onnx.ModelProto, Tuple[List]]:
+        """
+        Apply batchnorm folding.
+
+        NOTE: Input model is not mutated.
+
+        :param model: Model to apply batchnorm folding.
+        :return: Output model and folded pairs.
+        """
+        model = copy.deepcopy(model)
+        conv_bns, bn_convs = fold_all_batch_norms_to_weight(model)
+        return model, conv_bns + bn_convs
+
+    @staticmethod
+    @cache.mark("cle")
+    def _apply_cross_layer_equalization(model: ONNXModel) -> onnx.ModelProto:
+        """
+        Apply cross-layer equalization.
+
+        NOTE: Input model is not mutated.
+
+        :param model: Model to apply cross-layer-equalization.
+        :return: Output model.
+        """
+        model = copy.deepcopy(model)
+        equalize_model(model)
+        return model
+
+    @cache.mark("adaround")
+    def _apply_adaround(self, model: ONNXModel) -> Tuple[onnx.ModelProto, str]:
+        """
+        Apply adaround.
+
+        NOTE1: Input model is not mutated.
+        NOTE2: Parameters `param_bw_override_list` and `ignore_quant_ops_list` are always set to None.
+
+        :param model: Model to apply adaround.
+        :return: Output model and the path to the parameter encoding file.
+        """
+        filename_prefix = "adaround"
+        adaround_encoding_path = os.path.join(self.results_dir,
+                                              "{}.encodings".format(filename_prefix))
+
+        sim = self._create_quantsim_and_encodings(model)
+
+        _, activation_quantizers = sim.get_all_quantizers()
+        for quantizer in activation_quantizers:
+            quantizer.enabled = False
+
+        model = Adaround._apply_adaround(sim, model, self.adaround_params, # pylint: disable=protected-access
+                                         path=self.results_dir, filename_prefix=filename_prefix)
+
+        return model, adaround_encoding_path
+
+    def _optimize_helper(
+            self,
+            optimize_fn: Callable,
+            allowed_accuracy_drop: float) -> Tuple[ONNXModel, float, str]:
+        """
+        Integrate and apply post-training quantization techniques.
+
+        :param allowed_accuracy_drop: Maximum allowed accuracy drop
+        :return: Tuple of (best model, eval score, encoding path)
+        """
+        allowed_accuracy_drop = float(allowed_accuracy_drop)
+        if allowed_accuracy_drop < 0:
+            raise ValueError(
+                "`allowed_accuracy_drop` must be a positive value. Got {:.2f}"
+                .format(allowed_accuracy_drop)
+            )
+
+        self.eval_manager.clear()
+
+        try:
+            with cache.enable(self.cache_dir):
+                _logger.info("Starting AutoQuant")
+
+                if self._quantsim_params['use_cuda']:
+                    providers = [('CUDAExecutionProvider', {'device_id': self._quantsim_params['device']}), 'CPUExecutionProvider']
+                else:
+                    providers = ['CPUExecutionProvider']
+                fp32_model_session = QuantizationSimModel.build_session(self.fp32_model.model, providers)
+                self._fp32_acc = self._evaluate_model_performance(fp32_model_session)
+                target_acc = self._fp32_acc - allowed_accuracy_drop
+                _logger.info("Target eval score: %f", target_acc)
+                _logger.info("FP32 eval score (W32A32): %f", self._fp32_acc)
+
+                ret = optimize_fn(self.fp32_model, target_acc)
+
+                acc = ret["accuracy"]
+                if acc is not None:
+                    _logger.info("Best eval score: %f", acc)
+
+                    # Save the best model with "best_model_" as prefix
+                    best_res = self.eval_manager.get_best_ptq_result()
+                    best_res.save_result_as("best_model")
+
+                    if acc < target_acc:
+                        _logger.info(
+                            "AutoQuant is unable to match the target accuracy. "
+                            "Consider Quantization Aware Training."
+                        )
+
+                return ret
+        finally:
+            self.eval_manager.export_diagnostics()
+
+    def get_quant_scheme_candidates(self) -> Tuple[_QuantSchemePair, ...]:
+        """
+        Return the candidates for quant scheme search.
+        During :meth:`~AutoQuant.optimize`, the candidate with the highest accuracy
+        will be selected among them.
+
+        :return: Candidates for quant scheme search
+        """
+        return self._quant_scheme_candidates
+
+    def set_quant_scheme_candidates(self, candidates: Tuple[_QuantSchemePair, ...]):
+        """
+        Set candidates for quant scheme search.
+        During :meth:`~AutoQuant.optimize`, the candidate with the highest accuracy
+        will be selected among them.
+
+        :param candidates: Candidates for quant scheme search
+        """
+        self._quant_scheme_candidates = copy.copy(candidates)
+
+    def _choose_default_quant_scheme(self):
+        def eval_fn(pair: _QuantSchemePair):
+            sim = self._create_quantsim_and_encodings(
+                self.fp32_model,
+                param_quant_scheme=pair.param_quant_scheme,
+                param_percentile=pair.param_percentile,
+                output_quant_scheme=pair.output_quant_scheme,
+                output_percentile=pair.output_percentile,
+            )
+            eval_score = self._evaluate_model_performance(sim.session)
+            _logger.info("Evaluation finished: %s (eval score: %f)", pair, eval_score)
+            return eval_score
+
+        param_bw = self._quantsim_params["param_bw"]
+        output_bw = self._quantsim_params["output_bw"]
+
+        candidates = self.get_quant_scheme_candidates()
+
+        # If the weight representation has sufficient precision (i.e. bitwidth >= 16),
+        # always use tf scheme
+        if param_bw >= 16:
+            candidates = [
+                candidate for candidate in candidates
+                if candidate.param_quant_scheme == QuantScheme.post_training_tf
+            ]
+
+        # If the output representation has sufficient precision (i.e. bitwidth >= 16),
+        # always use tf scheme
+        if output_bw >= 16:
+            candidates = [
+                candidate for candidate in candidates
+                if candidate.output_quant_scheme == QuantScheme.post_training_tf
+            ]
+
+        # If we have only one candidate left, we don't need to evaluated
+        # the quant scheme for comparison
+        if len(candidates) == 1:
+            return candidates[0]
+
+        assert candidates
+
+        # Find the quant scheme that yields the best eval score
+        best_quant_scheme = max(candidates, key=eval_fn)
+        _logger.info("Best Quant Scheme: %s", best_quant_scheme)
+
+        return best_quant_scheme
+
+    def _optimize_main(self, fp32_model: ONNXModel, target_acc: float):
+        """
+        Helper function of apply().
+
+        :param fp32_model: Model to apply PTQ techniques.
+        :param target_acc: Target eval score.
+
+        :raises RuntimeError: If none of the PTQ techniques were finished successfully.
+
+        :return: The best ptq result as a dictionary.
+        """
+
+        # Choose best quant scheme automatically.
+        with self.eval_manager.session("QuantScheme Selection") as sess:
+            self._quantsim_params["quant_scheme"] = sess.wrap(self._choose_default_quant_scheme)()
+
+        # Early exit
+        with self.eval_manager.session("W32 Evaluation") as sess:
+            w32_eval_score = sess.wrap(sess.eval)(fp32_model, param_bw=32)
+            _logger.info("Evaluation finished: W32A%d (eval score: %f)",
+                         self._quantsim_params["output_bw"], w32_eval_score)
+
+            if w32_eval_score < target_acc:
+                _logger.info(
+                    "W32A%d eval score (%f) is lower "
+                    "than the target eval score (%f). This means it is unlikely that "
+                    "the target eval score can be met using PTQ techniques. "
+                    "Please consider finetuning the model using range learning.",
+                    self._quantsim_params["output_bw"], w32_eval_score, target_acc
+                )
+
+                # Since AutoQuant pipeline exited early, all the return values are set to None
+                return {
+                    "model": None,
+                    "accuracy": None,
+                    "encoding_path": None,
+                    "applied_techniques": None,
+                }
+
+            sess.result["target_satisfied"] = True
+
+        # Batchnorm Folding
+        with self.eval_manager.session("Batchnorm Folding", ptq=True) as sess:
+            model, _ = sess.wrap(self._apply_batchnorm_folding)(fp32_model)
+            if sess.ptq_result is None:
+                sess.set_ptq_result(model=model,
+                                    applied_techniques=["batchnorm_folding"])
+
+        best_result = self.eval_manager.get_best_ptq_result()
+        if best_result and best_result.accuracy >= target_acc:
+            sess.result["target_satisfied"] = True
+            return best_result.as_dict()
+
+        # Cross-Layer Equalization
+        with self.eval_manager.session("Cross-Layer Equalization", ptq=True) as sess:
+            model = sess.wrap(self._apply_cross_layer_equalization)(fp32_model)
+            if sess.ptq_result is None:
+                sess.set_ptq_result(model=model,
+                                    applied_techniques=["cross_layer_equalization"])
+
+        best_result = self.eval_manager.get_best_ptq_result()
+        if best_result and best_result.accuracy >= target_acc:
+            sess.result["target_satisfied"] = True
+            return best_result.as_dict()
+
+        if best_result is None:
+            model = fp32_model
+            applied_techniques = []
+        else:
+            if "cross_layer_equalization" not in best_result.applied_techniques:
+                sess.result["effective"] = False
+            model = best_result.load_model()
+            applied_techniques = best_result.applied_techniques
+
+        # AdaRound
+        with self.eval_manager.session("AdaRound", ptq=True) as sess:
+            model, encoding_path = sess.wrap(self._apply_adaround)(model)
+            if sess.ptq_result is None:
+                sess.set_ptq_result(model=model,
+                                    encoding_path=encoding_path,
+                                    applied_techniques=[*applied_techniques, "adaround"])
+
+        best_result = self.eval_manager.get_best_ptq_result()
+        if best_result:
+            if "adaround" not in best_result.applied_techniques:
+                sess.result["effective"] = False
+            if best_result.accuracy >= target_acc:
+                sess.result["target_satisfied"] = True
+            return best_result.as_dict()
+
+        raise RuntimeError("None of Batchnorm Folding, CLE, or Adaround "
+                           "has been finished successfully.")
+
+
+@dataclass
+class PtqResult:
+    """
+    Evaluation results.
+    :param tag: Identifier string of the evaluation result.
+    :param model_path: Path to the serialized model.
+    :param encoding_path: Path to the encoding file.
+    :param accuracy: Accuracy of the model.
+    """
+    model_path: str
+    encoding_path: str
+    accuracy: float
+    applied_techniques: List[str]
+
+    def load_model(self) -> ONNXModel:
+        """
+        Load model.
+        :return: Loaded model.
+        """
+        return ONNXModel(onnx.load(self.model_path))
+
+    def as_dict(self):
+        """Convert to dictionary"""
+        return dict(model=self.load_model(),
+                    accuracy=self.accuracy,
+                    encoding_path=self.encoding_path,
+                    applied_techniques=self.applied_techniques)
+
+    def save_result_as(self, prefix: str = "best_model"):
+        """
+        Creates the copy of the PTQ result files with the given prefix.
+        :param prefix: prefix to be added to the file's basename
+        """
+        src_files = [self.model_path, self.encoding_path]
+        for file in src_files:
+            name = os.path.basename(file)
+            dirname = os.path.dirname(file)
+            dest = os.path.join(dirname, prefix + "_" + name)
+            if os.path.exists(file):
+                if os.path.exists(dest):
+                    os.remove(dest)
+                shutil.copyfile(file, dest)
+
+
+class _EvalManager:
+    """
+    Evaluation manager for AutoQuant.
+    """
+    def __init__(self,
+                 quantsim_factory: Callable,
+                 eval_func: Callable[[ort.InferenceSession], float],
+                 results_dir: str,
+                 strict_validation: bool):
+        """
+        :param quantsim_factory: A factory function that returns QuantizationSimModel.
+        :param eval_func: Evaluation function.
+        :param results_dir: Base directory to save the temporary serialized model.
+        """
+        self._quantsim_factory = quantsim_factory
+        self._eval_func = eval_func
+        self._results_dir = results_dir
+        self._strict_validation = strict_validation
+
+        os.makedirs(self._results_dir, exist_ok=True)
+
+        self._all_sessions = OrderedDict() # type: OrderedDict[str, _EvalSession]
+
+    def clear(self):
+        """
+        Clear all the session status saved in the previous run
+        """
+        for sess in self._all_sessions.values():
+            sess.reset_status()
+
+    def get_best_ptq_result(self) -> Optional[PtqResult]:
+        """
+        Get the results with the highest evaluation score among the ptq results evaluated so far.
+        :return: The best evaluation result so far.
+        """
+        # pylint: disable=protected-access
+        ptq_results = [sess.ptq_result for sess in self._all_sessions.values()
+                       if sess.ptq_result is not None and sess._ptq]
+        if not ptq_results:
+            return None
+
+        return max(ptq_results, key=lambda ptq_result: ptq_result.accuracy)
+
+    def session(self, title: str, ptq: bool = False):
+        """
+        Session factory.
+        :param title: Title of the session.
+        :param ptq: True if this session is a ptq session
+        :return: Session object.
+        """
+        if title not in self._all_sessions:
+            session = _EvalSession(title,
+                                   self._quantsim_factory,
+                                   self._eval_func,
+                                   results_dir=os.path.join(self._results_dir, ".trace"),
+                                   strict_validation=self._strict_validation,
+                                   ptq=ptq)
+            self._all_sessions[title] = session
+        return self._all_sessions[title]
+
+    HTML_TEMPLATE_FILE = os.path.join(
+        os.path.dirname(os.path.abspath(__file__)),
+        "auto_quant_v2_diagnostics_template.html",
+    )
+
+    def export_diagnostics(self) -> str:
+        """
+        Export diagnostics in html format.
+        :return: Diagnostics string in html format.
+        """
+        loader = jinja2.FileSystemLoader(os.path.dirname(self.HTML_TEMPLATE_FILE))
+        env = jinja2.Environment(loader=loader)
+        template = env.get_template(os.path.basename(self.HTML_TEMPLATE_FILE))
+
+        if any(sess.diagnostics.contains_bokeh() for sess in self._all_sessions.values()):
+            head = CDN.render()
+        else:
+            head = ""
+
+        log = io.StringIO()
+        for sess in self._all_sessions.values():
+            if sess.diagnostics.is_empty():
+                continue
+            log.write(
+                f"<h1> {sess.title} </h1>\n"
+            )
+            content = "\n".join(
+                line.get_html_elem() for line in sess.diagnostics
+            )
+            log.write(f"{content}\n")
+
+        result = OrderedDict()
+        result["ptq_techniques"] = OrderedDict()
+
+        for sess in self._all_sessions.values():
+            if sess.is_ptq_session():
+                result["ptq_techniques"][sess.title_lowercase] = sess.result
+            else:
+                result[sess.title_lowercase] = sess.result
+
+        flowchart_metadata = _build_flowchart_metadata(result)
+
+        html = template.render(head=head, log=log.getvalue(), **flowchart_metadata)
+
+        filename = os.path.join(self._results_dir, "diagnostics.html")
+        with open(filename, "w") as f:
+            f.write(html)
+        return html
+
+
+class _EvalSession: # pylint: disable=too-many-instance-attributes
+    """
+    Evaluation session for AutoQuant.
+
+    Each session object contains a title and diagnostics produced during the session.
+    The collected diagnostics will be exported into a html file by _EvalManager.
+    """
+    def __init__(
+            self,
+            title: str,
+            quantsim_factory: Callable,
+            eval_func: Callable[[ort.InferenceSession], float],
+            results_dir: str,
+            strict_validation: bool,
+            ptq: bool,
+    ):
+        """
+        :param title: Title of the session.
+        :param quantsim_factory: A factory function that returns QuantizationSimModel.
+        :param eval_func: Evaluation function.
+        :param results_dir: Base directory to save the temporary serialized model.
+        :param ptq: True if this session is a ptq session
+        """
+        self.title = title
+        self._quantsim_factory = quantsim_factory
+        self._eval_func = eval_func
+        self._results_dir = results_dir
+        self._strict_validation = strict_validation
+        self._ptq = ptq
+
+        self._spinner = None
+
+        self.result = {
+            "status": None,
+            "error": None,
+            "target_satisfied": False,
+            "effective": True,
+        }
+
+        os.makedirs(self._results_dir, exist_ok=True)
+
+        self.diagnostics = Diagnostics()
+
+        # Map session title to file name.
+        # e.g. title: "Cross-Layer Equalization" -> filename: "cross_layer_equalization"
+        self.title_lowercase = self.title.lower().replace("-", " ")
+        self.title_lowercase = "_".join(self.title_lowercase.split())
+
+        stdout_write = sys.stdout.write
+        self._log = io.StringIO()
+
+        # Redirects stdout to self._log
+        def write_wrapper(*args, **kwargs):
+            self._log.write(*args, **kwargs)
+            return stdout_write(*args, **kwargs)
+
+        self._stdout_redirect = patch.object(sys.stdout, "write", write_wrapper)
+        self._ptq_result = None
+        self._cached_result = None
+
+    def is_ptq_session(self):
+        """
+        Getter method of self._ptq flag
+        """
+        return self._ptq
+
+    def reset_status(self):
+        """
+        Reset the session status saved in the previous run
+        """
+        self.result = {
+            "status": None,
+            "error": None,
+            "target_satisfied": False,
+            "effective": True,
+        }
+
+    def wrap(self, fn):
+        """
+        Return a wrapper function that caches the return value.
+
+        :param fn: Function to wrap.
+        :returns: Function whose return value is cached.
+        """
+        results_dir = self._results_dir
+        class CachedResult:
+            """Cached result """
+            def __init__(self, obj):
+                self._filename = os.path.join(results_dir, f".{uuid4()}")
+                while os.path.exists(self._filename):
+                    self._filename = os.path.join(results_dir, f".{uuid4()}")
+                with open(self._filename, "wb") as f:
+                    pickle.dump(obj, f)
+
+            def load(self):
+                """Load cached result """
+                with open(self._filename, "rb") as f:
+                    return pickle.load(f)
+
+        @functools.wraps(fn)
+        def wrapper(*args, **kwargs):
+            if self._cached_result:
+                return self._cached_result.load()
+            ret = fn(*args, **kwargs)
+            self._cached_result = CachedResult(ret)
+            return ret
+        return wrapper
+
+    def eval(self, model: ONNXModel, **kwargs):
+        """
+        Evaluate the model.
+        :param model: Model to evaluate.
+        :param **kwargs: Additional arguments to the quantsim factory.
+        :return: Eval score.
+        """
+        sim = self._quantsim_factory(model, **kwargs)
+        acc = self._eval_func(sim.session)
+        return acc
+
+    def __enter__(self):
+        self._spinner = Spinner(self.title)
+        self._spinner.__enter__()
+        self._stdout_redirect.start()
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        if self._ptq_result is not None:
+            _logger.info("Session finished: %s. (eval score: %f). Applied techniques: %s",
+                         self.title, self._ptq_result.accuracy, ' '.join(self._ptq_result.applied_techniques))
+
+        self._spinner.__exit__(exc_type, exc_val, exc_tb)
+
+        if exc_val:
+            buffer = io.StringIO()
+            traceback.print_exception(exc_type, exc_val, exc_tb, file=buffer)
+
+            if self._strict_validation:
+                print(buffer.getvalue())
+            else:
+                print(
+                    "################################################################\n"
+                    "################################################################\n"
+                    "################################################################\n"
+                    "WARNING: The following exception was raised but ignored:\n\n"
+                    f"{buffer.getvalue()}"
+                    "################################################################\n"
+                    "################################################################\n"
+                    "################################################################\n"
+                )
+
+        self._stdout_redirect.stop()
+        self.diagnostics.add(self._log.getvalue())
+
+        self.result["error"] = exc_val
+        if not exc_val:
+            self.result["status"] = "success"
+        elif self._strict_validation:
+            self.result["status"] = "error-failed"
+        else:
+            self.result["status"] = "error-ignored"
+
+        if exc_val and not self._strict_validation:
+            # Return True so that the error doesn't propagate further
+            return True
+        return None
+
+    @property
+    def ptq_result(self) -> Optional[PtqResult]:
+        """Getter of self._ptq_result."""
+        return self._ptq_result
+
+    def set_ptq_result(
+            self,
+            applied_techniques: List[str],
+            model: onnx.ModelProto = None,
+            sim: QuantizationSimModel = None,
+            acc: float = None,
+            **kwargs
+    ) -> None:
+        """
+        Set the result of PTQ. Should be called exactly once inside a with-as block.
+
+        Exactly one among model and (sim, acc) pair should be specified.
+        1) If sim and acc is specified, save them as the result of this session.
+        2) If model is specified, evaluate the quantized accuracy of the model and save the result.
+
+        :param model: Result of PTQ.
+        :param sim: Result of PTQ. The quamtization encoding (compute_encodings()) is
+                    assumed to have been computed in advance.
+        :param acc: Eval score.
+        :param **kwargs: Additional arguments to the quantsim factory.
+        :return: None
+        """
+
+        if sim is None:
+            assert acc is None
+            assert model is not None
+            sim = self._quantsim_factory(model, **kwargs)
+            acc = self._eval_func(sim.session)
+        else:
+            assert acc is not None
+            assert model is None
+
+        self._set_ptq_result(sim, acc, applied_techniques)
+
+    def _set_ptq_result(
+            self,
+            sim: QuantizationSimModel,
+            acc: float,
+            applied_techniques: List[str],
+    ) -> PtqResult:
+        """
+        Set the result of PTQ. Should be called exactly once inside a with-as block.
+
+        :param sim: Result of PTQ. The quamtization encoding (compute_encodings()) is
+                    assumed to have been computed in advance.
+        :param acc: Eval score.
+        :return: PtqResult object.
+        """
+        if self._ptq_result is not None:
+            raise RuntimeError(
+                "sess.eval() can be called only once per each _EvalSession instance."
+            )
+
+        model_path, encoding_path = self._export(sim)
+        self._ptq_result = PtqResult(
+            model_path=model_path,
+            encoding_path=encoding_path,
+            accuracy=acc,
+            applied_techniques=applied_techniques,
+        )
+        return self._ptq_result
+
+    def _export(self, sim: QuantizationSimModel) -> Tuple[str, str]:
+        """
+        Export quantsim.
+        :param sim: QuantizationSimModel object to export.
+        :return: The paths where model and encoding are saved
+        """
+        sim.export(path=self._results_dir,
+                   filename_prefix=self.title_lowercase)
+        model_path = os.path.join(self._results_dir, f"{self.title_lowercase}.onnx")
+        encoding_path = os.path.join(self._results_dir, f"{self.title_lowercase}.encodings")
+        _logger.info("The results of %s is saved in %s and %s.",
+                     self.title, model_path, encoding_path)
+        return model_path, encoding_path
+
+
+def _build_flowchart_metadata(result: Mapping) -> Dict: # pylint: disable=too-many-return-statements
+    """
+    Build flowchart metadata for the html template of summary report
+
+    :param result: Result of AutoQuant with the following format:
+
+        result := {
+            "quantscheme_selection": _stage_result,
+            "w32_evaluation": _stage_result,
+            "ptq_techniques" [
+                "batchnorm_folding": _stage_result,
+                "cross_layer_equalization": _stage_result,
+                "adaround": _stage_result,
+            ]
+
+        }
+
+        where _stage_result is a dictionary defined as below:
+
+        _stage_result := {
+            "status": str,
+            "error": Exception,
+            "target_satisfied": bool,
+            "effective": bool,
+        }
+
+    :return: Dictionary that contains flowchart metadata for html template
+    """
+    metadata = defaultdict(str)
+    metadata.update(
+        edge_quant_scheme_selection_in='data-visited="true"',
+    )
+    if "quantscheme_selection" in result:
+        status = result['quantscheme_selection']['status']
+        metadata.update(
+            node_quant_scheme_selection=f'data-visited="true" data-stage-result="{status}"',
+        )
+
+        if status == 'error-failed':
+            return metadata
+
+    metadata.update(
+        edge_quant_scheme_selection_out='data-visited="true"',
+        node_test_w32_eval_score='data-visited="true"',
+    )
+
+    if not result["w32_evaluation"]["target_satisfied"]:
+        metadata.update(
+            edge_test_w32_eval_score_if_false='data-visited="true"',
+            node_result_fail='data-visited="true"',
+        )
+        return metadata
+
+    metadata.update(
+        edge_test_w32_eval_score_if_true='data-visited="true"',
+    )
+
+    for ptq_name, ptq_result in result["ptq_techniques"].items():
+        status = ptq_result['status']
+        effective = ptq_result['effective']
+        if status == "success" and not effective:
+            status = "discarded"
+        metadata.update({
+            f"node_{ptq_name}": f'data-visited="true" data-stage-result="{status}"',
+        })
+
+        if status == 'error-failed':
+            return metadata
+
+        metadata.update({
+            f'edge_{ptq_name}_out': 'data-visited="true"',
+            f'node_test_{ptq_name}': 'data-visited="true"',
+        })
+
+        if ptq_result['target_satisfied']:
+            metadata.update({
+                f'edge_test_{ptq_name}_if_true': 'data-visited="true"',
+                'node_result_success': 'data-visited="true"',
+            })
+            return metadata
+
+        metadata.update({
+            f'edge_test_{ptq_name}_if_false': 'data-visited="true"',
+        })
+
+    metadata.update(
+        node_result_fail='data-visited="true"',
+    )
+
+    return metadata
+
+ParetoFrontType = List[Tuple[int, float, QuantizerGroup, Tuple]]
+
+
+@dataclass
+class _MixedPrecisionArgs:
+    """
+    Mixed-precision specific arguments.
+    """
+    candidates: List[AmpCandidate]
+    forward_pass_callback: CallbackFunc
+    eval_callback_factory: EvalCallbackFactory
+    num_samples_for_phase_1: int
+    eval_callback_for_phase2: CallbackFunc
+
+
+@dataclass
+class _MixedPrecisionResult:
+    """
+    Mixed precision result
+    """
+    pareto_list: ParetoFrontType
+    sim: QuantizationSimModel
+    final_eval_score: float
+    sensitivity_plot: bokeh.plotting.figure
+    pareto_plot: bokeh.plotting.figure
+
+
+# The number of samples to be used for performance evaluation and AMP.
+# NOTE: None means "all".
+DEFAULT_NUM_SAMPLES_FOR_AMP_PHASE_1 = EvalCallbackFactory._DEFAULT_SQNR_NUM_SAMPLES
+DEFAULT_NUM_SAMPLES_FOR_AMP_PHASE_2 = None
+
+
+
    
+[docs]
+class AutoQuantWithAutoMixedPrecision:
+    """
+    Integrate and apply post-training quantization techniques.
+
+    AutoQuant includes 1) batchnorm folding, 2) cross-layer equalization,
+    3) Adaround, and 4) Automatic Mixed Precision (if enabled).
+    These techniques will be applied in a best-effort manner until the model
+    meets the evaluation goal given as allowed_accuracy_drop.
+    """
+    def __init__( # pylint: disable=too-many-arguments, too-many-locals
+            self,
+            model: ONNXModel,
+            dummy_input: Dict[str, np.ndarray],
+            data_loader: DataLoader,
+            eval_callback: Callable[[ort.InferenceSession, int], float],
+            param_bw: int = 8,
+            output_bw: int = 8,
+            quant_scheme: QuantScheme = QuantScheme.post_training_tf_enhanced,
+            rounding_mode: str = 'nearest',
+            use_cuda: bool = True,
+            device: int = 0,
+            config_file: str = None,
+            results_dir: str = "/tmp",
+            cache_id: str = None,
+            strict_validation: bool = True) -> None:
+        """
+        :param model: Model to be quantized.
+        :param dummy_input: Dummy input dict for the model.
+        :param data_loader: A collection that iterates over an unlabeled dataset, used for computing encodings
+        :param eval_callback: Function that calculates the evaluation score given the model session
+        :param param_bw: Parameter bitwidth
+        :param output_bw: Output bitwidth
+        :param quant_scheme: Quantization scheme
+        :param rounding_mode: Rounding mode
+        :param use_cuda: True if using CUDA to run quantization op. False otherwise.
+        :param config_file: Path to configuration file for model quantizers
+        :param results_dir: Directory to save the results of PTQ techniques
+        :param cache_id: ID associated with cache results
+        :param strict_validation: Flag set to True by default.When False, AutoQuant will proceed with execution and handle errors internally if possible. This may produce unideal or unintuitive results.
+        """
+        self._auto_quant_base = AutoQuant(model=model,
+                                          dummy_input=dummy_input,
+                                          data_loader=data_loader,
+                                          eval_callback=eval_callback,
+                                          param_bw=param_bw,
+                                          output_bw=output_bw,
+                                          quant_scheme=quant_scheme,
+                                          rounding_mode=rounding_mode,
+                                          use_cuda=use_cuda,
+                                          device=device,
+                                          config_file=config_file,
+                                          results_dir=results_dir,
+                                          cache_id=cache_id,
+                                          strict_validation=strict_validation)
+        self._data_loader = data_loader
+        self._amp_args = None
+
+
    
+[docs]
+    def run_inference(self) -> Tuple[QuantizationSimModel, float]:
+        '''
+        Creates a quantization model and performs inference
+
+        :return: QuantizationSimModel, model accuracy as float
+        '''
+        return self._auto_quant_base.run_inference()
    
+
+
+
    
+[docs]
+    def optimize(self, allowed_accuracy_drop: float = 0.0)\
+            -> Tuple[ONNXModel, float, str, ParetoFrontType]:
+        """
+        Integrate and apply post-training quantization techniques.
+
+        :param allowed_accuracy_drop: Maximum allowed accuracy drop
+        :return: Tuple of  (best model, eval score, encoding path, pareto front).
+            Pareto front is None if AMP is not enabled or AutoQuant exits
+            without performing AMP.
+        """
+        html_template_file = os.path.join(
+            os.path.dirname(os.path.abspath(__file__)),
+            "auto_quant_v2_diagnostics_template_with_amp.html",
+        )
+        with patch.object(_EvalManager, "HTML_TEMPLATE_FILE", html_template_file):
+            result = self._auto_quant_base._optimize_helper(self._optimize_main,
+                                                            allowed_accuracy_drop)
+            return result["model"],\
+                   result["accuracy"],\
+                   result["encoding_path"],\
+                   result["pareto_list"]
    
+
+
+
    
+[docs]
+    def set_adaround_params(self, adaround_params: AdaroundParameters) -> None:
+        """
+        Set Adaround parameters.
+        If this method is not called explicitly by the user, AutoQuant will use
+        `data_loader` (passed to `__init__`) for Adaround.
+
+        :param adaround_params: Adaround parameters.
+        """
+        self._auto_quant_base.set_adaround_params(adaround_params)
    
+
+
+    # pylint: disable=unused-argument
+
    
+[docs]
+    def set_mixed_precision_params(
+            self,
+            candidates: List[CANDIDATE_WITH_DTYPE],
+            num_samples_for_phase_1: Optional[int] = DEFAULT_NUM_SAMPLES_FOR_AMP_PHASE_1,
+            forward_fn: Callable = _default_forward_fn,
+            num_samples_for_phase_2: Optional[int] = DEFAULT_NUM_SAMPLES_FOR_AMP_PHASE_2,
+    ) -> None:
+        """
+        Set mixed precision parameters.
+        NOTE: Automatic mixed precision will NOT be enabled unless this method
+        is explicitly called by the user.
+
+        :param candidates: List of tuples of candidate bitwidths and datatypes.
+        :param num_samples_for_phase_1: Number of samples to be used for performance
+                evaluation in AMP phase 1.
+        :param forward_fn: Function that runs forward pass and returns the output tensor.
+                which will be used for SQNR compuatation in phase 1.
+                This function is expected to take 1) a model and 2) a single batch
+                yielded from the data loader, and return a single np.ndarray object
+                which represents the output of the model.
+        :param num_samples_for_phase_2: Number of samples to be used for performance
+                evaluation in AMP phase 2.
+        """
+        if len(candidates) < 2:
+            raise ValueError(f"AMP requires at least two candidates. Got {len(candidates)}.")
+
+        baseline_param_bw = self._auto_quant_base._quantsim_params["param_bw"]
+        baseline_output_bw = self._auto_quant_base._quantsim_params["output_bw"]
+        baseline_candidate = (
+            (baseline_output_bw, QuantizationDataType.int),
+            (baseline_param_bw, QuantizationDataType.int),
+        )
+
+        if baseline_candidate not in candidates:
+            raise ValueError(
+                f"AMP candidate must contain W{baseline_param_bw}A{baseline_output_bw}, "
+                "which was passed to the constructor of AutoQuant as `param_bw` and `output_bw`."
+            )
+
+        for candidate in candidates:
+            ((output_bw, output_dtype), (param_bw, param_dtype)) = candidate
+
+            if output_dtype != param_dtype:
+                raise ValueError(
+                    "The data types of parameters and outputs should be the same. "
+                    f"Got {output_dtype} output and {param_dtype} for parameter."
+                )
+
+            if output_dtype == QuantizationDataType.float:
+                continue
+
+            # The param/output_bw passed to the constructor of AutoQuant
+            # must be the baseline-bitwidth candidate among all AMP candidates.
+            if output_bw < baseline_output_bw or param_bw < baseline_param_bw:
+                raise ValueError(
+                    "All AMP candidates should be strictly superior to the baseline "
+                    f"W{baseline_param_bw}A{baseline_output_bw}, which was passed "
+                    "to the constructor of AutoQuant. Please make sure that all the INT candidates "
+                    f"satisfy param_bw >= {baseline_param_bw} and output_bw >= {baseline_param_bw}."
+                )
+
+        factory = EvalCallbackFactory(self._data_loader, forward_fn=forward_fn)
+
+        candidates = [AmpCandidate(candidate) for candidate in set(candidates)]
+
+        self._amp_args = _MixedPrecisionArgs(
+            candidates=candidates,
+            forward_pass_callback=CallbackFunc(self._auto_quant_base.forward_pass_callback, None),
+            eval_callback_factory=factory,
+            num_samples_for_phase_1=num_samples_for_phase_1,
+            eval_callback_for_phase2=CallbackFunc(self._auto_quant_base.eval_callback,
+                                                  num_samples_for_phase_2)
+        )
    
+
+
+
    
+[docs]
+    def get_quant_scheme_candidates(self) -> Tuple[_QuantSchemePair, ...]:
+        """
+        Return the candidates for quant scheme search.
+        During :meth:`~AutoQuant.optimize`, the candidate with the highest accuracy
+        will be selected among them.
+
+        :return: Candidates for quant scheme search
+        """
+        return self._auto_quant_base.get_quant_scheme_candidates()
    
+
+
+
    
+[docs]
+    def set_quant_scheme_candidates(self, candidates: Tuple[_QuantSchemePair, ...]):
+        """
+        Set candidates for quant scheme search.
+        During :meth:`~AutoQuant.optimize`, the candidate with the highest accuracy
+        will be selected among them.
+
+        :param candidates: Candidates for quant scheme search
+        """
+        # pylint: disable=no-member
+        return self._auto_quant_base.set_quant_scheme_candidates(candidates)
    
+
+
+    def _apply_mixed_precision(
+            self,
+            model: ONNXModel,
+            target_acc: float,
+            amp_args: _MixedPrecisionArgs,
+            results_dir: str,
+            encoding_path: str = None,
+    ) -> _MixedPrecisionResult:
+        """
+        Apply mixed-precision and return the highest accuracy.
+
+        NOTE1: Input model is not mutated.
+        NOTE2: Parameter `clean_start` is always set to True.
+
+        :param model: Model to apply mixed precision.
+        :param target_acc: Minimum evaluation score required.
+        :param encoding_path: Path to parameter encodings file.
+        :param results_dir: Directory to save the results of AdaRound and mixed precision.
+        :return: MixedPrecisionAlgo object.
+        """
+        if not amp_args:
+            raise RuntimeError
+
+        sim = self._auto_quant_base._create_quantsim_and_encodings(model,
+                                                                   encoding_path=encoding_path)
+
+        eval_callback_for_phase1 = amp_args.eval_callback_factory.sqnr(sim=sim, num_samples=amp_args.num_samples_for_phase_1)
+
+        algo = GreedyMixedPrecisionAlgo(
+            sim,
+            amp_args.candidates,
+            eval_callback_for_phase1,
+            amp_args.eval_callback_for_phase2,
+            results_dir=results_dir,
+            clean_start=True,
+            forward_pass_callback=amp_args.forward_pass_callback
+        )
+
+        # Find baseline accuracy and bw corresponding to baseline accuracy
+        algo.set_baseline(fp32_accuracy=self._auto_quant_base._fp32_acc)
+        allowed_accuracy_drop = algo.fp32_accuracy - target_acc
+
+        algo.run(allowed_accuracy_drop)
+
+        sensitivity_plot = None
+        if algo.accuracy_list is not None:
+            # Visualize quantizer group sensitivity
+            sensitivity_plot = create_sensitivity_plot(algo.accuracy_list,
+                                                       algo.baseline_candidate,
+                                                       algo.fp32_accuracy)
+
+        pareto_plot = None
+        if algo.pareto_list is not None:
+            # Create pareto list curve
+            pareto_plot = create_pareto_curve(algo.pareto_list)
+
+        return _MixedPrecisionResult(algo.pareto_list,
+                                     algo._sim,
+                                     algo._final_eval_score,
+                                     sensitivity_plot,
+                                     pareto_plot)
+
+    def _optimize_main(self, fp32_model: ONNXModel, target_acc: float) -> Dict[str, Any]:
+        """
+        Helper function of apply().
+
+        :param fp32_model: Model to apply PTQ techniques.
+        :param target_acc: Target eval score.
+        :return: The best ptq result as a dictionary.
+        """
+        # pylint: disable=broad-except, too-many-locals, too-many-statements, too-many-branches
+
+        if self._amp_args:
+            candidates = copy.copy(self._amp_args.candidates)
+        else:
+            candidates = []
+
+        eval_manager = self._auto_quant_base.eval_manager
+        results_dir = self._auto_quant_base.results_dir
+        strict_validation = eval_manager._strict_validation
+
+        sess = eval_manager.session("")
+        _multiconfig_adaround_fn = _adaround_wrapper(self._auto_quant_base._apply_adaround,
+                                                     self._auto_quant_base,
+                                                     candidates,
+                                                     target_acc,
+                                                     sess.eval)
+        sess_eval_fn = _EvalSession.eval
+        def eval_fn(_, model, param_bw=None, output_bw=None, **kwargs):
+            if param_bw == 32:
+                # For W32 evaluation, use the highest output bitwidth
+                # among all the AMP candidates
+                output_bitwidths = [
+                    output_bw for (output_bw, output_dtype), _ in candidates
+                    if output_dtype == QuantizationDataType.int
+                ]
+                output_bitwidths.append(self._auto_quant_base._quantsim_params["output_bw"])
+                output_bw = max(output_bitwidths)
+            return sess_eval_fn(_, model, param_bw=param_bw, output_bw=output_bw, **kwargs)
+
+        with patch.object(self._auto_quant_base, "_apply_adaround", _multiconfig_adaround_fn),\
+                patch.object(_EvalSession, "eval", eval_fn):
+            try:
+                result = self._auto_quant_base._optimize_main(fp32_model, target_acc)
+
+                # Automatic Mixed Precision
+                result["pareto_list"] = None
+
+                # An empty `result` dict means AutoQuant early-exited
+                # because W32 eval score didn't meet the target accuracy.
+                # In this case, do not proceed to AMP and exit immediately.
+                if result["model"] is None and\
+                        result["accuracy"] is None and\
+                        result["encoding_path"] is None and\
+                        result["applied_techniques"] is None:
+                    return result
+
+                if result["accuracy"] >= target_acc or not self._amp_args:
+                    return result
+
+                if len(candidates) < 2:
+                    _logger.info(
+                        "After Adaround, we have only one Adarond-compatible candidate left for AMP (W%dA%d). "
+                        "Return without proceeding to AMP", candidates[0].param_bw, candidates[0].output_bw
+                    )
+                    return result
+
+                model = result["model"]
+                applied_techniques = result["applied_techniques"]
+                # Freeze weight encoding to adaround weight encoding
+                encoding_path = result["encoding_path"] if "adaround" in applied_techniques else None
+            except Exception:
+                if strict_validation:
+                    raise
+                result = {}
+                model = fp32_model
+                applied_techniques = []
+                encoding_path = None
+
+            amp_args = copy.copy(self._amp_args)
+            if amp_args:
+                amp_args.candidates = candidates
+
+        with eval_manager.session("Automatic Mixed Precision", ptq=True) as sess:
+            amp_result = self._apply_mixed_precision(
+                model, target_acc, amp_args, results_dir, encoding_path=encoding_path
+            )
+            result["pareto_list"] = amp_result.pareto_list
+
+            if amp_result.sensitivity_plot is not None:
+                sess.diagnostics.add(amp_result.sensitivity_plot)
+
+            if amp_result.pareto_plot is not None:
+                sess.diagnostics.add(amp_result.pareto_plot)
+
+            sess.set_ptq_result(sim=amp_result.sim, acc=amp_result.final_eval_score,
+                                applied_techniques=[*applied_techniques, "automatic_mixed_precision"])
+
+        best_result = eval_manager.get_best_ptq_result()
+        if best_result:
+            if "automatic_mixed_precision" not in best_result.applied_techniques:
+                sess.result["effective"] = False
+            if best_result.accuracy >= target_acc:
+                sess.result["target_satisfied"] = True
+            result.update(best_result.as_dict())
+            return result
+
+        raise RuntimeError("None of Batchnorm folding, CLE, Adaround or AMP "
+                           "has been finished successfully.")
    
+
+
+def _adaround_wrapper(apply_adaround_fn: Callable,
+                      auto_quant: AutoQuant,
+                      amp_candidates: List[AmpCandidate],
+                      target_acc: float,
+                      eval_fn: Callable):
+    @functools.wraps(apply_adaround_fn)
+    def _apply_adaround_wrapper(*args, **kwargs): # pylint: disable=too-many-locals
+        # If AMP candidates are empty (i.e. AMP is disabled),
+        # perform normal (single-round) adaround.
+        if not amp_candidates:
+            return apply_adaround_fn(*args, **kwargs)
+
+        def apply_adaround(param_bw: int):
+            _logger.info("Running Adaround with W%d", param_bw)
+
+            orig_param_bw = auto_quant._quantsim_params["param_bw"]
+            try:
+                auto_quant._quantsim_params["param_bw"] = param_bw
+                return apply_adaround_fn(*args, **kwargs)
+            finally:
+                auto_quant._quantsim_params["param_bw"] = orig_param_bw
+
+        int_candidates = [
+            candidate for candidate in amp_candidates
+            if candidate.param_dtype == QuantizationDataType.int
+        ]
+        sorted_int_candidates = sorted(int_candidates,
+                                       key=lambda candidate: (candidate.param_bw, candidate.output_bw))
+        # Run Adaround with the lowest-bitwidth candidate
+        lowest_candidate = sorted_int_candidates[0]
+        model, encoding_path = apply_adaround(param_bw=lowest_candidate.param_bw)
+
+        # If the lowest candidate is the only INT candidate, return immediately
+        if len(sorted_int_candidates) == 1:
+            return model, encoding_path
+
+        eval_score = eval_fn(model,
+                             param_bw=lowest_candidate.param_bw,
+                             output_bw=lowest_candidate.output_bw,
+                             encoding_path=encoding_path)
+        _logger.info("W%dA%d eval score after Adaround: %f",
+                     lowest_candidate.param_bw,
+                     lowest_candidate.output_bw,
+                     eval_score)
+
+        # If the lowest candidate satisfy the target accuracy, return immediately
+        if eval_score >= target_acc:
+            return model, encoding_path
+
+        # If the lowest candidate fails to meet the target accuracy,
+        # discard the lowest candidate, apply Adaround to the second-lowest candidate,
+        # and use it as the baseline for AMP.
+        second_lowest_candidate = sorted_int_candidates[1]
+
+        if second_lowest_candidate.param_bw != lowest_candidate.param_bw:
+            model = None
+            model, encoding_path = apply_adaround(param_bw=second_lowest_candidate.param_bw)
+            eval_score = eval_fn(model,
+                                 param_bw=second_lowest_candidate.param_bw,
+                                 output_bw=second_lowest_candidate.output_bw,
+                                 encoding_path=encoding_path)
+            _logger.info("W%dA%d eval score after Adaround: %f",
+                         second_lowest_candidate.param_bw,
+                         second_lowest_candidate.output_bw,
+                         eval_score)
+
+        # Only the candidates that are compatible with adaround can be used for AMP
+        adaround_compatible_amp_candidates = [
+            candidate for candidate in amp_candidates
+            if candidate.param_bw == second_lowest_candidate.param_bw or\
+                    candidate.param_dtype == QuantizationDataType.float
+        ]
+
+        # Fill in AMP candidates with Adaround-compatible candidates only
+        amp_candidates.clear()
+        amp_candidates.extend(adaround_compatible_amp_candidates)
+
+        return model, encoding_path
+
+    return _apply_adaround_wrapper
+
    +
    +
    +
    + +
    + + +
    +
    +
    +
    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
    + Made with Sphinx and @pradyunsg's + + Furo + +
    +
    +
    + + + + + + +
    +
    +
    + +
    +
    + +
    +
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    +

    Source code for aimet_onnx.batch_norm_fold

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2022, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
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+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+""" ONNX Code to fold batch-norm layers """
+
+from typing import Dict, List, Tuple
+import contextlib
+import numpy as np
+import onnx
+from onnx import numpy_helper
+from onnxruntime.quantization.onnx_quantizer import ONNXModel
+from packaging import version
+
+# pylint: disable=wrong-import-order
+from aimet_common.bias_correction import ConvBnPatternHandler
+from aimet_common.graph_pattern_matcher import PatternType
+from aimet_common.graph_searcher import GraphSearcher
+from aimet_common.connected_graph.connectedgraph_utils import get_ordered_ops
+import aimet_common.libpymo as libpymo
+from aimet_common.utils import AimetLogger
+
+from aimet_onnx.meta.connectedgraph import ConnectedGraph
+from aimet_onnx.meta.connectedgraph import WEIGHT_INDEX, BIAS_INDEX, RUNNING_MEAN_INDEX, RUNNING_VAR_INDEX
+from aimet_onnx.meta.operations import Op
+from aimet_onnx.utils import get_node_attribute, remove_node, transpose_tensor, ParamUtils, retrieve_constant_input
+
+# pylint: disable=no-name-in-module, ungrouped-imports
+if version.parse(onnx.__version__) >= version.parse("1.14.0"):
+    from onnx import NodeProto, TensorProto, ModelProto
+else:
+    from onnx.onnx_pb import NodeProto, TensorProto, ModelProto
+
+logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.BatchNormFolding)
+
+ConvType = ['Conv', 'ConvTranspose']
+LinearType = ['Gemm', 'MatMul']
+BatchNormType = ['BatchNormalization']
+
+class BNLayer:
+    """ Captures beta and gamma parameter for BatchNorm layers to be used during High Bias absorption """
+    def __init__(self):
+        self.bn_layer = None
+        self.gamma = None
+        self.beta = None
+
+
+def _find_conv_bn_pairs(connected_graph: ConnectedGraph) -> Dict:
+    """
+    Uses searcher to find preceding and next bn layers for a conv/linear layer
+    :param connected_graph: ConnectedGraph object.
+    :return: dictionary of conv/linear Op with associated bn op / activation info
+    """
+
+    # initialize all patterns to be matched and associated call back functions
+    patterns_with_callbacks = []
+    layer_select_handler = ConvBnPatternHandler()
+    preceding_linear_op_types = ['Flatten', 'Reshape']
+
+    # Linear layer combinations
+    for linear_op in LinearType:
+        for preceding_linear_op_type in preceding_linear_op_types:
+            # BN -> Linear
+            patterns_with_callbacks.append(PatternType(pattern=['BatchNormalization', preceding_linear_op_type, linear_op],
+                                                       action=layer_select_handler))
+
+    for op_type in ConvType + LinearType:
+        patterns_with_callbacks.append(PatternType(pattern=['BatchNormalization', op_type],
+                                                   action=layer_select_handler))
+        patterns_with_callbacks.append(PatternType(pattern=[op_type, 'BatchNormalization'],
+                                                   action=layer_select_handler))
+
+    # create graph searcher instance with connected graph and patterns to search
+    graph_searcher = GraphSearcher(connected_graph, patterns_with_callbacks)
+
+    # get all conv/linear and bn info
+    graph_searcher.find_all_patterns_in_graph_apply_actions()
+    convs_bn_activation_dict = layer_select_handler.get_conv_linear_bn_info_dict()
+
+    return convs_bn_activation_dict
+
+
+def find_all_batch_norms_to_fold(connected_graph: ConnectedGraph,
+                                 ) -> Tuple[List[Tuple[NodeProto, NodeProto]],
+                                            List[Tuple[NodeProto, NodeProto]]]:
+    """
+    Find all possible batch norm layers that can be folded. Returns a list of pairs such that (bn, layer)
+    means bn will be forward-folded into layer and (layer, bn) means bn will be backward-folded into layer
+    :param connected_graph: connected graph model to search
+    :return: A list of (layer, bn) pairs and a list of (bn, layer) pairs,
+             where `bn` can be folded into to `layer`.
+    """
+    conv_linear_bn_activation_info_dict = _find_conv_bn_pairs(connected_graph)
+    model = connected_graph.model
+    # To mark BN's already picked for backward folding
+    bn_picked_for_folding = set()
+
+    ordered_conv_fc_nodes = get_ordered_conv_linears(connected_graph)
+
+    conv_bn_pairs = []
+    # Backward fold is given priority over Forward fold
+    for node in ordered_conv_fc_nodes:
+        # Filter out combinations that are not supported
+        if node in conv_linear_bn_activation_info_dict.keys():
+            bn_info = conv_linear_bn_activation_info_dict[node]
+            if bn_info.output_bn and bn_info.output_bn not in bn_picked_for_folding:
+                if is_valid_bn_fold(node.get_module(), model, True):
+                    conv_bn_pairs.append((node.get_module(), bn_info.output_bn.get_module()))
+                    bn_picked_for_folding.add(bn_info.output_bn)
+                else:
+                    logger.info('...... invalid combination to fold %s', [node.name, bn_info.output_bn.name])
+
+    bn_conv_pairs = []
+    for node in ordered_conv_fc_nodes:
+        # Filter out combinations that are not supported
+        if node in conv_linear_bn_activation_info_dict.keys():
+            bn_info = conv_linear_bn_activation_info_dict[node]
+            if bn_info.input_bn and bn_info.input_bn not in bn_picked_for_folding:
+                if is_valid_bn_fold(node.get_module(), model, False):
+                    bn_conv_pairs.append((bn_info.input_bn.get_module(), node.get_module()))
+                    bn_picked_for_folding.add(bn_info.input_bn)
+                else:
+                    logger.info('...... invalid combination to fold %s', [bn_info.input_bn.name, node.name])
+
+    return conv_bn_pairs, bn_conv_pairs
+
+
+def get_ordered_conv_linears(conn_graph: ConnectedGraph) -> List[Op]:
+    """
+    helper to select a list of candidate layers for BatchNorm folding
+    :param conn_graph: connected graph to search
+    :return: List of conv/linear layers
+    """
+    # get ordered operations list from the connected graph
+    list_of_ordered_ops = get_ordered_ops(conn_graph.starting_ops)
+
+    # look for conv/linear layers
+    ordered_convs = []
+    for op in list_of_ordered_ops:
+        if op.type in ConvType + LinearType:
+            ordered_convs.append(op)
+    return ordered_convs
+
+
+def is_valid_bn_fold(conv_linear: NodeProto, model: ModelProto, fold_backward: bool) -> bool:
+    """
+    Determine if a given layer can successfully absorb a BatchNorm given the layer type and parameters
+    :param conv_linear: The Conv/Linear layer to fold a BatchNorm into.
+    :param model: The model to which the Conv/Linear layer belongs.
+    :param fold_backward: True if BatchNorm comes after Conv/Linear layer
+    :return: True if a BatchNorm layer can be folded without causing output error.
+    """
+    valid = True
+    if conv_linear.op_type in LinearType:
+        # Check if this is actually a fully connected layer or a dynamic matmul
+        w = retrieve_constant_input(conv_linear, model, WEIGHT_INDEX)[0]
+        if w is None:
+            valid = False
+    if not fold_backward:
+        # Cannot fold BN -> Conv with padding. AIMET does not support forward folding to grouped or DW Conv
+        if conv_linear.op_type == 'Conv':
+            valid &= all(item == 0 for item in get_node_attribute(conv_linear, "pads"))
+            valid &= get_node_attribute(conv_linear, "group") == 1
+        # AIMET does not support forward folding to ConvTranspose
+        elif conv_linear.op_type == 'ConvTranspose':
+            valid = False
+    else:
+        # AIMET does not support backwards folding to grouped ConvTranspose
+        if conv_linear.op_type == 'ConvTranspose':
+            valid &= get_node_attribute(conv_linear, "group") in (1, get_input_output_channels(conv_linear, model)[0])
+    return valid
+
+
+
    
+[docs]
+def fold_all_batch_norms_to_weight(model: ModelProto) -> [List]:
+    """
+    Fold all possible batch_norm layers in a model into the weight of the corresponding conv layers
+
+    :param model: onnx Model to perform BN fold on
+    :return: A list of pairs of layers [(Conv/Linear, BN layer that got folded)]
+    """
+    if isinstance(model, ONNXModel):
+        model = model.model
+    connected_graph = ConnectedGraph(model)
+    model = connected_graph.model
+    conv_bn_pairs, bn_conv_pairs = find_all_batch_norms_to_fold(connected_graph)
+    conv_bns = []
+    bn_convs = []
+    for conv, bn in conv_bn_pairs:
+        bn_layer = _fold_to_weight(model, conv, bn, True)
+        conv_bns.append((conv, bn_layer))
+        remove_node(bn, model.graph)
+
+    for bn, conv in bn_conv_pairs:
+        bn_layer = _fold_to_weight(model, conv, bn, False)
+        bn_convs.append((conv, bn_layer))
+        remove_node(bn, model.graph)
+
+    _update_standalone_batchnorm_ops(model)
+
+    return conv_bns, bn_convs
    
+
+
+
+def _fold_to_weight(model: ModelProto,
+                    conv_linear: NodeProto,
+                    bn: NodeProto,
+                    fold_backward: bool):
+    """
+    Fold BatchNorm into the weight and bias of the given layer.
+
+    :param model: onnx model to which the conv/bn pair belong
+    :param conv_linear: Conv or linear layer to fold BN into.
+    :param bn: BatchNorm to fold.
+    :param fold_backward: True if the BatchNorm comes after the Conv
+    """
+    # Must convert MatMul layers to Gemm to allow bias
+    if conv_linear.op_type == "MatMul":
+        _matmul_to_gemm(conv_linear, model)
+
+    weight = ParamUtils.get_param(model, conv_linear, WEIGHT_INDEX)
+    bias = ParamUtils.get_param(model, conv_linear, BIAS_INDEX)
+    groups = get_node_attribute(conv_linear, "group")
+
+    # If layer doesn't have bias, create a bias initializer and add it to the model, then retrieve it
+    if not bias:
+        bias_data = np.zeros(get_input_output_channels(conv_linear, model)[1])
+        bias_name = conv_linear.name + ".bias"
+        bias = numpy_helper.from_array(bias_data.astype(np.float32), name=bias_name)
+        model.graph.initializer.append(bias)
+        conv_linear.input.append(bias_name)
+        bias = ParamUtils.get_param(model, conv_linear, BIAS_INDEX)
+
+    # Transpose weights to C, N, H, W from N, C, H, W since axis are flipped for transposed conv
+    # However depthwise conv layers are always N, 1, H, W whether transposed-conv or not, so no need to transpose
+    # if conv_linear.type == "ConvTranspose" and conv_linear groups == 1:
+    if conv_linear.op_type == "ConvTranspose" and groups == 1:
+        weight = transpose_tensor(weight, (1, 0, 2, 3))
+    # Gemm layers may or may not need to have weights transposed depending on value of transB attribute
+    elif conv_linear.op_type in LinearType and not get_node_attribute(conv_linear, "transB"):
+        weight = transpose_tensor(weight, (1, 0))
+
+    channels = weight.dims[0] if fold_backward else weight.dims[1]
+    bn_param = get_bn_params(model, bn, channels)
+    bn_layer = copy_bn_params_to_bn_layer(bn, bn_param)
+
+    _call_mo_batch_norm_fold(weight, bias, bn_param, fold_backward=fold_backward)
+
+    # Transpose weight back to original configuration
+    if conv_linear.op_type == "ConvTranspose" and groups == 1:
+        weight = transpose_tensor(weight, (1, 0, 2, 3))
+    elif conv_linear.op_type in LinearType and not get_node_attribute(conv_linear, "transB"):
+        weight = transpose_tensor(weight, (1, 0))
+
+    weight_param = ParamUtils.get_param(model, conv_linear, WEIGHT_INDEX)
+    weight_param.raw_data = weight.raw_data
+    return bn_layer
+
+
+def _matmul_to_gemm(node: NodeProto, model: ModelProto):
+    """
+    Convert MatMul node to Gemm and initialize bias to zeros
+
+    :param node: MatMul node to convert to Gemm
+    :param model: model to which the node belongs
+    """
+    assert node.op_type == "MatMul"
+
+    weight, transposed = retrieve_constant_input(node, model, WEIGHT_INDEX)
+    if transposed:
+        node.input[WEIGHT_INDEX] = weight.name
+        model.graph.initializer.remove(weight)
+        weight = transpose_tensor(weight, (1, 0))
+        model.graph.initializer.append(weight)
+    node.op_type = "Gemm"
+    node.name = node.name.replace("MatMul", "Gemm")
+    # Create bias vector for Gemm operation
+    bias_name = node.name + ".bias"
+    bias_data = np.zeros(weight.dims[1])
+    bias = numpy_helper.from_array(bias_data.astype(np.float32), name=bias_name)
+    model.graph.initializer.append(bias)
+    node.input.append(bias_name)
+
+
+def _call_mo_batch_norm_fold(weight: TensorProto,
+                             bias: TensorProto,
+                             bn_params: libpymo.BNParams,
+                             fold_backward: bool):
+    """
+    Calls C++ batch norm folding API.
+
+    :param weight: Weight or scale tensor to fold BN into.
+    :param bias: Bias tensor to fold BN into.
+    :param bn_params: Batch Norm layer
+    :param fold_backward: True if BatchNorm comes after Conv/Linear layer
+    """
+    weight_tensor = libpymo.TensorParams()
+
+    weight_tensor.data = numpy_helper.to_array(weight).reshape(-1)
+    weight_tensor.shape = np.array(weight.dims)
+
+    bias_tensor = libpymo.TensorParams()
+
+    bias_tensor.data = numpy_helper.to_array(bias).reshape(-1)
+    bias_tensor.shape = np.array(bias.dims)
+    is_bias_valid = True
+
+    with _expand_shape_to_4d(weight_tensor):
+        _bias = libpymo.fold(bn_params, weight_tensor, bias_tensor, is_bias_valid, fold_backward)
+
+    bias.raw_data = np.asarray(_bias, dtype=np.float32).tobytes()
+    weight.raw_data = np.asarray(weight_tensor.data, dtype=np.float32).tobytes()
+
+
+def get_bn_params(model: ModelProto, bn: NodeProto, channels: int) -> libpymo.BNParams:
+    """
+    Returns the populated libpymo.BNParams object for the given BatchNormalization layer with
+    parameters repeated if necessary.
+
+    :param model: model to which the bn layer belongs
+    :param bn: BatchNormalization layer to retrieve the parameters from
+    :param channels: The effective number of channels the BatchNorm layer operates on (needed for Gemm layers)
+    :return: libpymo.BNParams object for the input BatchNorm layer
+    """
+    bn_params = libpymo.BNParams()
+    gamma = numpy_helper.to_array(ParamUtils.get_param(model, bn, WEIGHT_INDEX)).reshape(-1)
+    # In the case of BatchNorm2d -> Flatten -> Gemm, must resize the BN parameters to the Gemm input feature length
+    resize = channels / len(gamma)
+    bn_params.gamma = np.repeat(gamma, resize)
+    bn_params.beta = np.repeat(numpy_helper.to_array(ParamUtils.get_param(model, bn, BIAS_INDEX)).reshape(-1), resize)
+    bn_params.runningMean = np.repeat(
+        numpy_helper.to_array(ParamUtils.get_param(model, bn, RUNNING_MEAN_INDEX)).reshape(-1), resize)
+    runningVar = numpy_helper.to_array(ParamUtils.get_param(model, bn, RUNNING_VAR_INDEX))
+
+    epsilon = get_node_attribute(bn, "epsilon")
+    if epsilon is None:
+        epsilon = 1e-5 # Default onnx epsilon value
+    sigma = np.sqrt(runningVar + epsilon)
+    bn_params.runningVar = np.repeat(sigma.reshape(-1), resize)
+
+    return bn_params
+
+
+def copy_bn_params_to_bn_layer(bn: NodeProto, bn_params: libpymo.BNParams) -> BNLayer:
+    """
+    Copies bn params to a BN layer which can be used later by High bias absorption
+    :param bn: BN layer
+    :param bn_params: libpymo.BNParams object for the BatchNorm layer
+    :return BNLayer object
+    """
+    bn_layer = BNLayer()
+    bn_layer.bn_layer = bn
+    bn_layer.gamma = bn_params.gamma
+    bn_layer.beta = bn_params.beta
+    return bn_layer
+
+
+@contextlib.contextmanager
+def _expand_shape_to_4d(weight_tensor: libpymo.TensorParams):
+    """ Expand the shape of the weight into 4d.  """
+    dims = len(weight_tensor.shape)
+
+    if dims > 4:
+        raise RuntimeError
+
+    if dims == 4:
+        yield weight_tensor
+
+    else:
+        orig_shape = weight_tensor.shape
+        _4d_shape = np.append(orig_shape, [1 for _ in range(4-dims)]).astype(int)
+        try:
+            weight_tensor.shape = _4d_shape
+            yield weight_tensor
+        finally:
+            weight_tensor.shape = orig_shape
+
+
+def get_input_output_channels(node: NodeProto, model: ModelProto) -> Tuple[int, int]:
+    """
+    Find the input and output channels of a given layer.
+    :param node: The node to find the input/output channels of
+    :param model: The onnx model to which the layers belong
+    :return: Tuple of (num channels in, num channels out)
+    """
+    weight = ParamUtils.get_param(model, node, WEIGHT_INDEX)
+    groups = get_node_attribute(node, "group")
+    # If group atttribute does not exist in the node,then default is 1
+    if not groups:
+        groups = 1
+    if node.op_type == "Conv":
+        num_in_channels = weight.dims[1] * groups
+        num_out_channels = weight.dims[0]
+    elif node.op_type == "ConvTranspose":
+        num_in_channels = weight.dims[0]
+        num_out_channels = weight.dims[1] * groups
+    elif node.op_type == "Gemm":
+        transB = get_node_attribute(node, "transB")
+        if transB == 1:
+            num_out_channels = weight.dims[0]
+            num_in_channels = weight.dims[1]
+        else:
+            num_out_channels = weight.dims[1]
+            num_in_channels = weight.dims[0]
+    else:
+        num_out_channels = None
+        num_in_channels = None
+    return num_in_channels, num_out_channels
+
+
+# pylint: disable=too-many-locals
+def _update_standalone_batchnorm_ops(model: ModelProto):
+    """
+    Update weight and bias of standalone batchnorm ops in the model.
+    :param model: onnx Model for which batchnorm parameters are to be updated.
+    """
+
+    for node in model.graph.node:
+        if node.op_type in BatchNormType:
+
+            # get parameter names and indices
+            weight_name, bias_name, running_mean_name, running_var_name = node.input[1:]
+            init_w, init_b, init_rm, init_rv = [ParamUtils.get_param(model, node, idx) for idx in range(1, 5)]
+
+            attr = [item for item in node.attribute if item.name == "epsilon"]
+            if not attr:
+                attr = onnx.helper.make_attribute("epsilon", 1e-5) # Default epsilon value
+                node.attribute.append(attr)
+            else:
+                attr = attr[0]
+
+            epsilon = attr.f
+            tensor_w = numpy_helper.to_array(init_w)
+            tensor_b = numpy_helper.to_array(init_b)
+            tensor_rm = numpy_helper.to_array(init_rm)
+            tensor_rv = numpy_helper.to_array(init_rv)
+
+            # update values
+            inv_sigma = np.reciprocal(np.sqrt(tensor_rv + epsilon))
+            tensor_w = tensor_w * inv_sigma
+            tensor_b = tensor_b - tensor_rm * tensor_w
+            tensor_rm = np.zeros(tensor_w.shape, tensor_w.dtype)
+            tensor_rv = np.ones(tensor_w.shape, tensor_w.dtype)
+            attr.f = 0.
+
+            init_w_ = numpy_helper.from_array(tensor_w, weight_name)
+            init_b_ = numpy_helper.from_array(tensor_b, bias_name)
+            init_rm_ = numpy_helper.from_array(tensor_rm, running_mean_name)
+            init_rv_ = numpy_helper.from_array(tensor_rv, running_var_name)
+
+            # update initializers
+            init_w.CopyFrom(init_w_)
+            init_b.CopyFrom(init_b_)
+            init_rm.CopyFrom(init_rm_)
+            init_rv.CopyFrom(init_rv_)
+
    +
    +
    +
    + +
    + + +
    +
    +
    +
    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
    + Made with Sphinx and @pradyunsg's + + Furo + +
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    +
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    + +
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    + +
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    +

    Source code for aimet_onnx.cross_layer_equalization

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2022-2023, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+
+""" Cross Layer Equalization
+
+Some terminology for this code.
+CLS set: Set of layers (2 or 3) that can be used for cross-layer scaling
+Layer groups: Groups of layers that are immediately connected and can be decomposed further into CLS sets
+"""
+
+from typing import Tuple, List, Union
+import numpy as np
+import onnx
+from onnx import numpy_helper
+from onnxruntime.quantization.onnx_quantizer import ONNXModel
+from packaging import version
+
+# pylint: disable=wrong-import-order
+from aimet_common.utils import AimetLogger
+from aimet_common.connected_graph.connectedgraph import get_ordered_ops
+from aimet_common.cross_layer_equalization import GraphSearchUtils, CrossLayerScaling as CLS, ClsSetInfo, \
+    HighBiasFold as HBF
+import aimet_common.libpymo as libpymo      # pylint: disable=import-error
+
+from aimet_onnx.meta.connectedgraph import ConnectedGraph, WEIGHT_INDEX, BIAS_INDEX
+from aimet_onnx.meta.operations import Op
+from aimet_onnx.utils import transpose_tensor, ParamUtils, get_node_attribute, replace_relu6_with_relu
+from aimet_onnx.batch_norm_fold import BNLayer, fold_all_batch_norms_to_weight
+
+# pylint: disable=no-name-in-module, ungrouped-imports
+if version.parse(onnx.__version__) >= version.parse("1.14.0"):
+    from onnx import NodeProto, ModelProto
+else:
+    from onnx.onnx_pb import NodeProto, ModelProto
+
+logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.Quant)
+
+ClsSet = Union[Tuple['Conv', 'Conv'],
+               Tuple['Conv', 'Conv', 'Conv']]
+ScaleFactor = Union[np.ndarray, Tuple[np.ndarray]]
+cls_supported_layer_types = ['Conv', 'ConvTranspose']
+cls_supported_activation_types = ['Relu', 'PRelu']
+
+
+def get_ordered_list_of_conv_modules(list_of_starting_ops: List) -> List:
+    """
+    Finds order of nodes in graph
+    :param list_of_starting_ops: list of starting ops for the model
+    :return: List of names in graph in order
+    """
+    module_list = get_ordered_ops(list_of_starting_ops)
+    module_list = [[module.dotted_name, module] for module in module_list if module.type in cls_supported_layer_types]
+    return module_list
+
+
+class CrossLayerScaling(CLS):
+    """
+    Scales a model's layers to equalize the weights between consecutive layers
+    """
+    def __init__(self, model: ModelProto):
+        """
+        :param model: ONNX model
+        """
+        super().__init__()
+        self._model = model
+
+    def scale_model(self) -> List[ClsSetInfo]:
+        """
+        Uses cross-layer scaling to scale all applicable layers in the given model
+
+        :param model: Model to scale
+        :return: CLS information for each CLS set
+        """
+        # Find layer groups
+        connected_graph = ConnectedGraph(self._model)
+        ordered_module_list = get_ordered_list_of_conv_modules(connected_graph.starting_ops)
+        graph_search = GraphSearchUtils(connected_graph, ordered_module_list, cls_supported_layer_types,
+                                        cls_supported_activation_types)
+        layer_groups = graph_search.find_layer_groups_to_scale()
+
+        # Find cls sets from the layer groups
+        cls_sets = []
+        for layer_group in layer_groups:
+            cls_set = GraphSearchUtils.convert_layer_group_to_cls_sets(layer_group)
+            cls_sets += cls_set
+
+        # Scale the CLS sets
+        scale_factors = self.scale_cls_sets(cls_sets)
+
+        # Find if there were relu activations between layers of each cls set
+        is_relu_activation_in_cls_sets = graph_search.is_relu_activation_present_in_cls_sets(cls_sets)
+
+        # Convert to a list of cls-set-info elements
+        cls_set_info_list = CrossLayerScaling.create_cls_set_info_list(cls_sets, scale_factors,
+                                                                       is_relu_activation_in_cls_sets)
+
+        return cls_set_info_list
+
+    def _populate_libpymo_params(self, module: NodeProto,
+                                 layer_param: libpymo.EqualizationParams):
+        """
+        Populates libpymo weight parameter
+        """
+        weight = ParamUtils.get_param(self._model.model, module, WEIGHT_INDEX)
+        groups = get_node_attribute(module, "group")
+
+        # Transpose weights to C, N, H, W from N, C, H, W since axis are flipped for transposed conv
+        if module.op_type == "ConvTranspose" and groups == 1:
+            weight = transpose_tensor(weight, (1, 0, 2, 3))
+
+        layer_param.weight = numpy_helper.to_array(weight).reshape(-1)
+        weight_shape = get_weight_dimensions(np.array(weight.dims))
+        layer_param.weightShape = weight_shape
+
+    def _pack_params_for_conv(self,
+                              cls_set,
+                              prev_layer_params: libpymo.EqualizationParams,
+                              curr_layer_params: libpymo.EqualizationParams):
+        """
+        Prepare and pack data structure for previous and current layer in given cls set.
+
+        :param cls_set: Consecutive Conv layers Tuple whose weights and biases need to be equalized.
+        :param prev_layer_params: Data structure holding weight and bias for previous layer in cls set.
+        :param curr_layer_params: Data structure holding weight and bias for current layer in cls set.
+        """
+        self._populate_libpymo_params(cls_set[0].get_module(), prev_layer_params)
+        self._populate_libpymo_params(cls_set[1].get_module(), curr_layer_params)
+
+        cls_set_0_bias = ParamUtils.get_param(self._model.model, cls_set[0].get_module(), BIAS_INDEX)
+        if cls_set_0_bias is not None:
+            prev_layer_params.bias = numpy_helper.to_array(cls_set_0_bias).reshape(-1)
+        else:
+            prev_layer_params.isBiasNone = True
+
+    def _update_weight_for_layer_from_libpymo_obj(self, layer_param: libpymo.EqualizationParams,
+                                                  module: NodeProto):
+        """
+        Update weight parameter from libpymo object
+        """
+        weight = ParamUtils.get_param(self._model.model, module, WEIGHT_INDEX)
+        weight.raw_data = np.asarray(layer_param.weight, dtype=np.float32).tobytes()
+        groups = get_node_attribute(module, "group")
+        # Transpose weight back to original configuration
+        if module.op_type == "ConvTranspose" and groups == 1:
+            weight = transpose_tensor(weight, (1, 0, 2, 3))
+
+        weight_param = ParamUtils.get_param(self._model.model, module, WEIGHT_INDEX)
+        weight_param.raw_data = weight.raw_data
+
+    def _update_params_for_conv(self,
+                                cls_set,
+                                prev_layer_params: libpymo.EqualizationParams,
+                                curr_layer_params: libpymo.EqualizationParams):
+        """
+        Update weight and biases for cls set using updated data structures.
+
+        :param cls_set: Consecutive Conv layers Tuple whose weights and biases need to be equalized.
+        :param prev_layer_params: Data structure holding weight and bias for previous layer in cls set.
+        :param curr_layer_params: Data structure holding weight and bias for current layer in cls set.
+        """
+        self._update_weight_for_layer_from_libpymo_obj(prev_layer_params, cls_set[0].get_module())
+        self._update_weight_for_layer_from_libpymo_obj(curr_layer_params, cls_set[1].get_module())
+
+        if not prev_layer_params.isBiasNone:
+            bias_param = ParamUtils.get_param(self._model.model, cls_set[0].get_module(),
+                                              BIAS_INDEX)
+            bias_param.raw_data = np.asarray(prev_layer_params.bias, dtype=np.float32).tobytes()
+
+    def _pack_params_for_depthwise_conv(self, cls_set,
+                                        prev_layer_params: libpymo.EqualizationParams,
+                                        curr_layer_params: libpymo.EqualizationParams,
+                                        next_layer_params: libpymo.EqualizationParams):
+        """
+        Prepare and pack data structure for previous, current and next layer in given cls set.
+
+        :param cls_set: Consecutive Conv layers Tuple whose weights and biases need to be equalized.
+        :param prev_layer_params: Data structure holding weight and bias for previous layer in cls set.
+        :param curr_layer_params: Data structure holding weight and bias for current layer in cls set.
+        :param next_layer_params: Data structure holding weight and bias for next layer in cls set.
+        """
+
+        self._populate_libpymo_params(cls_set[0].get_module(), prev_layer_params)
+
+        assert cls_set[1].groups > 1
+
+        weight = ParamUtils.get_param(self._model.model, cls_set[1].get_module(), WEIGHT_INDEX)
+        curr_layer_params.weight = numpy_helper.to_array(weight).reshape(-1)
+        curr_layer_params.weightShape = np.array(weight.dims)
+
+        self._populate_libpymo_params(cls_set[2].get_module(), next_layer_params)
+
+        cls_set_0_bias = ParamUtils.get_param(self._model.model, cls_set[0].get_module(), BIAS_INDEX)
+        if cls_set_0_bias is not None:
+            prev_layer_params.bias = numpy_helper.to_array(cls_set_0_bias).reshape(-1)
+        else:
+            prev_layer_params.isBiasNone = True
+
+        cls_set_1_bias = ParamUtils.get_param(self._model.model, cls_set[1].get_module(), BIAS_INDEX)
+        if cls_set_1_bias is not None:
+            curr_layer_params.bias = numpy_helper.to_array(cls_set_1_bias).reshape(-1)
+        else:
+            curr_layer_params.isBiasNone = True
+
+    def _update_params_for_depthwise_conv(self, cls_set,
+                                          prev_layer_params: libpymo.EqualizationParams,
+                                          curr_layer_params: libpymo.EqualizationParams,
+                                          next_layer_params: libpymo.EqualizationParams):
+        """
+        Update weight and biases for cls set using updated data structures.
+
+        :param cls_set: Consecutive Conv layers Tuple whose weights and biases need to be equalized.
+        :param prev_layer_params: Data structure holding weight and bias for previous layer in cls set.
+        :param curr_layer_params: Data structure holding weight and bias for current layer in cls set.
+        :param next_layer_params: Data structure holding weight and bias for next layer in cls set.
+        """
+        self._update_weight_for_layer_from_libpymo_obj(prev_layer_params, cls_set[0].get_module())
+        self._update_weight_for_layer_from_libpymo_obj(curr_layer_params, cls_set[1].get_module())
+        self._update_weight_for_layer_from_libpymo_obj(next_layer_params, cls_set[2].get_module())
+
+        if not prev_layer_params.isBiasNone:
+            bias_param = ParamUtils.get_param(self._model.model, cls_set[0].get_module(),
+                                              BIAS_INDEX)
+            bias_param.raw_data = np.asarray(prev_layer_params.bias, dtype=np.float32).tobytes()
+
+        if not curr_layer_params.isBiasNone:
+            bias_param = ParamUtils.get_param(self._model.model, cls_set[1].get_module(),
+                                              BIAS_INDEX)
+            bias_param.raw_data = np.asarray(curr_layer_params.bias, dtype=np.float32).tobytes()
+
+
+class HighBiasFold(HBF):
+    """
+    Code to apply the high-bias-fold technique to a model
+    """
+    def __init__(self, model: ModelProto):
+        self._model = model
+
+    def _check_if_bias_is_none(self, layer: Op) -> bool:
+        """ Returns if bias is a None for a layer. True if bias is None"""
+        bias = ParamUtils.get_param(self._model.model, layer.get_module(), BIAS_INDEX)
+        return not bias
+
+    def _populate_bn_params_in_libpymo_obj(self, prev_layer_bn_params: libpymo.BNParamsHighBiasFold,
+                                           bn_layer: BNLayer):
+        """
+        Populates BatchNorm params in the libpymo object
+        :param prev_layer_bn_params: Data structure to pack batch norm parameter
+        :param bn_layer: BatchNorm layer
+        """
+        prev_layer_bn_params.gamma = bn_layer.gamma
+        prev_layer_bn_params.beta = bn_layer.beta
+
+    def _pack_previous_and_current_layer_params(self, cls_pair_info: ClsSetInfo.ClsSetLayerPairInfo,
+                                                prev_layer_params: libpymo.LayerParams,
+                                                curr_layer_params: libpymo.LayerParams):
+        """
+        Helper method to pack information of previous and current layer.
+
+        :param cls_pair_info: Layer pairs that were scaled using CLS and related information.
+        :param prev_layer_params: Data structure to pack previous layer parameters.
+        :param curr_layer_params: Data structure to pack current layer parameters.
+        """
+        prev_layer_params.activationIsRelu = cls_pair_info.relu_activation_between_layers
+
+        bias = ParamUtils.get_param(self._model.model, cls_pair_info.layer1.get_module(),
+                                    BIAS_INDEX)
+
+        prev_layer_params.bias = numpy_helper.to_array(bias).reshape(-1)
+
+        module = cls_pair_info.layer2.get_module()
+        weight = ParamUtils.get_param(self._model.model, module, WEIGHT_INDEX)
+        bias = ParamUtils.get_param(self._model.model, module, BIAS_INDEX)
+
+        groups = get_node_attribute(module, "group")
+
+        # Transpose weights to C, N, H, W from N, C, H, W since axis are flipped for transposed conv
+        if module.op_type == "ConvTranspose" and groups == 1:
+            weight = transpose_tensor(weight, (1, 0, 2, 3))
+
+        curr_layer_params.bias = numpy_helper.to_array(bias).reshape(-1)
+        curr_layer_params.weight = numpy_helper.to_array(weight).reshape(-1)
+        curr_layer_params.weightShape = get_weight_dimensions(np.array(weight.dims))
+
+    def _update_bias_for_layer_from_libpymo_obj(self, layer_param: libpymo.LayerParams,
+                                                module: NodeProto):
+        """
+        Update bias parameter from libpymo object
+        """
+        bias = ParamUtils.get_param(self._model.model, module, BIAS_INDEX)
+
+        bias.raw_data = np.asarray(layer_param.bias, dtype=np.float32).tobytes()
+
+    def _update_previous_and_current_layer_bias(self, cls_pair_info: ClsSetInfo.ClsSetLayerPairInfo,
+                                                prev_layer_params: libpymo.LayerParams,
+                                                curr_layer_params: libpymo.LayerParams):
+        """
+        Update biases for previous and current layer.
+
+        :param cls_pair_info: Layer pairs that were scaled using CLS and related information.
+        :param prev_layer_params: Data structure holding weight and bias for previous layer in cls set.
+        :param curr_layer_params: Data structure holding weight and bias for current layer in cls set.
+        """
+        self._update_bias_for_layer_from_libpymo_obj(prev_layer_params, cls_pair_info.layer1.get_module())
+        self._update_bias_for_layer_from_libpymo_obj(curr_layer_params, cls_pair_info.layer2.get_module())
+
+
+def get_weight_dimensions(weight_shape: np.array) -> np.array:
+    """
+    Returns a length 4 weight shape
+    :param weight_shape: shape of the weight tensor
+    """
+    dims = len(weight_shape)
+    if dims == 4:
+        return weight_shape
+    return np.append(weight_shape, [1 for _ in range(4 - dims)]).astype(int)
+
+
+
    
+[docs]
+def equalize_model(model: ModelProto):
+    """
+    High-level API to perform Cross-Layer Equalization (CLE) on the given model. The model is equalized in place.
+
+    :param model: Model to equalize
+    """
+    if not isinstance(model, ONNXModel):
+        model = ONNXModel(model)
+    conv_bn_pairs, bn_conv_pairs = fold_all_batch_norms_to_weight(model)
+
+    replace_relu6_with_relu(model)
+
+    bn_dict = {}
+    for conv_bn in conv_bn_pairs:
+        bn_dict[conv_bn[0].name] = conv_bn[1]
+
+    for bn_conv in bn_conv_pairs:
+        bn_dict[bn_conv[1].name] = bn_conv[0]
+
+    # perform cross-layer scaling on applicable layer sets
+    cls = CrossLayerScaling(model)
+    cls_set_info = cls.scale_model()
+
+    # high-bias fold
+    hbf = HighBiasFold(model)
+    hbf.bias_fold(cls_set_info, bn_dict)
    
+
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    +

    Source code for aimet_onnx.layer_output_utils

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2023, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+
+""" This module contains utilities to capture and save intermediate layer-outputs of a model """
+
+import copy
+from typing import List, Dict, Tuple, Union
+import re
+import numpy as np
+import onnxruntime as ort
+import onnx
+from packaging import version
+
+# pylint: disable=wrong-import-order
+from aimet_common.utils import AimetLogger
+from aimet_common.layer_output_utils import SaveInputOutput, save_layer_output_names
+
+from aimet_onnx.quantsim import QuantizationSimModel
+from aimet_onnx.utils import create_input_dict, add_hook_to_get_activation
+
+# pylint: disable=no-name-in-module, ungrouped-imports
+if version.parse(onnx.__version__) >= version.parse("1.14.0"):
+    from onnx import ModelProto
+else:
+    from onnx.onnx_pb import ModelProto
+
+logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.LayerOutputs)
+
+
+
    
+[docs]
+class LayerOutputUtil:
+    """ Implementation to capture and save outputs of intermediate layers of a model (fp32/quantsim) """
+
+    def __init__(self, model: ModelProto, dir_path: str, device: int = 0):
+        """
+        Constructor - It initializes the utility classes that captures and saves layer-outputs
+
+        :param model: ONNX model
+        :param dir_path: Directory wherein layer-outputs will be saved
+        :param device: CUDA device-id to be used
+        """
+        self.model = model
+
+        # Fetch appropriate execution providers depending on availability
+        providers = ['CPUExecutionProvider']
+        if 'CUDAExecutionProvider' in ort.get_available_providers():
+            providers = [('CUDAExecutionProvider', {'device_id': device}), 'CPUExecutionProvider']
+
+        # Utility to capture layer-outputs
+        self.layer_output = LayerOutput(model=model, providers=providers, dir_path=dir_path)
+
+        # Utility to save model inputs and their corresponding layer-outputs
+        self.save_input_output = SaveInputOutput(dir_path, 'NCHW')
+
+
    
+[docs]
+    def generate_layer_outputs(self, input_instance: Union[np.ndarray, List[np.ndarray], Tuple[np.ndarray]]):
+        """
+        This method captures output of every layer of a model & saves the inputs and corresponding layer-outputs to disk.
+
+        :param input_instance: Single input instance for which we want to obtain layer-outputs.
+        :return: None
+        """
+        logger.info("Generating layer-outputs for input instance %d", self.save_input_output.input_cntr+1)
+
+        input_dict = create_input_dict(self.model, input_instance)
+
+        layer_output_dict = self.layer_output.get_outputs(input_dict)
+        self.save_input_output.save(input_instance, layer_output_dict)
+
+        logger.info('Layer-outputs generated for input instance %d', self.save_input_output.input_cntr)
    
+
    
+
+
+
+class LayerOutput:
+    """
+    This class creates a layer-output name to layer-output dictionary.
+    """
+    def __init__(self, model: ModelProto, providers: List, dir_path: str):
+        """
+        Constructor - It initializes few lists that are required for capturing and naming layer-outputs.
+
+        :param model: ONNX model
+        :param providers: execution providers to execute onnxruntime
+        :param dir_path: directory to store topological order of layer-output names
+        """
+        self.model = copy.deepcopy(model)
+        self.activation_names = LayerOutput.get_activation_names(self.model)
+
+        quantized_activation_names = [name for name in self.activation_names if name.endswith('_updated')]
+        if quantized_activation_names:
+            self.activation_names = quantized_activation_names
+
+        LayerOutput.register_activations(self.model, self.activation_names)
+
+        self.session = QuantizationSimModel.build_session(self.model, providers)
+
+        # Replace special characters with underscore. This gives valid file names to store activation tensors.
+        self.sanitized_activation_names = [re.sub(r'\W+', "_", name.replace('_updated', '')) for name in self.activation_names]
+
+        # Save activation names which are in topological order of model graph. This order can be used while comparing layer-outputs.
+        save_layer_output_names(self.sanitized_activation_names, dir_path)
+
+    def get_outputs(self, input_dict: Dict) -> Dict[str, np.ndarray]:
+        """
+        This function creates layer-output name to layer-output dictionary.
+
+        :param input_dict: input name to input tensor map
+        :return: layer-output name to layer-output dictionary
+        """
+        activation_values = self.session.run(self.activation_names, input_dict)
+        return dict(zip(self.sanitized_activation_names, activation_values))
+
+    @staticmethod
+    def get_activation_names(model: ModelProto) -> List[str]:
+        """
+        This function fetches the activation names (layer-output names) of the given onnx model.
+
+        :param model: ONNX model
+        :return: list of activation names
+        """
+        activation_names = []
+        constant_activations = []  # No need to capture them
+        for node in model.graph.input:
+            activation_names.append(node.name)
+        for node in model.graph.node:
+            if node.op_type == 'Constant':
+                # Ignore and keep track to further ignore its quantized version
+                constant_activations.append(node.output[0]+'_updated')
+            else:
+                for output in node.output:
+                    if output not in constant_activations:
+                        activation_names.append(output)
+        return activation_names
+
+    @staticmethod
+    def register_activations(model: ModelProto, activation_names: List):
+        """
+        This function adds the intermediate activations into the model's ValueInfoProto so that they can be fetched via
+        running the session.
+
+        :param model: ONNX model
+        :param activation_names: list of activation names to be registered
+        :return:
+        """
+        for act_name in activation_names:
+            _ = add_hook_to_get_activation(model, act_name)
+
    +
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    + + + + + + + + + \ No newline at end of file diff --git a/releases/2.0.0/_modules/aimet_onnx/mixed_precision.html b/releases/2.0.0/_modules/aimet_onnx/mixed_precision.html new file mode 100644 index 0000000..ae141b4 --- /dev/null +++ b/releases/2.0.0/_modules/aimet_onnx/mixed_precision.html @@ -0,0 +1,457 @@ + + + + + + + + aimet_onnx.mixed_precision - AIMET + + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    +

    Source code for aimet_onnx.mixed_precision

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2024, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+""" Mixed precision inference """
+
+from typing import Union, Tuple, List
+
+from aimet_common.utils import AimetLogger
+from aimet_common.amp.utils import (
+    visualize_quantizer_group_sensitivity,
+    visualize_pareto_curve,
+    CANDIDATE_WITH_DTYPE,
+    AMPSearchAlgo,
+)
+from aimet_common.defs import CallbackFunc
+from aimet_onnx.quantsim import QuantizationSimModel
+from aimet_onnx.amp.mixed_precision_algo import GreedyMixedPrecisionAlgo
+from aimet_onnx.amp.quantizer_groups import QuantizerGroup
+
+logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.MixedPrecision)
+
+
+# pylint: disable=too-many-arguments
+
    
+[docs]
+def choose_mixed_precision(sim: QuantizationSimModel,
+                           candidates: List[CANDIDATE_WITH_DTYPE], eval_callback_for_phase1: CallbackFunc,
+                           eval_callback_for_phase2: CallbackFunc, allowed_accuracy_drop: Union[None, float],
+                           results_dir: str, clean_start: bool, forward_pass_callback: CallbackFunc,
+                           use_all_amp_candidates: bool = False, phase1_optimize: bool = True,
+                           amp_search_algo: AMPSearchAlgo = AMPSearchAlgo.Binary) -> \
+        Union[List[Tuple[int, float, QuantizerGroup, int]], None]:
+    """
+    High-level API to perform in place Mixed Precision evaluation on the given sim model. A pareto list is created and
+    a curve for Accuracy vs BitOps is saved under the results directory
+
+    :param sim: Quantized sim model
+    :param candidates: List of tuples for all possible bitwidth values for activations and parameters
+                    Suppose the possible combinations are-
+                    ((Activation bitwidth - 8, Activation data type - int), (Parameter bitwidth - 16, parameter data type - int))
+                    ((Activation bitwidth - 16, Activation data type - float), (Parameter bitwidth - 16, parameter data type - float))
+                    candidates will be [((8, QuantizationDataType.int), (16, QuantizationDataType.int)),
+                                        ((16, QuantizationDataType.float), (16, QuantizationDataType.float))]
+    :param eval_callback_for_phase1: An object of CallbackFunc class which takes in Eval function (callable) and eval
+                                     function parameters. This evaluation callback used to measure sensitivity of each
+                                     quantizer group during phase 1. The phase 1 involves finding accuracy list/sensitivity of each
+                                     module. Therefore, a user might want to run the phase 1 with a smaller dataset
+    :param eval_callback_for_phase2: An object of CallbackFunc class which takes in Eval function (callable) and eval
+                                     function parameters. Evaluation callback used to get accuracy of quantized model
+                                     for phase 2 calculations. The phase 2 involves finding pareto front curve
+    :param allowed_accuracy_drop: Maximum allowed drop in accuracy from FP32 baseline. The pareto front curve is plotted only till the point where the allowable
+                                  accuracy drop is met. To get a complete plot for picking points on the curve, the user
+                                  can set the allowable accuracy drop to None.
+    :param results_dir: Path to save results and cache intermediate results
+    :param clean_start: If true, any cached information from previous runs will be deleted prior to starting the
+                        mixed-precision analysis. If false, prior cached information will be used if applicable. Note
+                        it is the user's responsibility to set this flag to true if anything in the model or
+                        quantization parameters changes compared to the previous run.
+    :param forward_pass_callback: An object of CallbackFunc class which takes in Forward pass function (callable) and its
+                                  function parameters. Forward pass callback used to compute quantization encodings
+    :param use_all_amp_candidates: Using the “supported_kernels” field in the config file (under defaults
+                    and op_type sections), a list of supported candidates can be specified. All the AMP candidates
+                    which are passed through the “candidates” field may not be supported based on the data passed
+                    through “supported_kernels”. When the field “use_all_amp_candidates” is set to True, the AMP
+                    algorithm will ignore the "supported_kernels" in the config file and continue to use all candidates.
+    :phase1_optimize: If user set this parameter to false then phase1 default logic will be executed else optimized logic will be executed.
+    :param amp_search_algo: A valid value from the Enum AMPSearchAlgo. Defines the search algorithm to be used for
+                            the phase 2 of AMP.
+
+    :return: Pareto front list containing information including Bitops, QuantizerGroup candidates and
+             corresponding eval scores. The Pareto front list can be used for plotting a pareto front curve which
+             provides information regarding how bit ops vary w.r.t. accuracy. If the allowable accuracy drop is set to
+             100% then a user can use the pareto front curve to pick points and re-run,
+             None if we early exit the mixed precision algorithm.
+    """
+    mixed_precision_algo = GreedyMixedPrecisionAlgo(sim, candidates, eval_callback_for_phase1,
+                                                    eval_callback_for_phase2, results_dir, clean_start,
+                                                    forward_pass_callback, use_all_amp_candidates, phase1_optimize)
+    mixed_precision_algo.run(allowed_accuracy_drop, amp_search_algo)
+
+    if mixed_precision_algo.accuracy_list is not None and mixed_precision_algo.pareto_list is not None:
+        # Print mixed precision stats
+        logger.info(mixed_precision_algo)
+
+        # Visualize quantizer group sensitivity
+        visualize_quantizer_group_sensitivity(mixed_precision_algo.accuracy_list,
+                                              mixed_precision_algo.baseline_candidate,
+                                              mixed_precision_algo.fp32_accuracy,
+                                              results_dir=results_dir)
+        # Create pareto list curve
+        visualize_pareto_curve(mixed_precision_algo.pareto_list, results_dir)
+        return mixed_precision_algo.pareto_list
+
+    return None
    
+
+
    +
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    +
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    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
    + Made with Sphinx and @pradyunsg's + + Furo + +
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    + + + + + + + + + \ No newline at end of file diff --git a/releases/2.0.0/_modules/aimet_onnx/quant_analyzer.html b/releases/2.0.0/_modules/aimet_onnx/quant_analyzer.html new file mode 100644 index 0000000..f57959a --- /dev/null +++ b/releases/2.0.0/_modules/aimet_onnx/quant_analyzer.html @@ -0,0 +1,998 @@ + + + + + + + + aimet_onnx.quant_analyzer - AIMET + + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    +

    Source code for aimet_onnx.quant_analyzer

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2024, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+
+""" Quant Analyzer """
+
+import os
+import re
+from typing import Union, Tuple, Dict, List, Iterable
+import copy
+from collections import defaultdict
+
+import numpy as np
+from onnx import ModelProto
+import onnxruntime as ort
+from onnxruntime.quantization.onnx_model import ONNXModel
+from sklearn.metrics import mean_squared_error
+
+from aimet_common.utils import AimetLogger, CallbackFunc
+from aimet_common.defs import QuantScheme
+from aimet_common.quant_analyzer import save_json, export_per_layer_sensitivity_analysis_plot, \
+    create_and_export_min_max_ranges_plot, export_per_layer_mse_plot, export_stats_histogram_plot
+
+from aimet_onnx.qc_quantize_op import QcQuantizeOp
+from aimet_onnx.quantsim import QuantizationSimModel
+from aimet_onnx.batch_norm_fold import fold_all_batch_norms_to_weight
+from aimet_onnx.adaround.activation_sampler import ModuleData
+from aimet_onnx import utils
+from aimet_onnx.meta.operations import Op
+
+_logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.QuantAnalyzer)
+
+
+
    
+[docs]
+class QuantAnalyzer:
+    """
+    QuantAnalyzer provides following utilities:
+
+     1) model sensitivity to weight and activation quantization
+     2) per layer sensitivity analysis
+     3) per layer encoding (min - max range)
+     4) per layer quantizer historgram analysis and
+     5) per layer MSE analysis
+    """
+    def __init__(self,
+                 model: Union[ModelProto, ONNXModel],
+                 dummy_input: Dict[str, np.ndarray],
+                 forward_pass_callback: CallbackFunc,
+                 eval_callback: CallbackFunc,
+                 ):
+        """
+        :param model: FP32 model to analyze for quantization.
+        :param dummy_input: Dummy input to model.
+        :param forward_pass_callback: A callback function for model calibration that simply runs
+                forward passes on the model to compute encoding (delta/offset). This
+                callback function should use representative data and should be subset of
+                entire train/validation dataset (~1000 images/samples).
+        :param eval_callback: A callback function for model evaluation that determines model
+                performance. This callback function is expected to return scalar value
+                representing the model performance evaluated against entire test/evaluation dataset.
+        """
+        if not isinstance(forward_pass_callback, CallbackFunc):
+            raise ValueError('forward_pass_callback and its argument(s) are not encapsulated by CallbackFunc class.')
+        if not isinstance(eval_callback, CallbackFunc):
+            raise ValueError('eval_callback and its argument(s) are not encapsulated by CallbackFunc class.')
+
+        self._onnx_model = model
+        if not isinstance(self._onnx_model, ONNXModel):
+            self._onnx_model = ONNXModel(self._onnx_model)
+        self._dummy_input = dummy_input
+        self._forward_pass_callback = forward_pass_callback
+        self._eval_callback = eval_callback
+        self._unlabeled_dataset_iterable = None
+        self._num_batches = None
+
+
    
+[docs]
+    def analyze(self,
+                quant_scheme: QuantScheme = QuantScheme.post_training_tf_enhanced,
+                default_param_bw: int = 8,
+                default_activation_bw: int = 8,
+                config_file: str = None,
+                results_dir: str = "./tmp/",
+                ):
+        """
+        Analyzes model for quantization and point out sensitive parts/hotspots of the model by performing
+            1) model sensitivity to quantization,
+            2) perform per layer sensitivity analysis by enabling and disabling quantizers,
+            3) export per layer encodings min - max ranges,
+            4) export per layer quantizer stats histogram,
+            5) per layer MSE analysis
+
+        :param quant_scheme: Quantization scheme. Supported values are
+                QuantScheme.post_training_tf or QuantScheme.post_training_tf_enhanced.
+        :param default_param_bw: Default bitwidth (4-31) to use for quantizing layer parameters.
+        :param default_activation_bw: Default bitwidth (4-31) to use for quantizing layer inputs and outputs.
+        :param config_file: Path to configuration file for model quantizers.
+        :param results_dir: Directory to save the results.
+        """
+        sim = self.create_quantsim_and_encodings(quant_scheme,
+                                                 default_param_bw,
+                                                 default_activation_bw,
+                                                 config_file)
+
+        results_dir = os.path.abspath(results_dir)
+        os.makedirs(results_dir, exist_ok=True)
+
+        # Check model sensitivity to weight and activation quantization individually.
+        self.check_model_sensitivity_to_quantization(sim)
+
+        # Perform per layer analysis by enabling its quantizers (OPTION-1).
+        self.perform_per_layer_analysis_by_enabling_quantizers(sim, results_dir)
+
+        # Perform per layer analysis by disabling its quantizers (OPTION-2).
+        self.perform_per_layer_analysis_by_disabling_quantizers(sim, results_dir)
+
+        # Export encoding min-max range.
+        self.export_per_layer_encoding_min_max_range(sim, results_dir)
+
+        # Export PDF of statistics.
+        if quant_scheme == QuantScheme.post_training_tf_enhanced:
+            self.export_per_layer_stats_histogram(sim, results_dir)
+
+        # Export per layer MSE loss between fp32 and quantized output activations.
+        if self._unlabeled_dataset_iterable:
+            self.export_per_layer_mse_loss(sim, results_dir)
    
+
+
+
    
+[docs]
+    def create_quantsim_and_encodings(self, quant_scheme: QuantScheme, default_param_bw: int,
+                                      default_activation_bw: int, config_file: str) \
+            -> QuantizationSimModel:
+        """
+        Creates quantsim object and computes encodings.
+
+        :param quant_scheme: Quantization scheme.
+        :param default_param_bw: Default bitwidth (4-31) to use for quantizing layer parameters.
+        :param default_activation_bw: Default bitwidth (4-31) to use for quantizing layer inputs and outputs.
+        :param config_file: Path to configuration file for model quantizers.
+        :return: Quantsim object.
+        """
+        _ = fold_all_batch_norms_to_weight(self._onnx_model)
+        kwargs = dict(
+            quant_scheme=quant_scheme,
+            default_activation_bw=default_activation_bw,
+            default_param_bw=default_param_bw,
+            config_file=config_file,
+        )
+        sim = QuantizationSimModel(copy.deepcopy(self._onnx_model), self._dummy_input, **kwargs)
+        sim.compute_encodings(self._forward_pass_callback.func, self._forward_pass_callback.args)
+        return sim
    
+
+
+
    
+[docs]
+    def check_model_sensitivity_to_quantization(self,
+                                                sim: QuantizationSimModel,
+                                                ) -> Tuple[float, float, float]:
+        """
+        Performs model sensitivity analysis to weight and activation quantization individually.
+
+        :param sim: Quantsim model.
+        :return: FP32 eval score, weight-quantized eval score, act-quantized eval score.
+        """
+        # pylint: disable=protected-access
+        fp32_eval_score = self._eval_model(self._onnx_model.model)
+        _logger.info("FP32 eval score (W32A32): %f", fp32_eval_score)
+
+        weight_quantized_eval_score = self._eval_weight_quantized_model(sim)
+        _logger.info("Weight-quantized eval score (W%dA32): %f", sim._default_param_bw,
+                     weight_quantized_eval_score)
+
+        act_quantized_eval_score = self._eval_activation_quantized_model(sim)
+        _logger.info("Activation-quantized eval score (W32A%d): %f", sim._default_activation_bw,
+                     act_quantized_eval_score)
+
+        return fp32_eval_score, weight_quantized_eval_score, act_quantized_eval_score
    
+
+
+    def _eval_model(self, model):
+        """
+        Evaluate the model performance.
+
+        :param model: ONNX model to be evaluated.
+        :return: Scaler value representing model performance.
+        """
+        if 'CUDAExecutionProvider' in ort.get_available_providers():
+            providers = [('CUDAExecutionProvider', {'cudnn_conv_algo_search': 'DEFAULT'}), 'CPUExecutionProvider']
+        else:
+            providers = ['CPUExecutionProvider']
+        session = ort.InferenceSession(
+            path_or_bytes=model.SerializeToString(),
+            providers=providers,
+        )
+        return self._eval_callback.func(session, self._eval_callback.args)
+
+    def _eval_weight_quantized_model(self, sim: QuantizationSimModel)-> float:
+        """
+        Evaluate weight quantized model performance.
+        For weight quantized model performance, disable enabled activation quantizers, measure
+        eval score and enable again.
+
+        :param sim: Quantsim model.
+        :return: Quantized model performance.
+        """
+        enabled_activation_quantizers = self._get_enabled_activation_quantizers(sim)
+        self._enable_disable_quantizers(enabled_activation_quantizers, enabled=False)
+        eval_score = self._eval_callback.func(sim.session, self._eval_callback.args)
+        self._enable_disable_quantizers(enabled_activation_quantizers, enabled=True)
+        return eval_score
+
+    def _eval_activation_quantized_model(self, sim: QuantizationSimModel)-> float:
+        """
+        Evaluate activation quantized model performance.
+        For activation quantized model performance, disable enabled param quantizers, measure
+        eval score and enable again.
+
+        :param sim: Quantsim model.
+        :return: Quantized model performance.
+        """
+        enabled_param_quantizers = self._get_enabled_param_quantizers(sim)
+        self._enable_disable_quantizers(enabled_param_quantizers, enabled=False)
+        eval_score = self._eval_callback.func(sim.session, self._eval_callback.args)
+        self._enable_disable_quantizers(enabled_param_quantizers, enabled=True)
+        return eval_score
+
+    @staticmethod
+    def _get_enabled_param_quantizers(sim: QuantizationSimModel) -> List[QcQuantizeOp]:
+        """
+        For given quantsim model, get all enabled param quantizers.
+        :param sim: Quantsim model.
+        :return: List of enabled param quantizers.
+        """
+        param_quantizers, _ = sim.get_all_quantizers()
+        enabled_param_quantizers = [quantizer for quantizer in param_quantizers if quantizer.enabled]
+        return enabled_param_quantizers
+
+    @staticmethod
+    def _get_enabled_activation_quantizers(sim: QuantizationSimModel) -> List[QcQuantizeOp]:
+        """
+        For given quantsim model, get all enabled activation quantizers.
+        :param sim: Quantsim model.
+        :return: List of enabled activation quantizers.
+        """
+        _, act_quantizers = sim.get_all_quantizers()
+        enabled_activation_quantizers = [quantizer for quantizer in act_quantizers if quantizer.enabled]
+        return enabled_activation_quantizers
+
+    @staticmethod
+    def _enable_disable_quantizers(quantizers: List[QcQuantizeOp], enabled: bool):
+        """
+        For given list of quantizers, set (enable/disable) quantizer's enabled.
+
+        :param quantizers: List of quantizers.
+        :param enabled: Enabled flag.
+        """
+        for quantizer in quantizers:
+            quantizer.enabled = enabled
+
+
    
+[docs]
+    def perform_per_layer_analysis_by_enabling_quantizers(self,
+                                                          sim: QuantizationSimModel,
+                                                          results_dir: str,
+                                                          ) -> Dict:
+        """
+        Performs layer-wise quantization sensitivity analysis by enabling its quantizers
+
+        1. All parameter and activation quantizers are disabled.
+        2. For every layer, based on occurrence:
+              a. Each layer's parameters and activations quantizers are enabled as per JSON config file
+                 and set to bit-width specified.
+              b. Measure and record eval score on subset of dataset.
+              c. Disable enabled quantizers in step a.
+        3. Returns dictionary containing layer name and corresponding eval score.
+
+        :param sim: Quantsim model.
+        :param results_dir: Directory to save the results.
+        :return: layer wise eval score dictionary. dict[layer_name] = eval_score
+        """
+        results_dir = os.path.abspath(results_dir)
+        os.makedirs(results_dir, exist_ok=True)
+
+        _logger.info("OPTION-1: All the quantizers are disabled. "
+                     "Starting per-layer analysis by enabling layer-specific quantizers as per config file.")
+        layer_wise_eval_score_dict = self._perform_per_layer_analysis(sim,
+                                                                      disable_all_quantizers=True,
+                                                                      enabled_before=True,
+                                                                      enabled_after=False)
+        export_per_layer_sensitivity_analysis_plot(layer_wise_eval_score_dict,
+                                                   results_dir,
+                                                   title="per_layer_quant_enabled")
+        save_json(layer_wise_eval_score_dict,
+                  results_dir,
+                  title="per_layer_quant_enabled.json")
+        _logger.info("Exported per-layer quant analysis (enabled) plot.")
+        return layer_wise_eval_score_dict
    
+
+
+
    
+[docs]
+    def perform_per_layer_analysis_by_disabling_quantizers(self,
+                                                           sim: QuantizationSimModel,
+                                                           results_dir: str,
+                                                           ) -> Dict:
+        """
+        Performs layer-wise quantization sensitivity analysis by disabling its quantizers
+
+        1. All parameter and activation quantizers are enabled as per JSON config file
+           and set to bit-width specified.
+        2. For every layer, based on occurrence:
+              a. Each layer's parameters and activations quantizers are disabled.
+              b. Measure and record eval score on subset of dataset.
+              c. Enable disabled quantizers in step a.
+        3. Returns dictionary containing layer name and corresponding eval score.
+
+        :param sim: Quantsim model.
+        :param results_dir: Directory to save the results.
+        :return: layer wise eval score dictionary. dict[layer_name] = eval_score
+        """
+        results_dir = os.path.abspath(results_dir)
+        os.makedirs(results_dir, exist_ok=True)
+
+        _logger.info("OPTION-2: All the quantizers are enabled as per config file. "
+                     "Starting per-layer analysis by disabling layer-specific quantizers.")
+        layer_wise_eval_score_dict = self._perform_per_layer_analysis(sim,
+                                                                      disable_all_quantizers=False,
+                                                                      enabled_before=False,
+                                                                      enabled_after=True)
+        export_per_layer_sensitivity_analysis_plot(layer_wise_eval_score_dict,
+                                                   results_dir,
+                                                   title="per_layer_quant_disabled")
+        save_json(layer_wise_eval_score_dict,
+                  results_dir,
+                  title="per_layer_quant_disabled.json")
+        _logger.info("Exported per-layer quant analysis (disabled) plot.")
+        return layer_wise_eval_score_dict
    
+
+
+    def _perform_per_layer_analysis(self,
+                                    sim: QuantizationSimModel,
+                                    disable_all_quantizers: bool,
+                                    enabled_before: bool,
+                                    enabled_after: bool,
+                                    ) -> Dict:
+        """
+        Helper function for perform_per_layer_analysis_by_enabling_quantizers() and
+        perform_per_layer_analysis_by_disabling_quantizers()
+
+        :param sim: Quantsim model.
+        :param disable_all_quantizers: Flag to disable all the quantizers before per-layer analysis.
+        :param enabled_before: Flag to set enabled for quantizers before computing encodings.
+        :param enabled_after: Flag to set enabled for quantizers after computing encodings.
+        :return: layer wise eval score dictionary. dict[layer_name] = eval_score.
+        """
+
+        # Mapping of ops and their enabled quantizers
+        op_to_quantizers_dict = defaultdict(list)
+        for op in sim.connected_graph.ordered_ops:
+            input_quantizers, output_quantizers, param_quantizers = self._get_op_quantizers(op, sim)
+            if not input_quantizers and not output_quantizers and not param_quantizers:
+                continue
+            op_to_quantizers_dict[op.name_op].extend(input_quantizers)
+            op_to_quantizers_dict[op.name_op].extend(output_quantizers)
+            op_to_quantizers_dict[op.name_op].extend(list(param_quantizers.values()))
+
+        if disable_all_quantizers:
+            for enabled_quantizers in op_to_quantizers_dict.values():
+                self._enable_disable_quantizers(enabled_quantizers, enabled=False)
+
+        eval_score_dict = {}
+        for op_name, enabled_quantizers in op_to_quantizers_dict.items():
+            self._enable_disable_quantizers(enabled_quantizers, enabled=enabled_before)
+
+            # Record eval score.
+            eval_score_dict[op_name] = self._eval_callback.func(sim.session, self._eval_callback.args)
+            _logger.debug("For layer: %s, the eval score is: %f", op_name, eval_score_dict[op_name])
+
+            self._enable_disable_quantizers(enabled_quantizers, enabled=enabled_after)
+
+        if disable_all_quantizers:
+            for enabled_quantizers in op_to_quantizers_dict.values():
+                self._enable_disable_quantizers(enabled_quantizers, enabled=True)
+
+        return eval_score_dict
+
+    @staticmethod
+    def _get_op_quantizers(op: Op, sim: QuantizationSimModel) -> (List, List, Dict):
+        """
+        This function returns the enabled input, output and param quantizers of the given connected graph op.
+
+        :param op: Connected Graph Op
+        :param sim: QuantSim object
+        :return: list of input quantizers, list of output quantizers and dictionary of param quantizers
+        """
+        input_quantizers, output_quantizers, param_quantizers = sim.get_op_quantizers(op)
+        input_quantizers = [q for q in input_quantizers if q.enabled]
+        output_quantizers = [q for q in output_quantizers if q.enabled]
+        param_quantizers = {name: q for name, q in param_quantizers.items() if q.enabled}
+        return input_quantizers, output_quantizers, param_quantizers
+
+    # pylint: disable=no-self-use, too-many-branches, too-many-locals
+
    
+[docs]
+    def export_per_layer_encoding_min_max_range(self, sim: QuantizationSimModel, results_dir: str) -> Tuple[Dict, Dict]:
+        """
+        Exports encoding min and max range for all weights and activations. results_dir has
+        html files in following format.
+
+        -results_dir
+            -activations.html,
+            -weights.html
+
+        If per channel quantization(PCQ) is enabled then,
+
+        -results_dir
+            -activations.html,
+            -{layer_name}_{param_name}.html
+
+        :param sim: Quantsim model.
+        :param results_dir: Directory to save the results.
+        :return: layer wise min-max range for weights and activations.
+        """
+        min_max_ranges_dir = os.path.join(results_dir, "min_max_ranges")
+
+        min_max_range_for_activations_dict = {}
+        min_max_range_for_weights_dict = {}
+
+        for op in sim.connected_graph.ordered_ops:
+            input_quantizers, output_quantizers, param_quantizers = self._get_op_quantizers(op, sim)
+            op_name = re.sub(r'\W+', '_', op.name_op)
+
+            # Get input activations' encodings if starting op
+            for index, quantizer in enumerate(input_quantizers):
+                name = f"{op_name}_input_{index}"
+                encodings = quantizer.get_encodings()
+                min_max_range_for_activations_dict[name] = (encodings[0].min, encodings[0].max)
+
+            # Get output activations' encodings
+            for index, quantizer in enumerate(output_quantizers):
+                name = f"{op_name}_output_{index}"
+                encodings = quantizer.get_encodings()
+                min_max_range_for_activations_dict[name] = (encodings[0].min, encodings[0].max)
+
+            # Get parameters' encodings
+            for param_name, quantizer in param_quantizers.items():
+                name = re.sub(r'\W+', '_', f"{op_name}_{param_name}")
+                encodings = quantizer.get_encodings()
+                if len(encodings) > 1: # per-channel
+                    per_channel_encodings = {}
+                    for index, encoding in enumerate(encodings):
+                        per_channel_encodings[f"{name}_{index}"] = (encoding.min, encoding.max)
+                    min_max_range_for_weights_dict[name] = per_channel_encodings
+                else: # per-tensor
+                    min_max_range_for_weights_dict[name] = (encodings[0].min, encodings[0].max)
+
+        create_and_export_min_max_ranges_plot(min_max_range_for_weights_dict, min_max_ranges_dir, title="weights")
+        create_and_export_min_max_ranges_plot(min_max_range_for_activations_dict, min_max_ranges_dir, title="activations")
+
+        save_json(min_max_range_for_weights_dict, min_max_ranges_dir, title="weights.json")
+        save_json(min_max_range_for_activations_dict, min_max_ranges_dir, title="activations.json")
+
+        _logger.info("Exported per layer encodings min-max ranges plot(s).")
+
+        return min_max_range_for_weights_dict, min_max_range_for_activations_dict
    
+
+
+    # pylint: disable=too-many-branches, too-many-locals
+
    
+[docs]
+    def export_per_layer_stats_histogram(self,
+                                         sim: QuantizationSimModel,
+                                         results_dir: str,
+                                         ):
+        """
+        NOTE: Not to invoke when quantization scheme is not TF-Enhanced.
+
+        Exports histogram that represents a PDF of collected statistics by a quantizer.
+        After invoking this API, results_dir should have html files in following
+        format for every quantizers in the model.
+
+        -results_dir
+            -activations_pdf
+                name_{input/output}_{index}.html
+            -weights_pdf
+                -name
+                    param_name_{channel_index}.html
+
+        :param sim: Quantsim model.
+        :param results_dir: Directory to save the results.
+        """
+        weights_pdf_dir = os.path.join(results_dir, "weights_pdf")
+        activations_pdf_dir = os.path.join(results_dir, "activations_pdf")
+
+        for op in sim.connected_graph.ordered_ops:
+            input_quantizers, output_quantizers, param_quantizers = self._get_op_quantizers(op, sim)
+            op_name = re.sub(r'\W+', '_', op.name_op)
+
+            # Collect stats histogram of input activation quantizers
+            for index, quantizer in enumerate(input_quantizers):
+                self._create_and_export_stats_histogram_plot(quantizer,
+                                                             activations_pdf_dir,
+                                                             title=f"{op_name}_input_q{index}")
+
+            # Collect stats histogram of output activation quantizers
+            for index, quantizer in enumerate(output_quantizers):
+                self._create_and_export_stats_histogram_plot(quantizer,
+                                                             activations_pdf_dir,
+                                                             title=f"{op_name}_output_q{index}")
+
+            # Collect stats histogram of param quantizers
+            for param_name, quantizer in param_quantizers.items():
+                sanitized_param_name = re.sub(r'\W+', '_', param_name)
+                self._create_and_export_stats_histogram_plot(quantizer,
+                                                             os.path.join(weights_pdf_dir, op_name),
+                                                             title=f"{op_name}_{sanitized_param_name}")
+
+        _logger.info("Exported per layer stats histogram plot(s).")
    
+
+
+    @staticmethod
+    def _create_and_export_stats_histogram_plot(quantizer: QcQuantizeOp, results_dir: str, title: str):
+        """
+        For given quantizer, create and export histogram (PDF) of statistics in html format.
+
+        :param quantizer: Quantizer.
+        :param results_dir: Directory to save the results.
+        :param title: Title of the plot.
+        """
+        os.makedirs(results_dir, exist_ok=True)
+
+        histograms = quantizer.get_stats_histogram()
+        encodings = quantizer.get_encodings()
+
+        if not isinstance(encodings, List):
+            encodings = [encodings]
+
+        for index, (histogram, encoding) in enumerate(zip(histograms, encodings)):
+            export_stats_histogram_plot(histogram, encoding, results_dir, title=f"{title}_{index}")
+
+
    
+[docs]
+    def enable_per_layer_mse_loss(self, unlabeled_dataset_iterable: Iterable, num_batches: int):
+        """
+        Enables per layer MSE loss analysis.
+
+        :param unlabeled_dataset_iterable: A collection (i.e. iterable with `__len__`)
+                that iterates over an unlabeled dataset. The values yielded by this iterable are expected
+                to be able to be passed directly to the model.
+        :param num_batches: Number of batches. Approximately 256 samples/images are recommended,
+                so if batch size of data loader is 64, then 4 number of batches leads to 256 samples/images.
+        """
+        if len(unlabeled_dataset_iterable) < num_batches:
+            raise ValueError(f'Can not fetch {num_batches} batches from '
+                             f'a data loader of length {len(unlabeled_dataset_iterable)}.')
+
+        self._unlabeled_dataset_iterable = unlabeled_dataset_iterable
+        self._num_batches = num_batches
    
+
+
+
    
+[docs]
+    def export_per_layer_mse_loss(self, sim: QuantizationSimModel, results_dir: str) -> Dict:
+        """
+        Exports MSE loss between fp32 and quantized output activations for each layer.
+
+        :param sim: Quantsim model.
+        :param results_dir: Directory to save the results.
+        :return: layer wise MSE loss. dict[layer_name] = MSE loss.
+        """
+        results_dir = os.path.abspath(results_dir)
+        os.makedirs(results_dir, exist_ok=True)
+
+        mse_loss_dict = {}
+        for op_node in self._onnx_model.nodes():
+            if op_node.op_type == 'Constant':
+                continue
+            op_output = op_node.output[0]
+            if op_output in sim.qc_quantize_op_dict:
+                quantized_op_output = op_output + '_updated'
+                loss = self._compute_mse_loss(op_output, quantized_op_output, self._onnx_model, sim.model)
+                mse_loss_dict[op_node.name] = loss
+
+        export_per_layer_mse_plot(mse_loss_dict,
+                                  results_dir,
+                                  title="per_layer_mse_loss")
+        save_json(mse_loss_dict, results_dir, title="per_layer_mse_loss.json")
+        _logger.info("Exported per layer MSE loss plot.")
+        return mse_loss_dict
    
+
+
+    # pylint: disable=too-many-locals
+    def _compute_mse_loss(self, fp32_act_name: str, quantized_act_name: str,
+                          fp32_model: ONNXModel, quantized_model: ONNXModel) -> float:
+        """
+        Compute MSE loss between fp32 and quantized output activations for each batch, add for
+        all the batches and return averaged mse loss.
+
+        :param fp32_act_name: module from the fp32_model.
+        :param quantized_act_name: Corresponding quant wrapper from the QuantSim model.
+        :param fp32_model: PyTorch model.
+        :param sim: Quantsim model.
+        :return: MSE loss between fp32 and quantized output activations.
+        """
+
+        providers = ['CPUExecutionProvider']
+        if 'CUDAExecutionProvider' in ort.get_available_providers():
+            providers = [('CUDAExecutionProvider', {'cudnn_conv_algo_search': 'DEFAULT'}), 'CPUExecutionProvider']
+
+        # output activations collector.
+        orig_module_collector = ModuleData(fp32_model, fp32_act_name, providers)
+        quant_module_collector = ModuleData(quantized_model, quantized_act_name, providers)
+
+        total = 0
+        loss = 0.0
+        batch_index = 0
+        for model_inputs in self._unlabeled_dataset_iterable:
+            model_inputs = utils.create_input_dict(fp32_model.model, model_inputs)
+            _, quantized_out_acts = quant_module_collector.collect_inp_out_data(model_inputs,
+                                                                                collect_input=False,
+                                                                                collect_output=True)
+            _, fp32_out_acts = orig_module_collector.collect_inp_out_data(model_inputs,
+                                                                          collect_input=False,
+                                                                          collect_output=True)
+            loss += mean_squared_error(fp32_out_acts[0].reshape(fp32_out_acts[0].shape[0], -1),
+                                       quantized_out_acts[0].reshape(fp32_out_acts[0].shape[0], -1))
+            total += fp32_out_acts[0].shape[0]
+            batch_index += 1
+            if batch_index == self._num_batches:
+                break
+
+        average_loss = loss/total
+        return float(average_loss)
    
+
+
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    +

    Source code for aimet_onnx.quantsim

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2022-2024, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+""" Implementation for simulating models running on Quantized hardware """
+
+import contextlib
+import tempfile
+from dataclasses import dataclass
+from pathlib import Path
+import os
+from typing import Dict, List, Union, Tuple, Optional
+import json
+import warnings
+import numpy as np
+import onnx
+
+from onnx import helper
+import onnxruntime as ort
+from onnxruntime import SessionOptions, GraphOptimizationLevel, InferenceSession
+from onnxruntime.quantization.onnx_quantizer import ONNXModel
+from packaging import version
+
+# pylint: disable=wrong-import-order
+from aimet_common import libpymo, quantsim
+from aimet_common import libquant_info
+from aimet_common.defs import QuantScheme, QuantizationDataType
+from aimet_common.quantsim import extract_global_quantizer_args, VALID_ENCODING_VERSIONS
+from aimet_common.utils import save_json_yaml, AimetLogger, _red
+from aimet_common.connected_graph.product import Product
+from aimet_onnx import utils
+from aimet_onnx.meta.operations import Op
+from aimet_onnx.meta.utils import get_op_given_param_name, get_param_shape_using_connected_graph
+from aimet_onnx.meta.connectedgraph import ConnectedGraph
+from aimet_onnx.qc_quantize_op import QcQuantizeOp, OpMode, TensorQuantizerParams, GroupedBlockQuantizeDequantize
+from aimet_onnx.quantsim_config.quantsim_config import QuantSimConfigurator
+from aimet_onnx.utils import make_dummy_input, add_hook_to_get_activation, remove_activation_hooks
+
+logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.Quant)
+
+# pylint: disable=no-name-in-module, ungrouped-imports, too-many-lines
+if version.parse(onnx.__version__) >= version.parse("1.14.0"):
+    from onnx import ModelProto
+else:
+    from onnx.onnx_pb import ModelProto
+
+# List of ops whose outputs are not to be quantized
+op_outputs_to_ignore = ["branch", "Flatten", "Gather", "Reshape", "Shape", "Unsqueeze", "Squeeze", "Split",
+                        "Compress", "Tile", "Transpose", "Identity"]
+
+# List of ops whose params are not to be quantized
+op_params_to_ignore = ['Resize']
+
+allowed_op_type_for_per_channel = ['Conv', 'Gemm', 'MatMul', 'ConvTranspose']
+
+# List of op types whose input and output quantizers to be tied
+op_types_to_tie_qtzrs = ['Concat', 'MaxPool', 'AveragePool', 'Resize']
+_tie_qtzrs = False
+
+data_types_to_quantize = [np.float32]
+
+
+@contextlib.contextmanager
+def _apply_constraints(flag: bool):
+    """
+    Apply runtime specific constraints.
+    For certain ``op_types_to_tie_qtzrs``, runtime has constraints to have same encodings for
+     input and output quantizers.
+
+    NOTE: Default setting doesn't apply these constraints.
+    """
+    global _tie_qtzrs # pylint: disable=global-statement
+    orig_flag = _tie_qtzrs
+    try:
+        _tie_qtzrs = flag
+        yield
+    finally:
+        _tie_qtzrs = orig_flag
+
+
+@dataclass
+class EncodingMismatchInfo:
+    """
+    Dataclass tracking information about mismatched quantizer vs. encoding settings.
+    """
+    quantizer_name: str
+    enabled_mismatch: Optional[Tuple] = None
+    dtype_mismatch: Optional[Tuple] = None
+    bitwidth_mismatch: Optional[Tuple] = None
+    is_symmetric_mismatch: Optional[Tuple] = None
+    is_strict_symmetric_mismatch: Optional[Tuple] = None
+    is_unsigned_symmetric_mismatch: Optional[Tuple] = None
+
+    def has_mismatch(self) -> bool:
+        """
+        Returns True if there is a mismatched setting.
+
+        :return: True if there is a mismatched setting, False otherwise
+        """
+        return (self.enabled_mismatch is not None or
+                self.dtype_mismatch is not None or
+                self.bitwidth_mismatch is not None or
+                self.is_symmetric_mismatch is not None or
+                self.is_strict_symmetric_mismatch is not None or
+                self.is_unsigned_symmetric_mismatch is not None)
+
+
+
    
+[docs]
+class QuantizationSimModel:
+    """ Creates a QuantizationSimModel model by adding quantization simulations ops to a given model """
+
+    # pylint: disable=too-many-arguments, too-many-locals, too-many-instance-attributes
+    def __init__(self,
+                 model: ModelProto,
+                 dummy_input: Dict[str, np.ndarray] = None,
+                 quant_scheme: QuantScheme = QuantScheme.post_training_tf_enhanced,
+                 rounding_mode: str = 'nearest',
+                 default_param_bw: int = 8,
+                 default_activation_bw: int = 8,
+                 use_symmetric_encodings: bool = False, use_cuda: bool = True,
+                 device: int = 0, config_file: str = None,
+                 default_data_type: QuantizationDataType = QuantizationDataType.int,
+                 user_onnx_libs: List[str] = None, path: str = None):
+        """
+        Constructor
+
+        :param model: ONNX model
+        :param dummy_input: Dummy input to the model. If None, will attempt to auto-generate a dummy input
+        :param quant_scheme: Quantization scheme (e.g. QuantScheme.post_training_tf)
+        :param rounding_mode: Rounding mode (e.g. nearest)
+        :param default_param_bw: Quantization bitwidth for parameter
+        :param default_activation_bw: Quantization bitwidth for activation
+        :param use_symmetric_encodings: True if symmetric encoding is used.  False otherwise.
+        :param use_cuda: True if using CUDA to run quantization op. False otherwise.
+        :param config_file: Path to Configuration file for model quantizers
+        :param default_data_type: Default data type to use for quantizing all layer inputs, outputs and parameters.
+                                 Possible options are QuantizationDataType.int and QuantizationDataType.float.
+                                 Note that the mode default_data_type=QuantizationDataType.float is only supported with
+                                 default_output_bw=16 and default_param_bw=16
+        :param user_onnx_libs: List of paths to all compiled ONNX custom ops libraries
+        :param path: Directory to save the artifacts.
+        """
+        self.model = model
+        if not isinstance(model, ONNXModel):
+            self.model = ONNXModel(model)
+        if not dummy_input:
+            dummy_input = make_dummy_input(self.model.model)
+        self.qc_quantize_op_dict = {}
+        self.connected_graph = ConnectedGraph(self.model)
+        self._quant_scheme = quant_scheme
+        self._rounding_mode = rounding_mode
+        self._default_param_bw = default_param_bw
+        self._default_activation_bw = default_activation_bw
+        self._default_quantization_data_type = default_data_type
+        self._use_symmetric_encodings = use_symmetric_encodings
+        self._use_cuda = use_cuda
+        if 'CUDAExecutionProvider' not in ort.get_available_providers():
+            self._use_cuda = False
+        if self._use_cuda:
+            self._op_domain = "aimet.customop.cuda"
+            self.providers = [('CUDAExecutionProvider', {'device_id': device, 'cudnn_conv_algo_search': 'DEFAULT'}), 'CPUExecutionProvider']
+        else:
+            self._op_domain = "aimet.customop.cpu"
+            self.providers = ['CPUExecutionProvider']
+        self._user_onnx_libs = user_onnx_libs
+        self.param_names = []
+        self.input_quantizers_name = []
+        self.activation_names = []
+        self.activation_dtypes = {}
+        self._path = path if path else tempfile.mkdtemp()
+        if not os.path.exists(self._path):
+            os.makedirs(self._path, exist_ok=True)
+
+        # Get names of parameters and activations to quantize
+        self._get_param_names()
+        self._get_activations_to_quantize(dummy_input)
+
+        # Disable bias quantization
+        self._disable_bias_quantization()
+        self._add_quantization_nodes()
+
+        # Apply configurations based on provided config file.
+        quantsim_configurator = self._add_configuration_(config_file)
+        self._hw_version = quantsim_configurator._get_hw_version()
+        self._supported_kernels = quantsim_configurator.get_supported_kernels()
+        self._op_to_supported_kernel = quantsim_configurator.get_op_to_supported_kernels()
+        self.quant_args = extract_global_quantizer_args(quant_scheme, quantsim_configurator)
+        self._apply_exception_rules()
+        self._tie_quantizers()
+
+        # Build onnxruntime inference session
+        self.session = QuantizationSimModel.build_session(self.model.model, self.providers,
+                                                          user_onnx_libs=self._user_onnx_libs, path=self._path)
+
+    def get_supported_kernels(self) -> Dict:
+        """
+        Return _supported_kernels parsed from the config file
+        :return: Dictionary containing supported_kernels
+        """
+        return self._supported_kernels
+
+    def _add_configuration_(self, config_file: str):
+        """
+        Add configuration based on config file
+
+        :param config_file: Path to Configuration file for model quantizers
+        """
+        quantsim_configurator = QuantSimConfigurator(self.model, self.connected_graph, config_file,
+                                                     self._default_activation_bw, self._default_param_bw,
+                                                     self._default_quantization_data_type)
+        quantsim_configurator.configure_quantizers(self.qc_quantize_op_dict, self.param_names, self.activation_names,
+                                                   self.input_quantizers_name)
+
+        return quantsim_configurator
+
+    def _get_param_names(self):
+        """
+        Get the names of params
+        """
+        valid_ops = self._get_ops_with_parameter()
+        for op in valid_ops:
+            for param_info in op.parameters.values():
+                param, _ = param_info
+                if param.name and param.name not in self.param_names:
+                    self.param_names.append(param.name)
+
+    def _get_ops_with_parameter(self) -> List[Op]:
+        """
+        Gets ops with parameters to add quantization nodes for
+
+        :return: Connected graph ops
+        """
+        valid_ops = list(self.connected_graph.get_all_ops().values())
+        return valid_ops
+
+    def _get_activations_to_quantize(self, dummy_input: Dict[str, np.ndarray]):
+        """
+        Get the names of activations to quantize
+
+        :param dummy_input: Sample input to be run through the model
+        """
+        self.fill_activation_dtypes(dummy_input)
+        self.input_name_to_nodes = self.model.input_name_to_nodes()
+        self.output_name_to_node = self.model.output_name_to_node()
+
+        # Capture model inputs
+        for node in self.model.graph().input:
+            name = node.name
+            if name not in self.activation_names and name not in self.param_names and self._is_tensor_quantizable(name):
+                self.activation_names.append(name)
+
+        # Capture intermediate activations and model outputs
+        for node in self.model.nodes():
+            for name in node.input:
+                if name not in self.activation_names and name not in self.param_names and self._is_tensor_quantizable(name):
+                    self.activation_names.append(name)
+                    self.input_quantizers_name.append(name)
+
+            for name in node.output:
+                if name not in self.activation_names and name not in self.param_names and self._is_tensor_quantizable(name):
+                    self.activation_names.append(name)
+
+        # Rename model output node
+        for node in self.model.graph().output:
+            if node.name in self.activation_names:
+                node.name += '_updated'
+
+    def _is_tensor_quantizable(self, name: str) -> bool:
+        """
+        Checks whether the given tensor should be quantized
+
+        :param name: Name of the tensor
+        :return: True if the tensor should be quantized
+        """
+        # Check if the tensor data-type can be quantized
+        if name in self.model.get_initializer_name_set():  # static activation
+            if self.model.get_initializer(name).data_type != 1:  # 1 corresponds to float, dictionary can be found by using onnx.TensorProto.DataType.items()
+                return False
+        else:  # dynamic activation
+            if name not in self.activation_dtypes.keys() or self.activation_dtypes[name] not in data_types_to_quantize:
+                return False
+
+        # Check if the tensor is param to certain ops (eg: Resize)
+        consumer_nodes = self.input_name_to_nodes.get(name)
+        if consumer_nodes:
+            for consumer_node in consumer_nodes:
+                if consumer_node.op_type in op_params_to_ignore and \
+                        consumer_node.input[0] != name:  # except first input rest are params (only valid for unary ops)
+                    return False
+
+        # Check if the tensor is output of certain ops
+        producer_node = self.output_name_to_node.get(name)
+        if producer_node and producer_node.op_type in op_outputs_to_ignore:
+            return False
+
+        return True
+
+    def _disable_bias_quantization(self):
+        """
+        Exclude bias params from the quantization.
+        """
+        # TODO: hitameht: we should find better way to find such patterns and corner cases. May be
+        #  belongs to QuantSimConfigurator class.
+        for node in self.model.nodes():
+            if node.op_type == 'Add':
+                if self._check_matmul_add_patten(node):
+                    for inp_name in node.input:
+                        param_name = inp_name
+                        # ensure that the second input of Add op (bias) won't be configured
+                        # with activation precision.
+                        if (param_name in self.model.get_initializer_name_set() and
+                                len(self.model.get_initializer(param_name).dims) == 1 and
+                                param_name in self.activation_names):
+                            self.activation_names.remove(param_name)
+                            self.input_quantizers_name.remove(param_name)
+
+    def _check_matmul_add_patten(self, node: onnx.NodeProto) -> bool:
+        """
+        For given node, check if the previous and the current nodes are of type 'MatMul' and 'Add' respectively.
+
+        NOTE:
+        Linear = (Matmul -> Add) gets fused into a single MatMul / FullyConnected HTP op.
+        Second input of Add (Bias) needs to be either uint8 or int32.
+        This utility will find such pattern and help ensure that the second input of Add op (bias) won't be configured
+         with activation precision.
+
+        :param node: Onnx node
+        :return: True if the MatMul + Add pattern is found, False otherwise.
+        """
+        cg_op = self.connected_graph.get_op_from_module_name(node.name)
+        for inp in cg_op.input_ops:
+            prev_cg_op = inp
+            # Ensure that the MatMul isn't consumed by other op.
+            if prev_cg_op.type == 'MatMul' and cg_op.type == 'Add' and len(prev_cg_op.output_ops) == 1:
+                return True
+        return False
+
+    def fill_activation_dtypes(self, dummy_input: Dict[str, np.ndarray]):
+        """
+        Get the data type for each activation
+
+        :param dummy_input: Sample input to run through the model
+        """
+        activations = utils.get_graph_intermediate_activations(self.model.graph())
+        hooks = []
+        for name in activations:
+            hooks.append(add_hook_to_get_activation(self.model.model, name))
+        sess = QuantizationSimModel.build_session(self.model.model, ['CPUExecutionProvider'],
+                                                  user_onnx_libs=self._user_onnx_libs, path=self._path)
+        outputs = sess.run(None, dummy_input)
+        for idx in range(len(self.model.graph().output)):
+            act_name = self.model.graph().output[idx].name
+            dtype = outputs[idx].dtype
+            self.activation_dtypes[act_name] = dtype
+        remove_activation_hooks(self.model.model, hooks)
+
+    def _add_quantization_nodes(self):
+        """
+        Call insert functions for quantization nodes
+        """
+        self._insert_param_quantization_nodes()
+        self._insert_activation_quantization_nodes()
+
+    def _replace_input_of_all_nodes(self, old_name, new_name):
+        if old_name not in self.connected_graph.get_all_products():
+            raise ValueError(f"Tensor name {old_name} was not found in graph tensors "
+                             f"{self.connected_graph.get_all_products().keys()}.")
+
+        product = self.connected_graph.get_all_products()[old_name]
+        for consumer in product.consumers:
+            node = consumer.get_module()
+            for idx, tensor in enumerate(node.input):
+                if tensor == old_name:
+                    node.input[idx] = new_name
+
+    def _insert_param_quantization_nodes(self):
+        """
+        Insert quantization node for each param tensor
+        """
+        for name in self.param_names:
+            self._replace_input_of_all_nodes(name, name + '_qdq')
+
+            quant_info, tensor_quantizer_params = self._create_quant_info_object_for_param(name)
+            custom_node = helper.make_node(
+                op_type='QcQuantizeOp',
+                inputs=[name],
+                outputs=[name + '_qdq'],
+                name='QcQuantizeOp_' + name,
+                domain=self._op_domain,
+                op_name=name,
+                quant_info=libpymo.PtrToInt64(quant_info),
+            )
+            self.model.add_node(custom_node)
+            self.qc_quantize_op_dict[name] = QcQuantizeOp(quant_info=quant_info,
+                                                          quant_scheme=self._quant_scheme,
+                                                          rounding_mode=self._rounding_mode,
+                                                          op_mode=OpMode.oneShotQuantizeDequantize,
+                                                          bitwidth=self._default_param_bw,
+                                                          use_symmetric_encodings=self._use_symmetric_encodings,
+                                                          tensor_quantizer_params=tensor_quantizer_params)
+
+    def _create_quant_info_object_for_param(self, param_name: str):
+        """
+        Creates quant info object for QcQuantizeOp and QDQ node
+
+        :param param_name: Name of the parameter for which the quant info object will be created
+        :return: quant info object
+        """
+        quant_info = libquant_info.QcQuantizeInfo()
+        quant_info.usePerChannelMode = False
+        op = get_op_given_param_name(self.connected_graph, param_name)
+        param_shape = get_param_shape_using_connected_graph(self.connected_graph, param_name)
+        tensor_quantizer_params = TensorQuantizerParams(param_shape)
+
+        if len(param_shape) == 1:
+            tensor_quantizer_params.channel_axis = 0
+            tensor_quantizer_params.block_axis = None
+        else:
+            channel_axis, block_axis = self._get_quantization_axes(op)
+            tensor_quantizer_params.channel_axis = channel_axis
+            tensor_quantizer_params.block_axis = block_axis
+
+        return quant_info, tensor_quantizer_params
+
+    @staticmethod
+    def _get_quantization_axes(op: Op) -> Tuple[int, int]:
+        """
+        Gets quantization axes for per-channel and blockwise quantization
+
+        :param op: Connected graph op
+        return: axis
+        """
+        if op.type in ['Conv']:
+            return 0, 1
+        if op.type in ['ConvTranspose']:
+            return 1, 0
+        if op.type in ['Gemm', 'MatMul'] and op.transposed_params:
+            return 0, 1
+        if op.type in ['Gemm', 'MatMul']:
+            return 1, 0
+        return None, None
+
+    def _insert_activation_quantization_nodes(self):
+        """
+        Insert quantization node for each activation tensor
+        """
+        for name in self.activation_names:
+            self._replace_input_of_all_nodes(name, name + '_updated')
+            quant_info = libquant_info.QcQuantizeInfo()
+            custom_node = helper.make_node(
+                op_type='QcQuantizeOp',
+                inputs=[name],
+                outputs=[name + '_updated'],
+                name='QcQuantizeOp_' + name,
+                domain=self._op_domain,
+                op_name=name,
+                quant_info=libpymo.PtrToInt64(quant_info)
+            )
+            self.model.add_node(custom_node)
+            self.qc_quantize_op_dict[name] = QcQuantizeOp(quant_info=quant_info,
+                                                          quant_scheme=self._quant_scheme,
+                                                          rounding_mode=self._rounding_mode,
+                                                          op_mode=OpMode.updateStats,
+                                                          bitwidth=self._default_activation_bw,
+                                                          use_symmetric_encodings=self._use_symmetric_encodings)
+
+    @staticmethod
+    def build_session(model: onnx.ModelProto, providers: List, user_onnx_libs: List[str] = None, path: str = None):
+        """
+        Build and return onnxruntime inference session
+
+        :param model: onnx model
+        :param providers: providers to execute onnxruntime
+        :param user_onnx_libs: list of paths to user custom ONNX op libraries
+        :param path: path where to store model external data
+        """
+        sess_options = SessionOptions()
+        shared_library = os.path.dirname(libquant_info.__file__)
+        shared_library = os.path.join(shared_library, "libaimet_onnxrt_ops.so")
+        sess_options.register_custom_ops_library(shared_library)
+        if user_onnx_libs is not None:
+            for lib in user_onnx_libs:
+                sess_options.register_custom_ops_library(lib)
+        sess_options.graph_optimization_level = GraphOptimizationLevel.ORT_DISABLE_ALL
+
+        # Convert and save ONNX model to external data if larger than 2GB.
+        # External data will be saved under same directory.
+        path = path if path else tempfile.mkdtemp()
+        if not os.path.exists(path):
+            os.makedirs(path, exist_ok=True)
+        model_size = model.ByteSize()
+        save_as_external_data = model_size >= onnx.checker.MAXIMUM_PROTOBUF
+        output_path = os.path.join(path, 'model.onnx')
+        if save_as_external_data:
+            # Note: Saving as external data mutates the saved model, removing all initializer data
+            onnx.save_model(model, output_path, save_as_external_data=True, location=Path(output_path).name + ".data")
+            onnx.load_external_data_for_model(model, base_dir=path)
+
+        path_or_bytes = output_path if save_as_external_data else model.SerializeToString()
+        session = InferenceSession(
+            path_or_bytes=path_or_bytes,
+            sess_options=sess_options,
+            providers=providers,
+        )
+        return session
+
+    def get_qc_quantize_op(self):
+        """
+        Return dict of qc quantize ops
+        """
+        return self.qc_quantize_op_dict
+
+    def get_op_quantizers(self, op: Op) -> (List, List, Dict):
+        """
+        This function returns the input, output and param quantizers of the given connected graph op.
+
+        :param op: Connected Graph Op
+        :return: list of input quantizers, list of output quantizers and dictionary of param quantizers
+        """
+        input_quantizers = []
+        output_quantizers = []
+        param_quantizers = {}
+
+        # Capture as input quantizer if tensor is not a layer output or parameter
+        for cg_product in op.inputs:
+            if not cg_product.producer and not cg_product.is_parm:
+                input_name = cg_product.name
+                if input_name in self.qc_quantize_op_dict:
+                    input_quantizers.append(self.qc_quantize_op_dict[input_name])
+
+        # Capture output quantizers of the op
+        cg_product = op.output
+        if cg_product.name in self.qc_quantize_op_dict:
+            output_quantizers.append(self.qc_quantize_op_dict[cg_product.name])
+
+        # Capture param quantizers of the op
+        for param_name, (_, param_type) in op.parameters.items():
+            if param_name in self.qc_quantize_op_dict:
+                param_quantizers[param_type] = self.qc_quantize_op_dict[param_name]
+
+        return input_quantizers, output_quantizers, param_quantizers
+
+    def _apply_exception_rules(self):
+        """
+        Apply exception rules to specific op. For example, a rule can override high bitwidth to GroupNorm op.
+        """
+        for op in self.connected_graph.ordered_ops:
+            input_quantizers, output_quantizers, param_quantizers = self.get_op_quantizers(op)
+
+            if op.type == 'GroupNormalization':
+                if self._hw_version not in {'V73', 'V75', 'V79'}:
+                    continue
+                if 'weight' in param_quantizers:
+                    output_quantizer = output_quantizers[0]
+                    for _, param_quantizer in param_quantizers.items():
+                        param_quantizer.bitwidth = output_quantizer.bitwidth
+                        param_quantizer.use_symmetric_encodings = output_quantizer.use_symmetric_encodings
+
+            elif op.type == 'MatMul':
+                first_input_quantizer = input_quantizers[0] if input_quantizers else None
+                second_input_quantizer = list(param_quantizers.values())[0] if param_quantizers else None
+
+                first_input_op = op.inputs[0].producer if not first_input_quantizer else None
+                second_input_op = op.inputs[1].producer if not second_input_quantizer else None
+
+                target_quantizer_for_first_input = self._get_target_quantizer(first_input_quantizer, first_input_op)
+                target_quantizer_for_second_input = self._get_target_quantizer(second_input_quantizer, second_input_op)
+
+                # According to opdef for Matmul in HTP:
+                # 16bit Weight(second input for dynamic MatMul) must have 16bit Activation(first input for dynamic MatMul).
+                # 16bit Activation and 16bit Weight require minimum arch V73.
+                # 16bit Weight must be symmetric quantized.
+
+                # Below are the possible combinations for MatMul with 8/16 bitwidth:
+                # If version is V73/V75: {input0->8, input1->8 symm/asymm} {input0->16 , input1->8 symm/asymm} {input0->16, input1->16 symmetric}
+                # If version is lesser than V73: {input0->8, input1->8 symmetric} {input0->16, input1->8 symmetric}
+
+                if self._hw_version in {'V66', 'V68', 'V69'}:
+                    if target_quantizer_for_second_input is None:
+                        logger.warning("The target quantizer for second input could not be found. MatMul exception rule does not apply for op: %s.", op.name)
+                    else:
+                        target_quantizer_for_second_input.use_symmetric_encodings = True
+                        target_quantizer_for_second_input.bitwidth = 8
+                elif self._hw_version in {'V73', 'V75', 'V79'}:
+                    if target_quantizer_for_first_input is None or target_quantizer_for_second_input is None:
+                        logger.warning("The target quantizers could not be found. MatMul exception rule does not apply for op: %s.", op.name)
+                    elif target_quantizer_for_second_input.bitwidth == 16:
+                        target_quantizer_for_second_input.use_symmetric_encodings = True
+                        target_quantizer_for_first_input.bitwidth = 16
+
+    def _get_target_quantizer(self, input_quantizer: QcQuantizeOp, input_op: Op) -> QcQuantizeOp:
+        """
+        Returns input quantizer if enabled otherwise returns closest enabled parent output quantizer.
+
+        :param input_quantizer: Input quantizer
+        :param input_op: CG op
+        :return: Target quantizer
+        """
+        target_quantizer = None
+        if input_quantizer:
+            if input_quantizer.enabled:
+                target_quantizer = input_quantizer
+        else:
+            target_quantizer = self._get_closest_target_quantizer(input_op)
+        return target_quantizer
+
+    # pylint: disable=inconsistent-return-statements
+    def _get_closest_target_quantizer(self, op: Op) -> QcQuantizeOp:
+        """
+        Returns the closest parent output quantizer.
+
+        :param op: CG op whose output quantizer is to be analyzed
+        :return: Target quantizer
+        """
+        onnx_op = op.get_module()
+
+        if onnx_op is None:
+            return self._get_closest_target_quantizer(op.input_ops[0])
+
+        output_tensor = onnx_op.output[0]
+        target_quantizer = self.qc_quantize_op_dict.get(output_tensor, None)
+
+        if target_quantizer and target_quantizer.enabled:
+            return target_quantizer
+
+        if target_quantizer is None or not target_quantizer.enabled:
+            if len(op.input_ops) == 1:
+                return self._get_closest_target_quantizer(op.input_ops[0])
+            logger.warning("The op: %s with output quantization disabled has no input or more than one input exists. "
+                           "It's ambiguous to find the closest target quantizer in this case", op.name)
+            return None
+
+    def save_model_graph(self, filename_prefix: str):
+        """
+        Save model to given path
+
+        :param filename_prefix: filename to save the onnx model
+        """
+        self.model.save_model_to_file(os.path.join(self._path, filename_prefix) + '.onnx')
+
+
    
+[docs]
+    def compute_encodings(self, forward_pass_callback, forward_pass_callback_args):
+        """
+        Compute and return the encodings of each tensor quantizer
+
+        :param forward_pass_callback: A callback function that simply runs forward passes on the model. This callback
+            function should use representative data for the forward pass, so the calculated encodings work for all
+            data samples. This callback internally chooses the number of data samples it wants to use for calculating
+            encodings.
+        :param forward_pass_callback_args: These argument(s) are passed to the forward_pass_callback as-is. Up to
+            the user to determine the type of this parameter. E.g. could be simply an integer representing the number
+            of data samples to use. Or could be a tuple of parameters or an object representing something more complex.
+            If set to None, forward_pass_callback will be invoked with no parameters.
+        """
+        for op_name, qc_op in self.qc_quantize_op_dict.items():
+            qc_op.reset_encoding_stats()
+            if op_name in self.activation_names:
+                qc_op.op_mode = OpMode.updateStats
+            else:
+                qc_op.op_mode = OpMode.oneShotQuantizeDequantize
+                if qc_op.is_encoding_frozen():
+                    qc_op.op_mode = OpMode.quantizeDequantize
+
+        forward_pass_callback(self.session, forward_pass_callback_args)
+        for op_name, qc_op in self.qc_quantize_op_dict.items():
+            if qc_op.data_type == QuantizationDataType.int and not qc_op.is_encoding_frozen():
+                qc_op.compute_encodings()
+            qc_op.op_mode = OpMode.quantizeDequantize
    
+
+
+    def _get_encodings(self, quantizer_names, enc_version):
+        encoding_dict = {}
+        for name in quantizer_names:
+            encoding = self.qc_quantize_op_dict[name].export_encodings(enc_version)
+            if encoding is None:
+                continue
+            encoding_dict[name] = encoding
+
+        if version.parse(enc_version) < version.parse("1.0.0"):
+            return encoding_dict
+
+        for name, encoding in encoding_dict.items():
+            encoding["name"] = name
+        return list(encoding_dict.values())
+
+    def _export_encodings(self, encoding_file_path):
+        """
+        Export encodings to json file
+
+        :param encoding_file_path: path to save the encoding file
+        """
+        enc_version = quantsim.encoding_version
+        if enc_version not in VALID_ENCODING_VERSIONS:
+            raise NotImplementedError(f'Encoding version {enc_version} not in set of valid encoding '
+                                      f'versions {VALID_ENCODING_VERSIONS}.')
+
+        param_encodings = self._get_encodings(self.param_names, enc_version)
+        activation_encodings = self._get_encodings(self.activation_names, enc_version)
+
+        encodings_dict = {'version': enc_version,
+                          'activation_encodings': activation_encodings,
+                          'param_encodings': param_encodings,
+                          'quantizer_args': self.quant_args}
+
+        save_json_yaml(encoding_file_path, encodings_dict)
+
+    def remove_quantization_nodes(self):
+        """
+        Remove quantization nodes
+        """
+        self.model = self.remove_quantizers(self.model)
+
+    @staticmethod
+    def remove_quantizers(model: ONNXModel):
+        """
+        Removes all QcQuantizeOp layers from model
+        """
+        nodes_to_remove = []
+        for node in model.nodes():
+            if node.op_type == 'QcQuantizeOp':
+                nodes_to_remove.append(node)
+            else:
+                for name in node.input:
+                    model.replace_input_of_all_nodes(name, name.replace('_qdq', '').replace('_updated', ''))
+        model.remove_nodes(nodes_to_remove)
+
+        for node in model.graph().output:
+            node.name = node.name.replace('_updated', '')
+
+        return model
+
+
    
+[docs]
+    def export(self, path: str, filename_prefix: str):
+        """
+        Compute encodings and export to files
+
+        :param path: dir to save encoding files
+        :param filename_prefix: filename to save encoding files
+        """
+        if quantsim.encoding_version == '0.6.1':
+            msg = _red("Encoding version 0.6.1 will be deprecated in a future release, with version 1.0.0 becoming "
+                       "the default. If your code depends on parsing the exported encodings file, ensure that it is "
+                       "updated to be able to parse 1.0.0 format.\n"
+                       "To swap the encoding version to 1.0.0, run the following lines prior to calling quantsim "
+                       "export:\n\n"
+                       "from aimet_common import quantsim\n"
+                       "quantsim.encoding_version = '1.0.0'")
+            warnings.warn(msg, DeprecationWarning, stacklevel=2)
+        self._export_encodings(os.path.join(path, filename_prefix) + '.encodings')
+        self.remove_quantization_nodes()
+        if self.model.model.ByteSize() >= onnx.checker.MAXIMUM_PROTOBUF:
+            # Note: Saving as external data mutates the saved model, removing all initializer data
+            self.model.save_model_to_file(os.path.join(path, filename_prefix) + '.onnx', use_external_data_format=True)
+            onnx.load_external_data_for_model(self.model.model, base_dir=path)
+        else:
+            self.model.save_model_to_file(os.path.join(path, filename_prefix) + '.onnx')
    
+
+
+    def set_and_freeze_param_encodings(self, encoding_path: str):
+        """
+        Set and freeze parameter encodings from encodings JSON file
+
+        :param encoding_path: path from where to load parameter encodings file
+        """
+
+        # Load encodings file
+        with open(encoding_path) as json_file:
+            encodings = json.load(json_file)
+
+        for quantizer_name in encodings:
+            if quantizer_name in self.qc_quantize_op_dict:
+                libpymo_encodings = _create_libpymo_encodings(encodings[quantizer_name])
+                is_symmetric, is_strict_symmetric, is_unsigned_symmetric = \
+                    get_symmetric_properties(encodings[quantizer_name])
+                data_type = QuantizationDataType.int if encodings[quantizer_name][0]['dtype'] == 'int' else \
+                    QuantizationDataType.float
+                self.qc_quantize_op_dict[quantizer_name].update_quantizer_and_load_encodings(libpymo_encodings,
+                                                                                             is_symmetric,
+                                                                                             is_strict_symmetric,
+                                                                                             is_unsigned_symmetric,
+                                                                                             data_type)
+                self.qc_quantize_op_dict[quantizer_name].freeze_encodings()
+
+    def get_all_quantizers(self) -> Tuple[List, List]:
+        """
+        Returns all QcQuantizeOps through which TensorQuantizer's attributes can be accessed.
+        """
+        param_quantizers = []
+        activation_quantizers = []
+
+        for param in self.param_names:
+            param_quantizers.append(self.qc_quantize_op_dict[param])
+
+        for activation in self.activation_names:
+            activation_quantizers.append(self.qc_quantize_op_dict[activation])
+
+        return param_quantizers, activation_quantizers
+
+    def _tie_quantizers(self):
+        """
+        Tie the input and output quantizers for given op types.
+        """
+        if not _tie_qtzrs:
+            return
+
+        cg = self.connected_graph
+
+        def _set_quant_info(dst_qtzr_node_name: str, src_qtzr: QcQuantizeOp):
+            """
+            Set quant_info attribute (pointer to the libquant_info object)
+
+            :param dst_qtzr_node_name: destination quantizer node name in graph.
+            :param src_qtzr: source quantizer.
+            """
+            for node in self.model.graph().node:
+                if node.op_type == 'QcQuantizeOp' and node.name == dst_qtzr_node_name:
+                    for atr in node.attribute:
+                        if atr.name == "quant_info":
+                            atr.i = libpymo.PtrToInt64(src_qtzr.quant_info)
+                            return
+
+        def _set_qtzr(dst_qtzr: QcQuantizeOp, src_qtzr: QcQuantizeOp):
+            """
+            Set the dst quantizer by src quantizer and update quant_info attribute (pointer to the libquant_info object)
+             in the graph node.
+
+            :param dst_qtzr: destination quantizer.
+            :param src_qtzr: source quantizer
+            """
+            for name, qtzr in self.qc_quantize_op_dict.items():
+                if dst_qtzr == qtzr:
+                    self.qc_quantize_op_dict[name] = src_qtzr
+                    dst_qtzr_node_name = 'QcQuantizeOp_' + name
+                    # update quant_info attribute (pointer to the libquant_info object) in the graph node.
+                    _set_quant_info(dst_qtzr_node_name, src_qtzr)
+                    return
+
+        def _set_src_qtzr(x: Product, consumer: Op, src_qtzr):
+            producer = x.producer
+
+            if not producer:
+                # ``x`` is a root input (i.e. has no producer).
+                # In this case, set the input quantizer of the consumer to ``src_qtzr``
+                i = consumer.inputs.index(x)
+                inp_qtzr, _, __ = self.get_op_quantizers(consumer)
+                if i >= len(inp_qtzr):
+                    return
+
+                _set_qtzr(dst_qtzr=inp_qtzr[i], src_qtzr=src_qtzr)
+                return
+
+            _, out_qtzr, __ = self.get_op_quantizers(producer)
+
+            if out_qtzr:
+                # There exists output quantizer associated with the graph node ``producer``
+                # In this case, set the output quantizer of the producer to ``src_qtzr`
+                outputs = [producer.output]
+                i = outputs.index(x)
+                _set_qtzr(dst_qtzr=out_qtzr[i], src_qtzr=src_qtzr)
+
+            if not out_qtzr or producer.type in op_outputs_to_ignore:
+                # 1. There is no output quantizer associated with the graph node ``producer``, or
+                # 2. op is a math invariant op (reshape, permute, etc.).
+                # In these cases, propagate encoding further to the ancestors
+                for inp in producer.inputs:
+                    _set_src_qtzr(inp, consumer=producer, src_qtzr=src_qtzr)
+
+        for op in reversed(cg.ordered_ops):
+            if op.type not in op_types_to_tie_qtzrs:
+                continue
+
+            _, out_qtzr, __ = self.get_op_quantizers(op)
+
+            if not out_qtzr:
+                continue
+
+            if len(out_qtzr) != 1:
+                msg = 'Encoding propagation is only supported for ops with exactly ' \
+                      f'1 output quantizer, but found {len(out_qtzr)} ' \
+                      'output quantizers'
+                raise RuntimeError(msg)
+
+            for inp in op.inputs:
+                _set_src_qtzr(inp, consumer=op, src_qtzr=out_qtzr[0])
    
+
+
+
+
    
+[docs]
+def load_encodings_to_sim(quant_sim_model: QuantizationSimModel, onnx_encoding_path: str, strict=True) -> \
+        List[EncodingMismatchInfo]:
+    """
+    Loads the saved encodings to quant sim model. The encoding filename to load should end in .encodings,
+    generated as part of quantsim export.
+
+    :param quant_sim_model: Quantized model to load encodings for. Note: The model configuration should be the same as
+        when encodings were exported.
+    :param onnx_encoding_path: Path of the encodings file to load.
+    :param strict: If set to True and encoding settings between encodings to load do not line up with Quantsim
+        initialized settings, an assertion will be thrown. If set to False, quantizer settings will update to align with
+        encodings to load.
+    :return: List of EncodingMismatchInfo objects containing quantizer names and mismatched settings
+    """
+    mismatched_encodings = []
+
+    # Load encodings file
+    with open(onnx_encoding_path) as json_file:
+        encodings = json.load(json_file)
+
+    validate_encodings_to_load(encodings, quant_sim_model)
+
+    # First pass through quantizers to check for mismatched encodings
+    for quantizer_name, quantizer in quant_sim_model.qc_quantize_op_dict.items():
+        if quantizer_name not in encodings['activation_encodings'] and \
+                quantizer_name not in encodings['param_encodings']:
+            mismatched_info = get_encoding_mismatch_info(quantizer_name, quantizer, None)
+            if mismatched_info.has_mismatch():
+                mismatched_encodings.append(mismatched_info)
+            continue
+
+        if quantizer_name in encodings['activation_encodings']:
+            encodings_to_load = encodings['activation_encodings'][quantizer_name]
+        else:
+            encodings_to_load = encodings['param_encodings'][quantizer_name]
+
+        mismatched_info = get_encoding_mismatch_info(quantizer_name, quantizer, encodings_to_load)
+        if mismatched_info.has_mismatch():
+            mismatched_encodings.append(mismatched_info)
+
+    log_and_catch_mismatched_encodings(mismatched_encodings, strict)
+
+    # Second pass through quantizers to set quantizer settings
+    for quantizer_name, quantizer in quant_sim_model.qc_quantize_op_dict.items():
+        if quantizer_name not in encodings['activation_encodings'] and \
+                quantizer_name not in encodings['param_encodings']:
+            quantizer.enabled = False
+            continue
+
+        if quantizer_name in encodings['activation_encodings']:
+            encodings_to_load = encodings['activation_encodings'][quantizer_name]
+        else:
+            encodings_to_load = encodings['param_encodings'][quantizer_name]
+
+        is_symmetric, is_strict_symmetric, is_unsigned_symmetric = \
+            get_symmetric_properties(encodings_to_load)
+        data_type = QuantizationDataType.int if encodings_to_load[0]['dtype'] == 'int' else \
+                QuantizationDataType.float
+        libpymo_encodings = _create_libpymo_encodings(encodings_to_load)
+        quant_sim_model.qc_quantize_op_dict[quantizer_name].update_quantizer_and_load_encodings(
+            libpymo_encodings, is_symmetric, is_strict_symmetric, is_unsigned_symmetric, data_type)
+
+    return mismatched_encodings
    
+
+
+
+def validate_encodings_to_load(encodings_to_load: Dict, quant_sim_model: QuantizationSimModel):
+    """
+    Validate that all names of encodings to load are found in the model.
+
+    :param encodings_to_load: Encodings to load
+    :param quant_sim_model: Quantsim model to check for encoding names.
+    """
+    # Check that all encoding names in the encodings to load are found in the model. This check only works for verifying
+    # that names in encodings_to_load are valid. The reverse check will not work, since quantizers which are disabled
+    # will not show up in encodings_to_load.
+    encoding_names_not_found = []
+    for quantizer_name in (list(encodings_to_load['activation_encodings'].keys()) +
+                           list(encodings_to_load['param_encodings'].keys())):
+        if quantizer_name not in quant_sim_model.qc_quantize_op_dict:
+            encoding_names_not_found.append(quantizer_name)
+    if encoding_names_not_found:
+        logger.error('The following encoding names were present in the encodings to load but not found in the model: '
+                     '%s', str(encoding_names_not_found))
+        raise AssertionError('The following encoding names were present in the encodings to load but not found in the '
+                             'model: ' + str(encoding_names_not_found))
+
+
+def log_and_catch_mismatched_encodings(mismatched_encodings: List[EncodingMismatchInfo], strict: bool):
+    """
+    If mismatched_encodings is not empty, log details for each entry. If strict is True, raise an AssertionError.
+
+    :param mismatched_encodings: List of mismatched quantizer names and encoding settings
+    :param strict: If True, raise an AssertionError if there are mismatched settings
+    """
+    if mismatched_encodings:
+        logging_strings = ['The following quantizers had settings not matching with provided encodings to load:']
+        for mismatched_encoding_info in mismatched_encodings:
+            logging_strings.append(mismatched_encoding_info.quantizer_name + ':')
+            if mismatched_encoding_info.enabled_mismatch:
+                logging_strings.append(f'\tenabled: {mismatched_encoding_info.enabled_mismatch[0]}, '
+                                       f'loaded encoding enabled: '
+                                       f'{mismatched_encoding_info.enabled_mismatch[1]}')
+
+            if mismatched_encoding_info.dtype_mismatch:
+                logging_strings.append(f'\tdtype: {mismatched_encoding_info.dtype_mismatch[0]}, '
+                                       f'loaded encoding dtype: '
+                                       f'{mismatched_encoding_info.dtype_mismatch[1]}')
+
+            if mismatched_encoding_info.bitwidth_mismatch:
+                logging_strings.append(f'\tbitwidth: '
+                                       f'{mismatched_encoding_info.bitwidth_mismatch[0]}, loaded encoding bitwidth:'
+                                       f'{mismatched_encoding_info.bitwidth_mismatch[1]}')
+
+            if mismatched_encoding_info.is_symmetric_mismatch:
+                logging_strings.append(f'\tsymmetric: '
+                                       f'{mismatched_encoding_info.is_symmetric_mismatch[0]}, '
+                                       f'loaded encoding symmetric: '
+                                       f'{mismatched_encoding_info.is_symmetric_mismatch[1]}')
+
+            if mismatched_encoding_info.is_strict_symmetric_mismatch:
+                logging_strings.append(f'\tstrict symmetric: '
+                                       f'{mismatched_encoding_info.is_strict_symmetric_mismatch[0]}, '
+                                       f'loaded encoding strict symmetric: '
+                                       f'{mismatched_encoding_info.is_strict_symmetric_mismatch[1]}')
+
+            if mismatched_encoding_info.is_unsigned_symmetric_mismatch:
+                logging_strings.append(f'\tunsigned symmetric: '
+                                       f'{mismatched_encoding_info.is_unsigned_symmetric_mismatch[0]}, '
+                                       f'loaded encoding unsigned symmetric: '
+                                       f'{mismatched_encoding_info.is_unsigned_symmetric_mismatch[1]}')
+        log_message = '\n'.join(logging_strings)
+        if strict:
+            logger.error(log_message)
+            raise AssertionError(log_message)
+        logger.info(log_message)
+
+
+def _create_libpymo_encodings(encoding: Dict[str, Union[str, int, float]]) -> List[libpymo.TfEncoding]:
+    """
+    Given encoding dict, return a TfEncoding object with corresponding info.
+
+    :param encoding: Encoding dict to create TfEncoding object with
+    :return: TfEncoding object containing encoding dict info
+    """
+    libpymo_encodings = []
+    for enc_val in encoding:
+        enc = libpymo.TfEncoding()
+        enc.bw = enc_val['bitwidth']
+        enc.delta, enc.max, enc.min, enc.offset = 0.0, 0.0, 0.0, 0
+        if enc_val['dtype'] == 'int':
+            enc.delta, enc.max, enc.min, enc.offset = (enc_val['scale'], enc_val['max'], enc_val['min'],
+                                                       enc_val['offset'])
+        libpymo_encodings.append(enc)
+    return libpymo_encodings
+
+
+def get_symmetric_properties(encodings: List[Dict]) -> Tuple[Optional[bool], Optional[bool], Optional[bool]]:
+    """
+    Return symmetric properties of the given encodings. If encodings are float, return None for each.
+
+    :param encodings: Encodings to get symmetric properties for
+    :return: Tuple of is_symmetric, is_strict_symmetric, and is_unsigned symmetric properties
+    """
+    if encodings[0]['dtype'] == 'float':
+        return None, None, None
+
+    is_symmetric = encodings[0]['is_symmetric'] == 'True'
+
+    is_strict_symmetric = False
+    if is_symmetric and encodings[0]['offset'] == -2**(encodings[0]['bitwidth'] - 1) + 1:
+        is_strict_symmetric = True
+
+    # Note: Even if the original quantizer had is_unsigned_symmetric set to True, if any observed values were negative,
+    # the resulting encodings will look signed. This logic can only perform a best effort check to return True only if
+    # any encoding showed unsigned symmetric properties.
+    is_unsigned_symmetric = False
+    if is_symmetric:
+        for encoding in encodings:
+            if encoding['offset'] == 0:
+                is_unsigned_symmetric = True
+                break
+    return is_symmetric, is_strict_symmetric, is_unsigned_symmetric
+
+def get_encoding_mismatch_info(quantizer_name: str, quantizer: QcQuantizeOp,
+                               encodings_to_load: Optional[List[Dict]]) -> EncodingMismatchInfo:
+    """
+    Check that quantizer settings align with the settings in encodings_to_load. If settings do not align, track the
+    mismatching settings in a EncodingMismatchInfo object and add it to mismatched_encodings_info list.
+
+    :param quantizer_name: Name of quantizer to check
+    :param quantizer: Quantizer to check
+    :param encodings_to_load: Encodings to check
+    """
+    encoding_mismatch_info = EncodingMismatchInfo(quantizer_name)
+
+    # Match enabled state
+    if quantizer.enabled and encodings_to_load is None:
+        encoding_mismatch_info.enabled_mismatch = (quantizer.enabled, False)
+    if not quantizer.enabled and encodings_to_load is not None:
+        encoding_mismatch_info.enabled_mismatch = (quantizer.enabled, True)
+
+    if encodings_to_load is not None:
+        is_symmetric, is_strict_symmetric, is_unsigned_symmetric = get_symmetric_properties(encodings_to_load)
+
+        if quantizer.bitwidth != encodings_to_load[0]['bitwidth']:
+            encoding_mismatch_info.bitwidth_mismatch = (quantizer.bitwidth, encodings_to_load[0]['bitwidth'])
+        if quantizer.data_type.name != encodings_to_load[0]['dtype']:
+            encoding_mismatch_info.dtype_mismatch = (quantizer.data_type.name, encodings_to_load[0]['dtype'])
+        if quantizer.use_symmetric_encodings != is_symmetric:
+            encoding_mismatch_info.is_symmetric_mismatch = (quantizer.use_symmetric_encodings, is_symmetric)
+        if quantizer.use_strict_symmetric != is_strict_symmetric:
+            encoding_mismatch_info.is_strict_symmetric_mismatch = (quantizer.use_strict_symmetric, is_strict_symmetric)
+
+        # Unsigned symmetric is a special case because even if the setting is true, the encodings may appear to be
+        # signed symmetric if any observed tensor values were < 0.
+        # In this case, only mark a mismatch if quantizer was set to signed symmetric but an unsigned symmetric
+        # encoding was seen.
+        if quantizer.use_unsigned_symmetric != is_unsigned_symmetric and not quantizer.use_unsigned_symmetric:
+            encoding_mismatch_info.is_unsigned_symmetric_mismatch = (quantizer.use_unsigned_symmetric,
+                                                                     is_unsigned_symmetric)
+
+    return encoding_mismatch_info
+
+
+def set_blockwise_quantization_for_weights(sim: QuantizationSimModel,
+                                           op_types: Union[str, Tuple],
+                                           bitwidth: int,
+                                           symmetric: bool,
+                                           block_size: int,
+                                           strict: bool = False):
+    """
+    Set weight quantizers for the given operator types to use blockwise affine quantization.
+
+    :param sim: Quantsim object to configure weight quantizers for
+    :param op_types: Operator types for which to enable blockwise weight quantizaiton
+    :param bitwidth: Bitwidth for quantization
+    :param symmetric: True if quantization is symmetric, False otherwise
+    :param block_size: Block size for affine quantization. The block size will be applied to the weight's input features
+        dimension, while per-channel will be used for the weight's output features dimension
+    :param strict: If False, only enable blockwise quant for layers with dimensions evenly divisible by block_size.
+        If True, throw an error for layers with incompatible shapes.
+
+    Examples:
+
+        >>> # Assume 'sim' is a QuantizationSimModel object
+        >>> # Allows setting of all Linear and Conv weight quantizers to block_size 64 in the input_channels dimension:
+        >>> set_blockwise_quantization_for_weights(sim=sim,
+        ...                                        op_types=("Gemm", "MatMul", "Conv"),
+        ...                                        bitwidth=4,
+        ...                                        symmetric=True,
+        ...                                        block_size=64)
+    """
+
+    if isinstance(op_types, str):
+        op_types = (op_types, )
+
+    for op in sim.connected_graph.ordered_ops:
+        if op.type in op_types:
+            _, _, param_quantizers = sim.get_op_quantizers(op)
+
+            if "weight" in param_quantizers.keys():
+                weight_quantizer: QcQuantizeOp = param_quantizers["weight"]
+
+                try:
+                    weight_quantizer._enable_blockwise_quantization(block_size) # pylint:disable = protected-access
+                except ValueError as e:
+                    if strict:
+                        raise e
+                else:
+                    weight_quantizer.set_bitwidth(bitwidth)
+                    weight_quantizer.use_symmetric_encodings = symmetric
+                    weight_quantizer.data_type = QuantizationDataType.int
+
+
+
    
+[docs]
+def set_grouped_blockwise_quantization_for_weights(sim: QuantizationSimModel,
+                                                   op_types: Union[str, Tuple],
+                                                   bitwidth: int,
+                                                   decompressed_bw: int,
+                                                   block_size: int,
+                                                   strict: bool = False):
+    """
+    Set weight parameter quantizers of modules to grouped blockwise quantization.
+
+    :param sim: Quantsim to set weight quantizers for
+    :param op_types: Operator types for which to enable grouped blockwise weight quantizaiton
+    :param bitwidth: Bitwidth for affine quantization
+    :param decompressed_bw: Decompressed bw for grouped block quantization
+    :param block_size: Block size for affine quantization. The block size will be applied to the weight's input features
+        dimension, while per-channel will be used for the weight's output features dimension
+
+    Examples:
+
+        >>> # Assume 'sim' is a QuantizationSimModel object
+        >>> # Sets of all Gemm, MatMul, and Conv weight quantizers to block_size 64 in the input_channels dimension:
+        >>> set_grouped_blockwise_quantization_for_weights(sim=sim,
+        ...                                                op_types=("Gemm", "MatMul", "Conv"),
+        ...                                                bitwidth=4,
+        ...                                                decompressed_bw=8,
+        ...                                                block_size=64)
+    """
+
+    if isinstance(op_types, str):
+        op_types = (op_types, )
+
+    for op in sim.connected_graph.ordered_ops:
+
+        if op.type in op_types:
+            _, _, param_quantizers = sim.get_op_quantizers(op)
+
+
+            if "weight" in param_quantizers.keys():
+                weight_quantizer: QcQuantizeOp = param_quantizers["weight"]
+
+                try:
+                    grouped_quantizer = GroupedBlockQuantizeDequantize(weight_quantizer.quant_info,
+                                                                       bitwidth,
+                                                                       decompressed_bw,
+                                                                       block_size,
+                                                                       weight_quantizer.quant_scheme,
+                                                                       weight_quantizer.op_mode,
+                                                                       weight_quantizer.tensor_quantizer_params)
+                except ValueError as e:
+                    if strict:
+                        raise e
+                else:
+                    for name, quantizer in sim.qc_quantize_op_dict.items():
+                        if quantizer is weight_quantizer:
+                            sim.qc_quantize_op_dict[name] = grouped_quantizer
    
+
+
+
+# pylint: disable=protected-access
+def clamp_activation_encodings(quant_sim: QuantizationSimModel, clamp_val: float):
+    """
+    Clamp activations to specific range if out of bound.
+
+    :param quant_sim: quantsim object
+    :param clamp_val: positive float value
+    :return:
+    """
+    for act_name in quant_sim.activation_names:
+        quantizer = quant_sim.qc_quantize_op_dict.get(act_name)
+        is_clipped = quantizer.clip_and_recompute_encodings(clamp_val)
+        if is_clipped:
+            logger.info("Clamped tensor %s", act_name)
+
    +
    +
    +
    + +
    + + +
    +
    +
    +
    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
    + Made with Sphinx and @pradyunsg's + + Furo + +
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    + +
    +
    + +
    +
    + + + + + + + + + \ No newline at end of file diff --git a/releases/2.0.0/_modules/aimet_onnx/sequential_mse/seq_mse.html b/releases/2.0.0/_modules/aimet_onnx/sequential_mse/seq_mse.html new file mode 100644 index 0000000..be27513 --- /dev/null +++ b/releases/2.0.0/_modules/aimet_onnx/sequential_mse/seq_mse.html @@ -0,0 +1,979 @@ + + + + + + + + aimet_onnx.sequential_mse.seq_mse - AIMET + + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    +

    Source code for aimet_onnx.sequential_mse.seq_mse

    +# /usr/bin/env python
+# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2024, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+
+""" Sequential MSE implementation """
+
+# pylint: disable=no-name-in-module, ungrouped-imports, too-many-lines
+
+import copy
+from typing import List
+import numpy as np
+import torch
+from onnxruntime.quantization.onnx_quantizer import ONNXModel
+from onnx import numpy_helper
+from onnx.utils import Extractor
+
+# pylint: disable=wrong-import-order
+from aimet_onnx.quantsim import QuantizationSimModel
+from aimet_onnx.qc_quantize_op import QcQuantizeOp
+from aimet_onnx.sequential_mse.dependency_graph_utils import DependencyGraphUtils
+from aimet_onnx.sequential_mse.dependency_graph import DependencyGraph
+from aimet_onnx.sequential_mse.dependency_graph import DependencyNode
+from aimet_common.libpymo import TensorQuantizerOpMode
+from aimet_common.defs import QuantScheme
+from aimet_onnx.meta.connectedgraph import ConnectedGraph
+from aimet_common.utils import AimetLogger
+from dataclasses import dataclass
+
+from onnx import TensorProto
+
+
+SUPPORTED_MODULES = ("Conv", "Gemm", "MatMul")
+
+_logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.SeqMse)
+
+
    
+[docs]
+@dataclass
+class SeqMseParams:
+    """
+    Sequential MSE parameters
+
+    :param num_batches: Number of batches.
+    :param num_candidates: Number of candidates to perform grid search. Default 20.
+    :param inp_symmetry: Input symmetry. Available options are 'asym', 'symfp' and 'symqt'. Default 'symqt'.
+    :param loss_fn: Loss function. Available options are 'mse', 'l1' and 'sqnr'. Default 'mse'.
+    """
+    num_batches: int = 4
+    num_candidates: int = 20
+    inp_symmetry: str = 'symqt'
+    loss_fn: str = 'mse'
    
+
+
+# pylint: disable=too-many-instance-attributes
+class SequentialMse:
+    """
+    Sequentially minimizing activation MSE loss in layer-wise way to decide optimal param quantization encodings.
+    """
+
+    def __init__(self,
+                 model,
+                 sim: QuantizationSimModel,
+                 params: SeqMseParams,
+                 data_loader):
+
+        """
+        Initialize the sequential mse object
+
+        :param model: float model
+        :param sim: QuantizationSimModel object
+        :param data_loader: Torch Dataloader
+        :param params: Sequential MSE parameters
+        """
+
+        # pylint: disable=protected-access
+        assert sim._quant_scheme in (QuantScheme.post_training_tf, QuantScheme.training_range_learning_with_tf_init), \
+            "Use TF quant-scheme with sequential MSE."
+
+        self.sim = sim
+        self.model = model
+        self.params = params
+        self.node_name_to_input_names = dict()
+        self.static_tensor_name_to_proto = dict()
+
+        if not isinstance(self.model, ONNXModel):
+            self.model = ONNXModel(self.model)
+
+        self._fill_node_name_to_input_names()
+        self._fill_static_tensor_name_to_proto()
+
+        raw_data = dict()
+        for initializer in self.model.model.graph.initializer:
+            if initializer.HasField('raw_data'):
+                raw_data[initializer.name] = initializer.raw_data
+                initializer.ClearField("raw_data")
+
+        self._float_extractor = Extractor(self.model.model)
+
+        for initializer in self.model.model.graph.initializer:
+            if initializer.name in raw_data:
+                initializer.raw_data = raw_data[initializer.name]
+
+        for initializer in self._float_extractor.model.graph.initializer:
+            if initializer.name in raw_data:
+                initializer.raw_data = raw_data[initializer.name]
+
+        self._sim_extractor = copy.deepcopy(self._float_extractor)
+
+        self._update_value_info()
+
+        self._sim_extractor.model = self.sim.model.model
+        self._sim_extractor.graph = self.sim.model.model.graph
+
+        self.connected_graph = ConnectedGraph(self.model)
+
+        self.data_loader = data_loader
+
+        self.dependency_graph = DependencyGraph()
+        self.dependency_graph_utils = DependencyGraphUtils(self.connected_graph, self.node_name_to_input_names,
+                                                           self.static_tensor_name_to_proto)
+        self.quantizers_to_be_disabled = self._get_quantizers_to_be_disabled() # check this
+
+    def _update_value_info_for_output(self, node):
+        """
+        Updates the value info for output of a node in sim model.
+        Value info for QcQuantizeOp is not present in _sim_extractor
+
+        :param node: onnx node
+        """
+
+        input_name = node.input[0]
+        output_name = node.output[0]
+        if input_name in self._sim_extractor.vimap and output_name not in self._sim_extractor.vimap:
+            value_info_for_output = copy.deepcopy(self._sim_extractor.vimap[input_name])
+            value_info_for_output.name = node.output[0]
+            self._sim_extractor.vimap[node.output[0]] = value_info_for_output
+
+    def _update_value_info_for_input(self, node):
+        """
+        Updates the value info for input of a node in sim model.
+        Value info for QcQuantizeOp is not present in _sim_extractor
+
+        :param node: onnx node
+        """
+
+        input_name = node.input[0]
+        output_name = node.output[0]
+        if output_name in self._sim_extractor.vimap and input_name not in self._sim_extractor.vimap:
+            value_info_for_input = copy.deepcopy(self._sim_extractor.vimap[output_name])
+            value_info_for_input.name = node.input[0]
+            self._sim_extractor.vimap[node.input[0]] = value_info_for_input
+
+    def _update_value_info_for_graph_output(self):
+        """
+        Updates the value info for input of a node in sim model.
+        Value info for QcQuantizeOp is not present in _sim_extractor
+
+        :param node: onnx node
+        """
+
+        for value_info in self.model.model.graph.output:
+            self._float_extractor.vimap[value_info.name] = value_info
+
+        for value_info in self.sim.model.model.graph.output:
+            self._sim_extractor.vimap[value_info.name] = value_info
+
+    def _update_value_info(self):
+        """
+        Updates the value info for sim model.
+        Value info for QcQuantizeOp is not present in _sim_extractor
+        """
+
+        self._update_value_info_for_graph_output()
+
+        for node in self.sim.model.nodes():
+            if node.op_type == "QcQuantizeOp":
+                self._update_value_info_for_output(node)
+                self._update_value_info_for_input(node)
+
+    def _fill_static_tensor_name_to_proto(self):
+        """
+        Fills the mapping from static tensor name to the prop buf
+        """
+        for initializer in self.model.model.graph.initializer:
+            self.static_tensor_name_to_proto[initializer.name] = initializer
+
+        for node in self.model.model.graph.node:
+            if node.op_type == "Constant":
+                self.static_tensor_name_to_proto[node.output[0]] = node
+
+    # pylint: disable=inconsistent-return-statements
+    def _extract_float_data_from_proto(self, name):
+        """
+        returns the tensor value of the given name using static_tensor_name_to_proto dictionary
+        :param name: name of the static tensor
+        :return tensor value
+        """
+        if name in self.static_tensor_name_to_proto:
+            proto_buf = self.static_tensor_name_to_proto[name]
+            if isinstance(proto_buf, TensorProto):
+                return numpy_helper.to_array(proto_buf)
+
+            for attr in proto_buf.attribute:
+                if attr.name == "value":
+                    return numpy_helper.to_array(attr.t)
+        else:
+            raise ValueError(name, " is neither constant or initializer")
+
+    def _fill_node_name_to_input_names(self):
+        """
+        Fills the mapping from node name to input names
+        """
+        for node in self.model.nodes():
+            self.node_name_to_input_names[node.name] = node.input
+
+    @staticmethod
+    def apply_seq_mse(model, sim: QuantizationSimModel, params: SeqMseParams, data_loader):
+        """
+        It performs following steps:
+        1) creates seq_mse object
+        2) call apply_seq_algo() member function
+
+        :param model: float model
+        :param sim: QuantizationSimModel object
+        :param data_loader: Data loader
+        :param params: Sequential MSE parameters
+        """
+        seq_mse = SequentialMse(model, sim, params, data_loader)
+        seq_mse.apply_seq_mse_algo()
+
+    def apply_seq_mse_algo(self):
+        """
+        It performs following steps:
+        1) disable the quantizer for unsupported modules
+        2) create the dependency graph
+        3) run the onnx graph and compute encoding using seq mse algorithm
+        4) re-enable the quantizer disabled in first step
+        """
+
+        try:
+            self.temporarily_disable_quantizers()
+            self.dependency_graph = self.dependency_graph_utils.create_dependency_graph(self.data_loader,
+                                                                                        self.params.num_batches)
+            self._run_onnx_graph_dependency_graph_order()
+        finally:
+            self.re_enable_quantizers()
+
+    def _get_quantizers_to_be_disabled(self) -> List[QcQuantizeOp]:
+        """
+        :return Returns the quantizers of unsupported modules
+        """
+
+        quantizer_to_disable_name = list()
+        quantizer_to_not_disable_name = list()
+
+        for name, qc_quantize_op in self.sim.qc_quantize_op_dict.items():
+            if qc_quantize_op.enabled:
+                quantizer_to_disable_name.append(name)
+
+        for node in self.model.nodes():
+            if self.dependency_graph_utils.is_supported_module(node):
+                weight_node_name = node.input[1]
+                quantizer_to_not_disable_name.append(weight_node_name)
+
+        quantizer_to_disable_name = [name for name in quantizer_to_disable_name
+                                     if name not in quantizer_to_not_disable_name]
+
+        quantizer_to_disable = list()
+
+        for name in quantizer_to_disable_name:
+            if name in self.sim.qc_quantize_op_dict:
+                quantizer_to_disable.append(self.sim.qc_quantize_op_dict[name])
+
+        return quantizer_to_disable
+
+    def temporarily_disable_quantizers(self):
+        """
+        Disable quantizers needed to be disabled before applying sequential MSE.
+        """
+        for quantizer in self.quantizers_to_be_disabled:
+            quantizer.enabled = False
+
+    def re_enable_quantizers(self):
+        """
+        Re-enable quantizers that were disabled by temporarily_disable_quantizers method
+        """
+        for quantizer in self.quantizers_to_be_disabled:
+            quantizer.enabled = True
+
+    def _get_min_max_from_weights(self, dependency_node: DependencyNode):
+        """
+        Get per channel min/max values across output channel.
+
+        :param dependency_node: Dependevy node which is to be optimized
+        :return: per_channel_min and per_channel_max
+        """
+
+        weight_name = self.node_name_to_input_names[dependency_node.op_name][1]
+        weight_data = self._extract_float_data_from_proto(weight_name)
+
+        connected_op = self.connected_graph.get_op_from_module_name(dependency_node.op_name)
+        # pylint: disable=protected-access
+        channel_axis = QuantizationSimModel._get_quantization_axes(connected_op)[0]
+        # pylint: disable=consider-using-generator, use-a-generator
+        axis = tuple([i for i in range(len(weight_data.shape)) if i != channel_axis])
+
+        per_channel_max = np.max(abs(weight_data), axis=axis)
+
+        return [-per_channel_max, per_channel_max]
+
+    def _get_candidates(self, per_channel_max, per_channel_min):
+        """
+        Perform grid search.
+        :param per_channel_max: Per channel max values
+        :param per_channel_min: Per channel min values
+        :return: candidates
+        """
+        candidates = list()
+        num_candidates = self.params.num_candidates
+        for i in range(num_candidates):
+            cand_max = per_channel_max / num_candidates * (i + 1)
+            cand_min = per_channel_min / num_candidates * (i + 1)
+            candidates.append((cand_max, cand_min))
+        return candidates
+
+    def _compute_encoding_from_candidate(self, candidate, dependency_node: DependencyNode):
+        """
+        computes the encoding using candidate min and candidate max
+
+        :param candidate: list containing min and max value
+        :param dependency_node: Corresponding Dependency node
+        :return: encoding
+        """
+
+        cand_max = candidate[0]
+        cand_min = candidate[1]
+
+        cand = np.stack((cand_max, cand_min), axis=-1)
+
+        weight_name = self.node_name_to_input_names[dependency_node.op_name][1]
+
+        quantize_op = self.sim.qc_quantize_op_dict[weight_name]
+
+        quantize_op.reset_encoding_stats()
+
+        # pylint: disable=protected-access
+        tensor_quantizers = quantize_op._tensor_quantizer
+
+        if len(tensor_quantizers) != len(cand) and len(tensor_quantizers) != 1:
+            raise ValueError(weight_name, " should be per-tensor or number of "
+                                          "quantizer must match with number of channels")
+
+        # pylint: disable=protected-access
+        if len(tensor_quantizers) == 1:
+            tensor_quantizer = tensor_quantizers[0]
+            tensor_quantizer.updateStats(cand, False)
+        else:
+            for i, tensor_quantizer in enumerate(tensor_quantizers):
+                tensor_quantizer.updateStats(cand[i], False)
+
+        quantize_op.compute_encodings()
+
+        quantize_op.op_mode = TensorQuantizerOpMode.quantizeDequantize
+
+    def _freeze_encodings(self, dependency_node: DependencyNode):
+        """
+        Freezes the encoding after the node is optimized
+        :param dependency_node: Optimized dependency node
+        """
+        weight_name = self.node_name_to_input_names[dependency_node.op_name][1]
+        quantize_op = self.sim.qc_quantize_op_dict[weight_name]
+        quantize_op.freeze_encodings()
+
+    @staticmethod
+    def neg_sqnr(pred: torch.Tensor, target: torch.Tensor, eps=1e-10, reduction="none"):
+        """
+        Loss function to minimize negative SQNR which is equivalent to maximizing SQNR.
+
+        :param pred: X^Q^ quantized-dequantized values
+        :param target: XW FP32 values
+        :param eps: epsilon
+        :param reduction: unused arg
+        :return: Negative SQNR
+        """
+        # pylint: disable=unused-argument
+        quant_error = target - pred
+        exp_noise = torch.mean(quant_error ** 2, 0, keepdim=True) + eps
+        exp_signal = torch.mean(target ** 2, 0, keepdim=True)
+        sqnr = exp_signal / exp_noise
+        sqnr_db = 10 * torch.log10(sqnr)
+        return -sqnr_db
+
+    def _compute_recon_loss(self, sim_output, float_output, dependency_node):
+        """
+        Compute reconstruction loss and return the sum by reducing over all the dimensions except last channel dimension.
+
+        :param xqwq: X^Q^ quantized-dequantized values
+        :param xw: XW FP32 values
+        :param params: Sequential MSE parameters
+        :return: loss
+        """
+
+        xqwq = torch.from_numpy(sim_output)
+        xw = torch.from_numpy(float_output)
+
+        if dependency_node.op_type == "Conv":
+            permute_order = [0] + list(range(2, xw.dim())) + [1]
+            xqwq = xqwq.permute(permute_order)
+            xw = xw.permute(permute_order)
+
+        if self.params.loss_fn == "mse":
+            loss_fn = torch.nn.functional.mse_loss
+        elif self.params.loss_fn == "l1":
+            loss_fn = torch.nn.functional.l1_loss
+        elif self.params.loss_fn == "sqnr":
+            loss_fn = SequentialMse.neg_sqnr
+        else:
+            raise ValueError(f"Invalid loss function: {self.params.loss_fn}")
+
+
+
+        channel_dim = xqwq.shape[-1]
+        xqwq = xqwq.reshape(-1, channel_dim)
+        xw = xw.reshape(-1, channel_dim)
+        loss = loss_fn(xqwq, xw, reduction="none").sum(0)
+        assert loss.size() == torch.Size([channel_dim])
+        return np.array(loss)
+
+    # pylint: disable-msg=too-many-locals
+    def _do_seq_mse(self, dependency_node: DependencyNode):
+        """
+        Find and freeze optimal parameter encodings candidate for given dependency node.
+        :param dependency_node: Corresponding Dependency node
+        """
+        per_channel_min, per_channel_max = self._get_min_max_from_weights(dependency_node)
+
+        candidates = self._get_candidates(per_channel_max, per_channel_min)
+
+        total_loss = list()
+
+        float_split_model, sim_split_model = self._split_onnx_graph(dependency_node.op_input_names,
+                                                                    dependency_node.op_output_names)
+
+        _logger.info("Finding and freezing optimal param encodings candidate of op: %s", dependency_node.op_name)
+        # for different modes only inputs will change
+        if self.params.inp_symmetry == "asym":
+            float_inputs = self.dependency_graph.get_float_data(dependency_node)
+            sim_inputs = self.dependency_graph.get_sim_data(dependency_node)
+        elif self.params.inp_symmetry == "symfp":
+            float_inputs = self.dependency_graph.get_float_data(dependency_node)
+            sim_inputs = self.dependency_graph.get_float_data(dependency_node)
+        elif self.params.inp_symmetry == "symqt":
+            float_inputs = self.dependency_graph.get_sim_data(dependency_node)
+            sim_inputs = self.dependency_graph.get_sim_data(dependency_node)
+        else:
+            raise ValueError(f"Invalid inp_symmetry: {self.params.inp_symmetry}")
+
+        float_outputs = self._run_onnx_graph(float_split_model, float_inputs)
+        float_outputs = np.concatenate(float_outputs[0], axis=0)
+
+        for candidate in candidates:
+
+            self._compute_encoding_from_candidate(candidate, dependency_node)
+
+            sim_outputs = self._run_onnx_graph(sim_split_model, sim_inputs)
+            sim_outputs = np.concatenate(sim_outputs[0], axis=0)
+
+            loss = self._compute_recon_loss(sim_outputs, float_outputs, dependency_node)
+
+            total_loss.append(loss)
+
+        stacked_loss = np.stack(total_loss, axis=0)
+        arg_min_ = np.argmin(stacked_loss, axis=0, keepdims=True)
+
+        best_max = torch.stack([torch.tensor(cand_max) for cand_max, _ in candidates]).gather(0, torch.tensor(arg_min_))[0]
+        best_min = torch.stack([torch.tensor(cand_min) for _, cand_min in candidates]).gather(0, torch.tensor(arg_min_))[0]
+
+        best_candidate = (best_max, best_min)
+
+        self._compute_encoding_from_candidate(best_candidate, dependency_node)
+        self._freeze_encodings(dependency_node)
+
+    # pylint: disable=no-self-use
+    def _get_input_names_from_dependencies(self, dependency_node: DependencyNode):
+        """
+        Returns the input names for the op corresponding to dependency node
+
+        :param dependency_node: Corresponding Dependency node
+        :return: input names for the op corresponding to dependency node
+        """
+
+        input_names = set()
+
+        for inward_node in dependency_node.inward_nodes:
+            input_names.update(inward_node.op_input_names)
+
+        return list(input_names)
+
+    def _get_inputs_from_dependencies(self, dependency_node: DependencyNode):
+        """
+        Returns the input needed for the op corresponding to the dependency node
+
+        :param dependency_node: Corresponding dependency node
+        :return: float inputs and sim inputs
+        """
+
+        float_inputs = dict()
+        sim_inputs = dict()
+
+        for inward_node in dependency_node.inward_nodes:
+            float_inputs.update(self.dependency_graph.get_float_data(inward_node))
+            sim_inputs.update(self.dependency_graph.get_sim_data(inward_node))
+
+        return float_inputs, sim_inputs
+
+    def _split_onnx_graph(self, input_names, output_names):
+        """
+        Splits the onnx graph from input names to output names using extractor
+
+        :param input_names: input names of split graph
+        :param output_names: output names of split graph
+        :return: float split model and sim split model
+        """
+        float_split_model = self._float_extractor.extract_model(list(input_names), list(output_names))
+        sim_split_model = self._sim_extractor.extract_model(list(input_names), list(output_names))
+        return float_split_model, sim_split_model
+
+    def _run_onnx_graph(self, model, inputs):
+        """
+        Run the onnx graph using onnx runtime
+
+        :param model: Corresponding model
+        :param inputs: inputs to the model
+        :return: outputs
+        """
+        # pylint: disable=protected-access
+        session = QuantizationSimModel.build_session(model, self.sim.providers,
+                                                     user_onnx_libs=self.sim._user_onnx_libs, path=self.sim._path)
+
+        outputs = list()
+
+        num_batches = min(self.params.num_batches, len(self.data_loader.dataset) // self.data_loader.batch_size)
+
+        for i in range(num_batches):
+            input_batch = dict()
+            for name, data in inputs.items():
+                input_batch[name] = data[i]
+            output = session.run(None, input_batch)
+            if len(outputs) == 0:
+                outputs = [list() for _ in range(len(output))]
+            for idx, out in enumerate(output):
+                outputs[idx].append(out)
+
+        return outputs
+
+    def _process_dependency_nodes(self, dependency_node: DependencyNode):
+        """
+        1) Get input names, output names using dependency graph
+        2) Split the graph using input names and output names
+        3) Run the split graph
+        4) Decrease the out-degree of the inward nodes by -1, if outdegree becomes zero, then delete the data
+        5) Optimize the dependency node
+
+        :param dependency_node: Corresponding dependency node
+        """
+        # get input names and output names, split and run and do_seq_mse
+        # then make out_degree of the inward nodes -1, if that becomes zero delete the data
+
+        input_names = self._get_input_names_from_dependencies(dependency_node=dependency_node)
+
+        graph_inputs = [node.name for node in self.model.model.graph.input]
+        output_names = [name for name in dependency_node.op_input_names if name not in graph_inputs]
+
+        float_split_model, sim_split_model = self._split_onnx_graph(input_names=input_names, output_names=output_names)
+        float_inputs, sim_inputs = self._get_inputs_from_dependencies(dependency_node=dependency_node)
+
+        float_outputs = self._run_onnx_graph(model=float_split_model, inputs=float_inputs)
+        self.dependency_graph.update_float_data(output_names, float_outputs)
+
+        sim_outputs = self._run_onnx_graph(model=sim_split_model, inputs=sim_inputs)
+        self.dependency_graph.update_sim_data(output_names, sim_outputs)
+
+        for inward_node in dependency_node.inward_nodes:
+            inward_node.outdegree = inward_node.outdegree - 1
+            if inward_node.outdegree == 0:
+                self.dependency_graph.dec_ref_count(inward_node)
+
+        if dependency_node.op_type in SUPPORTED_MODULES:
+            self._do_seq_mse(dependency_node=dependency_node)
+
+    def _do_topo_sort_helper(self, dependency_node: DependencyNode):
+        """
+        1) Decrease indegree of the child ops by -1, if the indegree becomes zero, then process the node
+        2) run _do_topo_sort_helper for the child node
+
+        :param dependency_node: Corresponding dependency node
+        """
+        # make indegree of the child ops -1, if the indegree becomes zero split and run and do_seq_mse
+        # then make out_degree of the inward nodes -1, if that becomes zero delete the data
+        # then call _do_topo_sort_helper for that node
+
+        for child_node in dependency_node.outward_nodes:
+            child_node.indegree = child_node.indegree - 1
+            if child_node.indegree == 0:
+                self._process_dependency_nodes(dependency_node=child_node)
+                self._do_topo_sort_helper(dependency_node=child_node)
+
+    def _run_onnx_graph_dependency_graph_order(self):
+        """
+        Start the topo sort from the starting ops i.e. ops having indegree equal to zero
+        """
+
+        for start_op in self.dependency_graph.starting_ops:
+            if start_op.op_type in SUPPORTED_MODULES:
+                self._do_seq_mse(dependency_node=start_op)
+            self._do_topo_sort_helper(dependency_node=start_op)
+
    +
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    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
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    +

    Source code for aimet_tensorflow.keras.adaround_weight

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2022-2023, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+
+""" Top level API for Adaptive Rounding - Post-Training Quantization (PTQ) """
+
+import os
+import json
+from typing import Dict, List, Union, Iterable, Tuple
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.utils import Progbar
+
+import aimet_common.libpymo as libpymo
+from aimet_common.utils import AimetLogger
+from aimet_common.defs import QuantScheme
+from aimet_common.quantsim_config.json_config_importer import JsonConfigImporter, ConfigDictKeys, ConfigDictType
+from aimet_tensorflow.keras.adaround.activation_sampler import ActivationSampler
+from aimet_tensorflow.keras.adaround.adaround_loss import AdaroundHyperParameters
+from aimet_tensorflow.keras.adaround.adaround_wrapper import AdaroundWrapper
+from aimet_tensorflow.keras.adaround.adaround_optimizer import AdaroundOptimizer
+from aimet_tensorflow.keras.connectedgraph import ConnectedGraph, map_keras_types_to_onnx
+from aimet_tensorflow.keras.quantsim_config.quantsim_config import MAP_TF_PARAM_NAME_TO_QUANTSIM_NAME
+
+_logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.Quant)
+
+AdaroundSupportedOps = (tf.keras.layers.Conv2D, tf.keras.layers.DepthwiseConv2D, tf.keras.layers.Dense)
+
+
+
    
+[docs]
+class AdaroundParameters:
+    """
+    Configuration parameters for Adaround
+    """
+    def __init__(self, data_set: tf.data.Dataset, num_batches: int, default_num_iterations: int = 10000,
+                 default_reg_param: float = 0.01, default_beta_range: Tuple = (20, 2), default_warm_start: float = 0.2):
+        """
+        :param data_set: TF Data set
+        :param num_batches: Number of batches
+        :param default_num_iterations: Number of iterations to adaround each layer. Default 10000
+        :param default_reg_param: Regularization parameter, trading off between rounding loss vs reconstruction loss.
+         Default 0.01
+        :param default_beta_range: Start and stop beta parameter for annealing of rounding loss (start_beta, end_beta).
+         Default (20, 2)
+        :param default_warm_start: warm up period, during which rounding loss has zero effect. Default 20% (0.2)
+        """
+        self.data_set = data_set
+        self.num_batches = num_batches
+        self.num_iterations = default_num_iterations
+        self.reg_param = default_reg_param
+        self.beta_range = default_beta_range
+        self.warm_start = default_warm_start
+
+    def __eq__(self, other: "AdaroundParameters"):
+        return self.data_set == other.data_set and\
+               self.num_batches == other.num_batches and\
+               self.num_iterations == other.num_iterations and\
+               self.reg_param == other.reg_param and\
+               self.beta_range == other.beta_range and\
+               self.warm_start == other.warm_start
    
+
+
+
+class Adaround:
+    """
+    Weight-rounding mechanism for Post Training Quantization (PTQ)
+    """
+    # pylint: disable=too-many-locals
+    # pylint: disable=too-many-arguments
+    @classmethod
+    def apply_adaround(cls, model: tf.keras.Model, params: AdaroundParameters, path: str, filename_prefix: str,
+                       default_param_bw: int = 4,
+                       default_quant_scheme: QuantScheme = QuantScheme.post_training_tf_enhanced,
+                       config_file: str = None) -> tf.keras.Model:
+        """
+        Returns model with optimized weight rounding of every op (Conv and Linear) and also saves the
+        corresponding quantization encodings to a separate JSON-formatted file that can then be imported by
+        QuantSim for inference or QAT
+
+        :param model: Model to adaround
+        :param params: Parameters for adaround
+        :param path: path where to store parameter encodings
+        :param filename_prefix: Prefix to use for filename of the encodings file
+        :param default_param_bw: Default bitwidth (4-31) to use for quantizing layer parameters. Default 4
+        :param default_quant_scheme:  Quantization scheme. Supported options are QuantScheme.post_training_tf or
+         QuantScheme.post_training_tf_enhanced. Default QuantScheme.post_training_tf_enhanced
+        :param config_file: Configuration file for model quantizers
+        :return: Model with Adarounded weights
+        """
+
+        # Get parameters from config file. To allow one central place for Adaround and Quantsim
+        configs, strict_symmetric, unsigned_symmetric, per_channel_enabled = cls.get_config_dict_keys(config_file)
+
+        # Optimization Hyper parameters
+        opt_params = AdaroundHyperParameters(params.num_iterations, params.reg_param, params.beta_range,
+                                             params.warm_start)
+
+        # Activation sampler
+        act_sampler = ActivationSampler(params.data_set, params.num_batches)
+
+        hard_rounded_model = tf.keras.models.clone_model(model)
+        hard_rounded_model.set_weights(model.get_weights())
+        soft_rounded_model = tf.keras.models.clone_model(model)
+        soft_rounded_model.set_weights(model.get_weights())
+
+        ordered_layer_indices = cls._get_ordered_adaround_layer_indices(model)
+        module_act_func_pair = cls._get_module_act_func_pair(model)
+        param_encodings = {}
+
+        progbar = Progbar(len(ordered_layer_indices))
+        for idx in ordered_layer_indices:
+            use_symmetric_encodings = cls.get_is_symmetric_flag_for_op_param(configs, model.layers[idx],
+                                                                                    param_name='weight',
+                                                                                    framework_to_onnx_type_dict=map_keras_types_to_onnx)
+            cls.adaround_layer(act_sampler, use_symmetric_encodings, strict_symmetric, unsigned_symmetric,
+                               default_param_bw, default_quant_scheme, model, hard_rounded_model, soft_rounded_model,
+                               idx, module_act_func_pair, opt_params, param_encodings, per_channel_enabled)
+            progbar.add(1)
+
+        # Export quantization encodings to JSON-formatted file at provided path
+        cls.export_encoding_to_json(path, filename_prefix, param_encodings)
+        return soft_rounded_model
+
+    # pylint: disable=too-many-locals
+    # pylint: disable=too-many-arguments
+    @classmethod
+    def adaround_layer(cls, act_sampler, is_symmetric, strict_symmetric, unsigned_symmetric, default_param_bw,
+                       default_quant_scheme, orig_model, hard_rounded_model, soft_rounded_model, idx,
+                       module_act_func_pair, opt_params, param_encodings, per_channel_enabled):
+        """
+        Perform adaround on a specific layer.
+        :param act_sampler: Activation sampler
+        :param is_symmetric: True if symmetric encodings is used, else asymmetric encodings
+        :param strict_symmetric: Taken from config file, False by default
+        :param unsigned_symmetric: Taken from config file, True by default
+        :param default_param_bw: Default bitwidth (4-31) to use for quantizing layer parameters
+        :param default_quant_scheme: Quantization scheme. Supported options are QuantScheme.post_training_tf or
+            QuantScheme.post_training_tf_enhanced
+        :param orig_model: Original model
+        :param hard_rounded_model: Model with hard rounded weights
+        :param soft_rounded_model: Model with soft rounded weights
+        :param idx: Index of layer in model to Adaround
+        :param module_act_func_pair: Dictionary mapping modules to subsequent activation functions
+        :param opt_params: Adaround hyperparameters
+        :param param_encodings: Dictionary holding parameter encodings information
+        :param per_channel_enabled: Flag for per channel quantization
+        """
+        # Collect input and output activations data
+        all_inp_data, all_out_data = act_sampler.sample_activation(orig_model.layers[idx], orig_model,
+                                                                   hard_rounded_model.layers[idx],
+                                                                   hard_rounded_model)
+        # Get module's next following activation function
+        act_func = module_act_func_pair[orig_model.layers[idx]]
+
+        output_height, output_width, output_channels = None, None, None
+        if isinstance(orig_model.layers[idx], tf.keras.layers.Conv2DTranspose):
+            data_format = 'NHWC' if orig_model.layers[idx].data_format == 'channels_last' else 'NCHW'
+            output_height, output_width, output_channels = \
+                cls.get_conv2d_transpose_output_tensor_shape(data_format, all_out_data)
+
+        wrapper = AdaroundWrapper(orig_model.layers[idx], default_param_bw, default_quant_scheme,
+                                  is_symmetric, strict_symmetric, unsigned_symmetric, per_channel_enabled,
+                                  output_height, output_width, output_channels)
+        hard_rounded_weight, soft_rounded_weight = AdaroundOptimizer.adaround_wrapper(wrapper, act_func,
+                                                                                      all_inp_data, all_out_data,
+                                                                                      opt_params)
+        # Update param encodings dictionary
+        Adaround._update_param_encodings_dict(param_encodings, orig_model.layers[idx], wrapper.encoding,
+                                              is_symmetric)
+        hard_rounded_model.layers[idx].set_weights([hard_rounded_weight] +
+                                                   hard_rounded_model.layers[idx].get_weights()[1:])
+        soft_rounded_model.layers[idx].set_weights([soft_rounded_weight] +
+                                                   soft_rounded_model.layers[idx].get_weights()[1:])
+
+    @classmethod
+    def _get_module_act_func_pair(cls, model) -> Dict[tf.keras.layers.Layer, Union[None, tf.keras.layers.Layer]]:
+        """
+        Get dictionary mapping modules to subsequent activation functions. If the activation function is built into the
+        layer, map None to that layer.
+        :param model: Model to obtain module to activation info from
+        :return: Dictionary mapping modules to subsequent activation functions
+        """
+        conn_graph = ConnectedGraph(model)
+        module_act_func_pair = {}
+        activation_types = (tf.keras.layers.ELU, tf.keras.layers.PReLU, tf.keras.layers.Softmax, tf.keras.layers.ReLU,
+                            tf.keras.layers.LeakyReLU, tf.keras.layers.ThresholdedReLU, tf.keras.layers.Activation)
+        for op in conn_graph.get_all_ops().values():
+            # Get module associated with op
+            cur_module = op.get_module()
+            if cur_module:
+                module_act_func_pair[cur_module] = None
+                if hasattr(cur_module, 'activation'):
+                    if tf.keras.activations.serialize(cur_module.activation) != 'linear':
+                        # If the activation is not passthrough, it is built into the layer. Use None for the associated
+                        # activation since running forward pass on the layer will already include the activation.
+                        continue
+                if op.output:
+                    assert op.output.consumers, 'op output should have at least one consumer op.'
+                    # Get the next op
+                    next_op = op.output.consumers[0]
+                    # Get module associated with next op
+                    next_module = next_op.get_module()
+
+                    # Get the appropriate activation function
+                    if isinstance(next_module, activation_types):
+                        module_act_func_pair[cur_module] = next_module
+                        _logger.debug("Module: %s is followed by activation function: %s", op.dotted_name,
+                                      next_op.dotted_name)
+
+        return module_act_func_pair
+
+    @staticmethod
+    def _update_param_encodings_dict(encoding_dict: Dict, layer: tf.keras.layers.Layer,
+                                     encoding: Union[libpymo.TfEncoding, List[libpymo.TfEncoding]],
+                                     is_symmetric: bool):
+        """
+        Add layer's parameter encoding to dictionary to be used for exporting.
+        :param encoding_dict: Encoding dictionary
+        :param layer: Layer to obtain parameter encoding information for
+        :param encoding: Encoding
+        :param is_symmetric: Symmetric vs Asymmetric boolean
+        """
+        tensor_name = layer.weights[0].name
+        if not isinstance(encoding, Iterable):
+            encoding = [encoding]
+        encoding_dict[tensor_name] = [{'min': enc.min,
+                                       'max': enc.max,
+                                       'scale': enc.delta,
+                                       'offset': int(enc.offset),
+                                       'bitwidth': enc.bw,
+                                       'is_symmetric': str(is_symmetric)} for enc in encoding]
+
+    @staticmethod
+    def _get_ordered_adaround_layer_indices(model: tf.keras.Model) -> List[int]:
+        """
+        Get a list of ordered layer indices corresponding to layers to Adaround.
+        :param model: Model to find indices for
+        :return: List of ordered layer indices to Adaround
+        """
+        return [idx for idx, layer in enumerate(model.layers) if isinstance(layer, AdaroundSupportedOps)]
+
+    @staticmethod
+    def get_config_dict_keys(config_file: str) -> Tuple[ConfigDictType, bool, bool, bool]:
+        """
+        Get config dictionary keys from config file. Config file will default if no provided one.
+        :param config_file: configuration file.
+        :return: Config dictionary, strict symmetric flag, unsigned symmetric flag, enable per channel flag.
+        """
+        configs = JsonConfigImporter.import_json_config_file(config_file)
+        strict_symmetric = configs[ConfigDictKeys.DEFAULTS].get(ConfigDictKeys.STRICT_SYMMETRIC, False)
+        unsigned_symmetric = configs[ConfigDictKeys.DEFAULTS].get(ConfigDictKeys.UNSIGNED_SYMMETRIC, False)
+
+        # Read per-channel quantization field. Default = False
+        per_channel_enabled = configs[ConfigDictKeys.DEFAULTS].get(ConfigDictKeys.PER_CHANNEL_QUANTIZATION, False)
+
+        return configs, strict_symmetric, unsigned_symmetric, per_channel_enabled
+
+    @staticmethod
+    def get_is_symmetric_flag_for_op_param(configs: ConfigDictType, tf_op_type: str, param_name: str,
+                                           framework_to_onnx_type_dict: dict) -> bool:
+        """
+        NOTE: Checks config file in reverse order of specificity.
+        Returns is_symmetric flag for op's param if it is set in config file else returns
+        False. First check all ops of specific types, second check all params of specific
+        and lastly check for default types. If not specified, it will return default is
+        symmetric False.
+        :param configs: Dictionary containing configs.
+        :param tf_op_type: TF op type.
+        :param param_name: Parameter name.
+        :param framework_to_onnx_type_dict: Dictionary mapping framework type to ONNX type.
+        :return: is_symmetric flag for given op's param.
+        """
+        assert param_name in MAP_TF_PARAM_NAME_TO_QUANTSIM_NAME.keys(), "param name is invalid."
+
+        # third level of specificity which applies to specific op_type's parameters.
+        try:
+            onnx_type = framework_to_onnx_type_dict[tf_op_type]
+            return configs[ConfigDictKeys.OP_TYPE] \
+                [onnx_type] \
+                [ConfigDictKeys.PARAMS] \
+                [param_name] \
+                [ConfigDictKeys.IS_SYMMETRIC]
+        except KeyError:
+            pass
+
+        # Second level of specificity which applies to all parameters only.
+        try:
+            return configs[ConfigDictKeys.PARAMS] \
+                [param_name] \
+                [ConfigDictKeys.IS_SYMMETRIC]
+        except KeyError:
+            pass
+
+        # First level of specificity which applies to all the ops and parameters.
+        try:
+            return configs[ConfigDictKeys.DEFAULTS] \
+                [ConfigDictKeys.PARAMS] \
+                [ConfigDictKeys.IS_SYMMETRIC]
+        except KeyError:
+            pass
+
+        # Default is_symmetric False.
+        return False
+
+    @classmethod
+    def export_encoding_to_json(cls, path: str, filename_prefix: str, param_encodings: Dict):
+        """
+        Save Adadrounded op's parameter encodings to JSON file
+        :param path: path where to store param encodings
+        :param filename_prefix: filename to store exported weight encodings in JSON format
+        :param param_encodings: Parameter encodings dictionary
+        """
+        # export encodings to JSON file
+        os.makedirs(os.path.abspath(path), exist_ok=True)
+        encoding_file_path = os.path.join(path, filename_prefix + '.encodings')
+        with open(encoding_file_path, 'w') as encoding_fp:
+            json.dump(param_encodings, encoding_fp, sort_keys=True, indent=4)
+
+    @staticmethod
+    def get_conv2d_transpose_output_tensor_shape(data_format: str, output_data: np.ndarray):
+        """
+        Get output height, width, and channels from output_data for use in adarounding conv2d transpose op.
+        :param data_format: Data format for the op (NHWC or NCHW)
+        :param output_data: numpy array containing sampled output of the op
+        :return: Tuple containing output height, width, and channels of the op
+        """
+        if data_format == 'NHWC':
+            output_height = output_data.shape[1]
+            output_width = output_data.shape[2]
+            output_channels = output_data.shape[3]
+        else:
+            output_height = output_data.shape[2]
+            output_width = output_data.shape[3]
+            output_channels = output_data.shape[1]
+        return output_height, output_width, output_channels
+
    +
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    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
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    + + + + + + + + + \ No newline at end of file diff --git a/releases/2.0.0/_modules/aimet_tensorflow/keras/amp/quantizer_groups.html b/releases/2.0.0/_modules/aimet_tensorflow/keras/amp/quantizer_groups.html new file mode 100644 index 0000000..dc827eb --- /dev/null +++ b/releases/2.0.0/_modules/aimet_tensorflow/keras/amp/quantizer_groups.html @@ -0,0 +1,749 @@ + + + + + + + + aimet_tensorflow.keras.amp.quantizer_groups - AIMET + + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    +

    Source code for aimet_tensorflow.keras.amp.quantizer_groups

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2024, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+
+""" Find quantizer groups in a model """
+import itertools
+from typing import Dict, List, Tuple
+from collections import defaultdict
+from dataclasses import dataclass, field
+import tensorflow as tf
+
+
+from aimet_common.connected_graph.operation import Op
+from aimet_common.utils import AimetLogger
+from aimet_common.amp.utils import CANDIDATE_WITH_DTYPE
+from aimet_common.amp.quantizer_groups import QuantizerGroupBase
+
+from aimet_tensorflow.keras.connectedgraph import ConnectedGraph
+from aimet_tensorflow.keras.quantsim import QuantizationSimModel, substitutable_modules
+from aimet_tensorflow.keras.quant_sim.tensor_quantizer import TensorQuantizer
+
+logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.MixedPrecision)
+
+
+ops_to_skip = ['view', 'NumToTensor', 'Split', 'PythonOp']
+ops_not_to_traverse = ['size']
+INPUT_OPS_STR = 'input_ops'
+OUTPUT_OPS_STR = 'output_ops'
+
+
+
+
    
+[docs]
+@dataclass(frozen=True)
+class QuantizerGroup(QuantizerGroupBase):
+    """
+    Group of modules and quantizers
+    """
+    input_quantizers: Tuple[str, ...] = field(default_factory=tuple)
+    output_quantizers: Tuple[str, ...] = field(default_factory=tuple)
+    parameter_quantizers: Tuple[str, ...] = field(default_factory=tuple)
+
+
    
+[docs]
+    def get_candidate(self, name_to_quantizer_dict: Dict) -> CANDIDATE_WITH_DTYPE:
+        """
+        Gets Activation & parameter bitwidth
+        :param name_to_quantizer_dict: Gets module from module name
+        :return: Tuple of Activation, parameter bitwidth and data type
+        """
+        activation_bw, parameter_bw = None, None
+        activation_data_type, parameter_data_type = None, None
+        for module_name in self.input_quantizers:
+            module = self.lookup_quantizer(module_name, name_to_quantizer_dict)
+            for layer in module.input_quantizers.layers:
+                activation_bw = layer.bitwidth
+                activation_data_type = layer.data_type
+                break
+            break
+
+        for module_name in self.output_quantizers:
+            module = self.lookup_quantizer(module_name, name_to_quantizer_dict)
+            for layer in module.output_quantizers.layers:
+                activation_bw = layer.bitwidth
+                activation_data_type = layer.data_type
+                break
+            break
+
+        if self.parameter_quantizers:
+            for module_name in self.parameter_quantizers:
+                module = self.lookup_quantizer(module_name, name_to_quantizer_dict)
+                for layer in module.param_quantizers.layers:
+                    parameter_bw = layer.bitwidth
+                    parameter_data_type = layer.data_type
+                    break
+                break
+
+
+        return (activation_bw, activation_data_type), (parameter_bw, parameter_data_type)
    
+
+
+
    
+[docs]
+    @staticmethod
+    def lookup_quantizer(quantizer_name: str, name_to_quantizer_dict: Dict) -> tf.keras.layers.Layer:
+        """
+        Returns the quantizer layer corresponding to the name
+        :quantizer_name: Name of the quantizer
+        :name_to_quantizer_dict: Dictionary of mappings from quantizer name to quantizer layer
+        """
+        if isinstance(quantizer_name, tuple):
+            quantizer_name = quantizer_name[0]
+        module = name_to_quantizer_dict[quantizer_name]
+        return module
    
+
+
+
    
+[docs]
+    def set_quantizers_to_candidate(self,
+                                    name_to_quantizer_dict: Dict,
+                                    candidate: CANDIDATE_WITH_DTYPE) -> None:
+        """
+        Sets a quantizer group to a given candidate bitwidth
+        :param name_to_quantizer_dict: Gets module from module name
+        :param candidate: candidate with act and param bw and data types
+        """
+        (activation_bw, activation_data_type), (param_bw, param_data_type) = candidate
+        for module_name in self.input_quantizers:
+            module = self.lookup_quantizer(module_name, name_to_quantizer_dict)
+            for layer in module.input_quantizers.layers:
+                layer.bitwidth = activation_bw
+                layer.data_type = activation_data_type
+
+        for module_name in self.output_quantizers:
+            module = self.lookup_quantizer(module_name, name_to_quantizer_dict)
+            for layer in module.output_quantizers.layers:
+                layer.bitwidth = activation_bw
+                layer.data_type = activation_data_type
+
+        if not self.parameter_quantizers:
+            return
+
+        for module_name in self.parameter_quantizers:
+            module = self.lookup_quantizer(module_name, name_to_quantizer_dict)
+            for layer in module.param_quantizers:
+                layer.bitwidth = param_bw
+                layer.data_type = param_data_type
    
+
+
+
    
+[docs]
+    def to_list(self) -> List[Tuple[str, str]]:
+        """
+        Converts quantizer group to a list
+        :return: List containing input/output quantizers & weight quantizers
+        """
+        if self.parameter_quantizers:
+            ret_list = list(itertools.chain(
+                (("input", module_name) for module_name in self.input_quantizers),
+                (("output", module_name) for module_name in self.output_quantizers),
+                (("weight", module_name) for module_name in self.parameter_quantizers),
+            ))
+        else:
+            ret_list = list(itertools.chain(
+                (("input", module_name) for module_name in self.input_quantizers),
+                (("output", module_name) for module_name in self.output_quantizers),
+            ))
+        return ret_list
    
+
+
+
    
+[docs]
+    def get_active_quantizers(self, name_to_quantizer_dict: Dict) -> List[TensorQuantizer]:
+        """ Find all active tensor quantizers associated with this quantizer group """
+        quantizers = []
+        for module_name in self.input_quantizers:
+            module = self.lookup_quantizer(module_name, name_to_quantizer_dict)
+            quantizers += list(module.input_quantizers.layers)
+
+        for module_name in self.output_quantizers:
+            module = self.lookup_quantizer(module_name, name_to_quantizer_dict)
+            quantizers += list(module.output_quantizers.layers)
+
+        if self.parameter_quantizers:
+            for module_name in self.parameter_quantizers:
+                module = self.lookup_quantizer(module_name, name_to_quantizer_dict)
+                quantizers += list(module.param_quantizers.layers)
+
+        return list(set(quantizer for quantizer in quantizers if quantizer.is_enabled()))
    
+
+
+
    
+[docs]
+    def get_active_param_quantizers(self, name_to_quantizer_dict: Dict) -> List[TensorQuantizer]:
+        """ Find all active param tensor quantizers associated with this quantizer group
+        :param name_to_quantizer_dict: Contains mapping of module name to sim.quantizer_config object
+        """
+        quantizers = []
+        if self.parameter_quantizers:
+            for module_name in self.parameter_quantizers:
+                module = self.lookup_quantizer(module_name, name_to_quantizer_dict)
+                quantizers += list(module.param_quantizers.layers)
+        return list(set(quantizer for quantizer in quantizers if quantizer.is_enabled()))
    
+
    
+
+
+def find_output_quantizer_groups(op: Op, parent_child_op_groups: Dict, map_for_skipped_ops: Dict):
+    """
+    Finds quantizer groups along the parent to child flow
+    :param op: pytorch module
+    :param parent_child_op_groups: parent child relationships in graph
+    :param map_for_skipped_ops: map to find first skipped parents of skipped ops
+    """
+    output = op.output
+    if output:
+        consumers = output.consumers
+        for consumer in consumers:
+            name = op.name
+            if consumer.type in ops_not_to_traverse:
+                continue
+            if op.dotted_name in map_for_skipped_ops:
+                name = map_for_skipped_ops[op.name]
+
+            if consumer.type in ops_to_skip:
+                map_for_skipped_ops[consumer.name] = name
+                find_output_quantizer_groups(consumer, parent_child_op_groups, map_for_skipped_ops)
+            # If there is a one to one connection between quantizers
+            else:
+                if name in map_for_skipped_ops:
+                    name = map_for_skipped_ops[name]
+                parent_child_op_groups[name].append(consumer.name)
+    else:
+        if op.dotted_name in map_for_skipped_ops:
+            parent_child_op_groups[map_for_skipped_ops[op.dotted_name]] = []
+
+def find_op_groups(graph: ConnectedGraph) -> Dict:
+    """
+    Finds parent children relationship based on three rules
+    1) If there is a direct connection between two ops, op1 and op2, then op1 is parent of op2 and they form a group
+    2) If the input to an op (op1) is shared with another op (op2), the op producing the input (op0) is the parent, and op1 and op2 are the children
+    :param graph: connected graph
+    :return: Dict of parent (key) and children (value) relationship
+    """
+    parent_child_op_groups = defaultdict(list)
+    map_for_skipped_ops = {}
+
+    for op in graph.ordered_ops:
+        # Add 1st op as child
+        if not op.input_ops:
+            parent_child_op_groups[INPUT_OPS_STR].append(op.name)
+        # Add output op as child to put output of model as a quantizer group
+        if op.output is None:
+            parent_child_op_groups[OUTPUT_OPS_STR].append(op.name)
+
+    for op in graph.get_all_ops().values():
+        if op.type in ops_to_skip or op.type in ops_not_to_traverse:
+            continue
+        find_output_quantizer_groups(op, parent_child_op_groups, map_for_skipped_ops)
+
+    return parent_child_op_groups
+
+def get_module_name_to_module_dict(sim: QuantizationSimModel) -> Dict:
+    """
+    Creates a dictionary of wrapped module's name to quantizer module
+    :param sim: quantization sim
+    :return: Dict key: name of wrapped module value: quantization wrapper
+    """
+    module_name_to_quantizer_dict = {}
+    for layer in sim.quant_wrappers():
+        for quantizer in layer.input_quantizers:
+            module_name_to_quantizer_dict[quantizer.name] = layer
+        for quantizer in layer.output_quantizers:
+            module_name_to_quantizer_dict[quantizer.name] = layer
+        for quantizer in layer.param_quantizers:
+            module_name_to_quantizer_dict[quantizer.name] = layer
+
+    return module_name_to_quantizer_dict
+
+# pylint: disable-msg=too-many-locals
+# pylint: disable-msg=too-many-branches
+def find_quantizer_group(sim: QuantizationSimModel) -> Tuple[Dict, List[QuantizerGroup]]:
+    """
+    Finds quantizer groups in a quantization sim model
+    :param sim: Quantization sim
+    :return: List of Quantizer groups
+    """
+    # Get connected graph from quantsim for model without wrappers
+    connected_graph = sim.connected_graph
+
+    if connected_graph is None:
+        raise AssertionError('Aborting Auto Mixed Precision, connected graph needs to exist for Auto Mixed precision')
+
+    quantizer_groups = []
+
+    parent_child_op_groups = find_op_groups(connected_graph)
+
+    quantized_op_name_to_quantizer_dict = get_module_name_to_module_dict(sim)
+
+    if INPUT_OPS_STR in parent_child_op_groups:
+        for child in parent_child_op_groups[INPUT_OPS_STR]:
+            # Add one quantizer group for each input and it's weight param
+            layer = connected_graph.get_layer_from_op_name(child)
+            if isinstance(layer, tuple(substitutable_modules.keys())):
+                sub_quantizer_groups = get_quantizers_groups_substituted_layer(sim, layer)
+                quantizer_groups.extend(sub_quantizer_groups)
+                continue
+
+            input_quantizer_names, output_quantizer_names, param_quantizer_names = sim.get_quantizer_name_by_layer(layer)
+
+            if input_quantizer_names or param_quantizer_names:
+                quantizer_group = QuantizerGroup(
+                    input_quantizers=input_quantizer_names,
+                    parameter_quantizers=param_quantizer_names
+                )
+                quantizer_groups.append(quantizer_group)
+                logger.debug('\n Quantizer Group Added: %s', quantizer_group)
+
+    # Based on which quantizers are enabled, create a list of quantizer_groups
+    for parents in parent_child_op_groups:
+        if parents in [INPUT_OPS_STR, OUTPUT_OPS_STR]:
+            continue
+
+        if not isinstance(parents, tuple):
+            parents = [parents]
+
+        for parent in parents:
+            layer = connected_graph.get_layer_from_op_name(parent)
+            if isinstance(layer, tuple(substitutable_modules.keys())):
+                sub_quantizer_groups = get_quantizers_groups_substituted_layer(sim, layer)
+                quantizer_groups.extend(sub_quantizer_groups)
+                continue
+
+            input_quantizer_names, output_quantizer_names, param_quantizer_names = sim.get_quantizer_name_by_layer(layer)
+
+        # Don't add quantizer group if it is empty
+        if input_quantizer_names or output_quantizer_names or param_quantizer_names:
+            quantizer_group = QuantizerGroup(
+                input_quantizers=input_quantizer_names,
+                output_quantizers=output_quantizer_names,
+                parameter_quantizers=param_quantizer_names
+            )
+            quantizer_groups.append(quantizer_group)
+            logger.debug('\n Quantizer Group added: %s', quantizer_group)
+
+    if OUTPUT_OPS_STR in parent_child_op_groups:
+        for parent in parent_child_op_groups[OUTPUT_OPS_STR]:
+            # Add one quantizer group for each input and it's weight param
+
+            layer = connected_graph.get_layer_from_op_name(parent)
+            if isinstance(layer, tuple(substitutable_modules.keys())):
+                sub_quantizer_groups = get_quantizers_groups_substituted_layer(sim, layer)
+                quantizer_groups.extend(sub_quantizer_groups)
+                continue
+
+            input_quantizer_names, output_quantizer_names, param_quantizer_names = sim.get_quantizer_name_by_layer(layer)
+
+            if output_quantizer_names:
+                quantizer_group = QuantizerGroup(
+                    input_quantizers=input_quantizer_names,
+                    output_quantizers=output_quantizer_names,
+                    parameter_quantizers=param_quantizer_names
+                )
+                quantizer_groups.append(quantizer_group)
+                logger.debug('\n Quantizer Group added: %s', quantizer_group)
+
+    return quantized_op_name_to_quantizer_dict, quantizer_groups
+
+# pylint: disable=protected-access
+def get_quantizers_groups_substituted_layer(sim: QuantizationSimModel, layer) -> List[QuantizerGroup]:
+    """ Helper function to return the quantizer groups for the substituted layers """
+    layer = sim._substituted_layer[layer]
+    quantizer_groups = []
+    for quant_wrapper in layer.quant_wrappers():
+        input_quantizers = quant_wrapper.input_quantizers
+        output_quantizers = quant_wrapper.output_quantizers
+        param_quantizers = quant_wrapper.param_quantizers
+
+
+        input_quantizer_names = QuantizationSimModel._quantizer_to_name_tuple(input_quantizers)
+        output_quantizer_names = QuantizationSimModel._quantizer_to_name_tuple(output_quantizers)
+        param_quantizer_names = QuantizationSimModel._quantizer_to_name_tuple(param_quantizers)
+
+        if input_quantizer_names or output_quantizer_names or param_quantizer_names:
+            quantizer_group = QuantizerGroup(
+                input_quantizers=input_quantizer_names,
+                output_quantizers=output_quantizer_names,
+                parameter_quantizers=param_quantizer_names
+            )
+            quantizer_groups.append(quantizer_group)
+            logger.debug('\n Quantizer Group added: %s', quantizer_group)
+
+    return quantizer_groups
+
+
+def find_wrapper_module(op_name: str, module_name_to_quantizer_dict: Dict) -> Tuple[str, tf.keras.layers.Layer]:
+    """
+    Finds quantization (wrapping) module corresponding to the wrapper module's dotted name
+    :param op_name: Dotted name of op as represented in connected graph
+    :param module_name_to_quantizer_dict: Dict key: name of wrapped module value: quantization wrapper
+    :return: Module name and the corresponding quant-wrapper module in the sim
+    """
+    # pylint:disable = protected-access
+    module_name = op_name[op_name.find('.') + 1:]
+    if module_name in module_name_to_quantizer_dict:
+        return module_name, module_name_to_quantizer_dict[module_name]
+    # Else it is a functional op
+    raise KeyError
+
    +
    +
    +
    + +
    + + +
    +
    +
    +
    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
    + Made with Sphinx and @pradyunsg's + + Furo + +
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    +
    + + + + + + + + + \ No newline at end of file diff --git a/releases/2.0.0/_modules/aimet_tensorflow/keras/auto_quant_v2.html b/releases/2.0.0/_modules/aimet_tensorflow/keras/auto_quant_v2.html new file mode 100644 index 0000000..c044c48 --- /dev/null +++ b/releases/2.0.0/_modules/aimet_tensorflow/keras/auto_quant_v2.html @@ -0,0 +1,2018 @@ + + + + + + + + aimet_tensorflow.keras.auto_quant_v2 - AIMET + + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    AIMET
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    +

    Source code for aimet_tensorflow.keras.auto_quant_v2

    +#!/usr/bin/env python3
+# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2023, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+# pylint: disable=too-many-lines
+
+"""Automatic Post-Training Quantization"""
+import contextlib
+import itertools
+import math
+import functools
+import os
+import sys
+from collections import OrderedDict, defaultdict
+from dataclasses import dataclass
+from typing import List, Callable, Dict, Any, Tuple, Optional, Mapping
+import io
+from unittest.mock import patch
+import traceback
+import shutil
+import copy
+from tqdm import tqdm
+import jinja2
+import bokeh.plotting
+from bokeh.resources import CDN
+import tensorflow as tf
+
+from aimet_common.cache import Cache
+from aimet_common.defs import QuantScheme, QuantizationDataType, CallbackFunc
+from aimet_common.quantsim import validate_quantsim_inputs
+from aimet_common.utils import AimetLogger, Spinner
+from aimet_common.auto_quant import Diagnostics
+from aimet_common.amp.utils import CANDIDATE_WITH_DTYPE, create_sensitivity_plot, create_pareto_curve, AmpCandidate
+from aimet_tensorflow.keras.adaround_weight import AdaroundParameters
+from aimet_tensorflow.keras.adaround_weight import Adaround
+from aimet_tensorflow.keras.batch_norm_fold import fold_all_batch_norms
+from aimet_tensorflow.keras.cache import KerasModelSerializationProtocol
+from aimet_tensorflow.keras.cross_layer_equalization import equalize_model
+from aimet_tensorflow.keras.quantsim import QuantizationSimModel
+from aimet_tensorflow.keras.amp.quantizer_groups import QuantizerGroup
+from aimet_tensorflow.keras.amp.mixed_precision_algo import GreedyMixedPrecisionAlgo, EvalCallbackFactory, _default_forward_fn
+
+
+_logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.AutoQuant) # pylint: disable=invalid-name
+
+cache = Cache() # pylint: disable=invalid-name
+
+# The number of samples to be used for performance evaluation.
+# NOTE: None means "all".
+NUM_SAMPLES_FOR_PERFORMANCE_EVALUATION = None
+
+@dataclass(frozen=True)
+class _QuantSchemePair:
+    param_quant_scheme: QuantScheme
+    output_quant_scheme: QuantScheme
+    param_percentile: Optional[float] = None
+    output_percentile: Optional[float] = None
+
+    def __str__(self):
+        def scheme_to_str(quant_scheme, percentile):
+            if quant_scheme == QuantScheme.post_training_percentile:
+                return f"{percentile}%ile"
+            if quant_scheme in (QuantScheme.post_training_tf,
+                                QuantScheme.training_range_learning_with_tf_init):
+                return "tf"
+            if quant_scheme in (QuantScheme.post_training_tf_enhanced,
+                                QuantScheme.training_range_learning_with_tf_enhanced_init):
+                return "tf-enhanced"
+            raise ValueError
+
+        param_str = scheme_to_str(self.param_quant_scheme, self.param_percentile)
+        output_str = scheme_to_str(self.output_quant_scheme, self.output_percentile)
+        return f"W@{param_str} / A@{output_str}"
+
+
+_QUANT_SCHEME_CANDIDATES = (
+    # Weight:     tf
+    # Activation: tf
+    _QuantSchemePair(QuantScheme.post_training_tf,
+                     QuantScheme.post_training_tf),
+
+    # Weight:     tf_enhanced
+    # Activation: tf
+    _QuantSchemePair(QuantScheme.post_training_tf_enhanced,
+                     QuantScheme.post_training_tf),
+
+    # Weight:     tf_enhanced
+    # Activation: tf_enhanced
+    _QuantSchemePair(QuantScheme.post_training_tf_enhanced,
+                     QuantScheme.post_training_tf_enhanced),
+
+    # Weight:     tf_enhanced
+    # Activation: percentile(99.9)
+    _QuantSchemePair(QuantScheme.post_training_tf_enhanced,
+                     QuantScheme.post_training_percentile,
+                     output_percentile=99.9),
+
+    # Weight:     tf_enhanced
+    # Activation: percentile(99.99)
+    _QuantSchemePair(QuantScheme.post_training_tf_enhanced,
+                     QuantScheme.post_training_percentile,
+                     output_percentile=99.99),
+)
+
+def _validate_inputs(model: tf.keras.Model, # pylint: disable=too-many-arguments
+                     dataset: tf.data.Dataset,
+                     eval_callback: Callable[[tf.keras.Model], float],
+                     results_dir: str,
+                     strict_validation: bool,
+                     quant_scheme: QuantScheme,
+                     param_bw: int,
+                     output_bw: int,
+                     rounding_mode: str):
+    """
+    Confirms inputs are of the correct type
+    :param model: Model to be quantized
+    :param dataset: A collection that iterates over an unlabeled dataset,
+        used for computing encodings
+    :param eval_callback: Function that calculates the evaluation score
+    :param results_dir: Directory to save the results of PTQ techniques
+    :param strict_validation: Flag set to True by default.
+        When False, AutoQuant will proceed with execution
+        and try to handle errors internally if possible.
+        This may produce unideal or unintuitive results.
+    :param quant_scheme: Quantization scheme
+    :param param_bw: Parameter bitwidth
+    :param output_bw: Output bitwidth
+    :param rounding_mode: Rounding mode
+    """
+    if not isinstance(model, tf.keras.Model):
+        raise ValueError('Model must be of type tf.keras.Model, not ' + str(type(model).__name__))
+
+    if not isinstance(dataset, tf.data.Dataset):
+        raise ValueError('dataset must be of type tf.data.Dataset, not ' + str(
+            type(dataset).__name__))
+
+    if not isinstance(eval_callback, Callable):  # pylint: disable=isinstance-second-argument-not-valid-type
+        raise ValueError('eval_callback must be of type Callable, not ' +
+                         str(type(eval_callback).__name__))
+
+    if not isinstance(results_dir, str):
+        raise ValueError('results_dir must be of type str, not ' +
+                         str(type(results_dir).__name__))
+
+    results_dir = os.path.abspath(results_dir)
+    os.makedirs(results_dir, exist_ok=True)
+
+    if not isinstance(strict_validation, bool):
+        raise ValueError('strict_validation must be of type bool, not ' +
+                         str(type(strict_validation).__name__))
+
+    validate_quantsim_inputs(quant_scheme, rounding_mode, output_bw, param_bw)
+
+
+
+class AutoQuant: # pylint: disable=too-many-instance-attributes
+    """
+    Integrate and apply post-training quantization techniques.
+
+    AutoQuant includes 1) batchnorm folding, 2) cross-layer equalization,
+    and 3) Adaround.
+    These techniques will be applied in a best-effort manner until the model
+    meets the evaluation goal given as allowed_accuracy_drop.
+    """
+
+    # pylint: disable=too-many-arguments
+    def __init__(self, # pylint: disable=too-many-arguments, too-many-locals
+                 model: tf.keras.Model,
+                 eval_callback: Callable[[tf.keras.Model], float],
+                 dataset: tf.data.Dataset,
+                 param_bw: int = 8,
+                 output_bw: int = 8,
+                 quant_scheme: QuantScheme = QuantScheme.post_training_tf_enhanced,
+                 rounding_mode: str = "nearest",
+                 config_file: str = None,
+                 results_dir: str = "/tmp",
+                 cache_id: str = None,
+                 strict_validation: bool = True) -> None:
+        '''
+        :param model: Model to be quantized. Assumes model is on the correct device
+        :param eval_callback: Function that calculates the evaluation score
+        :param dataset: A collection that iterates over an unlabeled dataset,
+            used for computing encodings
+        :param param_bw: Parameter bitwidth
+        :param output_bw: Output bitwidth
+        :param quant_scheme: Quantization scheme
+        :param rounding_mode: Rounding mode
+        :param config_file: Path to configuration file for model quantizers
+        :param results_dir: Directory to save the results of PTQ techniques
+        :param cache_id: ID associated with cache results
+        :param strict_validation: Flag set to True by default.
+            When False, AutoQuant will proceed with execution and
+            handle errors internally if possible.
+            This may produce unideal or unintuitive results.
+        '''
+
+        _validate_inputs(model, dataset, eval_callback, results_dir, strict_validation, \
+                         quant_scheme, param_bw, output_bw, rounding_mode)
+        model.trainable = False
+        self.fp32_model = model
+        self._fp32_acc = None
+        self.dataset = dataset
+        self.eval_callback = eval_callback
+
+        self._quantsim_params = dict(
+            param_bw=param_bw,
+            output_bw=output_bw,
+            quant_scheme=_QuantSchemePair(quant_scheme, quant_scheme),
+            rounding_mode=rounding_mode,
+            config_file=config_file,
+        )
+
+        self.results_dir = results_dir
+        if cache_id:
+            self.cache_dir = os.path.join(results_dir, ".auto_quant_cache", cache_id)
+        else:
+            self.cache_dir = None
+
+        def forward_pass_callback(model, _: Any = None):
+            for input_data in tqdm(self.dataset):
+                model(input_data, training=False)
+
+        self.forward_pass_callback = forward_pass_callback
+
+        # Use at most 2000 samples for AdaRound.
+        batch_size = None
+        for data in self.dataset:
+            if not batch_size:
+                batch_size = len(data)
+                break
+        batch_size = batch_size or 1
+        num_batches = math.ceil(2000 / batch_size)
+        num_batches = min(num_batches, len(self.dataset))
+
+        self.adaround_params = AdaroundParameters(self.dataset,
+                                                  num_batches)
+        self.eval_manager = _EvalManager(
+            quantsim_factory=self._create_quantsim_and_encodings,
+            eval_func=self._evaluate_model_performance,
+            results_dir=self.results_dir,
+            strict_validation=strict_validation
+        )
+        self._quant_scheme_candidates = _QUANT_SCHEME_CANDIDATES
+
+    def _evaluate_model_performance(self, model: tf.keras.Model) -> float:
+        """
+        Evaluate the model performance
+
+        :param model: Model to evaluate
+        :return: Evaluation score
+        """
+        return self.eval_callback(model, NUM_SAMPLES_FOR_PERFORMANCE_EVALUATION)
+
+    def run_inference(self) -> Tuple[QuantizationSimModel, float]:
+
+        '''
+        Creates a quantization model and performs inference
+
+        :return: QuantizationSimModel, model accuracy as float
+        '''
+
+        model = self.fp32_model
+
+        with self.eval_manager.session("Batchnorm Folding", ptq=True) as sess:
+            model, _ = self._apply_batchnorm_folding(model)
+            if sess.ptq_result is None:
+                sess.set_ptq_result(applied_techniques=["batchnorm_folding"], model=model,)
+
+        sim = self._create_quantsim_and_encodings(model)
+
+        if sess.ptq_result is None:
+            # BN folding failed. Need to measure the eval score
+            acc = self._evaluate_model_performance(sim.model)
+        else:
+            # BN folding success. No need to measure the eval score again
+            acc = sess.ptq_result.accuracy
+
+        return sim, acc
+
+    def optimize(self, allowed_accuracy_drop: float = 0.0) -> Tuple[tf.keras.Model, float, str]:
+        """
+        Integrate and apply post-training quantization techniques.
+
+        :param allowed_accuracy_drop: Maximum allowed accuracy drop
+        :return: Tuple of (best model, eval score, encoding path)
+        """
+        result = self._optimize_helper(self._optimize_main, allowed_accuracy_drop)
+        return result["model"], \
+            result["accuracy"], \
+            result["encoding_path"]
+
+    def set_adaround_params(self, adaround_params: AdaroundParameters):
+        """
+        Set Adaround parameters.
+        If this method is not called explicitly by the user, AutoQuant will use
+        `dataset` (passed to `__init__`) for Adaround.
+
+        :param adaround_params: Adaround parameters.
+        """
+        self.adaround_params = adaround_params
+
+
+    def _create_quantsim_and_encodings(self, # pylint: disable=too-many-arguments, too-many-locals, too-many-branches
+                                       model: tf.keras.Model,
+                                       rounding_mode: str = None,
+                                       output_bw: int = None,
+                                       output_quant_scheme: QuantScheme = None,
+                                       output_percentile: float = None,
+                                       param_bw: int = None,
+                                       param_quant_scheme: QuantScheme = None,
+                                       param_percentile: float = None,
+                                       config_file: str = None,
+                                       encoding_path: str = None) -> QuantizationSimModel:
+        """
+
+        :param rounding_mode: Rounding mode. Defaults to self._quantsim_params["rounding_mode"].
+        :param output_bw: Default bitwidth (4-31) to use for quantizing layer inputs andoutputs.
+            Defaults to self._quantsim_params["output_bw"].
+        :param output_quant_scheme: Quantization scheme for output quantizers.
+            Defaults to self._quantsim_params["quant_scheme"].output_quant_scheme.
+        :param output_percentile: Percentile value for outputs.
+            Only valid if output quant scheme is percentile scheme.
+        :param param_bw: Default bitwidth (4-31) to use for quantizing layer parameters.
+            Defaults to self._quantsim_params["param_bw"].
+        :param param_quant_scheme: Quantization scheme for param quantizers.
+            Defaults to self._quantsim_params["quant_scheme"].param_quant_scheme.
+        :param param_percentile: Percentile value for parameters.
+            Only valid if param quant scheme is percentile scheme.
+        :param config_file: Path to configuration file for model quantizers.
+                            Defaults to self._quantsim_params["config_file"].
+        :param encoding_path: Path to parameter encodings file.
+        :return: Quantsim model.
+        """
+        if output_bw is not None:
+            assert output_bw <= 32
+
+        if param_bw is not None:
+            assert param_bw <= 32
+
+        if output_quant_scheme is None or param_quant_scheme is None:
+            assert self._quantsim_params["quant_scheme"] is not None
+
+        kwargs = dict(
+            rounding_mode=(rounding_mode or self._quantsim_params["rounding_mode"]),
+            default_output_bw=(output_bw or self._quantsim_params["output_bw"]),
+            default_param_bw=(param_bw or self._quantsim_params["param_bw"]),
+            config_file=(config_file or self._quantsim_params["config_file"]),
+        )
+        sim = QuantizationSimModel(model, **kwargs)
+        input_quantizers, param_quantizers, output_quantizers = sim._get_quantizer_list() # pylint: disable=protected-access
+
+        default_quant_scheme = self._quantsim_params.get("quant_scheme")
+
+        if default_quant_scheme is not None:
+            output_quant_scheme = output_quant_scheme or \
+                                  default_quant_scheme.output_quant_scheme
+            output_percentile = output_percentile or default_quant_scheme.output_percentile
+            param_quant_scheme = param_quant_scheme or \
+                                 default_quant_scheme.param_quant_scheme
+            param_percentile = param_percentile or default_quant_scheme.param_percentile
+
+        # Set input/output quantizers' quant schemes
+        for quantizer in itertools.chain(input_quantizers, output_quantizers):
+            quantizer.quant_scheme = output_quant_scheme
+            if quantizer.quant_scheme == QuantScheme.post_training_percentile and \
+                    output_percentile is not None:
+                quantizer.set_percentile_value(output_percentile)
+
+        # Set param quantizers' quant schemes
+        for quantizer in param_quantizers:
+            quantizer.quant_scheme = param_quant_scheme
+            if quantizer.quant_scheme == QuantScheme.post_training_percentile and \
+                    param_percentile is not None:
+                quantizer.set_percentile_value(param_percentile)
+
+        if encoding_path:
+            sim.set_and_freeze_param_encodings(encoding_path)
+
+        input_quantizers, param_quantizers, output_quantizers = sim._get_quantizer_list() # pylint: disable=protected-access
+
+        # Disable input/output quantizers, using fp32 to simulate int32.
+        if output_bw == 32:
+            for quantizer in input_quantizers + output_quantizers:
+                quantizer.disable()
+        # Disable param quantizers, using fp32 to simulate int32.
+        if param_bw == 32:
+            for quantizer in param_quantizers:
+                quantizer.disable()
+
+        # Skip encoding computation if none of the quantizers are enabled
+        if any(quantizer.is_enabled() for quantizer in param_quantizers + \
+                                                  input_quantizers + \
+                                                  output_quantizers):
+            sim.compute_encodings(self.forward_pass_callback, None)
+        return sim
+
+    # pylint: disable=no-self-use
+    def _apply_batchnorm_folding(self, model: tf.keras.Model) -> Tuple[tf.keras.Model, List[Tuple]]:
+        """
+        Apply batchnorm folding
+        Note: Input model is not mutated
+        :param model: Model to apply batchnorm folding
+        :return: Output model and folded pairs
+        """
+        original_weight = model.get_weights()
+        model = tf.keras.models.clone_model(model)
+        model.set_weights(original_weight)
+        folded_pairs, model = fold_all_batch_norms(model)
+        return model, folded_pairs
+
+    # pylint: disable=no-self-use
+    @cache.mark("cle", KerasModelSerializationProtocol())
+    def _apply_cross_layer_equalization(self, model: tf.keras.Model) -> tf.keras.Model:
+        """
+        Apply cross-layer equalization
+        Note: Input model is not mutated
+        :param model: Model to apply cross-layer-equalization
+        :return: CLE applied model
+        """
+        original_weight = model.get_weights()
+        model = tf.keras.models.clone_model(model)
+        model.set_weights(original_weight)
+        return equalize_model(model)
+
+    def _apply_adaround(self,
+                        model: tf.keras.Model) -> Tuple[tf.keras.Model, str]:
+        """
+        Apply adaround
+
+        NOTE: Input model is not mutated
+        :param model: Model to apply adaround
+        :param results_dir: Directory to save the results of AdaRound
+        :return: Output model and the path to the parameter encoding file
+        """
+        filename_prefix = "adaround"
+        adaround_encoding_path = os.path.join(self.results_dir,
+                                              f"{filename_prefix}.encodings")
+        if self._quantsim_params["param_bw"] == 4:
+            self.adaround_params.num_iterations = 15000
+
+        _apply_adaround_cached = cache.mark("adaround", KerasModelSerializationProtocol()) \
+            (Adaround.apply_adaround)
+
+        model = _apply_adaround_cached(model, # pylint: disable=protected-access
+                                       self.adaround_params,
+                                       path=self.results_dir,
+                                       filename_prefix=filename_prefix,
+                                       default_param_bw=self._quantsim_params["param_bw"],
+                                       default_quant_scheme=self._quantsim_params.get("quant_scheme").param_quant_scheme,
+                                       config_file=self._quantsim_params["config_file"])
+
+        return model, adaround_encoding_path
+
+    def _optimize_helper(
+            self,
+            optimize_fn: Callable,
+            allowed_accuracy_drop: float) -> Tuple[tf.keras.Model, float, str]:
+        """
+        Integrate and apply post-training quantization techniques.
+
+        :param allowed_accuracy_drop: Maximum allowed accuracy drop
+        :return: Tuple of (best model, eval score, encoding path)
+        """
+        allowed_accuracy_drop = float(allowed_accuracy_drop)
+        if allowed_accuracy_drop < 0:
+            raise ValueError(
+                "`allowed_accuracy_drop` must be a positive value. Got {:.2f}"
+                .format(allowed_accuracy_drop)
+            )
+
+        self.eval_manager.clear()
+
+        try:
+            with cache.enable(self.cache_dir):
+                _logger.info("Starting AutoQuant")
+                self._fp32_acc = self._evaluate_model_performance(self.fp32_model)
+                target_acc = self._fp32_acc - allowed_accuracy_drop
+                _logger.info("Target eval score: %f", target_acc)
+                _logger.info("FP32 eval score (W32A32): %f", self._fp32_acc)
+
+                ret = optimize_fn(self.fp32_model, target_acc)
+
+                acc = ret["accuracy"]
+                if acc is not None:
+                    _logger.info("Best eval score: %f", acc)
+
+                    # Save the best model with "best_model_" as prefix
+                    best_res = self.eval_manager.get_best_ptq_result()
+                    best_res.save_result_as("best_model")
+
+                    if acc < target_acc:
+                        _logger.info(
+                            "AutoQuant is unable to match the target accuracy. "
+                            "Consider Quantization Aware Training."
+                        )
+
+                return ret
+        finally:
+            self.eval_manager.export_diagnostics()
+
+    def get_quant_scheme_candidates(self) -> Tuple[_QuantSchemePair, ...]:
+        """
+        Return the candidates for quant scheme search.
+        During :meth:`~AutoQuant.optimize`, the candidate with the highest accuracy
+        will be selected among them.
+
+        :return: Candidates for quant scheme search
+        """
+        return self._quant_scheme_candidates
+
+    def set_quant_scheme_candidates(self, candidates: Tuple[_QuantSchemePair, ...]):
+        """
+        Set candidates for quant scheme search.
+        During :meth:`~AutoQuant.optimize`, the candidate with the highest accuracy
+        will be selected among them.
+
+        :param candidates: Candidates for quant scheme search
+        """
+        self._quant_scheme_candidates = copy.copy(candidates) # pylint: disable=E1101
+
+    def _choose_default_quant_scheme(self):
+        def eval_fn(pair: _QuantSchemePair):
+            sim = self._create_quantsim_and_encodings(
+                self.fp32_model,
+                param_quant_scheme=pair.param_quant_scheme,
+                param_percentile=pair.param_percentile,
+                output_quant_scheme=pair.output_quant_scheme,
+                output_percentile=pair.output_percentile,
+            )
+            return self._evaluate_model_performance(sim.model)
+
+        param_bw = self._quantsim_params["param_bw"]
+        output_bw = self._quantsim_params["output_bw"]
+
+        candidates = self.get_quant_scheme_candidates()
+
+        # If the weight representation has sufficient precision (i.e. bitwidth >= 16),
+        # always use tf scheme
+        if param_bw >= 16:
+            candidates = [
+                candidate for candidate in candidates
+                if candidate.param_quant_scheme == QuantScheme.post_training_tf
+            ]
+
+        # If the output representation has sufficient precision (i.e. bitwidth >= 16),
+        # always use tf scheme
+        if output_bw >= 16:
+            candidates = [
+                candidate for candidate in candidates
+                if candidate.output_quant_scheme == QuantScheme.post_training_tf
+            ]
+
+        # If we have only one candidate left, we don't need to evaluated
+        # the quant scheme for comparison
+        if len(candidates) == 1:
+            return candidates[0]
+
+        assert candidates
+
+        # Find the quant scheme that yields the best eval score
+        return max(candidates, key=eval_fn)
+
+
+    def _optimize_main(self, fp32_model: tf.keras.Model, target_acc: float):
+        """
+        Helper function of apply().
+
+        :param fp32_model: Model to apply PTQ techniques.
+        :param target_acc: Target eval score.
+
+        :raises RuntimeError: If none of the PTQ techniques were finished successfully.
+
+        :return: The best ptq result as a dictionary.
+        """
+
+        # Choose quant scheme automatically.
+        with self.eval_manager.session("QuantScheme Selection") as sess:
+            self._quantsim_params["quant_scheme"] = self._choose_default_quant_scheme()
+
+        with self.eval_manager.session("W32 Evaluation") as sess:
+            w32_eval_score = sess.eval(model=fp32_model, param_bw=32)
+            _logger.info("Evaluation finished: W32A%d (eval score: %f)",
+                         self._quantsim_params["output_bw"], w32_eval_score)
+
+            # Early exit
+            if w32_eval_score < target_acc:
+                _logger.info(
+                    "W32A%d eval score (%f) is lower "
+                    "than the target eval score (%f). This means it is unlikely that "
+                    "the target eval score can be met using PTQ techniques. "
+                    "Please consider finetuning the model using range learning.",
+                    self._quantsim_params["output_bw"], w32_eval_score, target_acc
+                )
+
+                # Since AutoQuant pipeline exited early, all the return values are set to None
+                return {
+                    "model": None,
+                    "accuracy": None,
+                    "encoding_path": None,
+                    "applied_techniques": None,
+                }
+
+            sess.result["target_satisfied"] = True
+
+        # Batchnorm Folding
+        with self.eval_manager.session("Batchnorm Folding", ptq=True) as sess:
+            model, _ = self._apply_batchnorm_folding(fp32_model)
+            if sess.ptq_result is None:
+                sess.set_ptq_result(model=model,
+                                    applied_techniques=["batchnorm_folding"])
+
+        best_result = self.eval_manager.get_best_ptq_result()
+        if best_result and best_result.accuracy >= target_acc:
+            sess.result["target_satisfied"] = True
+            return best_result.as_dict()
+
+        # Cross-Layer Equalization
+        with self.eval_manager.session("Cross-Layer Equalization", ptq=True) as sess:
+            model = self._apply_cross_layer_equalization(fp32_model)
+            if sess.ptq_result is None:
+                sess.set_ptq_result(model=model,
+                                    applied_techniques=["cross_layer_equalization"])
+
+        best_result = self.eval_manager.get_best_ptq_result()
+        if best_result and best_result.accuracy >= target_acc:
+            sess.result["target_satisfied"] = True
+            return best_result.as_dict()
+
+        if best_result is None:
+            model = fp32_model
+            applied_techniques = []
+        else:
+            if "cross_layer_equalization" not in best_result.applied_techniques:
+                sess.result["effective"] = False
+            model = best_result.load_model()
+            applied_techniques = best_result.applied_techniques
+
+        # AdaRound
+        with self.eval_manager.session("AdaRound", ptq=True) as sess:
+            model, encoding_path = self._apply_adaround(model)
+            if sess.ptq_result is None:
+                sess.set_ptq_result(model=model,
+                                    encoding_path=encoding_path,
+                                    applied_techniques=[*applied_techniques, "adaround"])
+
+        best_result = self.eval_manager.get_best_ptq_result()
+        if best_result:
+            if "adaround" not in best_result.applied_techniques:
+                sess.result["effective"] = False
+            if best_result.accuracy >= target_acc:
+                sess.result["target_satisfied"] = True
+            return best_result.as_dict()
+
+        raise RuntimeError("None of batchnorm folding, CLE, or Adaround "
+                           "has been finished successfully.")
+
+
+@dataclass
+class PtqResult:
+    """
+    Evaluation results
+    :param model_path: Path to the serialized model.
+    :param encoding_path: Path to the encoding file.
+    :param accuracy: Accuracy of the model.
+    :param applied_techniques: Applied ptq techniques.
+    """
+    model_path: str
+    encoding_path: str
+    accuracy: float
+    applied_techniques: List[str]
+
+    def load_model(self):
+        """
+        Load model
+        :return: Loaded model
+        """
+        return tf.keras.models.load_model(self.model_path)
+    def save_result_as(self, prefix: str = "best_model"):
+        """
+        Creates the copy of the PTQ result files with the given prefix.
+        :param prefix: prefix to be added to the file's basename
+        """
+        src_files = [self.model_path+".h5", self.model_path+"_converted.pb", self.encoding_path]
+        for file in src_files:
+            name = os.path.basename(file)
+            dirname = os.path.dirname(file)
+            dest = os.path.join(dirname, prefix + "_" + name)
+            if os.path.exists(file):
+                if os.path.exists(dest):
+                    os.remove(dest)
+                shutil.copyfile(file, dest)
+    def as_dict(self):
+        """Convert to dictionary"""
+        return dict(model=self.load_model(),
+                    accuracy=self.accuracy,
+                    encoding_path=self.encoding_path,
+                    applied_techniques=self.applied_techniques)
+
+
+class _EvalManager:
+    """
+    Evaluation manager for AutoQuant.
+    """
+
+    def __init__(self,
+                 quantsim_factory: Callable,
+                 eval_func: Callable[[tf.keras.Model], float],
+                 results_dir: str,
+                 strict_validation: bool):
+        """
+        :param quantsim_factory: A factory function that returns QuantizationSimModel.
+        :param eval_func: Evaluation function.
+        :param results_dir: Base directory to save the temporary serialized model.
+        :param strict_validation: Flag set to True by default.
+            When False, AutoQuant will proceed with execution and
+            handle errors internally if possible.
+            This may produce unideal or unintuitive results.
+        """
+        self._quantsim_factory = quantsim_factory
+        self._eval_func = eval_func
+        self._results_dir = results_dir
+        self._strict_validation = strict_validation
+        os.makedirs(self._results_dir, exist_ok=True)
+
+        self._all_sessions = OrderedDict()
+
+    def clear(self):
+        """
+        Clear all the session status saved in the previous run
+        """
+        for sess in self._all_sessions.values():
+            sess.reset_status()
+
+    def get_best_ptq_result(self) -> PtqResult:
+        """
+        Get the results with the highest evaluation score among the ptq results evaluated so far.
+        :return: The best evaluation result so far.
+        """
+        ptq_results = [sess.ptq_result for sess in self._all_sessions.values()
+                       if sess.ptq_result is not None]
+        if not ptq_results:
+            return None
+
+        return max(ptq_results, key=lambda ptq_result: ptq_result.accuracy)
+
+    def session(self, title: str, ptq: bool = False):
+        """
+        Session factory.
+        :param title: Title of the session.
+        :param ptq: True if this session is a ptq session
+        :return: Session object.
+        """
+        if title not in self._all_sessions:
+            session = _EvalSession(title,
+                                   self._quantsim_factory,
+                                   self._eval_func,
+                                   results_dir=os.path.join(self._results_dir, ".trace"),
+                                   strict_validation=self._strict_validation,
+                                   ptq=ptq)
+            self._all_sessions[title] = session
+        return self._all_sessions[title]
+
+    HTML_TEMPLATE_FILE = os.path.join(
+        os.path.dirname(os.path.abspath(__file__)),
+        "auto_quant_v2_diagnostics_template.html",
+    )
+
+    def export_diagnostics(self) -> str:
+        """
+        Export diagnostics in html format.
+        :return: Diagnostics string in html format.
+        """
+        loader = jinja2.FileSystemLoader(os.path.dirname(os.path.abspath(self.HTML_TEMPLATE_FILE)))
+        env = jinja2.Environment(loader=loader)
+        template = env.get_template(os.path.basename(self.HTML_TEMPLATE_FILE))
+
+        if any(sess.diagnostics.contains_bokeh() for sess in self._all_sessions.values()):
+            head = CDN.render()
+        else:
+            head = ""
+
+        log = io.StringIO()
+        for sess in self._all_sessions.values():
+            if sess.diagnostics.is_empty():
+                continue
+            log.write(
+                f"<h1> {sess.title} </h1>\n"
+            )
+            content = "\n".join(
+                line.get_html_elem() for line in sess.diagnostics
+            )
+            log.write(f"{content}\n")
+
+        result = OrderedDict()
+        result["ptq_techniques"] = OrderedDict()
+
+        for sess in self._all_sessions.values():
+            if sess.is_ptq_session():
+                result["ptq_techniques"][sess.title_lowercase] = sess.result
+            else:
+                result[sess.title_lowercase] = sess.result
+
+        flowchart_metadata = _build_flowchart_metadata(result)
+
+        html = template.render(head=head, log=log.getvalue(), **flowchart_metadata)
+
+        filename = os.path.join(self._results_dir, "diagnostics.html")
+        with open(filename, "w") as file_name:
+            file_name.write(html)
+        return html
+
+
+class _EvalSession: # pylint: disable=too-many-instance-attributes
+    """
+    Evaluation session for AutoQuant.
+
+    Each session object contains a title and diagnostics produced during the session.
+    The collected diagnostics will be exported into a html file by _EvalManager.
+    """
+    def __init__(self, # pylint: disable=too-many-arguments
+                 title: str,
+                 quantsim_factory: Callable,
+                 eval_func: Callable[[tf.keras.Model], float],
+                 results_dir: str,
+                 strict_validation: bool,
+                 ptq: bool,):
+        """
+        :param title: Title of the session.
+        :param quantsim_factory: A factory function that returns QuantizationSimModel.
+        :param eval_func: Evaluation function.
+        :param results_dir: Base directory to save the temporary serialized model.
+        :param strict_validation: Flag set to True by default.
+            When False, AutoQuant will proceed with execution
+            and handle errors internally if possible.
+            This may produce unideal or unintuitive results.
+        :param ptq: True if this session is a ptq session
+        """
+        self.title = title
+        self._quantsim_factory = quantsim_factory
+        self._eval_func = eval_func
+        self._results_dir = results_dir
+        self._strict_validation = strict_validation
+        self._ptq = ptq
+        self._spinner = None
+
+        self.result = {
+            "status": None,
+            "error": None,
+            "target_satisfied": False,
+            "effective": True,
+        }
+
+        os.makedirs(self._results_dir, exist_ok=True)
+
+        self.diagnostics = Diagnostics()
+
+        # Map session title to file name.
+        # e.g. title: "Cross-Layer Equalization" -> filename: "cross_layer_equalization"
+        self.title_lowercase = self.title.lower().replace("-", " ")
+        self.title_lowercase = "_".join(self.title_lowercase.split())
+
+        stdout_write = sys.stdout.write
+        self._log = io.StringIO()
+
+        # Redirects stdout to self._log
+        def write_wrapper(*args, **kwargs):
+            self._log.write(*args, **kwargs)
+            return stdout_write(*args, **kwargs)
+
+        self._stdout_redirect = patch.object(sys.stdout, "write", write_wrapper)
+        self._ptq_result = None
+        self._cached_result = None
+
+    def is_ptq_session(self):
+        """
+        Getter method of self._ptq flag
+        """
+        return self._ptq
+
+    def reset_status(self):
+        """
+        Reset the session status saved in the previous run
+        """
+        self.result = {
+            "status": None,
+            "error": None,
+            "target_satisfied": False,
+            "effective": True,
+        }
+
+    def eval(self, model: tf.keras.Model, **kwargs):
+        """
+        Evaluate the model
+        :param model: Model to evaluate.
+        :param kwargs: Additional arguments to the quantsim factory.
+        :return: Eval score
+        """
+        sim = self._quantsim_factory(model, **kwargs)
+        acc = self._eval_func(sim.model)
+        return acc
+
+    def __enter__(self):
+        self._spinner = Spinner(self.title)
+        self._spinner.__enter__()
+        self._stdout_redirect.start()
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        if self._ptq_result is not None:
+            _logger.info("Session finished: %s. (eval score: %f)",
+                         self.title, self._ptq_result.accuracy)
+        self._spinner.__exit__(exc_type, exc_val, exc_tb)
+
+        if exc_val:
+            buffer = io.StringIO()
+            traceback.print_exception(exc_type, exc_val, exc_tb, file=buffer)
+
+            if self._strict_validation:
+                print(buffer.getvalue())
+            else:
+                print(
+                    "################################################################\n"
+                    "################################################################\n"
+                    "################################################################\n"
+                    "WARNING: The following exception was raised but ignored:\n\n"
+                    f"{buffer.getvalue()}"
+                    "################################################################\n"
+                    "################################################################\n"
+                    "################################################################\n"
+                )
+
+        self._stdout_redirect.stop()
+        self.diagnostics.add(self._log.getvalue())
+
+        self.result["error"] = exc_val
+        if not exc_val:
+            self.result["status"] = "success"
+        elif self._strict_validation:
+            self.result["status"] = "error-failed"
+        else:
+            self.result["status"] = "error-ignored"
+
+        if exc_val and not self._strict_validation:
+            # Return True so that the error doesn't propagate further
+            return True
+        return None
+    @property
+    def ptq_result(self) -> PtqResult:
+        """Getter of self._ptq_result."""
+        return self._ptq_result
+
+    def set_ptq_result(self,
+                       applied_techniques: List[str],
+                       model: tf.keras.Model = None,
+                       sim: QuantizationSimModel = None,
+                       acc: float = None,
+                       **kwargs):
+        """
+        Set the result of PTQ. Should be called exactly once inside a with-as block
+
+        Exactly one among model and (sim, acc) pair should be specified
+        1) If sim and acc is specified, save them as the result of this session
+        2) If model is specified, evaluate the quantized accuracy of the model and save the result
+        :param applied_techniques: List of applied technique names
+        :param model: Result of PTQ
+        :param sim: Result of PTQ. The quantization encoding (compute_encodings()) is
+                    assumed to have been computed in advance
+        :param acc: Eval score
+        :param kwargs: Additional arguments to the quantsim factory
+        """
+        if sim is None:
+            assert acc is None
+            assert model is not None
+            sim = self._quantsim_factory(model, **kwargs)
+            acc = self._eval_func(sim.model)
+        else:
+            assert acc is not None
+            assert model is None
+
+        self._set_ptq_result(sim, acc, applied_techniques)
+
+    def _set_ptq_result(self,
+                        sim: QuantizationSimModel,
+                        acc: float,
+                        applied_techniques: List[str]) -> PtqResult:
+        """
+        Set the result of PTQ. Should be called exactly once inside a with-as block
+        :param sim: Result of PTQ. The quantization encoding (compute_encodings()) is
+                    assumed to have been computed in advance
+        :param acc: Eval score
+        :param applied_techniques: List of applied technique names
+        :return: PtqResult object
+        """
+        if self._ptq_result is not None:
+            raise RuntimeError(
+                "sess.eval() can be called only once per each _EvalSession instance."
+            )
+        model_path, encoding_path = self._export(sim)
+        self._ptq_result = PtqResult(model_path=model_path,
+                                     encoding_path=encoding_path,
+                                     accuracy=acc,
+                                     applied_techniques=applied_techniques)
+        _logger.info(self._ptq_result)
+        return self._ptq_result
+
+    def _export(self, sim: QuantizationSimModel) -> Tuple[str, str]:
+        """
+        Export quantsim
+        :param sim: QuantizationSimModel object to export
+        :return: The paths where model and encoding are saved
+        """
+        sim.export(path=self._results_dir, filename_prefix=self.title_lowercase)
+        model_path = os.path.join(self._results_dir, f"{self.title_lowercase}")
+        encoding_path = os.path.join(self._results_dir, f"{self.title_lowercase}.encodings")
+        _logger.info("The results of %s is saved in %s and %s.",
+                     self.title, model_path, encoding_path)
+        return model_path, encoding_path
+
+
+@contextlib.contextmanager
+def spy_auto_quant(auto_quant: AutoQuant):
+    """
+    Install a spy that collects the handles to the ptq result of
+    each stage of AutoQuant.
+
+    Typical usage::
+        >>> auto_quant = AutoQuant(...)
+        ... with auto_quant_spy(auto_quant) as spy:
+        ...     _ = auto_quant.apply(...)
+        ...
+        ... for result in spy.get_all_ptq_results():
+        ...     print(result.applied_techniques)
+        ...     print(result.accuracy)
+        ...     print(result.encoding_path)
+        ...     model = result.load_model()
+        ...     ...
+    """
+
+    # pylint: disable=protected-access
+    class Spy:
+        """
+        Spy that collects the handles to the ptq result of
+        each stage of AutoQuant.
+        """
+
+        def __init__(self, eval_manager):
+            self._eval_manager = eval_manager
+
+        def get_all_ptq_results(self) -> List[PtqResult]:
+            """Return handles to the results of AutoQuant"""
+            if self._eval_manager is None:
+                return []
+            return [sess.ptq_result for sess in self._eval_manager._all_sessions.values()
+                    if sess.ptq_result is not None]
+
+    spy = Spy(auto_quant.eval_manager)
+
+    _optimize_main = auto_quant._optimize_main
+
+    def _optimize_main_wrapper(fp32_model, target_acc):
+        return _optimize_main(fp32_model, target_acc)
+
+    try:
+        setattr(auto_quant, "_optimize_main", _optimize_main_wrapper)
+        yield spy
+    finally:
+        setattr(auto_quant, "_optimize_main", _optimize_main)
+
+
+def _build_flowchart_metadata(result: Mapping) -> Dict: # pylint: disable=too-many-return-statements
+    """
+    Build flowchart metadata for the html template of summary report
+
+    :param result: Result of AutoQuant with the following format:
+
+        result := {
+            "quantscheme_selection": _stage_result,
+            "w32_evaluation": _stage_result,
+            "ptq_techniques" [
+                "batchnorm_folding": _stage_result,
+                "cross_layer_equalization": _stage_result,
+                "adaround": _stage_result,
+            ]
+
+        }
+
+        where _stage_result is a dictionary defined as below:
+
+        _stage_result := {
+            "status": str,
+            "error": Exception,
+            "target_satisfied": bool,
+            "effective": bool,
+        }
+
+    :return: Dictionary that contains flowchart metadata for html template
+    """
+    metadata = defaultdict(str)
+
+    metadata.update(
+        edge_quant_scheme_selection_in='data-visited="true"',
+    )
+    if "quantscheme_selection" in result:
+        status = result['quantscheme_selection']['status']
+        metadata.update(
+            node_quant_scheme_selection=f'data-visited="true" data-stage-result="{status}"',
+        )
+
+        if status == 'error-failed':
+            return metadata
+
+    metadata.update(
+        edge_quant_scheme_selection_out='data-visited="true"',
+        node_test_w32_eval_score='data-visited="true"',
+    )
+
+    if not result["w32_evaluation"]["target_satisfied"]:
+        metadata.update(
+            edge_test_w32_eval_score_if_false='data-visited="true"',
+            node_result_fail='data-visited="true"',
+        )
+        return metadata
+
+    metadata.update(
+        edge_test_w32_eval_score_if_true='data-visited="true"',
+    )
+
+
+    for ptq_name, ptq_result in result["ptq_techniques"].items():
+        status = ptq_result['status']
+        effective = ptq_result['effective']
+        if status == "success" and not effective:
+            status = "discarded"
+        metadata.update({
+            f"node_{ptq_name}": f'data-visited="true" data-stage-result="{status}"',
+        })
+
+        if status == 'error-failed':
+            return metadata
+
+        metadata.update({
+            f'edge_{ptq_name}_out': 'data-visited="true"',
+            f'node_test_{ptq_name}': 'data-visited="true"',
+        })
+
+        if ptq_result['target_satisfied']:
+            metadata.update({
+                f'edge_test_{ptq_name}_if_true': 'data-visited="true"',
+                'node_result_success': 'data-visited="true"',
+            })
+            return metadata
+
+        metadata.update({
+            f'edge_test_{ptq_name}_if_false': 'data-visited="true"',
+        })
+
+    metadata.update(
+        node_result_fail='data-visited="true"',
+    )
+
+    return metadata
+
+
+ParetoFrontType = List[Tuple[int, float, QuantizerGroup, Tuple]]
+
+@dataclass
+class _MixedPrecisionArgs:
+    """
+    Mixed-precision specific arguments.
+    """
+    candidates: List[AmpCandidate]
+    forward_pass_callback: CallbackFunc
+    eval_callback_for_phase1: CallbackFunc
+    eval_callback_for_phase2: CallbackFunc
+
+@dataclass
+class _MixedPrecisionResult:
+    """
+    Mixed precision result
+    """
+    pareto_list: ParetoFrontType
+    sim: QuantizationSimModel
+    final_eval_score: float
+    sensitivity_plot: bokeh.plotting.figure
+    pareto_plot: bokeh.plotting.figure
+
+
+# The number of samples to be used for performance evaluation and AMP.
+# NOTE: None means "all".
+DEFAULT_NUM_SAMPLES_FOR_AMP_PHASE_1 = EvalCallbackFactory._DEFAULT_SQNR_NUM_SAMPLES # pylint: disable=protected-access
+DEFAULT_NUM_SAMPLES_FOR_AMP_PHASE_2 = None
+
+
    
+[docs]
+class AutoQuantWithAutoMixedPrecision:
+    """
+    Integrate and apply post-training quantization techniques.
+
+    AutoQuant includes 1) batchnorm folding, 2) cross-layer equalization,
+    3) Adaround, and 4) Automatic Mixed Precision (if enabled).
+    These techniques will be applied in a best-effort manner until the model
+    meets the evaluation goal given as allowed_accuracy_drop.
+    """
+
+    # pylint: disable=too-many-arguments
+    def __init__(self,
+                 model: tf.keras.Model,
+                 eval_callback: Callable[[tf.keras.Model], float],
+                 dataset: tf.data.Dataset,
+                 param_bw: int = 8,
+                 output_bw: int = 8,
+                 quant_scheme: QuantScheme = QuantScheme.post_training_tf_enhanced,
+                 rounding_mode: str = "nearest",
+                 config_file: str = None,
+                 results_dir: str = "/tmp",
+                 cache_id: str = None,
+                 strict_validation: bool = True):
+        """
+        :param model: Model to be quantized. Assumes model is on the correct device
+        :param eval_callback:  A function that maps model and the number samples
+                to the evaluation score. This callback is expected to return a
+                scalar value representing the model performance evaluated
+                against exactly `N` samples, where `N` is the number of samples
+                passed as the second argument of this callback.
+                NOTE: If `N` is None, the model is expected to be evaluated against
+                the whole evaluation dataset.
+        :param dataset: An unlabeled dataset for encoding computation.
+                By default, this dataset will be also used for Adaround unless
+                otherwise specified by `self.set_adaround_params`
+        :param param_bw: Parameter bitwidth
+        :param output_bw: Output bitwidth
+        :param quant_scheme: Quantization scheme
+        :param rounding_mode: Rounding mode
+        :param config_file: Path to configuration file for model quantizers
+        :param results_dir: Directory to save the results of PTQ techniques
+        :param cache_id: ID associated with cache results
+        :param strict_validation: Flag set to True by default.hen False, AutoQuant will proceed with execution and handle errors internally if possible. This may produce unideal or unintuitive results.
+        """
+        self._auto_quant_base = AutoQuant(model,
+                                          eval_callback,
+                                          dataset,
+                                          param_bw,
+                                          output_bw,
+                                          quant_scheme,
+                                          rounding_mode,
+                                          config_file,
+                                          results_dir,
+                                          cache_id,
+                                          strict_validation)
+        self.dataset = dataset
+        self._amp_args = None
+
+
    
+[docs]
+    def run_inference(self) -> Tuple[QuantizationSimModel, float]:
+        '''
+        Creates a quantization model and performs inference
+
+        :return: QuantizationSimModel, model accuracy as float
+        '''
+        return self._auto_quant_base.run_inference()
    
+
+
+
    
+[docs]
+    def optimize(self, allowed_accuracy_drop: float = 0.0) \
+            -> Tuple[tf.keras.Model, float, str, ParetoFrontType]:
+        """
+        Integrate and apply post-training quantization techniques.
+
+        :param allowed_accuracy_drop: Maximum allowed accuracy drop
+        :return: Tuple of  (best model, eval score, encoding path, pareto front).
+            Pareto front is None if AMP is not enabled or AutoQuant exits
+            without performing AMP.
+        """
+        html_template_file = os.path.join(
+            os.path.dirname(os.path.abspath(__file__)),
+            "auto_quant_v2_diagnostics_template_with_amp.html",
+        )
+        with patch.object(_EvalManager, "HTML_TEMPLATE_FILE", html_template_file):
+            result = self._auto_quant_base._optimize_helper(self._optimize_main, # pylint: disable=protected-access
+                                                            allowed_accuracy_drop)
+            return result["model"], \
+                result["accuracy"], \
+                result["encoding_path"], \
+                result["pareto_list"]
    
+
+
+
    
+[docs]
+    def set_adaround_params(self, adaround_params: AdaroundParameters):
+        """
+        Set Adaround parameters.
+        If this method is not called explicitly by the user, AutoQuant will use
+        `dataset` (passed to `__init__`) for Adaround.
+
+        :param adaround_params: Adaround parameters.
+        """
+        return self._auto_quant_base.set_adaround_params(adaround_params)
    
+
+
+
    
+[docs]
+    def set_mixed_precision_params(self, # pylint: disable=too-many-locals
+                                   candidates: List[CANDIDATE_WITH_DTYPE],
+                                   num_samples_for_phase_1: Optional[int] = DEFAULT_NUM_SAMPLES_FOR_AMP_PHASE_1,
+                                   forward_fn: Callable = _default_forward_fn,
+                                   num_samples_for_phase_2: Optional[int] = DEFAULT_NUM_SAMPLES_FOR_AMP_PHASE_2):
+        """
+        Set mixed precision parameters.
+        NOTE: Automatic mixed precision will NOT be enabled unless this method
+        is explicitly called by the user.
+
+        :param candidates: List of tuples of candidate bitwidths and datatypes.
+        :param num_samples_for_phase_1: Number of samples to be used for performance
+                evaluation in AMP phase 1.
+        :param forward_fn: Function that runs forward pass and returns the output tensor.
+                which will be used for SQNR compuatation in phase 1.
+                This function is expected to take 1) a model and 2) a single batch
+                yielded from the dataset, and return a single torch.Tensor object
+                which represents the output of the model.
+        :param num_samples_for_phase_2: Number of samples to be used for performance
+                evaluation in AMP phase 2.
+        """
+        if len(candidates) < 2:
+            raise ValueError(f"AMP requires at least two candidates. Got {len(candidates)}.")
+
+        baseline_param_bw = self._auto_quant_base._quantsim_params["param_bw"] # pylint: disable=protected-access
+        baseline_output_bw = self._auto_quant_base._quantsim_params["output_bw"] # pylint: disable=protected-access
+        baseline_candidate = (
+            (baseline_output_bw, QuantizationDataType.int),
+            (baseline_param_bw, QuantizationDataType.int),
+        )
+
+        if baseline_candidate not in candidates:
+            raise ValueError(
+                f"AMP candidate must contain W{baseline_param_bw}A{baseline_output_bw}, "
+                "which was passed to the constructor of AutoQuant as `param_bw` and `output_bw`."
+            )
+
+        for candidate in candidates:
+            ((output_bw, output_dtype), (param_bw, param_dtype)) = candidate
+
+            if output_dtype != param_dtype:
+                raise ValueError(
+                    "The data types of parameters and outputs should be the same. "
+                    f"Got {output_dtype} output and {param_dtype} for parameter."
+                )
+
+            if output_dtype == QuantizationDataType.float:
+                continue
+
+            # The param/output_bw passed to the constructor of AutoQuant
+            # must be the baseline-bitwidth candidate among all AMP candidates.
+            if output_bw < baseline_output_bw or param_bw < baseline_param_bw:
+                raise ValueError(
+                    "All AMP candidates should be strictly superior to the baseline "
+                    f"W{baseline_param_bw}A{baseline_output_bw}, which was passed "
+                    "to the constructor of AutoQuant. Please make sure that all the INT candidates "
+                    f"satisfy param_bw >= {baseline_param_bw} and output_bw >= {baseline_param_bw}."
+                )
+
+        def dataloader_wrapper():
+
+            return self.dataset
+
+        factory = EvalCallbackFactory(dataloader_wrapper, forward_fn=forward_fn)
+        sqnr_eval_callback = factory.sqnr(num_samples=num_samples_for_phase_1)
+
+        candidates = [AmpCandidate(candidate) for candidate in set(candidates)]
+
+        self._amp_args = _MixedPrecisionArgs(
+            candidates=candidates,
+            forward_pass_callback=CallbackFunc(self._auto_quant_base.forward_pass_callback, None),
+            eval_callback_for_phase1=sqnr_eval_callback,
+            eval_callback_for_phase2=CallbackFunc(self._auto_quant_base.eval_callback,
+                                                  num_samples_for_phase_2)
+        )
    
+
+
    
+[docs]
+    def get_quant_scheme_candidates(self) -> Tuple[_QuantSchemePair, ...]:
+        """
+        Return the candidates for quant scheme search.
+        During :meth:`~AutoQuant.optimize`, the candidate with the highest accuracy
+        will be selected among them.
+
+        :return: Candidates for quant scheme search
+        """
+        return self._auto_quant_base.get_quant_scheme_candidates()
    
+
+
+
    
+[docs]
+    def set_quant_scheme_candidates(self, candidates: Tuple[_QuantSchemePair, ...]):
+        """
+        Set candidates for quant scheme search.
+        During :meth:`~AutoQuant.optimize`, the candidate with the highest accuracy
+        will be selected among them.
+
+        :param candidates: Candidates for quant scheme search
+        """
+        return self._auto_quant_base.set_quant_scheme_candidates(candidates)
    
+
+
+    def _apply_mixed_precision(self,
+                               model: tf.keras.Model,
+                               target_acc: float,
+                               amp_args: _MixedPrecisionArgs,
+                               results_dir: str,
+                               encoding_path: str = None) -> _MixedPrecisionResult:
+        """
+        Apply mixed-precision and return the highest accuracy.
+
+        NOTE1: Input model is not mutated.
+        NOTE2: Parameter `clean_start` is always set to True.
+
+        :param model: tf.keras.Model to apply mixed precision.
+        :param target_acc: Minimum evaluation score required.
+        :param results_dir: Directory to save the results of AdaRound and mixed precision.
+        :param encoding_path: Path to parameter encodings file.
+        :return: MixedPrecisionAlgo object.
+        """
+        if not amp_args:
+            raise RuntimeError
+
+        sim = self._auto_quant_base._create_quantsim_and_encodings(model, # pylint: disable=protected-access
+                                                                   encoding_path=encoding_path)
+
+        algo = GreedyMixedPrecisionAlgo(sim,
+                                        amp_args.candidates,
+                                        amp_args.eval_callback_for_phase1,
+                                        amp_args.eval_callback_for_phase2,
+                                        results_dir=results_dir,
+                                        clean_start=True,
+                                        forward_pass_callback=amp_args.forward_pass_callback)
+
+        # Find baseline accuracy and bw corresponding to baseline accuracy
+        algo.set_baseline(fp32_accuracy=self._auto_quant_base._fp32_acc) # pylint: disable=protected-access
+        allowed_accuracy_drop = algo.fp32_accuracy - target_acc
+
+        algo.run(allowed_accuracy_drop)
+
+        sensitivity_plot = None
+        if algo.accuracy_list is not None:
+            # Visualize quantizer group sensitivity
+            sensitivity_plot = create_sensitivity_plot(algo.accuracy_list,
+                                                       algo.baseline_candidate,
+                                                       algo.fp32_accuracy)
+        pareto_plot = None
+        if algo.pareto_list is not None:
+            # Create pareto list curve
+            pareto_plot = create_pareto_curve(algo.pareto_list)
+
+        return _MixedPrecisionResult(algo.pareto_list,
+                                     algo._sim, # pylint: disable=protected-access
+                                     algo._final_eval_score, # pylint: disable=protected-access
+                                     sensitivity_plot,
+                                     pareto_plot)
+
+    def _optimize_main(self, fp32_model: tf.keras.Model, target_acc: float) -> Dict[str, Any]:
+        """
+        Helper function of apply().
+
+        :param fp32_model: Model to apply PTQ techniques.
+        :param target_acc: Target eval score.
+        :return: The best ptq result as a dictionary.
+        """
+        # pylint: disable=broad-except, too-many-locals, too-many-statements, too-many-branches
+
+        if self._amp_args:
+            candidates = copy.copy(self._amp_args.candidates)
+        else:
+            candidates = []
+
+        eval_manager = self._auto_quant_base.eval_manager
+        results_dir = self._auto_quant_base.results_dir
+        strict_validation = eval_manager._strict_validation # pylint: disable=protected-access
+
+        sess = eval_manager.session("")
+        _multiconfig_adaround_fn = _adaround_wrapper(self._auto_quant_base._apply_adaround, # pylint: disable=protected-access
+                                                     self._auto_quant_base,
+                                                     candidates,
+                                                     target_acc,
+                                                     sess.eval)
+        sess_eval_fn = _EvalSession.eval
+        def eval_fn(_, model, param_bw=None, output_bw=None, **kwargs):
+            if param_bw == 32:
+                # For W32 evaluation, use the highest output bitwidth
+                # among all the AMP candidates
+                output_bitwidths = [
+                    output_bw for (output_bw, output_dtype), _ in candidates
+                    if output_dtype == QuantizationDataType.int
+                ]
+                output_bitwidths.append(self._auto_quant_base._quantsim_params["output_bw"]) # pylint: disable=protected-access
+                output_bw = max(output_bitwidths)
+            return sess_eval_fn(_, model, param_bw=param_bw, output_bw=output_bw, **kwargs)
+
+        with patch.object(self._auto_quant_base, "_apply_adaround", _multiconfig_adaround_fn), \
+                patch.object(_EvalSession, "eval", eval_fn):
+            try:
+                result = self._auto_quant_base._optimize_main(fp32_model, target_acc) # pylint: disable=protected-access
+
+                # Automatic Mixed Precision
+                result["pareto_list"] = None
+
+                # An empty `result` dict means AutoQuant early-exited
+                # because W32 eval score didn't meet the target accuracy.
+                # In this case, do not proceed to AMP and exit immediately.
+                if result["model"] is None and \
+                        result["accuracy"] is None and \
+                        result["encoding_path"] is None and \
+                        result["applied_techniques"] is None:
+                    return result
+
+                if result["accuracy"] >= target_acc or not self._amp_args:
+                    return result
+
+                if len(candidates) < 2:
+                    _logger.info(
+                        "After Adaround, we have only one Adarond-compatible candidate left for AMP (W%dA%d). "
+                        "Return without proceeding to AMP", candidates[0].param_bw, candidates[0].output_bw
+                    )
+                    return result
+
+                model = result["model"]
+                applied_techniques = result["applied_techniques"]
+                # Freeze weight encoding to adaround weight encoding
+                encoding_path = result["encoding_path"] if "adaround" in applied_techniques else None
+            except Exception:
+                if strict_validation:
+                    raise
+                result = {}
+                model = fp32_model
+                applied_techniques = []
+                encoding_path = None
+
+            amp_args = copy.copy(self._amp_args)
+            if amp_args:
+                amp_args.candidates = candidates
+
+        with eval_manager.session("Automatic Mixed Precision", ptq=True) as sess:
+            amp_result = self._apply_mixed_precision(
+                model, target_acc, amp_args, results_dir, encoding_path=encoding_path
+            )
+            result["pareto_list"] = amp_result.pareto_list
+
+            if amp_result.sensitivity_plot is not None:
+                sess.diagnostics.add(amp_result.sensitivity_plot)
+
+            if amp_result.pareto_plot is not None:
+                sess.diagnostics.add(amp_result.pareto_plot)
+
+            sess.set_ptq_result(sim=amp_result.sim, acc=amp_result.final_eval_score,
+                                applied_techniques=[*applied_techniques, "automatic_mixed_precision"])
+
+        best_result = eval_manager.get_best_ptq_result()
+        if best_result:
+            if "automatic_mixed_precision" not in best_result.applied_techniques:
+                sess.result["effective"] = False
+            if best_result.accuracy >= target_acc:
+                sess.result["target_satisfied"] = True
+            result.update(best_result.as_dict())
+            return result
+
+        raise RuntimeError("None of batchnorm folding, CLE, or Adaround "
+                           "has been finished successfully.")
    
+
+
+def _adaround_wrapper(apply_adaround_fn: Callable,
+                      auto_quant: AutoQuant,
+                      amp_candidates: List[AmpCandidate],
+                      target_acc: float,
+                      eval_fn: Callable):
+    @functools.wraps(apply_adaround_fn)
+    def _apply_adaround_wrapper(*args, **kwargs): # pylint: disable=too-many-locals
+        # If AMP candidates are empty (i.e. AMP is disabled),
+        # perform normal (single-round) adaround.
+        original_model = args[0]
+        if not amp_candidates:
+            input_model = tf.keras.models.clone_model(original_model)
+            input_model.set_weights(original_model.get_weights())
+            new_args = (input_model,) + args[1:]
+            return apply_adaround_fn(*new_args, **kwargs)
+
+        def apply_adaround(param_bw: int):
+            _logger.info("Running Adaround with W%d", param_bw)
+
+            orig_param_bw = auto_quant._quantsim_params["param_bw"] # pylint: disable=protected-access
+            try:
+                auto_quant._quantsim_params["param_bw"] = param_bw # pylint: disable=protected-access
+                input_model = tf.keras.models.clone_model(original_model)
+                input_model.set_weights(original_model.get_weights())
+                new_args = (input_model,) + args[1:]
+                return apply_adaround_fn(*new_args, **kwargs)
+            finally:
+                auto_quant._quantsim_params["param_bw"] = orig_param_bw # pylint: disable=protected-access
+
+        int_candidates = [
+            candidate for candidate in amp_candidates
+            if candidate.param_dtype == QuantizationDataType.int
+        ]
+        sorted_int_candidates = sorted(int_candidates,
+                                       key=lambda candidate: (candidate.param_bw, candidate.output_bw))
+        # Run Adaround with the lowest-bitwidth candidate
+        lowest_candidate = sorted_int_candidates[0]
+        model, encoding_path = apply_adaround(param_bw=lowest_candidate.param_bw)
+
+        #If the lowest candidate is the only INT candidate, return immediately
+        if len(sorted_int_candidates) == 1:
+            return model, encoding_path
+
+        eval_score = eval_fn(model,
+                             param_bw=lowest_candidate.param_bw,
+                             output_bw=lowest_candidate.output_bw,
+                             encoding_path=encoding_path)
+        _logger.info("W%dA%d eval score after Adaround: %f",
+                     lowest_candidate.param_bw,
+                     lowest_candidate.output_bw,
+                     eval_score)
+
+        #If the lowest candidate satisfy the target accuracy, return immediately
+        if eval_score >= target_acc:
+            return model, encoding_path
+
+        # If the lowest candidate fails to meet the target accuracy,
+        # discard the lowest candidate, apply Adaround to the second-lowest candidate,
+        # and use it as the baseline for AMP.
+        second_lowest_candidate = sorted_int_candidates[1]
+
+        if second_lowest_candidate.param_bw != lowest_candidate.param_bw:
+            model = None
+            model, encoding_path = apply_adaround(param_bw=second_lowest_candidate.param_bw)
+            eval_score = eval_fn(model,
+                                 param_bw=second_lowest_candidate.param_bw,
+                                 output_bw=second_lowest_candidate.output_bw,
+                                 encoding_path=encoding_path)
+            _logger.info("W%dA%d eval score after Adaround: %f",
+                         second_lowest_candidate.param_bw,
+                         second_lowest_candidate.output_bw,
+                         eval_score)
+
+        # Only the candidates that are compatible with adaround can be used for AMP
+        adaround_compatible_amp_candidates = [
+            candidate for candidate in amp_candidates
+            if candidate.param_bw == second_lowest_candidate.param_bw or \
+               candidate.param_dtype == QuantizationDataType.float
+        ]
+
+        # Fill in AMP candidates with Adaround-compatible candidates only
+        amp_candidates.clear()
+        amp_candidates.extend(adaround_compatible_amp_candidates)
+
+        return model, encoding_path
+
+    return _apply_adaround_wrapper
+
    +
    +
    +
    + +
    + + +
    +
    +
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    +

    Source code for aimet_tensorflow.keras.batch_norm_fold

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2021-2023, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+
+#pylint: disable=too-many-lines
+""" Utility for batch norm fold in tf 2.x """
+from typing import Iterable, Optional, Tuple, Union, List, Dict, Set
+import numpy as np
+import tensorflow as tf
+import tensorflow.keras.backend as K
+from packaging import version  # pylint: disable=wrong-import-order
+
+if version.parse(tf.version.VERSION) >= version.parse("2.10"):
+    # Ignore pylint errors as keras module is not available in TF 2.4
+    from keras.layers.core.tf_op_layer import TFOpLambda # pylint: disable=import-error
+    from keras.engine.functional import Functional # pylint: disable=import-error
+else:
+    # Ignore pylint errors due to conditional imports
+    from tensorflow.python.keras.engine.functional import Functional # pylint: disable=ungrouped-imports
+    from tensorflow.python.keras.layers.core import TFOpLambda # pylint: disable=ungrouped-imports
+
+# pylint: disable=wrong-import-position
+from aimet_common.defs import QuantScheme, MAP_ROUND_MODE_TO_PYMO
+
+import aimet_common.libpymo as libpymo
+from aimet_common.utils import AimetLogger
+from aimet_tensorflow.keras.model_preparer import _KerasModelPreparer
+from aimet_tensorflow.keras.quant_sim.qc_quantize_wrapper import QcQuantizeWrapper
+from aimet_tensorflow.keras.quant_sim.tensor_quantizer import ParamPerTensorQuantizer
+from aimet_tensorflow.keras.quantsim import QuantizationSimModel
+from aimet_tensorflow.keras.utils import common
+from aimet_tensorflow.keras.utils.model_connection_utils import ModelLayerConnections, ModelLayerConnectionsProperties
+from aimet_tensorflow.keras.utils.quantizer_utils import get_wrappers_bias_quantizer, get_wrappers_weight_quantizer
+from aimet_tensorflow.keras.utils.weight_tensor_utils import WeightTensorUtils
+
+_logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.Utils)
+
+LayerType = Union[
+    tf.keras.layers.Conv2D,
+    tf.keras.layers.Dense,
+    tf.keras.layers.Conv2DTranspose,
+    tf.keras.layers.DepthwiseConv2D
+]
+_supported_layers = LayerType.__args__
+
+PairType = Union[Tuple[LayerType, tf.keras.layers.BatchNormalization, bool],
+                 Tuple[tf.keras.layers.BatchNormalization, LayerType, bool]]
+
+BatchNormType = tf.keras.layers.BatchNormalization
+_supported_batchnorms = BatchNormType
+
+# Todo: search for more types of convolution
+LinearType = tf.keras.layers.Dense
+ConvType = Union[tf.keras.layers.Conv1D,
+                 tf.keras.layers.Conv2D,
+                 tf.keras.layers.DepthwiseConv2D,
+                 tf.keras.layers.Conv2DTranspose]
+_supported_convs = ConvType.__args__
+
+FlattenType = Union[tf.keras.layers.Flatten, tf.keras.layers.Reshape]
+
+MAP_PYMO_TO_ROUND_MODE = {v: k for k, v in MAP_ROUND_MODE_TO_PYMO.items()}
+def _check_layer_to_find_pattern(cur_layer: tf.keras.layers.Layer,
+                                 conv_linear_with_bn_dict: Dict[Union[ConvType, LinearType],
+                                                                List[Union[None, BatchNormType]]],
+                                 layer_out_node_ref: Dict,
+                                 has_seen: List[Union[None, ConvType, BatchNormType, FlattenType]]):
+    """
+    find all paths in the model considering all inputs.
+
+    :param cur_layer: layer to investigate for finding a pattern
+    :param conv_linear_with_bn_dict: dictionary to store possible conv_bn pairs,
+        key: Dense or Conv layer & Value: list of BNS;
+        first index in this list shows bn_in and the second index shows bn_out
+    :param layer_out_node_ref: dictionary includes layer_ref as a key, outbound nodes as value
+    :param has_seen: for storing the layer which is useful for finding pattern in the next layers;
+        index 0 is for conv op, index 2 is for bn op and index 3 is for storing flatten/reshape op
+    """
+
+    # pylint: disable=too-many-branches
+    if isinstance(cur_layer, _supported_convs):
+        if has_seen[1] is not None:
+            conv_linear_with_bn_dict[cur_layer] = [has_seen[1], None]
+            has_seen[1] = None
+        if (cur_layer.activation is tf.keras.activations.linear) and \
+                (cur_layer in layer_out_node_ref) and len(layer_out_node_ref[cur_layer]) == 1:
+            has_seen[0] = cur_layer
+    elif isinstance(cur_layer, BatchNormType):
+        if has_seen[0] is not None:
+            if has_seen[0] in conv_linear_with_bn_dict:
+                conv_linear_with_bn_dict[has_seen[0]][1] = cur_layer
+            else:
+                conv_linear_with_bn_dict[has_seen[0]] = [None, cur_layer]
+            has_seen[0] = None
+        if (cur_layer in layer_out_node_ref) and len(layer_out_node_ref[cur_layer]) == 1:
+            has_seen[1] = cur_layer
+    elif isinstance(cur_layer, (tf.keras.layers.Flatten, tf.keras.layers.Reshape)):
+        if (cur_layer in layer_out_node_ref) and len(layer_out_node_ref[cur_layer]) == 1:
+            if has_seen[1]:
+                has_seen[2] = cur_layer
+            else:
+                has_seen[1] = None
+        if has_seen[0]:
+            has_seen[0] = None
+    elif isinstance(cur_layer, LinearType):
+        if has_seen[1] is not None and has_seen[2] is not None:
+            conv_linear_with_bn_dict[cur_layer] = [has_seen[1], None]
+        has_seen[2] = None
+        has_seen[1] = None
+    else:
+        has_seen[0] = None
+        has_seen[1] = None
+        has_seen[2] = None
+
+
+def _add_children_layer_before_parent_layer(cur_layer: tf.keras.layers.Layer, node_layer_map: Dict,
+                                            layer_out_node_map: Dict,
+                                            visited_layers: Set[tf.keras.layers.Layer],
+                                            reversed_ordered_layers: List[tf.keras.layers.Layer]):
+    """
+    Function to use topological sorting for finding all the layers which are accessible
+    from the specific input_layer in the opposite order of occurrence.
+
+    :param cur_layer:layer that we want to find path from
+    :param node_layer_map: dictionary includes node_ref as a key, in_layers and out_layer as value
+    :param layer_out_node_map: dictionary includes layer_ref as a key, outbound nodes as value
+    :param visited_layers: Set of all layers that have been visited
+    :param reversed_ordered_layers: List of layers in the opposite order of occurrence
+        for the layers that we have visited so far
+    """
+
+    # Mark the current layer as visited.
+    visited_layers.add(cur_layer)
+
+    if cur_layer in layer_out_node_map:
+        # Recur for all the layers adjacent to this layer
+        for next_node in layer_out_node_map[cur_layer]:
+            next_layer = node_layer_map[next_node][1]
+            if next_layer not in visited_layers:
+                _add_children_layer_before_parent_layer(next_layer, node_layer_map,
+                                                        layer_out_node_map, visited_layers,
+                                                        reversed_ordered_layers)
+            reversed_ordered_layers.append(cur_layer)
+    else:
+        reversed_ordered_layers.append(cur_layer)
+
+
+def _get_ordered_layers(node_layer_map: Dict,
+                        layer_out_node_map: Dict) -> List[tf.keras.layers.Layer]:
+    """
+    Function to return the list with all the layers in which layers come before parent layer.
+
+    :param node_layer_map: dictionary includes node_ref as a key, in_layers and out_layer as value
+    :param layer_out_node_map: dictionary includes layer_ref as a key, outbound nodes as value
+    :return: ordered_layers: List of all layers in the order of occurrence
+    """
+    # to find the input layers of the model
+    input_layers = common.find_input_layers(node_layer_map)
+
+    #  Set of all layers that have been visited (to cut short duplicate traversals)
+    visited_layers = set()
+
+    # List of all layers in the opposite of order of occurrence
+    reversed_ordered_layers = []
+
+    for input_layer in input_layers:
+        _add_children_layer_before_parent_layer(input_layer, node_layer_map, layer_out_node_map,
+                                                visited_layers, reversed_ordered_layers)
+
+    # reverse the list because layers are in reverse order
+    ordered_layers = reversed_ordered_layers[::-1]
+
+    # # filter ordered ops for only valid ops
+    # ordered_ops = [op for op in ordered_ops if op in valid_ops]
+
+    return ordered_layers
+
+
+def _get_ordered_conv_linears(node_layer_map: Dict,
+                              layer_out_node_map: Dict) -> List[Union[ConvType, LinearType]]:
+    """
+    helper to select a list of conv_linears in the order of occurence
+
+    :param node_layer_map: dictionary includes node_ref as a key, in_layers and out_layer as value
+    :param layer_out_node_map: dictionary includes layer_ref as a key, outbound nodes as value
+    :return: return List of conv/linear layer refs
+    """
+    # get ordered layers list in node_layer map dictionary
+    list_of_ordered_layers = _get_ordered_layers(node_layer_map, layer_out_node_map)
+
+    # look for conv layers
+    ordered_conv_linears = []
+    for layer in list_of_ordered_layers:
+        if isinstance(layer, _supported_layers):
+            ordered_conv_linears.append(layer)
+    return ordered_conv_linears
+
+
+def _fill_conv_linear_bn_dict(cur_layer: tf.keras.layers.Layer, node_layer_ref: Dict,
+                              layer_out_node_ref: Dict,
+                              has_seen: List[Union[None, ConvType, BatchNormType, FlattenType]],
+                              visited_layer: Set[tf.keras.layers.Layer],
+                              conv_linear_with_bn_dict: Dict[Union[ConvType, LinearType],
+                                                             List[Union[None, BatchNormType]]]):
+    """
+    fill conv_linear_bn_dict for the model
+
+    :param cur_layer: dictionary includes node_ref as a key, in_layers and out_layer as value
+    :param node_layer_ref: dictionary includes node_ref as a key, in_layers and out_layer as value
+    :param layer_out_node_ref: dictionary includes layer_ref as a key, outbound nodes as value
+    :paramm has_seen: for storing the layer which is useful for finding pattern in the next layers;
+        index 0 is for conv op, index 2 is for bn op and index 3 is for storing flatten/reshape op
+    :param visited_layer: to store all the layers that have been visited so far in the dictionary
+    :param conv_linear_with_bn_dict: dictionary of all possible conv_bn pairs,
+        key: Dense or Conv layer & Value: list of BNS;
+        first index in this list shows bn_in and the second index shows bn_out
+    """
+
+    # Mark the current layer as visited to prevent passing from one layer more than once
+    visited_layer.add(cur_layer)
+
+    _check_layer_to_find_pattern(cur_layer, conv_linear_with_bn_dict, layer_out_node_ref, has_seen)
+
+    if cur_layer in layer_out_node_ref:
+        for next_node in layer_out_node_ref[cur_layer]:
+            next_layer = node_layer_ref[next_node][1]
+            if next_layer not in visited_layer:
+                _fill_conv_linear_bn_dict(next_layer, node_layer_ref, layer_out_node_ref, has_seen,
+                                          visited_layer, conv_linear_with_bn_dict)
+            else:
+                has_seen[0] = None
+                has_seen[1] = None
+                has_seen[2] = None
+
+
+def _find_possible_convs_linears_bn(node_layer_map: Dict, layer_out_node_map: Dict)\
+        -> Dict[Union[ConvType, LinearType], List[Union[None, BatchNormType]]]:
+    """
+    find all possible convs_linears_bn by traversing all paths in the model considering all inputs
+
+    :param node_layer_map:  dictionary includes node_ref as a key, in_layers and out_layer as value
+    :param layer_out_node_map: dictionary includes layer_ref as a key, outbound nodes as value
+    :return: return dictionary of all possible conv_bn pairs,
+        key: Dense or Conv layer & Value: list of BNS;
+        first index in this list shows bn_in and the second index shows bn_out
+    """
+
+    input_layers = common.find_input_layers(node_layer_map)
+    visited_layer = set()
+    conv_linear_with_bn_dict = {}
+
+    for input_layer in input_layers:
+        _fill_conv_linear_bn_dict(input_layer, node_layer_map, layer_out_node_map,
+                                  [None, None, None], visited_layer, conv_linear_with_bn_dict)
+
+    return conv_linear_with_bn_dict
+
+
+def _get_bn_params(bn: tf.keras.layers.BatchNormalization) -> libpymo.BNParams():
+    """
+    helper to populate BN params from given BN Layer, required for fold
+
+    :param bn: BatchNorm Layer
+    :return: return bn params in libpymo.TensorParams() format.
+    """
+    if bn.gamma is None:
+        _logger.warning("Gamma for BatchNormalization '%s' is None. Setting to ones.", bn.name)
+        # Batch Normalization layers can having missing gammas with two different cases. One is that the 'gamma' attribute
+        # is set to None. The second is if `scale` is set to False upon creation of the layer which turns off gamma.
+        with tf.name_scope(bn.name):
+            weights_with_gamma_and_before_rebuild = [np.ones_like(bn.beta)] + bn.get_weights()
+            bn.scale = True
+            bn.build(bn.input.shape)
+            bn.set_weights(weights_with_gamma_and_before_rebuild)
+            bn.gamma = next(filter(lambda w: 'gamma' in w.name, bn.weights))
+
+    bn_params = libpymo.BNParams()
+
+    bn_params.gamma = bn.gamma.numpy().reshape(-1)
+    bn_params.beta = bn.beta.numpy().reshape(-1)
+    bn_params.runningMean = bn.moving_mean.numpy().reshape(-1)
+    bn_params.runningVar = bn.moving_variance.numpy().reshape(-1)
+    epsilon = bn.epsilon
+    var = bn.moving_variance.numpy()
+    var_with_epsilon = var + epsilon
+    sigma = np.sqrt(var_with_epsilon)
+    bn_params.runningVar = sigma
+
+    return bn_params
+
+
+def _get_bias_tensor(conv_linear: LayerType) -> libpymo.TensorParams():
+    """
+    Get bias tensor in given conv layer.
+
+    Packs bias in the format required for BN fold
+    (libpymo.TensorParams()).
+    :param conv_linear: conv Layer
+    :return: return bias param in libpymo.TensorParams() format.
+    """
+
+    bias_tensor = libpymo.TensorParams()
+    if conv_linear.bias is not None:
+        bias_tensor.data = conv_linear.bias.numpy().reshape(-1)
+        bias_tensor.shape = np.array(conv_linear.bias.shape)
+
+    return bias_tensor
+
+
+def _get_weight_tensor_transpose_reshape(conv_linear: LayerType) -> libpymo.TensorParams():
+    """
+    Get weight tensor from conv layer.
+
+    Converts to right format - performs transpose and reshape.
+    Packs it to the format required for BN fold (libpymo.TensorParams()).
+    :param conv_linear: conv layer
+    :return: return weight tensor in libpymo.TensorParams() format.
+    """
+
+    # Weight tensor libpymo format
+    weight_tensor = libpymo.TensorParams()
+
+    # linear array to be sent for bn fold
+    weight = conv_linear.get_weights()[0]
+    shape = weight.shape
+
+    if isinstance(conv_linear, tf.keras.layers.DepthwiseConv2D):
+        # Depthwise conv layers in TF have outputs(Noc) set to 1.
+        # we will use format [Nic, Noc, kh, kw] -
+        # to be compatible with cpp backend.
+        weight = np.transpose(weight, (2, 3, 0, 1))
+        # [Nic, Noc, kh, kw]
+        shape = np.array([shape[2], shape[3], shape[0], shape[1]])
+    elif isinstance(conv_linear, tf.keras.layers.Dense):
+        shape = np.concatenate((np.array([1, 1]), shape))
+        weight = np.transpose(weight, (1, 0))
+        # [Noc, Nic, kh, kw]
+        shape = np.array([shape[3], shape[2], shape[0], shape[1]])
+    elif isinstance(conv_linear, tf.keras.layers.Conv2DTranspose):
+        weight = np.transpose(weight, (2, 3, 0, 1))
+        # [Noc, Nic, kh, kw]
+        shape = np.array([shape[2], shape[3], shape[0], shape[1]])
+    elif isinstance(conv_linear, tf.keras.layers.Conv2D):
+        weight = np.transpose(weight, (3, 2, 0, 1))
+        # [Noc, Nic, kh, kw]
+        shape = np.array([shape[3], shape[2], shape[0], shape[1]])
+    else:
+        _logger.error("_get_weight_tensor_transpose_reshape(): Operation type unsupported")
+
+    weight_tensor.data = weight.reshape(-1)
+    weight_tensor.shape = shape
+
+    return weight_tensor
+
+
+class PassThroughOp(tf.keras.layers.Layer):
+    """
+    This is a pass-through op, used for purpose of making an op a no-op
+    """
+
+    # pylint: disable=arguments-differ
+    @staticmethod
+    def call(inputs):
+        """
+        This is a function to return input as an output
+        :param inputs: input to pass through
+        """
+        return inputs
+
+# pylint: disable=too-many-branches, protected-access, too-many-locals, too-many-nested-blocks
+@common.to_functional
+def _delete_bn_from_functional(model: tf.keras.Model,
+                               bn_layers_to_remove: List[tf.keras.layers.BatchNormalization]) -> tf.keras.Model:
+    """
+    This function is used to remove ALL batch normalization layers from a functional model passed via the
+    bn_layers_to_remove parameter. Removing in place is not possible for functional models as the layers inbound and
+    outbound connections are immutable. This function returns a new model with the batch normalization layers removed.
+
+    :param model: Model to remove bn_layers from
+    :param bn_layers_to_remove: List of batch normalization layers to remove from the model
+    :return: A new model with the batch normalization layers removed
+    """
+
+    # In order to do this, we first need to know the original models inbound and outbound connections to each layer.
+    # We then need to create a new model with the same inbound and outbound connections, but with the batch normalization
+    # layers removed. This is done by rerouting the inbound nodes of the batch normalization layers to the inbound nodes
+    # of the next layer. This can be seen in the following diagram:
+    #
+    # Original model flow ------------------------->
+    #   ______________        ______________        ______________
+    #  |             |       |             |       |             |
+    #  |    Conv     |  -X-> |  Batch Norm |  -X-> |    ReLU     |
+    #  |_____________|       |_____________|     ^ |_____________|
+    #  New model flow   \                       /
+    #                    \                     /
+    #                     \___________________/
+
+
+    def wrapped_bn_layer_in_bns_to_remove(layer: tf.keras.layers.Layer) -> bool:
+        return isinstance(layer, QcQuantizeWrapper) and layer._layer_to_wrap in bn_layers_to_remove
+
+    tf.keras.backend.clear_session() # clear session to not have tensor name conflicts
+
+    # Step 1: Get the inbound and outbound connections for each layer in the model
+    model_layer_connections = ModelLayerConnections.get_model_layers_connection_properties(model)
+
+    for inp in model.inputs:
+        model_layer_connections[ModelLayerConnectionsProperties.OUTPUT_TENSORS].update({inp.name: inp})
+
+    # Step 2: Create a new model with the batch normalization layers removed by iterating through the layers in the model
+    # and using the inbound and outbound connections to rerouting around the batch normalization layers.
+    batch_norms_replaced_with_names = {}
+    model_outputs = []
+    for current_layer in model.layers:
+        if isinstance(current_layer, tf.keras.layers.InputLayer):
+            continue
+
+        # Determine input tensors of the given layer
+        layer_input = [model_layer_connections[ModelLayerConnectionsProperties.OUTPUT_TENSORS][layer_aux]
+                       for layer_aux in model_layer_connections[ModelLayerConnectionsProperties.INBOUND_NODES][current_layer.name]]
+
+        layer_input = layer_input[0] if len(layer_input) == 1 else layer_input
+
+        # Reroute around batch normalization layers if the layer is in the list of layers to remove
+        if current_layer in bn_layers_to_remove or wrapped_bn_layer_in_bns_to_remove(current_layer):
+            _logger.debug("Removing Batch Normalization layer %s", current_layer.name)
+
+            for outbound_node in current_layer._outbound_nodes:  # pylint: disable=protected-access
+                # Find and replace the Batch Normalization output layers input that holds the Batch Normalization layer
+                # node and replace it with the input layers of the Batch Normalization layer.
+                # For example, if ReLU's inputs are [conv1_bn] and conv1_bn's inputs are [conv1], then we replace
+                # ReLU's inputs with [conv1]
+
+                all_batch_norms_inbound_layers_names = \
+                    [inbound_node.inbound_layers.name for inbound_node in current_layer._inbound_nodes]
+
+                # Go through all the outbound layers of the batch normalization layer and replace the batch normalization
+                # layer name with the input layer names of the batch normalization layer.
+                batch_norms_outbound_layers_new_inbound_layers_names = \
+                    [outlayer.replace(current_layer.name, *all_batch_norms_inbound_layers_names)
+                     for outlayer in model_layer_connections[ModelLayerConnectionsProperties.INBOUND_NODES][outbound_node.outbound_layer.name]]
+
+                # Keras Batch Norm only supports one input tensors. Meaning there is one singular layer coming into it.
+                # Hence, 'inbound_nodes[0]'.
+                batch_norms_replaced_with_names[current_layer.name] = current_layer._inbound_nodes[0].inbound_layers.name
+
+                model_layer_connections[ModelLayerConnectionsProperties.INBOUND_NODES].update(
+                    {outbound_node.outbound_layer.name: batch_norms_outbound_layers_new_inbound_layers_names})
+
+                # The above updates our dict for the mapping of the inputs, but we need to also update what Keras thinks
+                # the inputs are. This is done by updating the inbound nodes of the output layer of the Batch Normalization.
+                # THIS IS ONLY FOR MAPPING THE INPUTS TO BUILD A NEW MODEL. The original models underlying structure is
+                # not changed.
+                outbound_node.outbound_layer._inbound_nodes = current_layer.inbound_nodes  # pylint: disable=protected-access
+
+        # Otherwise, treat like a normal layer
+        else:
+            # For layers that have multiple inputs, order matters for what is fed into the layer. For example, if we have
+            # an Add layer with inputs from a ReLU and a Batch Norm, the order they go into the Add matters. Furthermore,
+            # if the Batch Norm is deleted, then it needs to be replaced with it's folded layer in the same order.
+
+            KERAS_SYMBOLIC_TENSORS_INDEX = 0
+            # Check if we need to change layer_input order. If there is just one input, there is no order.
+            # Special case when there is a Lambda layer with multiple inputs is handled seperately
+            if isinstance(layer_input, List) and not isinstance(current_layer, TFOpLambda):
+                # Original models keras symbolic tensor order
+                original_keras_symbolic_tensors_order = model_layer_connections[ModelLayerConnectionsProperties.CALL_ARGS][
+                    current_layer.name][KERAS_SYMBOLIC_TENSORS_INDEX]
+
+                # Special case for Lambda layers. Lambda layers can be thought of as z = x + y. Unfortunately, their call
+                # args for the keras symbolic tensors will ONLY have the x portion. In our layer_input we have both x and y.
+                # This statement is added to wrap the x portion of the original call args and check if it's a batch norm
+                # folded out.
+                if not isinstance(original_keras_symbolic_tensors_order, List):
+                    original_keras_symbolic_tensors_order = [original_keras_symbolic_tensors_order]
+
+                # Check if a Batch Norm that was deleted is in the original keras symbolic order.
+                name_of_bn_replaced = [
+                    tensor._keras_history.layer.name
+                    for tensor in original_keras_symbolic_tensors_order
+                    if tensor._keras_history.layer.name in batch_norms_replaced_with_names
+                ]
+
+                # If a Batch Norm is found, then the keras symbolic tensor order is slightly updated to replace the
+                # Batch Norm with the folded layer. Otherwise, we can just use the original keras symbolic tensor order.
+                if name_of_bn_replaced:
+
+                    updated_keras_symbolic_tensors_order = []
+                    for keras_symbolic_tensor in original_keras_symbolic_tensors_order:
+                        if (name_of_bn := keras_symbolic_tensor._keras_history.layer.name) in name_of_bn_replaced: #pylint: disable=superfluous-parens
+                            updated_keras_symbolic_tensors_order.append(
+                                model_layer_connections[ModelLayerConnectionsProperties.OUTPUT_TENSORS][
+                                    batch_norms_replaced_with_names[name_of_bn]
+                                ]
+                            )
+                        else:
+                            updated_keras_symbolic_tensors_order.append(keras_symbolic_tensor)
+
+                    # Dictionary of the keras symbolic tensor name to the order.
+                    ordered_inputs = {k.name: v for v, k in enumerate(updated_keras_symbolic_tensors_order)}
+
+                    # Sort layer_input based on the above dictionary.
+                    layer_input = sorted(layer_input, key=lambda current_input, oi=ordered_inputs: oi[current_input.name])
+
+            # Since we are rerouting around the batch normalization layers, we need to temporarily remove the inbound
+            # and outbound nodes of the batch normalization layers so that the model can be built correctly and not
+            # duplicate the non batch normalization layers inbound/outbound nodes.
+            current_layer._inbound_nodes = []  # pylint: disable=protected-access
+            # Special case for when there is a Lambda operation with multiple inputs. For example, z = x + y.
+            if isinstance(current_layer, TFOpLambda):
+                kmp = _KerasModelPreparer.get_instance_for_common_layer_passthrough_functions(model_layer_connections)
+                x = kmp._handle_normal_keras_layer(current_layer)  # pylint: disable=protected-access
+                # Updating the Model layer connections
+                kmp._update_output_tensors_in_model_layers_connections(  # pylint: disable=protected-access
+                    current_layer, x, model
+                )
+            else:
+                x = current_layer(layer_input)
+            current_layer._outbound_nodes = []  # pylint: disable=protected-access
+
+            # Set new output tensor (in this case, it will be the same as the original model)
+            model_layer_connections[ModelLayerConnectionsProperties.OUTPUT_TENSORS].update({current_layer.name: x})
+
+        # Save tensor in output list if it is output in the initial model
+        if current_layer.name in model.output_names:
+            model_outputs.append(x)
+
+    return tf.keras.Model(inputs=model.inputs, outputs=model_outputs)
+
+
+def _delete_bn_from_sequential(layer: tf.keras.layers.Layer,
+                               bn: tf.keras.layers.BatchNormalization):
+
+    """
+    This is the function for removing batch normalization layers that are layers of sequential model
+
+    :param layer: model to obtain bn_layer that we want to remove
+    :param bn: batch normalization layer that needs to be removed
+    """
+
+    layers_after_bn = []
+    visited = False
+    idx = None
+    # pylint: disable=protected-access
+    for index, inner_layer in enumerate(layer.layers):
+        if visited:
+            layers_after_bn.append(inner_layer)
+
+        elif inner_layer == bn:
+            visited = True
+            idx = index
+
+        elif inner_layer.submodules:
+            _delete_bn_for_non_subclassed_model(inner_layer, bn)
+
+    if visited and idx is not None:
+        # pylint: disable=protected-access
+        for _ in range(len(layer.layers) - idx):
+            layer.pop()
+        for layer_to_add in layers_after_bn:
+            layer.add(layer_to_add)
+
+
+def _delete_bn_for_non_subclassed_model(model: Union[tf.keras.Model, tf.keras.layers.Layer],
+                                        bn_layer: tf.keras.layers.BatchNormalization):
+    """
+    Remove bn layer for those model which are not part of model subclassing
+
+    :param model: model to delete bn layers from
+    :param bn_layer: bn layer that should be removed
+    """
+
+    if isinstance(model, tf.keras.Sequential):
+        _delete_bn_from_sequential(model, bn_layer)
+
+    # We are expecting to find sequential model in functional model
+    # or model subclassing in the elif statement
+    elif isinstance(model, (tf.keras.layers.Layer, tf.keras.Model)):
+        for layer in model.layers:
+            if layer.submodules:
+                _delete_bn_for_non_subclassed_model(layer, bn_layer)
+
+
+def _delete_bn_from_model_subclassing(module_to_name_map: Dict[tf.keras.layers.Layer,
+                                                               Tuple[tf.keras.Model, str]],
+                                      bn_layer: tf.keras.layers.BatchNormalization):
+    """
+    Remove bn layer which is part of model subclassing api
+    or model inheriting from tf.keras.layers.Layer
+
+    :param module_to_name_map: model to remove bn from
+    :param bn_layer: bn layer that should be removed
+    """
+
+    parent_ref, module_name = module_to_name_map[bn_layer]
+    op = PassThroughOp()
+    setattr(parent_ref, module_name, op)
+
+# pylint: disable=inconsistent-return-statements
+def _delete_all_bns_from_model(model: Union[tf.keras.Model, tf.keras.layers.Layer],
+                               bn_layers: List[tf.keras.layers.BatchNormalization]) -> Optional[tf.keras.Model]:
+    """
+    Remove all bn layers for a given model.
+
+    :param model: Model to have the bn layers removed from
+    :param bn_layers: bn layers that should be removed
+    :return: new model with bn layers removed, if model is functional else None
+    """
+    if bn_layers:
+        # QuantizationSimModel's model will fall into this case.
+        if isinstance(model, Functional) and not isinstance(model, tf.keras.Sequential) or any(isinstance(l, QcQuantizeWrapper) for l in model.layers):
+            return _delete_bn_from_functional(model, bn_layers)
+
+        module_to_name_map = common.module_to_name_map(model)
+
+        for bn_layer in bn_layers:
+            if bn_layer in module_to_name_map:
+                _delete_bn_from_model_subclassing(module_to_name_map, bn_layer)
+            else:
+                _delete_bn_for_non_subclassed_model(model, bn_layer)
+
+
+def _find_all_batch_norms_to_fold(model: tf.keras.Model) -> Tuple[List[PairType], List[PairType], Set[tf.keras.layers.BatchNormalization]]:
+    """
+    uses searcher to choose layers for bias correction
+
+    :param model: model to obtain conv_linear pairs for
+    :return: List of conv/linear layers with associated bn op / activation info and
+            a Set of all the batch norms which are marked for folding.
+    """
+
+    node_layer_map = common.create_node_to_layer_map(model)
+    layer_out_node_map = common.create_layer_to_out_node_map(model)
+
+    possible_convs_linears_bn = _find_possible_convs_linears_bn(node_layer_map, layer_out_node_map)
+
+    # get all ordered convs/ linears layers
+    ordered_conv_linears = _get_ordered_conv_linears(node_layer_map, layer_out_node_map)
+
+    bn_picked_for_folding = set()
+    def get_pairs(conv_is_first=False) -> List:
+        index = 1 if conv_is_first else 0
+
+        pairs_list = []
+        for conv_linear in ordered_conv_linears:
+            if conv_linear in possible_convs_linears_bn and (bn_info := possible_convs_linears_bn[conv_linear]):
+                if bn_info[index] and bn_info[index] not in bn_picked_for_folding:
+                    pairs_list.append((conv_linear, bn_info[index]) if conv_is_first else (bn_info[index], conv_linear))
+                    bn_picked_for_folding.add(bn_info[index])
+
+        return pairs_list
+
+    conv_bn_pairs = get_pairs(conv_is_first=True)
+    bn_conv_pairs = get_pairs(conv_is_first=False)
+
+    return conv_bn_pairs, bn_conv_pairs, bn_picked_for_folding
+
+
+
    
+[docs]
+def fold_all_batch_norms(model: tf.keras.Model) \
+        -> Tuple[List[Tuple[LayerType, BatchNormType]], tf.keras.Model]:
+    """
+    Fold all batch_norm layers in a model into corresponding conv/linear layers
+
+    :param model: model to find all batch norms for
+
+    :return: A tuple of List of conv/linear layers with associated bn op / activation info
+        and a new model with the Batch Normalization layers folded
+    """
+
+    conv_bn_pairs, bn_conv_pairs, folded_bns = _find_all_batch_norms_to_fold(model)
+
+    # Potential new model is returned in case the model is a functional model
+    potential_new_model = _fold_given_batch_norms(model, conv_bn_pairs, bn_conv_pairs)
+    model = potential_new_model if potential_new_model else model
+
+    # Convert the standalone BNs which are not folded
+    bn_converted = convert_standalone_batchnorms(model, folded_bns)
+    if bn_converted:
+        _logger.info("%d BatchNorms' weights got converted", len(bn_converted))
+        model.compile()
+    _logger.warning("A new model is returned with the Batch Normalization layers removed for Keras models. "
+                    "Please use this new model for the rest of the AIMET flow.")
+
+    return conv_bn_pairs + [(conv, bn) for bn, conv in bn_conv_pairs], model
    
+
+
+
+def convert_standalone_batchnorms(model: tf.keras.Model, folded_bns: set) -> List[tf.keras.layers.BatchNormalization]:
+    """
+    Converts the weights of standalone batch norms remaining in the model after BN folding
+    :param model: keras model on which batch norm folding is being performed
+    :param folded_bns: list of batch norms which got folded
+    :return: list of BatchNorms whose weights is converted
+    """
+
+    bn_converted = []
+    for layer in model.layers:
+        if isinstance(layer, tf.keras.layers.BatchNormalization) and layer not in folded_bns:
+            convert_batchnorm_parameters(layer)
+            _logger.debug("%s weights got converted", layer.name)
+            bn_converted.append(layer)
+    return bn_converted
+
+
+def convert_batchnorm_parameters(bn: tf.keras.layers.BatchNormalization):
+    """
+    Convert the weights of BN such that it works as y = weights * x + bias
+    :param bn: Batch Norm layer whose weights need to be converted
+    """
+    bn_params = _get_bn_params(bn)
+
+    # inv ::  1/ Sqrt(var + eps)
+    inv = tf.math.rsqrt(bn.moving_variance.numpy() + bn.epsilon)
+    weight = np.array(bn_params.gamma) * np.array(inv)
+    bias = np.array(bn_params.beta) - np.array(bn_params.runningMean) * weight
+
+    new_bn_weights = [weight.data, bias.data,
+                      np.zeros(shape=bn.moving_mean.shape, dtype=np.float32),
+                      np.ones(shape=bn.moving_variance.shape, dtype=np.float32)]
+    bn.trainable = False
+    bn.set_weights(new_bn_weights)
+    bn.epsilon = 0
+
+
+# pylint: disable=protected-access
+
    
+[docs]
+def fold_all_batch_norms_to_scale(sim: QuantizationSimModel) -> List[Tuple[QcQuantizeWrapper, QcQuantizeWrapper]]:
+    """
+    Fold all batch_norm layers in a model into the quantization scale parameter
+    of the corresponding conv layers
+
+    :param sim: QuantizationSimModel to be folded
+    :return: A list of pairs of layers [(Conv/Linear, BN layer that got folded)]
+    """
+
+    assert sim.model is not None, "QuantizationSimModel attribute 'model' is None."
+
+    model = sim._model_without_wrappers
+
+    quant_wrappers = {
+        quant_wrapper._layer_to_wrap: quant_wrapper
+        for quant_wrapper in sim.quant_wrappers()
+    }
+
+    conv_bn_pairs, bn_conv_pairs, _ = _find_all_batch_norms_to_fold(model)
+    conv_bn_pairs = [
+        (quant_wrappers[conv], quant_wrappers[bn]) for conv, bn in conv_bn_pairs
+    ]
+    bn_conv_pairs = [
+        (quant_wrappers[bn], quant_wrappers[conv]) for bn, conv in bn_conv_pairs
+    ]
+
+    old_model_without_wrappers = tf.keras.models.clone_model(model)
+    conv_bn_pairs_without_wrappers, _, _ = _find_all_batch_norms_to_fold(old_model_without_wrappers)
+    old_model_without_wrappers.set_weights(WeightTensorUtils.get_all_sim_models_layer_to_wrap_weights(sim.model))
+
+    # We fold both the sim.model and sim._model_without_wrappers because we rebuild the QuantizationSimModel during
+    # export and this utilizes the sim._model_without_wrappers to achieve this.
+    bn_fold_sim_model = _fold_given_batch_norms(sim.model, conv_bn_pairs, bn_conv_pairs)
+    sim.model = bn_fold_sim_model if bn_fold_sim_model else sim.model
+
+    bn_fold_model = _fold_given_batch_norms(old_model_without_wrappers, conv_bn_pairs_without_wrappers, [])
+    sim._model_without_wrappers = bn_fold_model if bn_fold_model else old_model_without_wrappers
+
+    return conv_bn_pairs + [(conv, bn) for bn, conv in bn_conv_pairs]
    
+
+
+def fold_given_batch_norms(model: tf.keras.Model, layer_pairs: List[PairType]) -> Optional[tf.keras.Model]:
+    """
+    Fold a given set of batch_norm layers into conv_linear layers
+
+    :param model: Either a Keras Model or a QuantizationSimModel's model
+    :param layer_pairs: Tuple of conv, bn layers and is_batch_norm_second flag
+    :return: new model with batch norm layers folded if model is a functional model, else None
+    """
+    # pylint: disable=protected-access
+    conv_bn_paris = []
+    bn_conv_pairs = []
+
+    def is_batchnorm(layer: tf.keras.layers.Layer) -> bool:
+        if isinstance(layer, QcQuantizeWrapper):
+            layer = layer._layer_to_wrap
+        return isinstance(layer, _supported_batchnorms)
+
+    def is_conv_linear(layer: tf.keras.layers.Layer) -> bool:
+        if isinstance(layer, QcQuantizeWrapper):
+            layer = layer._layer_to_wrap
+        return isinstance(layer, _supported_layers)
+
+    for x, y in layer_pairs:
+        if is_batchnorm(x):
+            assert is_conv_linear(y)
+            bn = x
+            conv = y
+            bn_conv_pairs.append((bn, conv))
+        else:
+            assert is_conv_linear(x)
+            assert is_batchnorm(y)
+            conv = x
+            bn = y
+            conv_bn_paris.append((conv, bn))
+
+    return _fold_given_batch_norms(model, conv_bn_paris, bn_conv_pairs)
+
+def _fold_given_batch_norms(model: tf.keras.Model,
+                            conv_bn_pairs: Iterable[Tuple[tf.keras.layers.Layer, tf.keras.layers.Layer]],
+                            bn_conv_pairs: Iterable[Tuple[tf.keras.layers.Layer, tf.keras.layers.Layer]]) -> \
+                                Optional[tf.keras.Model]:
+    """
+    Fold a given set of batch_norm layers into conv layers
+
+    :param model: Model
+    :param conv_bn_pairs: List of (conv, bn) pairs to fold
+    :param bn_conv_pairs: List of (bn, conv) pairs to fold
+    """
+    for bn, conv in bn_conv_pairs:
+        if isinstance(conv, QcQuantizeWrapper):
+            raise RuntimeError(f"Forward folding to scale is not possible. Got {conv.name}")
+
+    bn_layers = []
+
+    def _fold(conv, bn, fold_backward):
+        is_wrapped = isinstance(conv, QcQuantizeWrapper) or isinstance(bn, QcQuantizeWrapper)
+        try:
+            if is_wrapped:
+                assert isinstance(conv, QcQuantizeWrapper) and isinstance(bn, QcQuantizeWrapper)
+                bn._layer_to_wrap.trainable = False
+                _fold_to_scale(conv, bn)
+                bn_layers.append(bn._layer_to_wrap)
+            else:
+                bn.trainable = False
+                _fold_to_weight(conv, bn, fold_backward=fold_backward)
+        except _BatchNormFoldingNotSupported as e:
+            bn_name = bn._layer_to_wrap.name if is_wrapped else bn.name
+            conv_name = conv._layer_to_wrap.name if is_wrapped else conv.name
+            _logger.warning(
+                "Failed to fold %s to %s. [Reason] %s", bn_name, conv_name, str(e)
+            )
+        else:
+            bn_layers.append(bn._layer_to_wrap if is_wrapped else bn)
+
+    for conv, bn in conv_bn_pairs:
+        _fold(conv, bn, fold_backward=True)
+
+    for bn, conv in bn_conv_pairs:
+        _fold(conv, bn, fold_backward=False)
+
+    return _delete_all_bns_from_model(model, bn_layers)
+
+class _BatchNormFoldingNotSupported(RuntimeError):
+    pass
+
+def _fold_to_scale(conv_wrapper: QcQuantizeWrapper, bn_wrapper: QcQuantizeWrapper):
+    """
+    Fold BatchNorm into the scale and bias of the given layer.
+
+    :param conv_wrapper: QcQuantizeWrapper that wraps conv or linear layer
+    :param bn_wrapper: QcQuantizeWrapper that wraps the Batch Norm layer
+    """
+    # pylint: disable=protected-access, too-many-statements, too-many-locals
+    conv = conv_wrapper._layer_to_wrap
+    bn = bn_wrapper._layer_to_wrap
+
+    weight_quantizer = get_wrappers_weight_quantizer(conv_wrapper.param_quantizers)
+    bias_quantizer   = get_wrappers_bias_quantizer(conv_wrapper.param_quantizers)
+
+    # Checking QuantScheme as aimet_tensorflow.keras does not have LearnedGridTensorQuantizer
+    if weight_quantizer.quant_scheme not in [QuantScheme.training_range_learning_with_tf_init,
+                                             QuantScheme.training_range_learning_with_tf_enhanced_init]:
+        raise _BatchNormFoldingNotSupported(
+            "BatchNorm folding to scale supports training_range_learning_with_tf_init or "
+            "training_range_learning_with_tf_enhanced_init only. "
+            f"got {weight_quantizer.quant_scheme}"
+        )
+
+    output_quantizer = conv_wrapper.output_quantizers[0]
+
+    if output_quantizer.is_enabled():
+        raise _BatchNormFoldingNotSupported(
+            "BatchNorm should belong to the same supergroup with the layer to be folded to."
+        )
+
+    if bias_quantizer:
+        if bias_quantizer.is_enabled():
+            raise _BatchNormFoldingNotSupported(
+                "Can't fold BatchNorm to scale if bias quantizer is enabled."
+            )
+
+    enc_min = weight_quantizer._encoding_min
+    enc_max = weight_quantizer._encoding_max
+
+    if not weight_quantizer.is_encoding_valid():
+        raise RuntimeError
+
+    with bn_wrapper._quantize_params():
+        _fold_to_weight(conv, bn, fold_backward=True)
+
+        gamma = bn.gamma
+        sigma = K.sqrt(bn.moving_variance + bn.epsilon)
+
+        for i, c in enumerate(gamma/sigma):
+            c = float(c)
+            if c >= 0:
+                enc_max[i].assign(enc_max[i] * c)
+                enc_min[i].assign(enc_min[i] * c)
+            else:
+                enc_max_before_reassign = enc_max[i]
+                enc_max[i].assign(enc_min[i] * c)
+                enc_min[i].assign(enc_max_before_reassign * c)
+
+
+    # Copy batchnorm's output quantizers to conv output quantizers
+    for conv_output_quantizer, bn_output_quantizer in \
+            zip(conv_wrapper.output_quantizers, bn_wrapper.output_quantizers):
+        if bn_output_quantizer.encoding is not None:
+            conv_output_quantizer._encoding_min.assign(bn_output_quantizer._encoding_min)
+            conv_output_quantizer._encoding_max.assign(bn_output_quantizer._encoding_max)
+            conv_output_quantizer._is_encoding_valid = True
+
+            tensor_quantizers = conv_output_quantizer._tensor_quantizer if isinstance(conv_output_quantizer._tensor_quantizer, List) else [conv_output_quantizer._tensor_quantizer]
+            for tensor_quantizer in tensor_quantizers:
+                tensor_quantizer.isEncodingValid = True
+
+        if bn_output_quantizer.is_enabled():
+            conv_output_quantizer.enable()
+        else:
+            conv_output_quantizer.disable()
+
+
+
+        bn_output_quantizer.disable()
+
+    if bias_quantizer is None:
+        bias_quantizer = ParamPerTensorQuantizer(conv,
+                                                 conv.bias.name.split(':')[0],
+                                                 weight_quantizer.quant_scheme,
+                                                 MAP_PYMO_TO_ROUND_MODE[weight_quantizer.round_mode],
+                                                 weight_quantizer.bitwidth,
+                                                 weight_quantizer.data_type,
+                                                 weight_quantizer.is_symmetric,
+                                                 weight_quantizer.use_strict_symmetric,
+                                                 weight_quantizer.use_unsigned_symmetric,
+                                                 enabled=False)
+        tensor_quantizers = bias_quantizer._tensor_quantizer if isinstance(bias_quantizer._tensor_quantizer, List) else [bias_quantizer._tensor_quantizer]
+        for tensor_quantizer in tensor_quantizers:
+            tensor_quantizer.isEncodingValid = True
+        conv_wrapper.param_quantizers.append(bias_quantizer)
+
+
+def _fold_to_weight(conv_linear: LayerType, bn: BatchNormType, fold_backward: bool):
+    """
+    Fold BatchNorm into the weight and bias of the given layer.
+
+    :param conv_linear: Conv or linear layer to fold BN into.
+    :param bn: BatchNorm to fold.
+    :param fold_backward: To fold backwards or not
+    """
+
+    is_bias_valid = conv_linear.bias is not None
+
+    bn_params = _get_bn_params(bn)
+    weight_tensor = _get_weight_tensor_transpose_reshape(conv_linear)
+    bias_tensor = _get_bias_tensor(conv_linear)
+
+    # Updated weight and bias
+    bias = libpymo.fold(bn_params, weight_tensor, bias_tensor, is_bias_valid, fold_backward)
+
+    if isinstance(conv_linear, tf.keras.layers.DepthwiseConv2D):
+        # Depthwise conv layers in TF have outputs(Noc) set to 1.
+        # we send in format [Nic, Noc, kh, kw]
+        numpy_weight_reshaped = np.reshape(weight_tensor.data, weight_tensor.shape).transpose((2, 3, 0, 1))
+
+    elif isinstance(conv_linear, tf.keras.layers.Dense):
+        # o, i - convert to i , o
+        numpy_weight_reshaped = np.reshape(
+            weight_tensor.data,
+            [weight_tensor.shape[0], weight_tensor.shape[1]]).transpose(1, 0)
+
+    elif isinstance(conv_linear, tf.keras.layers.Conv2DTranspose):
+        # we sent in format [Noc, Nic, kh, kw]
+        numpy_weight_reshaped = np.reshape(weight_tensor.data, weight_tensor.shape).transpose((2, 3, 0, 1))
+
+    else:
+        # conv2D case
+        # we sent in format [Noc, Nic, kh, kw]
+        numpy_weight_reshaped = np.reshape(weight_tensor.data, weight_tensor.shape).transpose((2, 3, 1, 0))
+
+    # update bias tensor, even in case there was no existing bias add op in given conv2D op.
+    bias_tensor_shape = [weight_tensor.shape[0]]
+    numpy_bias_reshaped = np.reshape(bias, bias_tensor_shape)
+
+    if not is_bias_valid:
+        conv_linear.use_bias = True
+        conv_linear.bias = conv_linear.add_weight(name=f"{conv_linear.name}/bias",
+                                                  shape=(weight_tensor.shape[0],),
+                                                  dtype=conv_linear.dtype,
+                                                  trainable=True)
+    conv_linear.set_weights([numpy_weight_reshaped.data, numpy_bias_reshaped])
+
    +
    +
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    + + + + + + + + + \ No newline at end of file diff --git a/releases/2.0.0/_modules/aimet_tensorflow/keras/bn_reestimation.html b/releases/2.0.0/_modules/aimet_tensorflow/keras/bn_reestimation.html new file mode 100644 index 0000000..8307a0c --- /dev/null +++ b/releases/2.0.0/_modules/aimet_tensorflow/keras/bn_reestimation.html @@ -0,0 +1,463 @@ + + + + + + + + aimet_tensorflow.keras.bn_reestimation - AIMET + + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Source code for aimet_tensorflow.keras.bn_reestimation

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2022, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+
+"""BatchNorm Reestimation"""
+from typing import List, Dict
+import numpy as np
+import tensorflow as tf
+from aimet_common.utils import Handle, AimetLogger
+
+logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.Utils)
+
+def _get_bn_submodules(model: tf.keras.Model) -> List[tf.keras.layers.Layer]:
+    bn_layers = []
+    for layer in model.submodules:
+        if isinstance(layer, tf.keras.layers.BatchNormalization):
+            bn_layers.append(layer)
+    return bn_layers
+
+
+def _reset_bn_stats(bn_layers: List[tf.keras.layers.Layer], bn_mean_checkpoints: Dict, bn_var_checkpoints: Dict, bn_momentum_checkpoints: Dict) -> Handle:
+    """
+    reset bn stats
+    :param bn_layers: keras bn_layers
+    :param bn_mean_checkpoints: Dict for original bn mean
+    :param bn_var_checkpoints: Dict for original bn var
+    :param bn_momentum_checkpoints: Dict for original bn momentum
+    :return:
+    """
+
+    def cleanup():
+        """
+        Restore Bn stats
+        """
+        for layer in bn_layers:
+            move_mean = bn_mean_checkpoints[layer.name]
+            move_var = bn_var_checkpoints[layer.name]
+            gamma, beta, _, _ = layer.get_weights()
+            layer.set_weights([gamma, beta, move_mean, move_var])
+            layer.momentum = bn_momentum_checkpoints[layer.name]
+
+    try:
+        for layer in bn_layers:
+            layer.momentum = 0.0
+        return Handle(cleanup)
+    except:
+        cleanup()
+        raise ValueError('exception for reset_bn_stats')  # pylint: disable=raise-missing-from
+
+# pylint: disable=too-many-locals
+
    
+[docs]
+def reestimate_bn_stats(model: tf.keras.Model, bn_re_estimation_dataset: tf.data.Dataset,
+                        bn_num_batches: int = 100) -> Handle:
+    """
+
+    top level api for end user directly call
+
+    :param model: tf.keras.Model
+    :param bn_re_estimation_dataset: Training dataset
+    :param bn_num_batches: The number of batches to be used for reestimation
+    :returns: Handle that undos the effect of BN reestimation upon handle.remove()
+    """
+
+    bn_layers = _get_bn_submodules(model)
+
+    # save checkpoints
+    bn_mean_ori = {layer.name: layer.moving_mean.numpy() for layer in bn_layers}
+    bn_var_ori = {layer.name: layer.moving_variance.numpy() for layer in bn_layers}
+    bn_momentum_ori = {layer.name: layer.momentum for layer in bn_layers}
+    # 1. switch to re-estimation mode and setup remove
+    handle = _reset_bn_stats(bn_layers, bn_mean_ori, bn_var_ori, bn_momentum_ori)
+
+    # 2. mean &var initialization
+    mean_sum_dict = {layer.name: np.zeros(layer.moving_mean.shape, dtype=layer.moving_mean.dtype.as_numpy_dtype) for layer in bn_layers}
+    var_sum_dict = {layer.name: np.zeros(layer.moving_variance.shape, dtype=layer.moving_variance.dtype.as_numpy_dtype) for  layer in bn_layers}
+
+    # 3 per batch forward for BN re-estimation, accumulate into mean&var buffers
+    bn_dataset_iterator = iter(bn_re_estimation_dataset)
+    for batch_index in range(bn_num_batches):
+        try:
+            batch_data = next(bn_dataset_iterator)
+            model(batch_data, training=True)
+            for layer in bn_layers:
+                mean_sum_dict[layer.name] += layer.moving_mean.numpy()
+                var_sum_dict[layer.name] += layer.moving_variance.numpy()
+            if batch_index == bn_num_batches - 1:
+                break
+        except tf.errors.OutOfRangeError:
+            logger.info("tf.errors.OutOfRangeError:: End of dataset.")
+            break
+
+    # 4 average mean&var buffers, Override BN stats with the reestimated stats
+    for layer in bn_layers:
+        move_mean = mean_sum_dict[layer.name]/bn_num_batches
+        move_var = var_sum_dict[layer.name]/bn_num_batches
+        gamma, beta, _, _ = layer.get_weights()
+        layer.set_weights([gamma, beta, move_mean, move_var])
+
+    return handle
    
+
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    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
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    + + + + + + + + + \ No newline at end of file diff --git a/releases/2.0.0/_modules/aimet_tensorflow/keras/compress.html b/releases/2.0.0/_modules/aimet_tensorflow/keras/compress.html new file mode 100644 index 0000000..969230d --- /dev/null +++ b/releases/2.0.0/_modules/aimet_tensorflow/keras/compress.html @@ -0,0 +1,445 @@ + + + + + + + + aimet_tensorflow.keras.compress - AIMET + + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Source code for aimet_tensorflow.keras.compress

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2023, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+
+""" Top-level API for aimet compression library """
+
+from typing import Union, Tuple, Callable
+import tensorflow as tf
+
+from aimet_common.defs import CostMetric, CompressionScheme, EvalFunction, CompressionStats
+from aimet_common.bokeh_plots import BokehServerSession
+
+from aimet_tensorflow.keras.utils.graph_saver import keras_wrapper_func, keras_save_and_load_graph, keras_remove_hanging_nodes
+from aimet_tensorflow.keras.defs import SpatialSvdParameters
+from aimet_tensorflow.keras.compression_factory import CompressionFactory
+
+
+
    
+[docs]
+class ModelCompressor:
+    """ aimet model compressor: Enables model compression using various schemes """
+
+    # pylint: disable=too-many-arguments
+
+
    
+[docs]
+    @staticmethod
+    def compress_model(model: tf.keras.Model, eval_callback: EvalFunction, eval_iterations,
+                       compress_scheme: CompressionScheme, cost_metric: CostMetric,
+                       parameters: Union[SpatialSvdParameters],
+                       trainer: Callable = None, visualization_url: str = None) -> Tuple[tf.keras.Model, CompressionStats]:
+        """
+        Compress a given model using the specified parameters
+
+        :param model: Model, represented by a tf.keras.Model, to compress
+        :param eval_callback:  Evaluation callback. Expected signature is evaluate(model, iterations, use_cuda).
+                               Expected to return an accuracy metric.
+        :param eval_iterations: Iterations to run evaluation for.
+        :param compress_scheme: Compression scheme. See the enum for allowed values
+        :param cost_metric: Cost metric to use for the compression-ratio (either mac or memory)
+        :param parameters: Compression parameters specific to given compression scheme
+        :param trainer: Training function
+                        None: If per layer fine-tuning is not required while creating the final compressed model
+        :param visualization_url: url the user will need to input where visualizations will appear
+        :return: A tuple of the compressed model session, and compression statistics
+        """
+
+        # If no url is passed in, then do not create a bokeh server session
+        if not visualization_url:
+            bokeh_session = None
+        else:
+            # create a bokeh session to publish visualizations to the server document for compression
+            bokeh_session = BokehServerSession(url=visualization_url, session_id="compression")
+
+        if parameters.multiplicity < 1:
+            raise ValueError('Rounding Multiplicity should be greater than 1')
+
+        # wrapper_func saves and reloads the graph before evaluation
+        # In Keras after making changes to the graph you must save and reload, then evaluate
+        eval_callback = keras_wrapper_func(eval_callback)
+
+        if compress_scheme == CompressionScheme.spatial_svd:
+            algo = CompressionFactory.create_spatial_svd_algo(model, eval_callback, eval_iterations,
+                                                              cost_metric, parameters, bokeh_session)
+        elif compress_scheme == CompressionScheme.weight_svd:
+            raise NotImplementedError("Not yet implemented for: {}".format(compress_scheme))
+        elif compress_scheme == CompressionScheme.channel_pruning:
+            raise NotImplementedError("Not yet implemented for: {}".format(compress_scheme))
+        else:
+            raise ValueError("Compression scheme not supported: {}".format(compress_scheme))
+
+        compressed_layer_db, stats = algo.compress_model(cost_metric, trainer)
+
+        # In keras after making changes to the model you must save and reload, then evaluate
+        tmp_dir = './data/saved_model'
+        updated_model = keras_save_and_load_graph(tmp_dir, compressed_layer_db.model)
+
+        # Remove the hanging nodes
+        updated_model = keras_remove_hanging_nodes(updated_model)
+
+        return updated_model, stats
    
+
    
+
+
    +
    +
    +
    + +
    + + +
    +
    +
    +
    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
    + Made with Sphinx and @pradyunsg's + + Furo + +
    +
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    + +
    +
    + +
    +
    + + + + + + + + + \ No newline at end of file diff --git a/releases/2.0.0/_modules/aimet_tensorflow/keras/cross_layer_equalization.html b/releases/2.0.0/_modules/aimet_tensorflow/keras/cross_layer_equalization.html new file mode 100644 index 0000000..3b2a3a2 --- /dev/null +++ b/releases/2.0.0/_modules/aimet_tensorflow/keras/cross_layer_equalization.html @@ -0,0 +1,828 @@ + + + + + + + + aimet_tensorflow.keras.cross_layer_equalization - AIMET + + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    AIMET
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    +

    Source code for aimet_tensorflow.keras.cross_layer_equalization

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2021, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+"""Cross Layer Equalization"""
+
+import typing
+
+import numpy as np
+import tensorflow as tf
+
+import aimet_common.libpymo as libpymo
+from aimet_common.utils import AimetLogger
+from aimet_tensorflow.keras.batch_norm_fold import fold_all_batch_norms
+from aimet_tensorflow.keras.graphsearchtuils import GraphSearchUtils, ClsSet
+from aimet_tensorflow.keras.utils import model_transform_utils
+from aimet_tensorflow.keras.utils.weight_tensor_utils import WeightTensorUtils
+
+_logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.CrosslayerEqualization)
+
+BatchNormFoldedPair = typing.Union[typing.Tuple[tf.keras.layers.Conv2D,
+                                                tf.keras.layers.BatchNormalization],
+                                   typing.Tuple[tf.keras.layers.Dense,
+                                                tf.keras.layers.BatchNormalization]]
+
+ScaleFactor = typing.Union[np.ndarray, typing.Tuple[np.ndarray, np.ndarray]]
+ReluFlag = typing.Union[bool, typing.Tuple[bool, bool]]
+
+Convs = typing.Union[tf.keras.layers.Conv2D,
+                     tf.keras.layers.DepthwiseConv2D,
+                     tf.keras.layers.Conv2DTranspose]
+
+_supported_convs = Convs.__args__
+
+class ClsSetInfo:
+    """
+    This class hold information about the layers in a CLS set, along with corresponding scaling factors
+    and other information like if there is a ReLU activation function between the CLS set layers
+    """
+
+    class ClsSetLayerPairInfo:
+        """
+        Models a pair of layers that were scaled using CLS. And related information.
+        """
+
+        def __init__(self, layer1: tf.keras.layers.Conv2D, layer2: tf.keras.layers.Conv2D, scale_factor: np.ndarray,
+                     relu_activation_between_layers: bool):
+            """
+            :param layer1: Layer whose bias is folded
+            :param layer2: Layer to which bias of previous layer's bias is folded
+            :param scale_factor: Scale Factor found from Cross Layer Scaling to scale BN parameters
+            :param relu_activation_between_layers: If the activation between layer1 and layer2 is Relu
+            """
+            self.layer1 = layer1
+            self.layer2 = layer2
+            self.scale_factor = scale_factor
+            self.relu_activation_between_layers = relu_activation_between_layers
+
+        def __eq__(self, other):
+            if isinstance(self, other.__class__):
+                return self.layer1 == other.layer1 and \
+                       self.layer2 == other.layer2 and \
+                       np.allclose(self.scale_factor, other.scale_factor) and \
+                       self.relu_activation_between_layers == other.relu_activation_between_layers
+            return False
+
+    def __init__(self, cls_pair_1: ClsSetLayerPairInfo, cls_pair_2: ClsSetLayerPairInfo = None):
+        """
+        Constructor takes 2 pairs if Depth-wise separable layer is being folded
+        :param cls_pair_1: Pair between two conv or conv and depth-wise conv
+        :param cls_pair_2: Pair between depth-wise conv and point-wise conv
+        """
+        if cls_pair_2:
+            self.cls_pair_info_list = [cls_pair_1, cls_pair_2]
+        else:
+            self.cls_pair_info_list = [cls_pair_1]
+
+    def __eq__(self, other):
+        if isinstance(self, other.__class__):
+            return self.cls_pair_info_list == other.cls_pair_info_list
+
+        return False
+
+class CrossLayerScaling:
+    """
+    Code to apply the cross-layer-scaling technique to a model
+    """
+
+    @staticmethod
+    def scale_cls_set_with_conv_layers(
+            cls_set: typing.Tuple[tf.keras.layers.Conv2D, tf.keras.layers.Conv2D]) -> np.ndarray:
+        """
+        API to invoke equalize layer params (update for weights and bias is in place)
+        :param cls_set: Consecutive Conv layers Tuple whose weights and biases need to be equalized
+        :return: Scaling factor S_12 for each conv layer pair: numpy array
+        """
+
+        for layer in cls_set:
+            # NOTE: DepthwiseConv2D and Conv2DTranspose is subclass of Conv2D
+            #   The check below covers all of Conv2D, DepthwiseConv2D and Conv2DTranspose class
+            if not isinstance(layer, tf.keras.layers.Conv2D):
+                raise ValueError("Only Conv or Transposed Conv layers are supported for CLE")
+
+        scaling_factor, prev_layer_params, curr_layer_params = CrossLayerScaling.call_mo_scale(cls_set)
+
+        prev_layer, curr_layer = cls_set
+        weight_and_bias_0 = CrossLayerScaling._unpack_equalization_params(prev_layer, prev_layer_params,
+                                                                          unpack_bias=True)
+        prev_layer.set_weights(weight_and_bias_0)
+
+        weight_and_bias_1 = CrossLayerScaling._unpack_equalization_params(curr_layer, curr_layer_params,
+                                                                          unpack_bias=False)
+        curr_layer.set_weights(weight_and_bias_1)
+
+        return scaling_factor
+
+    @staticmethod
+    def call_mo_scale(cls_set: typing.Tuple[tf.keras.layers.Conv2D, tf.keras.layers.Conv2D]) \
+            -> typing.Tuple[np.ndarray, libpymo.EqualizationParams, libpymo.EqualizationParams]:
+        """
+        Invokes scale API in model optimization library
+        :param cls_set: Consecutive Conv layers Tuple whose weights and biases need to be equalized
+        :return: Scaling factor, prev and current layer updated parameters
+        """
+        prev_layer_params = CrossLayerScaling._pack_equalization_params(cls_set[0], pack_bias=True)
+        curr_layer_params = CrossLayerScaling._pack_equalization_params(cls_set[1], pack_bias=False)
+
+        scaling_factor = libpymo.scaleLayerParams(prev_layer_params, curr_layer_params)
+        return scaling_factor, prev_layer_params, curr_layer_params
+
+    @staticmethod
+    def scale_cls_set_with_depthwise_conv_layers(
+            cls_set: typing.Tuple[tf.keras.layers.Conv2D,
+                                  tf.keras.layers.DepthwiseConv2D,
+                                  tf.keras.layers.Conv2D]) -> typing.Tuple[np.ndarray, np.ndarray]:
+        """
+        API to invoke equalize layer params (update for weights and bias is in place)
+        :param cls_set: Consecutive Conv layers whose weights and biases need to be equalized.
+                        Second Conv layer is a depth-wise conv and third conv layer is point-wise conv
+        :return: Scaling factors S_12 and S_23 : numpy arrays
+        """
+
+        for layer in cls_set:
+            if not isinstance(layer, _supported_convs):
+                raise ValueError("Only Conv or Transposed Conv layers are supported for CLE")
+
+        scaling_params, prev_layer_params, curr_layer_params, next_layer_params = \
+            CrossLayerScaling.call_mo_scale_depthwise_separable_layer(cls_set)
+
+        prev_layer, curr_layer, next_layer = cls_set
+        weight_and_bias_0 = CrossLayerScaling._unpack_equalization_params(prev_layer,
+                                                                          prev_layer_params,
+                                                                          unpack_bias=True)
+        prev_layer.set_weights(weight_and_bias_0)
+
+        weight_and_bias_1 = CrossLayerScaling._unpack_equalization_params(curr_layer,
+                                                                          curr_layer_params,
+                                                                          unpack_bias=True)
+        curr_layer.set_weights(weight_and_bias_1)
+
+        weight_and_bias_2 = CrossLayerScaling._unpack_equalization_params(next_layer,
+                                                                          next_layer_params,
+                                                                          unpack_bias=False)
+        next_layer.set_weights(weight_and_bias_2)
+
+        return scaling_params.scalingMatrix12, scaling_params.scalingMatrix23
+
+    @staticmethod
+    def call_mo_scale_depthwise_separable_layer(
+            cls_set: typing.Tuple[tf.keras.layers.Conv2D,
+                                  tf.keras.layers.DepthwiseConv2D,
+                                  tf.keras.layers.Conv2D]) -> typing.Tuple[libpymo.RescalingParamsVectors,
+                                                                           libpymo.EqualizationParams,
+                                                                           libpymo.EqualizationParams,
+                                                                           libpymo.EqualizationParams]:
+        """
+        Invokes scale API in model optimization library
+        :param cls_set: Consecutive Conv layers whose weights and biases need to be equalized
+        :return: Scaling factors, prev, current and next layer updated parameters
+        """
+
+        prev_layer_params = CrossLayerScaling._pack_equalization_params(cls_set[0], pack_bias=True)
+        curr_layer_params = CrossLayerScaling._pack_equalization_params(cls_set[1], pack_bias=True)
+        next_layer_params = CrossLayerScaling._pack_equalization_params(cls_set[2], pack_bias=False)
+
+        scaling_params = libpymo.scaleDepthWiseSeparableLayer(prev_layer_params, curr_layer_params, next_layer_params)
+        return scaling_params, prev_layer_params, curr_layer_params, next_layer_params
+
+    @staticmethod
+    def _pack_equalization_params(layer: tf.keras.layers.Conv2D, pack_bias: bool) -> libpymo.EqualizationParams:
+        equalization_params = libpymo.EqualizationParams()
+
+        param_tensors = layer.get_weights()
+
+        weight_tensor = param_tensors[0]
+        weight_tensor = WeightTensorUtils.transpose_from_tf_to_libpymo_format(weight_tensor, layer)
+
+        equalization_params.weight = weight_tensor.reshape(-1)
+        equalization_params.weightShape = np.array(weight_tensor.shape)
+
+        if pack_bias:
+            if layer.use_bias:
+                equalization_params.bias = param_tensors[1]
+            else:
+                equalization_params.isBiasNone = True
+
+        return equalization_params
+
+    @staticmethod
+    def _unpack_equalization_params(layer: tf.keras.layers.Conv2D,
+                                    equalization_params: libpymo.EqualizationParams,
+                                    unpack_bias: bool) -> typing.List:
+
+        weight_tensor = np.reshape(equalization_params.weight, equalization_params.weightShape)
+        weight_tensor = WeightTensorUtils.transpose_from_libpymo_to_tf_format(weight_tensor, layer)
+
+        if layer.use_bias:
+            if unpack_bias:
+                bias_tensor = np.reshape(equalization_params.bias, equalization_params.weightShape[0])
+            else:
+                _, bias_tensor = layer.get_weights()
+
+            param_tensors = [weight_tensor, bias_tensor]
+        else:
+            param_tensors = [weight_tensor]
+
+        return param_tensors
+
+    @staticmethod
+    def scale_cls_sets(cls_sets: typing.List[ClsSet]) -> \
+            typing.List[typing.Union[np.ndarray, typing.Tuple[np.ndarray, np.ndarray]]]:
+        """
+        Scale each cls set
+        :param cls_sets: Cls sets to scale
+        :return: List of scale factors corresponding to each scaled cls set
+        """
+        scale_factor_list = []
+        for cls_set in cls_sets:
+            if len(cls_set) == 3:
+                scale_factor = CrossLayerScaling.scale_cls_set_with_depthwise_conv_layers(cls_set)
+            else:
+                scale_factor = CrossLayerScaling.scale_cls_set_with_conv_layers(cls_set)
+            scale_factor_list.append(scale_factor)
+        return scale_factor_list
+
+    @staticmethod
+    def create_cls_set_info_list(cls_sets: typing.List[ClsSet],
+                                 scale_factors: typing.List[ScaleFactor],
+                                 is_relu_activation_in_cls_sets: typing.List[ReluFlag]) -> typing.List[ClsSetInfo]:
+        """
+        Binds information from there separate lists into one [ClsInfoSet] data structure
+        :param cls_sets: List of CLS sets
+        :param scale_factors: List of scale factors for each cls set
+        :param is_relu_activation_in_cls_sets: List of ReLU flag whether there is ReLU activation in each cls set
+        :return: List of ClsSetInfo
+        """
+        assert len(cls_sets) == len(scale_factors) == len(is_relu_activation_in_cls_sets)
+
+        cls_set_info_list = []
+        for cls_set, scale_factor, has_relu_activation in zip(cls_sets,
+                                                              scale_factors,
+                                                              is_relu_activation_in_cls_sets):
+            # Depthwise separable convolution layer case (triplet of layers)
+            # Should have two scale factors and ReLU flags
+            if isinstance(scale_factor, tuple):
+                assert len(cls_set) == 3
+                assert len(scale_factor) == len(has_relu_activation) == 2
+
+                prev_layer, curr_layer, next_layer = cls_set
+                cls_pair_1 = ClsSetInfo.ClsSetLayerPairInfo(prev_layer, curr_layer,
+                                                            scale_factor[0], has_relu_activation[0])
+                cls_pair_2 = ClsSetInfo.ClsSetLayerPairInfo(curr_layer, next_layer,
+                                                            scale_factor[1], has_relu_activation[1])
+                cls_set_info = ClsSetInfo(cls_pair_1, cls_pair_2)
+
+            # Standard convolution layer case (tuple of layers)
+            # Should have one scale factor and ReLU flag
+            else:
+                prev_layer, curr_layer = cls_set
+                cls_pair = ClsSetInfo.ClsSetLayerPairInfo(prev_layer, curr_layer,
+                                                          scale_factor, has_relu_activation)
+                cls_set_info = ClsSetInfo(cls_pair)
+
+            cls_set_info_list.append(cls_set_info)
+
+        return cls_set_info_list
+
+    @staticmethod
+    def scale_model(model: tf.keras.Model) -> typing.List[ClsSetInfo]:
+        """
+        Uses cross-layer scaling to scale all applicable layers in the given model
+        :param model: tf.keras.Model
+        :return: CLS information for each CLS set
+        """
+
+        # Find layer groups
+        graph_search_util = GraphSearchUtils(model)
+        layer_groups = graph_search_util.find_layer_groups_to_scale()
+
+        # Find cls sets from the layer groups
+        cls_sets = []
+        for layer_group in layer_groups:
+            cls_set = GraphSearchUtils.convert_layer_group_to_cls_sets(layer_group)
+            cls_sets += cls_set
+
+        # Scale the CLS sets
+        scale_factors = CrossLayerScaling.scale_cls_sets(cls_sets)
+
+        # Find if there were ReLU activations between layers of each cls set
+        is_relu_activation_in_cls_sets = graph_search_util.is_relu_activation_present_in_cls_sets(cls_sets)
+
+        # Convert to a list of cls set info elements
+        cls_set_info_list = CrossLayerScaling.create_cls_set_info_list(cls_sets,
+                                                                       scale_factors,
+                                                                       is_relu_activation_in_cls_sets)
+
+        return cls_set_info_list
+
+
+class HighBiasFold:
+    """
+    Code to apply the high-bias-fold technique to a model
+    """
+
+    @staticmethod
+    def bias_fold(cls_set_info_list: typing.List[ClsSetInfo],
+                  bn_layers: typing.Dict[tf.keras.layers.Conv2D, tf.keras.layers.BatchNormalization]):
+        """
+        Folds bias values greater than 3 * sigma to next layer's bias
+        :param cls_set_info_list: List of info elements for each cls set
+        :param bn_layers: Key: Conv/Linear layer Value: Corresponding folded BN layer
+        """
+        if not bn_layers:
+            _logger.info('High Bias folding is not supported for models without BatchNorm Layers')
+            return
+
+        for cls_set_info in cls_set_info_list:
+            for cls_pair_info in cls_set_info.cls_pair_info_list:
+                if (not cls_pair_info.layer1.use_bias) or (not cls_pair_info.layer2.use_bias) or \
+                        (cls_pair_info.layer1 not in bn_layers):
+                    continue
+
+                prev_layer_params, curr_layer_params = HighBiasFold.call_mo_high_bias_fold(cls_pair_info, bn_layers)
+
+                layer1 = cls_pair_info.layer1
+                layer1_weight_tensor, _ = layer1.get_weights()
+                layer1_bias_tensor = np.array(prev_layer_params.bias)
+                layer1.set_weights([layer1_weight_tensor, layer1_bias_tensor])
+
+                layer2 = cls_pair_info.layer2
+                layer2_weight_tensor, _ = layer2.get_weights()
+                layer2_bias_tensor = np.array(curr_layer_params.bias)
+                layer2.set_weights([layer2_weight_tensor, layer2_bias_tensor])
+
+    @staticmethod
+    def call_mo_high_bias_fold(cls_pair_info: ClsSetInfo.ClsSetLayerPairInfo,
+                               bn_layers: typing.Dict[tf.keras.layers.Conv2D, tf.keras.layers.BatchNormalization]) \
+            -> typing.Tuple[libpymo.LayerParams, libpymo.LayerParams]:
+        """
+        Invokes high bias fold MO API
+        :param cls_pair_info: Pair of layers that were scaled using CLS and related information
+        :param bn_layers: Key: Conv/Linear layer Value: Corresponding folded BN layer
+        :return: Updated layer params
+        """
+
+        bn_layer = bn_layers[cls_pair_info.layer1]
+        prev_layer_bn_params = HighBiasFold._pack_bn_params_high_bias_fold(bn_layer, cls_pair_info.scale_factor)
+
+        prev_layer_params, curr_layer_params = HighBiasFold._pack_layer_params(cls_pair_info)
+
+        libpymo.updateBias(prev_layer_params, curr_layer_params, prev_layer_bn_params)
+        return prev_layer_params, curr_layer_params
+
+    @staticmethod
+    def _pack_bn_params_high_bias_fold(bn_layer: tf.keras.layers.BatchNormalization,
+                                       scaling_parameter: np.ndarray) -> libpymo.BNParamsHighBiasFold:
+        """
+        Helper method to pack BatchNormalization parameter for high bias fold
+        :param bn_layer: Target batch normalization layer
+        :param scaling_parameter: Scaling parameters for each channel obtained from cross layer scaling
+        :return: Packed BNParamsHighBiasFold
+        """
+        bn_params = libpymo.BNParamsHighBiasFold()
+
+        # Note: In BatchNormFold, we initialize gamma and beta to 1 and 0 respectively to work as Identity
+        # So if the original value was set, use it for High Bias Fold
+        if hasattr(bn_layer, "original_gamma") and hasattr(bn_layer, "original_beta"):
+            gamma, beta = bn_layer.original_gamma, bn_layer.original_beta
+        else:
+            gamma, beta, _, _ = bn_layer.get_weights()
+
+        if len(scaling_parameter) != len(gamma) or len(scaling_parameter) != len(beta):
+            raise ValueError("High Bias absorption is not supported for networks with fold-forward BatchNorms")
+
+        bn_params.gamma = np.divide(gamma, scaling_parameter)
+        bn_params.beta = np.divide(beta, scaling_parameter)
+
+        return bn_params
+
+    @staticmethod
+    def _pack_layer_params(cls_pair_info: ClsSetInfo.ClsSetLayerPairInfo) \
+            -> typing.Tuple[libpymo.LayerParams, libpymo.LayerParams]:
+        """
+        Helper method to pack information of previous and current layer
+        :param cls_pair_info: Pair of layers that were scaled using CLS and related information
+        :return: Packed layer parameter tuple
+        """
+        # Pack parameters for previous layer
+        prev_layer_params = libpymo.LayerParams()
+
+        prev_layer = cls_pair_info.layer1
+        prev_layer_params.activationIsRelu = cls_pair_info.relu_activation_between_layers
+
+        _, prev_layer_bias_tensor = prev_layer.get_weights()
+        prev_layer_params.bias = prev_layer_bias_tensor
+
+        # Pack parameters for current layer
+        curr_layer_params = libpymo.LayerParams()
+
+        curr_layer = cls_pair_info.layer2
+        curr_layer_weight_tensor, curr_layer_bias_tensor = curr_layer.get_weights()
+        curr_layer_weight_tensor = WeightTensorUtils.transpose_from_tf_to_libpymo_format(curr_layer_weight_tensor,
+                                                                                         curr_layer)
+
+        curr_layer_params.bias = curr_layer_bias_tensor
+        curr_layer_params.weight = curr_layer_weight_tensor.reshape(-1)
+        curr_layer_params.weightShape = np.array(curr_layer_weight_tensor.shape)
+
+        return prev_layer_params, curr_layer_params
+
+
+
    
+[docs]
+def equalize_model(model: tf.keras.Model) -> tf.keras.Model:
+    """
+    High-level API to perform Cross-Layer Equalization (CLE) on the given model
+
+    :param model: tf.keras.Model
+    :return: CLE applied tf.keras.Model
+    """
+    # replace any ReLU6 layers with ReLU
+    model_for_cle, _ = model_transform_utils.replace_relu6_with_relu(model)
+
+    folded_pairs, model_for_cle = fold_all_batch_norms(model_for_cle)
+
+    equalize_bn_folded_model(model_for_cle, folded_pairs)
+
+    return model_for_cle
    
+
+
+
+def equalize_bn_folded_model(model: tf.keras.Model,
+                             folded_pairs: typing.List[BatchNormFoldedPair]):
+    """
+    Perform Cross-Layer Scaling (CLS) and High Bias Folding (HBF) on a batchnorm-folded model in-place
+    :param model: BatchNorm-folded model to equalize
+    :param folded_pairs: List of pairs of folded layers
+    """
+    bn_dict = {}
+    for conv_or_linear, bn in folded_pairs:
+        bn_dict[conv_or_linear] = bn
+
+    # perform cross-layer scaling on applicable layer sets
+    cls_set_info_list = CrossLayerScaling.scale_model(model)
+
+    # high-bias fold
+    HighBiasFold.bias_fold(cls_set_info_list, bn_dict)
+
    +
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    + + + + + + + + + \ No newline at end of file diff --git a/releases/2.0.0/_modules/aimet_tensorflow/keras/defs.html b/releases/2.0.0/_modules/aimet_tensorflow/keras/defs.html new file mode 100644 index 0000000..a8435f0 --- /dev/null +++ b/releases/2.0.0/_modules/aimet_tensorflow/keras/defs.html @@ -0,0 +1,462 @@ + + + + + + + + aimet_tensorflow.keras.defs - AIMET + + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    +

    Source code for aimet_tensorflow.keras.defs

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2018-2023, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+
+""" Common type definitions that are used across aimet """
+
+from enum import Enum
+from typing import List, Optional, Union
+import tensorflow as tf
+
+from aimet_common.defs import GreedySelectionParameters
+
+
+# Ways to handle getting number of channels from axes. Default is to get it from the last dimension. For depthwise
+# conv2d, it will be obtained from the last two dimensions.
+class AxisHandling(Enum):
+    """
+    Enum for axis handling used as input variable to QcQuantizePerChannelOp. This defines how to interpret the
+    number of output channels from the weight dimensions.
+    """
+    LAST_AXIS = 0
+    LAST_TWO_AXES = 1
+
+
+class ModuleCompRatioPair:
+    """
+    Pair of tf.Operation and a compression-ratio
+    :ivar module: Module of type tf.Operation
+    :ivar comp_ratio: Compression ratio. Compression ratio is the ratio of cost of compressed model
+            to cost of the original model.
+    """
+
+    def __init__(self, module: tf.Operation, comp_ratio: float):
+        self.module = module
+        self.comp_ratio = comp_ratio
+
+
+
    
+[docs]
+class SpatialSvdParameters:
+    """ Configuration parameters for spatial svd compression """
+
+
    
+[docs]
+    class ManualModeParams:
+        """
+        Configuration parameters for manual-mode spatial svd compression
+        """
+
+        def __init__(self, list_of_module_comp_ratio_pairs: List[ModuleCompRatioPair]):
+            """
+            :param list_of_module_comp_ratio_pairs: List of (module, comp-ratio) pairs
+            """
+            self.list_of_module_comp_ratio_pairs = list_of_module_comp_ratio_pairs
    
+
+
+
    
+[docs]
+    class AutoModeParams:
+        """
+        Configuration parameters for auto-mode compression
+        """
+
+        def __init__(self, greedy_select_params: GreedySelectionParameters,
+                     modules_to_ignore: Optional[List[tf.Operation]] = None):
+            """
+            :param greedy_select_params: Params for greedy comp-ratio selection algorithm
+            :param modules_to_ignore: List of modules to ignore (None indicates nothing to ignore)
+            """
+            self.greedy_params = greedy_select_params
+            self.modules_to_ignore = [] if modules_to_ignore is None else modules_to_ignore
    
+
+
+
    
+[docs]
+    class Mode(Enum):
+        """ Mode enumeration """
+
+        manual = 1
+        """ Manual mode """
+
+        auto = 2
+        """ Auto mode """
    
+
+
+    def __init__(self, input_op_names: List[str], output_op_names: List[str], mode: Mode,
+                 params: Union[ManualModeParams, AutoModeParams], multiplicity=1):
+        """
+        :param input_op_names: list of input op names to the model
+        :param output_op_names: List of output op names of the model
+        :param mode: Either auto mode or manual mode
+        :param params: Parameters for the mode selected
+        :param multiplicity: The multiplicity to which ranks/input channels will get rounded. Default: 1
+        """
+        self.input_op_names = input_op_names
+        self.output_op_names = output_op_names
+        self.mode = mode
+        self.mode_params = params
+        self.multiplicity = multiplicity
    
+
+
    +
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    + +
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    +
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    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
    + Made with Sphinx and @pradyunsg's + + Furo + +
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    + + + + + + + + + \ No newline at end of file diff --git a/releases/2.0.0/_modules/aimet_tensorflow/keras/layer_output_utils.html b/releases/2.0.0/_modules/aimet_tensorflow/keras/layer_output_utils.html new file mode 100644 index 0000000..72cf796 --- /dev/null +++ b/releases/2.0.0/_modules/aimet_tensorflow/keras/layer_output_utils.html @@ -0,0 +1,493 @@ + + + + + + + + aimet_tensorflow.keras.layer_output_utils - AIMET + + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    +

    Source code for aimet_tensorflow.keras.layer_output_utils

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2023, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+
+""" This module contains utilities to capture and save intermediate layer-outputs of a model. """
+
+import os
+from typing import Union, List, Tuple
+import re
+from collections import OrderedDict
+import json
+import numpy as np
+import tensorflow as tf
+from aimet_tensorflow.keras.quantsim import QcQuantizeWrapper, QcQuantizableMultiHeadAttention
+from aimet_common.layer_output_utils import SaveInputOutput
+from aimet_common.utils import AimetLogger
+
+logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.LayerOutputs)
+
+
    
+[docs]
+class LayerOutputUtil:
+    """ Implementation to capture and save outputs of intermediate layers of a model (fp32/quantsim) """
+
+    def __init__(self, model: tf.keras.Model, save_dir: str = "./KerasLayerOutput"):
+        """
+        Constructor for LayerOutputUtil.
+
+        :param model: Keras (fp32/quantsim) model.
+        :param save_dir: Directory to save the layer outputs.
+        """
+        # Freeze the model weights and state
+        model.trainable = False
+
+        # Get intermediate model for layer-outputs
+        self.intermediate_model = self._get_intermediate_model(model)
+
+        # Get actual Layer output name to modified layer output name dict
+        self.original_name_to_modified_name_mapper = self._get_original_name_to_modified_name_mapper(model)
+
+        # Saving the actual layer output name to modified layer output name (valid file name to save) in a json file
+        os.makedirs(save_dir, exist_ok=True)
+        with open(os.path.join(save_dir, "LayerOutputNameMapper.json"), 'w', encoding='utf-8') as fp:
+            json.dump(self.original_name_to_modified_name_mapper, fp=fp, indent=4)
+
+        # Identify the axis-layout used for representing an image tensor
+        axis_layout = 'NHWC' if tf.keras.backend.image_data_format() == 'channels_last' else 'NCHW'
+
+        # Utility to save model inputs and their corresponding layer-outputs
+        self.save_inp_out_obj = SaveInputOutput(save_dir, axis_layout=axis_layout)
+
+    @classmethod
+    def _get_layer_output_name(cls, layer: Union[QcQuantizeWrapper, QcQuantizableMultiHeadAttention, tf.keras.layers.Layer]):
+        """
+        This function returns the actual layer output name for a given layer
+        :param layer: Keras model layer.
+        :return: Actual layer output name for the layer
+        """
+        if isinstance(layer, QcQuantizeWrapper):
+            return layer.original_layer.output.name
+        return layer.output.name
+
+    @classmethod
+    def _get_intermediate_model(cls, model: tf.keras.Model):
+        """
+        This function instantiates the feature extraction model for per layer outputs
+        :param model: Keras model.
+        :return: Intermediate keras model for feature extraction
+        """
+        outputs = [layer.output for layer in model.layers]
+        intermediate_model = tf.keras.models.Model(inputs=model.inputs, outputs=outputs)
+        intermediate_model.trainable = False
+        return intermediate_model
+
+    @classmethod
+    def _get_original_name_to_modified_name_mapper(cls, model: tf.keras.Model):
+        """
+        This function captures the per-layer output name and modifies it to make a valid file name
+        (by removing non-word characters) so that the layer output can be easily saved with the modified name.
+        :param model: Keras model.
+        :return: Actual layer name to modified layer name dict
+        """
+        original_name_to_modified_name_mapper = OrderedDict()
+        for layer in model.layers:
+            layer_output_name = cls._get_layer_output_name(layer)
+
+            # Replace all non-word characters with "_" to make it a valid file name for saving the results
+            # For Eg.: "conv2d/BiasAdd:0" gets converted to "conv2d_BiasAdd_0"
+            modified_layer_output_name = re.sub(r'\W+', "_", layer_output_name)
+
+            original_name_to_modified_name_mapper[layer_output_name] = modified_layer_output_name
+
+        return original_name_to_modified_name_mapper
+
+    def get_outputs(self, input_instance: Union[tf.Tensor, List[tf.Tensor], Tuple[tf.Tensor]]):
+        """
+        This function captures layer-outputs and renames them as per the AIMET exported model.
+        :param input_instance: Single input instance for which we want to obtain layer-outputs.
+        :return: layer-output name to layer-output dict
+        """
+        # Run in inference mode
+        outs = self.intermediate_model(input_instance, training=False)
+        output_pred = [out.numpy() for out in outs]
+
+        return dict(zip(self.original_name_to_modified_name_mapper.values(), output_pred))
+
+
    
+[docs]
+    def generate_layer_outputs(self, input_instance: Union[tf.Tensor, List[tf.Tensor], Tuple[tf.Tensor]]):
+        """
+        This method captures output of every layer of a model & saves the inputs and corresponding layer-outputs to disk.
+
+        :param input_instance: Single input instance for which layer output need to be generated
+        :return: None
+        """
+        layer_output_dict = self.get_outputs(input_instance)
+
+        # Skip constant scalar layer-outputs
+        const_scalar_layer_name = []
+        for layer_name, layer_output in layer_output_dict.items():
+            if not isinstance(layer_output, np.ndarray):
+                const_scalar_layer_name.append(layer_name)
+        for layer_name in const_scalar_layer_name:
+            logger.info("Skipping constant scalar output of layer %s", layer_name)
+            _ = layer_output_dict.pop(layer_name)
+
+        self.save_inp_out_obj.save(np.array(input_instance), layer_output_dict)
+
+        logger.info("Layer outputs saved for input instance %d", self.save_inp_out_obj.input_cntr)
    
+
    
+
+
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    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
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    + + + + + + + + + \ No newline at end of file diff --git a/releases/2.0.0/_modules/aimet_tensorflow/keras/mixed_precision.html b/releases/2.0.0/_modules/aimet_tensorflow/keras/mixed_precision.html new file mode 100644 index 0000000..c535b35 --- /dev/null +++ b/releases/2.0.0/_modules/aimet_tensorflow/keras/mixed_precision.html @@ -0,0 +1,520 @@ + + + + + + + + aimet_tensorflow.keras.mixed_precision - AIMET + + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    +

    Source code for aimet_tensorflow.keras.mixed_precision

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2024, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+
+""" Mixed precision inference """
+
+from typing import Union, Tuple, List, Callable
+
+from aimet_common.utils import AimetLogger
+from aimet_common.defs import CallbackFunc
+from aimet_common.amp.utils import (
+    visualize_quantizer_group_sensitivity,
+    visualize_pareto_curve,
+    CANDIDATE_WITH_DTYPE,
+    AMPSearchAlgo
+)
+
+from aimet_tensorflow.keras.quantsim import QuantizationSimModel
+from aimet_tensorflow.keras.amp.mixed_precision_algo import GreedyMixedPrecisionAlgo, EvalCallbackFactory
+from aimet_tensorflow.keras.amp.quantizer_groups import QuantizerGroup
+
+logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.MixedPrecision)
+
+
+# pylint: disable=too-many-arguments
+
    
+[docs]
+def choose_mixed_precision(sim: QuantizationSimModel, candidates: List[CANDIDATE_WITH_DTYPE],
+                           eval_callback_for_phase1: CallbackFunc, eval_callback_for_phase2: CallbackFunc,
+                           allowed_accuracy_drop: Union[float, None], results_dir: str,
+                           clean_start: bool, forward_pass_callback: CallbackFunc,
+                           amp_search_algo: AMPSearchAlgo = AMPSearchAlgo.Binary, phase1_optimize: bool = True) \
+        -> Union[List[Tuple[int, float, QuantizerGroup, int]], None]:
+    """
+    High-level API to perform in place Mixed Precision evaluation on the given sim model. A pareto list is created and
+    a curve for Accuracy vs BitOps is saved under the results directory
+
+    :param sim: Quantized sim model
+    :param input_shape: tuple or list of tuples of input shape to the model
+    :param starting_op_names: List of starting op names of the model
+    :param output_op_names: List of output op names of the model
+    :param candidates: List of tuples for all possible bitwidth values for activations and parameters
+                Suppose the possible combinations are-
+                ((Activation bitwidth - 8, Activation data type - int), (Parameter bitwidth - 16, parameter data type - int))
+                ((Activation bitwidth - 16, Activation data type - float), (Parameter bitwidth - 16, parameter data type - float))
+                candidates will be [((8, QuantizationDataType.int), (16, QuantizationDataType.int)),
+                                    ((16, QuantizationDataType.float), (16, QuantizationDataType.float))]
+    :param eval_callback_for_phase1: An object of CallbackFunc class which takes in Eval function (callable) and eval
+                                     function parameters. This evaluation callback used to measure sensitivity of each
+                                     quantizer group during phase 1. The phase 1 involves finding accuracy list/sensitivity of each
+                                     module. Therefore, a user might want to run the phase 1 with a smaller dataset
+    :param eval_callback_for_phase2: An object of CallbackFunc class which takes in Eval function (callable) and eval
+                                     function parameters. Evaluation callback used to get accuracy of quantized model
+                                     for phase 2 calculations. The phase 2 involves finding pareto front curve
+    :param allowed_accuracy_drop: Maximum allowed drop in accuracy from FP32 baseline. The pareto front curve is plotted only till the point where the allowable
+                                  accuracy drop is met. To get a complete plot for picking points on the curve, the user
+                                  can set the allowable accuracy drop to None.
+    :param results_dir: Path to save results and cache intermediate results
+    :param clean_start: If true, any cached information from previous runs will be deleted prior to starting the
+                        mixed-precision analysis. If false, prior cached information will be used if applicable. Note
+                        it is the user's responsibility to set this flag to true if anything in the model or
+                        quantization parameters changes compared to the previous run.
+    :param forward_pass_callback: An object of CallbackFunc class which takes in Forward pass function (callable) and its
+                                  function parameters. Forward pass callback used to compute quantization encodings
+    :param amp_search_algo: A valid value from the Enum AMPSearchAlgo. Defines the search algorithm to be used for
+                            the phase 2 of AMP. Default to BruteForce for regular AMP.
+    :param phase1_optimize: If user set this parameter to false then phase1 default logic will be executed else optimized logic will be executed.
+    :return: Pareto front list containing a list of (Relative bit ops wrt baseline candidate, eval score, quantizer group
+            and the candidate being used in each step). The Pareto front list can be used for plotting a pareto front
+            curve which provides information regarding how bit ops vary w.r.t. accuracy. If the allowable accuracy drop
+            is set to 100% then a user can use the pareto front curve to pick points and re-run,
+            None if we early exit the mixed precision algorithm.
+    """
+
+    mixed_precision_algo = GreedyMixedPrecisionAlgo(sim, candidates, eval_callback_for_phase1,
+                                                    eval_callback_for_phase2, results_dir,
+                                                    clean_start, forward_pass_callback, phase1_optimize=phase1_optimize)
+
+    return _run_amp(mixed_precision_algo, allowed_accuracy_drop, amp_search_algo, results_dir)
    
+
+
+
+# pylint: disable=too-many-arguments
+
    
+[docs]
+def choose_fast_mixed_precision(sim: QuantizationSimModel, candidates: List[CANDIDATE_WITH_DTYPE],
+                                data_loader_wrapper: Callable, eval_callback_for_phase2: CallbackFunc,
+                                allowed_accuracy_drop: Union[float, None], results_dir: str, clean_start: bool,
+                                forward_pass_callback: CallbackFunc, forward_pass_callback_2: Callable = None,
+                                amp_search_algo: AMPSearchAlgo = AMPSearchAlgo.Binary, phase1_optimize: bool = True) \
+        -> Union[List[Tuple[int, float, QuantizerGroup, int]], None]:
+    """
+    High-level API to perform in place Mixed Precision evaluation on the given sim model. A pareto list is created and
+    a curve for Accuracy vs BitOps is saved under the results directory
+
+    :param sim: Quantized sim model
+    :param candidates: List of tuples for all possible bitwidth values for activations and parameters
+                Suppose the possible combinations are-
+                ((Activation bitwidth - 8, Activation data type - int), (Parameter bitwidth - 16, parameter data type - int))
+                ((Activation bitwidth - 16, Activation data type - float), (Parameter bitwidth - 16, parameter data type - float))
+                candidates will be [((8, QuantizationDataType.int), (16, QuantizationDataType.int)),
+                                    ((16, QuantizationDataType.float), (16, QuantizationDataType.float))]
+    :param data_loader_wrapper: A Callable function which when called should return a dataloader to be used to do phase 1 forward pass.
+    :param eval_callback_for_phase2: An object of CallbackFunc class which takes in Eval function (callable) and eval
+                                     function parameters. Evaluation callback used to get accuracy of quantized model
+                                     for phase 2 calculations. The phase 2 involves finding pareto front curve
+    :param allowed_accuracy_drop: Maximum allowed drop in accuracy from FP32 baseline. The pareto front curve is plotted only till the point where the allowable
+                                  accuracy drop is met. To get a complete plot for picking points on the curve, the user
+                                  can set the allowable accuracy drop to None.
+    :param results_dir: Path to save results and cache intermediate results
+    :param clean_start: If true, any cached information from previous runs will be deleted prior to starting the
+                        mixed-precision analysis. If false, prior cached information will be used if applicable. Note
+                        it is the user's responsibility to set this flag to true if anything in the model or
+                        quantization parameters changes compared to the previous run.
+    :param forward_pass_callback: An object of CallbackFunc class which takes in Forward pass function (callable) and its
+                                  function parameters. Forward pass callback used to compute quantization encodings
+    :param forward_pass_callback_2: forward pass callback function which will take an input model and inputs and perform forward pass
+            on it and return the output nupy ndarray of the last layer. Can be kept None if the model works with the standard model.predict() forward pass
+    :param amp_search_algo: A valid value from the Enum AMPSearchAlgo. Defines the search algorithm to be used for
+                            the phase 2 of AMP. Default to Interpolation for fast AMP.
+    :param phase1_optimize: If user set this parameter to false then phase1 default logic will be executed else optimized logic will be executed.
+    :return: Pareto front list containing a list of (Relative bit ops wrt baseline candidate, eval score, quantizer group
+            and the candidate being used in each step). The Pareto front list can be used for plotting a pareto front
+            curve which provides information regarding how bit ops vary w.r.t. accuracy. If the allowable accuracy drop
+            is set to 100% then a user can use the pareto front curve to pick points and re-run,
+            None if we early exit the mixed precision algorithm.
+    """
+
+    # pylint: disable=protected-access
+    eval_callback_for_phase1 = EvalCallbackFactory(data_loader_wrapper, forward_pass_callback_2).sqnr(sim._model_without_wrappers)
+    mixed_precision_algo = GreedyMixedPrecisionAlgo(sim, candidates, eval_callback_for_phase1,
+                                                    eval_callback_for_phase2, results_dir,
+                                                    clean_start, forward_pass_callback, phase1_optimize=phase1_optimize)
+
+    return _run_amp(mixed_precision_algo, allowed_accuracy_drop, amp_search_algo, results_dir)
    
+
+
+
+def _run_amp(mixed_precision_algo: GreedyMixedPrecisionAlgo,
+             allowed_accuracy_drop: float,
+             amp_search_algo,
+             results_dir: str):
+    mixed_precision_algo.run(allowed_accuracy_drop, amp_search_algo)
+
+    if mixed_precision_algo.accuracy_list is not None and mixed_precision_algo.pareto_list is not None:
+        # Print mixed precision stats
+        logger.info(mixed_precision_algo)
+
+        # Visualize quantizer group sensitivity
+        visualize_quantizer_group_sensitivity(mixed_precision_algo.accuracy_list,
+                                              mixed_precision_algo.baseline_candidate,
+                                              mixed_precision_algo.fp32_accuracy,
+                                              results_dir=results_dir)
+        # Create pareto list curve
+        visualize_pareto_curve(mixed_precision_algo.pareto_list, results_dir)
+        return mixed_precision_algo.pareto_list
+
+    return None
+
    +
    +
    +
    + +
    + + +
    +
    +
    +
    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
    + Made with Sphinx and @pradyunsg's + + Furo + +
    +
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    + + + + + + +
    +
    +
    + +
    +
    + +
    +
    + + + + + + + + + \ No newline at end of file diff --git a/releases/2.0.0/_modules/aimet_tensorflow/keras/model_preparer.html b/releases/2.0.0/_modules/aimet_tensorflow/keras/model_preparer.html new file mode 100644 index 0000000..74d0cbc --- /dev/null +++ b/releases/2.0.0/_modules/aimet_tensorflow/keras/model_preparer.html @@ -0,0 +1,1302 @@ + + + + + + + + aimet_tensorflow.keras.model_preparer - AIMET + + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    +

    Source code for aimet_tensorflow.keras.model_preparer

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2022-2023, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+
+""" Implementation to automatically prepare keras models for AIMET by converting them to a functional model """
+import typing
+
+import inspect
+import logging
+from typing import Any, Dict, List, Set, Union, Optional
+import re
+import numpy as np
+import tensorflow as tf
+
+import tensorflow.keras.backend as K
+from packaging import version  # pylint: disable=wrong-import-order
+
+if version.parse(tf.version.VERSION) >= version.parse("2.10"):
+    # Ignore pylint errors as keras module is not available in TF 2.4
+    from keras.engine.base_layer_utils import is_subclassed  # pylint: disable=import-error
+    from keras.engine.functional import Functional  # pylint: disable=import-error
+    from keras.engine.keras_tensor import KerasTensor  # pylint: disable=import-error
+    from keras.layers.core.tf_op_layer import TFOpLambda  # pylint: disable=import-error
+    from keras.layers.merging.base_merge import _Merge as MergeLayersParentClass  # pylint: disable=import-error
+else:
+    # Ignore pylint errors due to conditional imports
+    from tensorflow.python.keras.engine.base_layer_utils import is_subclassed  # pylint: disable=ungrouped-imports
+    from tensorflow.python.keras.engine.keras_tensor import KerasTensor  # pylint: disable=ungrouped-imports
+    from tensorflow.python.keras.engine.functional import Functional  # pylint: disable=ungrouped-imports
+    from tensorflow.python.keras.layers.core import TFOpLambda  # pylint: disable=ungrouped-imports
+    # pylint: disable=ungrouped-imports
+    from tensorflow.python.keras.layers.merge import \
+        _Merge as MergeLayersParentClass
+
+# pylint: disable=wrong-import-position
+from aimet_tensorflow.keras.utils.model_connection_utils import ModelLayerConnections, ModelLayerConnectionsProperties
+from aimet_tensorflow.keras.utils.model_transform_utils import replace_separable_conv_with_depthwise_pointwise, \
+    replace_relu6_with_relu
+from aimet_common.utils import AimetLogger
+
+_logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.ModelPreparer)
+
+regex_for_camel_case_to_snake_case = re.compile(r'(?<!^)(?=[A-Z])')
+_TEMP_MODEL_NAME = "temp_aimet_intermediate_model"
+
+"""
+This file contains the implementation to automatically prepare keras models for AIMET by converting them to a functional model.
+"""
+
+
+class _KerasModelPreparer:
+
+    def __init__(
+        self,
+        original_model: Optional[tf.keras.Model] = None,
+        input_layer: Optional[tf.keras.layers.InputLayer] = None
+    ):
+        self.model_outputs = []  # Both normal init and "passthrough" init utilize this
+        if original_model:
+            self.input_layer = self._format_input_layer(original_model, input_layer)
+
+            if self._inherits_from_keras_model(original_model):
+                _logger.debug("This model inherits from tf.keras.Model. Need to connect model.")
+                self.original_model = self._connect_inherited_model(original_model, input_layer, is_original_model=True)
+
+            else:
+                self.original_model = original_model
+
+            # Used to fix weight names at end of unwrapping
+            # Originally set to the name of the original model's class in the case that there is an inherited model
+            self.class_names = self._get_class_names_in_model(self.original_model)
+
+            self.model_layers_connections = \
+                ModelLayerConnections.get_model_layers_connection_properties(self.original_model)
+            self._set_prepared_models_input_layer()
+
+            self.original_models_last_layer = self.original_model.layers[-1]
+
+            self.prepared_model = None
+            self.custom_objects = None
+            self.original_weights_in_prepared_model_order = None
+
+    @classmethod
+    def get_instance_for_common_layer_passthrough_functions(
+            cls, model_layers_connections: ModelLayerConnectionsProperties.TYPE
+    ):
+        """
+        Special function to __init__ for classes outside _KerasModelPreparer that want access to useful
+        functions like _handle_normal_keras_layer. ONLY use this for internal use. For normal Keras Model Preparer,
+        please utilize the prepare_model function.
+
+        :param model_layers_connections: Dictionary of Model Layer Connections for the functions to use.
+        :return: A slim instance of _KerasModelPreparer
+        """
+
+        self = cls(original_model=None, input_layer=None)
+        self.model_layers_connections = model_layers_connections
+        return self
+
+    def _get_original_models_weights_in_functional_model_order(self) -> List[np.ndarray]:
+        """
+        Map the original model's weights to the functional model's weights.
+
+        :return: A list of the original model's weights in the order of the functional model's weights
+        """
+        # Make the original model's weights into a dictionary for quick lookup by name
+        # The original subclassed layers names are removed to match the new functional model's names
+        original_model_weights = {}
+        for weight in self.original_model.weights:
+            # pop out class_names of weight name
+            weight_name = weight.name
+            for class_name in self.class_names:
+                weight_name = weight_name.replace(class_name + '/', '')
+            original_model_weights[weight_name] = weight.numpy()
+
+        # Get the functional model's weights in order as a dictionary for quick lookup where the key is the weight name
+        # and the position of the weight's order is the value
+        prepared_model_weight_order = {
+            weight.name: position
+            for position, weight in enumerate(self.prepared_model.weights)
+        }
+
+        # Using the functional model's weights order, get the original model's weights in the same order. The lambda
+        # here uses the weight's name to get position in the functional model's weights order and the sorts the
+        # original model's weights by that position.
+        self.original_weights_in_prepared_model_order = [
+            weight for _, weight in
+            sorted(original_model_weights.items(), key=lambda weight_info: prepared_model_weight_order[weight_info[0]])
+        ]
+
+        return self.original_weights_in_prepared_model_order
+
+    def _set_prepared_models_weights(self):
+        """
+        Set the functional model's weights to the original model's weights in the correct order
+        """
+
+        assert self.prepared_model, (
+            "The prepared model must be created before setting weights. Please call "
+                "prepare_model() before calling set_weights()."
+        )
+
+        try:
+            self.prepared_model.set_weights(self._get_original_models_weights_in_functional_model_order())
+        except ValueError:
+            _logger.error(
+                "Could not copy weights from original model to the prepared model. This can occur when "
+                "custom sublayers are defined not in the same order as the sublayers call method. Please ensure that "
+                "the sublayers internal layers are defined in the same order as the sublayers call method.")
+            raise
+
+        _logger.debug("Functional model weights copied.")
+        _logger.info("Model prepared for AIMET in Functional API format.")
+
+    @staticmethod
+    def _format_input_layer(
+            original_model: tf.keras.Model,
+            input_layer: Union[tf.keras.layers.InputLayer, List[tf.keras.layers.InputLayer], dict]
+    ) -> tf.keras.layers.Layer:
+        """
+        This function formats the input layer by either using the original models input layer or the user provided
+        input layer. This function will also raise an error if the model needs a defined input layer to be prepared
+        for AIMET.
+
+        :param original_model: The original model to be copied
+        :param input_layer: The input layer to be used for the functional model
+        :return: The input layer
+        """
+        if hasattr(original_model, "input"):
+            input_layer = original_model.input
+        elif isinstance(input_layer, tf.keras.layers.InputLayer):
+            _logger.info("Input layer explicitly passed in")
+            return input_layer
+        else:
+            _logger.info("Input layer not found. Using input layer passed in.")
+            if input_layer is None:
+                raise ValueError(
+                    "The top layer of this model is subclassed. Please provide an input layer via the "
+                    "\'input_layer\' parameter."
+                )
+
+        if isinstance(input_layer, dict):  # Keras allows passing in tensors via tensor_name : tensor
+            input_layer = list(input_layer.values())
+            if len(input_layer) == 1:
+                return input_layer[0]
+
+        return input_layer
+
+    @staticmethod
+    def _get_class_names_in_model(model: Union[tf.keras.Model, tf.keras.layers.Layer]) -> Set[str]:
+        """
+        Helper function to get the class name for a nested layer.
+
+        :param model: the 'layer' or 'model' to get the class name
+        :return: A set containing the class name
+        """
+        return {
+            regex_for_camel_case_to_snake_case.sub("_", name).lower()
+            for name in (model.name, model.__class__.__name__)
+        }
+
+    @staticmethod
+    def _is_nested_layer(layer: tf.keras.layers.Layer) -> bool:
+        """
+        Checks if the given layer is a nested layer.
+
+        :param layer: The layer to check
+        :return: True if the layer is a nested layer, False otherwise
+        """
+        keras_defined_subclassed_layer = is_subclassed(layer)
+        # pylint: disable=use-a-generator
+        is_aimet_defined_subclassed = keras_defined_subclassed_layer and any(
+            [isinstance(v, tf.keras.layers.Layer) for v in layer.__dict__.values()]
+        )  # check if the subclass is holding subclassed layer attributes. we only care if this is the case.
+
+        return (
+            is_aimet_defined_subclassed or
+            _KerasModelPreparer._is_functional_model(layer) or
+            _KerasModelPreparer._is_sequential_model(layer)
+        )
+
+    @staticmethod
+    def _is_functional_model(layer: tf.keras.layers.Layer) -> bool:
+        """
+        Checks if the given layer is a functional layer.
+
+        :param layer: The layer to check
+        :return: True if the layer is a functional layer, False otherwise
+        """
+        return isinstance(layer, Functional) and not isinstance(layer, tf.keras.Sequential)
+
+    @staticmethod
+    def _is_sequential_model(layer: tf.keras.layers.Layer) -> bool:
+        """
+        Checks if the given layer is a sequential layer.
+
+        :param layer: The layer to check
+        :return: True if the layer is a sequential layer, False otherwise
+        """
+        return isinstance(layer, tf.keras.Sequential)
+
+    def _set_prepared_models_input_layer(self):
+        """
+        This function sets the input layer of the model to the input layer of the functional model.
+        """
+
+        def set_input_layer_factory(input_layer: Union[tf.keras.layers.InputLayer, List[tf.keras.layers.InputLayer]]):
+            if isinstance(input_layer, list):
+                for inp in input_layer:
+                    self.model_layers_connections[ModelLayerConnectionsProperties.OUTPUT_TENSORS].update(
+                        {inp.name: inp}
+                    )
+            else:
+                self.model_layers_connections[ModelLayerConnectionsProperties.OUTPUT_TENSORS].update(
+                    {input_layer.name: input_layer}
+                )
+
+        try:
+            set_input_layer_factory(self.input_layer)
+        except AttributeError:
+            # For models that are not connected
+            _logger.info("Model is not connected. Setting input layer to input layer passed in.")
+
+            input_layer_name = [inp.name for inp in self.input_layer] if isinstance(self.input_layer, list) else \
+                [self.input_layer.name]
+            self.model_layers_connections[ModelLayerConnectionsProperties.INBOUND_NODES].update(
+                {self.original_model.layers[0].name: [*input_layer_name]}
+            )
+
+            set_input_layer_factory(self.input_layer)
+
+    def _get_layer_input(self, layer: tf.keras.layers.Layer) -> tf.keras.layers.Layer:
+        """
+        Helper function to get the input layer of a layer.
+
+        :param layer: The layer to get the input layer of
+        :return: The input layer of the layer
+        """
+        try:
+            layer_input = [
+                self.model_layers_connections[ModelLayerConnectionsProperties.OUTPUT_TENSORS][layer_aux]
+                for layer_aux in
+                self.model_layers_connections[ModelLayerConnectionsProperties.INBOUND_NODES][layer.name]
+            ]
+
+            if len(layer_input) == 1:
+                layer_input = layer_input[0]
+        except KeyError:
+            layer_input = self._get_most_recently_added_output_tensor()
+            self.model_layers_connections[ModelLayerConnectionsProperties.INBOUND_NODES].update(
+                {layer.name: [layer_input.name]}
+            )
+            _logger.warning(
+                "Could not find input tensor for layer: %s. Using %s as input, the most recent output tensor.",
+                layer.name, layer_input.name
+            )
+
+        return layer_input
+
+    @staticmethod
+    def _is_tf_or_keras_tensor_input(arg: Any) -> bool:
+        """
+        Helper function to check if a given argument is a valid Keras tensor.
+
+        :param arg: The argument in question
+        :return: True if it is valid, False if not
+        """
+        if arg is not None:
+            if isinstance(arg, List):
+                return all(isinstance(x, KerasTensor) for x in arg) or all(isinstance(x, tf.Tensor) for x in arg)
+            return isinstance(arg, (KerasTensor, tf.Tensor))
+        return False
+
+    def _get_updated_call_args(self, layer: tf.keras.layers.Layer) -> List[Union[KerasTensor, List[KerasTensor], Any]]:
+        """
+        Helper function to get the call arguments of a layer.
+
+        :param layer: The layer to get the call arguments of
+        :return: The call arguments of the layer
+        """
+
+        def _is_tf_tensor(arg_in_question: Any) -> bool:
+            return isinstance(arg_in_question, tf.Tensor)
+
+        try:
+            original_call_args = self.model_layers_connections[ModelLayerConnectionsProperties.CALL_ARGS][layer.name]
+        except KeyError:
+            _logger.warning("Could not find call args for layer: '%s'. Using keras tensor only as input.", layer.name)
+            return [self._get_layer_input(layer)]
+
+        updated_call_args = []
+        found_keras_tensor = False
+        for arg in original_call_args:
+            if self._is_tf_or_keras_tensor_input(arg):
+
+                if found_keras_tensor and _is_tf_tensor(arg):
+                    updated_call_args.append(arg)
+
+                elif not found_keras_tensor:
+                    layer_input = self._get_layer_input(layer)
+                    if isinstance(layer_input, List):
+                        updated_call_args.extend(layer_input)
+                    else:
+                        updated_call_args.append(layer_input)
+                    found_keras_tensor = True
+
+            else:
+                updated_call_args.append(arg)
+
+        assert found_keras_tensor, f"No keras tensor found in call args of layer {layer.name}"
+        return updated_call_args
+
+    def _get_call_kwargs(self, layer: tf.keras.layers.Layer) -> Dict[Union[KerasTensor, List[KerasTensor]], Any]:
+        """
+        Helper function to get call keyword arguments for a given layer.
+
+        :param layer: The layer to get the call keyword arguments of
+        :return: The call keyword arguments of the layer
+        """
+        if original_call_kwargs := \
+                self.model_layers_connections[ModelLayerConnectionsProperties.CALL_KWARGS][layer.name]:
+            call_kwargs = {}
+            for key, value in original_call_kwargs.items():
+                # The Keras tensor is already in the call args, so we don't need to add it again. call_kwargs are for
+                # keyword arguments that are not Keras tensors such as 'axis', 'training', etc.
+                if self._is_tf_or_keras_tensor_input(value):
+                    continue
+                call_kwargs[key] = value
+        else:
+            _logger.debug("No kwargs for layer: '%s'", layer.name)
+            return {}
+        return call_kwargs
+
+    def _update_nested_values(self, values: typing.List | typing.Tuple, old_to_new_tensor_mapper: typing.Dict):
+        """
+        Helper function to update the nested Lists/Tuples based on the dictionary provided
+        :param values: List/Tuple of values
+        :param old_to_new_tensor_mapper: Update dictionary
+        :return: Any
+        """
+        if isinstance(values, typing.Tuple):
+            return tuple(self._update_nested_values(x, old_to_new_tensor_mapper) for x in values)
+        if isinstance(values, typing.List):
+            return [self._update_nested_values(x, old_to_new_tensor_mapper) for x in values]
+        if isinstance(values, KerasTensor) and values.name in old_to_new_tensor_mapper:
+            return old_to_new_tensor_mapper[values.name]
+        return values
+
+    def _update_output_tensors_in_model_layers_connections(
+            self,
+            layer: tf.keras.layers.Layer,
+            new_output_tensor: KerasTensor,
+            model: tf.keras.Model
+    ):
+        """
+        Helper function to update the output tensors in the model layers connections dictionary.
+
+        :param layer: The layer to update the output tensors of
+        :param new_output_tensor: The new output tensor to update with
+        :param model: The model currently being checked. Used to add model outputs
+        """
+        # pylint: disable=too-many-nested-blocks, disable=protected-access, too-many-locals, too-many-branches
+        if isinstance(new_output_tensor, List):
+            # Handling case where layer has list of output tensors
+            old_tensors = self.model_layers_connections[ModelLayerConnectionsProperties.LAYER_OUTPUT_TENSOR_MAP][layer.name]
+            # Updating layer_output_tensor_map to new tensors
+            self.model_layers_connections[ModelLayerConnectionsProperties.LAYER_OUTPUT_TENSOR_MAP][layer.name] = [tensor.name for tensor in new_output_tensor]
+
+            old_to_new_tensor_mapper = {old_tensor_name: new_output_tensor[i] for i, old_tensor_name in
+                                        enumerate(old_tensors)}
+
+            old_name_of_inputs = None
+            if layer.name in self.model_layers_connections[ModelLayerConnectionsProperties.INBOUND_NODES]:
+                old_name_of_inputs = self.model_layers_connections[ModelLayerConnectionsProperties.INBOUND_NODES].pop(
+                    layer.name
+                )
+            for out_tensor in new_output_tensor:
+                new_name = out_tensor.name
+                if old_name_of_inputs is not None:
+                    self.model_layers_connections[ModelLayerConnectionsProperties.INBOUND_NODES].update(
+                        {new_name: old_name_of_inputs}
+                    )
+                self.model_layers_connections[ModelLayerConnectionsProperties.OUTPUT_TENSORS].update(
+                    {out_tensor.name: out_tensor}
+                )
+
+            # Update inbound_nodes with new tensor names
+            for node_name, values in self.model_layers_connections[ModelLayerConnectionsProperties.INBOUND_NODES].items():
+                if layer.name in values:
+                    # Update tensor for this entire layer
+                    tensor_list = self._flatten_list(self.model_layers_connections[ModelLayerConnectionsProperties.CALL_ARGS][node_name])
+                    tensor_list.extend(self._flatten_list(list(self.model_layers_connections[ModelLayerConnectionsProperties.CALL_KWARGS][node_name].values())))
+                    tensor_list = [tensor for tensor in tensor_list if isinstance(tensor, KerasTensor)]
+                    assert len(tensor_list) >= len(values), f"{node_name} has mismatched number of inbound nodes"
+                    for idx, value in enumerate(values):
+                        if value == layer.name and tensor_list[idx].name in old_to_new_tensor_mapper:
+                            values[idx] = old_to_new_tensor_mapper[tensor_list[idx].name].name
+
+            # Update call_kwargs with new tensors
+            for _, kwargs_dict in self.model_layers_connections[ModelLayerConnectionsProperties.CALL_KWARGS].items():
+                for key, values in kwargs_dict.items():
+                    kwargs_dict[key] = self._update_nested_values(values, old_to_new_tensor_mapper)
+
+            # Update call_args with new tensors
+            for key, args in self.model_layers_connections[ModelLayerConnectionsProperties.CALL_ARGS].items():
+                self.model_layers_connections[ModelLayerConnectionsProperties.CALL_ARGS][key] = self._update_nested_values(args, old_to_new_tensor_mapper)
+
+        elif layer.name != new_output_tensor.name:
+            new_name = new_output_tensor.name
+            old_name_of_inputs = self.model_layers_connections[ModelLayerConnectionsProperties.INBOUND_NODES].pop(
+                layer.name
+            )
+            self.model_layers_connections[ModelLayerConnectionsProperties.INBOUND_NODES].update(
+                {new_name: old_name_of_inputs}
+            )
+
+            # Replace values in model_layers_connections[NetworkDictProperties.INBOUND_NODES] with new_name
+            for value in self.model_layers_connections[ModelLayerConnectionsProperties.INBOUND_NODES].values():
+                if layer.name in value:
+                    idx = value.index(layer.name)
+                    value[idx] = new_name
+
+            # Update the kwargs dict in case there's keras tensor
+            # pylint: disable=protected-access
+            for _, kwargs_dict in self.model_layers_connections[ModelLayerConnectionsProperties.CALL_KWARGS].items():
+                for key, values in kwargs_dict.items():
+                    if isinstance(values, List):
+                        for i, value in enumerate(values):
+                            if isinstance(value, KerasTensor) and value._keras_history.layer.name == layer.name:
+                                values[i] = new_output_tensor
+                    elif isinstance(values, KerasTensor) and values._keras_history.layer.name == layer.name:
+                        kwargs_dict[key] = new_output_tensor
+
+
+            self.model_layers_connections[ModelLayerConnectionsProperties.OUTPUT_TENSORS].update(
+                {new_name: new_output_tensor}
+            )
+        else:
+            # Set new output tensor (in this case, it will be the same as the original model)
+            self.model_layers_connections[ModelLayerConnectionsProperties.OUTPUT_TENSORS].update(
+                {layer.name: new_output_tensor}
+            )
+
+        # Save tensor in output list if it is output in the initial model
+        # TODO: Update so that the last conditional is only checked when it's not the last layer.
+        if model.output_names and layer.name in model.output_names:
+            _logger.debug("Layer '%s' added as output layer", layer.name)
+            self.model_outputs.append(new_output_tensor)
+
+    def _get_most_recently_added_output_tensor(self) -> KerasTensor:
+        """
+        Helper function to get the most recently added output tensor from the model layers connections.
+
+        :return: The most recently added output tensor
+        """
+        return next(reversed(self.model_layers_connections[ModelLayerConnectionsProperties.OUTPUT_TENSORS].items()))[-1]
+
+    @staticmethod
+    def _get_temporary_model(layer: tf.keras.layers.Layer, layer_input: tf.keras.layers.Layer) -> tf.keras.Model:
+        """
+        Helper function to create a temporary functional model from a layer.
+
+        :param layer: The layer to create the temporary model from
+        :param layer_input: The input layer of the layer
+        :return: The temporary model
+        """
+
+        def verify_weights(original_layer_weights: Set[tf.Variable], temp_model_weights: Set[tf.Variable]):
+            if missing_weights := original_layer_weights.difference(temp_model_weights):
+                raise ValueError(f"""
+    The number of weights in the temporary model for unwrapping layer '{layer.name}' does not match the
+    number of weights of the original layer. The missing weight(s) are {missing_weights}. This occurs when the Keras 
+    Symbolic tensor passed into the layers call function does not interact with a layer defined inside of the nested 
+    layer. Please refer to the documentation for more information.
+
+    This is the call function that is causing this error:
+    {inspect.getsource(layer.call)}
+    """)
+
+        layer_input = layer_input if isinstance(layer_input, List) else [layer_input]
+        temp_inputs = [
+            tf.keras.layers.Input(shape=inp.shape[1:], name=inp.name.split(':')[0] + "_temp_input")
+            for inp in layer_input
+        ]
+        if len(temp_inputs) == 1:
+            temp_inputs = temp_inputs[0]
+
+        try:
+            if _KerasModelPreparer._inherits_from_keras_model(layer):
+                temp_model = _KerasModelPreparer._connect_inherited_model(layer, temp_inputs)
+            else:
+                temp_model = tf.keras.Model(inputs=temp_inputs,
+                                            outputs=layer.call(temp_inputs, training=False),
+                                            name=_TEMP_MODEL_NAME)
+            _logger.debug("Model created for layer '%s'", layer.name)
+        except TypeError as e:
+            if "call() got an unexpected keyword argument 'training'" in e.__str__():
+                _logger.error(
+                    "Model preparer calls subclassed layers call functions with the parameter 'training=False', "
+                    "in the case that the layer behaves differently during evaluation. Please add **kwargs to your "
+                    "call function for layer '%s.'",
+                    layer.name
+                )
+            raise
+
+        temp_model.summary(print_fn=_logger.debug)
+        verify_weights({w.name for w in layer.weights}, {w.name for w in temp_model.weights})
+
+        return temp_model
+
+    @staticmethod
+    def _update_temporary_model_layers_connections_inbound_nodes(
+            temp_model_model_layers_connections: ModelLayerConnectionsProperties.TYPE,
+            temp_model: tf.keras.Model,
+            layer_input: tf.keras.layers.Layer
+    ):
+        """
+        Helper function to update the inbound nodes of the temporary model layers connections dictionary.
+
+        :param temp_model_model_layers_connections: The temporary model layers connections dictionary
+        :param temp_model: The temporary model
+        :param layer_input: The input layer of the layer
+        """
+        temp_model_input_names = [inp.name for inp in temp_model.input] if isinstance(temp_model.input, List) else \
+            [temp_model.input.name]
+        layer_inputs_name = [
+            inp.name for inp in (layer_input if isinstance(layer_input, List) else [layer_input])
+        ]  # pylint: disable=superfluous-parens
+
+        for layers_name, input_tensor_name in temp_model_model_layers_connections[
+                ModelLayerConnectionsProperties.INBOUND_NODES].items():
+            for idx, current_input_name in enumerate(input_tensor_name):
+                if current_input_name in temp_model_input_names:
+                    if len(layer_inputs_name) == 1:  # Special case where the same input is feed in multiple times
+                        temp_model_model_layers_connections[ModelLayerConnectionsProperties.INBOUND_NODES][layers_name][
+                            idx] = layer_inputs_name[0]
+                    else:
+                        temp_model_model_layers_connections[ModelLayerConnectionsProperties.INBOUND_NODES][layers_name][
+                            idx] = layer_inputs_name[idx]
+
+    def _handle_nested_layer(self, layer: tf.keras.layers.Layer) -> KerasTensor:
+        """
+        Helper function to handle nested layers such as subclass, functional, or sequential.
+
+        :param layer: The layer to handle
+        :return: The output tensor of the layer
+        """
+        _logger.debug("Extracting layers for '%s'", layer.name)
+
+        # Converts CamelCase to snake_case of nested layers class name
+        self.class_names.update([layer.name] if self._inherits_from_keras_model(
+            layer) else self._get_class_names_in_model(layer))
+
+        # Create a model based on the nested layer.
+        # This is done with the layer input from the model layers connections dictionary.
+        # 1) The input layer is used to create the temporary functional model
+        # 2) The input layer is used in the nested layers call function as a symbolic tensor to get internal layers
+        layer_input = self._get_layer_input(layer)
+        temp_model = tf.keras.models.clone_model(self._get_temporary_model(layer, layer_input))
+
+        # Get the model layers connections dictionary for the temporary model and merge it with the model layers
+        # connections dictionary for the functional model. This is done, so we can keep track of the sublayer and
+        # their inputs and outputs.
+        temp_model_model_layers_connections = ModelLayerConnections.get_model_layers_connection_properties(temp_model)
+        self._update_temporary_model_layers_connections_inbound_nodes(
+            temp_model_model_layers_connections, temp_model, layer_input
+        )
+
+        self.model_layers_connections = ModelLayerConnections.merge_model_layers_connections(
+            self.model_layers_connections, temp_model_model_layers_connections
+        )
+
+        return self._prepare_model_helper(temp_model)
+
+    @staticmethod
+    def _get_keras_tensor_index(value: Any, search_list: List):
+        """
+        Helper function to check whether the value is a KerasTensor and return the index of it from the search_list
+        :param value: Value to search in the list
+        :param search_list: List to search
+        :return: Index of value in the search list
+        """
+        if not isinstance(value, KerasTensor):
+            return None
+        for idx, k_tensor in enumerate(search_list):
+            if isinstance(k_tensor, KerasTensor) and value.name == k_tensor.name:
+                return idx
+        return None
+
+    def _flatten_list(self, values: typing.List | typing.Tuple):
+        """
+        A helper function that returns flatten list of values in the given List or Tuple
+        :param values: List or Tuple of values
+        :return: List of flattened values
+        """
+        flat_vals = []
+        for val in values:
+            if isinstance(val, (typing.List, typing.Tuple)):
+                flat_vals.extend(self._flatten_list(val))
+            else:
+                flat_vals.append(val)
+        return flat_vals
+
+    def _handle_normal_keras_layer(self, layer: tf.keras.layers.Layer) -> KerasTensor:
+        """
+        Helper function to handle normal keras layers. This function will create a new output tensor for the layer
+        and return it.
+
+        :param layer: The layer to create the output tensor for
+        :return: The output tensor of the layer
+        """
+        # pylint: disable=too-many-branches, too-many-nested-blocks
+        call_args = self._get_updated_call_args(layer)
+
+        if isinstance(layer, TFOpLambda):
+            if call_kwargs := self._get_call_kwargs(layer):
+                # Special case for 'tf.concat' that takes a list of inputs with kwargs attached
+                # may need to updated in the future
+
+                # Remove keras tensor from call_args in case it is used in one of the keyword arguments
+                for _, values in call_kwargs.items():
+                    if not isinstance(values, List):
+                        values = [values]
+                    for value in values:
+                        keras_tensor_index = self._get_keras_tensor_index(value, call_args)
+                        if keras_tensor_index is not None:
+                            call_args.pop(keras_tensor_index)
+
+                if hasattr(layer, "function") and hasattr(layer.function, "__name__") and \
+                        layer.function.__name__ == "concat":
+                    new_output_tensor = layer.call([*call_args], **call_kwargs)
+                elif hasattr(layer, "function") and hasattr(layer.function, "__name__") and \
+                        layer.function.__name__ == "cast":
+                    # Handling the case for cast op where the dtype of input tensor and the cast op is same
+                    source_tensor = call_kwargs['x'] if 'x' in call_kwargs else call_args[0]
+                    source_dtype = source_tensor.dtype
+                    target_dtype = call_kwargs['dtype'] if 'dtype' in call_kwargs else call_args[1].dtype
+                    new_output_tensor = source_tensor if source_dtype == target_dtype else \
+                        layer.call(*call_args, **call_kwargs)
+                else:
+                    new_output_tensor = layer.call(*call_args, **call_kwargs)
+            else:
+                new_output_tensor = layer.call(*call_args)
+        # Special case for "Merge" layers that take a list of inputs such as "tf.keras.layers.Concatenate" and
+        # "tf.keras.layers.Add"
+        elif isinstance(layer, MergeLayersParentClass):
+            new_output_tensor = layer(call_args)
+        else:
+            new_output_tensor = layer(*call_args)
+
+        return new_output_tensor
+
+    def _prepare_model_helper(self, model: tf.keras.Model) -> KerasTensor:
+        """
+        Helper function to recursively prepare a model. This function will be recursively called if a nested layer is
+        found. This function will extract the layers from the nested layer and add them to the functional model.
+        Otherwise, it will add the layer to the functional model.
+
+        :param model: The model to prepare
+        :return: The last layer of the model
+        """
+        for current_layer in model.layers:
+            _logger.debug("Processing layer: '%s'", current_layer.name)
+            # Skip input layers
+            if isinstance(current_layer, tf.keras.layers.InputLayer):
+                continue
+
+            # If the current layer is either a subclassed layer, functional model or sequential model, we need to
+            # extract the layers from the nested layer and add them to the functional model.
+            if self._is_nested_layer(current_layer):
+                new_output_tensor = self._handle_nested_layer(current_layer)
+                # If we are at the end of the original model, we want the model_outputs to be the end model outputs
+                if current_layer == self.original_models_last_layer:
+                    _logger.debug(
+                        "Last layer was a nested layer. "
+                        "Using temp model's output from _handle_nested_layer as model_output"
+                    )
+                    continue
+                self.model_outputs.clear()
+            else:
+                new_output_tensor = self._handle_normal_keras_layer(current_layer)
+
+            self._update_output_tensors_in_model_layers_connections(current_layer, new_output_tensor, model)
+        return new_output_tensor
+
+    def prepare_model(self):
+        """
+        Function to get the prepared model. This function sets up the input layer and calls the helper function to
+        recursively prepare the model.
+        """
+        _ = self._prepare_model_helper(self.original_model)
+
+        # If the model outputs are empty, then we need to get the most recently added output tensor. This is the case
+        # when a model might be sparse and not fully connected or when a Functional model is inside an inherited model.
+        if not self.model_outputs:
+            _logger.warning(
+                "No model outputs found. This usually occurs when a models is made by inheriting from "
+                "'tf.keras.Model' and placing a Functional model inside. Using most recently added output tensor as "
+                "prepared models output."
+            )
+            self.model_outputs = self._get_most_recently_added_output_tensor()
+
+        setattr(self, "prepared_model", tf.keras.Model(
+            inputs=self.input_layer,
+            outputs=self.model_outputs,
+            name=f"{self.original_model.name}_prepared"
+        ))
+
+        # Cloning model to remove any references to the original model
+        K.clear_session()  # To avoid name conflicts
+        self.prepared_model = tf.keras.models.clone_model(self.prepared_model)
+        setattr(
+            self, "custom_objects",  # For acceptable subclass layers
+            self._get_models_custom_objects(self.prepared_model)
+        )
+        _logger.info("Prepared Model Summary: \n")
+        self.prepared_model.summary(print_fn=_logger.info)
+
+        # Copying over weights from original model to functional model
+        _logger.debug("Final class_names: %s", self.class_names)
+        self._set_prepared_models_weights()
+
+        # Extra prepare step to replace Separable Conv's with Depthwise Pointwise pattern.
+        self.prepared_model, _ = replace_separable_conv_with_depthwise_pointwise(
+            self.prepared_model,
+            custom_objects=self.custom_objects
+        )
+        self.prepared_model, _ = replace_relu6_with_relu(
+            self.prepared_model,
+            custom_objects=self.custom_objects
+        )
+
+        self.verify_prepared_model()
+
+    @staticmethod
+    def _get_models_custom_objects(model: tf.keras.Model) -> Optional[Dict[str, tf.keras.layers.Layer]]:
+        """
+        Helper function to return a models `custom_objects` if there are any present in the model.
+
+        :param model: The model to check
+        :return: A dictionary {layer name : layer obj} of the custom objects or None if there are not any
+        """
+
+        return {
+            layer.__class__.__name__: layer.__class__
+            for layer in model.layers
+            if not getattr(layer, "__module__", None).split(".")[0] == "keras" and  # TF 2.10.1 and up
+            not getattr(layer, "__module__", None).split(".")[0] == "tensorflow"    # TF 2.4.3 support
+        } or None
+
+    @staticmethod
+    def _model_has_nested_layers(model: tf.keras.Model) -> bool:
+        """
+        Helper function to check if a model is needed to be prepared or not based on if the model has nested layers such as
+        subclass, functional, or sequential.
+
+        :param model: The model to check
+        :return: If the model needs to be prepared or not
+        """
+        for layer in model.layers:
+            if _KerasModelPreparer._is_nested_layer(layer):
+                return True
+        return False
+
+    @staticmethod
+    def _inherits_from_keras_model(model: tf.keras.Model) -> bool:
+        """
+        Helper function to check if a model itself is inheriting from tf.keras.Model. If so, then the model needs to connected.
+
+        :param model: The model to check.
+        :return: If the model is inheriting from tf.keras.Model
+        """
+
+        return (
+            type(model).__bases__[0] == tf.keras.Model and
+            not _KerasModelPreparer._is_functional_model(model) and
+            not _KerasModelPreparer._is_sequential_model(model)
+        )
+
+    @staticmethod
+    def _connect_inherited_model(model: tf.keras.Model, input_layer: Union[
+            tf.keras.layers.InputLayer, List[tf.keras.layers.InputLayer]],
+                                 is_original_model: bool = False) -> tf.keras.Model:
+        """
+        Function to loop through models that inherit from tf.keras.Model and therefore could potentially have no
+        outbound nodes.
+
+        :param model: Model to connect.
+        :param input_layer: The input layer to connect the model.
+        :param is_original_model: Flag to clone the model if the original model is the one passed in.
+        This is to fix naming issues. Otherwise, the model is not cloned.
+        :return: A model with the outbound nodes generated.
+        """
+
+        # TODO: Fix case where the layers are all the same. Maybe user has to?
+        model = tf.keras.Model(inputs=input_layer, outputs=model.call(input_layer), name=_TEMP_MODEL_NAME)
+        if is_original_model:
+            try:
+                return tf.keras.models.clone_model(model)
+            except TypeError as e:
+                _logger.error("The layer %s inherits from tf.keras.Model and has layer that does not have a "
+                              "`get_config` defined. Due to this, Keras cannot clone this layer. Please override the "
+                              "`get_config` function and provide the missing keys mentioned in the Keras error logs.",
+                              model.name)
+                raise e
+        return model
+
+    def verify_prepared_model(self):
+        """
+        Function to verify that the prepared model is correct. This function will check that the prepared model has
+        the same weights as the original model and that the prepared model has the same outputs as the original model.
+        """
+
+        # Check that the prepared model has the same number of parameters as the original model
+        assert self.prepared_model.count_params() == self.original_model.count_params(), \
+            "Prepared model and original model do not have the same number of parameters"
+        _logger.debug("Prepared model and original model have the same number of parameters")
+
+        # Check the weights of the prepared model and the original model
+        for original_weight, prepared_weight in zip(
+                self.original_weights_in_prepared_model_order, self.prepared_model.get_weights()):
+            np.testing.assert_array_equal(
+                original_weight, prepared_weight,
+                err_msg="Weights of prepared model and original model do not match"
+            )
+        _logger.debug("Weights of prepared model and original model match")
+
+        # Create a random input to test the prepared model
+        if isinstance(self.prepared_model.input_shape, List):
+            random_input = []
+            for current_input_shape in self.original_model.input_shape:
+                input_shape = [shape if shape is not None else 1 for shape in current_input_shape]
+                random_input.append(np.random.rand(*input_shape).astype(np.float32))
+        else:
+            input_shape = [shape if shape is not None else 1 for shape in self.prepared_model.input_shape]
+            random_input = np.random.rand(*input_shape).astype(np.float32)
+
+        verbose = logging.DEBUG == _logger.level
+        original_model_output = self.original_model.predict(random_input, verbose=verbose)
+        prepared_model_output = self.prepared_model.predict(random_input, verbose=verbose)
+
+        # Check the outputs of the prepared model and the original model
+        err_msg = """
+        Outputs of prepared model and original model do not match. Since the weights match and params 
+        match, this is likely due to a mismatch in the model's architecture. Specifically, if there is a reuse of a 
+        layer, then the prepared model will not have the same output as the original model. For example, 
+        if a ReLU layer is defined once and then used twice, then the prepared model will only have one ReLU layer 
+        while the original model will have two ReLU layers. Please check the model's architecture to see if there are 
+        any layers that are reused.
+        """
+
+        if isinstance(original_model_output, Dict):
+            original_model_output = list(original_model_output.values())
+            if len(original_model_output) == 1:
+                original_model_output = original_model_output[0]
+
+        if isinstance(original_model_output, List):
+            for original_output, prepared_output in zip(original_model_output, prepared_model_output):
+                np.testing.assert_array_equal(original_output, prepared_output, err_msg=err_msg)
+        else:
+            np.testing.assert_array_equal(original_model_output, prepared_model_output, err_msg=err_msg)
+        _logger.debug("Outputs of prepared model and original model match")
+
+        _logger.info("Prepared model verified")
+
+
+
    
+[docs]
+def prepare_model(original_model: tf.keras.Model,
+                  input_layer: Union[tf.keras.layers.InputLayer, List[tf.keras.layers.InputLayer]] = None) \
+        -> tf.keras.Model:
+    """
+    This function prepares a Keras model before continuing on with AIMET. Specifically, it will convert the model into
+    a purely Functional API model and copy over the original models weights.
+
+    :param original_model: The original model to be prepared
+    :param input_layer: The input layer to be used for the new model. By default, the input layer is set to None. If the
+    beginning portion of the model is subclassed, then the input layer must be passed in.
+    :return: The prepared model if needed, or the original model
+    """
+
+    # Initial check to see if preparing model is necessary
+    # pylint: disable=protected-access
+    if not _KerasModelPreparer._model_has_nested_layers(original_model) and \
+            not _KerasModelPreparer._inherits_from_keras_model(original_model):
+        _logger.info("Model does not contain any nested layers. "
+                     "Returning original model after going through "
+                     "'replace_separable_conv_with_depthwise_pointwise' and 'replace_relu6_with_relu.")
+        custom_objects = _KerasModelPreparer._get_models_custom_objects(original_model)
+        prepared_model, _ = replace_relu6_with_relu(original_model, custom_objects=custom_objects)
+        prepared_model, _ = replace_separable_conv_with_depthwise_pointwise(prepared_model,
+                                                                            custom_objects=custom_objects)
+        return prepared_model
+
+    keras_model_preparer = _KerasModelPreparer(original_model, input_layer=input_layer)
+
+    keras_model_preparer.prepare_model()
+
+    return keras_model_preparer.prepared_model
    
+
+
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    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
    + Made with Sphinx and @pradyunsg's + + Furo + +
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    +

    Source code for aimet_tensorflow.keras.quant_analyzer

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2022-2023, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+"""Quant Analyzer"""
+import os
+from collections import OrderedDict, defaultdict
+from typing import Dict, List, Tuple
+
+import tensorflow as tf
+
+from aimet_common.defs import QuantScheme
+from aimet_common.quant_analyzer import export_per_layer_sensitivity_analysis_plot, save_json, \
+    create_and_export_min_max_ranges_plot, export_stats_histogram_plot, export_per_layer_mse_plot
+from aimet_common.utils import CallbackFunc, AimetLogger, Spinner
+from aimet_tensorflow.keras.batch_norm_fold import fold_all_batch_norms
+from aimet_tensorflow.keras.graphsearchtuils import GraphSearchUtils
+from aimet_tensorflow.keras.quant_sim.qc_quantize_wrapper import QcQuantizeWrapper
+from aimet_tensorflow.keras.quant_sim.tensor_quantizer import TensorQuantizer
+from aimet_tensorflow.keras.quantsim import QuantizationSimModel
+from aimet_tensorflow.keras.utils.quantizer_utils import get_enabled_activation_quantizers, enable_disable_quantizers, \
+    get_enabled_param_quantizers
+
+_logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.Quant)
+
+
+def _sort_quant_wrappers_based_on_occurrence(sim: QuantizationSimModel) -> Dict[str, QcQuantizeWrapper]:
+    """
+    Sort quant wrappers based on occurrence for given quantsim model.
+
+    :param sim: Quantsim model.
+    :return: Ordered dictionary which maps wrapped layer name to quant wrapper.
+    """
+    sorted_quant_wrappers_dict = OrderedDict()
+    for wrapper in sim.model.layers:
+        if not isinstance(wrapper, QcQuantizeWrapper):
+            continue
+
+        sorted_quant_wrappers_dict[wrapper.original_layer.name] = wrapper
+
+    return sorted_quant_wrappers_dict
+
+
+def _get_enabled_quantizers(sorted_quant_wrappers: Dict[str, QcQuantizeWrapper]) -> \
+        Dict[QcQuantizeWrapper, List[TensorQuantizer]]:
+    """
+    For given sorted quant wrappers dict, get enabled quantizers.
+
+    :param sorted_quant_wrappers: Dictionary containing quant wrappers sorted based on occurrence.
+    :return: Dictionary which maps a quant wrapper to a list of enabled quantizers in it.
+    """
+    enabled_quant_wrappers = defaultdict(list)
+
+    for quant_wrapper in sorted_quant_wrappers.values():
+        for quantizer in quant_wrapper.param_quantizers:
+            if quantizer.is_enabled():
+                enabled_quant_wrappers[quant_wrapper].append(quantizer)
+
+        for quantizer in quant_wrapper.output_quantizers:
+            if quantizer.is_enabled():
+                enabled_quant_wrappers[quant_wrapper].append(quantizer)
+
+        for quantizer in quant_wrapper.input_quantizers:
+            if quantizer.is_enabled():
+                enabled_quant_wrappers[quant_wrapper].append(quantizer)
+
+    return enabled_quant_wrappers
+
+
+def _get_output_of_intermediate_layer(model: tf.keras.Model,
+                                      input_tensor: tf.Tensor,
+                                      layer_index: int) -> tf.Tensor:
+    """
+    Return output tensor from model extracted up to target intermediate layer
+
+    :param model: tf.keras.Model
+    :param input_tensor: Input tensor to feed
+    :param layer_index: Index of layer
+    :return: Output tensor from intermediate layer
+    """
+    layer_output = model.get_layer(index=layer_index).output
+    extracted_model = tf.keras.Model(inputs=model.inputs, outputs=layer_output)
+
+    return extracted_model(input_tensor)
+
+
+
    
+[docs]
+class QuantAnalyzer:
+    """
+    QuantAnalyzer tool provides
+
+    1) model sensitivity to weight and activation quantization
+    2) per layer sensitivity analysis
+    3) per layer encoding (min - max range)
+    4) per PDF analysis and
+    5) per layer MSE analysis
+    """
+
+    def __init__(self,
+                 model: tf.keras.Model,
+                 forward_pass_callback: CallbackFunc,
+                 eval_callback: CallbackFunc):
+        """
+        :param model: FP32 model to analyze for quantization.
+        :param forward_pass_callback: A callback function for model calibration that simply runs
+                forward passes on the model to compute encoding (delta/offset). This
+                callback function should use representative data and should be subset of
+                entire train/validation dataset (~1000 images/samples).
+        :param eval_callback: A callback function for model evaluation that determines model
+                performance. This callback function is expected to return scalar value
+                representing the model performance evaluated against entire test/evaluation dataset.
+        """
+        if not isinstance(forward_pass_callback, CallbackFunc):
+            raise ValueError('forward_pass_callback and its argument(s) are not encapsulated by CallbackFunc class.')
+        if not isinstance(eval_callback, CallbackFunc):
+            raise ValueError('eval_callback and its argument(s) are not encapsulated by CallbackFunc class.')
+
+        self._model = model
+        self._forward_pass_callback = forward_pass_callback
+        self._eval_callback = eval_callback
+        self._unlabeled_dataset = None
+        self._num_batches = None
+
+    # pylint: disable=unused-argument, no-self-use
+
    
+[docs]
+    def analyze(self,
+                quant_scheme: QuantScheme = QuantScheme.post_training_tf_enhanced,
+                rounding_mode: str = "nearest",
+                default_param_bw: int = 8,
+                default_output_bw: int = 8,
+                config_file: str = None,
+                results_dir: str = "./tmp/"):
+        """
+        Analyze model for quantization and point out sensitive parts/hotspots of the model by performing
+            1) model sensitivity to quantization,
+            2) perform per layer sensitivity analysis by enabling and disabling quant wrappers,
+            3) export per layer encodings min - max ranges,
+            4) export per layer statistics histogram (PDF) when quant scheme is TF-Enhanced,
+            5) per layer MSE analysis
+
+        :param quant_scheme: Quantization scheme. Supported values are
+                QuantScheme.post_training_tf or QuantScheme.post_training_tf_enhanced.
+        :param rounding_mode: The round scheme to used. One of: 'nearest' or 'stochastic', defaults to 'nearest'
+        :param default_param_bw: Default bitwidth (4-31) to use for quantizing layer parameters.
+        :param default_output_bw: Default bitwidth (4-31) to use for quantizing layer inputs and outputs.
+        :param config_file: Path to configuration file for model quantizers.
+        :param results_dir: Directory to save the results.
+        """
+        results_dir = os.path.abspath(results_dir)
+        os.makedirs(results_dir, exist_ok=True)
+
+        sim = self._create_quantsim_and_encodings(quant_scheme,
+                                                  rounding_mode,
+                                                  default_param_bw,
+                                                  default_output_bw,
+                                                  config_file)
+
+        # Check model sensitivity to weight and activation quantization individually.
+        self.check_model_sensitivity_to_quantization(sim, default_param_bw, default_output_bw)
+
+        # Perform per layer analysis by enabling each quant wrapper (OPTION-1).
+        self.perform_per_layer_analysis_by_enabling_quant_wrappers(sim, results_dir)
+
+        # Perform per layer analysis by disabling each quant wrapper (OPTION-2).
+        self.perform_per_layer_analysis_by_disabling_quant_wrappers(sim, results_dir)
+
+        # Export encoding min-max range.
+        self.export_per_layer_encoding_min_max_range(sim, results_dir)
+
+        # Export PDF of statistics
+        if quant_scheme == QuantScheme.post_training_tf_enhanced:
+            self.export_per_layer_stats_histogram(sim, results_dir)
+
+        # Export per layer MSE loss between fp32 and quantized output activations.
+        if self._unlabeled_dataset and self._num_batches:
+            self.export_per_layer_mse_loss(sim, results_dir)
    
+
+
+    def _create_quantsim_and_encodings(self,
+                                       quant_scheme: QuantScheme,
+                                       rounding_mode: str,
+                                       default_param_bw: int,
+                                       default_output_bw: int,
+                                       config_file: str) -> QuantizationSimModel:
+        """
+        Create Quantsim and compute encodings.
+
+        :param quant_scheme: Quantization scheme.
+        :param rounding_mode: The round scheme to used. One of: 'nearest' or 'stochastic', defaults to 'nearest'
+        :param default_param_bw: Default bitwidth (4-31) to use for quantizing layer parameters.
+        :param default_output_bw: Default bitwidth (4-31) to use for quantizing layer inputs and outputs.
+        :param config_file: Path to configuration file for model quantizers.
+        :return: Quantsim model.
+        """
+        _, self._model = fold_all_batch_norms(self._model) # pylint: disable=attribute-defined-outside-init
+        sim = QuantizationSimModel(self._model,
+                                   quant_scheme=quant_scheme,
+                                   rounding_mode=rounding_mode,
+                                   default_output_bw=default_output_bw,
+                                   default_param_bw=default_param_bw,
+                                   config_file=config_file)
+
+        sim.compute_encodings(forward_pass_callback=self._forward_pass_callback.func,
+                              forward_pass_callback_args=self._forward_pass_callback.args)
+
+        return sim
+
+
    
+[docs]
+    def check_model_sensitivity_to_quantization(self,
+                                                sim: QuantizationSimModel,
+                                                default_param_bw: int,
+                                                default_output_bw: int):
+        """
+        Perform the sensitivity analysis to weight and activation quantization
+        individually.
+
+        :param sim: Quantsim model.
+        :param default_param_bw: Default bitwidth (4-31) to use for quantizing layer parameters.
+        :param default_output_bw: Default bitwidth (4-31) to use for quantizing layer inputs and outputs.
+        :return: FP32 eval score, weight-quantized eval score, act-quantized eval score.
+        """
+        fp32_eval_score = self._eval_model(self._model)
+        _logger.info("FP32 eval score (W32A32): %f", fp32_eval_score)
+
+        weight_quantized_eval_score = self._eval_weight_quantized_model(sim)
+        _logger.info("Weight-quantized eval score (W%dA32): %f", default_param_bw,
+                     weight_quantized_eval_score)
+
+        act_quantized_eval_score = self._eval_activation_quantized_model(sim)
+        _logger.info("Activation-quantized eval score (W32A%d): %f", default_output_bw,
+                     act_quantized_eval_score)
    
+
+
+    def _eval_model(self, model: tf.keras.Model) -> float:
+        """
+        Evaluate the model performance.
+        :param model: tf.keras.Model to be evaluated
+        :return: Scalar value representing model performance
+        """
+        return self._eval_callback.func(model, self._eval_callback.args)
+
+    def _eval_weight_quantized_model(self, sim: QuantizationSimModel) -> float:
+        """
+        Evaluate weight quantized model performance.
+        For weight quantized model performance, disable enabled activation quantizers, measure
+        eval score and enable again.
+
+        :param sim: Quantsim model.
+        :return: Quantized model performance.
+        """
+        enabled_activation_quantizers = get_enabled_activation_quantizers(sim)
+        enable_disable_quantizers(enabled_activation_quantizers, enabled=False)
+        eval_score = self._eval_model(sim.model)
+        enable_disable_quantizers(enabled_activation_quantizers, enabled=True)
+        return eval_score
+
+    def _eval_activation_quantized_model(self, sim: QuantizationSimModel) -> float:
+        """
+        Evaluate activation quantized model performance.
+        For activation quantized model performance, disable enabled param quantizers, measure
+        eval score and enable again.
+
+        :param sim: Quantsim model.
+        :return: Quantized model performance.
+        """
+        enabled_param_quantizers = get_enabled_param_quantizers(sim)
+        enable_disable_quantizers(enabled_param_quantizers, enabled=False)
+        eval_score = self._eval_model(sim.model)
+        enable_disable_quantizers(enabled_param_quantizers, enabled=True)
+        return eval_score
+
+
    
+[docs]
+    def perform_per_layer_analysis_by_enabling_quant_wrappers(self,
+                                                              sim: QuantizationSimModel,
+                                                              results_dir: str) -> Dict[str, float]:
+        """
+        NOTE: Option 1
+
+        1. All quant wrappers' parameters and activations quantizers are disabled.
+        2. For every quant wrappers, based on occurrence:
+            i. Each quant wrapper's parameters and activations quantizers are enabled as per JSON config file and set to bit-width specified.
+            ii. Measure and record eval score on subset of dataset.
+            iii. Disable enabled quantizers in step i.
+        3. Returns dictionary containing quant wrapper name and corresponding eval score.
+
+        :param sim: Quantsim model.
+        :param results_dir: Directory to save the results.
+        :return: layer-wise eval score dictionary. dict[layer_name] = eval_score
+        """
+        results_dir = os.path.abspath(results_dir)
+        os.makedirs(results_dir, exist_ok=True)
+
+        _logger.info("\nOPTION-1:\nAll the quant wrappers are disabled.\n"
+                     "Starting per-layer analysis by enabling quant wrappers as per config file.")
+
+        layer_wise_eval_score_dict = self._perform_per_layer_analysis(sim,
+                                                                      disable_all_quantizers=True,
+                                                                      enabled_before=True,
+                                                                      enabled_after=False)
+        export_per_layer_sensitivity_analysis_plot(layer_wise_eval_score_dict,
+                                                   results_dir,
+                                                   title="per_layer_quant_enabled")
+        save_json(layer_wise_eval_score_dict,
+                  results_dir,
+                  title="per_layer_quant_enabled.json")
+        _logger.info("Exported per-layer quant analysis (enabled) plot.")
+        return layer_wise_eval_score_dict
    
+
+
+
    
+[docs]
+    def perform_per_layer_analysis_by_disabling_quant_wrappers(self,
+                                                               sim: QuantizationSimModel,
+                                                               results_dir: str) -> Dict[str, float]:
+        """
+        NOTE: Option 2
+
+        1. All quant wrappers' parameters and activations quantizers are enabled as per JSON config file and set to bit-width specified.
+        2. For every quant wrappers, based on occurrence:
+            i. Each quant wrapper's parameters and activations quantizers are disabled.
+            ii. Measure and record eval score on subset of dataset.
+            iii. Enable disabled quantizers in step i.
+        3. Returns dictionary containing quant wrapper name and corresponding eval score.
+
+        :param sim: Quantsim model.
+        :param results_dir: Directory to save the results.
+        :return: layer wise eval score dictionary. dict[layer_name] = eval_score
+        """
+        results_dir = os.path.abspath(results_dir)
+        os.makedirs(results_dir, exist_ok=True)
+
+        _logger.info("\nOPTION-2:\nAll the quant wrappers are enabled as per config file.\n"
+                     "Starting per-layer analysis by disabling quant wrappers.")
+        layer_wise_eval_score_dict = self._perform_per_layer_analysis(sim,
+                                                                      disable_all_quantizers=False,
+                                                                      enabled_before=False,
+                                                                      enabled_after=True)
+        export_per_layer_sensitivity_analysis_plot(layer_wise_eval_score_dict,
+                                                   results_dir,
+                                                   title="per_layer_quant_disabled")
+        save_json(layer_wise_eval_score_dict,
+                  results_dir,
+                  title="per_layer_quant_disabled.json")
+        _logger.info("Exported per-layer quant analysis (disabled) plot.")
+        return layer_wise_eval_score_dict
    
+
+
+    def _perform_per_layer_analysis(self,
+                                    sim: QuantizationSimModel,
+                                    disable_all_quantizers: bool,
+                                    enabled_before: bool,
+                                    enabled_after: bool) -> Dict[str, float]:
+        """
+        Helper function for perform_per_layer_analysis_by_enabling_quant_wrappers() and
+        perform_per_layer_analysis_by_disabling_quant_wrappers()
+
+        :param sim: Quantsim model.
+        :param disable_all_quantizers: Flag to disable all the quantizers before per-layer analysis.
+        :param enabled_before: Flag to set enabled for quantizers before computing encodings.
+        :param enabled_after: Flag to set enabled for quantizers after computing encodings.
+        :return: layer wise eval score dictionary. dict[layer_name] = eval_score.
+        """
+        # Sorted quant wrappers based on occurrence.
+        # maps wrapped module name to a quant wrapper.
+        sorted_quant_wrappers = _sort_quant_wrappers_based_on_occurrence(sim)
+
+        # quant wrappers and it's enabled quantizers.
+        # maps quant wrapper to a list of enabled quantizers in it.
+        enabled_quant_wrappers = _get_enabled_quantizers(sorted_quant_wrappers)
+
+        if disable_all_quantizers:
+            for enabled_quantizers in enabled_quant_wrappers.values():
+                enable_disable_quantizers(enabled_quantizers, enabled=False)
+
+        eval_score_dict = {}
+        for name, quant_wrapper in sorted_quant_wrappers.items():
+            if quant_wrapper in enabled_quant_wrappers:
+                enabled_quantizers = enabled_quant_wrappers[quant_wrapper]
+                enable_disable_quantizers(enabled_quantizers, enabled=enabled_before)
+
+                # Record eval score.
+                eval_score_dict[name] = self._eval_model(sim.model)
+                _logger.debug("For layer: %s, the eval score is: %f", name, eval_score_dict[name])
+
+                enable_disable_quantizers(enabled_quantizers, enabled=enabled_after)
+
+        if disable_all_quantizers:
+            for enabled_quantizers in enabled_quant_wrappers.values():
+                enable_disable_quantizers(enabled_quantizers, enabled=True)
+
+        return eval_score_dict
+
+    # pylint: disable=no-self-use
+
    
+[docs]
+    def export_per_layer_encoding_min_max_range(self,
+                                                sim: QuantizationSimModel,
+                                                results_dir: str) -> Tuple[Dict, Dict]:
+        """
+        Export encoding min and max range for all weights and activations. results_dir should have
+        html files in following format.
+
+        -results_dir
+            -activations.html
+            -weights.html
+
+        If per channel quantization(PCQ) is enabled then,
+
+        -results_dir
+            -activations.html
+            -{wrapped_module_name}_{param_name}.html
+
+        :param sim: Quantsim model.
+        :param results_dir: Directory to save the results.
+        :return: layer wise min-max range for weights and activations.
+        """
+        min_max_ranges_dir = os.path.join(results_dir, "min_max_ranges")
+
+        min_max_range_for_activations_dict = {}
+        min_max_range_for_weights_dict = {}
+        for quant_wrapper in sim.quant_wrappers():
+            wrapped_layer_name = quant_wrapper.original_layer.name
+
+            for index, quantizer in enumerate(quant_wrapper.input_quantizers):
+                if quantizer.is_enabled():
+                    name = f"{wrapped_layer_name}_input_{index}"
+                    min_max_range_for_activations_dict[name] = (quantizer.encoding.min, quantizer.encoding.max)
+
+            for index, quantizer in enumerate(quant_wrapper.output_quantizers):
+                if quantizer.is_enabled():
+                    name = f"{wrapped_layer_name}_output_{index}"
+                    min_max_range_for_activations_dict[name] = (quantizer.encoding.min, quantizer.encoding.max)
+
+            for quantizer in quant_wrapper.param_quantizers:
+                if quantizer.is_enabled():
+                    # Keras parameter name usually contains slash (/) and it can cause incorrect file path when saving
+                    #   Replace slash (/) with dash (-) to avoid it
+                    quantizer_name = quantizer.name.replace("/", "-")
+                    name = f"{wrapped_layer_name}_{quantizer_name}"
+
+                    if isinstance(quantizer.encoding, List): # per-channel
+                        per_channel_encodings = {}
+                        for index, encoding in enumerate(quantizer.encoding):
+                            per_channel_encodings[f"{name}_{index}"] = (encoding.min, encoding.max)
+                        min_max_range_for_weights_dict[name] = per_channel_encodings
+                    else: # per-tensor
+                        min_max_range_for_weights_dict[name] = (quantizer.encoding.min, quantizer.encoding.max)
+
+        create_and_export_min_max_ranges_plot(min_max_range_for_weights_dict,
+                                              min_max_ranges_dir,
+                                              title="weights")
+        create_and_export_min_max_ranges_plot(min_max_range_for_activations_dict,
+                                              min_max_ranges_dir,
+                                              title="activations")
+        save_json(min_max_range_for_weights_dict, min_max_ranges_dir, title="weights.json")
+        save_json(min_max_range_for_activations_dict, min_max_ranges_dir, title="activations.json")
+        _logger.info("Exported per layer encodings min-max ranges plot(s).")
+        return min_max_range_for_weights_dict, min_max_range_for_activations_dict
    
+
+
+
    
+[docs]
+    def export_per_layer_stats_histogram(self, sim: QuantizationSimModel, results_dir: str) -> None:
+        """
+        NOTE: Not to invoke when quantization scheme is not TF-Enhanced.
+
+        Export histogram that represents a PDF of collected statistics by a quantizer for every
+        quant wrapper. After invoking this API, results_dir should have html files in following
+        format for every quantizers of quant wrappers.
+
+        -results_dir
+            -activations_pdf
+                name_{input/output}_{index}.html
+            -weights_pdf
+                -name
+                    param_name_{channel_index}.html
+
+        :param sim: Quantsim model.
+        :param results_dir: Directory to save the results.
+        """
+        weights_pdf_dir = os.path.join(results_dir, "weights_pdf")
+        activations_pdf_dir = os.path.join(results_dir, "activations_pdf")
+
+        for quant_wrapper in sim.quant_wrappers():
+            wrapped_layer_name = quant_wrapper.original_layer.name
+
+            for index, quantizer in enumerate(quant_wrapper.input_quantizers):
+                if quantizer.encoding:
+                    self._create_and_export_stats_histogram_plot(quantizer, activations_pdf_dir,
+                                                                 title=f"{wrapped_layer_name}_input_q{index}")
+
+            for index, quantizer in enumerate(quant_wrapper.output_quantizers):
+                if quantizer.encoding:
+                    self._create_and_export_stats_histogram_plot(quantizer, activations_pdf_dir,
+                                                                 title=f"{wrapped_layer_name}_output_q{index}")
+
+            for quantizer in quant_wrapper.param_quantizers:
+                if quantizer.encoding:
+                    # Keras parameter name usually contains slash (/) and it can cause incorrect file path when saving
+                    #   Replace slash (/) with dash (-) to avoid it
+                    param_name = quantizer.name.replace("/", "-")
+                    self._create_and_export_stats_histogram_plot(quantizer,
+                                                                 os.path.join(weights_pdf_dir, wrapped_layer_name),
+                                                                 title=f"{wrapped_layer_name}_{param_name}")
+        _logger.info("Exported per layer stats histogram plot(s).")
    
+
+
+    @staticmethod
+    def _create_and_export_stats_histogram_plot(quantizer: TensorQuantizer,
+                                                results_dir: str,
+                                                title: str) -> None:
+        """
+        For given quantizer, create and export histogram (PDF) of statistics in html format.
+
+        :param quantizer: Quantizer.
+        :param results_dir: Directory to save the results.
+        :param title: Title of the plot.
+        """
+        os.makedirs(results_dir, exist_ok=True)
+
+        histograms = quantizer.get_stats_histogram()
+        encodings = quantizer.encoding
+        if not isinstance(encodings, List):
+            encodings = [encodings]
+
+        for index, (histogram, encoding) in enumerate(zip(histograms, encodings)):
+            export_stats_histogram_plot(histogram, encoding, results_dir, title=f"{title}_{index}")
+
+
    
+[docs]
+    def export_per_layer_mse_loss(self,
+                                  sim: QuantizationSimModel,
+                                  results_dir: str) -> Dict[str, float]:
+        """
+        NOTE: Need to pass same model input data through both fp32 and quantsim model to
+        tap output activations of each layer.
+
+        Export MSE loss between fp32 and quantized output activations for each layer.
+        :param sim: Quantsim model.
+        :param results_dir: Directory to save the results.
+        :return layer wise MSE loss. dict[layer_name] = MSE loss.
+        """
+        results_dir = os.path.abspath(results_dir)
+        os.makedirs(results_dir, exist_ok=True)
+
+        mse_loss_dict = {}
+        with Spinner("Calculating per-layer MSE loss"):
+            for index, layer in enumerate(self._model.layers):
+                if isinstance(layer, tf.keras.layers.InputLayer) or \
+                        GraphSearchUtils.is_folded_batch_normalization(layer):
+                    continue
+
+                loss = self._compute_mse_loss(sim, index)
+                mse_loss_dict[layer.name] = loss
+
+        export_per_layer_mse_plot(mse_loss_dict,
+                                  results_dir,
+                                  title="per_layer_mse_loss")
+        save_json(mse_loss_dict, results_dir, title="per_layer_mse_loss.json")
+        _logger.info("Exported per layer MSE loss plot.")
+        return mse_loss_dict
    
+
+
+    def _compute_mse_loss(self,
+                          sim: QuantizationSimModel,
+                          index: int) -> float:
+        """
+        Compute MSE loss between fp32 and quantized output activations for each batch, add for
+        all the batches and return averaged mse loss.
+
+        :param sim: Quantsim model.
+        :param index: Index of layer
+        :return: MSE loss between fp32 and quantized output activations.
+        """
+        loss = 0.0
+        total = 0
+        mse = tf.keras.losses.MeanSquaredError()
+        for tensor in self._unlabeled_dataset.take(self._num_batches):
+            quantized_output = _get_output_of_intermediate_layer(sim.model, tensor, index)
+            fp32_output = _get_output_of_intermediate_layer(self._model, tensor, index)
+
+            loss += mse(quantized_output, fp32_output).numpy()
+            total += tensor.shape[0]
+
+        return loss / total
+
+
    
+[docs]
+    def enable_per_layer_mse_loss(self, unlabeled_dataset: tf.data.Dataset, num_batches: int) -> None:
+        """
+        Enable per layer MSE loss analysis.
+
+        :param unlabeled_dataset: tf.data.Dataset provided as input to the model
+            and used to calculate mse loss
+        :param num_batches: Maximum number of batches to be used for MSE loss calculation
+        """
+        self._unlabeled_dataset = unlabeled_dataset
+        self._num_batches = num_batches
    
+
    
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    +

    Source code for aimet_tensorflow.keras.quantsim

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2022-2023, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+""" Quantsim for Keras """
+from __future__ import annotations
+
+import contextlib
+from dataclasses import dataclass
+import json
+import os
+from typing import Union, Dict, Tuple, Optional, List
+
+import tensorflow as tf
+from aimet_common import libpymo
+
+from aimet_common.defs import QuantScheme, QuantizationDataType
+from aimet_common.utils import AimetLogger, save_json_yaml
+from aimet_common import quantsim
+from aimet_common.quantsim import extract_global_quantizer_args
+from aimet_tensorflow.keras.connectedgraph import ConnectedGraph
+from aimet_tensorflow.keras.graphsearchtuils import GraphSearchUtils
+from aimet_tensorflow.keras.quant_sim.qc_quantize_wrapper import QcQuantizeWrapper, QuantizerSettings
+from aimet_tensorflow.keras.quant_sim.qc_mha_wrapper import QcQuantizableMultiHeadAttention
+from aimet_tensorflow.keras.rnn.qc_quant_LSTM import QuantizedLSTM
+from aimet_tensorflow.keras.quant_sim.tensor_quantizer import TensorQuantizer, ActivationTensorQuantizer, \
+    ParamPerTensorQuantizer, StaticGridPerChannelQuantizer, ParamPerChannelQuantizer
+from aimet_tensorflow.keras.quantsim_config.quantsim_config import QuantSimConfigurator, INPUT_QUANTIZERS, \
+    OUTPUT_QUANTIZERS, PARAM_QUANTIZERS
+from aimet_tensorflow.keras.utils.common import convert_h5_model_to_pb_model
+
+from aimet_tensorflow.keras.defs import AxisHandling
+import aimet_tensorflow.keras.utils.common as keras_common_utils
+
+_logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.Quant)
+
+unquantizable_modules = (tf.keras.layers.InputLayer, QcQuantizeWrapper)
+substitutable_modules = {
+    tf.keras.layers.MultiHeadAttention: QcQuantizableMultiHeadAttention,
+    tf.keras.layers.LSTM: QuantizedLSTM
+}
+
+
+@dataclass
+class QuantizationSimModelParams:
+    """
+    Data class that holds parameters for QuantizationSimModel. Used specifically to rebuild after converting to TF frozen pb
+    """
+    quant_scheme: Union[QuantScheme, str] = 'tf_enhanced'
+    rounding_mode: str = 'nearest'
+    default_output_bw: int = 8
+    default_param_bw: int = 8
+    in_place: bool = False
+    config_file: str = None
+    default_data_type: QuantizationDataType = QuantizationDataType.int
+
+
+# pylint: disable=too-many-ancestors
+# pylint: disable=too-many-instance-attributes
+
    
+[docs]
+class QuantizationSimModel(tf.keras.Model):
+    """
+    Implements mechanism to add quantization simulations ops to a model. This allows for off-target simulation of
+    inference accuracy. Also allows the model to be fine-tuned to counter the effects of quantization.
+    """
+
+    # pylint: disable=too-many-arguments
+    # pylint: disable=unused-argument
+    def __init__(self, model, quant_scheme: Union[QuantScheme, str] = 'tf_enhanced', rounding_mode: str = 'nearest',
+                 default_output_bw: int = 8, default_param_bw: int = 8, in_place: bool = False,
+                 config_file: str = None, default_data_type: QuantizationDataType = QuantizationDataType.int):
+        """
+        :param model: Model to quantize
+        :param quant_scheme: Quantization Scheme, currently supported schemes are post_training_tf and
+               post_training_tf_enhanced, defaults to post_training_tf_enhanced
+        :param rounding_mode: The round scheme to used. One of: 'nearest' or 'stochastic', defaults to 'nearest'.
+        :param default_output_bw: bitwidth to use for activation tensors, defaults to 8
+        :param default_param_bw: bitwidth to use for parameter tensors, defaults to 8
+        :param in_place: If True, then the given 'model' is modified in-place to add quant-sim nodes.
+                Only suggested use of this option is when the user wants to avoid creating a copy of the model
+        :param config_file: Path to a config file to use to specify rules for placing quant ops in the model
+        :param default_data_type: Default data type to use for quantizing all layer parameters.
+                                 Possible options are QuantizationDataType.int and QuantizationDataType.float.
+                                 Note that the mode default_data_type=QuantizationDataType.float is only supported with
+                                 default_output_bw=16 and default_param_bw=16
+        """
+        super().__init__()
+
+        self._model_without_wrappers = model
+        if not in_place:
+            self._model_without_wrappers = tf.keras.models.clone_model(model)
+            n_weights = len(self._model_without_wrappers.weights)
+            self._model_without_wrappers.set_weights(model.get_weights()[:n_weights])
+        self._layer_name_to_quant_wrapper = {}
+        self._substituted_layer = {}  # to hold the substituted layers
+        self._validate_model()
+        self.connected_graph = ConnectedGraph(self._model_without_wrappers)
+        self._quantsim_configurator = self._initialize_quantsim_configurator(quant_scheme, rounding_mode,
+                                                                             default_output_bw, default_param_bw,
+                                                                             default_data_type, config_file)
+        self.quant_scheme = quant_scheme
+        self._percentile_value = 100  # default percentile value
+        self.per_channel_quantization_enabled = self._quantsim_configurator.per_channel_quantization_flag
+        self.model = self._add_quantization_wrappers(quant_scheme, rounding_mode,
+                                                     default_output_bw, default_param_bw, default_data_type)
+        self.quant_args = extract_global_quantizer_args(quant_scheme, self._quantsim_configurator)
+
+        self._params = QuantizationSimModelParams(quant_scheme, rounding_mode, default_output_bw, default_param_bw,
+                                                  in_place, config_file, default_data_type)
+
+    def _validate_model(self):
+        """
+        Check that model is appropriate for quantsim.
+        """
+        multiple_inbound_node_layers = []
+
+        for layer in self._model_without_wrappers.layers:
+            if len(layer.inbound_nodes) > 1:
+                multiple_inbound_node_layers.append(layer.name)
+
+        if multiple_inbound_node_layers:
+            error_msg = (f'Layers with more than one inbound nodes are unsupported. This may occur if a layer is '
+                         f'reused multiple times in the model definition.\n'
+                         f'Layers with multiple inbound nodes: {multiple_inbound_node_layers}')
+            _logger.error(error_msg)
+            raise NotImplementedError(error_msg)
+
+        sep_conv_found = self.check_separable_conv(self._model_without_wrappers)
+        if sep_conv_found:
+            # Raising an assertion error incase there's SeparableConv2D in the model because in this case we have two sets of weights: Depthwise
+            # and Pointwise. For depthwise kernels, LAST TWO AXIS should be considered and for pointwise kernels LAST AXIS
+            # should be considered, which is not handled here. Running model preparer beforehand will resolve this as there the
+            # SeparableConv2D is splitted into two layers Depthwise and Pointwise seperately.
+            raise AssertionError("SeparableConv2D found in the model. Please run model preparer before calling QuantizationSimModel")
+
+    def check_separable_conv(self, model: tf.keras.models.Model | tf.keras.Sequential) -> bool:
+        """
+        Checks for SeparableConv2D layer in the model
+        :param model: Keras Model
+        :return: Boolean value, True if SeperableConv layer is found else False
+        """
+        for layer in model.layers:
+            if isinstance(layer, tf.keras.Sequential):
+                if self.check_separable_conv(layer):
+                    return True
+            elif isinstance(layer, tf.keras.layers.SeparableConv2D):
+                return True
+        return False
+
+    def _get_quantizer_list(self) -> Tuple[List, List, List]:
+        """
+        Method to provide a list of input, output and parameter quantizers
+        :return: Three lists containing input, paramater and output quantizers respectively
+        """
+        input_quantizers = []
+        parameter_quantizers = []
+        output_quantizers = []
+
+        for wrapper in self.quant_wrappers():
+            for quantizer in wrapper.input_quantizers:
+                input_quantizers.append(quantizer)
+
+            for quantizer in wrapper.param_quantizers:
+                parameter_quantizers.append(quantizer)
+
+            for quantizer in wrapper.output_quantizers:
+                output_quantizers.append(quantizer)
+
+        return input_quantizers, parameter_quantizers, output_quantizers
+
+    def set_percentile_value(self, percentile_value: float):
+        """
+        Set the percentile value to be used while computing encodings for quantizers having percentile quant scheme.
+
+        :param percentile_value: Percentile value to be set to
+        """
+        if percentile_value < 90 or percentile_value > 100:
+            raise ValueError("Percentile value must be in range [90, 100]")
+        self._percentile_value = percentile_value
+
+        # Set the percentile value to the activation quantizers
+        input_quantizers, _, output_quantizers = self._get_quantizer_list()
+        for quantizer in input_quantizers + output_quantizers:
+            if quantizer.quant_scheme == QuantScheme.post_training_percentile:
+                quantizer.set_percentile_value(self._percentile_value)
+
+    def _initialize_quantsim_configurator(self, quant_scheme: Union[QuantScheme, str], rounding_mode: str,
+                                          default_output_bw: int, default_param_bw: int,
+                                          default_data_type: QuantizationDataType = QuantizationDataType.int,
+                                          config_file: str = None) -> QuantSimConfigurator:
+        """
+        Initialize quantsim configurator
+        :param quant_scheme: Quantization Scheme
+        :param rounding_mode: The round scheme to used
+        :param default_output_bw: bitwidth to use for activation tensors
+        :param default_param_bw: bitwidth to use for parameter tensors
+        :param default_data_type: data type to use for the parameter tensors
+        :param config_file: Path to a config file to use to specify rules for placing quant ops in the model
+        :return: QuantSimConfigurator
+        """
+        return QuantSimConfigurator(self.connected_graph, quant_scheme, rounding_mode,
+                                    default_output_bw, default_param_bw, default_data_type, config_file)
+
+    def _add_quantization_wrappers(self, quant_scheme, rounding_mode,
+                                   default_output_bw, default_param_bw, default_data_type):
+        """
+        Add quantization wrappers to the model and return a new model with the wrappers inserted.
+        :param quant_scheme: Quantization scheme to use
+        :param rounding_mode: Rounding mode to use
+        :param default_output_bw: Default bitwidth for activation quantizers
+        :param default_param_bw: Default bitwidth for param quantizers
+        :param default_data_type: data type to use for param quantizers
+        """
+
+        def wrap_layer(layer) -> tf.keras.layers.Layer:
+            """
+            Function to wrap layers with QcQuantizeWrappers, used by keras clone_model()
+            :param layer: Layer to wrap
+            :return: Wrapped layer, or original layer if layer is not to be wrapped
+            """
+            if isinstance(layer, tuple(substitutable_modules.keys())):
+                new_class = substitutable_modules[type(layer)]
+                config = layer.get_config()
+                config["copy_source_weights"] = layer.get_weights()
+
+                if isinstance(layer, tf.keras.layers.LSTM):
+                    if isinstance(self._model_without_wrappers, tf.keras.Sequential):
+                        config["is_sequential_model"] = True
+
+                    # pylint: disable=protected-access
+                    if self._quantsim_configurator._layer_to_config_dict[layer]["is_input_quantized"]["setting"]:
+                        config["is_input_quantized"] = True
+                    config["quant_scheme"] = quant_scheme
+                    config["rounding_mode"] = rounding_mode
+                    config["default_output_bw"] = default_output_bw
+                    config["default_param_bw"] = default_param_bw
+                    config["default_data_type"] = default_data_type
+
+                wrapped_layer = new_class.from_config(config)
+                self._substituted_layer[layer] = wrapped_layer
+                return wrapped_layer
+
+            if isinstance(layer, tf.keras.Sequential):
+                return tf.keras.models.clone_model(layer, clone_function=wrap_layer)
+
+            if isinstance(layer, unquantizable_modules) or layer.submodules:
+                return layer
+
+            activation_quant_settings = QuantizerSettings(default_output_bw, default_data_type, rounding_mode,
+                                                          quant_scheme, False, False, False)
+            param_quant_settings = QuantizerSettings(default_param_bw, default_data_type, rounding_mode,
+                                                     quant_scheme, False, False, False)
+
+            input_quantizers, output_quantizers, param_quantizers = self._get_quantizers_by_layer(layer)
+            wrapper = QcQuantizeWrapper(layer, activation_quant_settings, param_quant_settings,
+                                        num_inputs=len(layer.inbound_nodes[0].keras_inputs),
+                                        input_quantizers=input_quantizers,
+                                        output_quantizers=output_quantizers,
+                                        param_quantizers=param_quantizers,
+                                        per_channel_quantization_enabled=self.per_channel_quantization_enabled)
+            self._layer_name_to_quant_wrapper[layer.name] = wrapper
+            return wrapper
+
+        return tf.keras.models.clone_model(self._model_without_wrappers, clone_function=wrap_layer)
+
+    def _get_quantizers_by_layer(self, layer: tf.keras.layers.Layer) -> Tuple[Optional[ActivationTensorQuantizer],
+                                                                              Optional[ActivationTensorQuantizer],
+                                                                              Union[ParamPerTensorQuantizer,
+                                                                                    ParamPerChannelQuantizer]]:
+        """
+        Get input/output/param quantizers from quantizers dictionary or initialize quantizers if layer is not found
+        :param layer: Target layer
+        :return: tuple of input, output, param quantizers
+        """
+        quantizers_dict = self._quantsim_configurator.get_quantizers_dict(layer)
+        if quantizers_dict is None:
+            _logger.warning("%s not found in quantizers dict, will generate quantizers automatically", layer.name)
+            input_quantizers = None
+            output_quantizers = None
+            param_quantizers = None
+        else:
+            input_quantizers = quantizers_dict.get(INPUT_QUANTIZERS)
+            output_quantizers = quantizers_dict.get(OUTPUT_QUANTIZERS)
+            param_quantizers = quantizers_dict.get(PARAM_QUANTIZERS)
+
+        return input_quantizers, output_quantizers, param_quantizers
+
+    @staticmethod
+    def _quantizer_to_name_tuple(quantizers: List[TensorQuantizer]) -> Tuple[Optional[List[str]]]:
+        """
+        Converts a list of quantizers to a tuple of quantizer names
+        :param quantizers: quantizers
+        :return: tuple of quantizer names
+        """
+        quant_list = []
+        if not quantizers:
+            return None
+
+        for quantizer in quantizers:
+            quant_list.append(quantizer.name)
+        return tuple(quant_list)
+
+    def get_quantizer_name_by_layer(self, layer: tf.keras.layers.Layer) -> Tuple[Optional[List[str]],
+                                                                                 Optional[List[str]],
+                                                                                 Optional[List[str]]]:
+        """
+        Get the names of input, output and param quantizers
+        :param layer: the keras layer
+        :return: Tuple of quantizer names
+        """
+        input_quantizers, output_quantizers, param_quantizers = self._get_quantizers_by_layer(layer)
+        output_quantizers_names = self._quantizer_to_name_tuple(output_quantizers)
+        input_quantizers_names = self._quantizer_to_name_tuple(input_quantizers)
+        parameter_quantizers_names = self._quantizer_to_name_tuple(param_quantizers)
+
+        return input_quantizers_names, output_quantizers_names, parameter_quantizers_names
+
+    def _disable_quantizers_in_folded_batchnorm(self):
+        """
+        Disable input/output/param quantizers if layer is folded batch normalization
+        """
+        for quantsim_wrapper in self._layer_name_to_quant_wrapper.values():
+            if GraphSearchUtils.is_folded_batch_normalization(quantsim_wrapper.original_layer):
+                for q in quantsim_wrapper.input_quantizers:
+                    q.disable()
+                for q in quantsim_wrapper.output_quantizers:
+                    q.disable()
+                for q in quantsim_wrapper.param_quantizers:
+                    q.disable()
+
+    @staticmethod
+    def _get_encoding_dict_for_quantizer(quantizer: TensorQuantizer) -> Union[List[Dict[str, Union[str, int, float]]],
+                                                                              Dict[str, Union[str, int, float]]]:
+        """
+        Get encoding dict for a tensor quantizer.
+
+        :param quantizer: Quantizer to get encoding info from
+        :return: Dictionary or List of dictionaries containing encodings info for the tensor quantizer
+        """
+        if not isinstance(quantizer, ParamPerChannelQuantizer) or quantizer.data_type == QuantizationDataType.float:
+            quantizer_encodings = [quantizer.encoding]
+        else:
+            quantizer_encodings = quantizer.encoding
+        return [
+            {
+                'min': encoding.min,
+                'max': encoding.max,
+                'scale': encoding.delta,
+                'offset': int(encoding.offset),
+                'bitwidth': encoding.bw,
+                'is_symmetric': str(quantizer.is_symmetric),
+                'dtype': 'int'
+            } if quantizer.data_type == QuantizationDataType.int
+            else {'dtype': 'float', 'bitwidth': int(quantizer.bitwidth)}
+            for encoding in quantizer_encodings
+        ]
+
+    def get_encodings_dict(self) -> Dict[str, Union[str, Dict]]:
+        """
+        Get encodings dict containing all activation and parameter encodings info in the model
+        :return: Dictionary containing all activation and parameter encodings info in the model
+        """
+        # pylint: disable=protected-access, too-many-branches
+        model_input_tensor_names = [inp.name for inp in self.model.inputs]
+        activation_encodings = {}
+        param_encodings = {}
+        for wrapper in self.quant_wrappers():
+            for idx, input_quantizer in enumerate(wrapper.input_quantizers):
+                if input_quantizer.is_encoding_valid() or input_quantizer.data_type == QuantizationDataType.float:
+                    # because dense layers in quantizable MHA are not explicitly sublayers, they don't have their
+                    # inbound_nodes parameter populated, so the name of the quantizer is used instead
+                    if not wrapper._layer_to_wrap.inbound_nodes:
+                        tensor_name = wrapper.name + "/" + input_quantizer.name + ":0"
+                    else:
+                        tensor_name = wrapper._layer_to_wrap.inbound_nodes[0].keras_inputs[idx].name
+                    encoding_dict = self._get_encoding_dict_for_quantizer(input_quantizer)
+                    if tensor_name in model_input_tensor_names:
+                        tensor_name += ":0"
+                    activation_encodings[tensor_name] = encoding_dict
+            for idx, param_quantizer in enumerate(wrapper.param_quantizers):
+                if param_quantizer.is_encoding_valid() or param_quantizer.data_type == QuantizationDataType.float:
+                    param_name = wrapper._layer_to_wrap.weights[idx].name
+                    encoding_dict = self._get_encoding_dict_for_quantizer(param_quantizer)
+                    param_encodings[param_name] = encoding_dict
+            for idx, output_quantizer in enumerate(wrapper.output_quantizers):
+                if output_quantizer.is_encoding_valid() or output_quantizer.data_type == QuantizationDataType.float:
+                    # because dense layers in quantizable MHA are not explicitly sublayers, they don't have their
+                    # inbound_nodes parameter populated, so the name of the quantizer is used instead
+                    if not wrapper._layer_to_wrap.inbound_nodes:
+                        tensor_name = wrapper.name + ":0"
+                    elif isinstance(wrapper._layer_to_wrap.output, List):
+                        tensor_name = wrapper._layer_to_wrap.output[idx].name
+                    else:
+                        tensor_name = wrapper._layer_to_wrap.output.name
+                    encoding_dict = self._get_encoding_dict_for_quantizer(output_quantizer)
+                    activation_encodings[tensor_name] = encoding_dict
+        return {
+            'version': quantsim.encoding_version,
+            'activation_encodings': activation_encodings,
+            'param_encodings': param_encodings,
+            'quantizer_args': self.quant_args if hasattr(self, "quant_args") else {}
+        }
+
+
    
+[docs]
+    def compute_encodings(self, forward_pass_callback, forward_pass_callback_args):
+        """
+        Computes encodings for all quantization sim nodes in the model.
+
+        :param forward_pass_callback: A callback function that is expected to run forward passes on a model.
+            This callback function should use representative data for the forward pass, so the calculated encodings work for all data samples.
+        :param forward_pass_callback_args: These argument(s) are passed to the forward_pass_callback as-is.
+            Up to the user to determine the type of this parameter. E.g. could be simply an integer representing the number of data samples to use.
+            Or could be a tuple of parameters or an object representing something more complex.
+        """
+        ops_with_invalid_encodings = []
+        self._compute_and_set_parameter_encodings(ops_with_invalid_encodings)
+
+        self._set_op_mode_parameters(libpymo.TensorQuantizerOpMode.quantizeDequantize)
+
+        forward_pass_callback(self.model, forward_pass_callback_args)
+        for quant_wrapper in self.quant_wrappers():
+            quant_wrapper.compute_encoding(ops_with_invalid_encodings)
+
+        op_mode = self._param_op_mode_after_analysis(self.quant_scheme)
+
+        self._set_op_mode_parameters(op_mode)
+
+        if ops_with_invalid_encodings:
+            _logger.info('The following quantizers did not have valid encodings and have been set to passThrough mode: '
+                         '%s', ops_with_invalid_encodings)
+            _logger.info('This can be due to the quantizers not having been evaluated during the forward pass in '
+                         'compute encodings. Evaluation is required to collect statistics needed to compute valid '
+                         'encodings.\n'
+                         'As a result, the quantizers have been set to passThrough mode, meaning no quantization noise '
+                         'will be simulated for these ops if they are evaluated in the future.\n'
+                         'If this is not desired, amend the forward pass to evaluate tensors which require these ops '
+                         'to be evaluated, and recompute encodings.')
    
+
+
+    def _set_op_mode_parameters(self, op_mode: libpymo.TensorQuantizerOpMode):
+        """
+        Sets quant mode for parameters and if the encodings are invalid, then adds those wrappers
+        to wrappers_with_invalid_encodings
+        :param op_mode: Quant mode to set to
+        """
+
+        for quantizer_info in self.quant_wrappers():
+            for param_quantizer in quantizer_info.param_quantizers:
+                if param_quantizer.is_enabled():
+                    param_quantizer.quant_mode = op_mode
+
+    @staticmethod
+    @contextlib.contextmanager
+    def _set_encoding_version_to_0_6_1():
+        assert quantsim.encoding_version in {'0.6.1', '1.0.0'}
+        if quantsim.encoding_version == '1.0.0':
+            _logger.info('Exporting to encoding version 1.0.0 is not yet supported. Exporting using version 0.6.1 '
+                         'instead.')
+        old_encoding_version = quantsim.encoding_version
+        quantsim.encoding_version = '0.6.1'
+
+        yield
+
+        quantsim.encoding_version = old_encoding_version
+
+
    
+[docs]
+    def export(self, path, filename_prefix, custom_objects=None, convert_to_pb=True):
+        """
+        This method exports out the quant-sim model so it is ready to be run on-target. Specifically, the following are saved
+
+        1. The sim-model is exported to a regular Keras model without any simulation ops
+        2. The quantization encodings are exported to a separate JSON-formatted file that can then be imported by the on-target runtime (if desired)
+
+        :param path: path where to store model pth and encodings
+        :param filename_prefix: Prefix to use for filenames of the model pth and encodings files
+        :param custom_objects: If there are custom objects to load, Keras needs a dict of them to map them
+        """
+        with self._set_encoding_version_to_0_6_1():
+            model_path = os.path.join(path, filename_prefix)
+
+            #TF Version 2.4 has bug i.e. save() in tf format don't work for unrolled LSTM.
+            for layer in self._model_without_wrappers.layers:
+                if isinstance(layer, tf.keras.layers.LSTM):
+                    break
+            else:
+                self._model_without_wrappers.save(model_path)
+
+            self._model_without_wrappers.save(model_path + '.h5', save_format='h5')
+
+            # Conversion of saved h5 model to pb model for consumption by SNPE/QNN
+            try:
+                if convert_to_pb:
+                    convert_h5_model_to_pb_model(f'{model_path}.h5', custom_objects=custom_objects)
+            except ValueError:
+                _logger.error("Could not convert h5 to frozen pb. "
+                              "Please call export() again with custom_objects defined.")
+                raise
+            finally:
+                encodings_dict = self.get_encodings_dict()
+                encoding_file_path = os.path.join(path, filename_prefix + '.encodings')
+                save_json_yaml(encoding_file_path, encodings_dict)
    
+
+
+    def _compute_and_set_parameter_encodings(self, ops_with_invalid_encodings: List):
+        # pylint: disable=too-many-nested-blocks
+        for quantizer_wrapper in self.quant_wrappers():
+            for idx, param_quantizer in enumerate(quantizer_wrapper.param_quantizers):
+                if param_quantizer.is_enabled() and param_quantizer.data_type == QuantizationDataType.int:
+                    # 0th input to our quant wrapper is the tensor being quantized
+                    weight_tensor = quantizer_wrapper.original_layer.get_weights()[idx]
+
+                    # Per-channel
+                    if isinstance(param_quantizer, StaticGridPerChannelQuantizer):
+                        for index, tensor_quantizer in enumerate(param_quantizer.tensor_quantizer):
+                            if param_quantizer.axis_handling == AxisHandling.LAST_TWO_AXES.value:
+                                last_two_axes_combined_shape = list(weight_tensor.shape[:-2]) + [-1]
+                                channel_slice = weight_tensor.reshape(*last_two_axes_combined_shape)
+                                channel_slice = channel_slice.take(index, channel_slice.ndim - 1)
+                            elif isinstance(quantizer_wrapper.original_layer, tf.keras.layers.Conv2DTranspose):
+                                if weight_tensor.ndim == 4:
+                                    channel_slice = weight_tensor.take(index, weight_tensor.ndim - 2)
+                                else:
+                                    # For bias in Transpose layers
+                                    channel_slice = weight_tensor.take(index, weight_tensor.ndim - 1)
+                            else:
+                                channel_slice = weight_tensor.take(index, weight_tensor.ndim - 1)
+                            tensor_quantizer.updateStats(channel_slice, False)
+
+                    # Per-tensor
+                    else:
+                        tensor_quantizer = param_quantizer.tensor_quantizer
+                        tensor_quantizer.updateStats(weight_tensor, False)
+
+                    param_quantizer.compute_encoding(ops_with_invalid_encodings)
+
+    def set_and_freeze_param_encodings(self, encoding_path: str):
+        """
+        Set and freeze parameter encodings from encodings JSON file
+        :param encoding_path: path from where to load parameter encodings file
+        """
+        # Load parameter encodings file
+        with open(encoding_path) as json_file:
+            param_encodings = json.load(json_file)
+
+        for quant_wrapper in self.quant_wrappers():
+            quant_wrapper.set_and_freeze_param_encoding(param_encodings)
+
+    # pylint: disable=too-many-nested-blocks
+
    
+[docs]
+    def load_encodings_to_sim(self, encoding_file_path: str):
+        """
+        Loads the saved encodings to quant sim model
+
+        :param encoding_file_path: path from where to load encodings file
+        :return:
+        """
+        # pylint: disable=protected-access, too-many-branches, too-many-locals, too-many-statements
+        # Load encodings file
+        with open(encoding_file_path) as json_file:
+            encodings = json.load(json_file)
+
+        param_encodings = encodings['param_encodings']
+        activation_encodings = encodings['activation_encodings']
+
+        model_input_tensor_names = [inp.name for inp in self.model.inputs]
+
+        for wrapper in self.quant_wrappers():
+            for idx, input_quantizer in enumerate(wrapper.input_quantizers):
+                # because dense layers in quantizable MHA and RNN are not explicitly sublayers, they don't have their
+                # inbound_nodes parameter populated, so the name of the quantizer is used instead
+                if not wrapper._layer_to_wrap.inbound_nodes:
+                    tensor_name = wrapper.name + "/" + input_quantizer.name + ":0"
+                else:
+                    tensor_name = wrapper._layer_to_wrap.inbound_nodes[0].keras_inputs[idx].name
+                if tensor_name in model_input_tensor_names:
+                    tensor_name += ":0"
+
+                if tensor_name in activation_encodings:
+                    if not input_quantizer.is_enabled():
+                        _logger.info("Not loading encodings for quantizer: %s as it is disabled", tensor_name)
+                        continue
+                    encoding_dict = activation_encodings[tensor_name][0]
+                    if encoding_dict['dtype'] == 'int':
+                        encoding, is_symmetric = keras_common_utils.create_encoding_from_dict(encoding_dict)
+                        input_quantizer.tensor_quantizer.isEncodingValid = True
+                        input_quantizer.set_quantizer_encodings(encoding.bw, is_symmetric, encoding,
+                                                                libpymo.TensorQuantizerOpMode.quantizeDequantize)
+                        _logger.info("Setting encodings for : %s", tensor_name)
+                    elif encoding_dict['dtype'] == 'float':
+                        input_quantizer.data_type = QuantizationDataType.float
+                        input_quantizer.bitwidth = encoding_dict['bitwidth']
+                        _logger.info("Setting quantizer dtype to float for : %s", tensor_name)
+                    else:
+                        raise RuntimeError("Unrecognized dtype %s for: %s" % (encoding_dict['dtype'], tensor_name))
+                else:
+                    if input_quantizer.is_enabled():
+                        input_quantizer.disable()
+                        _logger.info("Encoding for quantizer: %s is not present thus disabling it.", tensor_name)
+
+            for idx, param_quantizer in enumerate(wrapper.param_quantizers):
+                param_name = wrapper._layer_to_wrap.weights[idx].name
+
+                if param_name in param_encodings:
+                    if not param_quantizer.is_enabled():
+                        _logger.info("Not loading encodings for parameter: %s as quantizer is disabled", param_name)
+                        continue
+                    if isinstance(param_quantizer, StaticGridPerChannelQuantizer):
+                        if param_encodings[param_name][0]['dtype'] == 'float':
+                            wrapper.param_quantizers[idx] = ParamPerTensorQuantizer(layer=param_quantizer._original_layer,
+                                                                                    name=param_quantizer.name,
+                                                                                    quant_scheme=param_quantizer._quant_scheme,
+                                                                                    round_mode='nearest',
+                                                                                    bitwidth=param_encodings[param_name][0]['bitwidth'],
+                                                                                    data_type=QuantizationDataType.float,
+                                                                                    is_symmetric=param_quantizer.is_symmetric,
+                                                                                    use_strict_symmetric=param_quantizer.use_strict_symmetric,
+                                                                                    use_unsigned_symmetric=param_quantizer.use_unsigned_symmetric,
+                                                                                    enabled=False)
+                        else:
+                            encoding, is_symmetric = keras_common_utils.create_encoding_from_dict(
+                                param_encodings[param_name])
+                            for tensor_quantizer in param_quantizer.tensor_quantizer:
+                                tensor_quantizer.isEncodingValid = True
+                            bw = encoding[0].bw
+                            param_quantizer.set_quantizer_encodings(bw, is_symmetric, encoding,
+                                                                    libpymo.TensorQuantizerOpMode.oneShotQuantizeDequantize)
+                        _logger.info("Setting encodings for : %s", param_name)
+                    else:
+                        encoding_dict = param_encodings[param_name][0]
+                        if encoding_dict['dtype'] == 'int':
+                            encoding, is_symmetric = keras_common_utils.create_encoding_from_dict(encoding_dict)
+                            param_quantizer.tensor_quantizer.isEncodingValid = True
+                            bw = encoding.bw
+                            param_quantizer.set_quantizer_encodings(bw, is_symmetric, encoding,
+                                                                    libpymo.TensorQuantizerOpMode.oneShotQuantizeDequantize)
+                            _logger.info("Setting encodings for : %s", param_name)
+                        elif encoding_dict['dtype'] == 'float':
+                            param_quantizer.data_type = QuantizationDataType.float
+                            param_quantizer.bitwidth = encoding_dict['bitwidth']
+                            _logger.info("Setting quantizer to float for : %s", param_name)
+                        else:
+                            raise RuntimeError("Unrecognized dtype %s for: %s" % (encoding_dict['dtype'], tensor_name))
+                else:
+                    if param_quantizer.is_enabled():
+                        param_quantizer.disable()
+                        _logger.info("Encoding for parameter: %s not present thus disabling this quantizer.",
+                                     param_name)
+
+            # Loading encodings means that compute encodings was called. Therefore, these two lines set the correct
+            # op mode for the correct quant scheme and if the quantization was per channel or not.
+            op_mode = self._param_op_mode_after_analysis(self.quant_scheme)
+            self._set_op_mode_parameters(op_mode)
+
+            for idx, output_quantizer in enumerate(wrapper.output_quantizers):
+                # because dense layers in quantizable MHA are not explicitly sublayers, they don't have their
+                # inbound_nodes parameter populated, so the name of the quantizer is used instead
+                if not wrapper._layer_to_wrap.inbound_nodes:
+                    tensor_names = [wrapper.name + ":0"]
+                else:
+                    # There can be multiple outputs if there is a
+                    # `tf.split` in the model.
+                    if isinstance(wrapper._layer_to_wrap.output, list):
+                        tensor_names = [
+                            output.name
+                            for output in wrapper._layer_to_wrap.output
+                        ]
+                    else:
+                        tensor_names = [wrapper._layer_to_wrap.output.name]
+
+                for tensor_name in tensor_names:
+                    if tensor_name in activation_encodings:
+                        if not output_quantizer.is_enabled():
+                            _logger.info("Not loading encodings for quantizer: %s as it is disabled", tensor_name)
+                            continue
+                        encoding_dict = activation_encodings[tensor_name][0]
+                        if encoding_dict['dtype'] == 'int':
+                            encoding, is_symmetric = keras_common_utils.create_encoding_from_dict(encoding_dict)
+                            output_quantizer.tensor_quantizer.isEncodingValid = True
+                            output_quantizer.set_quantizer_encodings(encoding.bw, is_symmetric, encoding,
+                                                                     libpymo.TensorQuantizerOpMode.quantizeDequantize)
+                            _logger.info("Setting encodings for : %s", tensor_name)
+                        elif encoding_dict['dtype'] == 'float':
+                            output_quantizer.data_type = QuantizationDataType.float
+                            output_quantizer.bitwidth = encoding_dict['bitwidth']
+                            _logger.info("Setting quantizer dtype to float for : %s", tensor_name)
+                        else:
+                            raise RuntimeError("Unrecognized dtype %s for: %s" % (encoding_dict['dtype'], tensor_name))
+                    else:
+                        if output_quantizer.is_enabled():
+                            output_quantizer.disable()
+                            _logger.info("Encoding for quantizer: %s is not present thus disabling it.", tensor_name)
    
+
+
+    def _param_op_mode_after_analysis(self, quant_scheme) -> libpymo.TensorQuantizerOpMode:
+        """
+        Returns quant mode to use for parameters after encodings have been computed
+        :param quant_scheme: Quantization scheme to use
+        :return: Quant mode to use
+        """
+        if quant_scheme in [QuantScheme.training_range_learning_with_tf_init,
+                            QuantScheme.training_range_learning_with_tf_enhanced_init] \
+                or self.per_channel_quantization_enabled:
+            return libpymo.TensorQuantizerOpMode.quantizeDequantize
+        return libpymo.TensorQuantizerOpMode.oneShotQuantizeDequantize
+
+    def quant_wrappers(self):
+        """
+        Generator for yielding all quantization wrappers
+        """
+        for layer in self.model.layers:
+            if isinstance(layer, QcQuantizeWrapper):
+                yield layer
+            if isinstance(layer, tuple(substitutable_modules.values())):
+                yield from layer.quant_wrappers()
+
+            # For Getting Quantizers from Sequantial Block
+            if isinstance(layer, tf.keras.Sequential):
+                yield from quant_wrappers_for_sequential_block(layer)
+
+    def get_quant_wrapper_for_layer_name(self, layer_name: str) -> QcQuantizeWrapper:
+        """
+        Return qc quant wrapper corresponding to a layer name
+        :param layer_name: Layer name to get quantize wrapper for
+        :return: Qc quant wrapper corresponding to a layer name
+        """
+        return self._layer_name_to_quant_wrapper.get(layer_name)
+
+    # pylint: disable=too-many-locals
+    def _fill_missing_encoding_min_max_gradients(self, gradients: list):
+        """
+        Computes the encoding min/max gradients and populates the gradients list
+        :param gradients: gradients computed using GradientTape(gradients for encoding min/max will be `None`)
+        """
+
+        def _find_weight_in_layer(weight_name: str, model_layer: tf.keras.layers.Layer):
+
+            for weight in model_layer.weights:
+                if weight.name.split(":")[0] == weight_name:
+                    return weight
+
+            return None
+
+        # Mapping used to get the gradients of weights(kernel, bias etc)
+        weight_name_to_gradient = dict(zip([weight.name.split(":")[0] for weight in self.model.trainable_weights],
+                                           gradients))
+
+        # Mapping used to get index of encoding min/max gradients (which would be `None`) and fill them
+        weight_name_to_index = dict(zip([weight.name for weight in self.model.trainable_weights],
+                                        range(len(self.model.trainable_weights))))
+
+        # Only process layers where 'param_quantizers' is defined (i.e. QcQuantizeWrapper layers)
+        for layer in filter(lambda _layer: hasattr(_layer, 'param_quantizers'), self.model.layers):
+            for param_quantizer in layer.param_quantizers:
+                if param_quantizer.name in weight_name_to_gradient:
+                    # Value of weight associated with this param quantizer
+                    weight_tensor = _find_weight_in_layer(param_quantizer.name, layer.original_layer)
+
+                    # Gradients of the weights
+                    grad = weight_name_to_gradient[param_quantizer.name]
+
+                    # Using the weights and it's gradients, compute gradients for encoding min/max
+                    dloss_by_dmin, dloss_by_dmax = param_quantizer.get_gradients_for_encoding_min_max(weight_tensor,
+                                                                                                      grad)
+
+                    enc_min_index = weight_name_to_index[param_quantizer.encoding_min.name]
+                    enc_max_index = weight_name_to_index[param_quantizer.encoding_max.name]
+
+                    gradients[enc_min_index] = dloss_by_dmin
+                    gradients[enc_max_index] = dloss_by_dmax
+
+        # TODO: Remove this logic once this has been resolved in QNN/SNPE
+        # Go through activation quantizers (Input/Output) and set any ReLU's encoding min to 0
+        relu_quantize_wrappers = [
+            _layer for _layer in self.model.layers
+            if isinstance(_layer, QcQuantizeWrapper) and isinstance(_layer.original_layer, tf.keras.layers.ReLU)
+        ]
+
+        def _set_encoding_min_grad_to_None(quantizer):
+            enc_min_index = weight_name_to_index[quantizer.encoding_min.name]
+            gradients[enc_min_index] = None
+
+        for relu_quantizer in relu_quantize_wrappers:
+            for output_quantizer in relu_quantizer.output_quantizers:
+                _set_encoding_min_grad_to_None(output_quantizer)
+
+    # pylint: disable=useless-super-delegation
+    def get_config(self):
+        return super().get_config()
+
+    def call(self, inputs, training=None, mask=None):
+        return self.model.call(inputs, training, mask)
+
+    def train_step(self, data):
+        """
+        Custom training loop, equivalent to overriding `keras.Model.fit` function
+        Reference: https://keras.io/guides/customizing_what_happens_in_fit/
+        Only relevant when using range-learning, otherwise equivalent to `keras.Model.fit`
+        Param quantizers are disconnected in the op graph of the wrapped model
+        Because of this, the gradients are not computed for encoding min/max(when range learning is enabled)
+        This custom train_step function computes the missing gradients for encoding min/max of param quantizers
+        """
+        x, y = data
+
+        with tf.GradientTape() as tape:
+            predictions = self(x, training=True)
+            loss = self.compiled_loss(y, predictions)
+
+        gradients = tape.gradient(loss, self.model.trainable_weights)
+
+        # Manually compute missing gradients for encoding min/max when using range learning
+        if self.quant_scheme in [QuantScheme.training_range_learning_with_tf_init,
+                                 QuantScheme.training_range_learning_with_tf_enhanced_init]:
+            self._fill_missing_encoding_min_max_gradients(gradients)
+
+        gradients_to_apply = [(gradient, weight) for gradient, weight in zip(gradients, self.model.trainable_weights)
+                              if gradient is not None]
+
+        self.optimizer.apply_gradients(gradients_to_apply)
+
+        self.compiled_metrics.update_state(y, predictions)
+
+        return {m.name: m.result() for m in self.metrics}
    
+
+
+
+def quant_wrappers_for_sequential_block(seq_block: tf.keras.Sequential):
+    """
+        Generator for yielding all quantization wrappers for a Sequantial Block
+    """
+    for layer in seq_block.layers:
+        if isinstance(layer, QcQuantizeWrapper):
+            yield layer
+        if isinstance(layer, tuple(substitutable_modules.values())):
+            yield from layer.quant_wrappers()
+
+        # in cases of nested Sequential Block
+        if isinstance(layer, tf.keras.Sequential):
+            yield from quant_wrappers_for_sequential_block(layer)
+
    +
    +
    +
    + +
    + + +
    +
    +
    +
    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
    + Made with Sphinx and @pradyunsg's + + Furo + +
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    +

    Source code for aimet_torch._base.adaround.adaround_weight

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2024, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+""" Defines Adaround base classes shared across aimet_torch.v1 and v2 """
+
+from abc import ABC, abstractmethod
+import os
+import contextlib
+import json
+import tempfile
+from typing import Tuple, Union, Dict, List, Callable, Optional, Any
+import torch
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+
+# Import AIMET specific modules
+from aimet_common.utils import AimetLogger, convert_configs_values_to_bool
+from aimet_common.defs import QuantScheme
+
+from aimet_torch import utils
+from aimet_torch.meta import connectedgraph_utils
+from aimet_torch._base.quantsim import _QuantizationSimModelInterface, _QuantizedModuleProtocol
+from aimet_torch._base.adaround.adaround_optimizer import AdaroundOptimizer
+from aimet_torch._base.adaround.adaround_loss import AdaroundHyperParameters
+from aimet_torch._base.adaround.activation_sampler import create_modulelist_for_group_modules, get_block_inputs, \
+    get_block_outputs, create_cached_block_schedule_list
+from aimet_torch.utils import get_named_module
+
+logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.Quant)
+
+# The following modules with weights are supported by Adaround
+AdaroundSupportedModules = (torch.nn.Conv2d, torch.nn.ConvTranspose2d, torch.nn.Linear)
+
+
+
    
+[docs]
+class AdaroundParameters:
+    """
+    Configuration parameters for Adaround
+    """
+    def __init__(self, data_loader: DataLoader, num_batches: int,
+                 default_num_iterations: int = None, default_reg_param: float = 0.01,
+                 default_beta_range: Tuple = (20, 2), default_warm_start: float = 0.2,
+                 forward_fn: Callable[[torch.nn.Module, Any], Any] = None):
+        """
+        :param data_loader: Data loader
+        :param num_batches: Number of batches to be used for Adaround.
+         A commonly recommended value for this parameter is the smaller value among (1) len(data_loader) and (2) ceil(2000/batch_size)
+        :param default_num_iterations: Number of iterations to adaround each layer.
+         The default value is 10K for models with 8- or higher bit weights, and 15K for models with lower than 8 bit weights.
+        :param default_reg_param: Regularization parameter, trading off between rounding loss vs reconstruction loss.
+         Default 0.01
+        :param default_beta_range: Start and stop beta parameter for annealing of rounding loss (start_beta, end_beta).
+         Default (20, 2)
+        :param default_warm_start: warm up period, during which rounding loss has zero effect. Default 20% (0.2)
+        :param forward_fn: Optional adapter function that performs forward pass given a model and inputs
+         yielded from the data loader. The function expects model as first argument and inputs to model
+         as second argument.
+        """
+        if len(data_loader) < num_batches:
+            raise ValueError(f'Can not fetch {num_batches} batches from '
+                             f'a data loader of length {len(data_loader)}.')
+
+        self.data_loader = data_loader
+        self.num_batches = num_batches
+        self.num_iterations = default_num_iterations
+        self.reg_param = default_reg_param
+        self.beta_range = default_beta_range
+        self.warm_start = default_warm_start
+        self.forward_fn = forward_fn
    
+
+
+
+class AdaroundBase(ABC):
+    """
+    Weight-rounding mechanism for Post Training Quantization (PTQ)
+    """
+    @classmethod
+    def apply_adaround(cls, model: torch.nn.Module, dummy_input: Union[torch.Tensor, Tuple], params: AdaroundParameters,
+                       path: str, filename_prefix: str, default_param_bw: int = 4,
+                       param_bw_override_list: List[Tuple[torch.nn.Module, int]] = None,
+                       ignore_quant_ops_list: List[torch.nn.Module] = None,
+                       default_quant_scheme: QuantScheme = QuantScheme.post_training_tf_enhanced,
+                       default_config_file: str = None) -> torch.nn.Module:
+        """
+        Returns model with optimized weight rounding of every module (Conv and Linear) and also saves the
+        corresponding quantization encodings to a separate JSON-formatted file that can then be imported by
+        QuantSim for inference or QAT
+
+        :param model: Model to Adaround
+        :param dummy_input: Dummy input to the model. Used to parse model graph. If the model has more than one input,
+                            pass a tuple. User is expected to place the tensors on the appropriate device.
+        :param params: Parameters for Adaround
+        :param path: path where to store parameter encodings
+        :param filename_prefix: Prefix to use for filename of the encodings file
+        :param default_param_bw: Default bitwidth (4-31) to use for quantizing layer parameters
+        :param param_bw_override_list: List of Tuples. Each Tuple is a module and the corresponding parameter bitwidth
+                                       to be used for that module.
+        :param ignore_quant_ops_list: Ops listed here are skipped during quantization needed for AdaRounding. Do not
+                                      specify Conv and Linear modules in this list. Doing so, will affect accuracy.
+        :param default_quant_scheme: Quantization scheme. Supported options are using Quant Scheme Enum
+                                    QuantScheme.post_training_tf or QuantScheme.post_training_tf_enhanced
+        :param default_config_file: Default configuration file for model quantizers
+        :return: Model with Adarounded weights and saves corresponding parameter encodings JSON file at provided path
+        """
+        # pylint: disable=too-many-arguments
+        # Create Quant sim with given parameters
+        quant_sim = cls._get_quantsim(model, dummy_input=dummy_input, quant_scheme=default_quant_scheme,
+                                      default_param_bw=default_param_bw,
+                                      config_file=default_config_file)
+
+        # For the modules in the param_bw_override_list, override the default parameter bitwidths in the QuantSim
+        if param_bw_override_list:
+            cls._override_param_bitwidth(model, quant_sim, param_bw_override_list)
+
+        if ignore_quant_ops_list:
+            cls._exclude_modules(model, quant_sim, ignore_quant_ops_list)
+
+        # Compute only param encodings
+        cls._compute_param_encodings(quant_sim)
+
+        return cls._apply_adaround(quant_sim, model, dummy_input, params, path, filename_prefix)
+
+    @classmethod
+    def _apply_adaround(cls, quant_sim: _QuantizationSimModelInterface, model: torch.nn.Module,
+                        dummy_input: Union[torch.Tensor, Tuple], params: AdaroundParameters,
+                        path: str, filename_prefix: str, checkpoints_config: str = None) -> torch.nn.Module:
+        """
+        Returns model with optimized weight rounding of every module (Conv and Linear) and also saves the
+        corresponding quantization encodings to a separate JSON-formatted file that can then be imported by
+        QuantSim for inference or QAT
+
+        :param quant_sim: QuantizationSimModel object to optimize weight rounding.
+                          The activation quantizers are expected to have been disabled.
+        :param model: Original fp32 model from which quant_sim was created.
+        :param dummy_input: Dummy input to the model. Used to parse model graph. If the model has more than one input,
+                            pass a tuple. User is expected to place the tensors on the appropriate device.
+        :param params: Parameters for Adaround
+        :param path: path where to store parameter encodings
+        :param filename_prefix: Prefix to use for filename of the encodings file
+        :param checkpoints_config: Config files to split fp32/quant model by checkpoints
+        :return: Model with Adarounded weights and saves corresponding parameter encodings JSON file at provided path
+        """
+
+        # Sanity check: All the input/output quantizers should be disabled
+        cls._check_input_output_quantizers_for_adaround(quant_sim.model)
+
+        # Get the module - activation function pair using ConnectedGraph
+        module_act_func_pair = connectedgraph_utils.get_module_act_func_pair(model, dummy_input)
+
+        cls._adaround_model(model, quant_sim, module_act_func_pair, params, dummy_input, checkpoints_config)
+
+        # Export quantization encodings to JSON-formatted file
+        cls._export_encodings_to_json(path, filename_prefix, quant_sim)
+
+        cls._remove_quantization_wrappers(quant_sim.model)
+        logger.info('Completed Adarounding Model')
+        return quant_sim.model
+
+    @classmethod
+    def _adaround_model(cls, model: torch.nn.Module, quant_sim: _QuantizationSimModelInterface, module_act_func_pair: Dict,
+                        params: AdaroundParameters, dummy_input: Union[torch.Tensor, Tuple],
+                        checkpoints_config: str = None):
+        """
+        Optimize weight rounding of every module (AdaroundSupportedModules) of model in sequential manner
+        based on occurrence
+
+        NOTE: When checkpoints_config file is provided, assumption is that the outputs from previous group modules (block)
+         should feed directly into next group modules (block)
+
+        :param model: Original fp32 model from which quant_sim was created.
+        :param quant_sim: QuantizationSimModel object to optimize weight rounding.
+                          The activation quantizers are expected to have been disabled.
+        :param module_act_func_pair: Dictionary of module to immediate following activation function
+        :param params: Adaround parameters
+        :param dummy_input: Dummy input to the model
+        :param checkpoints_config: Config files to split fp32/quant model by checkpoints to speedup activations sampling
+        """
+        # pylint: disable=too-many-locals, protected-access, too-many-branches, too-many-statements
+
+        num_iterations = params.num_iterations
+
+        if num_iterations is None:
+            lowest_weight_bw = cls._get_lowest_weight_bw(quant_sim.model)
+            # If the lowest wegith bitwidth is < 8, then set num_iterations to 15K by default
+            if lowest_weight_bw < 8:
+                num_iterations = 15000
+            else:
+                num_iterations = 10000
+
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            # Cache model input data to temporary directory
+            cached_dataset = utils.CachedDataset(params.data_loader, params.num_batches, tmp_dir)
+
+            # Optimization Hyper parameters
+            opt_params = AdaroundHyperParameters(num_iterations, params.reg_param, params.beta_range,
+                                                 params.warm_start)
+
+            # AdaRound must be applied to modules in the order of occurrence
+            if checkpoints_config:
+                # Load the predefined json file for checkpoints info
+                checkpoint_config = json.load(open(checkpoints_config))
+                convert_configs_values_to_bool(checkpoint_config)
+
+                assert 'cache_on_cpu' in checkpoint_config.keys(), \
+                    "Please define cache_on_cpu to determine whether to cache intermediate tensors on CPU"
+                cache_on_cpu = checkpoint_config['cache_on_cpu']
+
+                checkpoint_type = checkpoint_config.get('checkpoint_type', 'sequential')
+                if checkpoint_type == 'sequential':
+                    assert 'grouped_modules' in checkpoint_config.keys(), \
+                        "Please provide a dictionary of grouped_modules in the file to define checkpoints"
+                    assert 'include_static_inputs' in checkpoint_config.keys(), \
+                        "Please provide a dictionary of include_static_inputs in the file to define checkpoints"
+
+                    grouped_modules = checkpoint_config['grouped_modules']
+                    breakpoint_module_name = checkpoint_config['grouped_modules'][list(grouped_modules.keys())[0]][0]
+                    include_static_inputs = checkpoint_config['include_static_inputs']
+                    cached_fp_dataset, cached_quant_dataset = get_block_inputs(model, quant_sim,
+                                                                               breakpoint_module_name,
+                                                                               cached_dataset, cache_on_cpu,
+                                                                               params.forward_fn, params.num_batches,
+                                                                               tmp_dir)
+                    # Get the device of model to latter be used to place input tensor on the same device
+                    device = utils.get_device(model)
+                    model.cpu()
+                    quant_sim.model.cpu()
+
+                    # Forward function for the ModuleList object
+                    def fwd_mod_ls(mod_ls, x):
+                        for mod in mod_ls:
+                            x = params.forward_fn(mod, x)
+                        return x
+
+                    sub_fp_models, sub_sim_models = create_modulelist_for_group_modules(model, quant_sim, grouped_modules)
+                    for i, (fp_block, quant_sim_block, static_input) in enumerate(zip(sub_fp_models,
+                                                                                      sub_sim_models,
+                                                                                      include_static_inputs)):
+                        modules = utils.get_ordered_list_of_modules(fp_block, cached_fp_dataset[0], fwd_mod_ls)
+                        cls._run_adaround_model(modules, fp_block, quant_sim_block,
+                                                module_act_func_pair, opt_params,
+                                                fwd_mod_ls,
+                                                cached_fp_dataset, cached_quant_dataset)
+
+                        # Get the outputs from the current block and assign to be the inputs for next block
+                        # except for the last block
+                        if i < len(sub_fp_models) - 1:
+                            get_block_outputs(fp_block, quant_sim_block, static_input,
+                                              cached_fp_dataset, cached_quant_dataset, cache_on_cpu,
+                                              fwd_mod_ls, device, tmp_dir)
+
+                    # After finishing Adaround, placing the quant model back to its original device
+                    quant_sim.model.to(device)
+                else:
+                    assert 'cached_blocks' in checkpoint_config.keys(), \
+                        "Please provide a list of modules that  can be cached"
+
+                    block_list = create_cached_block_schedule_list(
+                        model, dummy_input, checkpoint_config['cached_blocks'], AdaroundSupportedModules)
+
+                    for block_cfg, modules in tqdm(block_list, desc='block'):
+                        if block_cfg is None: # doesn't belong to a cached block
+                            cls._run_adaround_model(modules, model, quant_sim.model, module_act_func_pair, opt_params,
+                                                    params.forward_fn, cached_dataset)
+                        else:
+                            block_name, fp_block = block_cfg
+                            quant_sim_block: torch.nn.Module = get_named_module(quant_sim.model, block_name)
+
+                            cached_fp_dataset, cached_quant_dataset = get_block_inputs(model, quant_sim,
+                                                                                       block_name,
+                                                                                       cached_dataset, cache_on_cpu,
+                                                                                       params.forward_fn,
+                                                                                       params.num_batches,
+                                                                                       tmp_dir,
+                                                                                       incl_kwargs=True)
+
+                            def block_fwd(_model, x):
+                                return _model(*x)
+
+                            cls._run_adaround_model(modules, fp_block, quant_sim_block, module_act_func_pair,
+                                                    opt_params,
+                                                    block_fwd, cached_fp_dataset, cached_quant_dataset)
+                            del cached_fp_dataset
+                            del cached_quant_dataset
+            else:
+                modules = utils.get_ordered_list_of_modules(model, dummy_input)
+                cls._run_adaround_model(modules, model, quant_sim.model, module_act_func_pair, opt_params,
+                                        params.forward_fn, cached_dataset)
+
+    @classmethod
+    def _run_adaround_model(cls, modules: List, model: torch.nn.Module, quant_sim_model: torch.nn.Module,
+                            module_act_func_pair: Dict, opt_params: AdaroundHyperParameters, forward_fn: Callable,
+                            cached_dataset: utils.CachedDataset,
+                            cached_quant_dataset: Optional[utils.CachedDataset] = None):
+        """
+        Iterate through all modules to find out Adaround supported modules and
+         apply Adaround optimization to those modules
+
+        :param modules: Candidate modules
+        :param model: Original fp32 model
+        :param quant_sim_model: QuantSim model
+        :param module_act_func_pair: Activation function pairs
+        :param opt_params: Optimization parameters
+        :param forward_fn: Adapter function that performs forward pass given a model and inputs
+         yielded from the data loader
+        :param cached_dataset: Cached dataset for the fp32 model
+        :param cached_quant_dataset: Cached dataset for the quant model
+        """
+        # pylint: disable=too-many-arguments, too-many-locals, protected-access
+        for name, module in tqdm(modules):
+            if isinstance(module, AdaroundSupportedModules):
+                # Using name, get corresponding quantized wrapper module from Quant sim model
+                quant_wrapper = cls._get_quant_wrapper(quant_sim_model, name)
+                if not quant_wrapper:
+                    continue
+
+                # Wraps the quant module with adaround wrapper
+                # and temporarily replace quant module with wrapped module
+                with cls._replace_quantization_layer(quant_sim_model, name) as adaround_wrapper:
+
+                    # Get module's next following activation function
+                    act_func = module_act_func_pair[module]
+
+                    logger.info("Started Optimizing weight rounding of module: %s", name)
+                    AdaroundOptimizer.adaround_module(module, adaround_wrapper, model, quant_sim_model, act_func,
+                                                      cached_dataset, forward_fn, opt_params, cached_quant_dataset)
+                    weight = adaround_wrapper.weight
+
+                    # Fold trained alpha to weight
+                    with torch.no_grad():
+                        # Use soft rounding to compute Adarounded weight
+                        adaround_wrapper.use_soft_rounding = True
+                        adarounded_weight = adaround_wrapper.apply_adaround(weight)
+                        weight.copy_(adarounded_weight)
+                        del adarounded_weight
+
+    @staticmethod
+    @abstractmethod
+    def _compute_param_encodings(quant_sim: _QuantizationSimModelInterface):
+        """
+        Compute encodings for parameters, needed for initializing Adaround quantizers
+        :param quant_sim: Quant sim
+        """
+
+    @staticmethod
+    @abstractmethod
+    def _get_quantsim(model: torch.nn.Module, dummy_input: torch.Tensor,
+                      quant_scheme: QuantScheme, default_param_bw: int, config_file: str):
+        ...
+
+    @staticmethod
+    @abstractmethod
+    def _get_adaround_wrapper(quant_module: _QuantizedModuleProtocol):
+        ...
+
+    @staticmethod
+    @abstractmethod
+    def _remove_quantization_wrappers(module: torch.nn.Module):
+        ...
+
+    @staticmethod
+    @contextlib.contextmanager
+    def _patch_module_layer(model, layer_name, new_layer):
+        """
+        Temporarily replace model layer
+        """
+        original_layer = getattr(model, layer_name)
+        setattr(model, layer_name, new_layer)
+        yield
+        setattr(model, layer_name, original_layer)
+
+    @staticmethod
+    @abstractmethod
+    def _validate_quant_module_for_adaround(quant_module: _QuantizedModuleProtocol):
+        ...
+
+    @staticmethod
+    @abstractmethod
+    def _check_input_output_quantizers_for_adaround(quant_model: torch.nn.Module):
+        ...
+
+    @staticmethod
+    @abstractmethod
+    def _get_lowest_weight_bw(quant_model: torch.nn.Module):
+        ...
+
+    @classmethod
+    @contextlib.contextmanager
+    def _replace_quantization_layer(cls, quant_sim_model: torch.nn.Module, module_name: str):
+        """
+        Replace the quantized module's weight tensor quantizer with the Adaround tensor quantizer
+        :param quant_module: quant module
+        """
+        quant_module = utils.get_named_module(quant_sim_model, module_name)
+        cls._validate_quant_module_for_adaround(quant_module)
+        adaround_layer = cls._get_adaround_wrapper(quant_module)
+
+        # We need to look for the container to patch for modules inside submodule
+        upper_module = quant_sim_model
+        upper_module_name, _, target_module_name = module_name.rpartition('.')
+        if upper_module_name:
+            upper_module = utils.get_named_module(quant_sim_model, upper_module_name)
+
+        # Temporarily replace quant module with wrapped module
+        with cls._patch_module_layer(upper_module, target_module_name, adaround_layer):
+            yield adaround_layer
+
+    @staticmethod
+    @abstractmethod
+    def _get_quant_wrapper(quant_sim_model: torch.nn.Module, module_name: str) -> Union[_QuantizedModuleProtocol, None]:
+        """
+        For given module name, get the quantized wrapper module from the QuantSim model
+        :param quant_sim_model: Model with simulation ops
+        :param module_name: Module name
+        :return: Quantized wrapper module or None
+        """
+        ...
+
+    @classmethod
+    def _export_encodings_to_json(cls, path: str, filename_prefix: str, quant_sim: _QuantizationSimModelInterface):
+        """
+        Save Adadrounded module's parameter encodings to JSON file
+        :param path: path where to store param encodings
+        :param filename_prefix: filename to store exported weight encodings in JSON format
+        :param quant_sim: QunatSim that contains the model and Adaround tensor quantizers
+        """
+        # pylint: disable=protected-access
+        # Create a dictionary to export to JSON file
+        param_encodings = {}
+
+        for name, quant_module in quant_sim.model.named_modules():
+            if isinstance(quant_module, _QuantizedModuleProtocol) and \
+                    isinstance(quant_module.get_original_module(), AdaroundSupportedModules):
+
+                if 'weight' in quant_module.param_quantizers:
+                    cls._update_param_encodings_dict(quant_module, name, param_encodings)
+
+        # Unify the encoding format to be same as that of full encoding export file
+        encoding = {'param_encodings': param_encodings}
+        # export encodings to JSON file
+        os.makedirs(os.path.abspath(path), exist_ok=True)
+        encoding_file_path = os.path.join(path, filename_prefix + '.encodings')
+        with open(encoding_file_path, 'w') as encoding_fp:
+            json.dump(encoding, encoding_fp, sort_keys=True, indent=4)
+
+    @classmethod
+    def _update_param_encodings_dict(cls, quant_module: _QuantizedModuleProtocol, name: str, param_encodings: Dict):
+        """
+        Add module's weight parameter encodings to dictionary to be used for exporting encodings
+        :param quant_module: quant module
+        :param name: name of module
+        :param param_encodings: Dictionary of param encodings
+        """
+        for orig_param_name, encodings in quant_module.export_param_encodings(encoding_version='0.6.1').items():
+            if orig_param_name == 'weight' and encodings:
+                param_name = name + '.' + orig_param_name
+                param_encodings[param_name] = encodings
+
+    @staticmethod
+    def _override_param_bitwidth(model: torch.nn.Module, quant_sim: _QuantizationSimModelInterface,
+                                 param_bw_override_list: List[Tuple[torch.nn.Module, int]]):
+        """
+        For the QuantSim, for the list of modules in the param_bw_override_list,
+        overrides the default parameter bitwidths with the provided bitwidth.
+
+        :param model: The original model
+        :param quant_sim: The QuantSim that was created using a deepcopy of the original model.
+        :param param_bw_override_list: List of Tuples. Each Tuple is a module and the corresponding parameter bitwidth
+                                       to be used for that module.
+        """
+        # Create a mapping of original model's AdaRoundable module and their name
+        module_to_name = {}
+        for name, module in model.named_modules():
+            if isinstance(module, AdaroundSupportedModules):
+                module_to_name[module] = name
+
+        # Create a mapping of QuantSim model's AdaRoundable module name and their module
+        name_to_module = {}
+        for q_name, q_module in quant_sim.model.named_modules():
+            if isinstance(q_module, _QuantizedModuleProtocol):
+                if isinstance(q_module.get_original_module(), AdaroundSupportedModules):  # pylint: disable=protected-access
+                    name_to_module[q_name] = q_module
+
+        # For the modules specified in the param_bw_override_list, set the weight quantizer bitwidth
+        for (module, bw) in param_bw_override_list:
+            module_name = module_to_name[module]
+            quant_wrapper = name_to_module[module_name]
+            quant_wrapper.param_quantizers['weight'].bitwidth = bw
+
+    @classmethod
+    def _exclude_modules(cls, model: torch.nn.Module, quant_sim: _QuantizationSimModelInterface,
+                         ignore_quant_ops_list: List[torch.nn.Module]):
+        """
+        For the modules mentioned in the ignore_quant_ops_list, remove the corresponding quant wrappers from the
+        quantSim and excludes modules from adaround optimization.
+
+        :param model: The original model
+        :param quant_sim: The QuantSim that was created using a deepcopy of the original model.
+        :param ignore_quant_ops_list: The list of modules for which the Quantization wrappers are removed from the
+                                      QuantSim object.
+        """
+        quant_wrappers_to_exclude = []
+        for module in ignore_quant_ops_list:
+            for m in module.modules():
+                name = utils.get_layer_name(model, m)
+                quant_wrapper = cls._get_quant_wrapper(quant_sim.model, name)
+                if quant_wrapper:
+                    quant_wrappers_to_exclude.append(quant_wrapper)
+
+        quant_sim.exclude_layers_from_quantization(quant_wrappers_to_exclude)
+
+    @classmethod
+    def apply_adaround_with_cache(cls, model: torch.nn.Module, dummy_input: Union[torch.Tensor, Tuple],
+                                  params: AdaroundParameters,
+                                  path: str, filename_prefix: str, default_param_bw: int = 4,
+                                  param_bw_override_list: List[Tuple[torch.nn.Module, int]] = None,
+                                  ignore_quant_ops_list: List[torch.nn.Module] = None,
+                                  default_quant_scheme: QuantScheme = QuantScheme.post_training_tf_enhanced,
+                                  default_config_file: str = None,
+                                  checkpoints_config: str = None) -> torch.nn.Module:
+        """
+        Returns model with optimized weight rounding of every module (Conv and Linear) and also saves the
+        corresponding quantization encodings to a separate JSON-formatted file that can then be imported by
+        QuantSim for inference or QAT
+        :param model: Model to Adaround
+        :param dummy_input: Dummy input to the model. Used to parse model graph. If the model has more than one input,
+                            pass a tuple. User is expected to place the tensors on the appropriate device.
+        :param params: Parameters for Adaround
+        :param path: path where to store parameter encodings
+        :param filename_prefix: Prefix to use for filename of the encodings file
+        :param default_param_bw: Default bitwidth (4-31) to use for quantizing layer parameters
+        :param param_bw_override_list: List of Tuples. Each Tuple is a module and the corresponding parameter bitwidth
+                                       to be used for that module.
+        :param ignore_quant_ops_list: Ops listed here are skipped during quantization needed for AdaRounding. Do not
+                                      specify Conv and Linear modules in this list. Doing so, will affect accuracy.
+        :param default_quant_scheme: Quantization scheme. Supported options are using Quant Scheme Enum
+                                    QuantScheme.post_training_tf or QuantScheme.post_training_tf_enhanced
+        :param default_config_file: Default configuration file for model quantizers
+        :param checkpoints_file: JSON file to define checkpoints for caching intermediate tensors of fp32/quant model
+        :return: Model with Adarounded weights and saves corresponding parameter encodings JSON file at provided path
+        """
+        # pylint: disable=too-many-arguments
+        assert checkpoints_config is not None, "To run Adaround with cached tensors, please provide a JSON file with checkpoints defined"
+        # Create Quant sim with given parameters
+        quant_sim = cls._get_quantsim(model, dummy_input=dummy_input, quant_scheme=default_quant_scheme,
+                                      default_param_bw=default_param_bw,
+                                      config_file=default_config_file)
+
+        # For the modules in the param_bw_override_list, override the default parameter bitwidths in the QuantSim
+        if param_bw_override_list:
+            cls._override_param_bitwidth(model, quant_sim, param_bw_override_list)
+
+        if ignore_quant_ops_list:
+            cls._exclude_modules(model, quant_sim, ignore_quant_ops_list)
+
+        # Compute only param encodings
+        cls._compute_param_encodings(quant_sim)
+
+        return cls._apply_adaround(quant_sim, model, dummy_input, params, path, filename_prefix, checkpoints_config)
+
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    +

    Source code for aimet_torch._base.amp.mixed_precision_algo

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2024, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+""" Evaluator class for mixed precision """
+import os
+from collections import defaultdict, OrderedDict
+import pickle
+import functools
+from typing import Any, Callable, Union, Tuple, List, Dict
+import numpy as np
+import torch
+from torch.utils.data import DataLoader
+from aimet_common.utils import AimetLogger
+from aimet_common.defs import CallbackFunc
+from aimet_common.amp.mixed_precision_algo import GreedyMixedPrecisionAlgo as MixedPrecisionAlgo
+from aimet_common.amp.quantizer_groups import reformat_supported_kernels
+from aimet_common.amp.utils import (
+    sort_accuracy_list,
+    CANDIDATE_WITH_DTYPE,
+    ACCURACY_LIST,
+    disable_quantizers,
+    enable_quantizers,
+)
+from aimet_common.amp.convert_ops_reduction import SamplingStrategy
+from aimet_torch import utils
+from aimet_torch._base.amp import utils as mixed_precision_utils
+from aimet_torch._base.amp.convert_ops_reduction import ReduceConvertOps
+from aimet_torch._base.amp.quantizer_groups import find_quantizer_group, QuantizerGroup, get_module_name_to_module_dict, find_supported_candidates
+from aimet_torch._base.quantsim import _QuantizationSimModelInterface
+
+
+logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.MixedPrecision)
+
+
+
    
+[docs]
+class EvalCallbackFactory:
+    """
+    Factory class for various built-in eval callbacks
+    """
+    def __init__(self,
+                 data_loader: DataLoader,
+                 forward_fn: Callable[[torch.nn.Module, Any], torch.Tensor] = None):
+        """
+
+        :param data_loader: Data loader to be used for evaluation
+        :param forward_fn: Function that runs forward pass and returns the output tensor.
+                           This function is expected to take 1) a model and 2) a single batch
+                           yielded from the data loader, and return a single torch.Tensor object
+                           which represents the output of the model.
+                           The default forward function is roughly equivalent to
+                           ``lambda model, batch: model(batch)``
+        """
+        self._data_loader = data_loader
+        self._forward_fn = forward_fn or _default_forward_fn
+
+        # storing batchwise fp32 outputs in the list
+        self._batchwise_fp32_outputs_list = []
+
+    def _forward_fn_wrapper(self, *args, **kwargs):
+        output = self._forward_fn(*args, **kwargs)
+        if not isinstance(output, torch.Tensor):
+            raise RuntimeError(
+                "Forward pass was expected to return a torch.Tensor, "
+                f"but returned an object of type {type(output)}. "
+                "Try specifying `forward_fn` to adapt the output."
+            )
+        return output
+
+    _DEFAULT_SQNR_NUM_SAMPLES = 128
+
+
+
    
+[docs]
+    def sqnr(self, num_samples: int = _DEFAULT_SQNR_NUM_SAMPLES) -> CallbackFunc:
+        """
+        Returns SQNR eval callback.
+
+        :param num_samples: Number of samples used for evaluation
+        :return: A callback function that evaluates the input model's SQNR
+                 between fp32 outputs and fake-quantized outputs
+        """
+        evaluate_sqnr = functools.partial(_evaluate_sqnr,
+                                          data_loader=self._data_loader,
+                                          forward_fn=self._forward_fn_wrapper,
+                                          num_samples=num_samples,
+                                          batchwise_fp32_outputs_list=self._batchwise_fp32_outputs_list)
+        return CallbackFunc(evaluate_sqnr)
    
+
    
+
+
+
+def _default_forward_fn(model, inputs):
+    if isinstance(inputs, torch.Tensor):
+        return model(inputs)
+
+    assert isinstance(inputs, (tuple, list))
+    return model(*inputs)
+
+
+def _evaluate_sqnr(model: torch.nn.Module, _: Any,
+                   data_loader: DataLoader,
+                   forward_fn: Callable[[torch.nn.Module, Any], torch.Tensor],
+                   num_samples: int,
+                   batchwise_fp32_outputs_list: list) -> float:
+    """
+    Compute SQNR given a model and a data loader.
+
+    :param model: Root module
+    :param _: Placeholder for CallbackFunc
+    :param data_loader: Data loader to evaluate SQNR from
+    :param forward_fn: Function that runs forward pass and returns the output tensor.
+    :param num_samples: Number of samples used for evaluation
+    :param batchwise_fp32_outputs_list: List to store the model FP32 final outputs
+    :return: SQNR in dB scale
+    """
+    # If capture_fp32_output_only_once is true then batchwise_fp32_outputs_list would be empty so we can calculate outputs by passing through model
+    # we can store those results and use instead of passing to model
+    # Initially capture_fp32_output_only_once is false and we make it to true if batchwise_fp32_outputs_list is empty to store the outputs in the list
+
+    capture_fp32_output_only_once = False
+    if not batchwise_fp32_outputs_list:
+        capture_fp32_output_only_once = True
+
+    sqnr = 0.0
+    batch_size = data_loader.batch_size or 1
+    device = utils.get_device(model)
+    with utils.in_eval_mode(model), torch.no_grad():
+        for i, x in enumerate(data_loader):
+            if i * batch_size < num_samples:
+                x = utils.change_tensor_device_placement(x, device)
+                # First time we pass input to model and store the outputs in batchwise_fp32_outputs_list
+                if capture_fp32_output_only_once:
+                    with utils.disable_all_quantizers(model):
+                        fp32_output = forward_fn(model, x)
+                    batchwise_fp32_outputs_list.append(fp32_output)
+                else:
+                    # Re-using the stored outputs instead of computing again
+                    fp32_output = batchwise_fp32_outputs_list[i]
+
+                quantized_output = forward_fn(model, x)
+
+                # Accumulate signal by noise ratio
+                # changed in_place to False otherwise it overwrites the batchwise_fp32_outputs_list
+                sqnr += _compute_sqnr(fp32_output, quantized_output, in_place=False)
+            else:
+                break
+
+    # Convert SQNR into dB scale
+    sqnr_db = 10 * np.log10(sqnr / num_samples)
+    return sqnr_db
+
+
+def _compute_sqnr(orig_tensor: torch.Tensor,
+                  noisy_tensor: torch.Tensor,
+                  in_place: bool = False) -> float:
+    """
+    Compute SQNR between two tensors.
+
+    IMPORTANT: If in_place is True, the input tensors will be used as in-place buffer
+               and hence will have been corrupted when this function returns.
+
+    :param orig_tensor: Original tensor
+    :param noisy_tensor: Noisy tensor
+    :param in_place: If True, use the input tensors as in-place buffer
+    :return: SQNR
+    """
+    assert orig_tensor.shape == noisy_tensor.shape
+    assert orig_tensor.dtype == noisy_tensor.dtype
+
+    # SQNR := E[signal**2] / E[noise**2]
+    if in_place:
+        signal = orig_tensor
+        noise = noisy_tensor.negative_().add_(orig_tensor)
+    else:
+        signal = orig_tensor.detach().clone()
+        noise = orig_tensor - noisy_tensor
+
+    sqnr = signal.square_().mean() / (noise.square_().mean() + 0.0001)
+    return float(sqnr)
+
+
+class GreedyMixedPrecisionAlgo(MixedPrecisionAlgo):
+    """ Naive Greedy MixedPrecisionAlgo class """
+
+    ENABLE_MP_PROFILE_OPTIMIZE = False # Enables op_graph.optimize() during Phase-2
+    ENABLE_CONVERT_OP_REDUCTION = False # Run phase-3
+
+    # pylint: disable=too-many-arguments
+    def __init__(self, sim: _QuantizationSimModelInterface,
+                 dummy_input: Union[torch.Tensor, Tuple],
+                 candidates: List[CANDIDATE_WITH_DTYPE],
+                 eval_callback_for_phase1: CallbackFunc,
+                 eval_callback_for_phase2: CallbackFunc,
+                 results_dir: str, clean_start: bool,
+                 forward_pass_callback: CallbackFunc,
+                 use_all_amp_candidates: bool = False,
+                 phase2_reverse: bool = False,
+                 phase1_optimize: bool = False):
+        """
+
+        :param sim: Quantized sim model
+        :param dummy_input: Dummy input to the model. If the model has more than one input, pass a tuple.
+                            User is expected to place the tensors on the appropriate device.
+        :param candidates: List of Tuple of all possible [bitwidth, QuantizationDataType] values to quantize to
+        :param eval_callback_for_phase1: An object of CallbackFunc class which takes in Eval function (callable) and eval
+                                     function parameters. This evaluation callback used to measure sensitivity of each
+                                     quantizer group during phase 1. The phase 1 involves finding accuracy list/sensitivity of each
+                                     module. Therefore, a user might want to run the phase 1 with a smaller dataset
+        :param eval_callback_for_phase2: An object of CallbackFunc class which takes in Eval function (callable) and eval
+                                     function parameters. Evaluation callback used to get accuracy of quantized model
+                                     for phase 2 calculations. The phase 2 involves finding pareto front curve
+        :param results_dir: Path to save results and cache intermediate results
+        :param clean_start: If true, any cached information from previous runs will be deleted prior to starting the
+                            mixed-precision analysis. If false, prior cached information will be used if applicable. Note
+                            it is the user's responsibility to set this flag to true if anything in the model or
+                            quantization parameters changes compared to the previous run.
+        :param forward_pass_callback: An object of CallbackFunc class which takes in Forward pass function (callable) and its
+                                  function parameters. Forward pass callback used to compute quantization encodings
+        :param use_all_amp_candidates: Using the “supported_kernels” field in the config file (under defaults
+                    and op_type sections), a list of supported candidates can be specified. All the AMP candidates
+                    which are passed through the “candidates” field may not be supported based on the data passed
+                    through “supported_kernels”. When the field “use_all_amp_candidates” is set to True, the AMP algo
+                    will ignore the "supported_kernels" in the config file and will continue to use all the candidates.
+        :param phase2_reverse: If user will set this parameter to True, then phase1 of amp algo, that is calculating accuracy list will not be changed,
+                            whereas the phase2 algo of amp, which generate the pareto list will be changed. In phase2, algo will start, model with all quantizer groups in least candidate, and
+                            one by one, it will put nodes in higher candidate till target accuracy does not meet.
+        :phase1_optimize: If user set this parameter to True then phase1 optimized logic will be executed else common code will be executed
+        """
+        mac_dict = mixed_precision_utils.create_mac_dict(sim.model, dummy_input)
+        self.phase1_optimize = phase1_optimize
+        self.dummy_input = dummy_input
+
+        super().__init__(sim, candidates,
+                                                       eval_callback_for_phase1,
+                                                       eval_callback_for_phase2,
+                                                       forward_pass_callback,
+                                                       mac_dict,
+                                                       results_dir, clean_start, phase2_reverse)
+
+        supported_kernels = reformat_supported_kernels(sim.get_supported_kernels())
+
+        # Find 1. candidates for each of the quantizers by using supported_kernels, candidates and
+        # use_all_amp_candidates (flag)
+        # 2. max_candidate_options based on the candidates which are present in all the quantizers
+        self._supported_candidates_per_quantizer_group, self._baseline_candidate_options = find_supported_candidates(
+            self.quantizer_groups,
+            candidates,
+            supported_kernels,
+            get_module_name_to_module_dict(sim),
+            use_all_amp_candidates)
+
+
+    def _create_and_save_accuracy_list_optimized(self, baseline_candidate) -> ACCURACY_LIST:
+        """
+        Create a list of tuples of (quantizer_group, bitwidth, accuracy score)
+
+        :param baseline_candidate: Candidate [bitwidth, dtype] which yields max accuracy
+        :return: Sorted accuracy list containing tuples of (quantizer, candidate, accuracy score, bit ops reduction)
+        """
+        # pylint: disable=too-many-locals, too-many-branches, too-many-statements
+        index_of_quantizer_group = {}
+        for index, quantizer_group in enumerate(self.quantizer_groups):
+            index_of_quantizer_group[quantizer_group] = index
+
+        accuracy_list: ACCURACY_LIST = []
+
+        file = os.path.join(self._results_dir, '.cache', 'accuracy_list.pkl')
+        combinations_already_computed = set()
+
+        if os.path.isfile(file):
+            if self._clean_start:
+                os.remove(file)
+                logger.info("Removed old cached files and restarting computation")
+            else:
+                with open(file, 'rb') as f:
+                    accuracy_list = pickle.load(f)
+
+                combinations_already_computed.update(
+                    (quantizer_group, candidate)
+                    for quantizer_group, candidate, _, _ in accuracy_list
+                )
+
+        disabled_quantizers = OrderedDict()
+
+        try:
+            # Disable all quantizers
+            for quantizer_group in self.quantizer_groups:
+                quantizers = quantizer_group.get_active_quantizers(self._module_name_dict)
+                disable_quantizers(quantizers)
+                disabled_quantizers[quantizer_group] = quantizers
+
+            # quantizer_groups_per_candidate = {"candidate1":[quantizer_group1,quantizer_group2,...]}
+            # quantizer_groups_per_candidate is the dictionary with keys as candidates and values as quantizer groups that supports the corresponding candidate
+            # quantizer_groups_per_candidate is like reverse mapping to self._supported_candidates_per_quantizer_group
+            quantizer_groups_per_candidate = defaultdict(list)
+            for quantizer_group, candidates in self._supported_candidates_per_quantizer_group.items():
+                for candidate in candidates:
+                    quantizer_groups_per_candidate[candidate].append(quantizer_group)
+
+            # Loop through all possible bitwidths(candidates). Set all the quantizer groups to the corresponding bitwidth(candidate)
+            # Compute encodings by disabling the parameters and  reuse the encodings
+            for candidate, quantizer_groups in quantizer_groups_per_candidate.items():
+                if candidate == baseline_candidate:
+                    continue
+
+                 # configure the sim model with the candidate by enabling the quantizers and set quantizers to corresponding candidate
+                for quantizer_group in quantizer_groups:
+                    quantizers = disabled_quantizers[quantizer_group]
+                    try:
+                        enable_quantizers(quantizers)
+                        # Set quantizer bitwidth to candidate (bitwidth)
+                        quantizer_group.set_quantizers_to_candidate(self._module_name_dict, candidate)
+                    except RuntimeError as e:
+                        logger.info("Exception occured while setting Quantizers to Candidate: %s", e)
+
+                # list to store all the param quantizers
+                param_quantizers = []
+
+                for _, wrapper in self._sim.quant_wrappers():
+                    for _, param_quantizer in wrapper.param_quantizers.items():
+                        if param_quantizer.enabled:
+                            param_quantizers.append(param_quantizer)
+
+                # disable the parameter quantization
+                disable_quantizers(param_quantizers)
+
+
+                # compute encodings with out parameter quantization
+                self._sim.compute_encodings(self.algo_params.forward_pass_callback,
+                                            self.algo_params.forward_pass_callback_args)
+
+                # enable the parameter quantization
+                enable_quantizers(param_quantizers)
+
+
+                # Disable all the quantizers
+                for quantizer_group in quantizer_groups:
+                    quantizers = quantizer_group.get_active_quantizers(self._module_name_dict)
+                    disable_quantizers(quantizers)
+                    disabled_quantizers[quantizer_group] = quantizers
+
+                # Loop over all the quantizer groups and enable one at a time and calculate resulting model accuracy and disable the enabled quantizer
+                # Accuracy list will contain tuples of the quantizer, bitwidth, and accuracy score
+                for quantizer_group in quantizer_groups:
+                    quantizers = disabled_quantizers[quantizer_group]
+                    try:
+                        enable_quantizers(quantizers)
+
+                        # If starting the computation from an already existing state, then check if that combination
+                        # has already been executed
+                        if (quantizer_group, candidate) in combinations_already_computed:
+                            continue
+                        # Compute accuracy of model with new candidate (bitwidth)
+                        eval_score = self.evaluate_model(self.algo_params.eval_callback_for_phase1)
+
+                        bit_ops_reduction = self._find_bit_ops_reduction_for_acc_list(quantizer_group,
+                                                                                      baseline_candidate,
+                                                                                      candidate)
+                        accuracy_list.append((quantizer_group, candidate, eval_score, bit_ops_reduction))
+                        # Sort accuracy list, first by descending accuracy score, then by descending order of addition of bitwidths if accuracy
+                        # scores are identical, if that is also identical we sort by relative bit ops change in descending order
+                        # If bit ops reduction is also the same, then we sort in ascending order based on occurence of
+                        # quantizer group in the model
+                        accuracy_list = sort_accuracy_list(accuracy_list, index_of_quantizer_group)
+                        self._export_accuracy_list(accuracy_list, self._results_dir)
+                        logger.info('\n Quantizer: %s candidate: %s eval_score: %f \n', quantizer_group,
+                                    candidate, eval_score)
+                    finally:
+                        # Disable the quantizer
+                        disable_quantizers(quantizers)
+        finally:
+
+            # set all quantizers to baseline candidate
+            for quantizer_group in self.quantizer_groups:
+                quantizers = disabled_quantizers[quantizer_group]
+                try:
+                    # Enable the disabled quantizers
+                    enable_quantizers(quantizers)
+                    quantizer_group.set_quantizers_to_candidate(self._module_name_dict, baseline_candidate)
+                except RuntimeError as e:
+                    logger.info("Exception occured while setting Quantizers to Candidate: %s", e)
+
+        logger.info('Completed Accuracy list computation')
+        # Recompute encodings after quantizer's bitwidth is set back to self._max_bitwidth
+        self._sim.compute_encodings(self.algo_params.forward_pass_callback, self.algo_params.forward_pass_callback_args)
+        return accuracy_list
+
+
+
+    def _evaluate_model(self, eval_callback) -> float:
+        """
+        Evaluates a model
+
+        :param eval_callback: Callback function that contains eval function and eval args
+        :return: Eval score
+        """
+        return eval_callback.func(self._sim.model, eval_callback.args)
+
+    def _find_quantizer_group(self, sim) -> Tuple[Dict, List[QuantizerGroup]]:
+        """
+        Finds quantizer groups in a quantization sim
+
+        :param sim: Quantization sim
+        :return: Dictionary mapping quantized op name to sim.quantizer_config,
+            and a List of quantizer groups
+        """
+        return find_quantizer_group(sim)
+
+    @property
+    def baseline_candidate_options(self) -> List[CANDIDATE_WITH_DTYPE]:
+        """
+        Returns the _baseline_candidate_options which is the intersection of amp candidates and candidates supported by
+        all the quantizer groups
+        """
+        return self._baseline_candidate_options
+
+    def _find_bit_ops_reduction_for_acc_list(
+            self,
+            quantizer_group: QuantizerGroup,
+            max_candidate: CANDIDATE_WITH_DTYPE,
+            candidate: CANDIDATE_WITH_DTYPE,
+    ) -> int:
+        """
+        Finds reduction in bit ops from max candidate to new candidate
+
+        :param quantizer_group: Quantizer group
+        :param max_candidate: Maximum bitwidth and data type for the TensorQuantizer
+        :param candidate: Activation bitwidth, parameter bitwidth
+        :return: Bit ops reduction
+        """
+        return mixed_precision_utils.find_bit_ops_reduction(quantizer_group, self._mac_dict,
+                                                            max_candidate, candidate)
+
+    def calculate_running_bit_ops(
+            self,
+            quantizer_group: QuantizerGroup,
+            module_bitwidth_dict: Dict,
+            max_candidate: CANDIDATE_WITH_DTYPE,
+            candidate: CANDIDATE_WITH_DTYPE,
+            running_bit_ops: int,
+    ) -> int:
+        """
+        Calculates running bit ops value for every quantizer group
+
+        :param quantizer_group: A group of activation & parameter quantizers
+        :param module_bitwidth_dict: Dict; Key: Module name value: Activation, parameter bitwidth of module
+        :param max_candidate: Maximum bitwidth and data type for the TensorQuantizer
+        :param candidate: candidate to change the quantizer group to
+        :param running_bit_ops: Running bit ops value calculated uptil the quantizer group
+        :return: Running bit ops value
+        """
+        running_bit_ops = mixed_precision_utils.calculate_running_bit_ops(self._mac_dict, quantizer_group,
+                                                                          module_bitwidth_dict,
+                                                                          max_candidate,
+                                                                          candidate,
+                                                                          running_bit_ops)
+        return running_bit_ops
+
+    def _create_and_save_accuracy_list(self, baseline_candidate):
+        def disable_all_quantizers():
+            return utils.disable_all_quantizers(self._sim.model)
+
+        # Note: "disable_all_quantizers" will be migrated to the open source codew
+        #        as a method of QuantizationSimModel.
+        try:
+            setattr(self._sim, "disable_all_quantizers", disable_all_quantizers)
+            if self.phase1_optimize:
+                return self._create_and_save_accuracy_list_optimized(baseline_candidate)
+            return super()._create_and_save_accuracy_list(baseline_candidate)
+        finally:
+            delattr(self._sim, "disable_all_quantizers")
+
+    def set_quantizer_groups_candidates(self, quantizer_group_candidates: List[Tuple]) -> None:
+        """
+        Setter function to set quantizer groups to given candidate. This method also computes the encodings following
+        the change in quantizer groups
+        :param quantizer_group_candidates: list of quantizer groups and their candidates
+        """
+        def validate_quantizer_candidate(qg: QuantizerGroup, qg_candidate) -> bool:
+            """
+            Helper method to validate whether candidate can be applied
+            :param qg: quantizer group whose candidate needs to be changed
+            :param qg_candidate: the new candidate which needs to be applied to the quantizer group
+            :return: boolean value True if success else False
+            """
+            supported_candidates = self._supported_candidates_per_quantizer_group.get(qg)
+            if not qg.parameter_quantizers:
+                (activation_bw, activation_data_type), _ = qg_candidate
+                # Since only activation quantizers are present, validate activation candidate
+                for supported_candidate in supported_candidates:
+                    if supported_candidate[0] == (activation_bw, activation_data_type):
+                        return True
+                return False
+
+            # both activation and param quantizers are present in the quantizer group
+            return qg_candidate in supported_candidates
+
+        for quantizer_group, candidate in quantizer_group_candidates:
+            assert validate_quantizer_candidate(quantizer_group, candidate)
+            quantizer_group.set_quantizers_to_candidate(self._module_name_dict, candidate)
+
+        self._sim.compute_encodings(self.algo_params.forward_pass_callback, self.algo_params.forward_pass_callback_args)
+
+    def _create_op_graph(self, sim):
+        """
+        Creates op graph
+
+        :param sim: QuantizationSimModel object
+        """
+        return None
+
+    def _optimize_mp_profile_and_evaluate_model(self):
+        """
+        Get the eval score of the model based on whether ENABLE_MP_PROFILE_OPTIMIZE is enabled.
+        """
+
+        # Recompute quantizer encodings
+        self._sim.compute_encodings(self.algo_params.forward_pass_callback,
+                                    self.algo_params.forward_pass_callback_args)
+        # Compute new accuracy score
+        eval_score = self.evaluate_model(self.algo_params.eval_callback_for_phase2)
+
+        return eval_score
+
+    # pylint: disable=protected-access
+    def _reduce_mp_convert_ops(self):
+        """
+        Reduce mixed precision convert ops if enabled and supported
+        """
+        if self.ENABLE_CONVERT_OP_REDUCTION:
+            reduce_convert_ops_algo = ReduceConvertOps(self._sim, self.quantizer_groups, self.algo_params.candidates, self._mac_dict)
+
+            # Check if phase 2 solution is all 8 bits
+            phase2_all_8bits = all(
+                reduce_convert_ops_algo._phase_two_sol[qg] == 8 for qg in
+                reduce_convert_ops_algo._phase_two_sol)
+
+            # Check if phase 2 solution is all 16 bits
+            phase2_all_16bits = all(
+                reduce_convert_ops_algo._phase_two_sol[qg] == 16 for qg in
+                reduce_convert_ops_algo._phase_two_sol)
+
+            dummy_input_cpu = reduce_convert_ops_algo.convert_tensor_to_cpu(self.dummy_input)
+
+            if phase2_all_8bits or phase2_all_16bits:
+                logger.warning('Skipping phase3 because there is no scope to reduce convert-op overhead')
+            else:
+                for alpha in reduce_convert_ops_algo.DEFAULT_ALPHA_OPTIONS:
+                    phase_three_sol, solve_data_dict = \
+                        reduce_convert_ops_algo.run_amp_phase_3(alpha, SamplingStrategy.weighted_with_predicted_convert_cost)
+                    qg_candidates = reduce_convert_ops_algo.generate_qg_solution(phase_three_sol)
+
+                    # save sim state with the new quantizer settings
+                    self.set_quantizer_groups_candidates(qg_candidates)
+
+                    # export
+                    self._sim.export(path=self._results_dir, filename_prefix=f'AMP_ph3_export_alpha_{alpha}',
+                                     dummy_input=dummy_input_cpu)
+
+                    reduce_convert_ops_algo.save_phase_3_data_as_json(solve_data_dict, self._results_dir,
+                                                                      filename_suffix="alpha_{}".format(alpha))
+
    +
    +
    +
    + +
    + + +
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    +

    Source code for aimet_torch._base.amp.quantizer_groups

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2024, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+
+""" Find quantizer groups in a model """
+import itertools
+from typing import Dict, List, Tuple, Optional
+from collections import defaultdict
+from dataclasses import dataclass, field
+import torch
+
+from aimet_common.connected_graph.connectedgraph_utils import CG_SPLIT
+from aimet_common.connected_graph.operation import Op
+from aimet_common.utils import AimetLogger
+from aimet_common.amp.utils import CANDIDATE_WITH_DTYPE
+from aimet_common.amp.quantizer_groups import QuantizerGroupBase, get_supported_candidates_for_quantizers, \
+    compute_baseline_candidate_options
+
+from aimet_torch.meta.connectedgraph import ConnectedGraph
+from aimet_torch._base.quantsim import _QuantizationSimModelInterface, _QuantizedModuleProtocol
+from aimet_torch import onnx_utils
+from aimet_torch.translation_mapping import aimet_op_to_backend_op_name_map
+
+logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.MixedPrecision)
+
+
+
    
+[docs]
+@dataclass(frozen=True)
+class QuantizerGroup(QuantizerGroupBase):
+    """
+    Group of modules and quantizers
+    """
+    input_quantizers: Tuple[str, ...] = field(default_factory=tuple)
+    output_quantizers: Tuple[str, ...] = field(default_factory=tuple)
+    parameter_quantizers: Tuple[str, ...] = field(default_factory=tuple)
+    supported_kernel_ops: Tuple[str, ...] = field(default_factory=tuple)
+
+
    
+[docs]
+    def get_candidate(self, name_to_quantizer_dict: Dict) -> CANDIDATE_WITH_DTYPE:
+        """
+        Gets Activation & parameter bitwidth
+        :param name_to_quantizer_dict: Gets module from module name
+        :return: Tuple of Activation, parameter bitwidth and data type
+        """
+        activation_bw, parameter_bw = None, None
+        activation_data_type, parameter_data_type = None, None
+
+        for quantizer in self._get_input_quantizers(name_to_quantizer_dict) +\
+                         self._get_output_quantizers(name_to_quantizer_dict):
+            activation_bw = quantizer.bitwidth
+            activation_data_type = quantizer.data_type
+            break
+
+        for quantizer in self._get_param_quantizers(name_to_quantizer_dict):
+            if quantizer.enabled:
+                parameter_bw = quantizer.bitwidth
+                parameter_data_type = quantizer.data_type
+                break
+
+        return (activation_bw, activation_data_type), (parameter_bw, parameter_data_type)
    
+
+
+
    
+[docs]
+    def set_quantizers_to_candidate(self,
+                                    name_to_quantizer_dict: Dict,
+                                    candidate: CANDIDATE_WITH_DTYPE) -> None:
+        """
+        Sets a quantizer group to a given candidate bitwidth
+        :param name_to_quantizer_dict: Gets module from module name
+        :param candidate: candidate with act and param bw and data types
+        """
+        if len(candidate) == 1:
+            (activation_bw, activation_data_type), = candidate
+            param_bw, param_data_type = None, None
+        else:
+            (activation_bw, activation_data_type), (param_bw, param_data_type) = candidate
+
+        for quantizer in self._get_input_quantizers(name_to_quantizer_dict) +\
+                         self._get_output_quantizers(name_to_quantizer_dict):
+            quantizer.bitwidth = activation_bw
+            quantizer.data_type = activation_data_type
+
+        if param_bw is not None:
+            for quantizer in self._get_param_quantizers(name_to_quantizer_dict):
+                quantizer.bitwidth = param_bw
+                quantizer.data_type = param_data_type
    
+
+
+
    
+[docs]
+    def to_list(self) -> List[Tuple[str, str]]:
+        """
+        Converts quantizer group to a list
+        :return: List containing input/output quantizers & weight quantizers
+        """
+        return list(itertools.chain(
+            (("input", module_name) for module_name in self.input_quantizers),
+            (("output", module_name) for module_name in self.output_quantizers),
+            (("weight", module_name) for module_name in self.parameter_quantizers),
+        ))
    
+
+
+
    
+[docs]
+    def get_active_quantizers(self, name_to_quantizer_dict):
+        """ Find all active tensor quantizers associated with this quantizer group """
+        quantizers = self._get_input_quantizers(name_to_quantizer_dict) +\
+                     self._get_output_quantizers(name_to_quantizer_dict) +\
+                     self._get_param_quantizers(name_to_quantizer_dict)
+        return [quantizer for quantizer in quantizers if quantizer.enabled]
    
+
+
+    def _get_input_quantizers(self, name_to_quantizer_dict):
+        result = []
+        for quantizer_name in self.input_quantizers:
+            out = quantizer_name.split("_input_quantizer_idx_")
+            assert len(out) == 2
+            module_name, quantizer_idx = out[0], int(out[1])
+            module = name_to_quantizer_dict[module_name]
+            result.append(module.input_quantizers[quantizer_idx])
+        return result
+
+    def _get_output_quantizers(self, name_to_quantizer_dict):
+        result = []
+        for module_name in self.output_quantizers:
+            module = name_to_quantizer_dict[module_name]
+            result += module.output_quantizers
+        return result
+
+    def _get_param_quantizers(self, name_to_quantizer_dict):
+        result = []
+        for module_name in self.parameter_quantizers:
+            module = name_to_quantizer_dict[module_name]
+            for _, param_quantizer in module.param_quantizers.items():
+                result.append(param_quantizer)
+        return result
+
+
    
+[docs]
+    def get_input_quantizer_modules(self):
+        """helper method to get the module names corresponding to input_quantizers"""
+        result = set()
+        for quantizer_name in self.input_quantizers:
+            out = quantizer_name.split("_input_quantizer_idx_")
+            assert len(out) == 2
+            result.add(out[0])
+        return tuple(sorted(result))
    
+
    
+
+
+
+def find_wrapper_module(op_name: str, module_name_to_quantizer_dict: Dict) -> \
+        Tuple[Optional[str], Optional[torch.nn.Module]]:
+    """
+    Finds quantization (wrapping) module corresponding to the wrapper module's dotted name
+    :param op_name: Dotted name of op as represented in connected graph
+    :param module_name_to_quantizer_dict: Dict key: name of wrapped module value: quantization wrapper
+    :return: Module name and the corresponding torch module in the sim
+    """
+    # pylint:disable = protected-access
+    module_name = op_name[op_name.find('.') + 1:]
+    if module_name in module_name_to_quantizer_dict:
+        return module_name, module_name_to_quantizer_dict[module_name]
+    # Else it is a functional op
+    return None, None
+
+
+def get_module_name_to_module_dict(sim: _QuantizationSimModelInterface) -> Dict:
+    """
+    Creates a dictionary of wrapped module's name to quantizer module
+    :param sim: quantization sim
+    :return: Dict key: name of wrapped module value: quantization wrapper
+    """
+    module_name_to_quantizer_dict = {}
+    for name, ref_module in sim.model.named_modules():
+        if isinstance(ref_module, _QuantizedModuleProtocol):
+            module_name_to_quantizer_dict[name] = ref_module
+    return module_name_to_quantizer_dict
+
+
+ops_to_skip = ['view',
+               'NumToTensor',
+               'Split',
+               CG_SPLIT,
+               'PythonOp',
+               'Tile',
+               'transpose',
+               'reshape',
+               'flatten',
+               'permute',
+               'Permute', # tensor.transpose() results in Permute. Name obtained after mpp
+               'Reshape', # Name obtained after MPP
+               'ChannelShuffle', # Obtained without going thru mpp. torch.nn.ChannelShuffle fails mpp
+               'TopK', # Name obtained after MPP
+               'PixelShuffle', # Name obtained after MPP
+               'Expand', # Name obtained after MPP. Reproduce using tensor.expand
+               'Pad', # Name obtained after MPP
+               'Slice', # Name obtained after MPP
+               'Gather', # Name obtained after MPP
+               'ScatterElements', # Name obtained after MPP
+               'ReduceMin',  # Name obtained after MPP
+               'ReduceMax', # Name obtained after MPP
+               'Upsample', # Name obtained after MPP
+               'RoIPool', # Name obtained after MPP
+               'MaxPool', # Name obtained after MPP
+               'Transpose' # Name obtained after MPP
+               ]
+ops_not_to_traverse = ['size']
+
+def find_output_quantizer_groups(op: Op, parent_child_op_groups: Dict, map_for_skipped_ops: Dict):
+    """
+    Finds quantizer groups along the parent to child flow
+    :param op: pytorch module
+    :param parent_child_op_groups: parent child relationships in graph
+    :param map_for_skipped_ops: map to find first skipped parents of skipped ops
+    """
+    output = op.output
+    if output:
+        consumers = output.consumers
+        for consumer in consumers:
+            if consumer.type in ops_not_to_traverse:
+                continue
+            dotted_name = op.dotted_name
+            if op.dotted_name in map_for_skipped_ops:
+                dotted_name = map_for_skipped_ops[op.dotted_name]
+            if consumer.type in ops_to_skip:
+                map_for_skipped_ops[consumer.dotted_name] = dotted_name
+                find_output_quantizer_groups(consumer, parent_child_op_groups, map_for_skipped_ops)
+            # If there is a one to one connection between quantizers
+            else:
+                parent_child_op_groups[dotted_name].append(consumer.dotted_name)
+    else:
+        if op.dotted_name in map_for_skipped_ops:
+            parent_child_op_groups[map_for_skipped_ops[op.dotted_name]] = []
+
+
+def find_op_groups(graph: ConnectedGraph) -> Dict:
+    """
+    Finds parent children relationship based on three rules
+    1) If there is a direct connection between two ops, op1 and op2, then op1 is parent of op2 and they form a group
+    2) If the input to an op (op1) is shared with another op (op2), the op producing the input (op0) is the parent, and op1 and op2 are the children
+    :param graph: connected graph
+    :return: Dict of parent (key) and children (value) relationship
+    """
+    parent_child_op_groups = defaultdict(list)
+    map_for_skipped_ops = {}
+
+    for op in graph.ordered_ops:
+        # Add 1st op as child
+        if not op.input_ops:
+            parent_child_op_groups['input_ops'].append(op.dotted_name)
+        # Add output op as child to put output of model as a quantizer group
+        if op.output is None:
+            parent_child_op_groups['output_ops'].append(op.dotted_name)
+    for op in graph.get_all_ops().values():
+        if op.type in ops_to_skip or op.type in ops_not_to_traverse:
+            continue
+        find_output_quantizer_groups(op, parent_child_op_groups, map_for_skipped_ops)
+
+    return parent_child_op_groups
+
+
+# This code is not currently called anywhere but it can be used to combine two ops who feed into an elementwise op
+def find_input_quantizer_groups(graph, map_for_skipped_ops, parent_child_op_groups):
+    """
+    Combines two groups which share the same output
+    :param graph: connected graph
+    :param map_for_skipped_ops: map to find first skipped parents of skipped ops
+    :param parent_child_op_groups: parent child relationships in graph
+    """
+
+    for op in graph.get_all_ops().values():
+        inputs = op.input_ops
+        if len(inputs) > 1:
+            new_parents = set()
+            new_children = set()
+            for input_op in inputs:
+                dotted_name = input_op.dotted_name
+
+                if input_op.type in ops_to_skip:
+                    dotted_name = map_for_skipped_ops[dotted_name]
+
+                new_parents.add(dotted_name)
+
+                if dotted_name in parent_child_op_groups:
+                    for name in parent_child_op_groups[dotted_name]:
+                        new_children.add(name)
+                    del parent_child_op_groups[dotted_name]
+            if len(new_parents) == 1:
+                parent_child_op_groups[tuple(new_parents)[0]] = new_children
+            else:
+                parent_child_op_groups[tuple(new_parents)] = new_children
+
+
+def get_input_and_param_quantizers(
+        child: str, module_name_to_module_dict: Dict
+) -> Tuple[Tuple[str, ...], Tuple[str, ...]]:
+    """
+    Adds child's input quantizer and param quantizer to quantizer group
+    :param child: name of child
+    :param module_name_to_module_dict: name to module ref dict
+    :param quantizer_group: quantizer group
+    """
+    input_quantizers = []
+    parameter_quantizers = []
+    module_name, module = find_wrapper_module(child, module_name_to_module_dict)
+    if module_name is not None:
+        for idx, input_quantizer in enumerate(module.input_quantizers):
+            if input_quantizer.enabled:
+                input_quantizers.append(module_name + '_input_quantizer_idx_' + str(idx))
+        for _, param_quantizer in module.param_quantizers.items():
+            if param_quantizer.enabled:
+                parameter_quantizers.append(module_name)
+    return tuple(input_quantizers), tuple(parameter_quantizers)
+
+
+# pylint: disable=too-many-branches, too-many-locals
+def find_quantizer_group(sim: _QuantizationSimModelInterface) -> Tuple[Dict, List[QuantizerGroup]]:
+    """
+    Finds quantizer groups in a quantization sim model
+    :param sim: Quantization sim
+    :return: List of Quantizer groups
+    """
+    # Get connected graph from quantsim for model without wrappers
+    connected_graph = sim.connected_graph
+
+    if connected_graph is None:
+        raise AssertionError('Aborting Auto Mixed Precision, connected graph needs to exist for Auto Mixed precision')
+
+    quantizer_groups = []
+
+    parent_child_op_groups = find_op_groups(connected_graph)
+
+    module_name_to_module_dict = get_module_name_to_module_dict(sim)
+
+    if 'input_ops' in parent_child_op_groups:
+        for child in parent_child_op_groups['input_ops']:
+            # Add one quantizer group for each input and it's weight param
+            input_quantizers, parameter_quantizers = get_input_and_param_quantizers(child, module_name_to_module_dict)
+            if input_quantizers or parameter_quantizers:
+                child_module_name, _ = find_wrapper_module(child, module_name_to_module_dict)
+                supported_kernel_ops = []
+                if child_module_name is not None:
+                    supported_kernel_ops.append(child_module_name)
+                quantizer_group = QuantizerGroup(
+                    input_quantizers=input_quantizers,
+                    parameter_quantizers=parameter_quantizers,
+                    supported_kernel_ops=tuple(supported_kernel_ops)
+                )
+                quantizer_groups.append(quantizer_group)
+                logger.debug('\n Quantizer Group Added: %s', quantizer_group)
+
+    # Based on which quantizers are enabled, create a list of quantizer_groups
+    for parents, children in parent_child_op_groups.items():
+        input_quantizers = ()
+        output_quantizers = ()
+        parameter_quantizers = ()
+        if parents in ['input_ops', 'output_ops']:
+            continue
+        if not isinstance(parents, tuple):
+            parents = [parents]
+        for parent in parents:
+            module_name, module = find_wrapper_module(parent, module_name_to_module_dict)
+            if module is not None:
+                for output_quantizer in module.output_quantizers:
+                    if output_quantizer.enabled:
+                        output_quantizers += (module_name,)
+
+        supported_kernel_ops = []
+        for child in children:
+            input_q, param_q = get_input_and_param_quantizers(child, module_name_to_module_dict)
+            input_quantizers += input_q
+            parameter_quantizers += param_q
+            child_module_name, _ = find_wrapper_module(child, module_name_to_module_dict)
+            if child_module_name is not None:
+                supported_kernel_ops.append(child_module_name)
+
+        # Don't add quantizer group if it is empty
+        if input_quantizers or output_quantizers or parameter_quantizers:
+            quantizer_group = QuantizerGroup(
+                input_quantizers=input_quantizers,
+                output_quantizers=output_quantizers,
+                parameter_quantizers=parameter_quantizers,
+                supported_kernel_ops=tuple(supported_kernel_ops)
+            )
+            quantizer_groups.append(quantizer_group)
+            logger.debug('\n Quantizer Group added: %s', quantizer_group)
+
+    if 'output_ops' in parent_child_op_groups:
+        for parent in parent_child_op_groups['output_ops']:
+            # Add one quantizer group for each input and it's weight param
+            module_name, module = find_wrapper_module(parent, module_name_to_module_dict)
+            if module is not None:
+                for output_quantizer in module.output_quantizers:
+                    if output_quantizer.enabled:
+                        # Using empty supported kernel ops so that model output quantizers are able to consider all
+                        # default candidates
+                        quantizer_group = QuantizerGroup(
+                            output_quantizers=(module_name,),
+                            supported_kernel_ops=tuple()
+                        )
+                        quantizer_groups.append(quantizer_group)
+                        logger.debug('\n Quantizer Group added: %s', quantizer_group)
+
+    return module_name_to_module_dict, quantizer_groups
+
+
+def find_supported_candidates(quantizer_groups: List[QuantizerGroup],
+                              amp_candidates: List[CANDIDATE_WITH_DTYPE],
+                              supported_kernels: Dict,
+                              module_name_to_module_dict: Dict,
+                              use_all_amp_candidates: bool) -> Tuple[Dict, List]:
+    """
+    Computes 1. a list of supported candidates per Quantizer and 2. List of candidate options for max_candidate
+    :param quantizer_groups: List of quantizer groups computed for the given model
+    :param amp_candidates: List of candidates specified by the user to be used for the AMP algorithm
+    :param supported_kernels: Dict of supported kernels for a given op/defaults specified in the config file
+    :param module_name_to_module_dict: Dict mapping module name to module/quantizer
+    :param use_all_amp_candidates: Boolean value representing whether the unsupported candidates in the
+    "candidates" list need to be considered for creating the output lists. If set to True, all the AMP candidates are
+    directly used for all the Quantizers, else the candidates per Quantizers are computed.
+    """
+
+    quantizers_with_supported_candidates = defaultdict(list)
+
+    # pylint: disable=too-many-nested-blocks
+    # pylint: disable=protected-access
+    for quantizer_group in quantizer_groups:
+        quantizers = sorted(set(itertools.chain(quantizer_group.get_input_quantizer_modules(),
+                                                quantizer_group.output_quantizers,
+                                                quantizer_group.parameter_quantizers)))
+
+        # quantizers are now unique ops present in the given quantizer_group
+        onnx_ops = defaultdict(list)
+        supported_kernel_types = set()
+        for supported_kernel_op in quantizer_group.supported_kernel_ops:
+            try:
+                module = module_name_to_module_dict[supported_kernel_op]._module_to_wrap
+            except AttributeError:
+                module = module_name_to_module_dict[supported_kernel_op].get_original_module()
+            backend_type = aimet_op_to_backend_op_name_map.get(type(module))
+
+            if backend_type in supported_kernels:
+                supported_kernel_types.add(backend_type)
+            else:
+                onnx_types = onnx_utils.map_torch_types_to_onnx.get(type(module), [])
+
+                if not onnx_types:
+                    logger.warning("No mapping found for %s in the torch to onnx op type mapping dictionary.",
+                                   type(module))
+
+                supported_kernel_types.update(onnx_types)
+
+                for onnx_type in onnx_types:
+                    if onnx_type not in supported_kernels.keys():
+                        if module in supported_kernels:
+                            supported_kernels[onnx_type] = supported_kernels[module]
+
+        for quantizer in quantizers:
+            onnx_ops[quantizer] = list(supported_kernel_types)
+
+        supported_kernels_for_quantizers = get_supported_candidates_for_quantizers(quantizers,
+                                                                                   onnx_ops,
+                                                                                   supported_kernels,
+                                                                                   amp_candidates,
+                                                                                   use_all_amp_candidates)
+
+        quantizers_with_supported_candidates[quantizer_group] = supported_kernels_for_quantizers.copy()
+
+    max_candidate_options = compute_baseline_candidate_options(quantizers_with_supported_candidates, amp_candidates,
+                                                               use_all_amp_candidates)
+
+    return quantizers_with_supported_candidates, max_candidate_options
+
    +
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    + + + + + + + + + \ No newline at end of file diff --git a/releases/2.0.0/_modules/aimet_torch/_base/mixed_precision.html b/releases/2.0.0/_modules/aimet_torch/_base/mixed_precision.html new file mode 100644 index 0000000..51bc108 --- /dev/null +++ b/releases/2.0.0/_modules/aimet_torch/_base/mixed_precision.html @@ -0,0 +1,464 @@ + + + + + + + + aimet_torch._base.mixed_precision - AIMET + + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    +

    Source code for aimet_torch._base.mixed_precision

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2024, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+
+""" Mixed precision inference """
+
+from typing import Union, Tuple, List
+import torch
+
+from aimet_common.utils import AimetLogger
+from aimet_common.amp.utils import (
+    visualize_quantizer_group_sensitivity,
+    visualize_pareto_curve,
+    CANDIDATE_WITH_DTYPE as TORCH_CANDIDATE,
+    AMPSearchAlgo,
+)
+from aimet_common.defs import CallbackFunc
+from .quantsim import _QuantizationSimModelInterface
+from .amp.mixed_precision_algo import GreedyMixedPrecisionAlgo
+from .amp.quantizer_groups import QuantizerGroup
+
+logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.MixedPrecision)
+
+
+# pylint: disable=too-many-arguments
+
    
+[docs]
+def choose_mixed_precision(sim: _QuantizationSimModelInterface, dummy_input: Union[torch.Tensor, Tuple],
+                           candidates: List[TORCH_CANDIDATE], eval_callback_for_phase1: CallbackFunc,
+                           eval_callback_for_phase2: CallbackFunc, allowed_accuracy_drop: Union[None, float],
+                           results_dir: str, clean_start: bool, forward_pass_callback: CallbackFunc,
+                           use_all_amp_candidates: bool = False, phase2_reverse: bool = False,
+                           phase1_optimize: bool = True, amp_search_algo: AMPSearchAlgo = AMPSearchAlgo.Binary) -> \
+        Union[List[Tuple[int, float, QuantizerGroup, int]], None]:
+    """
+    High-level API to perform in place Mixed Precision evaluation on the given sim model. A pareto list is created and
+    a curve for Accuracy vs BitOps is saved under the results directory
+
+    :param sim: Quantized sim model
+    :param dummy_input: Dummy input to the model. If the model has more than one input, pass a tuple.
+                        User is expected to place the tensors on the appropriate device.
+    :param candidates: List of tuples for all possible bitwidth values for activations and parameters
+                    Suppose the possible combinations are-
+                    ((Activation bitwidth - 8, Activation data type - int), (Parameter bitwidth - 16, parameter data type - int))
+                    ((Activation bitwidth - 16, Activation data type - float), (Parameter bitwidth - 16, parameter data type - float))
+                    candidates will be [((8, QuantizationDataType.int), (16, QuantizationDataType.int)),
+                                        ((16, QuantizationDataType.float), (16, QuantizationDataType.float))]
+    :param eval_callback_for_phase1: An object of CallbackFunc class which takes in Eval function (callable) and eval
+                                     function parameters. This evaluation callback used to measure sensitivity of each
+                                     quantizer group during phase 1. The phase 1 involves finding accuracy list/sensitivity of each
+                                     module. Therefore, a user might want to run the phase 1 with a smaller dataset
+    :param eval_callback_for_phase2: An object of CallbackFunc class which takes in Eval function (callable) and eval
+                                     function parameters. Evaluation callback used to get accuracy of quantized model
+                                     for phase 2 calculations. The phase 2 involves finding pareto front curve
+    :param allowed_accuracy_drop: Maximum allowed drop in accuracy from FP32 baseline. The pareto front curve is plotted only till the point where the allowable
+                                  accuracy drop is met. To get a complete plot for picking points on the curve, the user
+                                  can set the allowable accuracy drop to None.
+    :param results_dir: Path to save results and cache intermediate results
+    :param clean_start: If true, any cached information from previous runs will be deleted prior to starting the
+                        mixed-precision analysis. If false, prior cached information will be used if applicable. Note
+                        it is the user's responsibility to set this flag to true if anything in the model or
+                        quantization parameters changes compared to the previous run.
+    :param forward_pass_callback: An object of CallbackFunc class which takes in Forward pass function (callable) and its
+                                  function parameters. Forward pass callback used to compute quantization encodings
+    :param use_all_amp_candidates: Using the “supported_kernels” field in the config file (under defaults
+                    and op_type sections), a list of supported candidates can be specified. All the AMP candidates
+                    which are passed through the “candidates” field may not be supported based on the data passed
+                    through “supported_kernels”. When the field “use_all_amp_candidates” is set to True, the AMP
+                    algorithm will ignore the "supported_kernels" in the config file and continue to use all candidates.
+    :param phase2_reverse: If user will set this parameter to True, then phase1 of amp algo, that is calculating accuracy list
+                            will not be changed, whereas the phase2 algo of amp, which generate the pareto list will be changed. In phase2, algo will start,
+                            model with all quantizer groups in least candidate, and one by one, it will put nodes in higher candidate till target accuracy does not meet.
+    :param phase1_optimize: If user set this parameter to false then phase1 default logic will be executed else optimized logic will be executed.
+    :param amp_search_algo: A valid value from the Enum AMPSearchAlgo. Defines the search algorithm to be used for
+                            the phase 2 of AMP.
+
+    :return: Pareto front list containing information including Bitops, QuantizerGroup candidates and
+             corresponding eval scores. The Pareto front list can be used for plotting a pareto front curve which
+             provides information regarding how bit ops vary w.r.t. accuracy. If the allowable accuracy drop is set to
+             100% then a user can use the pareto front curve to pick points and re-run,
+             None if we early exit the mixed precision algorithm.
+    """
+    mixed_precision_algo = GreedyMixedPrecisionAlgo(sim, dummy_input, candidates, eval_callback_for_phase1,
+                                                    eval_callback_for_phase2, results_dir, clean_start,
+                                                    forward_pass_callback, use_all_amp_candidates, phase2_reverse, phase1_optimize)
+    mixed_precision_algo.run(allowed_accuracy_drop, amp_search_algo)
+
+    if mixed_precision_algo.accuracy_list is not None and mixed_precision_algo.pareto_list is not None:
+        # Print mixed precision stats
+        logger.info(mixed_precision_algo)
+
+        # Visualize quantizer group sensitivity
+        visualize_quantizer_group_sensitivity(mixed_precision_algo.accuracy_list,
+                                              mixed_precision_algo.baseline_candidate,
+                                              mixed_precision_algo.fp32_accuracy,
+                                              results_dir=results_dir)
+        # Create pareto list curve
+        visualize_pareto_curve(mixed_precision_algo.pareto_list, results_dir)
+        return mixed_precision_algo.pareto_list
+
+    return None
    
+
+
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    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
    + Made with Sphinx and @pradyunsg's + + Furo + +
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    + + + + + + + + + \ No newline at end of file diff --git a/releases/2.0.0/_modules/aimet_torch/_base/seq_mse.html b/releases/2.0.0/_modules/aimet_torch/_base/seq_mse.html new file mode 100644 index 0000000..ee328d6 --- /dev/null +++ b/releases/2.0.0/_modules/aimet_torch/_base/seq_mse.html @@ -0,0 +1,904 @@ + + + + + + + + aimet_torch._base.seq_mse - AIMET + + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    +

    Source code for aimet_torch._base.seq_mse

    +# /usr/bin/env python
+# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2024, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+
+""" Sequential MSE base """
+
+from abc import abstractmethod, ABC
+import json
+import os
+import tempfile
+import contextlib
+from dataclasses import dataclass
+from typing import Optional, Tuple, List, Callable
+import torch
+import torch.nn.functional as functional
+from torch.utils.data import DataLoader
+
+from aimet_common.utils import AimetLogger
+
+from aimet_torch.utils import CachedDataset, get_ordered_list_of_modules, in_eval_mode, StopForwardException,\
+    change_tensor_device_placement, get_device
+from aimet_torch._base.adaround.activation_sampler import create_modulelist_for_group_modules,\
+    get_block_inputs, get_block_outputs
+
+# The following modules with weights are supported
+SUPPORTED_MODULES = (torch.nn.Linear, torch.nn.Conv2d, )
+
+# Skip running Sequential MSE if param BW is higher than supported PARAM_BW.
+SUPPORTED_PARAM_BW = 4
+
+_logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.SeqMse)
+
+
+def default_forward_fn(model, inputs):
+    """
+    Default forward function.
+    :param model: pytorch model
+    :param inputs: model inputs
+    """
+    if isinstance(inputs, torch.Tensor):
+        inputs = [inputs]
+    return model(*inputs)
+
+
+
    
+[docs]
+@dataclass
+class SeqMseParams:
+    """
+    Sequential MSE parameters
+
+    :param num_batches: Number of batches.
+    :param num_candidates: Number of candidates to perform grid search. Default 20.
+    :param inp_symmetry: Input symmetry. Available options are 'asym', 'symfp' and 'symqt'. Default 'symqt'.
+    :param loss_fn: Loss function. Available options are 'mse', 'l1' and 'sqnr'. Default 'mse'.
+    :param forward_fn: Optional adapter function that performs forward pass given a model and inputs
+     yielded from the data loader. The function expects model as first argument and inputs to model as second argument.
+    """
+    num_batches: int
+    num_candidates: int = 20
+    inp_symmetry: str = 'symqt'
+    loss_fn: str = 'mse'
+    forward_fn: Callable = default_forward_fn
    
+
+
+
+class SequentialMseBase(ABC):
+    """
+    Sequentially minimizing activation MSE loss in layer-wise way to decide optimal param quantization encodings.
+    """
+    @classmethod
+    def apply_seq_mse(cls,
+                      model: torch.nn.Module,
+                      sim,
+                      data_loader: DataLoader,
+                      params: SeqMseParams,
+                      modules_to_exclude: Optional[List[torch.nn.Module]] = None,
+                      checkpoints_config: Optional[str] = None):
+        """
+        Sequentially minimizing activation MSE loss in layer-wise way to decide optimal param quantization encodings.
+
+            1 Disable all input/output quantizers, param quantizers of non-supported modules
+            2 Find and feeze optimal parameter encodings candidate for remaining supported modules
+            3 Re-enable disabled quantizers from step 1
+
+        Example userflow:
+        model = Model().eval()
+        sim = QuantizationSimModel(...)
+        apply_seq_mse(...)
+        sim.compute_encodings(...) [compute encodings for all activations and parameters of non-supported modules]
+        sim.export(...)
+
+        NOTE:
+        1) module reference passed to modules_to_exclude should be from FP32 model.
+        2) module from modules_to_exclude won't be quantized and skipped when applying sequential MSE.
+        3) Except finding param encodings for supported modules, config JSON file will be respected and
+        final state of sim will be unchanged.
+
+        :param model: Original fp32 model
+        :param sim: Corresponding QuantizationSimModel object
+        :param data_loader: Data loader
+        :param params: Sequential MSE parameters
+        :param modules_to_exclude: List of supported type module(s) to exclude when applying Sequential MSE
+        :param checkpoints_config: Config files to split fp32/quant model by checkpoints to speedup activations sampling
+        """
+        # disable all input/output activation quantizers and
+        # param quantizers of all the non-supported modules and from modules_to_exclude list.
+        # then, re-enable disabled quantizers after running sequential mse.
+        # this ensures that config JSON file will be respected and final state of sim will be unchanged.
+        with cls.temporarily_disable_quantizers(model, sim, modules_to_exclude), \
+            tempfile.TemporaryDirectory() as tempdir:
+
+            # Initialize param encodings of modules of supported types.
+            cls.compute_all_param_encodings(sim)
+
+            cached_dataset = CachedDataset(data_loader, params.num_batches, os.path.join(tempdir, 'cached_dataset'))
+            if checkpoints_config:
+                cls.apply_seq_mse_using_opt_sampling(checkpoints_config, model, sim, modules_to_exclude, cached_dataset, params,
+                                                     tempdir)
+            else:
+                dummy_input = change_tensor_device_placement(next(iter(data_loader)), get_device(model))
+                fp32_modules = get_ordered_list_of_modules(model, dummy_input, fwd_func=params.forward_fn)
+                fp32_modules = [(name, module) for name, module in fp32_modules if isinstance(module, SUPPORTED_MODULES)]
+                if modules_to_exclude:
+                    fp32_modules = [(name, module) for name, module in fp32_modules if not module in modules_to_exclude]
+
+                # Find and freeze optimal param encodings candidate
+                cls.run_seq_mse(fp32_modules, model, sim.model, params, params.forward_fn,
+                                cached_dataset, cached_quant_dataset=None)
+
+    @classmethod
+    def apply_seq_mse_using_opt_sampling(cls,
+                                         checkpoints_config: str,
+                                         model: torch.nn.Module,
+                                         sim,
+                                         modules_to_exclude: Optional[List[torch.nn.Module]],
+                                         cached_dataset: CachedDataset,
+                                         params: SeqMseParams,
+                                         tempdir: str):
+        """
+        Apply sequential MSE using optimized sampling of intermediate data. When checkpoints_config file is provided,
+        intermediate activations from breakpoint are treated as model inputs for next blocks.
+
+        NOTE: Assumption is that the outputs from the current block are fed directly to following block
+        and there are no functional operations in-between.
+
+        :param checkpoints_config: Config files to split fp32/quant model by checkpoints to speedup activations sampling
+        :param model: Original fp32 model
+        :param sim: Corresponding QuantizationSimModel object
+        :param modules_to_exclude: List of supported type module(s) to exclude when applying Sequential MSE
+        :param cached_dataset: Cached dataset
+        :param params: Sequential MSE parameters
+        :param tempdir: temporary working directory
+        """
+        # pylint: disable=too-many-locals
+        ckpts_file = json.load(open(checkpoints_config))
+        assert 'grouped_modules' in ckpts_file.keys(), \
+            "Please provide a dictionary of grouped_modules in the file to define checkpoints"
+        assert 'include_static_inputs' in ckpts_file.keys(), \
+            "Please provide a dictionary of include_static_inputs in the file to define checkpoints"
+        assert 'cache_on_cpu' in ckpts_file.keys(), \
+            "Please define cache_on_cpu to determine whether to cache intermediate tensors on CPU"
+
+        grouped_modules = ckpts_file['grouped_modules']
+        breakpoint_module_name = ckpts_file['grouped_modules'][list(grouped_modules.keys())[0]][0]
+        include_static_inputs = ckpts_file['include_static_inputs']
+        cache_on_cpu = ckpts_file['cache_on_cpu']
+        cached_fp_dataset, cached_quant_dataset = get_block_inputs(model, sim,
+                                                                   breakpoint_module_name,
+                                                                   cached_dataset, cache_on_cpu,
+                                                                   params.forward_fn, params.num_batches,
+                                                                   tempdir)
+        device = get_device(model)
+        model.cpu()
+        sim.model.cpu()
+
+        # Forward function for the ModuleList object
+        def fwd_fn_modulelist(modulelists, x):
+            for mod in modulelists:
+                x = mod(*x) if isinstance(x, (tuple, list)) else mod(x)
+            return x
+
+        sub_fp_models, sub_sim_models = create_modulelist_for_group_modules(model, sim, grouped_modules)
+        for i, (fp_block, quant_sim_block, static_input) in enumerate(zip(sub_fp_models,
+                                                                          sub_sim_models,
+                                                                          include_static_inputs)):
+            fp32_modules = get_ordered_list_of_modules(fp_block, cached_fp_dataset[0], fwd_func=fwd_fn_modulelist)
+            fp32_modules = [(name, module) for name, module in fp32_modules if isinstance(module, SUPPORTED_MODULES)]
+            if modules_to_exclude:
+                fp32_modules = [(name, module) for name, module in fp32_modules if not module in modules_to_exclude]
+
+            cls.run_seq_mse(fp32_modules, fp_block, quant_sim_block, params, fwd_fn_modulelist,
+                            cached_fp_dataset, cached_quant_dataset=cached_quant_dataset)
+
+            # Get the outputs from the current block and assign to be the inputs for next block
+            # except for the last block
+            if i < len(sub_fp_models) - 1:
+                get_block_outputs(fp_block, quant_sim_block, static_input,
+                                  cached_fp_dataset, cached_quant_dataset, cache_on_cpu,
+                                  fwd_fn_modulelist, device, tempdir)
+        model.to(device)
+        sim.model.to(device)
+
+    @classmethod
+    def run_seq_mse(cls,
+                    fp32_modules: List[Tuple[str, torch.nn.Module]],
+                    model: torch.nn.Module,
+                    quant_model: torch.nn.Module,
+                    params: SeqMseParams,
+                    forward_fn: Callable,
+                    cached_fp_dataset: CachedDataset,
+                    cached_quant_dataset: Optional[CachedDataset] = None,
+                    ):
+        """
+        Run Sequential MSE
+
+        :param fp32_modules: List of FP32 candidate modules in order of occurence
+        :param model: FP32 model
+        :param quant_model: QuantizationSimModel object
+        :param params: Sequential MSE parameters
+        :param forward_fn: Optional adapter function that performs forward pass given a model and inputs
+        yielded from the data loader. The function expects model as first argument and inputs to model as second argument.
+        :param cached_fp_dataset: Cached dataset object
+        :param cached_quant_dataset: Cached dataset object
+        """
+        name_to_quant_module = {}
+        for name, quant_module in quant_model.named_modules():
+            name_to_quant_module[name] = quant_module
+
+        if not cached_quant_dataset:
+            cached_quant_dataset = cached_fp_dataset
+
+        for module_qualified_name, fp32_module in fp32_modules:
+            try:
+                quant_module = name_to_quant_module[module_qualified_name]
+            except KeyError:
+                continue
+
+            if quant_module.param_quantizers['weight'].bitwidth > SUPPORTED_PARAM_BW:
+                continue
+
+            _logger.info("Finding and freezing optimal param encodings candidate of module: %s", module_qualified_name)
+            if params.inp_symmetry == "asym":
+                fp32_inp_acts = cls.get_module_inp_acts(fp32_module, model, params, forward_fn, cached_fp_dataset)
+                quant_inp_acts = cls.get_module_inp_acts(quant_module, quant_model, params, forward_fn, cached_quant_dataset)
+                cls.optimize_module(quant_module, fp32_inp_acts, quant_inp_acts, params)
+            elif params.inp_symmetry == "symfp":
+                fp32_inp_acts = cls.get_module_inp_acts(fp32_module, model, params, forward_fn, cached_fp_dataset)
+                cls.optimize_module(quant_module, fp32_inp_acts, fp32_inp_acts, params)
+            elif params.inp_symmetry == "symqt":
+                quant_inp_acts = cls.get_module_inp_acts(quant_module, quant_model, params, forward_fn, cached_quant_dataset)
+                cls.optimize_module(quant_module, quant_inp_acts, quant_inp_acts, params)
+            else:
+                raise ValueError(f"Invalid inp_symmetry: {params.inp_symmetry}")
+
+    @staticmethod
+    def get_module_inp_acts(module: torch.nn.Module,
+                            model: torch.nn.Module,
+                            params: SeqMseParams,
+                            forward_fn: Callable,
+                            cached_dataset: CachedDataset,
+                            ) -> torch.Tensor:
+        """
+        For given module, get inputs to the module.
+
+        :param module: FP32/quant module
+        :param model: FP32/quant model
+        :param params: Sequential MSE parameters
+        :param forward_fn: Optional adapter function that performs forward pass given a model and inputs
+        yielded from the data loader. The function expects model as first argument and inputs to model as second argument.
+        :param cached_dataset: Cached dataset
+        :return: Concatenated inputs
+        """
+        inp_acts = []
+        def hook_fn(_, inp, __):
+            if isinstance(inp, tuple):
+                inp_acts.append(inp[0])
+            raise StopForwardException
+        handle = module.register_forward_hook(hook_fn)
+
+        iterator = iter(cached_dataset)
+        for _ in range(params.num_batches):
+            batch = change_tensor_device_placement(next(iterator), get_device(model))
+            try:
+                with in_eval_mode(model), torch.no_grad():
+                    forward_fn(model, batch)
+            except StopForwardException:
+                pass
+        handle.remove()
+
+        inp_acts = torch.stack(inp_acts)
+        return inp_acts
+
+    @staticmethod
+    def _get_quantizers_to_be_disabled(
+            model: torch.nn.Module,
+            sim,
+            modules_to_exclude: Optional[List[torch.nn.Module]],
+    ):
+        """
+        For given quantsim model, get all quantizers to be disabled before applying sequential MSE.
+        """
+        # pylint: disable=protected-access
+        name_to_fp32_module_dict = {}
+        for name, fp32_module in model.named_modules():
+            name_to_fp32_module_dict[name] = fp32_module
+
+        quantizers_to_be_disabled = []
+        for name, quant_wrapper in sim.quant_wrappers():
+            for quantizer in quant_wrapper.input_quantizers:
+                if quantizer.enabled:
+                    quantizers_to_be_disabled.append(quantizer)
+
+            for quantizer in quant_wrapper.output_quantizers:
+                if quantizer.enabled:
+                    quantizers_to_be_disabled.append(quantizer)
+
+            for quantizer in quant_wrapper.param_quantizers.values():
+                if not isinstance(quant_wrapper._module_to_wrap, SUPPORTED_MODULES) and quantizer.enabled:
+                    quantizers_to_be_disabled.append(quantizer)
+
+            # disable param quantizers from exclusion list
+            if modules_to_exclude:
+                with contextlib.suppress(KeyError):
+                    fp32_module = name_to_fp32_module_dict[name]
+                    if fp32_module in modules_to_exclude:
+                        for quantizer in quant_wrapper.param_quantizers.values():
+                            if quantizer.enabled:
+                                quantizers_to_be_disabled.append(quantizer)
+
+        return quantizers_to_be_disabled
+
+    @staticmethod
+    def get_candidates(num_candidates: int,
+                       per_channel_max: torch.Tensor,
+                       per_channel_min: Optional[torch.Tensor]) -> List[Tuple[torch.Tensor, torch.Tensor]]:
+        """
+        Perform grid search.
+
+        :param num_candidates: Number of candidates
+        :param per_channel_max: Per channel max values
+        :param per_channel_min: Per channel min values
+        :return: candidates
+        """
+        candidates = []
+        if per_channel_min is not None:
+            for cand in range(num_candidates):
+                cand_max = torch.tensor(per_channel_max / num_candidates * (cand + 1))
+                cand_min = torch.tensor(per_channel_min / num_candidates * (cand + 1))
+                candidates.append((cand_max, cand_min))
+        else:
+            for cand in range(num_candidates):
+                cand_max = torch.tensor(per_channel_max / num_candidates * (cand + 1))
+                cand_min = -cand_max
+                candidates.append((cand_max, cand_min))
+        return candidates
+
+    @staticmethod
+    def compute_recon_loss(xqwq: torch.Tensor, xw: torch.Tensor, params: SeqMseParams):
+        """
+        Compute reconstruction loss and return the sum by reducing over all the dimensions except last channel dimension.
+
+        :param xqwq: X^Q^ quantized-dequantized values
+        :param xw: XW FP32 values
+        :param params: Sequential MSE parameters
+        :return: loss
+        """
+        if params.loss_fn == "mse":
+            loss_fn = functional.mse_loss
+        elif params.loss_fn == "l1":
+            loss_fn = functional.l1_loss
+        elif params.loss_fn == "sqnr":
+            loss_fn = neg_sqnr
+        else:
+            raise ValueError(f"Invalid loss function: {params.loss_fn}")
+
+        channel_dim = xqwq.shape[-1]
+        xqwq = xqwq.reshape(-1, channel_dim)
+        xw = xw.reshape(-1, channel_dim)
+        loss = loss_fn(xqwq, xw, reduction="none").sum(0)
+        assert loss.size() == torch.Size([channel_dim])
+        return loss
+
+    @classmethod
+    def get_per_channel_min_and_max(cls, quant_module) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Get per channel min/max values across output channel.
+
+        :param quant_module: Quant module to be optimized
+        :return:
+        """
+        # pylint: disable=protected-access
+        module = cls._get_original_module(quant_module)
+
+        if isinstance(module, torch.nn.Conv2d):
+            channel_dim = module.out_channels
+            weight = module.weight.reshape(channel_dim, -1)
+        elif isinstance(module, torch.nn.Linear):
+            weight = module.weight
+        else:
+            raise ValueError('Unsupported module: ', module)
+
+        if cls._is_symmetric_quantizer(quant_module.param_quantizers["weight"]):
+            per_channel_max = torch.max(weight.abs(), dim=1)[0].detach()
+            per_channel_min = None
+        else:
+            per_channel_max = torch.max(weight, dim=1)[0].detach()
+            per_channel_min = torch.min(weight, dim=1)[0].detach()
+
+        return per_channel_min, per_channel_max
+
+    @classmethod
+    def compute_outputs(cls,
+                        quant_module,
+                        x: torch.Tensor,
+                        xq: torch.Tensor,
+                        w: torch.Tensor,
+                        wq: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Compute X^W^ and XW output activations.
+
+        :param quant_module: Wrapper module to be optimized
+        :param x: Inputs from FP32 model
+        :param xq: Inputs from QuantSim model
+        :param w: FP32 weights
+        :param wq: Quantized-dequantized weights
+        :return: xqwq, xw
+        """
+        # pylint: disable=protected-access
+        module = cls._get_original_module(quant_module)
+
+        if isinstance(module, torch.nn.Linear):
+            xqwq = functional.linear(xq, wq)
+            xw = functional.linear(x, w)
+        elif isinstance(module, torch.nn.Conv2d):
+            xqwq = functional.conv2d(xq, wq, stride=module.stride, dilation=module.dilation,
+                                     padding=module.padding, groups=module.groups)
+            xw = functional.conv2d(x, w, stride=module.stride, dilation=module.dilation,
+                                   padding=module.padding, groups=module.groups)
+
+            # [N, C, H, W] --> [N, H, W, C], so that loss can be computed across channel dimension.
+            xqwq = xqwq.permute(0, 2, 3, 1)
+            xw = xw.permute(0, 2, 3, 1)
+        else:
+            raise ValueError('Unsupported module: ', module)
+        return xqwq, xw
+
+    @classmethod
+    @abstractmethod
+    def temporarily_disable_quantizers(
+            cls,
+            model: torch.nn.Module,
+            sim,
+            modules_to_exclude: Optional[List[torch.nn.Module]],
+    ):
+        """
+        For given quantsim model, disable quantizers needed to be disabled before applying sequential MSE.
+
+        :param model: Original fp32 model
+        :param sim: QuantizationSimModel object
+        :param modules_to_exclude: List of supported modules to exclude when applying Sequential MSE
+        :return: List of quantizers to be disabled.
+        """
+
+    @classmethod
+    @abstractmethod
+    def compute_all_param_encodings(cls, sim):
+        """
+        Compute encodings for all parameters, needed for initializing Sequential MSE
+
+        :param sim: Quant sim
+        """
+
+    @classmethod
+    @abstractmethod
+    def optimize_module(cls,
+                        quant_module,
+                        x: torch.Tensor,
+                        xq: torch.Tensor,
+                        params: SeqMseParams):
+        """
+        Find and freeze optimal parameter encodings candidate for given module.
+
+        :param quant_module: Quant module to be optimized
+        :param x: Inputs to module from FP32 model
+        :param xq: Inputs to module from QuantSim model
+        :param params: Sequenial MSE parameters
+        """
+
+    @classmethod
+    @abstractmethod
+    def compute_param_encodings(cls,
+                                quantizer,
+                                x_min: torch.Tensor,
+                                x_max: torch.Tensor):
+        """
+        Compute encodings for parameter quantizer using given x_min and x_max values.
+
+        :param quantizer: Tensor quantizer
+        :param x_min: min values
+        :param x_max: max values
+        """
+
+    @classmethod
+    @abstractmethod
+    def _is_symmetric_quantizer(cls, quantizer):
+        ...
+
+    @classmethod
+    @abstractmethod
+    def _freeze_quantizer_encoding(cls, quantizer):
+        ...
+
+    @classmethod
+    @abstractmethod
+    def _get_quantized_weight(cls, quant_module):
+        ...
+
+    @classmethod
+    @abstractmethod
+    def _get_original_module(cls, quant_module):
+        ...
+
+
+def neg_sqnr(pred: torch.Tensor, target: torch.Tensor, eps=1e-10, reduction="none"):
+    """
+    Loss function to minimize negative SQNR which is equivalent to maximizing SQNR.
+
+    :param pred: X^Q^ quantized-dequantized values
+    :param target: XW FP32 values
+    :param eps: epsilon
+    :param reduction: unused arg
+    :return: Negative SQNR
+    """
+    # pylint: disable=unused-argument
+    quant_error = target - pred
+    exp_noise = torch.mean(quant_error ** 2, 0, keepdim=True) + eps
+    exp_signal = torch.mean(target ** 2, 0, keepdim=True)
+    sqnr = exp_signal / exp_noise
+    sqnr_db = 10 * torch.log10(sqnr)
+    return -sqnr_db
+
    +
    +
    +
    + +
    + + +
    +
    +
    +
    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
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    +

    Source code for aimet_torch.bn_reestimation

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2022, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+
+"""BatchNorm Reestimation"""
+
+import itertools
+from typing import Iterable, List, Callable, Any
+
+from tqdm import tqdm
+import torch
+from torch.utils.data import DataLoader
+from torch.nn.modules.batchnorm import _BatchNorm
+from aimet_torch.utils import in_eval_mode, in_train_mode
+from aimet_common.utils import Handle
+
+def _get_active_bn_modules(model: torch.nn.Module) -> Iterable[_BatchNorm]:
+    for module in model.modules():
+        if isinstance(module, _BatchNorm):
+            bn = module
+            if bn.running_mean is not None and bn.running_var is not None:
+                yield bn
+
+
+def _for_each_module(modules: Iterable[torch.nn.Module],
+                     action: Callable[[torch.nn.Module], Handle]) -> Handle:
+    """
+    Apply an undoable action to each module.
+
+    :param modules: Modules to apply the action.
+    :param action: Action to be applied to the modules.
+    :returns: Handle that undos the applied action.
+    """
+
+    handles: List[Handle] = []
+
+    def cleanup():
+        for handle in handles:
+            handle.remove()
+
+    try:
+        for module in modules:
+            handle = action(module)
+            assert isinstance(handle, Handle)
+            handles.append(handle)
+        return Handle(cleanup)
+    except:
+        cleanup()
+        raise
+
+
+def _reset_bn_stats(module: _BatchNorm) -> Handle:
+    """
+    Reset BN statistics to the initial values.
+
+    :param module: BatchNorm module.
+    :returns: Handle that restores the original BN statistics upon handle.remove().
+    """
+    orig_running_mean = module.running_mean.clone()
+    orig_running_var = module.running_var.clone()
+    orig_num_batches_tracked = module.num_batches_tracked.clone()
+
+    def cleanup():
+        module.running_mean.copy_(orig_running_mean)
+        module.running_var.copy_(orig_running_var)
+        module.num_batches_tracked.copy_(orig_num_batches_tracked)
+
+    try:
+        module.reset_running_stats()
+        return Handle(cleanup)
+    except:
+        cleanup()
+        raise
+
+
+def _reset_momentum(module: _BatchNorm) -> Handle:
+    """
+    Set BN momentum to 1.0.
+
+    :param module: BatchNorm module.
+    :returns: Handle that restores the original BN momentum upon handle.remove().
+    """
+    momentum = module.momentum
+
+    def cleanup():
+        module.momentum = momentum
+
+    try:
+        module.momentum = 1.0
+        return Handle(cleanup)
+    except:
+        cleanup()
+        raise
+
+
+DEFAULT_NUM_BATCHES = 100
+
+
+
    
+[docs]
+def reestimate_bn_stats(model: torch.nn.Module,
+                        dataloader: DataLoader,
+                        num_batches: int = DEFAULT_NUM_BATCHES,
+                        forward_fn: Callable[[torch.nn.Module, Any], Any] = None) -> Handle:
+    """
+    Reestimate BatchNorm statistics (running mean and var).
+
+    :param model: Model to reestimate the BN stats.
+    :param dataloader: Training dataset.
+    :param num_batches: The number of batches to be used for reestimation.
+    :param forward_fn: Optional adapter function that performs forward pass
+                       given a model and a input batch yielded from the data loader.
+    :returns: Handle that undos the effect of BN reestimation upon handle.remove().
+    """
+    forward_fn = forward_fn or (lambda model, data: model(data))
+    bn_modules = tuple(_get_active_bn_modules(model))
+
+    # Set all the layers to eval mode except batchnorm layers
+    with in_eval_mode(model), in_train_mode(bn_modules), torch.no_grad():
+        with _for_each_module(bn_modules, action=_reset_momentum):
+            handle = _for_each_module(bn_modules, action=_reset_bn_stats)
+
+            try:
+                # Batchnorm statistics accumulation buffer
+                buffer = {
+                    bn: {"sum_mean": torch.zeros_like(bn.running_mean),
+                         "sum_var":  torch.zeros_like(bn.running_var)}
+                    for bn in bn_modules
+                }
+
+                num_batches = min(len(dataloader), num_batches)
+                dataloader_slice = itertools.islice(dataloader, num_batches)
+
+                for data in tqdm(dataloader_slice,
+                                 total=num_batches,
+                                 desc="batchnorm reestimation"):
+                    forward_fn(model, data)
+
+                    for bn in bn_modules:
+                        buffer[bn]["sum_mean"] += bn.running_mean
+                        buffer[bn]["sum_var"] += bn.running_var
+
+                for bn in bn_modules:
+                    sum_mean = buffer[bn]["sum_mean"]
+                    sum_var = buffer[bn]["sum_var"]
+
+                    # Override BN stats with the reestimated stats.
+                    bn.running_mean.copy_(sum_mean / min(len(dataloader), num_batches))
+                    bn.running_var.copy_(sum_var / min(len(dataloader), num_batches))
+
+                return handle
+            except:
+                handle.remove()
+                raise
    
+
+
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    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
    + Made with Sphinx and @pradyunsg's + + Furo + +
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    + + + + + + + + + \ No newline at end of file diff --git a/releases/2.0.0/_modules/aimet_torch/compress.html b/releases/2.0.0/_modules/aimet_torch/compress.html new file mode 100644 index 0000000..aa7cef0 --- /dev/null +++ b/releases/2.0.0/_modules/aimet_torch/compress.html @@ -0,0 +1,448 @@ + + + + + + + + aimet_torch.compress - AIMET + + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    AIMET
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    +

    Source code for aimet_torch.compress

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2018, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+
+""" Top-level API to AIMET compression library """
+
+from typing import Union, Tuple
+import torch
+
+from aimet_common.defs import CostMetric, CompressionScheme, EvalFunction, CompressionStats
+from aimet_common.bokeh_plots import BokehServerSession
+
+from aimet_torch.defs import SpatialSvdParameters, WeightSvdParameters, ChannelPruningParameters
+from aimet_torch.compression_factory import CompressionFactory
+
+
+
    
+[docs]
+class ModelCompressor:
+    """ AIMET model compressor: Enables model compression using various schemes """
+    # Too many arguments in this function, disabling pylint for now
+
    
+[docs]
+    @staticmethod
+    def compress_model(model: torch.nn.Module, eval_callback: EvalFunction, eval_iterations,
+                       input_shape: Tuple,
+                       compress_scheme: CompressionScheme, cost_metric: CostMetric,
+                       parameters: Union[SpatialSvdParameters,
+                                         WeightSvdParameters,
+                                         ChannelPruningParameters],
+                       trainer=None, visualization_url=None) -> Tuple[torch.nn.Module, CompressionStats]:
+
+        """
+        Compress a given model using the specified parameters
+
+        :param model: Model to compress
+        :param eval_callback:  Evaluation callback. Expected signature is evaluate(model, iterations, use_cuda).
+                               Expected to return an accuracy metric.
+        :param eval_iterations: Iterations to run evaluation for
+        :param trainer: Training Class: Contains a callable, train_model, which takes model, layer which is being fine
+                        tuned and an optional parameter train_flag as a parameter
+                        None: If per layer fine tuning is not required while creating the final compressed model
+        :param input_shape: Shape of the input tensor for model
+        :param compress_scheme: Compression scheme. See the enum for allowed values
+        :param cost_metric: Cost metric to use for the compression-ratio (either mac or memory)
+        :param parameters: Compression parameters specific to given compression scheme
+        :param visualization_url: url the user will need to input where visualizations will appear
+        :return: A tuple of the compressed model, and compression statistics
+        """
+        # pylint:disable=too-many-arguments
+        # If no url is passed in, then do not create a bokeh server session
+        if not visualization_url:
+            bokeh_session = None
+        else:
+            # create a bokeh session to publish visualizations to the server document for compression
+            bokeh_session = BokehServerSession(url=visualization_url, session_id="compression")
+
+        # put model in eval mode. This is important because otherwise running a forward pass can change module buffers
+        # e.g. for batchnorm layers that can affect model evaluation results
+        if trainer is not None:
+            trainer.train_model(model, model, train_flag=False)
+
+        model = model.eval()
+
+        if parameters.multiplicity < 1:
+            raise ValueError('Rounding Multiplicity should be greater than 1')
+
+        if compress_scheme == CompressionScheme.spatial_svd:
+            algo = CompressionFactory.create_spatial_svd_algo(model, eval_callback, eval_iterations,
+                                                              input_shape, cost_metric, parameters, bokeh_session)
+
+        elif compress_scheme == CompressionScheme.weight_svd:
+            algo = CompressionFactory.create_weight_svd_algo(model, eval_callback, eval_iterations,
+                                                             input_shape, cost_metric, parameters, bokeh_session)
+
+        elif compress_scheme == CompressionScheme.channel_pruning:
+            algo = CompressionFactory.create_channel_pruning_algo(model, eval_callback, eval_iterations,
+                                                                  input_shape, cost_metric, parameters, bokeh_session)
+
+        else:
+            raise ValueError("Compression scheme not supported: {}".format(compress_scheme))
+
+        compressed_layer_db, stats = algo.compress_model(cost_metric, trainer)
+        return compressed_layer_db.model, stats
    
+
    
+
+
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    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
    + Made with Sphinx and @pradyunsg's + + Furo + +
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    + + + + + + + + + \ No newline at end of file diff --git a/releases/2.0.0/_modules/aimet_torch/cross_layer_equalization.html b/releases/2.0.0/_modules/aimet_torch/cross_layer_equalization.html new file mode 100644 index 0000000..fd56bb9 --- /dev/null +++ b/releases/2.0.0/_modules/aimet_torch/cross_layer_equalization.html @@ -0,0 +1,1079 @@ + + + + + + + + aimet_torch.cross_layer_equalization - AIMET + + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    AIMET
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    +

    Source code for aimet_torch.cross_layer_equalization

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2019-2024, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+
+""" Cross Layer Equalization
+
+Some terminology for this code.
+CLS set: Set of layers (2 or 3) that can be used for cross-layer scaling
+Layer groups: Groups of layers that are immediately connected and can be decomposed further into CLS sets
+"""
+# pylint: disable=too-many-lines
+
+from typing import Tuple, List, Union, Dict
+import numpy as np
+import torch
+
+from aimet_common.utils import AimetLogger
+from aimet_common.cross_layer_equalization import ClsLayerType, ClsSetInfo, ClsImpl, HbfImpl
+from aimet_torch import utils
+from aimet_torch.meta.connectedgraph import ConnectedGraph
+from aimet_torch.batch_norm_fold import fold_all_batch_norms
+from aimet_torch.utils import (get_device, get_ordered_list_of_modules, create_rand_tensors_given_shapes,
+                               place_model)
+
+logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.CrosslayerEqualization)
+
+ClsSet = Union[Tuple[torch.nn.Conv2d, torch.nn.Conv2d],
+               Tuple[torch.nn.Conv2d, torch.nn.Conv2d, torch.nn.Conv2d]]
+
+ClsSupportedLayer = Union[torch.nn.Conv1d, torch.nn.Conv2d, torch.nn.ConvTranspose1d, torch.nn.ConvTranspose2d]
+
+ScaleFactor = Union[np.ndarray, Tuple[np.ndarray]]
+
+cls_supported_layers = (torch.nn.Conv2d, torch.nn.ConvTranspose2d, torch.nn.Conv1d, torch.nn.ConvTranspose1d)
+cls_supported_activations = (torch.nn.ReLU, torch.nn.PReLU)
+
+
+def get_ordered_list_of_conv_modules(model: torch.nn.Module, dummy_input: Union[torch.Tensor, Tuple]) -> List:
+    """
+    Finds order of nodes in graph
+    :param model: model
+    :param dummy_input: Dummy input to the model. Used to parse model graph.
+    :return: List of names in graph in order
+    """
+    module_list = get_ordered_list_of_modules(model, dummy_input)
+    module_list = [[name, module] for name, module in module_list if isinstance(module, cls_supported_layers)]
+    return module_list
+
+
+class GraphSearchUtils:
+    """
+    Code to search a model graph to find nodes to use for cross-layer-scaling and high-bias-fold
+    """
+
+    def __init__(self, model: torch.nn.Module, input_shapes: Union[Tuple, List[Tuple]],
+                 dummy_input: Union[torch.Tensor, List[torch.Tensor]] = None):
+        """
+
+        :param model: PyTorch model.
+        :param input_shapes: Input shape for the model (can be one or multiple inputs)
+        :param dummy_input: Dummy input to the model. Used to parse model graph. dummy_input is expected to be placed
+         on the same device as model.
+        """
+        if dummy_input is None:
+            inp_tensor_list = tuple(utils.create_rand_tensors_given_shapes(input_shapes, get_device(model)))
+        else:
+            inp_tensor_list = dummy_input
+        self._connected_graph = ConnectedGraph(model, inp_tensor_list)
+        self._ordered_module_list = get_ordered_list_of_conv_modules(model, inp_tensor_list)
+
+
+    @staticmethod
+    def find_downstream_layer_groups_to_scale(op, layer_groups, current_group=None, visited_nodes=None):
+        """
+        Recursive function to find cls layer groups downstream from a given op
+        :param op: Starting op to search from
+        :param layer_groups: Running list of layer groups
+        :param current_group: Running current layer group
+        :param visited_nodes: Running list of visited nodes (to short-circuit recursion)
+        :return: None
+        """
+
+        if not visited_nodes:
+            visited_nodes = []
+        if not current_group:
+            current_group = []
+
+        if op in visited_nodes:
+            return
+        visited_nodes.append(op)
+        # print("Visiting node: {}".format(op.dotted_name))
+
+        # If current node is Conv2D, add to the current group
+        if op.model_module and isinstance(op.model_module.get_module(), cls_supported_layers):
+            current_group.append(op.model_module.get_module())
+
+        # Terminating condition for current group
+        if not op.model_module or not isinstance(op.model_module.get_module(),
+                                                 cls_supported_layers + cls_supported_activations):
+
+            if (len(current_group) > 1) and (current_group not in layer_groups):
+                layer_groups.append(current_group)
+            current_group = []
+
+        if op.output:
+            for consumer in op.output.consumers:
+                GraphSearchUtils.find_downstream_layer_groups_to_scale(consumer, layer_groups,
+                                                                       current_group, visited_nodes)
+
+        # Reached a leaf.. See if the current group has something to grab
+        if (len(current_group) > 1) and (current_group not in layer_groups):
+            layer_groups.append(current_group)
+
+    @staticmethod
+    def convert_layer_group_to_cls_sets(layer_group):
+        """
+        Helper function to convert a layer group to a list of cls sets
+        :param layer_group: Given layer group to generate cls sets
+        :return: List of cls sets
+
+        Supported layer combinations for CLS are:
+        1. Conv + Conv
+        2. DepthwiseConv + Conv
+        3. Conv + DepthwiseConv + Conv
+
+        Can be rewritten as,
+        Conv
+            -> Conv
+            -> DepthwiseConv
+                -> Conv
+        DepthwiseConv
+            -> Conv
+
+        If a combination is partially supported, the cls_set is completely omitted and restarted from the next
+        supported layer
+        For example: Consider Conv + DepthwiseConv + Depthwise(unsupported)
+        - Since Depthwise(unsupported) is the last layer encountered, we need to omit all the three layers and restart
+        the cls sets from the next supported layer.
+
+        """
+
+        # pylint: disable=too-many-branches
+        def convert_to_cls_layer_type(layer: ClsSupportedLayer) -> Tuple[ClsLayerType, ClsSupportedLayer]:
+            """
+            Given the layer, check if its supported in CLS
+            :param layer: layer to check
+            :return: Tuple of ClsLayerType and the layer
+            """
+            if layer.groups == 1:
+                layer_type = ClsLayerType.Conv
+            elif layer.groups == layer.in_channels and layer.in_channels == layer.out_channels:
+                # depthwiseConv layer with depth multiplier = 1
+                layer_type = ClsLayerType.DepthwiseConv
+            else:
+                layer_type = ClsLayerType.Unsupported
+            return layer_type, layer
+
+        def get_next_layer() -> Union[Tuple[ClsLayerType, Union[ClsSupportedLayer, None]]]:
+            """
+            :return: Tuple of ClsLayerType and the next layer in layer_group
+            """
+            if not layer_group:
+                return ClsLayerType.Unsupported, None
+            layer = layer_group.pop(0)
+            return convert_to_cls_layer_type(layer)
+
+        cls_sets = []
+
+        first_layer_to_scale = (ClsLayerType.Unsupported, None)
+        while layer_group:
+            while layer_group and first_layer_to_scale[0] is ClsLayerType.Unsupported:
+                first_layer_to_scale = get_next_layer()
+                if first_layer_to_scale[0] is ClsLayerType.Unsupported:
+                    logger.info('Layer %s is not supported. Ignoring for cls', first_layer_to_scale[1])
+
+            second_layer_to_scale = get_next_layer()
+            if first_layer_to_scale[0] == ClsLayerType.Conv:
+                if second_layer_to_scale[0] == ClsLayerType.Conv:
+                    cls_sets.append((first_layer_to_scale[1], second_layer_to_scale[1]))
+                    first_layer_to_scale = second_layer_to_scale
+                elif second_layer_to_scale[0] == ClsLayerType.DepthwiseConv:
+                    if layer_group:
+                        # do not pop third layer yet, determine its type and then pop it
+                        third_layer_to_scale = convert_to_cls_layer_type(layer_group[0])
+                        if third_layer_to_scale[0] == ClsLayerType.Conv:
+                            cls_sets.append(
+                                (first_layer_to_scale[1], second_layer_to_scale[1], third_layer_to_scale[1]))
+                            # adding third_layer_to_scale for the next round of CLS set determination
+                            first_layer_to_scale = get_next_layer()
+                        else:
+                            # unsupported combination encountered
+                            first_layer_to_scale = second_layer_to_scale
+                else:
+                    logger.info('Layer %s is not supported. Ignoring for cls', second_layer_to_scale[1])
+                    first_layer_to_scale = (ClsLayerType.Unsupported, None)
+            elif first_layer_to_scale[0] == ClsLayerType.DepthwiseConv:
+                if second_layer_to_scale[0] == ClsLayerType.Conv:
+                    cls_sets.append((first_layer_to_scale[1], second_layer_to_scale[1]))
+                first_layer_to_scale = second_layer_to_scale
+            else:
+                logger.info('Layer %s is not supported. Ignoring for cls', first_layer_to_scale[1])
+                first_layer_to_scale = second_layer_to_scale
+
+        return cls_sets
+
+    def find_layer_groups_to_scale(self) -> List[List[torch.nn.Conv2d]]:
+        """
+        :return: List of groups of layers. Each group can be independently equalized
+        """
+
+        # Find the input node(s) in the graph
+        input_nodes = []
+        for op in self._connected_graph.get_all_ops().values():
+            if op.inputs and op.inputs[0].is_model_input:
+                input_nodes.append(op)
+
+        layer_groups = []
+        for op in input_nodes:
+            self.find_downstream_layer_groups_to_scale(op, layer_groups)
+
+        # Sort the layer groups in order of occurrence in the model
+        ordered_layer_groups = []
+        for _, module in self._ordered_module_list:
+            for layer_group in layer_groups:
+                if layer_group[0] is module:
+                    ordered_layer_groups.append(layer_group)
+
+        return ordered_layer_groups
+
+    @staticmethod
+    def does_module_have_relu_activation(connected_graph: ConnectedGraph, module: torch.nn.Module) -> bool:
+        """
+        Finds if a given module has a ReLU activation
+        :param connected_graph: Reference to ConnectedGraph instance
+        :param module: PyTorch module to find activation for
+        :return: True if module has a relu activation
+        """
+
+        for op in connected_graph.get_all_ops().values():
+
+            if op.model_module and op.model_module.get_module() is module:
+                assert len(op.output.consumers) == 1
+                is_relu_activation = isinstance(op.output.consumers[0].model_module.get_module(),
+                                                (torch.nn.ReLU, torch.nn.PReLU))
+                return is_relu_activation
+
+        return False
+
+    def is_relu_activation_present_in_cls_sets(self, cls_sets: List[ClsSet]):
+        """
+        :param cls_sets: CLS sets to find relu activations in
+        :return: List of groups of layers. Each group can be independently equalized
+        """
+
+        is_relu_activation_in_cls_sets = []
+        for cls_set in cls_sets:
+
+            # We need to check activation functions for all layers but the last one in the set
+            # Because we are only interested in checking activation functions between the layers we will scale
+            cls_set = cls_set[:-1]
+
+            is_relu_activation_in_cls_set = ()
+            for module in cls_set:
+                is_relu_activation_in_cls_set += (self.does_module_have_relu_activation(self._connected_graph,
+                                                                                        module), )
+
+            if len(is_relu_activation_in_cls_set) == 1:
+                is_relu_activation_in_cls_set = is_relu_activation_in_cls_set[0]
+
+            is_relu_activation_in_cls_sets.append(is_relu_activation_in_cls_set)
+
+        return is_relu_activation_in_cls_sets
+
+
+class CrossLayerScaling:
+    """
+    Code to apply the cross-layer-scaling technique to a model
+    """
+
+    @staticmethod
+    def scale_cls_sets(cls_sets: List[ClsSet]) -> List[ScaleFactor]:
+        """
+        Scale multiple CLS sets
+
+        :param cls_sets: List of CLS sets
+        :return: Scaling factors calculated and applied for each CLS set in order
+        """
+        scale_factor_list = []
+        for cls_set in cls_sets:
+            scale_factor = CrossLayerScaling.scale_cls_set(cls_set)
+            scale_factor_list.append(scale_factor)
+        return scale_factor_list
+
+    @staticmethod
+    def scale_cls_set(cls_set: ClsSet) -> ScaleFactor:
+        """
+        Scale a CLS set
+        :param cls_set: Either a pair or regular conv layers or a triplet of depthwise separable layers
+        :return: Scaling factor calculated and applied
+        """
+        if len(cls_set) == 3:
+            scale_factor = CrossLayerScaling.scale_cls_set_with_depthwise_layers(cls_set)
+        else:
+            scale_factor = CrossLayerScaling.scale_cls_set_with_conv_layers(cls_set)
+
+        return scale_factor
+
+    @classmethod
+    def scale_cls_set_with_conv_layers(cls, cls_set: ClsSet) -> np.ndarray:
+        """
+        API to invoke equalize layer params (update for weights and bias is in place)
+
+        :param cls_set: Consecutive Conv layers Tuple whose weights and biases need to be equalized
+        :return: Scaling factor S_12 for each conv layer pair: numpy array
+        """
+        on_gpu = False
+        for module in cls_set:
+            if not isinstance(module, cls_supported_layers):
+                raise ValueError(f"Only Conv or Transposed Conv layers are supported for cross layer equalization."
+                                 f" Layer class {str(module.__class__)} is not supported.")
+            if module.weight.is_cuda:
+                on_gpu = True
+                module.cpu()
+
+        cls_impl = PythonClsImpl()
+        scaling_factor = cls_impl.scale_cls_set_with_conv_layers(cls_set)
+
+        if on_gpu:
+            for module in cls_set:
+                module.to(device="cuda")
+
+        return scaling_factor
+
+    @classmethod
+    def scale_cls_set_with_depthwise_layers(cls, cls_set: ClsSet) -> [np.ndarray, np.ndarray]:
+        """
+        API to invoke equalize layer params for depth wise separable layers(update for weights and bias is in place)
+
+        :param cls_set: Consecutive Conv layers whose weights and biases need to be equalized.
+                        Second Conv layer is a depth-wise conv and third conv layer is point-wise conv
+        :return: Scaling factors S_12 and S_23 : numpy arrays
+        """
+        on_gpu = False
+        for module in cls_set:
+            if not isinstance(module, cls_supported_layers):
+                raise ValueError(f"Only Conv or Transposed Conv layers are supported for cross layer equalization."
+                                 f" Layer class {str(module.__class__)} is not supported.")
+            if module.weight.is_cuda:
+                on_gpu = True
+                module.cpu()
+
+        cls_impl = PythonClsImpl()
+        scaling_factors = cls_impl.scale_cls_set_with_depthwise_layers(cls_set)
+
+        if on_gpu:
+            for module in cls_set:
+                module.to(device="cuda")
+
+        return scaling_factors
+
+    @staticmethod
+    def create_cls_set_info_list(cls_sets: List[ClsSet], scale_factors: List[ScaleFactor],
+                                 is_relu_activation_in_cls_sets):
+        """
+        Binds information from there separate lists into one [ClsInfoSet] data-structure
+        :param cls_sets: List of CLS sets
+        :param scale_factors: Scale-factors for each cls-set
+        :param is_relu_activation_in_cls_sets: Information if there is relu activation in each cls-set
+        :return: List of ClsSetInfo
+        """
+        cls_set_info_list = []
+        assert len(cls_sets) == len(scale_factors) == len(is_relu_activation_in_cls_sets)
+
+        for index, cls_set in enumerate(cls_sets):
+
+            if isinstance(scale_factors[index], tuple):
+                # If we are dealing with a triplet of layers, then we should have 2 scale factors and 2 relu flags
+                # Assert that this is true
+                assert len(cls_set) == 3
+                assert len(scale_factors[index]) == len(is_relu_activation_in_cls_sets[index]) == 2
+
+                cls_pair_1 = ClsSetInfo.ClsSetLayerPairInfo(cls_set[0], cls_set[1], scale_factors[index][0],
+                                                            is_relu_activation_in_cls_sets[index][0])
+                cls_pair_2 = ClsSetInfo.ClsSetLayerPairInfo(cls_set[1], cls_set[2], scale_factors[index][1],
+                                                            is_relu_activation_in_cls_sets[index][1])
+
+                cls_set_info = ClsSetInfo(cls_pair_1, cls_pair_2)
+
+            else:
+                cls_pair = ClsSetInfo.ClsSetLayerPairInfo(cls_set[0], cls_set[1], scale_factors[index],
+                                                          is_relu_activation_in_cls_sets[index])
+
+                cls_set_info = ClsSetInfo(cls_pair)
+
+            cls_set_info_list.append(cls_set_info)
+
+        return cls_set_info_list
+
+    @staticmethod
+    def scale_model(model: torch.nn.Module, input_shapes: Union[Tuple, List[Tuple]] = None,
+                    dummy_input: Union[torch.Tensor, List[torch.Tensor]] = None) -> List[ClsSetInfo]:
+        """
+        Uses cross-layer scaling to scale all applicable layers in the given model
+
+        :param model: Model to scale
+        :param input_shapes: Input shape for the model (can be one or multiple inputs)
+        :param dummy_input: A dummy input to the model. Can be a Tensor or a Tuple of Tensors. dummy_input will be
+         placed on CPU if not already.
+        :return: CLS information for each CLS set
+        """
+        if isinstance(model, torch.nn.DataParallel):
+            return CrossLayerScaling.scale_model(model.module, input_shapes, dummy_input=dummy_input)
+
+        # The use of input_shapes will be removed in the future release. It is maintained now for backward compatibility.
+        if input_shapes and dummy_input is None:
+            dummy_input = create_rand_tensors_given_shapes(input_shapes, torch.device('cpu'))
+        if input_shapes is None and dummy_input is None:
+            raise ValueError("Both input_shapes and dummy_input can't be None")
+
+        # Place model and dummy input on the cpu.
+        with place_model(model, torch.device("cpu")):
+            dummy_input = utils.change_tensor_device_placement(dummy_input, device=torch.device('cpu'))
+
+            # Find layer groups
+            graph_search = GraphSearchUtils(model, input_shapes, dummy_input=dummy_input)
+            layer_groups = graph_search.find_layer_groups_to_scale()
+
+            # Find cls sets from the layer groups
+            cls_sets = []
+            for layer_group in layer_groups:
+                cls_set = GraphSearchUtils.convert_layer_group_to_cls_sets(layer_group)
+                cls_sets += cls_set
+
+            # Scale the CLS sets
+            scale_factors = CrossLayerScaling.scale_cls_sets(cls_sets)
+
+            # Find if there were relu activations between layers of each cls set
+            is_relu_activation_in_cls_sets = graph_search.is_relu_activation_present_in_cls_sets(cls_sets)
+
+            # Convert to a list of cls-set-info elements
+            cls_set_info_list = CrossLayerScaling.create_cls_set_info_list(cls_sets, scale_factors,
+                                                                           is_relu_activation_in_cls_sets)
+        return cls_set_info_list
+
+
+class PythonClsImpl(ClsImpl):
+    """
+    This class implements the CLS algorithm using Python version while following the base Implementation interface.
+    """
+    def scale_cls_set_with_depthwise_layers(self, cls_set) -> [np.ndarray, np.ndarray]:
+        """
+        API to invoke equalize layer params for depth wise separable layers(update for weights and bias is in place)
+
+        :param cls_set: Consecutive Conv layers whose weights and biases need to be equalized.
+                        Second Conv layer is a depth-wise conv and third conv layer is point-wise conv
+        :return: Scaling factors S_12 and S_23 : numpy arrays
+        """
+        weight_0 = self._prepare_params(cls_set[0])
+        assert cls_set[1].groups > 1
+        weight_1 = self._prepare_params(cls_set[1])
+        weight_2 = self._prepare_params(cls_set[2])
+        weight_0 = weight_0.numpy()
+        weight_1 = weight_1.numpy()
+        weight_2 = weight_2.numpy()
+
+        bias_0 = None
+        if cls_set[0].bias is not None:
+            bias_0 = cls_set[0].bias.detach().cpu().numpy()
+        bias_1 = None
+        if cls_set[1].bias is not None:
+            bias_1 = cls_set[1].bias.detach().cpu().numpy()
+
+        # compute scaling factors and folded parameters.
+        s_12, s_23 = self.compute_scaling_params_for_depthwise_conv(weight_0, weight_1, weight_2)
+        _weight_0, _weight_1, _weight_2, _bias_0, _bias_1 = (
+            self.fold_scaling_params_for_depthwise_conv(weight_0, weight_1, weight_2, bias_0, bias_1, s_12, s_23))
+
+        with torch.no_grad():
+            self._restore_params(cls_set[0], torch.from_numpy(_weight_0))
+            self._restore_params(cls_set[1], torch.from_numpy(_weight_1))
+            self._restore_params(cls_set[2], torch.from_numpy(_weight_2))
+
+            if cls_set[0].bias is not None:
+                cls_set[0].bias.copy_(torch.from_numpy(_bias_0).reshape_as(cls_set[0].bias)).to(device=cls_set[0].bias.device,
+                                                                                                dtype=cls_set[0].bias.dtype)
+            if cls_set[1].bias is not None:
+                cls_set[1].bias.copy_(torch.from_numpy(_bias_1).reshape_as(cls_set[1].bias)).to(device=cls_set[1].bias.device,
+                                                                                                dtype=cls_set[1].bias.dtype)
+        return s_12, s_23
+
+    def scale_cls_set_with_conv_layers(self, cls_set) -> np.ndarray:
+        """
+        API to invoke equalize layer params for regular conv layers (update for weights and bias is in place)
+
+        :param cls_set: Consecutive Conv layers Tuple whose weights and biases need to be equalized
+        :return: Scaling factor S_12 for each conv layer pair: numpy array
+        """
+        weight_0 = self._prepare_params(cls_set[0])
+        weight_1 = self._prepare_params(cls_set[1])
+        weight_0 = weight_0.numpy()
+        weight_1 = weight_1.numpy()
+
+        bias_0 = None
+        if cls_set[0].bias is not None:
+            bias_0 = cls_set[0].bias.detach().cpu().numpy()
+
+        # compute scaling factors and folded parameters.
+        scale_factor = self.compute_scaling_params_for_conv(weight_0, weight_1)
+        _weight_0, _weight_1, _bias_0 = (
+            self.fold_scaling_params_for_conv(weight_0, weight_1, bias_0, scale_factor))
+
+        with torch.no_grad():
+            self._restore_params(cls_set[0], torch.from_numpy(_weight_0))
+            self._restore_params(cls_set[1], torch.from_numpy(_weight_1))
+            if cls_set[0].bias is not None:
+                cls_set[0].bias.copy_(torch.from_numpy(_bias_0).reshape_as(cls_set[0].bias)).to(device=cls_set[0].bias.device,
+                                                                                                dtype=cls_set[0].bias.dtype)
+        return scale_factor
+
+    @staticmethod
+    def _transpose_tensor(module: torch.nn.Module, tensor: torch.Tensor) -> torch.Tensor:
+        """
+        During preparation:
+        For TransposeConv2d, Transpose tensor in the common format [Noc, Nin, Kh, Kw].
+        For TransposeConv1d, Transpose tensor in common format [Noc, Nin, K].
+
+        During restoration:
+        For TransposeConv2d, Transpose tensor in the original format [Nin, Noc, Kh, Kw].
+        For TransposeConv1d, Transpose tensor in back in original format [Nin, Noc, K].
+
+        :param module: Module.
+        :param tensor: Input tensor.
+        :return: Output tensor.
+        """
+        if isinstance(module, torch.nn.ConvTranspose2d) and module.groups == 1:
+            tensor = tensor.permute(1, 0, 2, 3).contiguous()
+
+        if isinstance(module, torch.nn.ConvTranspose1d) and module.groups == 1:
+            tensor = tensor.permute(1, 0, 2).contiguous()
+        return tensor
+
+    @staticmethod
+    def _make_4d_tensor(module: torch.nn.Module, tensor: torch.Tensor) -> torch.Tensor:
+        """
+        Return 4 dimensional tensor by adding a dimension on the end if the tensor is not 4d.
+
+        :param module: Module.
+        :param tensor: Input tensor.
+        :return: Output tensor.
+        """
+        if isinstance(module, (torch.nn.Conv1d, torch.nn.ConvTranspose1d)):
+            assert len(tensor.shape) == 3, "Module should have 3d weight tensor."
+            tensor = torch.unsqueeze(tensor, dim=-1)
+        return tensor
+
+    def _prepare_params(self, module: torch.nn.Module) -> torch.Tensor:
+        """
+        Prepare weight parameters for CLS.
+
+        :param module: PyTorch module.
+        :return: Prepared weight.
+        """
+        weight = module.weight.detach().cpu()
+        weight = self._transpose_tensor(module, weight)
+        weight = self._make_4d_tensor(module, weight)
+        return weight
+
+    def _restore_params(self, module: torch.nn.Module, tensor: torch.Tensor):
+        """
+        Restore the weight parameters.
+
+        :param module: PyTorch module.
+        :param tensor: updated parameters.
+        """
+        if isinstance(module, (torch.nn.Conv1d, torch.nn.ConvTranspose1d)):
+            tensor = torch.squeeze(tensor, dim=-1)
+
+        _weight_0 = self._transpose_tensor(module, tensor)
+        module.weight.copy_(_weight_0.reshape_as(module.weight)).to(device=module.weight.device,
+                                                                    dtype=module.weight.dtype)
+
+
+class HighBiasFold:
+    """
+    Code to apply the high-bias-fold technique to a model
+    """
+
+    ActivationIsReluForFirstModule = bool
+    ScaleForFirstModule = np.ndarray
+
+    @classmethod
+    def bias_fold(cls, cls_set_info_list: List[ClsSetInfo],
+                  bn_layers: Dict[Union[torch.nn.Conv2d, torch.nn.ConvTranspose2d], torch.nn.BatchNorm2d]):
+        """
+        Folds bias values greater than 3 * sigma to next layer's bias
+
+        :param cls_set_info_list: List of info elements for each cls set
+        :param bn_layers: Key: Conv/Linear layer Value: Corresponding folded BN layer
+        :return: None
+        """
+        if not bn_layers:
+            logger.info('High Bias folding is not supported for models without BatchNorm Layers')
+            return
+
+        for cls_set_info in cls_set_info_list:
+            for cls_pair_info in cls_set_info.cls_pair_info_list:
+
+                if (cls_pair_info.layer1.bias is None) or (cls_pair_info.layer2.bias is None) or \
+                        (cls_pair_info.layer1 not in bn_layers):
+                    continue
+
+                hbf_impl = PythonHbfImpl()
+                hbf_impl.bias_fold(cls_pair_info, bn_layers)
+
+
+class PythonHbfImpl(HbfImpl):
+    """
+    This class implements the HBF algorithm using python version while following the base Implementation interface.
+    """
+    # pylint: disable=no-self-use
+    def bias_fold(self, cls_pair_info, bn_layers):
+        """
+        Bias fold implementation using python version.
+
+        :param cls_pair_info: Layer pairs that were scaled using CLS and related information.
+        :param bn_layers: Dictionary with Key being Conv/Linear layer and value being corresponding folded BN layer.
+        """
+        weight = cls_pair_info.layer2.weight.detach().cpu()
+        if isinstance(cls_pair_info.layer2, (torch.nn.Conv1d, torch.nn.ConvTranspose1d)):
+            weight = torch.unsqueeze(weight, dim=-1)
+        # Transpose weights to C, N, H, W from N, C, H, W since axis are flipped for transposed conv
+        if isinstance(cls_pair_info.layer2, (torch.nn.ConvTranspose1d, torch.nn.ConvTranspose2d)) and \
+                cls_pair_info.layer2.groups == 1:
+            weight = weight.permute(1, 0, 2, 3)
+        weight = weight.numpy()
+
+        activation_is_relu = cls_pair_info.relu_activation_between_layers
+
+        beta = bn_layers[cls_pair_info.layer1].bias.detach().cpu().numpy() / cls_pair_info.scale_factor
+        gamma = bn_layers[cls_pair_info.layer1].weight.detach().cpu().numpy() / cls_pair_info.scale_factor
+
+        bias_prev_layer = cls_pair_info.layer1.bias.detach().cpu().numpy()
+        bias_curr_layer = cls_pair_info.layer2.bias.detach().cpu().numpy()
+
+        # Absorb high biases
+        _bias_prev_layer, _bias_curr_layer = (
+            self._absorb_bias(activation_is_relu, beta, gamma, weight, bias_curr_layer, bias_prev_layer))
+
+        with torch.no_grad():
+            cls_pair_info.layer1.bias.copy_(torch.from_numpy(_bias_prev_layer).reshape_as(cls_pair_info.layer1.bias)).to(
+                device=cls_pair_info.layer1.bias.device, dtype=cls_pair_info.layer1.bias.dtype)
+            cls_pair_info.layer2.bias.copy_(torch.from_numpy(_bias_curr_layer).reshape_as(cls_pair_info.layer2.bias)).to(
+                device=cls_pair_info.layer2.bias.device, dtype=cls_pair_info.layer2.bias.dtype)
+
+
    
+[docs]
+def equalize_model(model: torch.nn.Module, input_shapes: Union[Tuple, List[Tuple]] = None,
+                   dummy_input: Union[torch.Tensor, Tuple] = None):
+    """
+    High-level API to perform Cross-Layer Equalization (CLE) on the given model. The model is equalized in place.
+
+    :param model: Model to equalize
+    :param input_shapes: Shape of the input (can be a tuple or a list of tuples if multiple inputs)
+    :param dummy_input: A dummy input to the model. Can be a Tensor or a Tuple of Tensors. dummy_input will be
+     placed on CPU if not already.
+    """
+    if isinstance(model, torch.nn.DataParallel):
+        equalize_model(model.module, input_shapes, dummy_input)
+    else:
+        # The use of input_shapes will be removed in the future release. It is maintained now for backward compatibility.
+        if input_shapes and dummy_input is None:
+            dummy_input = create_rand_tensors_given_shapes(input_shapes, torch.device('cpu'))
+        if input_shapes is None and dummy_input is None:
+            raise ValueError("Both input_shapes and dummy_input can't be None")
+
+        # Place model and dummy input on the cpu.
+        with place_model(model, torch.device('cpu')):
+            dummy_input = utils.change_tensor_device_placement(dummy_input, device=torch.device('cpu'))
+
+            # fold batchnorm layers and perform CLE on the folded model.
+            folded_pairs = fold_all_batch_norms(model, input_shapes, dummy_input=dummy_input)
+            equalize_bn_folded_model(model, input_shapes, folded_pairs,
+                                     dummy_input=dummy_input)
    
+
+
+
+def equalize_bn_folded_model(model: torch.nn.Module,
+                             input_shapes: Union[Tuple, List[Tuple]],
+                             folded_pairs: List[Tuple[torch.nn.Module, torch.nn.BatchNorm2d]],
+                             dummy_input: Union[torch.Tensor, Tuple] = None
+                             ):
+    """
+    Perform Cross-Layer Scaling (CLS) and High Bias Folding (HBF) on a batchnorm-folded model.
+    The model is equalized in place.
+
+    :param model: Batchnorm-folded model to equalize
+    :param input_shapes: Shape of the input (can be a tuple or a list of tuples if multiple inputs)
+    :param dummy_input: A dummy input to the model. Can be a Tensor or a Tuple of Tensors. dummy_input will be
+     placed on CPU if not already.
+    :param folded_pairs: List of pairs of folded layers
+    """
+    if isinstance(model, torch.nn.DataParallel):
+        equalize_bn_folded_model(model.module, input_shapes, folded_pairs, dummy_input=dummy_input)
+    else:
+        bn_dict = {}
+        for conv_bn in folded_pairs:
+            bn_dict[conv_bn[0]] = conv_bn[1]
+
+        with place_model(model, torch.device('cpu')):
+            # replace any ReLU6 layers with ReLU
+            utils.replace_modules_of_type1_with_type2(model, torch.nn.ReLU6, torch.nn.ReLU)
+
+            # perform cross-layer scaling on applicable layer sets
+            cls_set_info_list = CrossLayerScaling.scale_model(model, input_shapes, dummy_input=dummy_input)
+
+            # high-bias fold
+            HighBiasFold.bias_fold(cls_set_info_list, bn_dict)
+
    +
    +
    +
    + +
    + + +
    +
    +
    +
    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
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    +

    Source code for aimet_torch.defs

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2018, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+
+""" Common type definitions that are used across AIMET """
+
+from enum import Enum
+from typing import List, Optional, Union
+
+import torch.utils.data
+
+from aimet_common.defs import GreedySelectionParameters, RankSelectScheme
+
+
+class ModuleCompRatioPair:
+    """
+    Pair of torch.nn.module and a compression-ratio
+
+    :ivar module: Module of type torch.nn.module
+    :ivar comp_ratio: Compression ratio. Compression ratio is the ratio of cost of compressed model
+            to cost of the original model.
+    """
+
+    def __init__(self, module: torch.nn.Module, comp_ratio: float):
+        self.module = module
+        self.comp_ratio = comp_ratio
+
+
+class OpToIOTensors:
+    """
+    Data class to store the input and output tensor names of an operation as a lists.
+    """
+    def __init__(self, node_inputs: List[str], node_outputs: List[str]):
+        """
+        :param node_inputs: name of inputs to the node
+        :param node_outputs: name of output from the node
+        """
+
+        self.inputs = node_inputs
+        self.outputs = node_outputs
+
+
+
    
+[docs]
+class SpatialSvdParameters:
+    """ Configuration parameters for spatial svd compression """
+
+
    
+[docs]
+    class ManualModeParams:
+        """
+        Configuration parameters for manual-mode spatial svd compression
+        """
+
+        def __init__(self, list_of_module_comp_ratio_pairs: List[ModuleCompRatioPair]):
+            """
+            :param list_of_module_comp_ratio_pairs: List of (module, comp-ratio) pairs
+            """
+            self.list_of_module_comp_ratio_pairs = list_of_module_comp_ratio_pairs
    
+
+
+
    
+[docs]
+    class AutoModeParams:
+        """
+        Configuration parameters for auto-mode compression
+        """
+
+        def __init__(self, greedy_select_params: GreedySelectionParameters,
+                     modules_to_ignore: Optional[List[torch.nn.Module]] = None):
+            """
+            :param greedy_select_params: Params for greedy comp-ratio selection algorithm
+            :param modules_to_ignore: List of modules to ignore (None indicates nothing to ignore)
+            """
+            self.greedy_params = greedy_select_params
+            self.modules_to_ignore = [] if modules_to_ignore is None else modules_to_ignore
    
+
+
+
    
+[docs]
+    class Mode(Enum):
+        """ Mode enumeration """
+
+        manual = 1
+        """ Manual mode """
+
+        auto = 2
+        """ Auto mode """
    
+
+
+    def __init__(self, mode: Mode, params: Union[ManualModeParams, AutoModeParams], multiplicity=1):
+        """
+        :param mode: Either auto mode or manual mode
+        :param params: Parameters for the mode selected
+        :param multiplicity: The multiplicity to which ranks/input channels will get rounded. Default: 1
+        """
+        self.mode = mode
+        self.mode_params = params
+        self.multiplicity = multiplicity
    
+
+
+
+
    
+[docs]
+class ChannelPruningParameters:
+    """ Configuration parameters for channel pruning compression """
+
+
    
+[docs]
+    class ManualModeParams:
+        """
+        Configuration parameters for manual-mode channel pruning compression
+        """
+
+        def __init__(self, list_of_module_comp_ratio_pairs: List[ModuleCompRatioPair]):
+            """
+            :param list_of_module_comp_ratio_pairs: List of (module, comp-ratio) pairs
+            """
+            self.list_of_module_comp_ratio_pairs = list_of_module_comp_ratio_pairs
    
+
+
+
    
+[docs]
+    class AutoModeParams:
+        """
+        Configuration parameters for auto-mode compression
+        """
+
+        def __init__(self, greedy_select_params: GreedySelectionParameters,
+                     modules_to_ignore: Optional[List[torch.nn.Module]] = None):
+            """
+            :param greedy_select_params: Params for greedy comp-ratio selection algorithm
+            :param modules_to_ignore: List of modules to ignore (None indicates nothing to ignore)
+            """
+            self.greedy_params = greedy_select_params
+            self.modules_to_ignore = [] if modules_to_ignore is None else modules_to_ignore
    
+
+
+
    
+[docs]
+    class Mode(Enum):
+        """ Mode enumeration """
+
+        manual = 1
+        """ Manual mode: User specifies comp-ratio per layer """
+
+        auto = 2
+        """ Auto mode: AIMET computes optimal comp-ratio per layer """
    
+
+
+    def __init__(self, data_loader: torch.utils.data.DataLoader, num_reconstruction_samples: int,
+                 allow_custom_downsample_ops: bool,
+                 mode: Mode, params: Union[ManualModeParams, AutoModeParams], multiplicity=1):
+        self.data_loader = data_loader
+        self.num_reconstruction_samples = num_reconstruction_samples
+        self.allow_custom_downsample_ops = allow_custom_downsample_ops
+        self.mode = mode
+        self.mode_params = params
+        self.multiplicity = multiplicity
    
+
+
+
+
    
+[docs]
+class WeightSvdParameters:
+    """ Configuration parameters for weight svd compression """
+
+
    
+[docs]
+    class ManualModeParams:
+        """
+        Configuration parameters for manual-mode weight svd compression
+        """
+
+        def __init__(self, list_of_module_comp_ratio_pairs: List[ModuleCompRatioPair]):
+            """
+            :param list_of_module_comp_ratio_pairs: List of (module, comp-ratio) pairs
+            """
+            self.list_of_module_comp_ratio_pairs = list_of_module_comp_ratio_pairs
    
+
+
+
    
+[docs]
+    class AutoModeParams:
+        """
+        Configuration parameters for auto-mode compression
+        """
+
+        def __init__(self,
+                     rank_select_scheme: RankSelectScheme,
+                     select_params: Union[GreedySelectionParameters, ],
+                     modules_to_ignore: Optional[List[torch.nn.Module]] = None):
+            """
+            :param rank_select_scheme: supports two options greedy and tar
+            :param select_params: Params for greedy/TAR comp-ratio selection algorithm
+            :param modules_to_ignore: List of modules to ignore (None indicates nothing to ignore)
+            """
+            self.rank_select_scheme = rank_select_scheme
+            self.select_params = select_params
+            self.modules_to_ignore = [] if modules_to_ignore is None else modules_to_ignore
    
+
+
+
    
+[docs]
+    class Mode(Enum):
+        """ Mode enumeration """
+
+        manual = 1
+        """ Manual mode """
+
+        auto = 2
+        """ Auto mode """
    
+
+
+    def __init__(self, mode: Mode, params: Union[ManualModeParams, AutoModeParams], multiplicity=1):
+        """
+        :param mode: Either auto mode or manual mode
+        :param params: Parameters for the mode selected
+        :param multiplicity: The multiplicity to which ranks/input channels will get rounded. Default: 1
+        """
+        self.mode = mode
+        self.mode_params = params
+        self.multiplicity = multiplicity
    
+
+
+
+class PassThroughOp(torch.nn.Module):
+    """
+    This is a pass-through op, used for purpose of making an op a no-op
+    """
+    # pylint:disable=arguments-differ
+    @staticmethod
+    def forward(inputx):
+        """
+        Forward pass for passthrough op
+        """
+        return inputx
+
    +
    +
    +
    + +
    + + +
    +
    +
    +
    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
    + Made with Sphinx and @pradyunsg's + + Furo + +
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    +

    Source code for aimet_torch.layer_output_utils

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2023, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+
+""" This module contains utilities to capture and save intermediate layer-outputs of a model. """
+
+import os
+from typing import Union, Dict, List, Tuple
+from enum import Enum
+import shutil
+import re
+
+import numpy as np
+import onnx
+import torch
+
+from aimet_common.utils import AimetLogger
+from aimet_common.layer_output_utils import SaveInputOutput, save_layer_output_names
+
+from aimet_torch._base.quantsim import _QuantizedModuleProtocol
+from aimet_torch import utils
+from aimet_torch import torchscript_utils
+from aimet_torch.onnx_utils import OnnxSaver, OnnxExportApiArgs
+from aimet_torch.v2.nn.base import BaseQuantizationMixin
+
+try:
+    from aimet_torch.v1.qc_quantize_recurrent import QcQuantizeRecurrent
+    _quantized_module_types = (_QuantizedModuleProtocol, QcQuantizeRecurrent)
+except ImportError:
+    _quantized_module_types = (_QuantizedModuleProtocol,)
+
+
+logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.LayerOutputs)
+
+
+
    
+[docs]
+class NamingScheme(Enum):
+    """ Enumeration of layer-output naming schemes. """
+
+    PYTORCH = 1
+    """ Names outputs according to exported pytorch model. Layer names are used. """
+    ONNX = 2
+    """ Names outputs according to exported onnx model. Layer output names are generally numeric. """
+    TORCHSCRIPT = 3
+    """ Names outputs according to exported torchscript model. Layer output names are generally numeric. """
    
+
+
+
+
    
+[docs]
+class LayerOutputUtil:
+    """ Implementation to capture and save outputs of intermediate layers of a model (fp32/quantsim). """
+
+    def __init__(self, model: torch.nn.Module, dir_path: str, naming_scheme: NamingScheme = NamingScheme.PYTORCH,
+                 dummy_input: Union[torch.Tensor, Tuple, List] = None, onnx_export_args: Union[OnnxExportApiArgs, Dict] = None):
+        """
+        Constructor for LayerOutputUtil.
+
+        :param model: Model whose layer-outputs are needed.
+        :param dir_path: Directory wherein layer-outputs will be saved.
+        :param naming_scheme: Naming scheme to be followed to name layer-outputs. There are multiple schemes as per
+            the exported model (pytorch, onnx or torchscript). Refer the NamingScheme enum definition.
+        :param dummy_input: Dummy input to model. Required if naming_scheme is 'NamingScheme.ONNX' or 'NamingScheme.TORCHSCRIPT'.
+        :param onnx_export_args: Should be same as that passed to quantsim export API to have consistency between
+            layer-output names present in exported onnx model and generated layer-outputs. Required if naming_scheme is
+            'NamingScheme.ONNX'.
+        """
+
+        # Utility to capture layer-outputs
+        self.layer_output = LayerOutput(model=model, naming_scheme=naming_scheme, dir_path=dir_path, dummy_input=dummy_input,
+                                        onnx_export_args=onnx_export_args)
+
+        # Utility to save model inputs and their corresponding layer-outputs
+        self.save_input_output = SaveInputOutput(dir_path=dir_path, axis_layout='NCHW')
+
+
    
+[docs]
+    def generate_layer_outputs(self, input_instance: Union[torch.Tensor, List[torch.Tensor], Tuple[torch.Tensor]]):
+        """
+        This method captures output of every layer of a model & saves the single input instance and corresponding layer-outputs to disk.
+
+        :param input_instance: Single input instance for which we want to obtain layer-outputs.
+        :return: None
+        """
+
+        logger.info("Generating layer-outputs for input instance %d", self.save_input_output.input_cntr+1)
+
+        # Obtain layer-output name to output dictionary
+        layer_output_dict = self.layer_output.get_outputs(input_instance)
+
+        # Place inputs and layer-outputs on CPU
+        input_instance = LayerOutputUtil._get_input_in_numpy(input_instance)
+        layer_output_dict = LayerOutputUtil._get_layer_output_in_numpy(layer_output_dict)
+
+        # Save inputs and layer-outputs
+        self.save_input_output.save(input_instance, layer_output_dict)
+
+        logger.info('Successfully generated layer-outputs for input instance %d', self.save_input_output.input_cntr)
    
+
+
+    @staticmethod
+    def _get_input_in_numpy(input_instance: Union[torch.Tensor, List[torch.Tensor], Tuple[torch.Tensor]]) -> \
+            Union[np.ndarray, List[np.ndarray], Tuple[np.ndarray]]:
+        """
+        Coverts the torch tensors into numpy arrays
+        :param input_instance: Single input instance with torch tensors
+        :return: Input with numpy arrays
+        """
+        if isinstance(input_instance, (List, Tuple)):
+            numpy_input = []
+            for ith_input in input_instance:
+                numpy_input.append(ith_input.cpu().numpy())
+            return numpy_input
+        return input_instance.cpu().numpy()
+
+    @staticmethod
+    def _get_layer_output_in_numpy(layer_output_dict: Dict[str, torch.Tensor]) -> Dict[str, np.ndarray]:
+        """
+        Converts the torch tensors into numpy arrays
+        :param layer_output_dict: layer output dictionary with torch tensors
+        :return: layer output dictionary with numpy arrays
+        """
+        layer_output_numpy_dict = {}
+        for output_name, output_tensor in layer_output_dict.items():
+            layer_output_numpy_dict[output_name] = output_tensor.cpu().numpy()
+        return layer_output_numpy_dict
    
+
+
+
+class LayerOutput:
+    """
+    This class creates a layer-output name to layer-output dictionary. The layer-output names are as per the AIMET exported
+    pytorch/onnx/torchscript model.
+    """
+    def __init__(self, model: torch.nn.Module, dir_path: str, naming_scheme: NamingScheme = NamingScheme.PYTORCH,
+                 dummy_input: Union[torch.Tensor, Tuple, List] = None, onnx_export_args: Union[OnnxExportApiArgs, Dict] = None):
+        """
+        Constructor - It initializes few dictionaries that are required for capturing and naming layer-outputs.
+
+        :param model: Model whose layer-outputs are needed.
+        :param dir_path: Directory wherein layer-output names arranged in topological order will be saved. It will also
+            be used to temporarily save onnx/torchscript equivalent of the given model.
+        :param naming_scheme: Naming scheme to be followed to name layer-outputs. There are multiple schemes as per
+            the exported model (pytorch, onnx or torchscript). Refer the NamingScheme enum definition.
+        :param dummy_input: Dummy input to model (required if naming_scheme is 'onnx').
+        :param onnx_export_args: Should be same as that passed to quantsim export API to have consistency between
+            layer-output names present in exported onnx model and generated layer-outputs (required if naming_scheme is
+            'onnx').
+        """
+        self.model = model
+        self.module_to_name_dict = utils.get_module_to_name_dict(model=model, prefix='')
+
+        # Check whether the given model is quantsim model
+        self.is_quantsim_model = any(isinstance(module, _quantized_module_types) for module in model.modules())
+
+        # Obtain layer-name to layer-output name mapping
+        self.layer_name_to_layer_output_dict = {}
+        self.layer_name_to_layer_output_name_dict = {}
+        if naming_scheme == NamingScheme.PYTORCH:
+            for name, module in model.named_modules():
+                if utils.is_leaf_module(module) or isinstance(module, BaseQuantizationMixin):
+                    name = name.replace('._module_to_wrap', '')
+                    self.layer_name_to_layer_output_name_dict[name] = name
+        else:
+            self.layer_name_to_layer_output_name_dict = LayerOutput.get_layer_name_to_layer_output_name_map(
+                self.model, naming_scheme, dummy_input, onnx_export_args, dir_path)
+
+        # Replace any delimiter in layer-output name string with underscore
+        for layer_name, output_name in self.layer_name_to_layer_output_name_dict.items():
+            self.layer_name_to_layer_output_name_dict[layer_name] = re.sub(r'\W+', "_", output_name)
+
+        # Save layer-output names which are in topological order of model graph. This order can be used while comparing layer-outputs.
+        layer_output_names = list(self.layer_name_to_layer_output_name_dict.values())
+        save_layer_output_names(layer_output_names, dir_path)
+
+    def get_outputs(self, input_instance: Union[torch.Tensor, List[torch.Tensor], Tuple[torch.Tensor]]) -> Dict[str, torch.Tensor]:
+        """
+        This function captures layer-outputs and renames them as per the AIMET exported pytorch/onnx/torchscript model.
+
+        :param input_instance: Single input instance for which we want to obtain layer-outputs.
+        :return: layer-name to layer-output dict
+        """
+
+        # Fetch outputs of all the layers
+        self.layer_name_to_layer_output_dict = {}
+        if self.is_quantsim_model:
+            # Apply record-output hook to QuantizeWrapper modules (one node above leaf node in model graph)
+            utils.run_hook_for_layers_with_given_input(self.model, input_instance, self.record_outputs,
+                                                       module_type_for_attaching_hook=_quantized_module_types,
+                                                       leaf_node_only=False)
+        else:
+            # Apply record-output hook to Original modules (leaf node in model graph)
+            utils.run_hook_for_layers_with_given_input(self.model, input_instance, self.record_outputs, leaf_node_only=True)
+
+        # Rename outputs according to pytorch/onnx/torchscript model
+        layer_output_name_to_layer_output_dict = LayerOutput.rename_layer_outputs(self.layer_name_to_layer_output_dict,
+                                                                                  self.layer_name_to_layer_output_name_dict)
+
+        return layer_output_name_to_layer_output_dict
+
+    def record_outputs(self, module: torch.nn.Module, _, output: torch.Tensor):
+        """
+        Hook function to capture output of a layer.
+
+        :param module: Layer-module in consideration.
+        :param _: Placeholder for the input of the layer-module.
+        :param output: Output of the layer-module.
+        :return: None
+        """
+        layer_name = self.module_to_name_dict[module]
+        if isinstance(output, torch.Tensor):
+            self.layer_name_to_layer_output_dict[layer_name] = output.clone()
+        else:
+            logger.info("Skipping constant scalar output of layer %s", layer_name)
+
+    @staticmethod
+    def rename_layer_outputs(layer_name_to_layer_output_dict: Dict[str, torch.Tensor],
+                             layer_name_to_layer_output_name_dict: Dict[str, str]) -> Dict[str, torch.Tensor]:
+        """
+        Rename layer-outputs based on the layer-name to layer-output name map
+
+        :param layer_name_to_layer_output_dict: Dict containing layer-outputs
+        :param layer_name_to_layer_output_name_dict: Dict containing layer-output names
+        :return: layer_output_name_to_layer_output_dict
+        """
+        layer_names = list(layer_name_to_layer_output_dict.keys())
+
+        for layer_name in layer_names:
+            if layer_name in layer_name_to_layer_output_name_dict:
+                # Rename the layer-output by using layer-output name, instead of layer-name
+                layer_output_name = layer_name_to_layer_output_name_dict[layer_name]
+                layer_name_to_layer_output_dict[layer_output_name] = layer_name_to_layer_output_dict.pop(layer_name)
+            else:
+                # Delete the layer-output as it doesn't have a name
+                layer_name_to_layer_output_dict.pop(layer_name)
+
+        return layer_name_to_layer_output_dict
+
+    @staticmethod
+    def get_layer_name_to_layer_output_name_map(model, naming_scheme: NamingScheme, dummy_input: Union[torch.Tensor, Tuple, List],
+                                                onnx_export_args: Union[OnnxExportApiArgs, Dict], dir_path: str) -> Dict[str, str]:
+        """
+        This function produces layer-name to layer-output name map w.r.t the AIMET exported onnx/torchscript model. If a
+        layer gets expanded into multiple layers in the exported model then the intermediate layers are ignored and
+        output-name of last layer is used.
+
+        :param model: model
+        :param naming_scheme: onnx/torchscript
+        :param dummy_input: dummy input that is used to construct onnx/torchscript model
+        :param onnx_export_args: OnnxExportApiArgs instance same as that passed to quantsim export API
+        :param dir_path: directory to temporarily save the constructed onnx/torchscrip model
+        :return: dictionary of layer-name to layer-output name
+        """
+        # pylint: disable=import-outside-toplevel, cyclic-import
+        if any(isinstance(module, BaseQuantizationMixin) for module in model.modules()):
+            from aimet_torch.v2.quantsim import QuantizationSimModel
+        else:
+            from aimet_torch.v1.quantsim import QuantizationSimModel
+
+        # Restore original model by removing quantization wrappers if present.
+        original_model = QuantizationSimModel.get_original_model(model)
+
+        # Set path to store exported onnx/torchscript model.
+        LayerOutput._validate_dir_path(dir_path)
+        exported_model_dir = os.path.join(dir_path, 'exported_models')
+        os.makedirs(exported_model_dir, exist_ok=True)
+
+        # Get node to i/o tensor name map from the onnx/torchscript model
+        if naming_scheme == NamingScheme.ONNX:
+            exported_model_node_to_io_tensor_map = LayerOutput.get_onnx_node_to_io_tensor_map(
+                original_model, exported_model_dir, dummy_input, onnx_export_args)
+        else:
+            exported_model_node_to_io_tensor_map = LayerOutput.get_torchscript_node_to_io_tensor_map(
+                original_model, exported_model_dir, dummy_input)
+
+        layer_names_list = [name for name, module in original_model.named_modules() if utils.is_leaf_module(module)]
+        layers_missing_in_exported_model = []
+        layer_name_to_layer_output_name_map = {}
+
+        # Get mapping between layer names and layer-output names.
+        logger.info("Layer Name to Layer Output-name Mapping")
+        # pylint: disable=protected-access
+        for layer_name in layer_names_list:
+            if layer_name in exported_model_node_to_io_tensor_map:
+                # pylint: disable=protected-access, unused-variable
+                layer_output_names, intermediate_layer_output_names = QuantizationSimModel._get_layer_activation_tensors(
+                    layer_name, exported_model_node_to_io_tensor_map)
+                layer_name_to_layer_output_name_map[layer_name] = layer_output_names[0]
+                logger.info("%s -> %s", layer_name, layer_output_names[0])
+            else:
+                layers_missing_in_exported_model.append(layer_name)
+
+        if layers_missing_in_exported_model:
+            logger.warning("The following layers were not found in the exported model:\n"
+                           "%s\n"
+                           "This can be due to below reason:\n"
+                           "\t- The layer was not seen while exporting using the dummy input provided in sim.export(). "
+                           "Ensure that the dummy input covers all layers.",
+                           layers_missing_in_exported_model)
+
+        # Delete onnx/torchscript models
+        shutil.rmtree(exported_model_dir, ignore_errors=False, onerror=None)
+
+        return layer_name_to_layer_output_name_map
+
+    @staticmethod
+    def get_onnx_node_to_io_tensor_map(model: torch.nn.Module, exported_model_dir: str, dummy_input: Union[torch.Tensor, Tuple, List],
+                                       onnx_export_args: Union[OnnxExportApiArgs, Dict]) -> Dict[str, Dict]:
+        """
+        This function constructs an onnx model equivalent to the give pytorch model and then generates node-name to i/o
+        tensor-name map.
+        :param model: pytorch model without quantization wrappers
+        :param exported_model_dir: directory to save onnx model
+        :param dummy_input: dummy input to be used for constructing onnx model
+        :param onnx_export_args: configurations to generate onnx model
+        :return: onnx_node_to_io_tensor_map
+        """
+        LayerOutput._validate_dummy_input(dummy_input)
+        LayerOutput._validate_onnx_export_args(onnx_export_args)
+
+        onnx_path = os.path.join(exported_model_dir, 'model.onnx')
+
+        OnnxSaver.create_onnx_model_with_pytorch_layer_names(onnx_model_path=onnx_path, pytorch_model=model,
+                                                             dummy_input=dummy_input, onnx_export_args=onnx_export_args)
+        onnx_model = onnx.load(onnx_path)
+        onnx_node_to_io_tensor_map, _ = OnnxSaver.get_onnx_node_to_io_tensor_names_map(onnx_model)
+
+        return onnx_node_to_io_tensor_map
+
+    @staticmethod
+    def get_torchscript_node_to_io_tensor_map(model: torch.nn.Module, exported_model_dir: str,
+                                              dummy_input: Union[torch.Tensor, Tuple, List]) -> Dict[str, Dict]:
+        """
+        This function constructs a torchscript model equivalent to the give pytorch model and then generates node-name to i/o
+        tensor-name map.
+        :param model: pytorch model without quantization wrappers
+        :param exported_model_dir: directory to save onnx model
+        :param dummy_input: dummy input to be used for constructing onnx model
+        :return: torchscript_node_to_io_tensor_map
+        """
+        LayerOutput._validate_dummy_input(dummy_input)
+
+        ts_path = os.path.join(exported_model_dir, 'model.torchscript.pth')
+
+        with utils.in_eval_mode(model), torch.no_grad():
+            torchscript_utils.create_torch_script_model(ts_path, model, dummy_input)
+            trace = torch.jit.load(ts_path)
+            torch_script_node_to_io_tensor_map, _ = \
+                torchscript_utils.get_node_to_io_tensor_names_map(model, trace, dummy_input)
+
+        return torch_script_node_to_io_tensor_map
+
+    @staticmethod
+    def _validate_dir_path(dir_path: str):
+        """
+        Validate directory path in which onnx/torchscript models will be temporarily saved
+        :param dir_path: directory path
+        :return:
+        """
+        if dir_path is None:
+            raise ValueError("Missing directory path to save onnx/torchscript models")
+
+    @staticmethod
+    def _validate_dummy_input(dummy_input: Union[torch.Tensor, Tuple, List]):
+        """
+        Validates dummy input which is used to generate onnx/torchscript model
+        :param dummy_input: single input instance
+        :return:
+        """
+        if not isinstance(dummy_input, (torch.Tensor, tuple, list)):
+            raise ValueError("Invalid dummy_input data-type")
+
+    @staticmethod
+    def _validate_onnx_export_args(onnx_export_args: Union[OnnxExportApiArgs, Dict]):
+        """
+        Validates export arguments which are used to generate an onnx model
+        :param onnx_export_args: export arguments
+        :return:
+        """
+        if onnx_export_args is None:
+            onnx_export_args = OnnxExportApiArgs()
+        if not isinstance(onnx_export_args, (OnnxExportApiArgs, dict)):
+            raise ValueError("Invalid onnx_export_args data-type")
+
    +
    +
    +
    + +
    + + +
    +
    +
    +
    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
    + Made with Sphinx and @pradyunsg's + + Furo + +
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    +

    Source code for aimet_torch.model_preparer

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2021-2024, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  From PyTorch:
+#
+#  Copyright (c) 2016-     Facebook, Inc            (Adam Paszke)
+#  Copyright (c) 2014-     Facebook, Inc            (Soumith Chintala)
+#  Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
+#  Copyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)
+#  Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
+#  Copyright (c) 2011-2013 NYU                      (Clement Farabet)
+#  Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
+#  Copyright (c) 2006      Idiap Research Institute (Samy Bengio)
+#  Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
+#
+#  From Caffe2:
+#
+#  Copyright (c) 2016-present, Facebook Inc. All rights reserved.
+#
+#  All contributions by Facebook:
+#  Copyright (c) 2016 Facebook Inc.
+#
+#  All contributions by Google:
+#  Copyright (c) 2015 Google Inc.
+#  All rights reserved.
+#
+#  All contributions by Yangqing Jia:
+#  Copyright (c) 2015 Yangqing Jia
+#  All rights reserved.
+#
+#  All contributions by Kakao Brain:
+#  Copyright 2019-2020 Kakao Brain
+#
+#  All contributions by Cruise LLC:
+#  Copyright (c) 2022 Cruise LLC.
+#  All rights reserved.
+#
+#  All contributions from Caffe:
+#  Copyright(c) 2013, 2014, 2015, the respective contributors
+#  All rights reserved.
+#
+#  All other contributions:
+#  Copyright(c) 2015, 2016 the respective contributors
+#  All rights reserved.
+#
+#  Caffe2 uses a copyright model similar to Caffe: each contributor holds
+#  copyright over their contributions to Caffe2. The project versioning records
+#  all such contribution and copyright details. If a contributor wants to further
+#  mark their specific copyright on a particular contribution, they should
+#  indicate their copyright solely in the commit message of the change when it is
+#  committed.
+#
+#  All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright
+#     notice, this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright
+#     notice, this list of conditions and the following disclaimer in the
+#     documentation and/or other materials provided with the distribution.
+#
+#  3. Neither the names of Facebook, Deepmind Technologies, NYU, NEC Laboratories America
+#     and IDIAP Research Institute nor the names of its contributors may be
+#     used to endorse or promote products derived from this software without
+#     specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+
+""" Implementation to automatically prepare pytorch models for AIMET features """
+
+# --------------------------------------------------------------------------------------------------------
+# Reference : https://github.com/pytorch/pytorch/blob/main/torch/fx/proxy.py#L26
+#             https://github.com/pytorch/pytorch/blob/main/torch/fx/proxy.py#L57
+
+# Above PyTorch code is used to get node_name_to_scope information by overriding call_module and create_node methods
+# of torch.fx.Tracer base class:
+# TODO: node_name_to_scope should be removed and instead use node.meta[] after upgrading to torch 2.0
+# ----------------------------------------------------------------------------------------------------------
+
+import copy
+import re
+from typing import Any, Optional, Dict, Union, List, Callable, Tuple
+import torch
+import torch.fx
+from aimet_common.utils import AimetLogger
+from aimet_torch.utils import in_eval_mode
+from aimet_torch.utils import replace_modules_of_type1_with_type2
+import aimet_torch._base.nn.modules.custom as aimet_modules
+
+logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.ModelPreparer)
+
+# this is a map of torch.nn.functional type to corresponding module type
+functional_op_to_module_map = {
+    torch.nn.functional.relu: torch.nn.ReLU,
+    torch.nn.functional.gelu: torch.nn.GELU
+}
+
+# In this functional --> module map, corresponding model is of type torch.nn and stateful.
+functional_with_stateful_api = {
+    'relu'          : torch.nn.ReLU,
+    'relu6'         : torch.nn.ReLU6,
+    'hardtanh'      : torch.nn.Hardtanh,
+    'hardwish'      : torch.nn.Hardswish,
+    'elu'           : torch.nn.ELU,
+    'selu'          : torch.nn.SELU,
+    'celu'          : torch.nn.CELU,
+    'leaky_relu'    : torch.nn.LeakyReLU,
+    'prelu'         : torch.nn.PReLU,
+    'rrelu'         : torch.nn.RReLU,
+    'glu'           : torch.nn.GLU,
+    'gelu'          : torch.nn.GELU,
+    'logsigmoid'    : torch.nn.LogSigmoid,
+    'hardshrink'    : torch.nn.Hardshrink,
+    'tanhshrink'    : torch.nn.Tanhshrink,
+    'softsign'      : torch.nn.Softsign,
+    'softplus'      : torch.nn.Softplus,
+    'softmin'       : torch.nn.Softmin,
+    'softmax'       : torch.nn.Softmax,
+    'softshrink'    : torch.nn.Softshrink,
+    'log_softmax'   : torch.nn.LogSoftmax,
+    'tanh'          : torch.nn.Tanh,
+    'sigmoid'       : torch.nn.Sigmoid,
+    'hardsigmoid'   : torch.nn.Hardsigmoid,
+    'silu'          : torch.nn.SiLU,
+    'scaled_dot_product_attention': aimet_modules.ScaledDotProductAttention,
+}
+
+
+# Function that requires special transformation.
+functional_with_special_handling = {
+    'cat'           : aimet_modules.Concat,
+    'conv2d'        : torch.nn.Conv2d
+}
+
+# In this functional --> module map, corresponding custom module is of type torch.nn and uses stateless API.
+functional_with_stateless_api = {
+    '_pad'                      : aimet_modules.Pad,
+    'pad'                       : aimet_modules.Pad,
+    'sum'                       : aimet_modules.Sum,
+    'add'                       : aimet_modules.Add,
+    'subtract'                  : aimet_modules.Subtract,
+    'sub'                       : aimet_modules.Subtract,
+    'mul'                       : aimet_modules.Multiply,
+    'div'                       : aimet_modules.Divide,
+    'truediv'                   : aimet_modules.Divide,
+    'floordiv'                  : aimet_modules.FloorDivide,
+    'matmul'                    : aimet_modules.MatMul,
+    'exp'                       : aimet_modules.Exponential,
+    'interpolate'               : aimet_modules.Interpolate,
+    'max_pool2d'                : aimet_modules.MaxPool2d,
+    'max_pool2d_with_indices'   : aimet_modules.MaxPool2d,
+    'adaptive_avg_pool2d'       : aimet_modules.AdaptiveAvgPool2d,
+    'avg_pool2d'                : aimet_modules.AvgPool2d,
+    'norm'                      : aimet_modules.Norm,
+    'batch_norm'                : aimet_modules.BatchNorm,
+    'group_norm'                : aimet_modules.GroupNorm,
+    'mean'                      : aimet_modules.Mean,
+    'pow'                       : aimet_modules.Pow,
+    'where'                     : aimet_modules.Where,
+    'addmm'                     : aimet_modules.Addmm,
+    'bmm'                       : aimet_modules.Bmm,
+    'baddbmm'                   : aimet_modules.Baddbmm,
+    'cumsum'                    : aimet_modules.CumSum,
+    'masked_fill'               : aimet_modules.MaskedFill,
+    'square'                    : aimet_modules.Square,
+    'rsqrt'                     : aimet_modules.RSqrt,
+}
+
+
+class Scope:
+    """
+    Code adapted from: https://github.com/pytorch/pytorch/blob/main/torch/fx/proxy.py#L26
+
+    Scope object that records the module path and the module type of module.
+    Scope is used to track the information of the module that contains a Node
+    in a Graph of GraphModule.
+    """
+    def __init__(self, module_path: str, module_type: Any):
+        super().__init__()
+        self.module_path = module_path
+        self.module_type = module_type
+
+
+class ScopeContextManager:
+    """
+    Code adapted from: https://github.com/pytorch/pytorch/blob/main/torch/fx/proxy.py#L57
+
+    A context manager to track the Scope of Node during symbolic tracing.
+    When entering a forward function of a Module, we'll update the scope information of
+    the current module, and when we exit, we'll restore the previous scope information.
+    """
+    def __init__(self, scope: Scope, current_scope: Scope):
+        super().__init__()
+        # Keep a copy of prev scope.
+        self._prev_scope = copy.copy(scope)
+        # Update scope to current scope
+        scope.module_path = current_scope.module_path
+        scope.module_type = current_scope.module_type
+        # Save a reference so, we can restore tracer.scope with prev scope on exit.
+        self._scope = scope
+
+    def __enter__(self):
+        return
+
+    def __exit__(self, *args):
+        self._scope.module_path = self._prev_scope.module_path
+        self._scope.module_type = self._prev_scope.module_type
+
+
+def conv2d_create_node(traced_model: torch.fx.GraphModule, module_name: str, node: torch.fx.node) \
+        -> torch.fx.node:
+    """
+    Create the node to be inserted in the graph model.
+
+    :param traced_model: Symbolically traced model
+    :param module_name: Qualified module name in symbolic_traced_model hierarchy corresponding to new node
+    :param node: Current node in the graph after which new node will be inserted
+    :return: torch.fx.node to be inserted in the graph
+    """
+
+    n_args = len(node.args)
+    # input tensors must be passed as args, not kwargs for QcQuantizeWrapper
+    input_tensor = []
+    # input and weight is guaranteed to exist, but bias can be None
+    # Since None cannot be passed as args in QcQuantizeWrapper, do not add it to input_tensor
+    for index, key in [[0, 'input'], [1, 'weight'], [2, ' bias']]:
+        value = None
+        if n_args > index:
+            value = node.args[index]
+        elif key in node.kwargs:
+            value = node.kwargs[key]
+
+        if value is not None:
+            input_tensor.append(value)
+        else:
+            break
+
+    with traced_model.graph.inserting_after(node):
+        if check_dynamic_conv2d(traced_model, module_name):
+            new_node = traced_model.graph.call_module(module_name, args=tuple(input_tensor))
+        else:
+            new_node = traced_model.graph.call_module(module_name, args=tuple([input_tensor[0]]))
+        return new_node
+
+
+def check_dynamic_conv2d(traced_model: torch.fx.GraphModule, module_name: str) -> bool:
+    """
+    return True if the module is dynamic conv2d.
+    """
+    m = traced_model
+    for name in module_name.split('.'):
+        m = getattr(m, name)
+
+    return isinstance(m, aimet_modules.DynamicConv2d)
+
+
+def conv2d_create_module(node: torch.fx.node) -> torch.nn.Module:
+    """
+    Create the replacement module.
+
+    :param node: Current node in the graph after which new node will be inserted
+    :return: New module.
+    """
+
+    # Get weight and bias from argument
+    params = merge_args_and_kwargs(node, {1: 'weight', 2: 'bias'})
+
+    # Convert F.Conv2D arguments to nn.Conv2D arguments
+    kwargs = merge_args_and_kwargs(node, {3: 'stride', 4: 'padding', 5: 'dilation', 6: 'groups'})
+
+    # If weight or bias is from activation of another layer, use dynamic_conv2d
+    use_dynamic_conv2d = False
+    for key, param in params.items():
+        if param.op != 'get_attr':
+            use_dynamic_conv2d = True
+            break
+
+    if use_dynamic_conv2d:
+        module = aimet_modules.DynamicConv2d(**kwargs)
+    else:
+        for key, param_node in params.items():
+            params[key] = get_node_attr(param_node)
+
+        # Fetch additional info using parameters
+        out_channels, in_channels, kernel_size, _ = params['weight'].shape
+        bias = 'bias' in params
+
+        # For Depthwise Conv, multiply in_channels by number of groups
+        # if groups is not passed as arg, use its default value 1
+        kwargs['in_channels'] = in_channels * kwargs.get('groups', 1)
+        kwargs['out_channels'] = out_channels
+        kwargs['kernel_size'] = kernel_size
+        kwargs['bias'] = bias
+
+        module = torch.nn.Conv2d(**kwargs)
+        # Replace nn.Conv2D params using F.Conv2D arguments
+        module.weight = torch.nn.Parameter(params['weight'])
+        if bias:
+            module.bias = torch.nn.Parameter(params['bias'])
+    return module
+
+
+def merge_args_and_kwargs(node: torch.fx.node, arguments_to_fetch: Dict) -> Dict:
+    """
+    Merge args and kwargs into a single kwargs and return it
+    :param node: node to fetch args and kwargs from
+    :param arguments_to_fetch: dictionary containing arguments' indices in args and keys in kwargs
+    :return: single merged kwargs
+    """
+    n_args = len(node.args)
+    kwargs = {}
+    for index, key in arguments_to_fetch.items():
+        value = None
+        if n_args > index:
+            value = node.args[index]
+        elif key in node.kwargs:
+            value = node.kwargs[key]
+
+        if value is not None:
+            kwargs[key] = value
+    return kwargs
+
+
+def get_node_attr(node: torch.fx.node):
+    """
+    Codes modified from https://pytorch.org/docs/stable/fx.html#the-interpreter-pattern
+
+    :param node: node to fetch data from
+    :return: value returned from node
+    """
+    def fetch_attr(target: str):
+        target_atoms = target.split('.')
+        attr_itr = node.graph.owning_module
+        for i, atom in enumerate(target_atoms):
+            if not hasattr(attr_itr, atom):
+                raise RuntimeError(f"Node referenced nonexistant target {'.'.join(target_atoms[:i])}")
+            attr_itr = getattr(attr_itr, atom)
+        return attr_itr
+
+    assert node.op == 'get_attr'
+
+    return fetch_attr(node.target)
+
+
+def concat_create_node(traced_model: torch.fx.GraphModule, module_name: str, node: torch.fx.node) \
+        -> torch.fx.node:
+    """
+    Create the node to be inserted in the graph model.
+
+    :param traced_model: Symbolically traced model
+    :param module_name: Qualified module name in symbolic_traced_model hierarchy corresponding to new node
+    :param node: Current node in the graph after which new node will be inserted
+    :return: torch.fx.node to be inserted in the graph
+    """
+
+    with traced_model.graph.inserting_after(node):
+        # call_module only accepts tuple as args but node.args[0] can be a list. Convert it into a tuple
+        # If node.args[0] is already a tuple, tuple() will do nothing
+        new_node = traced_model.graph.call_module(module_name, args=tuple(node.args[0]))
+        return new_node
+
+
+def concat_create_module(node: torch.fx.node) -> torch.nn.Module:
+    """
+    Create the replacement module.
+
+    :param node: Current node in the graph after which new node will be inserted
+    :return: New module.
+    """
+
+    num_args = len(node.args)
+    if num_args == 1 and 'dim' not in node.kwargs:
+        # Handle torch.cat being called with default parameter dim
+        kwargs = node.kwargs
+        module = aimet_modules.Concat()
+    else:
+        axis = node.args[1] if num_args > 1 else node.kwargs['dim']
+        module = aimet_modules.Concat(axis)
+        kwargs = {'axis': axis}
+
+    for key, value in kwargs.items():
+        setattr(module, key, value)
+
+    return module
+
+special_handler_functions = {
+    # Special handling functions for creating node and module
+    'cat': {'node_fn': concat_create_node, 'module_fn': concat_create_module},
+    'conv2d': {'node_fn': conv2d_create_node, 'module_fn': conv2d_create_module}
+}
+
+
+
    
+[docs]
+def prepare_model(model: torch.nn.Module,
+                  modules_to_exclude: List[torch.nn.Module] = None,
+                  module_classes_to_exclude: List[Callable] = None,
+                  concrete_args: Optional[Dict[str, Any]] = None) -> torch.fx.GraphModule:
+    """
+    Prepare and modify the pytorch model for AIMET features using torch.FX symbolic tracing API.
+
+    1. Replace torch.nn.functional by module of type torch.nn.Module
+    2. Create new independent torch.nn.Module instances for reused/duplicate module
+
+    :param model: pytorch Model to be modified.
+    :param modules_to_exclude: List of modules to exclude when tracing.
+    :param module_classes_to_exclude: List of module classes to exclude when tracing.
+    :param concrete_args: Allows you to partially specialize your function, whether it's to remove control flow or
+     data structures. If the model has control flow, torch.fx won't be able to trace the model. Check
+     torch.fx.symbolic_trace API in detail.
+    :return: Modified pytorch Model
+    """
+    with in_eval_mode(model):
+        traced_model, node_name_to_scope = \
+            _trace_model(model, modules_to_exclude, module_classes_to_exclude, concrete_args)
+
+    # Prepare model and perform checks to make sure the graph is well-formed.
+    _prepare_traced_model(traced_model, node_name_to_scope)
+    return traced_model
    
+
+
+
+def _trace_model(model: torch.nn.Module,
+                 modules_to_exclude: Optional[List[torch.nn.Module]],
+                 module_classes_to_exclude: Optional[List[Callable]],
+                 concrete_args: Optional[Dict[str, Any]]) -> [torch.fx.GraphModule, Dict]:
+    """
+    Returns traced model and dictionary of node name to the scope of module which contains the node.
+
+    :param model: pytorch Model to be modified.
+    :param modules_to_exclude: List of modules to exclude when tracing.
+    :param module_classes_to_exclude: List of module classes to exclude when tracing.
+    :param concrete_args: Concrete arguments that should not be treated as Proxies.
+    :return: (Traced model, node_name_to_scope)
+    """
+    class Tracer(torch.fx.Tracer):
+        """
+        Override is_leaf_module(), call_module() and create_node() methods of parent class.
+        """
+        def __init__(self):
+            super().__init__()
+            self.scope = Scope("", None)
+            self.node_name_to_scope = {}
+
+        def is_leaf_module(self, m: torch.nn.Module, module_qualified_name: str) -> bool:
+            return (
+                modules_to_exclude and m in modules_to_exclude
+                or module_classes_to_exclude and type(m) in module_classes_to_exclude # pylint: disable=unidiomatic-typecheck
+                or super().is_leaf_module(m, module_qualified_name)
+            )
+
+        def call_module(self, m: torch.nn.Module, forward: Callable[..., Any], args: Tuple[Any, ...],
+                        kwargs: Dict[str, Any]) -> Any:
+            module_qualified_name = self.path_of_module(m)
+            with ScopeContextManager(self.scope, Scope(module_qualified_name, type(m))):
+                return super().call_module(m, forward, args, kwargs)
+
+        def create_node(self, kind: str, target, args, kwargs, name: Optional[str] = None,
+                        type_expr: Optional[Any] = None) -> torch.fx.Node:
+            node = super().create_node(kind, target, args, kwargs, name, type_expr)
+            self.node_name_to_scope[node.name] = (self.scope.module_path, self.scope.module_type)
+            return node
+
+    # Symbolic tracing frontend - captures the semantics of the module
+    tracer = Tracer()
+    graph = tracer.trace(model, concrete_args=concrete_args)
+    traced_model = torch.fx.GraphModule(tracer.root, graph)
+    return traced_model, tracer.node_name_to_scope
+
+
+def _prepare_traced_model(traced_model: torch.fx.GraphModule,
+                          node_name_to_scope: Dict[str, Tuple[str, type]] = None):
+    """
+    Helper for prepare_model(). This prepares the given traced_model in-place.
+
+    :param traced_model: Symbolically traced model.
+    :param node_name_to_scope: Mapping from node name to the scope of module which contains the node.
+    """
+    unique_nodes = set()
+
+    # Modify the symbolically traced model by iterating over all the nodes
+    for node in traced_model.graph.nodes:
+
+        # Create new module for functional nodes
+        if node.op in ['call_function', 'call_method']:
+            functional_name = _find_functional_name_for_node(node.name)
+            if functional_name:
+                # Instantiate new module for functional node
+                new_module = _create_module_for_functional_node(node, functional_name)
+                parent_module, new_module_name, new_module_qualified_name = \
+                    _get_info_for_functional_node(traced_model, node, node_name_to_scope)
+                setattr(parent_module, new_module_name, new_module)
+                # Insert the node for new module in the graph
+                _insert_node_for_new_module(traced_model, node, new_module_qualified_name, functional_name)
+                logger.info("Functional         : Adding new module for node: {%s} ", new_module_qualified_name)
+
+        # Create new module for reused/duplicate nodes
+        elif node.target in unique_nodes:
+            if node.op == 'call_module':
+                # Instantiate new module for reused node
+                new_module = _create_module_for_reused_node(node, traced_model)
+                parent_module, new_module_name, new_module_qualified_name = \
+                    _get_info_for_reused_node(traced_model, node, node_name_to_scope)
+                setattr(parent_module, new_module_name, new_module)
+                # Insert the node for new module in the graph
+                _insert_node_for_new_module(traced_model, node, new_module_qualified_name)
+                logger.info("Reused/Duplicate   : Adding new module for node: {%s} ", new_module_qualified_name)
+        else:
+            unique_nodes.add(node.target)
+
+    _verify_traced_model(traced_model)
+
+    # Replace SiLU with CustomSiLU
+    replace_modules_of_type1_with_type2(traced_model, torch.nn.SiLU, aimet_modules.CustomSiLU)
+
+
+def _verify_traced_model(traced_model: torch.fx.GraphModule):
+    """
+    Does some checks to make sure the graph is well-formed and recompile the forward() method of symbolic_traced
+    model from its graph
+
+    :param traced_model: Symbolically traced model
+    """
+    traced_model.graph.lint()
+    traced_model.recompile()
+
+
+def _insert_node_for_new_module(traced_model: torch.fx.GraphModule,
+                                node: torch.fx.node,
+                                module_qualified_name: str,
+                                functional_name: str = None):
+    """
+    Insert 'call module' node into graph and replace all the uses of 'node' with newly added node and erase the
+    old node from graph
+    :param traced_model: Symbolically traced model
+    :param node: Current node in the graph after which new node will be inserted
+    :param module_qualified_name: Qualified module name in symbolic_traced_model hierarchy corresponding to new node
+    :param functional_name: Original functional name
+    """
+    with traced_model.graph.inserting_after(node):
+        if functional_name:
+            if functional_name in functional_with_special_handling:
+                new_node = special_handler_functions[functional_name]['node_fn'](traced_model, module_qualified_name, node)
+            elif functional_name in functional_with_stateless_api:
+                new_node = traced_model.graph.call_module(module_qualified_name, args=node.args, kwargs=node.kwargs)
+            elif functional_name in functional_with_stateful_api:
+                new_node = traced_model.graph.call_module(module_qualified_name, args=node.args)
+            else:
+                raise ValueError("Unsupported module: {}".format(functional_name))
+        else:
+            new_node = traced_model.graph.call_module(module_qualified_name, args=node.args)
+
+        node.replace_all_uses_with(new_node)
+    traced_model.graph.erase_node(node)
+
+
+def _find_functional_name_for_node(node_name: str) -> Union[str, None]:
+    """
+    For given node name, find corresponding functional name from combined lookup
+
+    :param node_name: torch.fx Node name
+    :return: corresponding functional name if found, else None
+    """
+    combined_lookup = {**functional_with_stateful_api, **functional_with_special_handling, **functional_with_stateless_api}
+
+    # Functional operations with similar names are differentiated using "_count" suffix
+    # when symbolically traced. For example, two add operations will have name 'add' and 'add_1'.
+    # Split given node name by occurrence of pattern. \d is used to match [0-9] followed by '_'.
+    strings = re.split(pattern=r'_\d', string=node_name)
+    for string in strings:
+        if string in combined_lookup.keys():
+            return string
+
+    logger.debug("Couldn't find functional: %s in the lookup. If functional op isn't math invariant,"
+                 " add an entry in the lookup.", node_name)
+    return None
+
+
+def _create_module_for_functional_node(node: torch.fx.node, functional_name: str) -> torch.nn.Module:
+    """
+    For given node and functional name, create torch.nn.Module with same parameters as functional node parameters
+    :param node: torch.fx Node
+    :param functional_name: Functional name for given node
+    :return: New module
+    """
+    # Instantiate new module from lookup
+    if functional_name in functional_with_stateful_api:
+        module = functional_with_stateful_api[functional_name]()
+        # Set the parameters for module from node.kwargs
+        for key, value in node.kwargs.items():
+            setattr(module, key, value)
+    elif functional_name in functional_with_special_handling:
+        module = special_handler_functions[functional_name]['module_fn'](node)
+    elif functional_name in functional_with_stateless_api:
+        module = functional_with_stateless_api[functional_name]()
+    else:
+        raise ValueError("Unsupported module: {}".format(functional_name))
+    return module
+
+
+def _create_module_for_reused_node(node: torch.fx.node, symbolic_traced_model: torch.fx.GraphModule) ->\
+        torch.nn.Module:
+    """
+    For given reused/Duplicate node in symbolically traced model, create new module with same parameters as
+    original module
+    :param node: Reused/Duplicate torch.fx Node
+    :param symbolic_traced_model: Symbolically traced model
+    :return: New module
+    """
+    # Get the original module and return newly deep copied module
+    module = _get_module_for_dotted_name(symbolic_traced_model, node.target)
+    new_module = copy.deepcopy(module)
+
+    return new_module
+
+
+def _get_module_for_dotted_name(module: torch.fx.GraphModule, dotted_name: str) -> torch.nn.Module:
+    """
+    For given dotted name, find the module
+    :param module: module to be found
+    :param dotted_name: dotted name of module
+    :return: module
+    """
+    if '.' in dotted_name:
+        module_name, _, remainder = dotted_name.partition('.')
+        return _get_module_for_dotted_name(module._modules[module_name], remainder) # pylint: disable=protected-access
+
+    return getattr(module, dotted_name)
+
+
+def get_module_for_activation_fn(act_fn: torch.nn.functional):
+    """
+    returns module instance for functional tyoe handled within PT transformers for activation functions
+    :param act_fn: activation function implemented as a functional.
+    :return: module equivalent for the activation function.
+    """
+
+    if act_fn not in functional_op_to_module_map:
+        logger.error("Unsupported activation function {%s}", act_fn)
+        return None
+    module = functional_op_to_module_map[act_fn]()
+    return module
+
+
+def prepare_pt_transformer_for_quantsim(transformer_model: torch.nn.Module):
+    """
+    Replaces functionals with modules for activation function, updates model in-place
+    :param transformer_model: model with PyTorch nn.Transformer layer
+    :return: updated model with modules for activation function.
+    """
+
+    for module in transformer_model.modules():
+
+        # encoder layer or decoder layer type is the leaf level node to be updated within nn.transformer layer
+        if isinstance(module, torch.nn.TransformerEncoderLayer) and not isinstance(module.activation, torch.nn.Module):
+            module.activation = get_module_for_activation_fn(module.activation)
+
+        if isinstance(module, torch.nn.TransformerDecoderLayer) and not isinstance(module.activation, torch.nn.Module):
+            module.activation = get_module_for_activation_fn(module.activation)
+
+
+def _get_info_for_functional_node(traced_model: torch.fx.GraphModule,
+                                  node: torch.fx.Node,
+                                  node_name_to_scope: Dict[str, Tuple[str, type]])\
+        -> Tuple[torch.fx.GraphModule, str, str]:
+    """
+    For functional node, get module which contains the node, corresponding new module's name and fully qualified name.
+    This information will be used to add new module at either module-level scope or model-level scope.
+
+    NOTE: If node_name_to_scope is not provided, then the corresponding new module will be added at model-level scope.
+    Also, if exception is raised, new module will be added at model-level scope.
+
+    :param traced_model: Traced model
+    :param node: torch.fx Node
+    :param node_name_to_scope: Mapping from node name to the scope of module which contains the node.
+    :return: (parent_module, new_module_name, new_module_qualified_name)
+    """
+    parent_module = traced_model
+    new_module_name = "module_" + node.name
+    new_module_qualified_name = new_module_name
+
+    if node_name_to_scope:
+        try:
+            module_path, _ = node_name_to_scope[node.name]
+            parent_module = traced_model.get_submodule(module_path)
+            if module_path == "":
+                new_module_qualified_name = new_module_name
+            else:
+                new_module_qualified_name = module_path + "." + new_module_name
+        except (KeyError, AttributeError):
+            pass
+
+    return parent_module, new_module_name, new_module_qualified_name
+
+
+def _get_info_for_reused_node(traced_model: torch.fx.GraphModule,
+                              node: torch.fx.Node,
+                              node_name_to_scope: Dict[str, Tuple[str, type]])\
+        -> Tuple[torch.fx.GraphModule, str, str]:
+    """
+    For reused node, get module which contains the node, corresponding new module's name and fully qualified name.
+    This information will be used to add new module at either module-level scope or model-level scope.
+
+    NOTE: If node_name_to_scope is not provided, then the corresponding new module will be added at model-level scope.
+    Also, if exception is raised, new module will be added at model-level scope.
+
+    :param traced_model: Traced model
+    :param node: torch.fx Node
+    :param node_name_to_scope: Mapping from node name to the scope of module which contains the node.
+    :return: (parent_module, new_module_name, new_module_qualified_name)
+    """
+    parent_module = traced_model
+    new_module_name = "module_" + node.name
+    new_module_qualified_name = new_module_name
+
+    if node_name_to_scope:
+        try:
+            module_path, _ = node_name_to_scope[node.name]
+            if "." in module_path:
+                parent_name, child_name = module_path.rsplit(".", maxsplit=1)
+            else:
+                parent_name, child_name = "", module_path
+            parent_module = traced_model.get_submodule(parent_name)
+            new_module_name = "module_" + child_name + "_" + node.name.rsplit("_", maxsplit=1)[1]
+            if parent_name == "":
+                new_module_qualified_name = new_module_name
+            else:
+                new_module_qualified_name = parent_name + "." + new_module_name
+        except (KeyError, AttributeError):
+            pass
+
+    return parent_module, new_module_name, new_module_qualified_name
+
    +
    +
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    + +
    + + +
    +
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    +
    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
    + Made with Sphinx and @pradyunsg's + + Furo + +
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    + + + + + + + + + \ No newline at end of file diff --git a/releases/2.0.0/_modules/aimet_torch/model_validator/model_validator.html b/releases/2.0.0/_modules/aimet_torch/model_validator/model_validator.html new file mode 100644 index 0000000..813aa16 --- /dev/null +++ b/releases/2.0.0/_modules/aimet_torch/model_validator/model_validator.html @@ -0,0 +1,434 @@ + + + + + + + + aimet_torch.model_validator.model_validator - AIMET + + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    +

    Source code for aimet_torch.model_validator.model_validator

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2021, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+
+""" Utility for validating pytorch models prior to using AIMET features """
+
+from typing import Tuple, Union, Callable
+import torch
+
+from aimet_common.utils import AimetLogger
+import aimet_torch.model_validator.validation_checks as val_checks
+
+logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.Utils)
+
+
+
    
+[docs]
+class ModelValidator:
+    """
+    ModelValidator object for validating that AIMET features can be applied on the Pytorch model.
+    """
+    _validation_checks = [
+        val_checks.validate_for_reused_modules,
+        val_checks.validate_for_missing_modules
+    ]
+
+
    
+[docs]
+    @staticmethod
+    def add_check(validation_check: Callable):
+        """
+        Add a validation check function to be used for validating the model. Validation check functions must take the
+        model, model inputs, and kwargs as inputs.
+        The validation check must output True if the model passes the check, and False otherwise.
+        :param validation_check: Validation check function for validating the model.
+        """
+        ModelValidator._validation_checks.append(validation_check)
    
+
+
+
    
+[docs]
+    @staticmethod
+    def validate_model(model: torch.nn.Module, model_input: Union[torch.Tensor, Tuple], **kwargs) -> bool:
+        """
+        Validate the pytorch model by running all validation check functions and returning True if all pass, False
+        otherwise.
+        Keyword arguments can be used to pass specific arguments to particular validation checkers.
+        Currently supported keyword arguments:
+        layers_to_exclude: List of torch.nn.Modules to be excluded in the check for missing modules. These layers and
+        all of their sublayers will not be flagged if they do not have a corresponding Pytorch module.
+        :param model: Pytorch model to validate
+        :param model_input: Dummy input to the model
+        :return True if pytorch model is valid, False otherwise
+        """
+        is_valid_model = True
+        failed_val_checks = set()
+        for val_check in ModelValidator._validation_checks:
+            logger.info('Running validator check %s', val_check)
+            val_check_result = val_check(model, model_input, **kwargs)
+            if not val_check_result:
+                failed_val_checks.add(val_check)
+            is_valid_model = is_valid_model and val_check_result
+        if not is_valid_model:
+            logger.info('The following validator checks failed:')
+            for val_check in failed_val_checks:
+                logger.info('\t%s', val_check)
+            return is_valid_model
+
+        logger.info('All validation checks passed.')
+        return is_valid_model
    
+
    
+
+
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    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
    + Made with Sphinx and @pradyunsg's + + Furo + +
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    +
    + + + + + + + + + \ No newline at end of file diff --git a/releases/2.0.0/_modules/aimet_torch/peft.html b/releases/2.0.0/_modules/aimet_torch/peft.html new file mode 100644 index 0000000..26f9ddb --- /dev/null +++ b/releases/2.0.0/_modules/aimet_torch/peft.html @@ -0,0 +1,840 @@ + + + + + + + + aimet_torch.peft - AIMET + + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    +

    Source code for aimet_torch.peft

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2024, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+
+""" Implementation for handling LoRA adapters added using PEFT """
+from typing import Dict, Type
+import os
+import pickle
+from collections import defaultdict
+import torch.nn as nn
+import torch
+from safetensors.torch import save_file
+from safetensors import safe_open
+
+# pylint: disable=import-error
+# pylint: disable=no-name-in-module
+from peft.tuners.lora.layer import LoraLayer as PeftLoraLayer
+from peft.tuners.lora.layer import Conv2d as PeftConv2d
+
+from aimet_torch.utils import replace_modules_of_type1_using_constructor
+from aimet_torch._base.nn.modules.custom import Add, Multiply
+from aimet_torch.v2.quantsim import QuantizationSimModel
+from aimet_torch.v2.quantization.affine import QuantizeDequantize
+from aimet_torch._base.quantsim import _QuantizedModuleProtocol
+from aimet_torch.v2.nn import BaseQuantizationMixin
+
+
+class LoraLayer(torch.nn.Module):
+    """
+    Quantizable lora layer
+    """
+    # pylint: disable=too-many-instance-attributes
+    def __init__(self, lora_layer: PeftLoraLayer):
+        """
+        :param lora_layer: Lora layer we want to replace
+        """
+        super().__init__()
+        self.base_layer = lora_layer.base_layer
+        self.r = lora_layer.r
+        self.lora_alpha = lora_layer.lora_alpha
+        self.scaling = [torch.nn.Parameter(torch.as_tensor(scale), requires_grad=False).to(self.base_layer.weight.device)
+                        for scale in lora_layer.scaling.values()]
+        self.lora_dropout = nn.ModuleList({})
+        self.adapter_name_to_index = {}
+        self.index_to_adapter_name = {}
+        self.lora_A = nn.ModuleList([])
+        self.lora_B = nn.ModuleList([])
+        self.active_adapters = {}
+        self._swap_module_dict_with_list(lora_layer)
+        self.in_features = lora_layer.in_features
+        self.out_features = lora_layer.out_features
+        self.add_lora_to_res = Add()
+        self.mul_scale = Multiply()
+
+    def _swap_module_dict_with_list(self, lora_layer):
+        for index, adapter_name in enumerate(lora_layer.lora_A):
+            self.lora_A.append(lora_layer.lora_A[adapter_name])
+            self.lora_B.append(lora_layer.lora_B[adapter_name])
+            self.lora_dropout.append(lora_layer.lora_dropout[adapter_name])
+            self.adapter_name_to_index[adapter_name] = index
+            if adapter_name in lora_layer.active_adapter:
+                self.active_adapters[adapter_name] = True
+            else:
+                self.active_adapters[adapter_name] = False
+        for adapter_name in self.adapter_name_to_index:
+            self.index_to_adapter_name[self.adapter_name_to_index[adapter_name]] = adapter_name
+
+    def forward(self, x: torch.Tensor, *args, **kwargs) -> torch.Tensor:
+        """ Forward pass for replaced layer"""
+        result = self.base_layer(x, *args, **kwargs)
+        torch_result_dtype = result.dtype
+        for active_adapter in self.active_adapters:
+            if active_adapter not in self.adapter_name_to_index:
+                continue
+            lora_A = self.lora_A[self.adapter_name_to_index[active_adapter]]
+            lora_B = self.lora_B[self.adapter_name_to_index[active_adapter]]
+            dropout = self.lora_dropout[self.adapter_name_to_index[active_adapter]]
+            scaling = self.scaling[self.adapter_name_to_index[active_adapter]]
+            x = x.to(lora_A.weight.dtype)
+
+            result = self.add_lora_to_res(result, lora_B(self.mul_scale(lora_A(dropout(x)), scaling.detach())))
+
+        result = result.to(torch_result_dtype)
+        return result
+
+
+def replace_lora_layers_with_quantizable_layers(model: torch.nn.Module):
+    """
+    Utility to replace lora layers with Quantizable Lora layers
+
+    :param model: PEFT model
+    """
+    replace_modules_of_type1_using_constructor(model, PeftLoraLayer, LoraLayer)
+    replace_modules_of_type1_using_constructor(model, PeftConv2d, LoraLayer)
+
+
+
    
+[docs]
+class AdapterMetaData:
+    """
+    Tracks meta data for lora layers. Tracks names of lora_a & b as well as alpha values
+    Attributes:
+        lora_A, lora_B, alpha
+    """
+    def __init__(self):
+        self.lora_A = []
+        self.lora_B = []
+        self.alpha = None
+        self.mul_scale = []
    
+
+
+
+def track_lora_meta_data(model: torch.nn.Module, path: str, filename_prefix: str,
+                         replaced_module_type: Type[torch.nn.Module] = None) -> Dict[str, AdapterMetaData]:
+    """
+    Utility to track and save meta data for adapters. The meta data has adapter names and corresponding lora layers & alphas
+
+    :param model: PEFT model
+    :param path: path where to store model pth and encodings
+    :param filename_prefix: Prefix to use for filenames
+    :param replaced_module_type: If lora linear layer is replaced by another torch module, then replaced_module_type
+                                represents the type with which linear layer was replaced. Otherwise pass None
+    """
+    module_to_name_d = {}
+
+    for name, module in model.named_modules():
+        module_to_name_d[module] = name
+
+    adapter_name_to_meta_data = defaultdict(AdapterMetaData)
+    for name, module in model.named_modules():
+        if isinstance(module, LoraLayer):
+            for index, lora_layer in enumerate(module.lora_A):
+                if replaced_module_type and isinstance(lora_layer, replaced_module_type):
+                    lora_layer = lora_layer.conv2d
+                adapter_name_to_meta_data[module.index_to_adapter_name[index]].lora_A.append(
+                    module_to_name_d[lora_layer])
+            for index, lora_layer in enumerate(module.lora_B):
+                if replaced_module_type and isinstance(lora_layer, replaced_module_type):
+                    lora_layer = lora_layer.conv2d
+                adapter_name_to_meta_data[module.index_to_adapter_name[index]].lora_B.append(
+                    module_to_name_d[lora_layer])
+            for lora_adapter_name in module.lora_alpha:
+                adapter_name_to_meta_data[lora_adapter_name].alpha = module.lora_alpha[lora_adapter_name]
+            adapter_name_to_meta_data[module.index_to_adapter_name[index]].mul_scale.append(module_to_name_d[module.mul_scale])
+
+
+    file_name = os.path.join(path, f"{filename_prefix}.pkl")
+    with open(file_name, 'wb') as file:
+        pickle.dump(adapter_name_to_meta_data, file)
+    return adapter_name_to_meta_data
+
+
+
    
+[docs]
+class PeftQuantUtils:
+    """
+    Utilities for quantizing peft model
+    """
+    def __init__(self, adapater_name_to_meta_data: Dict[str, AdapterMetaData], name_to_module_dict=None):
+        """
+        Init for Peft utilities for quantization
+
+        :param adapater_name_to_meta_data: Dict mapping adapter name to meta data. Output of track_meta_data
+        :param name_to_module_dict: PT Name to module prepared model name mapping
+        """
+        self.adapter_name_to_meta_data = adapater_name_to_meta_data
+        self.lora_layers = self._get_lora_layers()
+        self.pt_name_to_prepared_name, self.prepared_name_to_pt_name = None, None
+        self.pt_to_lora_name = dict.fromkeys(self.lora_layers, '')
+        if name_to_module_dict:
+            self.pt_name_to_prepared_name, self.prepared_name_to_pt_name = self._get_pytorch_name_to_prepared_name(name_to_module_dict)
+            self.lora_to_pt_name, self.pt_to_lora_name = self._get_lora_name_to_pytorch_name()
+            self.mul_names = self._get_prepared_name_for_mul()
+
+    @staticmethod
+    def _get_pytorch_name_to_prepared_name(name_to_module_dict):
+        """
+        Gets onnx names to pytorch names mapping and vice versa
+        """
+        pt_name_to_onnx_name = {}
+        onnx_name_to_pt_name = {}
+        for pytorch_name in name_to_module_dict:
+            onnx_name = name_to_module_dict[pytorch_name][0]
+            pt_name_to_onnx_name[pytorch_name] = onnx_name
+            onnx_name_to_pt_name[onnx_name] = pytorch_name
+        return pt_name_to_onnx_name, onnx_name_to_pt_name
+
+    def _get_prepared_name_for_mul(self):
+        """
+        Gets onnx names to pytorch names mapping and vice versa
+        """
+        names = set()
+        for adapter_name in self.adapter_name_to_meta_data:
+            adapter_data = self.adapter_name_to_meta_data[adapter_name]
+            for index, _ in enumerate(adapter_data.mul_scale):
+                lora_prepared_name = self.pt_name_to_prepared_name[self.lora_to_pt_name[adapter_data.lora_A[index]]]
+                prepared_name = lora_prepared_name[0:lora_prepared_name.find('_lora_A')] + '_mul_scale_Mul'
+                names.add(prepared_name)
+        return names
+
+
    
+[docs]
+    def quantize_lora_scale_with_fixed_range(self, sim, bitwidth, scale_min=0, scale_max=1e-5):
+        """
+        Add input quantizer for scale(alpha/rank) and provide min max values to it
+
+        :param sim: QuantSim model
+        :param bitwidth: Bitwidth for input quantizer to Mul/ bitwidth for scale
+        :param scale_min: min value of lora alpha to be used
+        :param scale_max: max value of lora alpha to be used
+        """
+
+        def _create_quantizer():
+            quantizer = QuantizeDequantize(shape=(), bitwidth=bitwidth, symmetric=False)
+            quantizer.set_range(torch.as_tensor(scale_min), torch.as_tensor(scale_max))
+            self._freeze_quantizer(quantizer)
+            return quantizer
+
+        for name, module in sim.model.named_modules():
+            if not self.prepared_name_to_pt_name:
+                if isinstance(module, LoraLayer):
+                    module.mul_scale.input_quantizers[1] = _create_quantizer()
+            elif name in self.mul_names:
+                module.input_quantizers[1] = _create_quantizer()
    
+
+
+    def _get_lora_name_to_pytorch_name(self):
+        """
+        Gets most similar pytorch name for every lora name
+        """
+        lora_to_pytorch_name = {}
+        pytorch_to_lora_name = {}
+        for pt_name in self.pt_name_to_prepared_name:
+            for lora_name in self.lora_layers:
+                if pt_name in lora_name:
+                    lora_to_pytorch_name[lora_name] = pt_name
+                    pytorch_to_lora_name[pt_name] = lora_name
+        return lora_to_pytorch_name, pytorch_to_lora_name
+
+    def _get_lora_layers(self) -> set:
+        """
+        Gets all lora layers
+        """
+        lora_layers = set()
+        for adapter_name in self.adapter_name_to_meta_data:
+            for lora_module in self.adapter_name_to_meta_data[adapter_name].lora_A:
+                lora_layers.add(lora_module)
+            for lora_module in self.adapter_name_to_meta_data[adapter_name].lora_B:
+                lora_layers.add(lora_module)
+        return lora_layers
+
+    @staticmethod
+    def _freeze_quantizer(quantizer):
+        """
+        Disables compute encodings and gradient update for a quantizer
+
+        :param quantizer: Param, output or Input quantizer
+        """
+        # pylint:disable = protected-access
+        quantizer._allow_overwrite = False
+        quantizer.requires_grad_(False)
+
+
    
+[docs]
+    def freeze_base_model_param_quantizers(self, sim: QuantizationSimModel):
+        """
+        Freeze parameter quantizers of base model
+
+        :param sim: QuantSim model
+        """
+        for module_name, module in sim.model.named_modules():
+            module_name = self._get_module_name(module_name)
+            if isinstance(module, BaseQuantizationMixin) and module_name not in self.pt_to_lora_name:
+                for _, param_quantizer in module.param_quantizers.items():
+                    if param_quantizer:
+                        self._freeze_quantizer(param_quantizer)
    
+
+
+
    
+[docs]
+    def freeze_base_model_activation_quantizers(self, sim: QuantizationSimModel):
+        """
+        Freeze activation quantizers of base model
+
+        :param sim: QuantSim model
+        """
+        for module_name, module in sim.model.named_modules():
+            module_name = self._get_module_name(module_name)
+            if isinstance(module, BaseQuantizationMixin) and module_name not in self.pt_to_lora_name:
+                for input_quantizer, output_quantizer in zip(module.input_quantizers, module.output_quantizers):
+                    if input_quantizer:
+                        self._freeze_quantizer(input_quantizer)
+                    if output_quantizer:
+                        self._freeze_quantizer(output_quantizer)
    
+
+
+
    
+[docs]
+    def freeze_base_model(self, sim: QuantizationSimModel):
+        """
+        Freeze entire base model
+
+        :param sim: QuantSim model
+        """
+        self.freeze_base_model_activation_quantizers(sim)
+        self.freeze_base_model_param_quantizers(sim)
    
+
+
+
    
+[docs]
+    def set_bitwidth_for_lora_adapters(self, sim: QuantizationSimModel,
+                                       output_bw: int, param_bw: int):
+        """
+        Sets output and param bitwidth for all Lora adapters added to the model
+
+        :param sim: QuantSim model
+        :param output_bw: Output BW
+        :param param_bw: Parameter BW
+        """
+        for module_name, module in sim.model.named_modules():
+            module_name = self._get_module_name(module_name)
+            if isinstance(module, BaseQuantizationMixin) and module_name in self.pt_to_lora_name:
+                self._set_bitwidth_for_module(module, output_bw, param_bw)
    
+
+
+    def _get_module_name(self, module_name: str) -> str:
+        """
+        Gets module name from prepared model's names if prepared model is being used, else returns the pytorch name
+        :param module_name: pytorch name
+        """
+        if self.prepared_name_to_pt_name and module_name in self.prepared_name_to_pt_name:
+            module_name = self.prepared_name_to_pt_name[module_name]
+        return module_name
+
+
    
+[docs]
+    def get_quantized_lora_layer(self, sim: QuantizationSimModel):
+        """
+        This function can be used to generate lora quantized layers
+        Use cases: 1) New quantizers can be created and assigned to lora quantized layer.
+                   New quantizers may be required if changing - Changing dtype, per channel to per tensor
+                   and vice versa
+                   2) Assign new values to symmetric, bitwidth
+
+        :param sim: QuantSim model
+        """
+        for module_name, module in sim.model.named_modules():
+            module_name = self._get_module_name(module_name)
+            if isinstance(module, BaseQuantizationMixin) and module_name in self.pt_to_lora_name:
+                yield module_name, module
    
+
+
+
    
+[docs]
+    def get_fp_lora_layer(self, model):
+        """
+        This Function can be used to get lora layers for a model
+
+        :param model: FP32 model
+        """
+        for module_name, module in model.named_modules():
+            module_name = self._get_module_name(module_name)
+            if module_name in self.pt_to_lora_name:
+                yield module_name, module
    
+
+
+    @staticmethod
+    def _set_bitwidth_for_module(module: BaseQuantizationMixin, output_bw: int, param_bw: int):
+        """
+        Sets bitwidth for a QcQuantizeWrapper module
+
+        :param module: QcQuantize wrapper module
+        :param output_bw: Output BW
+        :param param_bw: Parameter BW
+        """
+        for output_quantizer in module.output_quantizers:
+            output_quantizer.bitwidth = output_bw
+        for _, param_quantizer in module.param_quantizers.items():
+            param_quantizer.bitwidth = param_bw
+
+
    
+[docs]
+    def export_adapter_weights(self, sim: QuantizationSimModel, path: str, filename_prefix: str):
+        """
+        Exports adapter weights to safetensor format
+
+        :param sim: QuantSim model
+        :param path: path where to store model pth and encodings
+        :param filename_prefix: Prefix to use for filenames of the model pth and encodings files
+        """
+        tensors = {}
+
+        for module_name, module in sim.model.named_modules():
+            if not isinstance(module, _QuantizedModuleProtocol):
+                continue
+            org_name = module_name
+            pt_name = self._get_module_name(module_name)
+            if self.prepared_name_to_pt_name and pt_name in self.pt_to_lora_name:
+                module_name = self.pt_to_lora_name[pt_name]
+            if module_name in self.lora_layers:
+                for param_name, param in module.named_parameters():
+                    if param_name in ['weight', 'bias']:
+                        tensor_name = org_name + '.' + param_name
+                        tensors[tensor_name] = param
+        filename_prefix = filename_prefix + '.safetensor'
+        model_params_path = os.path.join(path, filename_prefix)
+        save_file(tensors, model_params_path)
    
+
+
+
    
+[docs]
+    def enable_adapter_and_load_weights(self, sim: QuantizationSimModel, adapter_weights_path,
+                                        use_safetensor: bool = True):
+        """
+        Enables adapter effect on base model by loading weights to model
+
+        :param sim: QuantSim model
+        :param adapter_weights_path: Path to adapter weights (adapter weights should be either bin file or safetensor)
+        :param use_safetensor: True if adapter weights path point to a safetensor file. False if points to bin file
+        """
+        tensors = _load_weights(adapter_weights_path, use_safetensor)
+        lora_layer_names_set = set(self.lora_layers)
+        onnx_names_tensors = {}
+        for key in tensors.keys():
+            tensor_name = key
+            temp_key = key[0:key.find('.weight')]
+            if self.prepared_name_to_pt_name:
+                tensor_name = self.pt_name_to_prepared_name[self.lora_to_pt_name[temp_key]] + '.weight'
+            lora_layer_names_set.remove(temp_key)
+            onnx_names_tensors[tensor_name] = tensors[key]
+
+        if lora_layer_names_set:
+            raise KeyError("Lora layer weights missing for the following names", lora_layer_names_set)
+
+        sim.model.load_state_dict(onnx_names_tensors, strict=False)
    
+
+
+
    
+[docs]
+    def disable_lora_adapters(self, sim: QuantizationSimModel):
+        """
+        Disables adapter (zero out weights for lora A & B) effect on base model by loading weights to model
+
+        :param sim: QuantSim model
+        """
+        tensors = {}
+        for module_name, module in sim.model.named_modules():
+            org_name = module_name
+            pt_name = self._get_module_name(module_name)
+            if self.prepared_name_to_pt_name and pt_name in self.pt_to_lora_name:
+                module_name = self.pt_to_lora_name[pt_name]
+            if module_name in self.lora_layers:
+                for param_name, param in module.named_parameters():
+                    if param_name in ['weight', 'bias']:
+                        tensor_name = org_name + '.' + param_name
+                        tensors[tensor_name] = torch.zeros_like(param)
+
+        sim.model.load_state_dict(tensors, strict=False)
    
+
    
+
+
+
+def _load_weights(adapter_weights_path: str, use_safetensor: bool = True) -> Dict:
+    """
+    Util to load weights
+
+    :param adapter_weights_path: Path to adapter weights (adapter weights should be either bin file or safetensor)
+    :param use_safetensor: True if adapter weights path point to a safetensor file. False if points to bin file
+    """
+    tensors = {}
+    if use_safetensor:
+        with safe_open(adapter_weights_path, framework="pt", device=0) as f:
+            for key in f.keys():
+                tensors[key] = f.get_tensor(key)
+    else:
+        tensors = torch.load(adapter_weights_path)
+
+    return tensors
+
    +
    +
    +
    + +
    + + +
    +
    +
    +
    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
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    +

    Source code for aimet_torch.v1.auto_quant

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2022, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+# pylint: disable=too-many-lines, disable=protected-access
+
+""" Implementation of AIMET AutoQuantBase and v1 AutoQuant """
+import copy
+import functools
+import itertools
+import os
+from unittest.mock import patch
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+import torch
+from torch.utils.data import DataLoader
+
+from aimet_torch import utils
+from aimet_torch._base.auto_quant import (
+    AutoQuantBase,
+    _EvalManager,
+    _QuantSchemePair,
+    _EvalSession,
+    cache,
+    _MixedPrecisionArgs,
+    _MixedPrecisionResult,
+    ParetoFrontType,
+)
+from aimet_torch.v1.adaround.adaround_weight import Adaround
+from aimet_torch._base.adaround.adaround_weight import AdaroundParameters
+from aimet_torch.v1.quantsim import QuantizationSimModel
+from aimet_torch.utils import get_all_quantizers
+from aimet_torch.onnx_utils import OnnxExportApiArgs
+from aimet_torch.amp.mixed_precision_algo import GreedyMixedPrecisionAlgo, EvalCallbackFactory, _default_forward_fn
+
+from aimet_common.defs import QuantScheme, CallbackFunc, QuantizationDataType
+from aimet_common.utils import AimetLogger
+from aimet_common.amp.utils import (
+    create_sensitivity_plot,
+    create_pareto_curve,
+    CANDIDATE_WITH_DTYPE,
+    AmpCandidate,
+)
+
+
+__all__ = [
+    'AutoQuant',
+    'AutoQuantWithAutoMixedPrecision',
+]
+
+
+_logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.AutoQuant)
+
+
+class AutoQuant(AutoQuantBase): # pylint: disable=too-many-instance-attributes
+    """
+    Integrate and apply post-training quantization techniques.
+
+    AutoQuant includes 1) batchnorm folding, 2) cross-layer equalization,
+    and 3) Adaround.
+    These techniques will be applied in a best-effort manner until the model
+    meets the evaluation goal given as allowed_accuracy_drop.
+    """
+
+    @staticmethod
+    def _get_adaround():
+        """ returns AdaRound """
+        return Adaround
+
+    @staticmethod
+    def _get_quantsim(model, dummy_input, **kwargs):
+        return QuantizationSimModel(model, dummy_input, **kwargs)
+
+    def _configure_quantsim(self, # pylint: disable=too-many-arguments
+                            sim,
+                            output_bw,
+                            output_quant_scheme,
+                            output_percentile,
+                            param_bw,
+                            param_quant_scheme,
+                            param_percentile,
+                            encoding_path):
+
+        param_quantizers, input_quantizers, output_quantizers = utils.get_all_quantizers(sim.model)
+
+        # Set input/output quantizers' quant schemes
+        for quantizer in itertools.chain(input_quantizers, output_quantizers):
+            quantizer.quant_scheme = output_quant_scheme
+            if quantizer.quant_scheme == QuantScheme.post_training_percentile and\
+                    output_percentile is not None:
+                quantizer.set_percentile_value(output_percentile)
+
+        # Set param quantizers' quant schemes
+        for quantizer in param_quantizers:
+            quantizer.quant_scheme = param_quant_scheme
+            if quantizer.quant_scheme == QuantScheme.post_training_percentile and\
+                    param_percentile is not None:
+                quantizer.set_percentile_value(param_percentile)
+
+        if encoding_path:
+            sim.set_and_freeze_param_encodings(encoding_path)
+
+        param_quantizers, input_quantizers, output_quantizers = utils.get_all_quantizers(sim.model)
+
+        # Disable input/output quantizers, using fp32 to simulate int32.
+        if output_bw == 32:
+            for quantizer in input_quantizers + output_quantizers:
+                quantizer.enabled = False
+
+        # Disable param quantizers, using fp32 to simulate int32.
+        if param_bw == 32:
+            for quantizer in param_quantizers:
+                quantizer.enabled = False
+
+    @staticmethod
+    def _has_enabled_quantizers(sim):
+        param_quantizers, input_quantizers, output_quantizers = utils.get_all_quantizers(sim.model)
+        return any(quantizer.enabled for quantizer in param_quantizers +\
+                                                      input_quantizers +\
+                                                      output_quantizers)
+
+    @staticmethod
+    def _disable_activation_quantizers(sim):
+        _, input_quantizers, output_quantizers = get_all_quantizers(sim.model)
+        for quantizer in itertools.chain(input_quantizers, output_quantizers):
+            quantizer.enabled = False
+
+
+
+# The number of samples to be used for performance evaluation and AMP.
+# NOTE: None means "all".
+DEFAULT_NUM_SAMPLES_FOR_AMP_PHASE_1 = EvalCallbackFactory._DEFAULT_SQNR_NUM_SAMPLES
+DEFAULT_NUM_SAMPLES_FOR_AMP_PHASE_2 = None
+
+
+
    
+[docs]
+class AutoQuantWithAutoMixedPrecision:
+    """
+    Integrate and apply post-training quantization techniques.
+
+    AutoQuant includes 1) batchnorm folding, 2) cross-layer equalization,
+    3) Adaround, and 4) Automatic Mixed Precision (if enabled).
+    These techniques will be applied in a best-effort manner until the model
+    meets the evaluation goal given as allowed_accuracy_drop.
+    """
+    def __init__( # pylint: disable=too-many-arguments, too-many-function-args
+            self,
+            model: torch.nn.Module,
+            dummy_input: Union[torch.Tensor, Tuple],
+            data_loader: DataLoader,
+            eval_callback: Callable[[torch.nn.Module], float],
+            param_bw: int = 8,
+            output_bw: int = 8,
+            quant_scheme: QuantScheme = QuantScheme.post_training_tf_enhanced,
+            rounding_mode: str = 'nearest',
+            config_file: str = None,
+            results_dir: str = "/tmp",
+            cache_id: str = None,
+            strict_validation: bool = True,
+            model_prepare_required: bool = True,) -> None:
+        """
+        :param model: Model to be quantized. Assumes model is on the correct device
+        :param dummy_input: Dummy input for the model. Assumes that dummy_input is on the correct device
+        :param data_loader: A collection that iterates over an unlabeled dataset, used for computing encodings
+        :param eval_callback: Function that calculates the evaluation score
+        :param param_bw: Parameter bitwidth
+        :param output_bw: Output bitwidth
+        :param quant_scheme: Quantization scheme
+        :param rounding_mode: Rounding mode
+        :param config_file: Path to configuration file for model quantizers
+        :param results_dir: Directory to save the results of PTQ techniques
+        :param cache_id: ID associated with cache results
+        :param strict_validation: Flag set to True by default.hen False, AutoQuant will proceed with execution and handle errors internally if possible. This may produce unideal or unintuitive results.
+        :param model_prepare_required: Flag set to True by default.If False, AutoQuant will skip model prepare block in the pipeline.
+        """
+        self._auto_quant_base = AutoQuant(model,
+                                          dummy_input,
+                                          data_loader,
+                                          eval_callback,
+                                          param_bw,
+                                          output_bw,
+                                          quant_scheme,
+                                          rounding_mode,
+                                          config_file,
+                                          results_dir,
+                                          cache_id,
+                                          strict_validation,
+                                          model_prepare_required)
+        self._data_loader = data_loader
+        self._amp_args = None
+
+
    
+[docs]
+    def run_inference(self) -> Tuple[QuantizationSimModel, float]:
+        '''
+        Creates a quantization model and performs inference
+
+        :return: QuantizationSimModel, model accuracy as float
+        '''
+        return self._auto_quant_base.run_inference()
    
+
+
+
    
+[docs]
+    def optimize(self, allowed_accuracy_drop: float = 0.0)\
+            -> Tuple[torch.nn.Module, float, str, ParetoFrontType]:
+        """
+        Integrate and apply post-training quantization techniques.
+
+        :param allowed_accuracy_drop: Maximum allowed accuracy drop
+        :return: Tuple of  (best model, eval score, encoding path, pareto front).
+            Pareto front is None if AMP is not enabled or AutoQuant exits
+            without performing AMP.
+        """
+        html_template_file = os.path.join(
+            os.path.dirname(os.path.abspath(__file__)),
+            "auto_quant_diagnostics_template_with_amp.html",
+        )
+        with patch.object(_EvalManager, "HTML_TEMPLATE_FILE", html_template_file):
+            result = self._auto_quant_base._optimize_helper(self._optimize_main,
+                                                            allowed_accuracy_drop)
+            return result["model"],\
+                   result["accuracy"],\
+                   result["encoding_path"],\
+                   result["pareto_list"]
    
+
+
+
    
+[docs]
+    def set_adaround_params(self, adaround_params: AdaroundParameters) -> None:
+        """
+        Set Adaround parameters.
+        If this method is not called explicitly by the user, AutoQuant will use
+        `data_loader` (passed to `__init__`) for Adaround.
+
+        :param adaround_params: Adaround parameters.
+        """
+        return self._auto_quant_base.set_adaround_params(adaround_params)
    
+
+
+
    
+[docs]
+    def set_export_params(self,
+                          onnx_export_args: OnnxExportApiArgs = -1,
+                          propagate_encodings: bool = None) -> None:
+        """
+        Set parameters for QuantizationSimModel.export.
+
+        :param onnx_export_args: optional export argument with onnx specific overrides
+                if not provide export via torchscript graph
+        :param propagate_encodings: If True, encoding entries for intermediate ops
+                (when one PyTorch ops results in multiple ONNX nodes) are filled with
+                the same BW and data_type as the output tensor for that series of ops.
+        """
+        return self._auto_quant_base.set_export_params(onnx_export_args, propagate_encodings)
    
+
+
+
    
+[docs]
+    def set_mixed_precision_params(
+            self,
+            candidates: List[CANDIDATE_WITH_DTYPE],
+            num_samples_for_phase_1: Optional[int] = DEFAULT_NUM_SAMPLES_FOR_AMP_PHASE_1,
+            forward_fn: Callable = _default_forward_fn,
+            num_samples_for_phase_2: Optional[int] = DEFAULT_NUM_SAMPLES_FOR_AMP_PHASE_2,
+    ) -> None:
+        """
+        Set mixed precision parameters.
+        NOTE: Automatic mixed precision will NOT be enabled unless this method
+        is explicitly called by the user.
+
+        :param candidates: List of tuples of candidate bitwidths and datatypes.
+        :param num_samples_for_phase_1: Number of samples to be used for performance
+                evaluation in AMP phase 1.
+        :param forward_fn: Function that runs forward pass and returns the output tensor.
+                which will be used for SQNR compuatation in phase 1.
+                This function is expected to take 1) a model and 2) a single batch
+                yielded from the data loader, and return a single torch.Tensor object
+                which represents the output of the model.
+                The default forward function is roughly equivalent to
+                ``lambda model, batch: model(batch)``
+        :param num_samples_for_phase_2: Number of samples to be used for performance
+                evaluation in AMP phase 2.
+        """
+        if len(candidates) < 2:
+            raise ValueError(f"AMP requires at least two candidates. Got {len(candidates)}.")
+
+        baseline_param_bw = self._auto_quant_base._quantsim_params["param_bw"]
+        baseline_output_bw = self._auto_quant_base._quantsim_params["output_bw"]
+        baseline_candidate = (
+            (baseline_output_bw, QuantizationDataType.int),
+            (baseline_param_bw, QuantizationDataType.int),
+        )
+
+        if baseline_candidate not in candidates:
+            raise ValueError(
+                f"AMP candidate must contain W{baseline_param_bw}A{baseline_output_bw}, "
+                "which was passed to the constructor of AutoQuant as `param_bw` and `output_bw`."
+            )
+
+        for candidate in candidates:
+            ((output_bw, output_dtype), (param_bw, param_dtype)) = candidate
+
+            if output_dtype != param_dtype:
+                raise ValueError(
+                    "The data types of parameters and outputs should be the same. "
+                    f"Got {output_dtype} output and {param_dtype} for parameter."
+                )
+
+            if output_dtype == QuantizationDataType.float:
+                continue
+
+            # The param/output_bw passed to the constructor of AutoQuant
+            # must be the baseline-bitwidth candidate among all AMP candidates.
+            if output_bw < baseline_output_bw or param_bw < baseline_param_bw:
+                raise ValueError(
+                    "All AMP candidates should be strictly superior to the baseline "
+                    f"W{baseline_param_bw}A{baseline_output_bw}, which was passed "
+                    "to the constructor of AutoQuant. Please make sure that all the INT candidates "
+                    f"satisfy param_bw >= {baseline_param_bw} and output_bw >= {baseline_param_bw}."
+                )
+
+        factory = EvalCallbackFactory(self._data_loader, forward_fn=forward_fn)
+        sqnr_eval_callback = factory.sqnr(num_samples_for_phase_1)
+
+        candidates = [AmpCandidate(candidate) for candidate in set(candidates)]
+
+        self._amp_args = _MixedPrecisionArgs(
+            candidates=candidates,
+            forward_pass_callback=CallbackFunc(self._auto_quant_base.forward_pass_callback, None),
+            eval_callback_for_phase1=sqnr_eval_callback,
+            eval_callback_for_phase2=CallbackFunc(self._auto_quant_base.eval_callback,
+                                                  num_samples_for_phase_2),
+        )
    
+
+
+
    
+[docs]
+    def set_model_preparer_params(
+            self,
+            modules_to_exclude: List[torch.nn.Module] = None,
+            concrete_args: Optional[Dict[str, Any]] = None,
+    ):
+        """
+        Set parameters for model preparer.
+
+        :param modules_to_exclude: List of modules to exclude when tracing.
+        :param concrete_args: Parameter for model preparer. Allows you to partially specialize
+            your function, whether it's to remove control flow or data structures. If the
+            model has control flow, torch.fx won't be able to trace the model. Check
+            torch.fx.symbolic_trace API in detail.
+        """
+        return self._auto_quant_base.set_model_preparer_params(modules_to_exclude, concrete_args)
    
+
+
+
    
+[docs]
+    def get_quant_scheme_candidates(self) -> Tuple[_QuantSchemePair, ...]:
+        """
+        Return the candidates for quant scheme search.
+        During :meth:`~AutoQuant.optimize`, the candidate with the highest accuracy
+        will be selected among them.
+
+        :return: Candidates for quant scheme search
+        """
+        return self._auto_quant_base.get_quant_scheme_candidates()
    
+
+
+
    
+[docs]
+    def set_quant_scheme_candidates(self, candidates: Tuple[_QuantSchemePair, ...]):
+        """
+        Set candidates for quant scheme search.
+        During :meth:`~AutoQuant.optimize`, the candidate with the highest accuracy
+        will be selected among them.
+
+        :param candidates: Candidates for quant scheme search
+        """
+        return self._auto_quant_base.set_quant_scheme_candidates(candidates)
    
+
+
+    @cache.mark("mixed_precision")
+    def _apply_mixed_precision(
+            self,
+            model: torch.nn.Module,
+            dummy_input: Union[torch.Tensor, Tuple],
+            target_acc: float,
+            amp_args: _MixedPrecisionArgs,
+            results_dir: str,
+            encoding_path: str = None,
+    ) -> _MixedPrecisionResult:
+        """
+        Apply mixed-precision and return the highest accuracy.
+
+        NOTE1: Input model is not mutated.
+        NOTE2: Parameter `clean_start` is always set to True.
+
+        :param model: Model to apply mixed precision.
+        :param dummy_input: Dummy input to the model.
+        :param target_acc: Minimum evaluation score required.
+        :param encoding_path: Path to parameter encodings file.
+        :param results_dir: Directory to save the results of AdaRound and mixed precision.
+        :return: MixedPrecisionAlgo object.
+        """
+        if not amp_args:
+            raise RuntimeError
+
+        sim = self._auto_quant_base._create_quantsim_and_encodings(model,
+                                                                   encoding_path=encoding_path)
+
+        algo = GreedyMixedPrecisionAlgo(
+            sim,
+            dummy_input,
+            amp_args.candidates,
+            amp_args.eval_callback_for_phase1,
+            amp_args.eval_callback_for_phase2,
+            results_dir=results_dir,
+            clean_start=True,
+            forward_pass_callback=amp_args.forward_pass_callback
+        )
+
+        # Find baseline accuracy and bw corresponding to baseline accuracy
+        algo.set_baseline(fp32_accuracy=self._auto_quant_base._fp32_acc)
+        allowed_accuracy_drop = algo.fp32_accuracy - target_acc
+
+        algo.run(allowed_accuracy_drop)
+
+        sensitivity_plot = None
+        if algo.accuracy_list is not None:
+            # Visualize quantizer group sensitivity
+            sensitivity_plot = create_sensitivity_plot(algo.accuracy_list,
+                                                       algo.baseline_candidate,
+                                                       algo.fp32_accuracy)
+        pareto_plot = None
+        if algo.pareto_list is not None:
+            # Create pareto list curve
+            pareto_plot = create_pareto_curve(algo.pareto_list)
+
+        return _MixedPrecisionResult(algo.pareto_list,
+                                     algo._sim,
+                                     algo._final_eval_score,
+                                     sensitivity_plot,
+                                     pareto_plot)
+
+    def _optimize_main(self, fp32_model: torch.nn.Module, target_acc: float) -> Dict[str, Any]:
+        """
+        Helper function of apply().
+
+        :param fp32_model: Model to apply PTQ techniques.
+        :param target_acc: Target eval score.
+        :return: The best ptq result as a dictionary.
+        """
+        # pylint: disable=broad-except, too-many-locals, too-many-statements, too-many-branches
+
+        if self._amp_args:
+            candidates = copy.copy(self._amp_args.candidates)
+        else:
+            candidates = []
+
+        eval_manager = self._auto_quant_base.eval_manager
+        dummy_input = self._auto_quant_base.dummy_input
+        results_dir = self._auto_quant_base.results_dir
+        strict_validation = eval_manager._strict_validation
+
+        sess = eval_manager.session("")
+        _multiconfig_adaround_fn = _adaround_wrapper(self._auto_quant_base._apply_adaround,
+                                                     self._auto_quant_base,
+                                                     candidates,
+                                                     target_acc,
+                                                     sess.eval)
+        sess_eval_fn = _EvalSession.eval
+        def eval_fn(_, model, param_bw=None, output_bw=None, **kwargs):
+            if param_bw == 32:
+                # For W32 evaluation, use the highest output bitwidth
+                # among all the AMP candidates
+                output_bitwidths = [
+                    output_bw for (output_bw, output_dtype), _ in candidates
+                    if output_dtype == QuantizationDataType.int
+                ]
+                output_bitwidths.append(self._auto_quant_base._quantsim_params["output_bw"])
+                output_bw = max(output_bitwidths)
+            return sess_eval_fn(_, model, param_bw=param_bw, output_bw=output_bw, **kwargs)
+
+        with patch.object(self._auto_quant_base, "_apply_adaround", _multiconfig_adaround_fn),\
+                patch.object(_EvalSession, "eval", eval_fn):
+            try:
+                result = self._auto_quant_base._optimize_main(fp32_model, target_acc)
+
+                # Automatic Mixed Precision
+                result["pareto_list"] = None
+
+                # An empty `result` dict means AutoQuant early-exited
+                # because W32 eval score didn't meet the target accuracy.
+                # In this case, do not proceed to AMP and exit immediately.
+                if result["model"] is None and\
+                        result["accuracy"] is None and\
+                        result["encoding_path"] is None and\
+                        result["applied_techniques"] is None:
+                    return result
+
+                if result["accuracy"] >= target_acc or not self._amp_args:
+                    return result
+
+                if len(candidates) < 2:
+                    _logger.info(
+                        "After Adaround, we have only one Adarond-compatible candidate left for AMP (W%dA%d). "
+                        "Return without proceeding to AMP", candidates[0].param_bw, candidates[0].output_bw
+                    )
+                    return result
+
+                model = result["model"]
+                applied_techniques = result["applied_techniques"]
+                # Freeze weight encoding to adaround weight encoding
+                encoding_path = result["encoding_path"] if "adaround" in applied_techniques else None
+            except Exception:
+                if strict_validation:
+                    raise
+                result = {}
+                model = fp32_model
+                applied_techniques = []
+                encoding_path = None
+
+            amp_args = copy.copy(self._amp_args)
+            if amp_args:
+                amp_args.candidates = candidates
+
+        with eval_manager.session("Automatic Mixed Precision", ptq=True) as sess:
+            amp_result = self._apply_mixed_precision(
+                model, dummy_input, target_acc, amp_args, results_dir, encoding_path=encoding_path
+            )
+            result["pareto_list"] = amp_result.pareto_list
+
+            if amp_result.sensitivity_plot is not None:
+                sess.diagnostics.add(amp_result.sensitivity_plot)
+
+            if amp_result.pareto_plot is not None:
+                sess.diagnostics.add(amp_result.pareto_plot)
+
+            sess.set_ptq_result(sim=amp_result.sim, acc=amp_result.final_eval_score,
+                                applied_techniques=[*applied_techniques, "automatic_mixed_precision"])
+
+        best_result = eval_manager.get_best_ptq_result()
+        if best_result:
+            if "automatic_mixed_precision" not in best_result.applied_techniques:
+                sess.result["effective"] = False
+            if best_result.accuracy >= target_acc:
+                sess.result["target_satisfied"] = True
+            result.update(best_result.as_dict())
+            return result
+
+        raise RuntimeError("None of batchnorm folding, CLE, or Adaround "
+                           "has been finished successfully.")
    
+
+
+def _adaround_wrapper(apply_adaround_fn: Callable,
+                      auto_quant: AutoQuantBase,
+                      amp_candidates: List[AmpCandidate],
+                      target_acc: float,
+                      eval_fn: Callable):
+    @functools.wraps(apply_adaround_fn)
+    def _apply_adaround_wrapper(*args, **kwargs): # pylint: disable=too-many-locals
+        # If AMP candidates are empty (i.e. AMP is disabled),
+        # perform normal (single-round) adaround.
+        if not amp_candidates:
+            return apply_adaround_fn(*args, **kwargs)
+
+        def apply_adaround(param_bw: int):
+            _logger.info("Running Adaround with W%d", param_bw)
+
+            orig_param_bw = auto_quant._quantsim_params["param_bw"]
+            try:
+                auto_quant._quantsim_params["param_bw"] = param_bw
+                return apply_adaround_fn(*args, **kwargs)
+            finally:
+                auto_quant._quantsim_params["param_bw"] = orig_param_bw
+
+        int_candidates = [
+            candidate for candidate in amp_candidates
+            if candidate.param_dtype == QuantizationDataType.int
+        ]
+        sorted_int_candidates = sorted(int_candidates,
+                                       key=lambda candidate: (candidate.param_bw, candidate.output_bw))
+        # Run Adaround with the lowest-bitwidth candidate
+        lowest_candidate = sorted_int_candidates[0]
+        model, encoding_path = apply_adaround(param_bw=lowest_candidate.param_bw)
+
+        # If the lowest candidate is the only INT candidate, return immediately
+        if len(sorted_int_candidates) == 1:
+            return model, encoding_path
+
+        eval_score = eval_fn(model,
+                             param_bw=lowest_candidate.param_bw,
+                             output_bw=lowest_candidate.output_bw,
+                             encoding_path=encoding_path)
+        _logger.info("W%dA%d eval score after Adaround: %f",
+                     lowest_candidate.param_bw,
+                     lowest_candidate.output_bw,
+                     eval_score)
+
+        # If the lowest candidate satisfy the target accuracy, return immediately
+        if eval_score >= target_acc:
+            return model, encoding_path
+
+        # If the lowest candidate fails to meet the target accuracy,
+        # discard the lowest candidate, apply Adaround to the second-lowest candidate,
+        # and use it as the baseline for AMP.
+        second_lowest_candidate = sorted_int_candidates[1]
+
+        if second_lowest_candidate.param_bw != lowest_candidate.param_bw:
+            model = None
+            model, encoding_path = apply_adaround(param_bw=second_lowest_candidate.param_bw)
+            eval_score = eval_fn(model,
+                                 param_bw=second_lowest_candidate.param_bw,
+                                 output_bw=second_lowest_candidate.output_bw,
+                                 encoding_path=encoding_path)
+            _logger.info("W%dA%d eval score after Adaround: %f",
+                         second_lowest_candidate.param_bw,
+                         second_lowest_candidate.output_bw,
+                         eval_score)
+
+        # Only the candidates that are compatible with adaround can be used for AMP
+        adaround_compatible_amp_candidates = [
+            candidate for candidate in amp_candidates
+            if candidate.param_bw == second_lowest_candidate.param_bw or\
+                    candidate.param_dtype == QuantizationDataType.float
+        ]
+
+        # Fill in AMP candidates with Adaround-compatible candidates only
+        amp_candidates.clear()
+        amp_candidates.extend(adaround_compatible_amp_candidates)
+
+        return model, encoding_path
+
+    return _apply_adaround_wrapper
+
    +
    +
    +
    + +
    + + +
    +
    +
    +
    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
    + Made with Sphinx and @pradyunsg's + + Furo + +
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    +

    Source code for aimet_torch.v1.quant_analyzer

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2022-2023, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+
+""" Quant Analyzer """
+
+import os
+import contextlib
+from typing import Tuple, List, Type, Generator
+
+from aimet_common.quant_analyzer import export_stats_histogram_plot
+from aimet_common.utils import AimetLogger
+from aimet_torch import utils
+from aimet_torch._base.quant_analyzer import QuantAnalyzerBase
+from aimet_torch.v1.tensor_quantizer import TensorQuantizer, StaticGridTensorQuantizer
+from aimet_torch.v1.qc_quantize_op import QcQuantizeWrapper
+from aimet_torch.v1.qc_quantize_recurrent import QcQuantizeRecurrent
+from aimet_torch.v1.quantsim import QuantizationSimModel
+from aimet_torch.v1.batch_norm_fold import fold_all_batch_norms
+
+_logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.QuantAnalyzer)
+
+DEFAULT_BOKEH_FIGURE_HEIGHT = 300
+
+
+
    
+[docs]
+class QuantAnalyzer(QuantAnalyzerBase):
+    """
+    QuantAnalyzer tool provides
+
+     1) model sensitivity to weight and activation quantization
+     2) per layer sensitivity analysis
+     3) per layer encoding (min - max range)
+     4) per PDF analysis and
+     5) per layer MSE analysis
+    """
+
+    @staticmethod
+    def _enable_disable_quantizers(quantizers: List[TensorQuantizer], enabled: bool):
+        """
+        For given list of quantizers, set (enable/disable) quantizer's enabled.
+
+        :param quantizers: List of quantizers.
+        :param enabled: Enabled flag.
+        """
+        for quantizer in quantizers:
+            quantizer.enabled = enabled
+
+    # pylint: disable=no-self-use
+    def _create_and_export_stats_histogram_plot(self,
+                                                quantizer: StaticGridTensorQuantizer,
+                                                results_dir: str,
+                                                title: str,
+                                                ):
+        """
+        For given quantizer, create and export histogram (PDF) of statistics in html format.
+
+        :param quantizer: Quantizer.
+        :param results_dir: Directory to save the results.
+        :param title: Title of the plot.
+        """
+        os.makedirs(results_dir, exist_ok=True)
+
+        histograms = quantizer.get_stats_histogram()
+        encodings = quantizer.encoding
+        if not isinstance(encodings, List):
+            encodings = [encodings]
+
+        for index, (histogram, encoding) in enumerate(zip(histograms, encodings)):
+            export_stats_histogram_plot(histogram, encoding, results_dir, title=f"{title}_{index}")
+
+    @staticmethod
+    def patch_quantsim_to_store_histogram(_):
+        """
+        Placeholder function to prevent patching v1 quantsim
+        """
+
+    @staticmethod
+    def _get_quantsim_cls() -> Type[QuantizationSimModel]:
+        return QuantizationSimModel
+
+    @staticmethod
+    def _get_quant_wrapper_type() -> Tuple[Type]:
+        return (QcQuantizeWrapper, QcQuantizeRecurrent)
+
+    @staticmethod
+    def _is_quantizer_enabled(quantizer: TensorQuantizer):
+        return quantizer.enabled
+
+    @staticmethod
+    def _get_quantizer_encodings(quantizer: TensorQuantizer):
+        if quantizer.encoding and not isinstance(quantizer.encoding, List):
+            return [quantizer.encoding]
+        return quantizer.encoding
+
+    @classmethod
+    @contextlib.contextmanager
+    def _disable_param_quantizers(cls, sim: QuantizationSimModel):
+        enabled_param_quantizers = cls._get_enabled_param_quantizers(sim)
+        cls._enable_disable_quantizers(enabled_param_quantizers, enabled=False)
+        yield
+        cls._enable_disable_quantizers(enabled_param_quantizers, enabled=True)
+
+    @classmethod
+    @contextlib.contextmanager
+    def _disable_activation_quantizers(cls, sim: QuantizationSimModel):
+        enabled_activation_quantizers = cls._get_enabled_activation_quantizers(sim)
+        cls._enable_disable_quantizers(enabled_activation_quantizers, enabled=False)
+        yield
+        cls._enable_disable_quantizers(enabled_activation_quantizers, enabled=True)
+
+    @staticmethod
+    def _disable_quant_wrapper(module: QcQuantizeWrapper):
+        return utils.disable_all_quantizers(module)
+
+    @staticmethod
+    def _get_quantized_modules(sim: QuantizationSimModel) -> Generator[QcQuantizeWrapper, None, None]:
+        for module in sim.model.modules():
+            if isinstance(module, (QcQuantizeWrapper, QcQuantizeRecurrent)):
+                yield module
+
+    @staticmethod
+    def _fold_all_batch_norms(*args, **kwargs):
+        return fold_all_batch_norms(*args, **kwargs)
    
+
+
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    +

    Source code for aimet_torch.v1.quantsim

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2019-2024, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+
+""" Implementation for simulating models running on Quantized hardware """
+import contextlib
+import os
+import io
+import copy
+from typing import Tuple, List, Union, Dict, Callable, Optional, Any
+import torch
+
+from aimet_common.utils import AimetLogger
+from aimet_common.defs import QuantScheme, QuantizationDataType
+from aimet_common.utils import deprecated
+
+from aimet_torch.v1.qc_quantize_op import QcQuantizeStandAloneBase, QcQuantizeWrapper, QcQuantizeOpMode, \
+    StaticGridQuantWrapper, LearnedGridQuantWrapper, NativeTorchQuantWrapper
+from aimet_torch.v1.tensor_quantizer import initialize_learned_grid_quantizer_attributes
+from aimet_torch.v1.qc_quantize_op import get_encoding_by_quantizer as _get_encoding_by_quantizer
+from aimet_torch import utils
+from aimet_torch.v1.utils import create_encoding_dict
+from aimet_torch.onnx_utils import OnnxSaver, OnnxExportApiArgs
+from aimet_torch.v1.qc_quantize_recurrent import QcQuantizeRecurrent
+from aimet_torch.quantsim_config.builder import LazyQuantizeWrapper
+from aimet_torch.v1._builder import _V1LazyQuantizeWrapper
+from aimet_torch._base.quantsim import (
+    _QuantizationSimModelBase,
+    _QuantizedModuleProtocol,
+    unquantizable_modules,
+    QuantParams,
+    ExportableQuantModule,
+    save_checkpoint,
+    load_checkpoint,
+    check_accumulator_overflow,
+)
+
+__all__ = [
+    'QuantizationSimModel',
+    'QuantParams',
+    'ExportableQuantModule',
+    'save_checkpoint',
+    'load_checkpoint',
+    'check_accumulator_overflow',
+    'load_encodings_to_sim',
+    'compute_encodings_for_sims',
+]
+
+
+logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.Quant)
+
+# If a torch module type is in this dictionary, call the corresponding quantized module constructor instead of wrapping
+# it with QcQuantizeWrapper.
+qc_quantize_modules_dict = {
+    torch.nn.RNN: QcQuantizeRecurrent,
+    torch.nn.LSTM: QcQuantizeRecurrent,
+    torch.nn.GRU: QcQuantizeRecurrent
+}
+
+
+# Types of modules which cannot be quantized
+quantized_modules = (
+    QcQuantizeWrapper,
+    QcQuantizeStandAloneBase,
+    QcQuantizeRecurrent,
+    _QuantizedModuleProtocol,
+    LazyQuantizeWrapper,
+)
+
+
+
    
+[docs]
+class QuantizationSimModel(_QuantizationSimModelBase):
+    """
+    Implements mechanism to add quantization simulations ops to a model. This allows for off-target simulation of
+    inference accuracy. Also allows the model to be fine-tuned to counter the effects of quantization.
+    """
+    # pylint: disable=too-many-arguments, too-many-locals, too-many-public-methods
+    _quantized_modules = quantized_modules
+
+    def _realize_quant_wrappers_in_model(self, model: torch.nn.Module):
+        """
+        Prepare QuantSim for compute encodings. Resets encodings for each quantizable layer and sets mode to Analysis.
+        Realize quant wrappers using collected information in LazyQuantWrapper.
+
+        :param model: model containing modules wrapped with LazyQuantWrapper
+        """
+        for module_name, module_ref in model.named_children():
+            if isinstance(module_ref, LazyQuantizeWrapper):
+                quantized_module = module_ref.realize()
+                setattr(model, module_name, quantized_module)
+
+            elif not utils.is_leaf_module(module_ref):
+                self._realize_quant_wrappers_in_model(module_ref)
+
+    def __str__(self):
+        """
+        Pretty-printed output indicating where in the model, quantizers have been activated
+        :return:
+        """
+
+        def print_quantizer_state(stream, quantizer, prefix_string):
+            if quantizer.enabled:
+                stream.write(f'  {prefix_string}: bw={quantizer.bitwidth}, '
+                             f'encoding-present={bool(quantizer.encoding)}\n')
+
+                if quantizer.encoding:
+                    stream.write(f'    {quantizer}')
+            else:
+                stream.write(f'  {prefix_string}: Not quantized\n')
+
+            stream.write('  -------\n')
+
+        stream = io.StringIO(newline='\n')
+        stream.write("-------------------------\n")
+        stream.write("Quantized Model Report\n")
+        stream.write("-------------------------\n")
+
+        for layer_name, layer in self._get_qc_quantized_layers(self.model):
+            stream.write('----------------------------------------------------------\n')
+            stream.write('Layer: {}\n'.format(layer_name))
+
+            # Inputs
+            if isinstance(layer.input_quantizers, dict):
+                for name, quantizer in layer.input_quantizers.items():
+                    print_quantizer_state(stream, quantizer, prefix_string=f"Input[{name}]")
+            else:
+                for index, quantizer in enumerate(layer.input_quantizers):
+                    print_quantizer_state(stream, quantizer, prefix_string=f"Input[{index}]")
+
+            # Params
+            for param_name, quantizer in layer.param_quantizers.items():
+                print_quantizer_state(stream, quantizer, prefix_string=f"Param[{param_name}]")
+
+            # Outputs
+            if isinstance(layer.output_quantizers, dict):
+                for name, quantizer in layer.output_quantizers.items():
+                    print_quantizer_state(stream, quantizer, prefix_string=f"Output[{name}]")
+            else:
+                for index, quantizer in enumerate(layer.output_quantizers):
+                    print_quantizer_state(stream, quantizer, prefix_string=f"Output[{index}]")
+
+        return stream.getvalue()
+
+    @staticmethod
+    def prepare_sim_for_compute_encodings(sim: 'QuantizationSimModel'):
+        """
+        Prepare QuantSim for compute encodings. Resets encodings for each quantizable layer and sets mode to Analysis.
+
+        :param sim: QuantSim to prepare
+        """
+        # pylint: disable=protected-access
+        quantized_layers = sim._get_qc_quantized_layers(sim.model)
+
+        for _, layer in quantized_layers:
+            # Clear stats and encodings if they are present
+            layer.reset_encodings()
+
+            # And set the mode to analysis
+            layer.set_mode(QcQuantizeOpMode.ANALYSIS)
+
+        for _, layer in quantized_layers:
+            # call only when quant scheme is percentile
+            if sim._quant_scheme == QuantScheme.post_training_percentile:
+                layer.set_percentile_value(sim._percentile_value)
+
+    @staticmethod
+    def compute_layer_encodings_for_sim(sim: 'QuantizationSimModel'):
+        """
+        Compute encodings for each quantizable layer in sim after forward pass has been called.
+
+        :param sim: QuantSim to compute encodings for
+        """
+        # pylint: disable=protected-access
+        quantized_layers = sim._get_qc_quantized_layers(sim.model)
+        # Get the computed per-layer encodings and log them
+        for name, layer in quantized_layers:
+            layer.compute_encoding()
+
+            # Before we return we set the mode to active - meaning ready for quantize/de-quantize
+            # for layers with valid_encoding, otherwise we set to pass through
+            if isinstance(layer, QcQuantizeRecurrent):
+                sim.set_mode_for_recurrent_module(layer, name)
+            else:
+                # By default we want to set the Quantization wrappers to ACTIVE mode
+                layer.set_mode(QcQuantizeOpMode.ACTIVE)
+
+        sim.replace_wrappers_for_quantize_dequantize()
+
+
    
+[docs]
+    def compute_encodings(self, forward_pass_callback, forward_pass_callback_args): # pylint: disable=arguments-differ
+        """
+        Computes encodings for all quantization sim nodes in the model. It is also used to find initial encodings for
+        Range Learning
+
+        :param forward_pass_callback: A callback function that simply runs forward passes on the model. This callback
+            function should use representative data for the forward pass, so the calculated encodings work for all
+            data samples. This callback internally chooses the number of data samples it wants to use for calculating
+            encodings.
+        :param forward_pass_callback_args: These argument(s) are passed to the forward_pass_callback as-is. Up to
+            the user to determine the type of this parameter. E.g. could be simply an integer representing the number
+            of data samples to use. Or could be a tuple of parameters or an object representing something more complex.
+            If set to None, forward_pass_callback will be invoked with no parameters.
+        :return: None
+
+        """
+
+        QuantizationSimModel.prepare_sim_for_compute_encodings(self)
+
+        # Run forward iterations so we can collect statistics to compute the appropriate encodings
+        with utils.in_eval_mode(self.model), torch.no_grad():
+            _ = forward_pass_callback(self.model, forward_pass_callback_args)
+
+        QuantizationSimModel.compute_layer_encodings_for_sim(self)
    
+
+
+    @classmethod
+    def set_mode_for_recurrent_module(cls, layer: QcQuantizeRecurrent, name: str):
+        """
+        Sets Recurrent module to active or pass through mode based on quantizer state
+
+        :param layer:  Qc Quantizer layer for recurrent module
+        :param name:  layer name
+        :return: True if the encoding is invalid
+
+        """
+        for quantizer_name, output_quantizer in layer.output_quantizers.items():
+            if output_quantizer.enabled:
+                if output_quantizer.encoding:
+                    encoding = output_quantizer.encoding
+                    logger.debug("Encoding for %s-%s: min=%f, max=%f, offset=%f. delta=%f, bw=%f",
+                                 name, quantizer_name, encoding.min, encoding.max,
+                                 encoding.delta, encoding.offset, encoding.bw)
+
+        for quantizer_name, input_quantizer in layer.input_quantizers.items():
+            if input_quantizer.enabled:
+                if input_quantizer.encoding:
+                    encoding = input_quantizer.encoding
+                    logger.debug("Encoding for %s-%s: min=%f, max=%f, offset=%f. delta=%f, bw=%f",
+                                 name, quantizer_name, encoding.min, encoding.max,
+                                 encoding.delta, encoding.offset, encoding.bw)
+
+        layer.set_mode(QcQuantizeOpMode.ACTIVE)
+
+    def set_percentile_value(self, percentile_value: float):
+        """
+        Set the percentile value to be used while computing encodings
+        """
+        if percentile_value < 90 or percentile_value > 100:
+            raise ValueError("Percentile value must be in range [90, 100]")
+        self._percentile_value = percentile_value
+
+    def _replace_quantization_wrapper(self, model, device):
+        """
+        Recursively remove quantization wrappers from all appropriate modules starting with a given module
+        :param model: model for which PostTrainingWrapper gets replaced with Trainable wrapped module
+        :param device: device on which model is present
+        :return: None
+        """
+        for module_name, module_ref in model.named_children():
+
+            if isinstance(module_ref, StaticGridQuantWrapper):
+                # Create a Trainable wrapper and copy properties of PostTrainingWrapper to the Trainable wrapper
+                quantized_module = self._construct_and_initialize_trainable_wrapper(module_ref, device)
+                setattr(model, module_name, quantized_module)
+
+            elif isinstance(module_ref, QcQuantizeRecurrent):
+                # Set Recurrent layer for training mode
+                module_ref.construct_and_initialize_trainable_quantizers(self._quant_scheme)
+
+            # Recursively call children modules if present
+            if not utils.is_leaf_module(module_ref):
+                self._replace_quantization_wrapper(module_ref, device)
+
+    def _construct_and_initialize_trainable_wrapper(self, post_training_module: StaticGridQuantWrapper,
+                                                    device: torch.device) -> LearnedGridQuantWrapper:
+        """
+        Copies following tensor quantizer attributes from StaticGridQuantWrapper to LearnedGridQuantWrapper
+        to avoid any mismatch.
+            - enabled
+            - bitwidth
+            - encoding
+            - use_symmetric_encodings
+            - use_strict_symmetric
+            - use_unsigned_symmetric
+
+        :param post_training_module: StaticGridQuantWrapper wrapped module
+        :param device: device on which model is present
+        :return: trainable_module: QcTrainable wrapper module
+        """
+
+        # pylint: disable=protected-access
+        module = post_training_module._module_to_wrap
+
+        num_inputs = len(post_training_module.input_quantizers)
+        num_outputs = len(post_training_module.output_quantizers)
+
+        # Creating a LearnedGridQuantWrapper module
+        trainable_module = LearnedGridQuantWrapper(module, self._default_param_bw,
+                                                   self._default_output_bw, self._rounding_mode, self._quant_scheme,
+                                                   device=device, num_inputs=num_inputs, num_outputs=num_outputs,
+                                                   data_type=QuantizationDataType.int)
+        # Copy user settable attributes for outputs
+        for index, quantizer in enumerate(post_training_module.output_quantizers):
+            initialize_learned_grid_quantizer_attributes(trainable_module.output_quantizers[index], quantizer)
+            if trainable_module.output_quantizers[index].encoding_min_max_fixed_vals is not None:
+                trainable_module.output_quantizers[index].freeze_encoding()
+        # Copy user settable attributes for inputs
+        for index, quantizer in enumerate(post_training_module.input_quantizers):
+            initialize_learned_grid_quantizer_attributes(trainable_module.input_quantizers[index], quantizer)
+            if trainable_module.input_quantizers[index].encoding_min_max_fixed_vals is not None:
+                trainable_module.input_quantizers[index].freeze_encoding()
+        # Copy user settable attributes for params
+        for name, quantizer in post_training_module.param_quantizers.items():
+            learned_grid_quantizer = trainable_module.param_quantizers[name]
+            initialize_learned_grid_quantizer_attributes(learned_grid_quantizer, quantizer)
+            if learned_grid_quantizer.encoding_min_max_fixed_vals is not None:
+                learned_grid_quantizer.freeze_encoding()
+
+        return trainable_module
+
+    def replace_wrappers_for_quantize_dequantize(self):
+        """
+        Replaces StaticGridWrapper with LearnedGridWrapper
+        """
+        if self._quant_scheme == QuantScheme.training_range_learning_with_tf_init or self._quant_scheme == \
+                QuantScheme.training_range_learning_with_tf_enhanced_init:
+            try:
+                device = utils.get_device(self.model)
+            except StopIteration:
+                # Model doesn't have any parameter.
+                # Set device to cpu by default.
+                device = torch.device('cpu')
+
+            self._replace_quantization_wrapper(self.model, device)
+
+    def _create_quantizer_module(self, module_to_quantize: torch.nn.Module, num_inout_tensors: Dict,
+                                 data_type: QuantizationDataType) -> torch.nn.Module:
+        """Instantiates wrapper based on quant scheme
+        """
+        assert self._quant_scheme in [QuantScheme.post_training_tf, QuantScheme.post_training_tf_enhanced,
+                                      QuantScheme.training_range_learning_with_tf_enhanced_init,
+                                      QuantScheme.training_range_learning_with_tf_init,
+                                      QuantScheme.post_training_percentile]
+
+        # We lookup the number of input and output tensors already determined
+        # Special case, we are adding a wrapper for a module not in the forward pass: Use default of 1, 1
+        num_in_tensors, num_out_tensors = num_inout_tensors.get(module_to_quantize, (1, 1))
+
+        # Set quantizer to be a module replacer if it is in qc_quantize_modules_dict, otherwise set as
+        # StaticGridQuantWrapper.
+        quantizer_wrapper_type = qc_quantize_modules_dict.get(type(module_to_quantize), _V1LazyQuantizeWrapper)
+
+        if issubclass(quantizer_wrapper_type, LazyQuantizeWrapper):
+            quant_scheme_for_initialization = self._quant_scheme
+        else:
+            quant_scheme_for_initialization = utils.get_v1_quant_scheme_for_initialization(self._quant_scheme)
+
+        # TODO add quant_scheme_for_initialization for FP8 case
+        quantized_module = quantizer_wrapper_type(module_to_quantize, self._default_param_bw, self._default_output_bw,
+                                                  self._rounding_mode, quant_scheme_for_initialization, num_inputs=num_in_tensors,
+                                                  num_outputs=num_out_tensors, data_type=data_type)
+
+        return quantized_module
+
+    @classmethod
+    def _is_quantizable_module(cls, module: torch.nn.Module):
+        # pylint: disable=unidiomatic-typecheck
+        return type(module) != torch.nn.Module and\
+               not isinstance(module, unquantizable_modules) and\
+               not cls._is_quantized_module(module)
+
+    @classmethod
+    def _is_quantized_module(cls, module: torch.nn.Module):
+        return isinstance(module, quantized_modules)
+
+    def _add_quantization_wrappers(self, module, num_inout_tensors, default_data_type: QuantizationDataType):
+        """Recursively add quantization wrappers to all appropriate modules starting with module
+        """
+        if self._is_quantized_module(module):
+            return
+
+        for module_name, module_ref in module.named_children():
+            logger.debug("nn.Module found : %s", module_ref)
+
+            if self._is_quantizable_module(module_ref) and utils.is_leaf_module(module_ref):
+                # Create a new QcQuantize wrapper module
+                quantized_module = self._create_quantizer_module(module_ref, num_inout_tensors, default_data_type)
+                setattr(module, module_name, quantized_module)
+            else:
+                self._add_quantization_wrappers(module_ref, num_inout_tensors, default_data_type)
+
+    # pylint: disable=too-many-arguments
+    @classmethod
+    def _update_encoding_dicts_for_layer(cls, layer: _QuantizedModuleProtocol, layer_name: str, activation_encodings_onnx: Dict,
+                                         activation_encodings_torch: Dict, param_encodings: Dict,
+                                         op_to_io_tensor_map: Dict, valid_param_set: set, propagate_encodings: bool,
+                                         tensor_to_consumer_map: Dict[str, str],
+                                         layers_to_onnx_op_names: Dict[str, str],
+                                         tensor_to_quantizer_map: Dict):
+        """
+        Add given layer param and activation encodings to respective dictionaries to be used for exporting encodings
+        :param layer: layer as torch.nn.Module
+        :param layer_name: Name of the layer
+        :param activation_encodings_onnx: dictionary of activation encodings which maps onnx attribute to encodings
+        :param activation_encodings_torch: dictionary of activation encodings which maps pytorch names to encodings
+        :param param_encodings: dictionary of param encodings
+        :param op_to_io_tensor_map: ONNX or Torch Script map of layer name to it's input/output tensors
+        :param valid_param_set: a set of valid param input names in model
+        :param propagate_encodings: If True, encoding entries for intermediate ops (when one PyTorch ops results in
+                multiple ONNX nodes) are filled with the same BW and data_type as the output tensor for that series of
+                ops.
+        :param tensor_to_consumer_map: Dictionary mapping tensor names to op names which consume the tensor
+        :param layers_to_onnx_op_names: Dictionary mapping PyTorch layer names to names of corresponding ONNX ops
+        """
+        if isinstance(layer, QcQuantizeRecurrent):
+            # Update encodings for Recurrent layers
+            QuantizationSimModel._update_encoding_dict_for_recurrent_layers(layer, layer_name, op_to_io_tensor_map,
+                                                                            activation_encodings_onnx,
+                                                                            param_encodings, propagate_encodings,
+                                                                            tensor_to_quantizer_map)
+        else:
+            super()._update_encoding_dicts_for_layer(layer, layer_name, activation_encodings_onnx,
+                                                     activation_encodings_torch,
+                                                     param_encodings, op_to_io_tensor_map,
+                                                     valid_param_set, propagate_encodings,
+                                                     tensor_to_consumer_map, layers_to_onnx_op_names,
+                                                     tensor_to_quantizer_map)
+
+    @staticmethod
+    def _update_encoding_dict_for_recurrent_layers(layer: torch.nn.Module, layer_name: str, op_to_io_tensor_map: Dict,
+                                                   activation_encodings_onnx: Dict, param_encodings: Dict,
+                                                   propagate_encodings: bool, tensor_to_quantizer_map: Dict):
+        """
+
+        :param layer:
+        :param layer_name:
+        :param op_to_io_tensor_map:
+        :param activation_encodings_onnx:
+        :param param_encodings:
+        :param propagate_encodings:
+        :return:
+        """
+
+        # pylint: disable=too-many-nested-blocks
+        # pylint: disable=too-many-locals
+
+        onnx_activations_to_quantizers, onnx_params_to_quantizers = \
+            layer.get_activation_param_quantizers_for_onnx_tensors(op_to_io_tensor_map[layer_name +
+                                                                                       '#root_node'])
+        # ------------------
+        # Activations
+        # ------------------
+        quantizer = None
+        for tensor, quantizer in onnx_activations_to_quantizers.items():
+            quantizer_encoding = _get_encoding_by_quantizer(quantizer)
+            encoding = create_encoding_dict(quantizer_encoding, quantizer, propagate_encodings=False)
+            activation_encodings_onnx[tensor] = [encoding]
+            tensor_to_quantizer_map[tensor] = quantizer
+
+        if propagate_encodings and quantizer:
+            _, op_names = QuantizationSimModel.find_op_names_for_layer(layer_name, op_to_io_tensor_map, None, None)
+            for op_name in op_names:
+                io_tensor_list = op_to_io_tensor_map[op_name]
+                if not isinstance(io_tensor_list, list):
+                    io_tensor_list = [io_tensor_list]
+
+                for io_tensors in io_tensor_list:
+
+                    if io_tensors.outputs:
+                        for output_tensor in io_tensors.outputs:
+                            if output_tensor in onnx_activations_to_quantizers:
+                                continue
+                            quantizer_encoding = _get_encoding_by_quantizer(quantizer)
+                            encoding = create_encoding_dict(quantizer_encoding, quantizer, True)
+
+                            activation_encodings_onnx[output_tensor] = [encoding]
+                            tensor_to_quantizer_map[output_tensor] = quantizer
+
+        # ------------------
+        # Params
+        # ------------------
+        for tensor, quantizer in onnx_params_to_quantizers.items():
+            quantizer_encoding = _get_encoding_by_quantizer(quantizer)
+            encoding = create_encoding_dict(quantizer_encoding, quantizer, propagate_encodings=False)
+            param_encodings[tensor] = [encoding]
+            tensor_to_quantizer_map[tensor] = quantizer
+
+    @staticmethod
+    def _get_qc_quantized_layers(model) -> List[Tuple[str, QcQuantizeWrapper]]:
+        quantized_layers = []
+        for name, module in model.named_modules():
+            if isinstance(module, (QcQuantizeRecurrent, LazyQuantizeWrapper, _QuantizedModuleProtocol)):
+                quantized_layers.append((name, module))
+        return quantized_layers
+
+    @classmethod
+    def _remove_quantization_wrappers(cls, starting_module, list_of_modules_to_exclude):
+        """
+        Recursively remove quantization wrappers from all appropriate modules starting with a given module
+        :param starting_module: Module to recursive search downstream from
+        :param list_of_modules_to_exclude: List of torch modules to remove quantization wrappers from (if present)
+        :return: None
+        """
+        for module_name, module_ref in starting_module.named_children():
+
+            # If modules is in the exclude list, remove the wrapper
+            if module_ref in list_of_modules_to_exclude:
+                if isinstance(module_ref, (_QuantizedModuleProtocol, QcQuantizeRecurrent)):
+                    orig_module = module_ref.get_original_module()
+                elif isinstance(module_ref, QcQuantizeStandAloneBase):
+                    orig_module = torch.nn.Identity()
+                else:
+                    orig_module = None
+
+                if orig_module:
+                    setattr(starting_module, module_name, orig_module)
+                    module_ref = orig_module
+
+            # Recursively call children modules if present
+            if not utils.is_leaf_module(module_ref):
+                cls._remove_quantization_wrappers(module_ref, list_of_modules_to_exclude)
+
+    @classmethod
+    @torch.no_grad()
+    def _apply_qdq_to_model_parameters(cls, model: torch.nn.Module):
+        """
+        Applies quant-dequant to the parameters of a PyTorch model
+        to avoid rounding error during weight quantization.
+
+        :param model: The PyTorch model whose parameters will be quant-dequantized.
+        """
+        # pylint: disable=protected-access
+        for module in model.modules():
+            if isinstance(module, (QcQuantizeRecurrent, StaticGridQuantWrapper)):
+                with utils.in_eval_mode(module):
+                    module._quantize_dequantize_params()
+            elif isinstance(module, (LearnedGridQuantWrapper)):
+                with utils.in_eval_mode(module):
+                    module._quantize_params()
+                    cls._update_parameters_by_attr(module._module_to_wrap)
+
+    def named_qmodules(self):
+        """Generator that yields all quantized modules in the model and their names
+        """
+        for name, module in self.model.named_modules():
+            if isinstance(module, (QcQuantizeWrapper, QcQuantizeRecurrent, LazyQuantizeWrapper)):
+                yield name, module
+
+    quant_wrappers = named_qmodules
+
+    @staticmethod
+    def _replace_quantization_wrapper_with_native_torch_quantization_nodes(quant_sim_model, device: torch.device):
+        """
+        Recursively remove quantization wrappers from all appropriate modules starting with a given module
+        :param quant_sim_model: model for which QcQuantizeWrapper gets replaced with wrapped module using
+        native torch quantization nodes
+        :param device: device on which model is present
+        :return:
+        """
+        # Recursively replace quantization wrappers to native torch quantization nodes
+        for module_name, module_ref in quant_sim_model.named_children():
+            # Create a native torch quantization node
+            if isinstance(module_ref, QcQuantizeWrapper):
+                embedded_module = NativeTorchQuantWrapper(module_ref, '_module_to_wrap', device)
+                setattr(quant_sim_model, module_name, embedded_module)
+
+            elif isinstance(module_ref, QcQuantizeRecurrent):
+                logger.error('Do not support save model embedded native torch quantization nodes using QcQuantizeRecurrent.')
+                raise AssertionError
+
+            # Recursively call children modules if present
+            if not utils.is_leaf_module(module_ref):
+                QuantizationSimModel._replace_quantization_wrapper_with_native_torch_quantization_nodes(module_ref, device)
+
+    @classmethod
+    def save_model_with_embedded_quantization_nodes(cls, sim_model, path: str, filename_prefix: str, dummy_input: Union[torch.Tensor, Tuple],
+                                                    onnx_export_args: Optional[Union[OnnxExportApiArgs, Dict]] = None,
+                                                    export_to_torchscript: bool = False, is_conditional: bool = False):
+        """
+        Export model embedded with native torch quantization nodes. These nodes will be exported
+        as default onnx or torch script quantized nodes.
+        :param sim_model: model with the quantsim wrappers
+        :param path: path where to store model pth and encodings
+        :param filename_prefix: Prefix to use for filenames of the model pth and encodings files
+        :param dummy_input: Dummy input to the model. Used to parse model graph
+        :param onnx_export_args: optional export argument with onnx specific overrides if not provide export via
+                torchscript graph. Int16 can only be exported by torchscript
+        :param export_to_torchscript: If True, export to torchscript. Export to onnx otherwise. Defaults to False.
+        :param is_conditional: True if model is conditional, False otherwise
+        :return:
+        """
+        def _validate_torchquantizer(quant_sim_model):
+            # To avoid non 8 bit TorchQuantizer are exported to ONNX
+            for _, module in quant_sim_model.named_modules():
+                if isinstance(module, NativeTorchQuantWrapper):
+                    quantizers = module.input_quantizers + module.output_quantizers
+                    if 'weight' in module.param_quantizers:
+                        quantizers += [module.param_quantizers['weight']]
+                    if 'bias' in module.param_quantizers:
+                        quantizers += [module.param_quantizers['bias']]
+
+                    for quantizer in quantizers:
+                        if quantizer.enabled and quantizer.data_type == QuantizationDataType.int and quantizer.bitwidth != 8:
+                            raise ValueError('Only 8 bit quantizers are supported by exporting to ONNX model.'
+                                             'Please enable export_to_torchscript if you want to export non 8 bit quantizers.')
+
+        model_filename = filename_prefix + '_embedded' + '.onnx'
+        model_path = os.path.join(path, model_filename)
+        quant_sim_model = copy.deepcopy(sim_model)
+
+        device = utils.get_device(quant_sim_model)
+        if isinstance(dummy_input, torch.Tensor):
+            dummy_input = dummy_input.to(device)
+        else:
+            dummy_input = tuple([input.to(device) for input in dummy_input])  # pylint: disable=consider-using-generator
+        QuantizationSimModel._replace_quantization_wrapper_with_native_torch_quantization_nodes(quant_sim_model, device)
+
+        if export_to_torchscript:
+            with utils.in_eval_mode(quant_sim_model), torch.no_grad():
+                trace = torch.jit.trace(quant_sim_model, dummy_input)
+                ts_path = os.path.join(path, filename_prefix + '_embedded' + '.torchscript.pth')
+                trace.save(ts_path)
+        else:
+            _validate_torchquantizer(quant_sim_model)
+            OnnxSaver._export_model_to_onnx(quant_sim_model, dummy_input, model_path, is_conditional, onnx_export_args) # pylint: disable=protected-access
    
+
+
+
+
+@deprecated("Use QuantizationSimModel.load_encodings instead.")
+def load_encodings_to_sim(quant_sim_model: _QuantizationSimModelBase, pytorch_encoding_path: str):
+    """
+    Loads the saved encodings to quant sim model. The encoding filename to load should end in _torch.encodings,
+    generated as part of quantsim export.
+
+    :param quant_sim_model: Quantized model to load encodings for. Note: The model configuration should be the same as
+        when encodings were exported.
+    :param pytorch_encoding_path: Path of the encodings file to load.
+    """
+    for module in quant_sim_model.model.modules():
+        if isinstance(module, QcQuantizeWrapper):
+            module.set_mode(QcQuantizeOpMode.ACTIVE)
+
+    quant_sim_model.load_encodings(pytorch_encoding_path,
+                                   strict=True,
+                                   partial=False,
+                                   requires_grad=None,
+                                   allow_overwrite=None)
+
+    if isinstance(quant_sim_model, QuantizationSimModel):
+        # Only for V1 quantsim
+        quant_sim_model.replace_wrappers_for_quantize_dequantize()
+
+
+def compute_encodings_for_sims(sim_list: List[QuantizationSimModel], forward_pass_callback: Callable,
+                               forward_pass_callback_args: Any):
+    """
+    Compute encodings for a list of QuantSims.
+
+    :param sim_list: List of QuantSims to compute encodings for.
+    :param forward_pass_callback: A callback function that simply runs forward passes on the models. This callback
+        function should use representative data for the forward pass, so the calculated encodings work for all
+        data samples. This callback internally chooses the number of data samples it wants to use for calculating
+        encodings.
+        The callback expects exactly two inputs:
+            - List of models which are involved in the forward pass. The models are taken directly from calling
+            sim.model for each sim in sim_list, passed in the same order in which the sims appear in sim_list.
+            - Forward pass callback args
+    :param forward_pass_callback_args: These argument(s) are passed to the forward_pass_callback as-is. Up to
+        the user to determine the type of this parameter. E.g. could be simply an integer representing the number
+        of data samples to use. Or could be a tuple of parameters or an object representing something more complex.
+        If set to None, forward_pass_callback will be invoked with no parameters.
+    """
+    ctx_managers = [torch.no_grad()]
+    for sim in sim_list:
+        ctx_managers.append(utils.in_eval_mode(sim.model))
+        QuantizationSimModel.prepare_sim_for_compute_encodings(sim)
+
+    with contextlib.ExitStack() as stack:
+        for mgr in ctx_managers:
+            stack.enter_context(mgr)
+        _ = forward_pass_callback([sim.model for sim in sim_list], forward_pass_callback_args)
+
+    for sim in sim_list:
+        QuantizationSimModel.compute_layer_encodings_for_sim(sim)
+
    +
    +
    +
    + +
    + + +
    +
    +
    +
    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
    + Made with Sphinx and @pradyunsg's + + Furo + +
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    +
    + + + + + + + + + \ No newline at end of file diff --git a/releases/2.0.0/_modules/aimet_torch/v2/auto_quant.html b/releases/2.0.0/_modules/aimet_torch/v2/auto_quant.html new file mode 100644 index 0000000..29259cf --- /dev/null +++ b/releases/2.0.0/_modules/aimet_torch/v2/auto_quant.html @@ -0,0 +1,1035 @@ + + + + + + + + aimet_torch.v2.auto_quant - AIMET + + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    +

    Source code for aimet_torch.v2.auto_quant

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2024, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+# pylint: disable=too-many-lines, disable=protected-access
+""" Concrete implementation for AIMET AutoQuant using v2 QuantSim """
+
+import functools
+import itertools
+import copy
+import os
+from unittest.mock import patch
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+import torch
+from torch.utils.data import DataLoader
+
+import aimet_torch.v2.quantization as Q
+from aimet_torch._base.auto_quant import (
+    AutoQuantBase,
+    _EvalManager,
+    _QuantSchemePair,
+    _EvalSession,
+    _MixedPrecisionArgs,
+    _MixedPrecisionResult,
+    _logger,
+    ParetoFrontType,
+)
+from aimet_torch._base.adaround.adaround_weight import AdaroundParameters
+from aimet_torch.v2.adaround import Adaround
+from aimet_torch.v2.quantsim import QuantizationSimModel
+from aimet_torch.v2.nn import BaseQuantizationMixin
+from aimet_torch.v2.quantization import encoding_analyzer
+from aimet_torch.v2.utils import flatten_nn_module_list
+from aimet_torch.v2.amp.utils import _mock_v1_quantizers
+from aimet_torch.amp.mixed_precision_algo import GreedyMixedPrecisionAlgo, EvalCallbackFactory, _default_forward_fn
+from aimet_torch.onnx_utils import OnnxExportApiArgs
+from aimet_common.defs import QuantScheme, CallbackFunc, QuantizationDataType
+from aimet_common.amp.utils import (
+    create_sensitivity_plot,
+    create_pareto_curve,
+    CANDIDATE_WITH_DTYPE,
+    AmpCandidate,
+)
+
+
+__all__ = [
+    'AutoQuant',
+    'AutoQuantWithAutoMixedPrecision',
+]
+
+_MAP_QSCHEME_TO_ENCODING_ANALYZER = {
+    QuantScheme.post_training_tf: encoding_analyzer.MinMaxEncodingAnalyzer,
+    QuantScheme.post_training_percentile: encoding_analyzer.PercentileEncodingAnalyzer,
+    QuantScheme.post_training_tf_enhanced: encoding_analyzer.SqnrEncodingAnalyzer,
+    QuantScheme.training_range_learning_with_tf_init: encoding_analyzer.MinMaxEncodingAnalyzer,
+    QuantScheme.training_range_learning_with_tf_enhanced_init: encoding_analyzer.SqnrEncodingAnalyzer,
+}
+
+
+class AutoQuant(AutoQuantBase): # pylint: disable=too-many-instance-attributes
+    """
+    Integrate and apply post-training quantization techniques.
+
+    AutoQuant includes 1) batchnorm folding, 2) cross-layer equalization,
+    and 3) Adaround.
+    These techniques will be applied in a best-effort manner until the model
+    meets the evaluation goal given as allowed_accuracy_drop.
+    """
+
+    @staticmethod
+    def _get_adaround():
+        """ returns AdaRound """
+        return Adaround
+
+    @functools.wraps(AutoQuantBase.__init__)
+    def __init__(self, *args, rounding_mode: str = 'nearest', **kwargs):
+        if rounding_mode == 'stochastic':
+            raise ValueError("Stochastic rounding mode is not supported.")
+        super().__init__(*args, **kwargs)
+
+    @staticmethod
+    def _get_quantsim(model, dummy_input, **kwargs):
+        return QuantizationSimModel(model, dummy_input, **kwargs)
+
+    def _configure_quantsim(self, # pylint: disable=too-many-arguments
+                            sim,
+                            output_bw,
+                            output_quant_scheme,
+                            output_percentile,
+                            param_bw,
+                            param_quant_scheme,
+                            param_percentile,
+                            encoding_path):
+
+        for module in sim.model.modules():
+            if isinstance(module, BaseQuantizationMixin):
+                # Set input/output quantizers' quant schemes
+                for quantizer in itertools.chain(flatten_nn_module_list(module.input_quantizers),
+                                                 flatten_nn_module_list(module.output_quantizers)):
+                    self._set_quantizer_qscheme(quantizer, output_quant_scheme, output_percentile)
+
+                # Set param quantizers' quant schemes
+                for quantizer in module.param_quantizers.values():
+                    self._set_quantizer_qscheme(quantizer, param_quant_scheme, param_percentile)
+
+        if encoding_path:
+            sim.set_and_freeze_param_encodings(encoding_path)
+
+        if output_bw == 32:
+            self._disable_activation_quantizers(sim)
+
+        if param_bw == 32:
+            self._disable_param_quantizers(sim)
+
+
+    @staticmethod
+    def _set_quantizer_qscheme(quantizer, quant_scheme, percentile):
+        if quantizer is None:
+            return
+
+        if quant_scheme in (QuantScheme.post_training_percentile, QuantScheme.post_training_tf,
+                            QuantScheme.post_training_tf_enhanced):
+            quantizer.requires_grad_(False)
+
+        elif QuantScheme in (QuantScheme.training_range_learning, QuantScheme.training_range_learning_with_tf_init,
+                             QuantScheme.training_range_learning_with_tf_enhanced_init):
+            quantizer.requires_grad_(True)
+
+        enc_analyzer = _MAP_QSCHEME_TO_ENCODING_ANALYZER[quant_scheme](quantizer.shape)
+        if isinstance(enc_analyzer, encoding_analyzer.PercentileEncodingAnalyzer) and percentile is not None:
+            enc_analyzer.set_percentile(percentile)
+
+        quantizer.encoding_analyzer = enc_analyzer
+
+
+    @staticmethod
+    def _has_enabled_quantizers(sim):
+        for module in sim.model.modules():
+            if isinstance(module, Q.base.QuantizerBase):
+                return True
+        return False
+
+    @staticmethod
+    def _disable_activation_quantizers(sim):
+        def recursive_disable_quantizers(quantizer_list):
+            for idx, quantizer in enumerate(quantizer_list):
+                if isinstance(quantizer, (list, tuple, torch.nn.ModuleList)):
+                    recursive_disable_quantizers(quantizer)
+                else:
+                    quantizer_list[idx] = None
+
+        for module in sim.model.modules():
+            if isinstance(module, BaseQuantizationMixin):
+                recursive_disable_quantizers(module.input_quantizers)
+                recursive_disable_quantizers(module.output_quantizers)
+
+    @staticmethod
+    def _disable_param_quantizers(sim):
+        for module in sim.model.modules():
+            if isinstance(module, BaseQuantizationMixin):
+                for name, _ in module.param_quantizers.items():
+                    module.param_quantizers[name] = None
+
+
+
+# The number of samples to be used for performance evaluation and AMP.
+# NOTE: None means "all".
+DEFAULT_NUM_SAMPLES_FOR_AMP_PHASE_1 = EvalCallbackFactory._DEFAULT_SQNR_NUM_SAMPLES
+DEFAULT_NUM_SAMPLES_FOR_AMP_PHASE_2 = None
+
+
+
    
+[docs]
+class AutoQuantWithAutoMixedPrecision:
+    """
+    Integrate and apply post-training quantization techniques.
+
+    AutoQuant includes 1) batchnorm folding, 2) cross-layer equalization,
+    3) Adaround, and 4) Automatic Mixed Precision (if enabled).
+    These techniques will be applied in a best-effort manner until the model
+    meets the evaluation goal given as allowed_accuracy_drop.
+    """
+    def __init__( # pylint: disable=too-many-arguments, too-many-function-args
+            self,
+            model: torch.nn.Module,
+            dummy_input: Union[torch.Tensor, Tuple],
+            data_loader: DataLoader,
+            eval_callback: Callable[[torch.nn.Module], float],
+            param_bw: int = 8,
+            output_bw: int = 8,
+            quant_scheme: QuantScheme = QuantScheme.post_training_tf_enhanced,
+            rounding_mode: str = 'nearest',
+            config_file: str = None,
+            results_dir: str = "/tmp",
+            cache_id: str = None,
+            strict_validation: bool = True,
+            model_prepare_required: bool = True,) -> None:
+        """
+        :param model: Model to be quantized. Assumes model is on the correct device
+        :param dummy_input: Dummy input for the model. Assumes that dummy_input is on the correct device
+        :param data_loader: A collection that iterates over an unlabeled dataset, used for computing encodings
+        :param eval_callback: Function that calculates the evaluation score
+        :param param_bw: Parameter bitwidth
+        :param output_bw: Output bitwidth
+        :param quant_scheme: Quantization scheme
+        :param rounding_mode: Rounding mode
+        :param config_file: Path to configuration file for model quantizers
+        :param results_dir: Directory to save the results of PTQ techniques
+        :param cache_id: ID associated with cache results
+        :param strict_validation: Flag set to True by default.hen False, AutoQuant will proceed with execution and handle errors internally if possible. This may produce unideal or unintuitive results.
+        :param model_prepare_required: Flag set to True by default.If False, AutoQuant will skip model prepare block in the pipeline.
+        """
+        self._auto_quant_base = AutoQuant(model,
+                                          dummy_input,
+                                          data_loader,
+                                          eval_callback,
+                                          param_bw,
+                                          output_bw,
+                                          quant_scheme,
+                                          rounding_mode,
+                                          config_file,
+                                          results_dir,
+                                          cache_id,
+                                          strict_validation,
+                                          model_prepare_required)
+        self._data_loader = data_loader
+        self._amp_args = None
+
+
    
+[docs]
+    def run_inference(self) -> Tuple[QuantizationSimModel, float]:
+        '''
+        Creates a quantization model and performs inference
+
+        :return: QuantizationSimModel, model accuracy as float
+        '''
+        return self._auto_quant_base.run_inference()
    
+
+
+
    
+[docs]
+    def optimize(self, allowed_accuracy_drop: float = 0.0)\
+            -> Tuple[torch.nn.Module, float, str, ParetoFrontType]:
+        """
+        Integrate and apply post-training quantization techniques.
+
+        :param allowed_accuracy_drop: Maximum allowed accuracy drop
+        :return: Tuple of  (best model, eval score, encoding path, pareto front).
+            Pareto front is None if AMP is not enabled or AutoQuant exits
+            without performing AMP.
+        """
+        html_template_file = os.path.join(
+            os.path.dirname(os.path.abspath(__file__)),
+            "auto_quant_diagnostics_template_with_amp.html",
+        )
+        with patch.object(_EvalManager, "HTML_TEMPLATE_FILE", html_template_file):
+            result = self._auto_quant_base._optimize_helper(self._optimize_main,
+                                                            allowed_accuracy_drop)
+            return result["model"],\
+                   result["accuracy"],\
+                   result["encoding_path"],\
+                   result["pareto_list"]
    
+
+
+
    
+[docs]
+    def set_adaround_params(self, adaround_params: AdaroundParameters) -> None:
+        """
+        Set Adaround parameters.
+        If this method is not called explicitly by the user, AutoQuant will use
+        `data_loader` (passed to `__init__`) for Adaround.
+
+        :param adaround_params: Adaround parameters.
+        """
+        return self._auto_quant_base.set_adaround_params(adaround_params)
    
+
+
+
    
+[docs]
+    def set_export_params(self,
+                          onnx_export_args: OnnxExportApiArgs = -1,
+                          propagate_encodings: bool = None) -> None:
+        """
+        Set parameters for QuantizationSimModel.export.
+
+        :param onnx_export_args: optional export argument with onnx specific overrides
+                if not provide export via torchscript graph
+        :param propagate_encodings: If True, encoding entries for intermediate ops
+                (when one PyTorch ops results in multiple ONNX nodes) are filled with
+                the same BW and data_type as the output tensor for that series of ops.
+        """
+        return self._auto_quant_base.set_export_params(onnx_export_args, propagate_encodings)
    
+
+
+
    
+[docs]
+    def set_mixed_precision_params(
+            self,
+            candidates: List[CANDIDATE_WITH_DTYPE],
+            num_samples_for_phase_1: Optional[int] = DEFAULT_NUM_SAMPLES_FOR_AMP_PHASE_1,
+            forward_fn: Callable = _default_forward_fn,
+            num_samples_for_phase_2: Optional[int] = DEFAULT_NUM_SAMPLES_FOR_AMP_PHASE_2,
+    ) -> None:
+        """
+        Set mixed precision parameters.
+        NOTE: Automatic mixed precision will NOT be enabled unless this method
+        is explicitly called by the user.
+
+        :param candidates: List of tuples of candidate bitwidths and datatypes.
+        :param num_samples_for_phase_1: Number of samples to be used for performance
+                evaluation in AMP phase 1.
+        :param forward_fn: Function that runs forward pass and returns the output tensor.
+                which will be used for SQNR compuatation in phase 1.
+                This function is expected to take 1) a model and 2) a single batch
+                yielded from the data loader, and return a single torch.Tensor object
+                which represents the output of the model.
+                The default forward function is roughly equivalent to
+                ``lambda model, batch: model(batch)``
+        :param num_samples_for_phase_2: Number of samples to be used for performance
+                evaluation in AMP phase 2.
+        """
+        if len(candidates) < 2:
+            raise ValueError(f"AMP requires at least two candidates. Got {len(candidates)}.")
+
+        baseline_param_bw = self._auto_quant_base._quantsim_params["param_bw"]
+        baseline_output_bw = self._auto_quant_base._quantsim_params["output_bw"]
+        baseline_candidate = (
+            (baseline_output_bw, QuantizationDataType.int),
+            (baseline_param_bw, QuantizationDataType.int),
+        )
+
+        if baseline_candidate not in candidates:
+            raise ValueError(
+                f"AMP candidate must contain W{baseline_param_bw}A{baseline_output_bw}, "
+                "which was passed to the constructor of AutoQuant as `param_bw` and `output_bw`."
+            )
+
+        for candidate in candidates:
+            ((output_bw, output_dtype), (param_bw, param_dtype)) = candidate
+
+            if output_dtype != param_dtype:
+                raise ValueError(
+                    "The data types of parameters and outputs should be the same. "
+                    f"Got {output_dtype} output and {param_dtype} for parameter."
+                )
+
+            if output_dtype == QuantizationDataType.float:
+                continue
+
+            # The param/output_bw passed to the constructor of AutoQuant
+            # must be the baseline-bitwidth candidate among all AMP candidates.
+            if output_bw < baseline_output_bw or param_bw < baseline_param_bw:
+                raise ValueError(
+                    "All AMP candidates should be strictly superior to the baseline "
+                    f"W{baseline_param_bw}A{baseline_output_bw}, which was passed "
+                    "to the constructor of AutoQuant. Please make sure that all the INT candidates "
+                    f"satisfy param_bw >= {baseline_param_bw} and output_bw >= {baseline_param_bw}."
+                )
+
+        factory = EvalCallbackFactory(self._data_loader, forward_fn=forward_fn)
+        sqnr_eval_callback = factory.sqnr(num_samples_for_phase_1)
+
+        candidates = [AmpCandidate(candidate) for candidate in set(candidates)]
+
+        self._amp_args = _MixedPrecisionArgs(
+            candidates=candidates,
+            forward_pass_callback=CallbackFunc(self._auto_quant_base.forward_pass_callback, None),
+            eval_callback_for_phase1=sqnr_eval_callback,
+            eval_callback_for_phase2=CallbackFunc(self._auto_quant_base.eval_callback,
+                                                  num_samples_for_phase_2),
+        )
    
+
+
+
    
+[docs]
+    def set_model_preparer_params(
+            self,
+            modules_to_exclude: List[torch.nn.Module] = None,
+            concrete_args: Optional[Dict[str, Any]] = None,
+    ):
+        """
+        Set parameters for model preparer.
+
+        :param modules_to_exclude: List of modules to exclude when tracing.
+        :param concrete_args: Parameter for model preparer. Allows you to partially specialize
+            your function, whether it's to remove control flow or data structures. If the
+            model has control flow, torch.fx won't be able to trace the model. Check
+            torch.fx.symbolic_trace API in detail.
+        """
+        return self._auto_quant_base.set_model_preparer_params(modules_to_exclude, concrete_args)
    
+
+
+
    
+[docs]
+    def get_quant_scheme_candidates(self) -> Tuple[_QuantSchemePair, ...]:
+        """
+        Return the candidates for quant scheme search.
+        During :meth:`~AutoQuant.optimize`, the candidate with the highest accuracy
+        will be selected among them.
+
+        :return: Candidates for quant scheme search
+        """
+        return self._auto_quant_base.get_quant_scheme_candidates()
    
+
+
+
    
+[docs]
+    def set_quant_scheme_candidates(self, candidates: Tuple[_QuantSchemePair, ...]):
+        """
+        Set candidates for quant scheme search.
+        During :meth:`~AutoQuant.optimize`, the candidate with the highest accuracy
+        will be selected among them.
+
+        :param candidates: Candidates for quant scheme search
+        """
+        return self._auto_quant_base.set_quant_scheme_candidates(candidates)
    
+
+
+    # @cache.mark("mixed_precision")
+    def _apply_mixed_precision(
+            self,
+            model: torch.nn.Module,
+            dummy_input: Union[torch.Tensor, Tuple],
+            target_acc: float,
+            amp_args: _MixedPrecisionArgs,
+            results_dir: str,
+            encoding_path: str = None,
+    ) -> _MixedPrecisionResult:
+        """
+        Apply mixed-precision and return the highest accuracy.
+
+        NOTE1: Input model is not mutated.
+        NOTE2: Parameter `clean_start` is always set to True.
+
+        :param model: Model to apply mixed precision.
+        :param dummy_input: Dummy input to the model.
+        :param target_acc: Minimum evaluation score required.
+        :param encoding_path: Path to parameter encodings file.
+        :param results_dir: Directory to save the results of AdaRound and mixed precision.
+        :return: MixedPrecisionAlgo object.
+        """
+        if not amp_args:
+            raise RuntimeError
+
+        sim = self._auto_quant_base._create_quantsim_and_encodings(model,
+                                                                   encoding_path=encoding_path)
+
+        with _mock_v1_quantizers(sim):
+            algo = GreedyMixedPrecisionAlgo(
+                sim,
+                dummy_input,
+                amp_args.candidates,
+                amp_args.eval_callback_for_phase1,
+                amp_args.eval_callback_for_phase2,
+                results_dir=results_dir,
+                clean_start=True,
+                forward_pass_callback=amp_args.forward_pass_callback
+            )
+
+            # Find baseline accuracy and bw corresponding to baseline accuracy
+            algo.set_baseline(fp32_accuracy=self._auto_quant_base._fp32_acc)
+            allowed_accuracy_drop = algo.fp32_accuracy - target_acc
+
+            algo.run(allowed_accuracy_drop)
+
+            sensitivity_plot = None
+            if algo.accuracy_list is not None:
+                # Visualize quantizer group sensitivity
+                sensitivity_plot = create_sensitivity_plot(algo.accuracy_list,
+                                                        algo.baseline_candidate,
+                                                        algo.fp32_accuracy)
+            pareto_plot = None
+            if algo.pareto_list is not None:
+                # Create pareto list curve
+                pareto_plot = create_pareto_curve(algo.pareto_list)
+
+            return _MixedPrecisionResult(algo.pareto_list,
+                                        algo._sim,
+                                        algo._final_eval_score,
+                                        sensitivity_plot,
+                                        pareto_plot)
+
+    def _optimize_main(self, fp32_model: torch.nn.Module, target_acc: float) -> Dict[str, Any]:
+        """
+        Helper function of apply().
+
+        :param fp32_model: Model to apply PTQ techniques.
+        :param target_acc: Target eval score.
+        :return: The best ptq result as a dictionary.
+        """
+        # pylint: disable=broad-except, too-many-locals, too-many-statements, too-many-branches
+
+        if self._amp_args:
+            candidates = copy.copy(self._amp_args.candidates)
+        else:
+            candidates = []
+
+        eval_manager = self._auto_quant_base.eval_manager
+        dummy_input = self._auto_quant_base.dummy_input
+        results_dir = self._auto_quant_base.results_dir
+        strict_validation = eval_manager._strict_validation
+
+        sess = eval_manager.session("")
+        _multiconfig_adaround_fn = _adaround_wrapper(self._auto_quant_base._apply_adaround,
+                                                     self._auto_quant_base,
+                                                     candidates,
+                                                     target_acc,
+                                                     sess.eval)
+        sess_eval_fn = _EvalSession.eval
+        def eval_fn(_, model, param_bw=None, output_bw=None, **kwargs):
+            if param_bw == 32:
+                # For W32 evaluation, use the highest output bitwidth
+                # among all the AMP candidates
+                output_bitwidths = [
+                    output_bw for (output_bw, output_dtype), _ in candidates
+                    if output_dtype == QuantizationDataType.int
+                ]
+                output_bitwidths.append(self._auto_quant_base._quantsim_params["output_bw"])
+                output_bw = max(output_bitwidths)
+            return sess_eval_fn(_, model, param_bw=param_bw, output_bw=output_bw, **kwargs)
+
+        with patch.object(self._auto_quant_base, "_apply_adaround", _multiconfig_adaround_fn),\
+                patch.object(_EvalSession, "eval", eval_fn):
+            try:
+                result = self._auto_quant_base._optimize_main(fp32_model, target_acc)
+
+                # Automatic Mixed Precision
+                result["pareto_list"] = None
+
+                # An empty `result` dict means AutoQuant early-exited
+                # because W32 eval score didn't meet the target accuracy.
+                # In this case, do not proceed to AMP and exit immediately.
+                if result["model"] is None and\
+                        result["accuracy"] is None and\
+                        result["encoding_path"] is None and\
+                        result["applied_techniques"] is None:
+                    return result
+
+                if result["accuracy"] >= target_acc or not self._amp_args:
+                    return result
+
+                if len(candidates) < 2:
+                    _logger.info(
+                        "After Adaround, we have only one Adarond-compatible candidate left for AMP (W%dA%d). "
+                        "Return without proceeding to AMP", candidates[0].param_bw, candidates[0].output_bw
+                    )
+                    return result
+
+                model = result["model"]
+                applied_techniques = result["applied_techniques"]
+                # Freeze weight encoding to adaround weight encoding
+                encoding_path = result["encoding_path"] if "adaround" in applied_techniques else None
+            except Exception:
+                if strict_validation:
+                    raise
+                result = {}
+                model = fp32_model
+                applied_techniques = []
+                encoding_path = None
+
+            amp_args = copy.copy(self._amp_args)
+            if amp_args:
+                amp_args.candidates = candidates
+
+        with eval_manager.session("Automatic Mixed Precision", ptq=True) as sess:
+            amp_result = self._apply_mixed_precision(
+                model, dummy_input, target_acc, amp_args, results_dir, encoding_path=encoding_path
+            )
+            result["pareto_list"] = amp_result.pareto_list
+
+            if amp_result.sensitivity_plot is not None:
+                sess.diagnostics.add(amp_result.sensitivity_plot)
+
+            if amp_result.pareto_plot is not None:
+                sess.diagnostics.add(amp_result.pareto_plot)
+
+            sess.set_ptq_result(sim=amp_result.sim, acc=amp_result.final_eval_score,
+                                applied_techniques=[*applied_techniques, "automatic_mixed_precision"])
+
+        best_result = eval_manager.get_best_ptq_result()
+        if best_result:
+            if "automatic_mixed_precision" not in best_result.applied_techniques:
+                sess.result["effective"] = False
+            if best_result.accuracy >= target_acc:
+                sess.result["target_satisfied"] = True
+            result.update(best_result.as_dict())
+            return result
+
+        raise RuntimeError("None of batchnorm folding, CLE, or Adaround "
+                           "has been finished successfully.")
    
+
+
+
+
+def _adaround_wrapper(apply_adaround_fn: Callable,
+                      auto_quant: AutoQuantBase,
+                      amp_candidates: List[AmpCandidate],
+                      target_acc: float,
+                      eval_fn: Callable):
+    @functools.wraps(apply_adaround_fn)
+    def _apply_adaround_wrapper(*args, **kwargs): # pylint: disable=too-many-locals
+        # If AMP candidates are empty (i.e. AMP is disabled),
+        # perform normal (single-round) adaround.
+        if not amp_candidates:
+            return apply_adaround_fn(*args, **kwargs)
+
+        def apply_adaround(param_bw: int):
+            _logger.info("Running Adaround with W%d", param_bw)
+
+            orig_param_bw = auto_quant._quantsim_params["param_bw"]
+            try:
+                auto_quant._quantsim_params["param_bw"] = param_bw
+                return apply_adaround_fn(*args, **kwargs)
+            finally:
+                auto_quant._quantsim_params["param_bw"] = orig_param_bw
+
+        int_candidates = [
+            candidate for candidate in amp_candidates
+            if candidate.param_dtype == QuantizationDataType.int
+        ]
+        sorted_int_candidates = sorted(int_candidates,
+                                       key=lambda candidate: (candidate.param_bw, candidate.output_bw))
+        # Run Adaround with the lowest-bitwidth candidate
+        lowest_candidate = sorted_int_candidates[0]
+        model, encoding_path = apply_adaround(param_bw=lowest_candidate.param_bw)
+
+        # If the lowest candidate is the only INT candidate, return immediately
+        if len(sorted_int_candidates) == 1:
+            return model, encoding_path
+
+        eval_score = eval_fn(model,
+                             param_bw=lowest_candidate.param_bw,
+                             output_bw=lowest_candidate.output_bw,
+                             encoding_path=encoding_path)
+        _logger.info("W%dA%d eval score after Adaround: %f",
+                     lowest_candidate.param_bw,
+                     lowest_candidate.output_bw,
+                     eval_score)
+
+        # If the lowest candidate satisfy the target accuracy, return immediately
+        if eval_score >= target_acc:
+            return model, encoding_path
+
+        # If the lowest candidate fails to meet the target accuracy,
+        # discard the lowest candidate, apply Adaround to the second-lowest candidate,
+        # and use it as the baseline for AMP.
+        second_lowest_candidate = sorted_int_candidates[1]
+
+        if second_lowest_candidate.param_bw != lowest_candidate.param_bw:
+            model = None
+            model, encoding_path = apply_adaround(param_bw=second_lowest_candidate.param_bw)
+            eval_score = eval_fn(model,
+                                 param_bw=second_lowest_candidate.param_bw,
+                                 output_bw=second_lowest_candidate.output_bw,
+                                 encoding_path=encoding_path)
+            _logger.info("W%dA%d eval score after Adaround: %f",
+                         second_lowest_candidate.param_bw,
+                         second_lowest_candidate.output_bw,
+                         eval_score)
+
+        # Only the candidates that are compatible with adaround can be used for AMP
+        adaround_compatible_amp_candidates = [
+            candidate for candidate in amp_candidates
+            if candidate.param_bw == second_lowest_candidate.param_bw or\
+                    candidate.param_dtype == QuantizationDataType.float
+        ]
+
+        # Fill in AMP candidates with Adaround-compatible candidates only
+        amp_candidates.clear()
+        amp_candidates.extend(adaround_compatible_amp_candidates)
+
+        return model, encoding_path
+
+    return _apply_adaround_wrapper
+
    +
    +
    +
    + +
    + + +
    +
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    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
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    +

    Source code for aimet_torch.v2.mixed_precision

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2024, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+# pylint: disable=all
+
+from .manual_mixed_precision import MixedPrecisionConfigurator
+from .utils import Precision, SupportedDType
+
+from aimet_torch._base.mixed_precision import choose_mixed_precision as _choose_mixed_precision
+from aimet_torch.v2.quantsim import QuantizationSimModel
+from aimet_torch.v2.amp.utils import _mock_v1_quantizers
+
+
+
    
+[docs]
+def choose_mixed_precision(sim: QuantizationSimModel, *args, **kwargs):
+    __doc__ = _choose_mixed_precision.__doc__ # pylint: disable=redefined-builtin, unused-variable
+
+    with _mock_v1_quantizers(sim):
+        return _choose_mixed_precision(sim, *args, **kwargs)
    
+
+
    +
    +
    +
    + +
    + + +
    +
    +
    +
    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
    + Made with Sphinx and @pradyunsg's + + Furo + +
    +
    +
    + + + + + + +
    +
    +
    + +
    +
    + +
    +
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    Source code for aimet_torch.v2.mixed_precision.manual_mixed_precision

    +# /usr/bin/env python
+# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2024, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+""" Manual mixed precision configurator """
+
+from typing import overload, Union, List, Tuple, Dict, get_args, Type, Optional, IO
+import torch
+
+from aimet_common.utils import AimetLogger
+from aimet_torch.v2.utils import flatten_list
+from aimet_torch.v2.mixed_precision.utils import UserRequest, RequestType, SupportedDType, ModuleProduct, broadcast_tuples
+from aimet_torch.v2.mixed_precision.manual_mixed_precision_handler import MpHandler
+from aimet_torch.v2.quantsim import QuantizationSimModel
+
+logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.Quant)
+
+
+
    
+[docs]
+class MixedPrecisionConfigurator:
+    """
+    Mixed Precision Configurator helps set up a mixed precision profile in the QuantSim object. The user is expected to
+    follow the below steps to set the sim in Mixed Precision.
+
+    1. Create QuantSim object
+    2. Create the MixedPrecisionConfigurator object by passing in the QuantSim object
+    3. Make a series of set_precision/set_model_input_precision/set_model_output_precision calls
+    4. Call apply() method by passing in the config file and strict flag
+    5. Run compute_encodings on the above QuantSim object
+    6. Export the encodings/onnx artifacts
+    """
+
+    def __init__(self, sim: QuantizationSimModel):
+        """
+        :param sim: QuantSim object
+        """
+        self._sim = sim
+        self.user_requests = []
+        self.mp_handler = MpHandler(sim)
+
+    def _store_user_request(self, request_type: RequestType, module: Union[torch.nn.Module, Type, ModuleProduct],
+                            activation: Union[List[SupportedDType], SupportedDType] = None,
+                            param: Optional[Dict[str, SupportedDType]] = None):
+        self.user_requests.append(UserRequest(request_type=request_type,
+                                              module=module,
+                                              activation=activation,
+                                              param=param))
+
+    @overload
+    def set_precision(self, module: torch.nn.Module,
+                      activation: Union[List[SupportedDType], SupportedDType],
+                      param: Optional[Dict[str, SupportedDType]] = None):
+        ...
+
+    @overload
+    def set_precision(self, module_type: Type[torch.nn.Module],
+                      activation: Union[List[SupportedDType], SupportedDType],
+                      param: Optional[Dict[str, SupportedDType]] = None):
+        ...
+
+
    
+[docs]
+    def set_precision(self, arg: Union[torch.nn.Module, Type[torch.nn.Module]],
+                      activation: Union[List[SupportedDType], SupportedDType],
+                      param: Optional[Dict[str, SupportedDType]] = None):
+        """
+        :param arg: Module can be of type torch.nn.Module or the type of the module.
+        :param activation: A string representing the activation dtype of the module input(s)
+        :param param: Dict with name of the param as key and its dtype as value
+
+        - If the 'module' is a leaf-module(the module doesnt compose of other torch.nn.module), the specified settings
+          would be applied to the module.
+        - If the 'module' is a non-leaf-module (module is composed of other torch.nn.module), the specified settings
+          would be applied to all the leaf modules in 'module'.
+        - If the 'module' is Type of module, all the modules in the model which satisfy the specified module type would
+          be set to the specified activation and param settings
+        - If the same 'module' is specified through multiple set_precision(...) calls, the latest one will be applied.
+
+        Examples: TODO
+
+        """
+        # pylint: disable=too-many-branches
+        if activation:
+            if isinstance(activation, List):
+                for act in activation:
+                    if act not in get_args(SupportedDType):
+                        raise ValueError("Supported inputs for activation are ", get_args(SupportedDType))
+            else:
+                if activation not in get_args(SupportedDType):
+                    raise ValueError("Supported inputs for activation are ", get_args(SupportedDType))
+        if param:
+            for param_name, dtype in param.items():
+                if dtype not in get_args(SupportedDType):
+                    raise ValueError(f"Supported inputs for param: {param_name} are ", get_args(SupportedDType))
+
+        if isinstance(arg, type):
+            self._store_user_request(RequestType.set_precision_by_module_type, arg, activation, param)
+        elif isinstance(arg, torch.nn.Module):
+            if arg in self._sim.model.modules():
+                self._store_user_request(RequestType.set_precision_by_module, arg, activation, param)
+            else:
+                raise ValueError(f"Specified module {arg} is not part of the sim object")
+        else:
+            raise TypeError("arg is neither a torch.nn.Module nor of Type[torch.nn.Module]")
    
+
+
+
    
+[docs]
+    def set_model_input_precision(self, activation: Union[List[Optional[SupportedDType]], Tuple[Optional[SupportedDType]], SupportedDType]):
+        """
+        Activation precision which needs to be set to the model inputs
+
+        :param activation: Activation dtypes for inputs of the model
+        """
+        broadcasted_activations = broadcast_tuples(activation, self.mp_handler.cg_traverser.model_inputs)
+        for act, model_input in zip(flatten_list(broadcasted_activations),
+                                           flatten_list(self.mp_handler.cg_traverser.model_inputs)):
+            if act:
+                if act not in get_args(SupportedDType):
+                    raise ValueError("Supported inputs for activation are ", get_args(SupportedDType))
+                self._store_user_request(RequestType.set_model_input_precision, model_input, act)
    
+
+
+
    
+[docs]
+    def set_model_output_precision(self, activation: Union[List[Optional[SupportedDType]], Tuple[Optional[SupportedDType]], SupportedDType]):
+        """
+        Activation precision which needs to be set to the model outputs
+
+        :param activation: Activation dtypes for outputs of the model
+        """
+        broadcasted_activations = broadcast_tuples(activation, self.mp_handler.cg_traverser.model_outputs)
+        for act, model_output in zip(flatten_list(broadcasted_activations),
+                                            flatten_list(self.mp_handler.cg_traverser.model_outputs)):
+            if act:
+                if act not in get_args(SupportedDType):
+                    raise ValueError("Supported inputs for activation are ", get_args(SupportedDType))
+                self._store_user_request(RequestType.set_model_output_precision, model_output, act)
    
+
+
+    @overload
+    def apply(self, log_file: str = './mmp_log.txt', strict: bool = True):
+        ...
+
+    @overload
+    def apply(self, log_file: IO, strict: bool = True):
+        ...
+
+
+
    
+[docs]
+    def apply(self, log_file: Optional[Union[IO, str]] = './mmp_log.txt', strict: bool = True):
+        """
+        Apply the mp settings specified through the set_precision/set_model_input_precision/set_model_output_precision
+        calls to the QuantSim object
+
+        :param log_file: log_file to store the logs. log_file can either be a string representing the path or the IO
+                        object to write the logs into.
+        :param strict: Boolean flag to indicate whether to fail (strict=True) on incorrect/conflicting inputs made by
+                       the user or (strict=False) take a best-effort approach to realize the MP settings
+        """
+        if isinstance(log_file, str):
+            with open(log_file, 'w') as f:
+                self.mp_handler.apply(f, self.user_requests, strict)
+        else:
+            self.mp_handler.apply(log_file, self.user_requests, strict)
+
+        self.user_requests.clear()
    
+
    
+
+
    +
    +
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    + +
    + + +
    +
    +
    +
    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
    + Made with Sphinx and @pradyunsg's + + Furo + +
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    +
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    + +
    +
    + +
    +
    + + + + + + + + + \ No newline at end of file diff --git a/releases/2.0.0/_modules/aimet_torch/v2/nn/true_quant.html b/releases/2.0.0/_modules/aimet_torch/v2/nn/true_quant.html new file mode 100644 index 0000000..aec4703 --- /dev/null +++ b/releases/2.0.0/_modules/aimet_torch/v2/nn/true_quant.html @@ -0,0 +1,2791 @@ + + + + + + + + aimet_torch.v2.nn.true_quant - AIMET + + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    +

    Source code for aimet_torch.v2.nn.true_quant

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2024, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+# pylint: disable=too-many-lines, wrong-import-order, redefined-builtin
+""" Quantized modules"""
+
+from packaging import version
+import contextlib
+import itertools
+from inspect import signature
+from abc import abstractmethod, ABCMeta
+from collections import OrderedDict
+from typing import Type, Any, Optional, Callable, Dict
+from weakref import WeakKeyDictionary
+import warnings
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import Tensor
+from torch.overrides import BaseTorchFunctionMode, get_overridable_functions
+from torch._VF import ( # pylint: disable=no-name-in-module
+    gru as _gru,
+    gru_cell as _gru_cell,
+    lstm as _lstm,
+    lstm_cell as _lstm_cell,
+    rnn_relu as _rnn_relu,
+    rnn_tanh as _rnn_tanh,
+    rnn_relu_cell as _rnn_relu_cell,
+    rnn_tanh_cell as _rnn_tanh_cell,
+)
+
+from aimet_torch.v2.quantization.base import QuantizerBase
+from aimet_torch.v2.quantization.tensor import QuantizedTensorBase
+from aimet_torch.v2.utils import patch_attr, _ContextManager, allow_recompute
+from .base import BaseQuantizationMixin # pylint: disable=import-error
+
+
+def _quantize_if_applicable(data: Any, quantizer: Optional[QuantizerBase]):
+    """
+    Quantize data if it is a quantizable type and quantize is not None
+    """
+    if quantizer and isinstance(data, Tensor) and data.is_floating_point():
+        if isinstance(data, QuantizedTensorBase):
+            data = data.dequantize()
+        return quantizer(data)
+
+    if isinstance(data, QuantizedTensorBase):
+        return data.quantize()
+
+    return data
+
+
+def _dequantize_if_applicable(data: torch.Tensor):
+    return data.dequantize() if isinstance(data, QuantizedTensorBase) else data
+
+
+def _quantize_dequantize_if_applicable(data, quantizer):
+    if quantizer and isinstance(data, Tensor) and data.is_floating_point():
+        if isinstance(data, QuantizedTensorBase):
+            data = data.dequantize()
+        data = quantizer(data)
+
+    if isinstance(data, QuantizedTensorBase):
+        return data.dequantize()
+
+    return data
+
+
+_QUANTIZED_MODULES_UNDER_COMPUTE_ENCODINGS = WeakKeyDictionary()
+
+
+def _is_computing_encodings(qmodule):
+    return _QUANTIZED_MODULES_UNDER_COMPUTE_ENCODINGS.get(qmodule, 0) > 0
+
+
+def _enter_computing_encodings(qmodule):
+    if qmodule not in _QUANTIZED_MODULES_UNDER_COMPUTE_ENCODINGS:
+        _QUANTIZED_MODULES_UNDER_COMPUTE_ENCODINGS[qmodule] = 0
+    _QUANTIZED_MODULES_UNDER_COMPUTE_ENCODINGS[qmodule] += 1
+
+
+def _exit_compute_encodings(qmodule):
+    assert _QUANTIZED_MODULES_UNDER_COMPUTE_ENCODINGS[qmodule] > 0
+    _QUANTIZED_MODULES_UNDER_COMPUTE_ENCODINGS[qmodule] -= 1
+
+
+class QuantizationMixinMeta(ABCMeta):
+    """Sets :meth:`forward` to :meth:`quantized_forward` if only :meth:`quantized_forward` is defined
+    """
+
+    def __new__(mcs, name, bases, namespace, **kwargs):
+        if "quantized_forward" in namespace and "forward" not in namespace:
+            warnings.warn("Support for defining `quantized_forward` in place of `forward` method will be deprecated, "
+                          "please use `forward` instead.",
+                          DeprecationWarning, stacklevel=2)
+            namespace["forward"] = namespace["quantized_forward"]
+        return super().__new__(mcs, name, bases, namespace, **kwargs)
+
+
+
    
+[docs]
+class QuantizationMixin(BaseQuantizationMixin, metaclass=QuantizationMixinMeta): # pylint: disable=abstract-method
+    """Quantization mixin class for torch.nn.Module.
+
+    Specifically, a quantized module will quantize input, output, and parameter tensors with
+    its held :class:`QuantizerBase` objects during the :meth:`forward` method and use the inherited :class:`torch.nn.Module`
+    forward method to compute the layer operation. If all input, output, and parameter quantizers are ``None``, a
+    quantized module will behave exactly the same as its parent :class:`torch.nn.Module`.
+
+    Attributes:
+        input_quantizers: :class:`torch.nn.ModuleList` containing :class:`QuantizerBase` objects to be applied
+            to the layer's input tensors
+        output_quantizers: :class:`torch.nn.ModuleList` containing :class:`QuantizerBase` objects to be applied
+            to the layer's output tensors
+        param_quantizers: :class:`torch.nn.ModuleDict` mapping parameter names to associated :class:`QuantizerBase`
+            objects
+
+    Examples:
+
+        >>> qlinear = QuantizedLinear(in_features=10, out_features=10)
+        >>> print(qlinear)
+        QuantizedLinear(
+          in_features=10, out_features=10, bias=True
+          (param_quantizers): ModuleDict(
+            (weight): None
+            (bias): None
+          )
+          (input_quantizers): ModuleList(
+            (0): None
+          )
+          (output_quantizers): ModuleList(
+            (0): None
+          )
+        )
+
+    """
+
+    cls_to_qcls = OrderedDict()  # quantized class -> original class
+    qcls_to_cls = OrderedDict()  # original class -> quantized class
+
+    _default_kernel: Optional[Callable] = None
+    _kernels = WeakKeyDictionary()  # instance -> instance_kernel
+
+
    
+[docs]
+    @abstractmethod
+    def forward(self, *args, **kwargs):
+        """Computes a quantized version of the parent module's forward method.
+
+        The :meth:`forward` method should perform the following logic in order:
+
+            1) Apply existing input quantizers to input tensors
+            2) Apply existing param quantizers to the layer's parameters
+            3) Call the inherited :class:`torch.nn.Module` forward method with quantized inputs and parameters
+            4) Apply existing output quantizers to the outputs of the forward method
+
+        If all input, output, and parameter quantizers are ``None``, this method will behave exactly the same as
+        its parent module's forward pass.
+        """
+        return super().forward(*args, **kwargs)
    
+
+
+
    
+[docs]
+    @classmethod
+    def set_default_kernel(cls, kernel: Callable):
+        """Set default kernel for the class.
+
+        In general, this signature will follow the signature of the equivalent :mod:`torch.nn.functional` function,
+        but should return a :class:`QuantizedTensor` object and take in the additional keyword argument ``output_encodings``.
+
+        Once set, all instances of cls will call into kernel in the forward pass unless:
+
+            1) The instance is within the :meth:`compute_encodings` context, or
+            2) The kernel has been overridden by a :meth:`set_kernel` call
+
+        Args:
+            kernel: Callable object to be used as the default kernel by all the instances of this class.
+
+
+        Example:
+
+            >>> from aimet_torch.v2 import quantization as Q
+            >>> def int_multiply(a, b, output_encodings=None):
+            ...     encodings = [a.encoding, b.encoding, output_encodings]
+            ...     if not all(enc.mapping == "affine" for enc in encodings):
+            ...             raise NotImplementedError
+            ...     q_output = (a.quantized_repr() + a.encoding.offset) * (b.quantized_repr() + b.encoding.offset)
+            ...     dq_output = q_output *  (a.encoding.scale * b.encoding.scale)
+            ...     return Q.QuantizedTensor(output_encodings.quantize(dq_output), encoding=output_encodings)
+            ...
+            >>> QuantizedMultiply.set_default_kernel(int_multiply)
+            >>> qmult = QuantizedMultiply()
+            >>> qmult.get_kernel()
+            <function int_multiply at ...>
+
+        """
+        cls._default_kernel = kernel
    
+
+
+
    
+[docs]
+    @classmethod
+    def get_default_kernel(cls) -> Optional[Callable]:
+        """Return the default kernel of the class
+
+        Returns:
+            Default kernel of the class. None if the default kernel is not set.
+
+        """
+        return cls._default_kernel
    
+
+
+
    
+[docs]
+    def set_kernel(self, kernel: Callable):
+        """Set kernel for this instance of quantized module.
+
+        In general, this signature will follow the signature of the equivalent :mod:`torch.nn.functional` function,
+        but should return a :class:`QuantizedTensor` object and take in the additional keyword argument ``output_encodings``.
+
+        Once set, the layer will call into ``kernel`` in the forward pass unless within the :meth:`compute_encodings`
+        context.
+
+        Args:
+            kernel: Callable object to be used as the underlying kernel.
+
+        Example:
+
+            >>> from aimet_torch.v2 import quantization as Q
+            >>> def int_multiply(a, b, output_encodings=None):
+            ...     encodings = [a.encoding, b.encoding, output_encodings]
+            ...     if not all(enc.mapping == "affine" for enc in encodings):
+            ...             raise NotImplementedError
+            ...     q_output = (a.quantized_repr() + a.encoding.offset) * (b.quantized_repr() + b.encoding.offset)
+            ...     dq_output = q_output *  (a.encoding.scale * b.encoding.scale)
+            ...     return Q.QuantizedTensor(output_encodings.quantize(dq_output), encoding=output_encodings)
+            ...
+            >>> qmult = QuantizedMultiply()
+            >>> qmult.set_kernel(int_multiply)
+
+        """
+        QuantizationMixin._kernels[self] = kernel
    
+
+
+
    
+[docs]
+    def get_kernel(self) -> Optional[Callable]:
+        """Return the kernel to be used by this instance of quantized module.
+
+        If the current instance does not have any kernel set, it will retrieve the default kernel of the class.
+
+        Returns:
+            The kernel to be used by this instance.
+
+        """
+        if self in QuantizationMixin._kernels:
+            return QuantizationMixin._kernels[self]
+        return self.get_default_kernel()
    
+
+
+
    
+[docs]
+    @contextlib.contextmanager
+    def compute_encodings(self):  # pylint: disable=missing-function-docstring
+        ctx = _ContextManager(action=lambda: _enter_computing_encodings(self),
+                              cleanup=lambda: _exit_compute_encodings(self))
+        with super().compute_encodings(), ctx:
+            yield
    
+
+
+    def _patch_dequantized_parameters(self):
+        stack = contextlib.ExitStack()
+        for param_name, _ in self.param_quantizers.items():
+            qparam = getattr(self, param_name)
+            ctx = patch_attr(self, param_name, _dequantize_if_applicable(qparam))
+            stack.enter_context(ctx)
+
+        return stack
+
+
    
+[docs]
+    @classmethod
+    def wrap(cls, module_cls: Type[nn.Module]) -> Type[nn.Module]:
+        """
+        Wrap a regular module class into a quantized module class
+        """
+        if not issubclass(module_cls, nn.Module):
+            raise ValueError("Expected module_cls to be a subclass of torch.nn.Module. "
+                             f"Got {module_cls}.")
+        if module_cls in cls.cls_to_qcls:
+            return cls.cls_to_qcls[module_cls]
+
+        quantized_cls_name = f"Quantized{module_cls.__name__}"
+        base_classes = (cls, module_cls)
+        quantized_cls = type(quantized_cls_name, base_classes, {'__module__': __name__})
+        return cls.implements(module_cls)(quantized_cls)
    
+
+
+
    
+[docs]
+    @classmethod
+    def from_module(cls, module: nn.Module):
+        r"""Create an instance of quantized module from a regular module instance.
+
+        The resulting quantized module contains the same attributes and parameters as the original module, but may
+        be assigned input, output and parameter quantizers.
+
+        :param module: Floating point module to quantize
+        :return: Quantized version of the original module
+
+        Example:
+
+            >>> linear = torch.nn.Linear(10, 10)
+            >>> quantized_linear = QuantizationMixin.from_module(linear)
+            >>> print(quantized_linear.param_quantizers)
+            QuantizedLinear(
+              in_features=10, out_features=10, bias=True
+              (param_quantizers): ModuleDict(
+                (weight): None
+                (bias): None
+              )
+              (input_quantizers): ModuleList(
+                (0): None
+              )
+              (output_quantizers): ModuleList(
+                (0): None
+              )
+            )
+            >>> print(quantized_linear.weight is linear.weight)
+            True
+        """
+        return super().from_module(module)
    
+
+
+
    
+[docs]
+    @classmethod
+    def implements(cls, module_cls):
+        r"""
+        Decorator for registering quantized definition of the given base class.
+
+        Even though AIMET supports quantization of all built-in modules in torch.nn subpackage
+        such as ``torch.nn.Conv2d`` or ``torch.nn.Linear`` that AIMET is already aware of,
+        :class:`QuantizationSimModel` will throw a runtime error when it encounters custom modules
+        defined by the users, asking the users to provide the quantized definition of the custom modules
+        that AIMET doesn't know of.
+
+        To declare the quantized definition of a module, :class:`QuantizationSimModel` requires you
+        to define a subclass of your module decorated with :meth:`implements`,
+        in which you will implement ``__quant_init__`` and ``forward`` methods.
+
+        As an example, given a custom module as below::
+
+            class MaskedAdd(torch.nn.Module):
+               def forward(self, input: torch.Tensor, mask: torch.Tensor, value: torch.Tensor):
+                   return input + mask * value
+
+        its quantized definition should be declared before creating :class:`QuantizationSimModel`, typically as below::
+
+
+            @QuantizationMixin.implements(MaskedAdd)
+            class QuantizedMaskedAdd(QuantizationMixin, MaskedAdd):
+                # The quantized definition of MaskedAdd should be a subclass of
+                # QuantizationMixin and MaskedAdd (Order matters!)
+                def __quant_init__(self):
+                    super().__quant_init__()
+
+                    # Declare the number of input/output quantizers
+                    self.input_quantizers = torch.nn.ModuleList([None, None, None])
+                    self.output_quantizers = torch.nn.ModuleList([None])
+
+               def forward(self, input: torch.Tensor, mask: torch.Tensor, value: torch.Tensor):
+                   input_qtzr  = self.input_quantizers[0]
+                   _           = self.input_quantizers[1] # I don't want to quantize the boolean masks!
+                   value_qtzr  = self.input_quantizers[2]
+                   output_qtzr = self.output_quantizers[0]
+
+                   if input_qtzr is not None:
+                       input = input_qtzr(input)
+
+                   if value_qtzr is not None:
+                       value = value_qtzr(value)
+
+                   output = super().forward(input, mask, value)
+
+                   if output_qtzr is not None:
+                       output = output_qtzr(output)
+
+                   return output
+        """
+        return super().implements(module_cls)
    
+
    
+
+
+
+# pylint: disable=too-many-ancestors
+
+
+_dispatch_table: Dict[Callable, Optional[Callable]]
+_dispatch_table = {
+    torch_fn: None
+    for torch_fn in itertools.chain(*get_overridable_functions().values())
+}
+
+# NOTE: ``torch.overrides.get_overridable_functions()`` doesn't include
+#       F.hardswish, F.hardsigmoid, or Tensor.unflatten, even though
+#       they are implemented in a perfectly dispatchable manner.
+_dispatch_table[F.hardswish] = None
+_dispatch_table[F.hardsigmoid] = None
+_dispatch_table[Tensor.unflatten] = None
+
+
+class _Dispatcher(BaseTorchFunctionMode):
+    def __torch_function__(self, func, types, args=(), kwargs=None):
+        impl = _dispatch_table.get(func, None)
+
+        if impl is None:
+            impl = func
+
+        return super().__torch_function__(impl, types, args, kwargs)
+
+
+@contextlib.contextmanager
+def _dispatch(torch_func: Callable, custom_impl: Callable):
+    try:
+        orig = _dispatch_table[torch_func]
+    except KeyError as e:
+        raise RuntimeError(f"PyTorch doesn't support overriding {torch_func}") from e
+
+    try:
+        _dispatch_table[torch_func] = custom_impl
+
+        with _Dispatcher():
+            yield
+    finally:
+        _dispatch_table[torch_func] = orig
+
+
+class _DispatchMeta(QuantizationMixinMeta):
+    def __new__(mcs, name, bases, namespace, **kwargs):
+        """
+        Sanity check for class definitions of dispatch-based quantized modules
+        """
+        if '_builtin_torch_fn' in namespace:
+            torch_fn = namespace['_builtin_torch_fn']
+            if torch_fn and torch_fn not in _dispatch_table:
+                raise RuntimeError(f"PyTorch doesn't support overriding {torch_fn}")
+        return super().__new__(mcs, name, bases, namespace, **kwargs)
+
+
+class _DispatchMixin(metaclass=_DispatchMeta):
+    _builtin_torch_fn: Optional[Callable] = None
+
+    def _get_builtin_torch_fn(self):
+        return type(self)._builtin_torch_fn
+
+    def forward(self, *args, **kwargs):  # pylint: disable=missing-function-docstring
+        kernel = self.get_kernel()
+        builtin_torch_fn = self._get_builtin_torch_fn()
+
+        if not kernel or _is_computing_encodings(self):
+            kernel = self._builtin_torch_fn_helper(builtin_torch_fn)
+        else:
+            kernel = self._custom_kernel_helper(kernel)
+
+        with self._patch_quantized_parameters():
+            with _dispatch(builtin_torch_fn, kernel):
+                output = super().forward(*args, **kwargs)
+
+        return _dequantize_if_applicable(output)
+
+    def _builtin_torch_fn_helper(self, fn: Callable[..., Tensor]):
+        def wrapper(*args, **kwargs):
+            qtzd_args = (
+                _quantize_dequantize_if_applicable(x, qtzr)
+                for x, qtzr in zip(args, self.input_quantizers)
+            )
+            others = (
+                _dequantize_if_applicable(x)
+                for x in args[len(self.input_quantizers):]
+            )
+            kwargs = {
+                key: _dequantize_if_applicable(value)
+                for key, value in kwargs.items()
+            }
+
+            output = fn(*qtzd_args, *others, **kwargs)
+
+            return _quantize_dequantize_if_applicable(output, self.output_quantizers[0])
+
+        return wrapper
+
+    def _custom_kernel_helper(self, fn: Callable[..., QuantizedTensorBase]):
+        def wrapper(*args, **kwargs):
+            qtzd_args = (
+                _quantize_if_applicable(x, qtzr)
+                for x, qtzr in zip(args, self.input_quantizers)
+            )
+            others = args[len(self.input_quantizers):]
+
+            output_encodings = self.output_quantizers[0].get_encodings() if self.output_quantizers[0] else None
+            kwargs.update(output_encodings=output_encodings)
+            return fn(*qtzd_args, *others, **kwargs)
+
+        return wrapper
+
+
+def __nullary__(self):
+    super(type(self), self).__quant_init__()
+    self.input_quantizers = nn.ModuleList([])
+
+
+def __unary__(self):
+    super(type(self), self).__quant_init__()
+
+
+def __binary__(self):
+    super(type(self), self).__quant_init__()
+    self.input_quantizers = nn.ModuleList([None, None])
+
+
+def __ternary__(self):
+    super(type(self), self).__quant_init__()
+    self.input_quantizers = nn.ModuleList([None, None, None])
+
+
+def _generate_docstring(parent_cls):
+    return \
+    f"""
+    Quantized subclass of torch.nn.{parent_cls.__name__}
+
+    .. method:: forward{str(signature(parent_cls.forward))}
+        :noindex:
+
+        Quantized forward of torch.nn.{parent_cls.__name__}.
+
+        The input(s), parameter(s) (if any), and output(s) will be quantized with
+        ``self.input_quantizers``, ``self.param_quantizers``, and ``self.output_quantizers`` respectively.
+
+        For more information, see :class:`QuantizationMixin`.
+    """
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.AdaptiveAvgPool1d)
+class QuantizedAdaptiveAvgPool1d(_DispatchMixin, QuantizationMixin, nn.AdaptiveAvgPool1d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(nn.AdaptiveAvgPool1d)
+    _builtin_torch_fn = F.adaptive_avg_pool1d
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.AdaptiveAvgPool2d)
+class QuantizedAdaptiveAvgPool2d(_DispatchMixin, QuantizationMixin, nn.AdaptiveAvgPool2d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.AdaptiveAvgPool2d)
+    _builtin_torch_fn = F.adaptive_avg_pool2d
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.AdaptiveAvgPool3d)
+class QuantizedAdaptiveAvgPool3d(_DispatchMixin, QuantizationMixin, nn.AdaptiveAvgPool3d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.AdaptiveAvgPool3d)
+    _builtin_torch_fn = F.adaptive_avg_pool3d
+    __quant_init__ = __unary__
    
+
+
+
+# @QuantizationMixin.implements(nn.AdaptiveLogSoftmaxWithLoss)
+# class QuantizedAdaptiveLogSoftmaxWithLoss(_DispatchMixin, QuantizationMixin, nn.AdaptiveLogSoftmaxWithLoss):
+#     _builtin_torch_fn = ...
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.AdaptiveMaxPool1d)
+class QuantizedAdaptiveMaxPool1d(_DispatchMixin, QuantizationMixin, nn.AdaptiveMaxPool1d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.AdaptiveMaxPool1d)
+    _builtin_torch_fn = F.adaptive_max_pool1d
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.AdaptiveMaxPool2d)
+class QuantizedAdaptiveMaxPool2d(_DispatchMixin, QuantizationMixin, nn.AdaptiveMaxPool2d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.AdaptiveMaxPool2d)
+    _builtin_torch_fn = F.adaptive_max_pool2d
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.AdaptiveMaxPool3d)
+class QuantizedAdaptiveMaxPool3d(_DispatchMixin, QuantizationMixin, nn.AdaptiveMaxPool3d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.AdaptiveMaxPool3d)
+    _builtin_torch_fn = F.adaptive_max_pool3d
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.AlphaDropout)
+class QuantizedAlphaDropout(_DispatchMixin, QuantizationMixin, nn.AlphaDropout):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.AlphaDropout)
+    _builtin_torch_fn = F.alpha_dropout
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.AvgPool1d)
+class QuantizedAvgPool1d(_DispatchMixin, QuantizationMixin, nn.AvgPool1d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.AvgPool1d)
+    _builtin_torch_fn = F.avg_pool1d
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.AvgPool2d)
+class QuantizedAvgPool2d(_DispatchMixin, QuantizationMixin, nn.AvgPool2d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.AvgPool2d)
+    _builtin_torch_fn = F.avg_pool2d
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.AvgPool3d)
+class QuantizedAvgPool3d(_DispatchMixin, QuantizationMixin, nn.AvgPool3d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.AvgPool3d)
+    _builtin_torch_fn = F.avg_pool3d
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.BCELoss)
+class QuantizedBCELoss(_DispatchMixin, QuantizationMixin, nn.BCELoss):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.BCELoss)
+    _builtin_torch_fn = F.binary_cross_entropy
+    __quant_init__ = __binary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.BCEWithLogitsLoss)
+class QuantizedBCEWithLogitsLoss(_DispatchMixin, QuantizationMixin, nn.BCEWithLogitsLoss):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.BCEWithLogitsLoss)
+    _builtin_torch_fn = F.binary_cross_entropy_with_logits
+    __quant_init__ = __binary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.BatchNorm1d)
+class QuantizedBatchNorm1d(_DispatchMixin, QuantizationMixin, nn.BatchNorm1d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.BatchNorm1d)
+    _builtin_torch_fn = F.batch_norm
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.BatchNorm2d)
+class QuantizedBatchNorm2d(_DispatchMixin, QuantizationMixin, nn.BatchNorm2d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.BatchNorm2d)
+    _builtin_torch_fn = F.batch_norm
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.BatchNorm3d)
+class QuantizedBatchNorm3d(_DispatchMixin, QuantizationMixin, nn.BatchNorm3d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.BatchNorm3d)
+    _builtin_torch_fn = F.batch_norm
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.Bilinear)
+class QuantizedBilinear(_DispatchMixin, QuantizationMixin, nn.Bilinear):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.Bilinear)
+    _builtin_torch_fn = F.bilinear
+    __quant_init__ = __binary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.CELU)
+class QuantizedCELU(_DispatchMixin, QuantizationMixin, nn.CELU):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.CELU)
+    _builtin_torch_fn = F.celu
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.CTCLoss)
+class QuantizedCTCLoss(_DispatchMixin, QuantizationMixin, nn.CTCLoss):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.CTCLoss)
+    _builtin_torch_fn = F.ctc_loss
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.ChannelShuffle)
+class QuantizedChannelShuffle(_DispatchMixin, QuantizationMixin, nn.ChannelShuffle):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.ChannelShuffle)
+    _builtin_torch_fn = F.channel_shuffle
+    __quant_init__ = __unary__
    
+
+
+
+if version.parse(torch.__version__) >= version.parse("2.1.0"):
+
    
+[docs]
+    @QuantizationMixin.implements(nn.CircularPad1d)
+    class QuantizedCircularPad1d(_DispatchMixin, QuantizationMixin, nn.CircularPad1d):
+        # pylint: disable=missing-class-docstring
+        __doc__ = _generate_docstring(parent_cls=nn.CircularPad1d)
+        _builtin_torch_fn = F.pad
+        __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+    @QuantizationMixin.implements(nn.CircularPad2d)
+    class QuantizedCircularPad2d(_DispatchMixin, QuantizationMixin, nn.CircularPad2d):
+        # pylint: disable=missing-class-docstring
+        __doc__ = _generate_docstring(parent_cls=nn.CircularPad2d)
+        _builtin_torch_fn = F.pad
+        __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+    @QuantizationMixin.implements(nn.CircularPad3d)
+    class QuantizedCircularPad3d(_DispatchMixin, QuantizationMixin, nn.CircularPad3d):
+        # pylint: disable=missing-class-docstring
+        __doc__ = _generate_docstring(parent_cls=nn.CircularPad3d)
+        _builtin_torch_fn = F.pad
+        __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.ConstantPad1d)
+class QuantizedConstantPad1d(_DispatchMixin, QuantizationMixin, nn.ConstantPad1d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.ConstantPad2d)
+    _builtin_torch_fn = F.pad
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.ConstantPad2d)
+class QuantizedConstantPad2d(_DispatchMixin, QuantizationMixin, nn.ConstantPad2d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.ConstantPad2d)
+    _builtin_torch_fn = F.pad
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.ConstantPad3d)
+class QuantizedConstantPad3d(_DispatchMixin, QuantizationMixin, nn.ConstantPad3d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.ConstantPad3d)
+    _builtin_torch_fn = F.pad
+    __quant_init__ = __unary__
    
+
+
+
+# @QuantizationMixin.implements(nn.Container)
+# class QuantizedContainer(_DispatchMixin, QuantizationMixin, nn.Container):
+#     _builtin_torch_fn = ...
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.Conv1d)
+class QuantizedConv1d(_DispatchMixin, QuantizationMixin, nn.Conv1d):  # pylint: disable=too-many-ancestors
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.Conv1d)
+    _builtin_torch_fn = F.conv1d
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.Conv2d)
+class QuantizedConv2d(_DispatchMixin, QuantizationMixin, nn.Conv2d):  # pylint: disable=too-many-ancestors
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.Conv2d)
+    _builtin_torch_fn = F.conv2d
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.Conv3d)
+class QuantizedConv3d(_DispatchMixin, QuantizationMixin, nn.Conv3d):  # pylint: disable=too-many-ancestors
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.Conv3d)
+    _builtin_torch_fn = F.conv3d
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.ConvTranspose1d)
+class QuantizedConvTranspose1d(_DispatchMixin, QuantizationMixin, nn.ConvTranspose1d): # pylint: disable=too-many-ancestors
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.ConvTranspose1d)
+    _builtin_torch_fn = F.conv_transpose1d
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.ConvTranspose2d)
+class QuantizedConvTranspose2d(_DispatchMixin, QuantizationMixin, nn.ConvTranspose2d): # pylint: disable=too-many-ancestors
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.ConvTranspose2d)
+    _builtin_torch_fn = F.conv_transpose2d
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.ConvTranspose3d)
+class QuantizedConvTranspose3d(_DispatchMixin, QuantizationMixin, nn.ConvTranspose3d): # pylint: disable=too-many-ancestors
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.ConvTranspose3d)
+    _builtin_torch_fn = F.conv_transpose3d
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.CosineEmbeddingLoss)
+class QuantizedCosineEmbeddingLoss(_DispatchMixin, QuantizationMixin, nn.CosineEmbeddingLoss):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.CosineEmbeddingLoss)
+    _builtin_torch_fn = F.cosine_embedding_loss
+    __quant_init__ = __binary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.CosineSimilarity)
+class QuantizedCosineSimilarity(_DispatchMixin, QuantizationMixin, nn.CosineSimilarity):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.CosineSimilarity)
+    _builtin_torch_fn = F.cosine_similarity
+    __quant_init__ = __binary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.CrossEntropyLoss)
+class QuantizedCrossEntropyLoss(_DispatchMixin, QuantizationMixin, nn.CrossEntropyLoss):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.CrossEntropyLoss)
+    _builtin_torch_fn = F.cross_entropy
+    __quant_init__ = __binary__
    
+
+
+
+# @QuantizationMixin.implements(nn.CrossMapLRN2d)
+# class QuantizedCrossMapLRN2d(_DispatchMixin, QuantizationMixin, nn.CrossMapLRN2d):
+#     _builtin_torch_fn = ...
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.Dropout)
+class QuantizedDropout(_DispatchMixin, QuantizationMixin, nn.Dropout):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.Dropout)
+    _builtin_torch_fn = F.dropout
+    __quant_init__ = __unary__
    
+
+
+
+if version.parse(torch.__version__) >= version.parse("1.12.0"):
+
    
+[docs]
+    @QuantizationMixin.implements(nn.Dropout1d)
+    class QuantizedDropout1d(_DispatchMixin, QuantizationMixin, nn.Dropout1d):
+        # pylint: disable=missing-class-docstring
+        __doc__ = _generate_docstring(parent_cls=nn.Dropout1d)
+        _builtin_torch_fn = F.dropout1d
+        __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.Dropout2d)
+class QuantizedDropout2d(_DispatchMixin, QuantizationMixin, nn.Dropout2d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.Dropout2d)
+    _builtin_torch_fn = F.dropout2d
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.Dropout3d)
+class QuantizedDropout3d(_DispatchMixin, QuantizationMixin, nn.Dropout3d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.Dropout3d)
+    _builtin_torch_fn = F.dropout3d
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.ELU)
+class QuantizedELU(_DispatchMixin, QuantizationMixin, nn.ELU):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.ELU)
+    _builtin_torch_fn = F.elu
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.Embedding)
+class QuantizedEmbedding(_DispatchMixin, QuantizationMixin, nn.Embedding):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.Embedding)
+    _builtin_torch_fn = F.embedding
+    __quant_init__ = __nullary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.EmbeddingBag)
+class QuantizedEmbeddingBag(_DispatchMixin, QuantizationMixin, nn.EmbeddingBag):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.EmbeddingBag)
+    _builtin_torch_fn = F.embedding_bag
+
+    def _builtin_torch_fn_helper(self, fn: Callable[..., Tensor]):
+        def embedding_bag(input: Tensor, # pylint: disable=redefined-builtin, too-many-arguments
+                          weight: Tensor,
+                          offsets: Optional[Tensor] = None,
+                          max_norm: Optional[float] = None,
+                          norm_type: float = 2,
+                          scale_grad_by_freq: bool = False,
+                          mode: str = "mean",
+                          sparse: bool = False,
+                          per_sample_weights: Optional[Tensor] = None,
+                          include_last_offset: bool = False,
+                          padding_idx: Optional[int] = None):
+
+            if per_sample_weights is not None:
+                qtzr = self.input_quantizers[0]
+                per_sample_weights = _quantize_dequantize_if_applicable(per_sample_weights, qtzr)
+
+            output = fn(input,
+                        weight,
+                        offsets=offsets,
+                        max_norm=max_norm,
+                        norm_type=norm_type,
+                        scale_grad_by_freq=scale_grad_by_freq,
+                        mode=mode,
+                        sparse=sparse,
+                        per_sample_weights=per_sample_weights,
+                        include_last_offset=include_last_offset,
+                        padding_idx=padding_idx)
+
+            return _quantize_dequantize_if_applicable(output, self.output_quantizers[0])
+
+        return embedding_bag
+
+    def _custom_kernel_helper(self, fn: Callable[..., QuantizedTensorBase]):
+        def embedding_bag(input: Tensor, # pylint: disable=redefined-builtin, too-many-arguments
+                          weight: Tensor,
+                          offsets: Optional[Tensor] = None,
+                          max_norm: Optional[float] = None,
+                          norm_type: float = 2,
+                          scale_grad_by_freq: bool = False,
+                          mode: str = "mean",
+                          sparse: bool = False,
+                          per_sample_weights: Optional[Tensor] = None,
+                          include_last_offset: bool = False,
+                          padding_idx: Optional[int] = None):
+
+            if per_sample_weights is not None:
+                qtzr = self.input_quantizers[0]
+                per_sample_weights = _quantize_if_applicable(per_sample_weights, qtzr)
+
+            output_encodings = self.output_quantizers[0].get_encodings() if self.output_quantizers[0] else None
+
+            return fn(input,
+                      weight,
+                      offsets=offsets,
+                      max_norm=max_norm,
+                      norm_type=norm_type,
+                      scale_grad_by_freq=scale_grad_by_freq,
+                      mode=mode,
+                      sparse=sparse,
+                      per_sample_weights=per_sample_weights,
+                      include_last_offset=include_last_offset,
+                      padding_idx=padding_idx,
+                      output_encodings=output_encodings)
+
+        return embedding_bag
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.FeatureAlphaDropout)
+class QuantizedFeatureAlphaDropout(_DispatchMixin, QuantizationMixin, nn.FeatureAlphaDropout):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.FeatureAlphaDropout)
+    _builtin_torch_fn = F.feature_alpha_dropout
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.Flatten)
+class QuantizedFlatten(_DispatchMixin, QuantizationMixin, nn.Flatten):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.Flatten)
+    def _get_builtin_torch_fn(self):
+        return Tensor.flatten
+
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.Fold)
+class QuantizedFold(_DispatchMixin, QuantizationMixin, nn.Fold):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.Fold)
+    _builtin_torch_fn = F.fold
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.FractionalMaxPool2d)
+class QuantizedFractionalMaxPool2d(_DispatchMixin, QuantizationMixin, nn.FractionalMaxPool2d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.FractionalMaxPool2d)
+    _builtin_torch_fn = F.fractional_max_pool2d
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.FractionalMaxPool3d)
+class QuantizedFractionalMaxPool3d(_DispatchMixin, QuantizationMixin, nn.FractionalMaxPool3d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.FractionalMaxPool3d)
+    _builtin_torch_fn = F.fractional_max_pool3d
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.GELU)
+class QuantizedGELU(_DispatchMixin, QuantizationMixin, nn.GELU):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.GELU)
+    _builtin_torch_fn = F.gelu
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.GLU)
+class QuantizedGLU(_DispatchMixin, QuantizationMixin, nn.GLU):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.GLU)
+    _builtin_torch_fn = F.glu
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.GRU)
+class QuantizedGRU(_DispatchMixin, QuantizationMixin, nn.GRU):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.GRU)
+    _builtin_torch_fn = _gru
+
+    def __quant_init__(self):
+        super().__quant_init__()
+        # pylint: disable=attribute-defined-outside-init
+        self.input_quantizers = nn.ModuleList([None, None])
+        self.output_quantizers = nn.ModuleList([None, None])
+
+    def _quantize_inputs(self, args, apply):
+        if args[1].is_floating_point():
+            input, hx, *others = args
+            batch_sizes = None
+        else:
+            input, batch_sizes, hx, *others = args
+
+        input = apply(input, self.input_quantizers[0])
+        hx = apply(hx, self.input_quantizers[1])
+
+        if batch_sizes is None:
+            return input, hx, *others
+        return input, batch_sizes, hx, *others
+
+    def _builtin_torch_fn_helper(self, fn: Callable[..., Tensor]):
+        assert fn == _gru
+        apply = _quantize_dequantize_if_applicable
+
+        def gru(*args):
+            args = self._quantize_inputs(args, apply)
+            output, h_n = fn(*args)
+            return (
+                apply(output, self.output_quantizers[0]),
+                apply(h_n, self.output_quantizers[1]),
+            )
+
+        return gru
+
+    def _custom_kernel_helper(self, fn: Callable[..., QuantizedTensorBase]):
+        apply = _quantize_if_applicable
+
+        def gru(*args):
+            args = self._quantize_inputs(args, apply)
+            output_encodings = tuple(qtzr and qtzr.get_encodings() for qtzr in self.output_quantizers)
+            return fn(*args, output_encodings=output_encodings)
+
+        return gru
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.GRUCell)
+class QuantizedGRUCell(_DispatchMixin, QuantizationMixin, nn.GRUCell):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.GRUCell)
+    _builtin_torch_fn = _gru_cell
+
+    def __quant_init__(self):
+        super().__quant_init__()
+        # pylint: disable=attribute-defined-outside-init
+        self.input_quantizers = nn.ModuleList([None, None])
+        self.output_quantizers = nn.ModuleList([None])
+
+    def _builtin_torch_fn_helper(self, fn: Callable[..., Tensor]):
+        assert fn == _gru_cell
+        apply = _quantize_dequantize_if_applicable
+
+        def gru_cell(input, hx, *args, **kwargs):
+            input = apply(input, self.input_quantizers[0])
+            hx = apply(hx, self.input_quantizers[1])
+            output = fn(input, hx, *args, **kwargs)
+            return apply(output, self.output_quantizers[0])
+
+        return gru_cell
+
+    def _custom_kernel_helper(self, fn: Callable[..., QuantizedTensorBase]):
+        apply = _quantize_if_applicable
+
+        def gru_cell(input, hx, *args, **kwargs):
+            input = apply(input, self.input_quantizers[0])
+            hx = apply(hx, self.input_quantizers[1])
+            output_encodings = self.output_quantizers[0] and self.output_quantizers[0].get_encodings()
+            return fn(input, hx, *args, **kwargs, output_encodings=output_encodings)
+
+        return gru_cell
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.GaussianNLLLoss)
+class QuantizedGaussianNLLLoss(_DispatchMixin, QuantizationMixin, nn.GaussianNLLLoss):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.GaussianNLLLoss)
+    _builtin_torch_fn = F.gaussian_nll_loss
+    __quant_init__ = __ternary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.GroupNorm)
+class QuantizedGroupNorm(_DispatchMixin, QuantizationMixin, nn.GroupNorm):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.GroupNorm)
+    _builtin_torch_fn = F.group_norm
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.Hardshrink)
+class QuantizedHardshrink(_DispatchMixin, QuantizationMixin, nn.Hardshrink):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.Hardshrink)
+    _builtin_torch_fn = F.hardshrink
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.Hardsigmoid)
+class QuantizedHardsigmoid(_DispatchMixin, QuantizationMixin, nn.Hardsigmoid):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.Hardsigmoid)
+    _builtin_torch_fn = F.hardsigmoid
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.Hardswish)
+class QuantizedHardswish(_DispatchMixin, QuantizationMixin, nn.Hardswish):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.Hardswish)
+    _builtin_torch_fn = F.hardswish
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.Hardtanh)
+class QuantizedHardtanh(_DispatchMixin, QuantizationMixin, nn.Hardtanh):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.Hardtanh)
+    _builtin_torch_fn = F.hardtanh
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.HingeEmbeddingLoss)
+class QuantizedHingeEmbeddingLoss(_DispatchMixin, QuantizationMixin, nn.HingeEmbeddingLoss):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.HingeEmbeddingLoss)
+    _builtin_torch_fn = F.hinge_embedding_loss
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.HuberLoss)
+class QuantizedHuberLoss(_DispatchMixin, QuantizationMixin, nn.HuberLoss):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.HuberLoss)
+    _builtin_torch_fn = F.huber_loss
+    __quant_init__ = __binary__
    
+
+
+
+# @QuantizationMixin.implements(nn.Identity)
+# class QuantizedIdentity(_DispatchMixin, QuantizationMixin, nn.Identity):
+#     _builtin_torch_fn = ...
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.InstanceNorm1d)
+class QuantizedInstanceNorm1d(_DispatchMixin, QuantizationMixin, nn.InstanceNorm1d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.InstanceNorm1d)
+    _builtin_torch_fn = F.instance_norm
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.InstanceNorm2d)
+class QuantizedInstanceNorm2d(_DispatchMixin, QuantizationMixin, nn.InstanceNorm2d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.InstanceNorm2d)
+    _builtin_torch_fn = F.instance_norm
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.InstanceNorm3d)
+class QuantizedInstanceNorm3d(_DispatchMixin, QuantizationMixin, nn.InstanceNorm3d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.InstanceNorm3d)
+    _builtin_torch_fn = F.instance_norm
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.KLDivLoss)
+class QuantizedKLDivLoss(_DispatchMixin, QuantizationMixin, nn.KLDivLoss):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.KLDivLoss)
+    _builtin_torch_fn = F.kl_div
+    __quant_init__ = __binary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.L1Loss)
+class QuantizedL1Loss(_DispatchMixin, QuantizationMixin, nn.L1Loss):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.L1Loss)
+    _builtin_torch_fn = F.l1_loss
+    __quant_init__ = __binary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.LPPool1d)
+class QuantizedLPPool1d(_DispatchMixin, QuantizationMixin, nn.LPPool1d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.LPPool1d)
+    _builtin_torch_fn = F.lp_pool1d
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.LPPool2d)
+class QuantizedLPPool2d(_DispatchMixin, QuantizationMixin, nn.LPPool2d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.LPPool2d)
+    _builtin_torch_fn = F.lp_pool2d
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.LSTM)
+class QuantizedLSTM(_DispatchMixin, QuantizationMixin, nn.LSTM):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.LSTM)
+    _builtin_torch_fn = _lstm
+
+    def __quant_init__(self):
+        super().__quant_init__()
+        # pylint: disable=attribute-defined-outside-init
+        self.input_quantizers = nn.ModuleList([None, None, None])
+        self.output_quantizers = nn.ModuleList([None, None, None])
+
+    def _quantize_inputs(self, args, apply):
+        if isinstance(args[1], Tensor):
+            input, batch_sizes, hx, *others = args
+        else:
+            input, hx, *others = args
+            batch_sizes = None
+
+        input = apply(input, self.input_quantizers[0])
+        h, c = hx
+        h_qtzr, c_qtzr = self.input_quantizers[1:]
+        hx = (apply(h, h_qtzr), apply(c, c_qtzr))
+
+        if batch_sizes is None:
+            return input, hx, *others
+        return input, batch_sizes, hx, *others
+
+    def _builtin_torch_fn_helper(self, fn: Callable[..., Tensor]):
+        assert fn == _lstm
+        apply = _quantize_dequantize_if_applicable
+
+        def lstm(*args):
+            args = self._quantize_inputs(args, apply)
+            output, h_n, c_n = fn(*args)
+            return (
+                apply(output, self.output_quantizers[0]),
+                apply(h_n, self.output_quantizers[1]),
+                apply(c_n, self.output_quantizers[2]),
+            )
+
+        return lstm
+
+    def _custom_kernel_helper(self, fn: Callable[..., QuantizedTensorBase]):
+        apply = _quantize_if_applicable
+
+        def lstm(*args):
+            args = self._quantize_inputs(args, apply)
+            output_encodings = tuple(qtzr and qtzr.get_encodings() for qtzr in self.output_quantizers)
+            return fn(*args, output_encodings=output_encodings)
+
+        return lstm
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.LSTMCell)
+class QuantizedLSTMCell(_DispatchMixin, QuantizationMixin, nn.LSTMCell):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.LSTMCell)
+    _builtin_torch_fn = _lstm_cell
+
+    def __quant_init__(self):
+        super().__quant_init__()
+        # pylint: disable=attribute-defined-outside-init
+        self.input_quantizers = nn.ModuleList([None, None, None])
+        self.output_quantizers = nn.ModuleList([None, None])
+
+    def _builtin_torch_fn_helper(self, fn: Callable[..., Tensor]):
+        assert fn == _lstm_cell
+        apply = _quantize_dequantize_if_applicable
+
+        def lstm_cell(input, hx, *args, **kwargs):
+            input = apply(input, self.input_quantizers[0])
+            h, c = hx
+            h_qtzr, c_qtzr = self.input_quantizers[1:]
+            hx = (apply(h, h_qtzr), apply(c, c_qtzr))
+
+            hx, cx = fn(input, hx, *args, **kwargs)
+            return (
+                apply(hx, self.output_quantizers[0]),
+                apply(cx, self.output_quantizers[1]),
+            )
+
+        return lstm_cell
+
+    def _custom_kernel_helper(self, fn: Callable[..., QuantizedTensorBase]):
+        apply = _quantize_if_applicable
+
+        def lstm_cell(input, hx, *args, **kwargs):
+            input = apply(input, self.input_quantizers[0])
+            h, c = hx
+            h_qtzr, c_qtzr = self.input_quantizers[1:]
+            hx = (apply(h, h_qtzr), apply(c, c_qtzr))
+
+            output_encodings = tuple(qtzr and qtzr.get_encodings() for qtzr in self.output_quantizers)
+            return fn(input, hx, *args, **kwargs, output_encodings=output_encodings)
+
+        return lstm_cell
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.LayerNorm)
+class QuantizedLayerNorm(_DispatchMixin, QuantizationMixin, nn.LayerNorm):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.LayerNorm)
+    _builtin_torch_fn = F.layer_norm
+    __quant_init__ = __unary__
    
+
+
+
+# @QuantizationMixin.implements(nn.LazyBatchNorm1d)
+# class QuantizedLazyBatchNorm1d(_DispatchMixin, QuantizationMixin, nn.LazyBatchNorm1d):
+#     _builtin_torch_fn = ...
+
+
+# @QuantizationMixin.implements(nn.LazyBatchNorm2d)
+# class QuantizedLazyBatchNorm2d(_DispatchMixin, QuantizationMixin, nn.LazyBatchNorm2d):
+#     _builtin_torch_fn = ...
+
+
+# @QuantizationMixin.implements(nn.LazyBatchNorm3d)
+# class QuantizedLazyBatchNorm3d(_DispatchMixin, QuantizationMixin, nn.LazyBatchNorm3d):
+#     _builtin_torch_fn = ...
+
+
+# @QuantizationMixin.implements(nn.LazyConv1d)
+# class QuantizedLazyConv1d(_DispatchMixin, QuantizationMixin, nn.LazyConv1d):
+#     _builtin_torch_fn = ...
+
+
+# @QuantizationMixin.implements(nn.LazyConv2d)
+# class QuantizedLazyConv2d(_DispatchMixin, QuantizationMixin, nn.LazyConv2d):
+#     _builtin_torch_fn = ...
+
+
+# @QuantizationMixin.implements(nn.LazyConv3d)
+# class QuantizedLazyConv3d(_DispatchMixin, QuantizationMixin, nn.LazyConv3d):
+#     _builtin_torch_fn = ...
+
+
+# @QuantizationMixin.implements(nn.LazyConvTranspose1d)
+# class QuantizedLazyConvTranspose1d(_DispatchMixin, QuantizationMixin, nn.LazyConvTranspose1d):
+#     _builtin_torch_fn = ...
+
+
+# @QuantizationMixin.implements(nn.LazyConvTranspose2d)
+# class QuantizedLazyConvTranspose2d(_DispatchMixin, QuantizationMixin, nn.LazyConvTranspose2d):
+#     _builtin_torch_fn = ...
+
+
+# @QuantizationMixin.implements(nn.LazyConvTranspose3d)
+# class QuantizedLazyConvTranspose3d(_DispatchMixin, QuantizationMixin, nn.LazyConvTranspose3d):
+#     _builtin_torch_fn = ...
+
+
+# @QuantizationMixin.implements(nn.LazyInstanceNorm1d)
+# class QuantizedLazyInstanceNorm1d(_DispatchMixin, QuantizationMixin, nn.LazyInstanceNorm1d):
+#     _builtin_torch_fn = ...
+
+
+# @QuantizationMixin.implements(nn.LazyInstanceNorm2d)
+# class QuantizedLazyInstanceNorm2d(_DispatchMixin, QuantizationMixin, nn.LazyInstanceNorm2d):
+#     _builtin_torch_fn = ...
+
+
+# @QuantizationMixin.implements(nn.LazyInstanceNorm3d)
+# class QuantizedLazyInstanceNorm3d(_DispatchMixin, QuantizationMixin, nn.LazyInstanceNorm3d):
+#     _builtin_torch_fn = ...
+
+
+# @QuantizationMixin.implements(nn.LazyLinear)
+# class QuantizedLazyLinear(_DispatchMixin, QuantizationMixin, nn.LazyLinear):
+#     _builtin_torch_fn = ...
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.LeakyReLU)
+class QuantizedLeakyReLU(_DispatchMixin, QuantizationMixin, nn.LeakyReLU):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.LeakyReLU)
+    _builtin_torch_fn = F.leaky_relu
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.Linear)
+class QuantizedLinear(_DispatchMixin, QuantizationMixin, nn.Linear):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.Linear)
+    _builtin_torch_fn = F.linear
+    __quant_init__ = __unary__
+
+    # Only allow activation recompute (a.k.a activation checkpointing) for QuantizedLinear.
+    # This is mainly to reduce memory footprint of QAT of large language models.
+
    
+[docs]
+    @allow_recompute
+    def forward(self, *args, **kwargs):
+        # Workaround for deepspeed.
+        # Deepspeed zero3 sometimes forcefully mokey-patches F.linear to torch.addmm,
+        # which collides with the core assumption of our dispatch mechanism
+        # that nn.Linear invokes F.linear.
+        # To circumvent this issue, we temporarily restore the original F.linear
+        # before running forward.
+        with patch_attr(F, 'linear', type(self)._builtin_torch_fn):
+            return super().forward(*args, **kwargs)
    
+
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.LocalResponseNorm)
+class QuantizedLocalResponseNorm(_DispatchMixin, QuantizationMixin, nn.LocalResponseNorm):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.LocalResponseNorm)
+    _builtin_torch_fn = F.local_response_norm
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.LogSigmoid)
+class QuantizedLogSigmoid(_DispatchMixin, QuantizationMixin, nn.LogSigmoid):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.LogSigmoid)
+    _builtin_torch_fn = F.logsigmoid
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.LogSoftmax)
+class QuantizedLogSoftmax(_DispatchMixin, QuantizationMixin, nn.LogSoftmax):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.LogSoftmax)
+    _builtin_torch_fn = F.log_softmax
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.MSELoss)
+class QuantizedMSELoss(_DispatchMixin, QuantizationMixin, nn.MSELoss):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.MSELoss)
+    _builtin_torch_fn = F.mse_loss
+    __quant_init__ = __binary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.MarginRankingLoss)
+class QuantizedMarginRankingLoss(_DispatchMixin, QuantizationMixin, nn.MarginRankingLoss):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.MarginRankingLoss)
+    _builtin_torch_fn = F.margin_ranking_loss
+    __quant_init__ = __binary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.MaxPool1d)
+class QuantizedMaxPool1d(_DispatchMixin, QuantizationMixin, nn.MaxPool1d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.MaxPool1d)
+    _builtin_torch_fn = F.max_pool1d
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.MaxPool2d)
+class QuantizedMaxPool2d(_DispatchMixin, QuantizationMixin, nn.MaxPool2d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.MaxPool2d)
+    _builtin_torch_fn = F.max_pool2d
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.MaxPool3d)
+class QuantizedMaxPool3d(_DispatchMixin, QuantizationMixin, nn.MaxPool3d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.MaxPool3d)
+    _builtin_torch_fn = F.max_pool3d
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.MaxUnpool1d)
+class QuantizedMaxUnpool1d(_DispatchMixin, QuantizationMixin, nn.MaxUnpool1d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.MaxUnpool1d)
+    _builtin_torch_fn = F.max_unpool1d
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.MaxUnpool2d)
+class QuantizedMaxUnpool2d(_DispatchMixin, QuantizationMixin, nn.MaxUnpool2d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.MaxUnpool2d)
+    _builtin_torch_fn = F.max_unpool2d
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.MaxUnpool3d)
+class QuantizedMaxUnpool3d(_DispatchMixin, QuantizationMixin, nn.MaxUnpool3d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.MaxUnpool3d)
+    _builtin_torch_fn = F.max_unpool3d
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.Mish)
+class QuantizedMish(_DispatchMixin, QuantizationMixin, nn.Mish):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.Mish)
+    _builtin_torch_fn = F.mish
+    __quant_init__ = __unary__
    
+
+
+
+# @QuantizationMixin.implements(nn.Module)
+# class QuantizedModule(_DispatchMixin, QuantizationMixin, nn.Module):
+#     _builtin_torch_fn = ...
+
+
+# @QuantizationMixin.implements(nn.ModuleDict)
+# class QuantizedModuleDict(_DispatchMixin, QuantizationMixin, nn.ModuleDict):
+#     _builtin_torch_fn = ...
+
+
+# @QuantizationMixin.implements(nn.ModuleList)
+# class QuantizedModuleList(_DispatchMixin, QuantizationMixin, nn.ModuleList):
+#     _builtin_torch_fn = ...
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.MultiLabelMarginLoss)
+class QuantizedMultiLabelMarginLoss(_DispatchMixin, QuantizationMixin, nn.MultiLabelMarginLoss):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.MultiLabelMarginLoss)
+    _builtin_torch_fn = F.multilabel_margin_loss
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.MultiLabelSoftMarginLoss)
+class QuantizedMultiLabelSoftMarginLoss(_DispatchMixin, QuantizationMixin, nn.MultiLabelSoftMarginLoss):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.MultiLabelSoftMarginLoss)
+    _builtin_torch_fn = F.multilabel_soft_margin_loss
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.MultiMarginLoss)
+class QuantizedMultiMarginLoss(_DispatchMixin, QuantizationMixin, nn.MultiMarginLoss):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.MultiMarginLoss)
+    _builtin_torch_fn = F.multi_margin_loss
+    __quant_init__ = __unary__
    
+
+
+
+# @QuantizationMixin.implements(nn.MultiheadAttention)
+# class QuantizedMultiheadAttention(_DispatchMixin, QuantizationMixin, nn.MultiheadAttention):
+#     _builtin_torch_fn = ...
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.NLLLoss)
+class QuantizedNLLLoss(_DispatchMixin, QuantizationMixin, nn.NLLLoss):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.NLLLoss)
+    _builtin_torch_fn = F.nll_loss
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.NLLLoss2d)
+class QuantizedNLLLoss2d(_DispatchMixin, QuantizationMixin, nn.NLLLoss2d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.NLLLoss2d)
+    _builtin_torch_fn = F.nll_loss
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.PReLU)
+class QuantizedPReLU(_DispatchMixin, QuantizationMixin, nn.PReLU):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.PReLU)
+    _builtin_torch_fn = F.prelu
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.PairwiseDistance)
+class QuantizedPairwiseDistance(_DispatchMixin, QuantizationMixin, nn.PairwiseDistance):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.PairwiseDistance)
+    _builtin_torch_fn = F.pairwise_distance
+    __quant_init__ = __binary__
    
+
+
+
+# @QuantizationMixin.implements(nn.ParameterDict)
+# class QuantizedParameterDict(_DispatchMixin, QuantizationMixin, nn.ParameterDict):
+#     _builtin_torch_fn = ...
+
+
+# @QuantizationMixin.implements(nn.ParameterList)
+# class QuantizedParameterList(_DispatchMixin, QuantizationMixin, nn.ParameterList):
+#     _builtin_torch_fn = ...
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.PixelShuffle)
+class QuantizedPixelShuffle(_DispatchMixin, QuantizationMixin, nn.PixelShuffle):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.PixelShuffle)
+    _builtin_torch_fn = F.pixel_shuffle
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.PixelUnshuffle)
+class QuantizedPixelUnshuffle(_DispatchMixin, QuantizationMixin, nn.PixelUnshuffle):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.PixelUnshuffle)
+    _builtin_torch_fn = F.pixel_unshuffle
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.PoissonNLLLoss)
+class QuantizedPoissonNLLLoss(_DispatchMixin, QuantizationMixin, nn.PoissonNLLLoss):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.PoissonNLLLoss)
+    _builtin_torch_fn = F.poisson_nll_loss
+    __quant_init__ = __binary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.RNN)
+class QuantizedRNN(_DispatchMixin, QuantizationMixin, nn.RNN):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.RNN)
+    def _get_builtin_torch_fn(self):
+        assert self.mode in ('RNN_TANH', 'RNN_RELU')
+        if self.mode == 'RNN_TANH':
+            return _rnn_tanh
+        return _rnn_relu
+
+    def __quant_init__(self):
+        super().__quant_init__()
+        # pylint: disable=attribute-defined-outside-init
+        self.input_quantizers = nn.ModuleList([None, None])
+        self.output_quantizers = nn.ModuleList([None, None])
+
+    def _quantize_inputs(self, args, apply):
+        if args[1].is_floating_point():
+            input, hx, *others = args
+            batch_sizes = None
+        else:
+            input, batch_sizes, hx, *others = args
+
+        input = apply(input, self.input_quantizers[0])
+        hx = apply(hx, self.input_quantizers[1])
+
+        if batch_sizes is None:
+            return input, hx, *others
+        return input, batch_sizes, hx, *others
+
+    def _builtin_torch_fn_helper(self, fn: Callable[..., Tensor]):
+        assert fn in (_rnn_tanh, _rnn_relu)
+        apply = _quantize_dequantize_if_applicable
+
+        def rnn(*args):
+            args = self._quantize_inputs(args, apply)
+            output, h_n = fn(*args)
+            return (
+                apply(output, self.output_quantizers[0]),
+                apply(h_n, self.output_quantizers[1]),
+            )
+
+        return rnn
+
+    def _custom_kernel_helper(self, fn: Callable[..., QuantizedTensorBase]):
+        apply = _quantize_if_applicable
+
+        def rnn(*args):
+            args = self._quantize_inputs(args, apply)
+            output_encodings = tuple(qtzr and qtzr.get_encodings() for qtzr in self.output_quantizers)
+            return fn(*args, output_encodings=output_encodings)
+
+        return rnn
    
+
+
+
+# @QuantizationMixin.implements(nn.RNNBase)
+# class QuantizedRNNBase(_DispatchMixin, QuantizationMixin, nn.RNNBase):
+#     _builtin_torch_fn = ...
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.RNNCell)
+class QuantizedRNNCell(_DispatchMixin, QuantizationMixin, nn.RNNCell):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.RNNCell)
+    def _get_builtin_torch_fn(self):
+        assert self.nonlinearity in ("tanh", "relu")
+
+        if self.nonlinearity == "tanh":
+            return _rnn_tanh_cell
+        return _rnn_relu_cell
+
+    def __quant_init__(self):
+        super().__quant_init__()
+        # pylint: disable=attribute-defined-outside-init
+        self.input_quantizers = nn.ModuleList([None, None])
+        self.output_quantizers = nn.ModuleList([None])
+
+    def _builtin_torch_fn_helper(self, fn: Callable[..., Tensor]):
+        assert fn in (_rnn_tanh_cell, _rnn_relu_cell)
+        apply = _quantize_dequantize_if_applicable
+
+        def rnn_cell(input, hx, *args, **kwargs):
+            input = apply(input, self.input_quantizers[0])
+            hx = apply(hx, self.input_quantizers[1])
+            output = fn(input, hx, *args, **kwargs)
+            return apply(output, self.output_quantizers[0])
+
+        return rnn_cell
+
+    def _custom_kernel_helper(self, fn: Callable[..., QuantizedTensorBase]):
+        apply = _quantize_if_applicable
+
+        def rnn_cell(input, hx, *args, **kwargs):
+            input = apply(input, self.input_quantizers[0])
+            hx = apply(hx, self.input_quantizers[1])
+            output_encodings = self.output_quantizers[0] and self.output_quantizers[0].get_encodings()
+            return fn(input, hx, *args, **kwargs, output_encodings=output_encodings)
+
+        return rnn_cell
    
+
+
+
+# @QuantizationMixin.implements(nn.RNNCellBase)
+# class QuantizedRNNCellBase(_DispatchMixin, QuantizationMixin, nn.RNNCellBase):
+#     _builtin_torch_fn = ...
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.RReLU)
+class QuantizedRReLU(_DispatchMixin, QuantizationMixin, nn.RReLU):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.RReLU)
+    _builtin_torch_fn = F.rrelu
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.ReLU)
+class QuantizedReLU(_DispatchMixin, QuantizationMixin, nn.ReLU):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.ReLU)
+    _builtin_torch_fn = F.relu
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.ReLU6)
+class QuantizedReLU6(_DispatchMixin, QuantizationMixin, nn.ReLU6):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.ReLU6)
+    _builtin_torch_fn = F.hardtanh
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.ReflectionPad1d)
+class QuantizedReflectionPad1d(_DispatchMixin, QuantizationMixin, nn.ReflectionPad1d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.ReflectionPad1d)
+    _builtin_torch_fn = F.pad
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.ReflectionPad2d)
+class QuantizedReflectionPad2d(_DispatchMixin, QuantizationMixin, nn.ReflectionPad2d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.ReflectionPad2d)
+    _builtin_torch_fn = F.pad
+    __quant_init__ = __unary__
    
+
+
+
+if version.parse(torch.__version__) >= version.parse("1.10.0"):
+
    
+[docs]
+    @QuantizationMixin.implements(nn.ReflectionPad3d)
+    class QuantizedReflectionPad3d(_DispatchMixin, QuantizationMixin, nn.ReflectionPad3d):
+        # pylint: disable=missing-class-docstring
+        __doc__ = _generate_docstring(parent_cls=nn.ReflectionPad3d)
+        _builtin_torch_fn = F.pad
+        __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.ReplicationPad1d)
+class QuantizedReplicationPad1d(_DispatchMixin, QuantizationMixin, nn.ReplicationPad1d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.ReplicationPad1d)
+    _builtin_torch_fn = F.pad
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.ReplicationPad2d)
+class QuantizedReplicationPad2d(_DispatchMixin, QuantizationMixin, nn.ReplicationPad2d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.ReplicationPad2d)
+    _builtin_torch_fn = F.pad
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.ReplicationPad3d)
+class QuantizedReplicationPad3d(_DispatchMixin, QuantizationMixin, nn.ReplicationPad3d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.ReplicationPad3d)
+    _builtin_torch_fn = F.pad
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.SELU)
+class QuantizedSELU(_DispatchMixin, QuantizationMixin, nn.SELU):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.SELU)
+    _builtin_torch_fn = F.selu
+    __quant_init__ = __unary__
    
+
+
+
+# @QuantizationMixin.implements(nn.Sequential)
+# class QuantizedSequential(_DispatchMixin, QuantizationMixin, nn.Sequential):
+#     _builtin_torch_fn = ...
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.SiLU)
+class QuantizedSiLU(_DispatchMixin, QuantizationMixin, nn.SiLU):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.SiLU)
+    _builtin_torch_fn = F.silu
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.Sigmoid)
+class QuantizedSigmoid(_DispatchMixin, QuantizationMixin, nn.Sigmoid):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.Sigmoid)
+    _builtin_torch_fn = torch.sigmoid
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.SmoothL1Loss)
+class QuantizedSmoothL1Loss(_DispatchMixin, QuantizationMixin, nn.SmoothL1Loss):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.SmoothL1Loss)
+    _builtin_torch_fn = F.smooth_l1_loss
+    __quant_init__ = __binary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.SoftMarginLoss)
+class QuantizedSoftMarginLoss(_DispatchMixin, QuantizationMixin, nn.SoftMarginLoss):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.SoftMarginLoss)
+    _builtin_torch_fn = F.soft_margin_loss
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.Softmax)
+class QuantizedSoftmax(_DispatchMixin, QuantizationMixin, nn.Softmax):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.Softmax)
+    _builtin_torch_fn = F.softmax
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.Softmax2d)
+class QuantizedSoftmax2d(_DispatchMixin, QuantizationMixin, nn.Softmax2d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.Softmax2d)
+    _builtin_torch_fn = F.softmax
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.Softmin)
+class QuantizedSoftmin(_DispatchMixin, QuantizationMixin, nn.Softmin):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.Softmin)
+    _builtin_torch_fn = F.softmin
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.Softplus)
+class QuantizedSoftplus(_DispatchMixin, QuantizationMixin, nn.Softplus):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.Softplus)
+    _builtin_torch_fn = F.softplus
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.Softshrink)
+class QuantizedSoftshrink(_DispatchMixin, QuantizationMixin, nn.Softshrink):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.Softshrink)
+    _builtin_torch_fn = F.softshrink
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.Softsign)
+class QuantizedSoftsign(_DispatchMixin, QuantizationMixin, nn.Softsign):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.Softsign)
+    _builtin_torch_fn = F.softsign
+    __quant_init__ = __unary__
    
+
+
+
+# @QuantizationMixin.implements(nn.SyncBatchNorm)
+# class QuantizedSyncBatchNorm(_DispatchMixin, QuantizationMixin, nn.SyncBatchNorm):
+#     _builtin_torch_fn = ...
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.Tanh)
+class QuantizedTanh(_DispatchMixin, QuantizationMixin, nn.Tanh):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.Tanh)
+    _builtin_torch_fn = torch.tanh
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.Tanhshrink)
+class QuantizedTanhshrink(_DispatchMixin, QuantizationMixin, nn.Tanhshrink):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.Tanhshrink)
+    _builtin_torch_fn = F.tanhshrink
+    __quant_init__ = __unary__
    
+
+
+
+# @QuantizationMixin.implements(nn.Threshold)
+
    
+[docs]
+@QuantizationMixin.implements(nn.Threshold)
+class QuantizedThreshold(_DispatchMixin, QuantizationMixin, nn.Threshold):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.Threshold)
+    _builtin_torch_fn = F.threshold
+    __quant_init__ = __unary__
    
+
+
+
+# @QuantizationMixin.implements(nn.Transformer)
+# class QuantizedTransformer(_DispatchMixin, QuantizationMixin, nn.Transformer):
+#     _builtin_torch_fn = ...
+
+
+# @QuantizationMixin.implements(nn.TransformerDecoder)
+# class QuantizedTransformerDecoder(_DispatchMixin, QuantizationMixin, nn.TransformerDecoder):
+#     _builtin_torch_fn = ...
+
+
+# @QuantizationMixin.implements(nn.TransformerDecoderLayer)
+# class QuantizedTransformerDecoderLayer(_DispatchMixin, QuantizationMixin, nn.TransformerDecoderLayer):
+#     _builtin_torch_fn = ...
+
+
+# @QuantizationMixin.implements(nn.TransformerEncoder)
+# class QuantizedTransformerEncoder(_DispatchMixin, QuantizationMixin, nn.TransformerEncoder):
+#     _builtin_torch_fn = ...
+
+
+# @QuantizationMixin.implements(nn.TransformerEncoderLayer)
+# class QuantizedTransformerEncoderLayer(_DispatchMixin, QuantizationMixin, nn.TransformerEncoderLayer):
+#     _builtin_torch_fn = ...
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.TripletMarginLoss)
+class QuantizedTripletMarginLoss(_DispatchMixin, QuantizationMixin, nn.TripletMarginLoss):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.TripletMarginLoss)
+    _builtin_torch_fn = F.triplet_margin_loss
+    __quant_init__ = __ternary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.TripletMarginWithDistanceLoss)
+class QuantizedTripletMarginWithDistanceLoss(_DispatchMixin, QuantizationMixin, nn.TripletMarginWithDistanceLoss):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.TripletMarginWithDistanceLoss)
+    _builtin_torch_fn = F.triplet_margin_with_distance_loss
+    __quant_init__ = __ternary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.Unflatten)
+class QuantizedUnflatten(_DispatchMixin, QuantizationMixin, nn.Unflatten):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.Unflatten)
+    def _get_builtin_torch_fn(self):
+        return Tensor.unflatten
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.Unfold)
+class QuantizedUnfold(_DispatchMixin, QuantizationMixin, nn.Unfold):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.Unfold)
+    _builtin_torch_fn = F.unfold
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.Upsample)
+class QuantizedUpsample(_DispatchMixin, QuantizationMixin, nn.Upsample):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.Upsample)
+    _builtin_torch_fn = F.interpolate
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.UpsamplingBilinear2d)
+class QuantizedUpsamplingBilinear2d(_DispatchMixin, QuantizationMixin, nn.UpsamplingBilinear2d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.UpsamplingBilinear2d)
+    _builtin_torch_fn = F.interpolate
+    __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.UpsamplingNearest2d)
+class QuantizedUpsamplingNearest2d(_DispatchMixin, QuantizationMixin, nn.UpsamplingNearest2d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.UpsamplingNearest2d)
+    _builtin_torch_fn = F.interpolate
+    __quant_init__ = __unary__
    
+
+
+
+if version.parse(torch.__version__) >= version.parse("2.1.0"):
+
    
+[docs]
+    @QuantizationMixin.implements(nn.ZeroPad1d)
+    class QuantizedZeroPad1d(_DispatchMixin, QuantizationMixin, nn.ZeroPad1d):
+        # pylint: disable=missing-class-docstring
+        __doc__ = _generate_docstring(parent_cls=nn.ZeroPad1d)
+        _builtin_torch_fn = F.pad
+        __quant_init__ = __unary__
    
+
+
+
+
    
+[docs]
+@QuantizationMixin.implements(nn.ZeroPad2d)
+class QuantizedZeroPad2d(_DispatchMixin, QuantizationMixin, nn.ZeroPad2d):
+    # pylint: disable=missing-class-docstring
+    __doc__ = _generate_docstring(parent_cls=nn.ZeroPad2d)
+    _builtin_torch_fn = F.pad
+    __quant_init__ = __unary__
    
+
+
+
+if version.parse(torch.__version__) >= version.parse("2.1.0"):
+
    
+[docs]
+    @QuantizationMixin.implements(nn.ZeroPad3d)
+    class QuantizedZeroPad3d(_DispatchMixin, QuantizationMixin, nn.ZeroPad3d):
+        # pylint: disable=missing-class-docstring
+        __doc__ = _generate_docstring(parent_cls=nn.ZeroPad3d)
+        _builtin_torch_fn = F.pad
+        __quant_init__ = __unary__
    
+
+
+
+del __nullary__
+del __unary__
+del __binary__
+del __ternary__
+
    +
    +
    +
    + +
    + + +
    +
    +
    +
    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
    + Made with Sphinx and @pradyunsg's + + Furo + +
    +
    +
    + + + + + + +
    +
    +
    + +
    +
    + +
    +
    + + + + + + + + + \ No newline at end of file diff --git a/releases/2.0.0/_modules/aimet_torch/v2/quant_analyzer.html b/releases/2.0.0/_modules/aimet_torch/v2/quant_analyzer.html new file mode 100644 index 0000000..48ec50a --- /dev/null +++ b/releases/2.0.0/_modules/aimet_torch/v2/quant_analyzer.html @@ -0,0 +1,503 @@ + + + + + + + + aimet_torch.v2.quant_analyzer - AIMET + + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
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    +
    AIMET
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    + + + + + Back to top + +
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    + +
    +
    +

    Source code for aimet_torch.v2.quant_analyzer

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2024, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+
+""" Quant Analyzer for AIMET v2"""
+
+import os
+import contextlib
+from collections import namedtuple
+from typing import Tuple, List, Type, Optional, Generator
+import torch
+
+from aimet_common.quant_analyzer import export_stats_histogram_plot
+from aimet_torch._base.quant_analyzer import QuantAnalyzerBase
+from aimet_torch.v2.quantsim import QuantizationSimModel
+from aimet_torch.v2.nn.base import BaseQuantizationMixin
+from aimet_torch.v2.quantization.base import QuantizerBase
+from aimet_torch.v2.quantization.encoding_analyzer import _HistogramObserver, _Histogram
+from aimet_torch.v2.batch_norm_fold import fold_all_batch_norms
+
+
+V1Encoding = namedtuple('V1Encoding', ['min', 'max'])
+
+
+
    
+[docs]
+class QuantAnalyzer(QuantAnalyzerBase):
+    """
+    QuantAnalyzer tool provides
+
+     1) model sensitivity to weight and activation quantization
+     2) per layer sensitivity analysis
+     3) per layer encoding (min - max range)
+     4) per PDF analysis and
+     5) per layer MSE analysis
+    """
+    @staticmethod
+    def _get_quantsim_cls() -> Type[QuantizationSimModel]:
+        return QuantizationSimModel
+
+    @staticmethod
+    def _get_quant_wrapper_type() -> Tuple[Type]:
+        return (BaseQuantizationMixin,)
+
+    # pylint: disable=no-self-use
+    def _create_and_export_stats_histogram_plot(self,
+                                                quantizer: QuantizerBase,
+                                                results_dir: str,
+                                                title: str,
+                                                ):
+        """
+        For given quantizer, create and export histogram (PDF) of statistics in html format.
+
+        :param quantizer: Quantizer.
+        :param results_dir: Directory to save the results.
+        :param title: Title of the plot.
+        """
+        os.makedirs(results_dir, exist_ok=True)
+
+        assert isinstance(quantizer.encoding_analyzer.observer, _HistogramObserver)
+        v2_histograms = quantizer.encoding_analyzer.observer.get_stats()
+        histograms = self._convert_to_v1_histograms(v2_histograms)
+        encodings = self._get_quantizer_encodings(quantizer)
+
+        for index, (histogram, encoding) in enumerate(zip(histograms, encodings)):
+            export_stats_histogram_plot(histogram, encoding, results_dir, title=f"{title}_{index}")
+
+    @staticmethod
+    def _enable_disable_quantizers(quantizers: List[QuantizerBase], enabled: bool):
+        """
+        For given list of quantizers, set (enable/disable) quantizer's enabled.
+
+        :param quantizers: List of quantizers.
+        :param enabled: Enabled flag.
+        """
+        raise RuntimeError("Changing enabled attribute is not allowed in quantsim v2")
+
+    @classmethod
+    def _disable_param_quantizers(cls, sim: QuantizationSimModel):
+        # pylint: disable=protected-access
+        ctx = contextlib.ExitStack()
+        for quant_wrapper in cls._get_quantized_modules(sim):
+            ctx.enter_context(quant_wrapper._remove_param_quantizers())
+        return ctx
+
+    @classmethod
+    def _disable_activation_quantizers(cls, sim: QuantizationSimModel):
+        # pylint: disable=protected-access
+        ctx = contextlib.ExitStack()
+        for quant_wrapper in cls._get_quantized_modules(sim):
+            ctx.enter_context(quant_wrapper._remove_activation_quantizers())
+        return ctx
+
+    @staticmethod
+    def _disable_quant_wrapper(module: BaseQuantizationMixin):
+        # pylint: disable=protected-access
+        return module._remove_all_quantizers()
+
+    @staticmethod
+    def _convert_to_v1_histograms(histograms: List[_Histogram]) -> List:
+        v1_histograms = []
+        for hist in histograms:
+            assert hist is not None, "Cannot find histogram data in quantizer"
+            hist_sum = torch.sum(hist.histogram).item()
+            v1_hist = []
+            for bin_edge, hist_value in zip(hist.bin_edges, hist.histogram):
+                v1_hist.append((bin_edge.item(), hist_value.item() / hist_sum))
+            v1_histograms.append(v1_hist)
+
+        return v1_histograms
+
+    @staticmethod
+    def _is_quantizer_enabled(quantizer: Optional[QuantizerBase]):
+        return quantizer is not None
+
+    @classmethod
+    def _get_quantizer_encodings(cls, quantizer: QuantizerBase) -> Optional[List]:
+        v1_encodings = []
+
+        encoding = quantizer.get_encodings()
+        if not encoding:
+            return None
+
+        flatten_min = encoding.min.flatten()
+        flatten_max = encoding.max.flatten()
+
+        for encoding_min, encoding_max in zip(flatten_min, flatten_max):
+            v1_encodings.append(V1Encoding(min=encoding_min.item(), max=encoding_max.item()))
+
+        return v1_encodings
+
+    @staticmethod
+    def _get_quantized_modules(sim: QuantizationSimModel) -> Generator[BaseQuantizationMixin, None, None]:
+        for module in sim.model.modules():
+            if isinstance(module, BaseQuantizationMixin):
+                yield module
+
+    @staticmethod
+    def _fold_all_batch_norms(*args, **kwargs):
+        return fold_all_batch_norms(*args, **kwargs)
    
+
+
    +
    +
    +
    + +
    + + +
    +
    +
    +
    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
    + Made with Sphinx and @pradyunsg's + + Furo + +
    +
    +
    + + + + + + +
    +
    +
    + +
    +
    + +
    +
    + + + + + + + + + \ No newline at end of file diff --git a/releases/2.0.0/_modules/aimet_torch/v2/quantization/affine/backends.html b/releases/2.0.0/_modules/aimet_torch/v2/quantization/affine/backends.html new file mode 100644 index 0000000..9af5dfb --- /dev/null +++ b/releases/2.0.0/_modules/aimet_torch/v2/quantization/affine/backends.html @@ -0,0 +1,730 @@ + + + + + + + + aimet_torch.v2.quantization.affine.backends - AIMET + + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
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    +
    AIMET
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    +

    Source code for aimet_torch.v2.quantization.affine.backends

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2023, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+# pylint: disable=all
+
+import math
+from itertools import chain, repeat
+from typing import overload, Union, Tuple, Optional
+import torch
+from .utils import *
+
+
+@overload
+def quantize(tensor: torch.Tensor, scale: torch.Tensor, offset: torch.Tensor,
+             bitwidth: Union[int, float], signed: bool = False,
+             block_size: Optional[Tuple[int, ...]] = None):
+    ...
+
+@overload
+def quantize(tensor: torch.Tensor, scale: torch.Tensor, offset: torch.Tensor, *,
+             num_steps: int, signed: bool = False, block_size: Optional[Tuple[int, ...]] = None):
+    ...
+
+@overload
+def quantize(tensor: torch.Tensor, scale: torch.Tensor, offset: torch.Tensor,
+             qmin: int, qmax: int, block_size: Optional[Tuple[int, ...]] = None):
+    ...
+
+
+
    
+[docs]
+def quantize(tensor: torch.Tensor, scale: torch.Tensor, offset: torch.Tensor,
+             *args, **kwargs):
+    r"""
+    Applies quantization to the input.
+
+    Precisely,
+
+    .. math::
+        out = clamp\left(\left\lceil\frac{input}{scale}\right\rfloor - offset, qmin, qmax\right)
+
+    If block size :math:`B = \begin{pmatrix} B_0  & B_1  & \cdots & B_{D-1} \end{pmatrix}` is specified,
+    this equation will be further generalized as
+
+    .. math::
+        out_{j_0 \cdots j_{D-1}} & = clamp\left(
+            \left\lceil\frac{input_{j_0 \cdots j_{D-1}}}{scale_{i_0 \cdots i_{D-1}}}\right\rfloor
+            - offset_{i_0 \cdots i_{D-1}}, qmin, qmax\right)\\
+
+        \text{where} \quad \forall_{0 \leq d < D} \quad i_d = \left\lfloor \frac{j_d}{B_d} \right\rfloor
+
+    This function is overloaded with the signatures listed below:
+
+
+    .. function:: quantize(tensor, scale, offset, bitwidth, signed=False, block_size=None)
+       :noindex:
+
+       Equivalent to:
+
+       .. math::
+           qmin= 
+           \begin{cases}
+               -\left\lceil\frac{2^{bitwidth}-1}{2}\right\rceil,& \text{if } signed\\
+               0,                                               & \text{otherwise   (default)}
+           \end{cases}
+           qmax= 
+           \begin{cases}
+               \left\lfloor\frac{2^{bitwidth}-1}{2}\right\rfloor,& \text{if } signed\\
+               2^{bitwidth}-1,                                   & \text{otherwise   (default)}
+           \end{cases}
+
+       :param Tensor tensor: Tensor to quantize
+       :param Tensor scale: Scale for quantization
+       :param Tensor offset: Offset for quantization
+       :param int bitwidth: Bitwidth of quantized tensor based on which :math:`qmin` and :math:`qmax` will be derived
+       :param bool signed: If false, the output will be mapped to positive integers only.
+           Otherwise, it will range over both positive and negative integers.
+       :param block_size: Block size
+       :type block_size: Tuple[int, ...], optional
+
+    .. function:: quantize(tensor, scale, offset, *, num_steps, signed=False, block_size=None)
+       :noindex:
+
+       Equivalent to:
+
+       .. math::
+           qmin= 
+           \begin{cases}
+               -\left\lceil\frac{num\_steps}{2}\right\rceil,& \text{if } signed\\
+               0,                                           & \text{otherwise   (default)}
+           \end{cases}
+           qmax= 
+           \begin{cases}
+               \left\lfloor\frac{num\_steps}{2}\right\rfloor,& \text{if } signed\\
+               num\_steps,                                   & \text{otherwise   (default)}
+           \end{cases}
+
+
+       :param Tensor tensor: Tensor to quantize
+       :param Tensor scale: Scale for quantization
+       :param Tensor offset: Offset for quantization
+       :param int num_steps: The number of steps in the quantization range based on which :math:`qmin` and :math:`qmax` will be derived
+       :param bool signed: If false, the output will be mapped to positive integers only.
+           Otherwise, it will range over both positive and negative integers.
+       :param block_size: Block size
+       :type block_size: Tuple[int, ...], optional
+
+    .. function:: quantize(tensor, scale, offset, *, qmin, qmax, block_size=None)
+       :noindex:
+
+       :param Tensor tensor: Tensor to quantize
+       :param Tensor scale: Scale for quantization
+       :param Tensor offset: Offset for quantization
+       :param int qmin: Minimum value of the quantization range
+       :param int qmax: Maximum value of the quantization range
+       :param block_size: Block size
+       :type block_size: Tuple[int, ...], optional
+
+
+    Examples:
+
+        >>> import aimet_torch.v2.quantization as Q
+        >>> input = torch.arange(start=-0.3, end=1.3, step=0.05)
+        >>> print(input)
+        tensor([-3.0000e-01, -2.5000e-01, -2.0000e-01, -1.5000e-01, -1.0000e-01,
+                -5.0000e-02, -1.1921e-08,  5.0000e-02,  1.0000e-01,  1.5000e-01,
+                2.0000e-01,  2.5000e-01,  3.0000e-01,  3.5000e-01,  4.0000e-01,
+                4.5000e-01,  5.0000e-01,  5.5000e-01,  6.0000e-01,  6.5000e-01,
+                7.0000e-01,  7.5000e-01,  8.0000e-01,  8.5000e-01,  9.0000e-01,
+                9.5000e-01,  1.0000e+00,  1.0500e+00,  1.1000e+00,  1.1500e+00,
+                1.2000e+00,  1.2500e+00])
+        >>> scale = torch.tensor(1/15)
+        >>> offset = torch.tensor(0.0)
+        >>> Q.affine.quantize(input, scale, offset, bitwidth=4)
+        tensor([ 0.,  0.,  0.,  0.,  0.,  0., -0.,  1.,  2.,  2.,  3.,  4.,  4.,  5.,
+                 6.,  7.,  7.,  8.,  9., 10., 10., 11., 12., 13., 13., 14., 15., 15.,
+                 15., 15., 15., 15.])
+        >>> Q.affine.quantize(input, scale, offset, num_steps=15)
+        tensor([ 0.,  0.,  0.,  0.,  0.,  0., -0.,  1.,  2.,  2.,  3.,  4.,  4.,  5.,
+                 6.,  7.,  7.,  8.,  9., 10., 10., 11., 12., 13., 13., 14., 15., 15.,
+                 15., 15., 15., 15.])
+        >>> Q.affine.quantize(input, scale, offset, qmin=0, qmax=15)
+        tensor([ 0.,  0.,  0.,  0.,  0.,  0., -0.,  1.,  2.,  2.,  3.,  4.,  4.,  5.,
+                 6.,  7.,  7.,  8.,  9., 10., 10., 11., 12., 13., 13., 14., 15., 15.,
+                 15., 15., 15., 15.])
+    """
+    qmin, qmax, block_size = _parse_args(args, kwargs)
+    return get_backend().quantize(tensor, scale, offset, qmin, qmax, block_size)
    
+
+
+
+@overload
+def quantize_dequantize(tensor: torch.Tensor, scale: torch.Tensor, offset: torch.Tensor,
+                        bitwidth: Union[int, float], signed: bool = False,
+                        block_size: Optional[Tuple[int, ...]] = None):
+    ...
+
+@overload
+def quantize_dequantize(tensor: torch.Tensor, scale: torch.Tensor, offset: torch.Tensor, *,
+                        num_steps: int, signed: bool = False, block_size: Optional[Tuple[int, ...]] = None):
+    ...
+
+@overload
+def quantize_dequantize(tensor: torch.Tensor, scale: torch.Tensor, offset: torch.Tensor,
+                        qmin: int, qmax: int, block_size: Optional[Tuple[int, ...]] = None):
+    ...
+
+
+
    
+[docs]
+def quantize_dequantize(tensor: torch.Tensor, scale: torch.Tensor, offset: torch.Tensor,
+                        *args, **kwargs):
+    r"""
+    Applies fake-quantization by quantizing and dequantizing the input.
+
+    Precisely,
+
+    .. math::
+        out = (\overline{input} + offset) * scale
+
+    where
+
+    .. math::
+        \overline{input} = clamp\left(\left\lceil\frac{input}{scale}\right\rfloor - offset, qmin, qmax\right)
+
+
+    If block size :math:`B = \begin{pmatrix} B_0  & B_1  & \cdots & B_{D-1} \end{pmatrix}` is specified,
+    this equation will be further generalized as
+
+    .. math::
+        out_{j_0 \cdots j_{D-1}} &= (\overline{input}_{j_0 \cdots j_{D-1}} + offset_{i_0 \cdots i_{D-1}}) * scale_{i_0 \cdots i_{D-1}}\\
+        \overline{input}_{j_0 \cdots j_{D-1}} &= clamp\left(
+            \left\lceil\frac{input_{j_0 \cdots j_{D-1}}}{scale_{i_0 \cdots i_{D-1}}}\right\rfloor
+            - offset_{i_0 \cdots i_{D-1}}, qmin, qmax\right)\\
+
+        \text{where } \quad \forall_{0 \leq d < D} \quad i_d = \left\lfloor \frac{j_d}{B_d} \right\rfloor
+
+
+    This function is overloaded with the signatures listed below:
+
+
+    .. function:: quantize_dequantize(tensor, scale, offset, bitwidth, signed=False, block_size=None)
+       :noindex:
+
+       Equivalent to:
+
+       .. math::
+           qmin= 
+           \begin{cases}
+               -\left\lceil\frac{2^{bitwidth}-1}{2}\right\rceil,& \text{if } signed\\
+               0,                                               & \text{otherwise   (default)}
+           \end{cases}
+           qmax= 
+           \begin{cases}
+               \left\lfloor\frac{2^{bitwidth}-1}{2}\right\rfloor,& \text{if } signed\\
+               2^{bitwidth}-1,                                   & \text{otherwise   (default)}
+           \end{cases}
+
+       :param Tensor tensor: Tensor to quantize
+       :param Tensor scale: Scale for quantization
+       :param Tensor offset: Offset for quantization
+       :param int bitwidth: Bitwidth of quantized tensor based on which :math:`qmin` and :math:`qmax` will be derived
+       :param bool signed: If false, :math:`\overline{input}` will be mapped to positive integers only.
+           Otherwise, :math:`\overline{input}` will range over both positive and negative integers.
+       :param block_size: Block size
+       :type block_size: Tuple[int, ...], optional
+
+    .. function:: quantize_dequantize(tensor, scale, offset, *, num_steps, signed=False, block_size=None)
+       :noindex:
+
+       Equivalent to:
+
+       .. math::
+           qmin= 
+           \begin{cases}
+               -\left\lceil\frac{num\_steps}{2}\right\rceil,& \text{if } signed\\
+               0,                                           & \text{otherwise   (default)}
+           \end{cases}
+           qmax= 
+           \begin{cases}
+               \left\lfloor\frac{num\_steps}{2}\right\rfloor,& \text{if } signed\\
+               num\_steps,                                   & \text{otherwise   (default)}
+           \end{cases}
+
+
+       :param Tensor tensor: Tensor to quantize
+       :param Tensor scale: Scale for quantization
+       :param Tensor offset: Offset for quantization
+       :param int num_steps: The number of steps in the quantization range based on which :math:`qmin` and :math:`qmax` will be derived
+       :param bool signed: If false, :math:`\overline{input}` will be mapped to positive integers only.
+           Otherwise, :math:`\overline{input}` will range over both positive and negative integers.
+       :param block_size: Block size
+       :type block_size: Tuple[int, ...], optional
+
+    .. function:: quantize_dequantize(tensor, scale, offset, *, qmin, qmax, block_size=None)
+       :noindex:
+
+       :param Tensor tensor: Tensor to quantize
+       :param Tensor scale: Scale for quantization
+       :param Tensor offset: Offset for quantization
+       :param int qmin: Minimum value of the quantization range
+       :param int qmax: Maximum value of the quantization range
+       :param block_size: Block size
+       :type block_size: Tuple[int, ...], optional
+
+
+    Examples:
+
+        >>> import aimet_torch.v2.quantization as Q
+        >>> input = torch.arange(start=-0.3, end=1.3, step=0.05)
+        >>> print(input)
+        tensor([-3.0000e-01, -2.5000e-01, -2.0000e-01, -1.5000e-01, -1.0000e-01,
+                -5.0000e-02, -1.1921e-08,  5.0000e-02,  1.0000e-01,  1.5000e-01,
+                2.0000e-01,  2.5000e-01,  3.0000e-01,  3.5000e-01,  4.0000e-01,
+                4.5000e-01,  5.0000e-01,  5.5000e-01,  6.0000e-01,  6.5000e-01,
+                7.0000e-01,  7.5000e-01,  8.0000e-01,  8.5000e-01,  9.0000e-01,
+                9.5000e-01,  1.0000e+00,  1.0500e+00,  1.1000e+00,  1.1500e+00,
+                1.2000e+00,  1.2500e+00])
+        >>> scale = torch.tensor(1/15)
+        >>> offset = torch.tensor(0.0)
+        >>> Q.affine.quantize_dequantize(input, scale, offset, bitwidth=4)
+        tensor([0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0667, 0.1333,
+                0.1333, 0.2000, 0.2667, 0.2667, 0.3333, 0.4000, 0.4667, 0.4667, 0.5333,
+                0.6000, 0.6667, 0.6667, 0.7333, 0.8000, 0.8667, 0.8667, 0.9333, 1.0000,
+                1.0000, 1.0000, 1.0000, 1.0000, 1.0000])
+        >>> Q.affine.quantize_dequantize(input, scale, offset, num_steps=15)
+        tensor([0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0667, 0.1333,
+                0.1333, 0.2000, 0.2667, 0.2667, 0.3333, 0.4000, 0.4667, 0.4667, 0.5333,
+                0.6000, 0.6667, 0.6667, 0.7333, 0.8000, 0.8667, 0.8667, 0.9333, 1.0000,
+                1.0000, 1.0000, 1.0000, 1.0000, 1.0000])
+        >>> Q.affine.quantize_dequantize(input, scale, offset, qmin=0, qmax=15)
+        tensor([0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0667, 0.1333,
+                0.1333, 0.2000, 0.2667, 0.2667, 0.3333, 0.4000, 0.4667, 0.4667, 0.5333,
+                0.6000, 0.6667, 0.6667, 0.7333, 0.8000, 0.8667, 0.8667, 0.9333, 1.0000,
+                1.0000, 1.0000, 1.0000, 1.0000, 1.0000])
+    """
+    qmin, qmax, block_size = _parse_args(args, kwargs)
+    return get_backend().quantize_dequantize(tensor, scale, offset, qmin, qmax, block_size)
    
+
+
+
+
    
+[docs]
+def dequantize(tensor: torch.Tensor, scale: torch.Tensor, offset: torch.Tensor,
+               block_size: Optional[Tuple[int, ...]] = None):
+    r"""
+    Applies dequantization to the input.
+
+    Precisely,
+
+    .. math::
+        out = (input + offset) * scale
+
+    If block size :math:`B = \begin{pmatrix} B_0  & B_1  & \cdots & B_{D-1} \end{pmatrix}` is specified,
+    this equation will be further generalized as
+
+    .. math::
+        out_{j_0 \cdots j_{D-1}} & = (input_{j_0 \cdots j_{D-1}} + offset_{i_0 \cdots i_{D-1}}) * scale_{i_0 \cdots i_{D-1}}
+
+        \text{where} \quad \forall_{0 \leq d < D} \quad i_d = \left\lfloor \frac{j_d}{B_d} \right\rfloor
+
+    :param Tensor tensor: Tensor to dequantize
+    :param Tensor scale: Scale for dequantization
+    :param Tensor offset: Offset for dequantization
+    :param block_size: Block size
+    :type block_size: Tuple[int, ...], optional
+    """
+    return get_backend().dequantize(tensor, scale, offset, block_size)
    
+
+
+
+def _parse_args(args, kwargs) -> Tuple[int, int, Optional[Tuple[int, ...]]]:
+    bitwidth = num_steps = signed = qmin = qmax = None
+
+    # Pad positional args with None's such that len(args) == 3
+    args = tuple(chain(args, repeat(None, 3 - len(args))))
+    arg0 = kwargs.get('qmin', kwargs.get('bitwidth', args[0]))
+    arg1 = kwargs.get('qmax', kwargs.get('signed', args[1]))
+    block_size = kwargs.get('block_size', None) or args[2]
+
+    if arg0 is None:
+        num_steps = kwargs['num_steps']
+        signed = kwargs['signed']
+        qmin, qmax = _derive_qmin_qmax(num_steps=num_steps, signed=signed)
+    elif arg1 is None or isinstance(arg1, bool):
+        bitwidth, signed = arg0, bool(arg1)
+        qmin, qmax = _derive_qmin_qmax(bitwidth=bitwidth, signed=signed)
+    else:
+        qmin, qmax = arg0, arg1
+
+    assert qmin is not None
+    assert qmax is not None
+
+    return qmin, qmax, block_size
+
+
+def _derive_qmin_qmax(*, bitwidth: int = None, num_steps: int = None, signed: bool):
+    if bitwidth is not None:
+        num_steps = 2 ** bitwidth - 1
+
+    if signed:
+        qmin = -math.ceil(num_steps/2)
+        qmax = math.floor(num_steps/2)
+    else:
+        qmin = 0
+        qmax = num_steps
+
+    return qmin, qmax
+
    +
    +
    +
    + +
    + + +
    +
    +
    +
    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
    + Made with Sphinx and @pradyunsg's + + Furo + +
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    +

    Source code for aimet_torch.v2.quantization.affine.quantizer

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2023-2024, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+# pylint: disable=redefined-builtin
+""" Affine quantizers """
+
+import abc
+from itertools import chain, repeat
+from typing import Optional, List, Dict, Tuple, overload
+import contextlib
+import functools
+
+import torch
+from torch import nn
+
+from aimet_torch.v2.utils import patch_attr, _is_expandable, StatisticsNotFoundError, docstring
+from aimet_torch.v2.quantization.encoding_analyzer import EncodingAnalyzer, MinMaxEncodingAnalyzer, _flag_extreme_min_max
+from aimet_torch.v2.quantization.affine import AffineEncoding, GroupedBlockEncoding
+from aimet_torch.v2.quantization.tensor import QuantizedTensor, DequantizedTensor
+from aimet_torch.v2.quantization.base import QuantizerBase
+from aimet_torch.v2.quantization.affine.backends import quantize, quantize_dequantize, torch_builtins, _derive_qmin_qmax
+from aimet_torch.v2.utils import ste_round
+from aimet_torch.v2.deepspeed_utils import SafeGatheredParameters
+from ._utils import _GridMixin, _register_signature # pylint: disable=import-error
+
+
+__all__ = ['AffineQuantizerBase', 'MinMaxQuantizer', 'Quantize', 'QuantizeDequantize',
+           'GroupedBlockQuantizeDequantize']
+
+
+
+class AffineQuantizerBase(QuantizerBase, _GridMixin):
+    """
+    Base class for linear quantization modules.
+
+    Args:
+        shape (tuple): Shape of the quantization parameters
+        bitwidth (int): Quantization bitwidth
+        symmetric (bool): If True, performs symmetric quantization;
+                          otherwise, performs asymmetric quantization
+        encoding_analyzer (EncodingAnalyzer, optional): Encoding analyzer for calibrating quantization encodings
+                                                        (default: absolute min-max encoding analyzer)
+
+    """
+    _init_signatures = []
+
+    @overload
+    @_register_signature(_init_signatures)
+    def __init__(self, shape, qmin: int, qmax: int, symmetric: bool, encoding_analyzer: EncodingAnalyzer = None,
+                 block_size: Optional[Tuple[int, ...]] = None):
+        ...
+
+    @overload
+    @_register_signature(_init_signatures)
+    def __init__(self, shape, bitwidth: int, symmetric: bool, encoding_analyzer: EncodingAnalyzer = None,
+                 block_size: Optional[Tuple[int, ...]] = None):
+        ...
+
+    def __init__(self, shape, *args, **kwargs):
+        super().__init__()
+        if isinstance(shape, int):
+            shape = (shape,)
+        self.shape = tuple(shape)
+        full_args = (shape, *args)
+
+        # Pad positional args with None's such that len(args) == 5
+        args = tuple(chain(args, repeat(None, 5 - len(args))))
+        arg0 = kwargs.pop('qmin', kwargs.pop('bitwidth', args[0]))
+        arg1 = kwargs.pop('qmax', args[1])
+
+        if arg1 is not None and not isinstance(arg1, bool):
+            # (arg0, arg1, arg2) == (qmin, qmax, symmetric)
+            qmin, qmax = arg0, arg1
+            symmetric = kwargs.pop('symmetric', args[2])
+
+            if (qmin is None) or (qmax is None) or (symmetric is None):
+                raise self._arg_parsing_error(full_args, kwargs)
+
+            encoding_analyzer = kwargs.pop('encoding_analyzer', args[3])
+            block_size = kwargs.pop('block_size', args[4])
+        else:
+            # (arg0, arg1) == (bitwidth, symmetric)
+            bitwidth = arg0
+            symmetric = kwargs.pop('symmetric', args[1])
+
+            if (bitwidth is None) or (symmetric is None):
+                raise self._arg_parsing_error(full_args, kwargs)
+
+            # We support two quantization modes: (unsigned) asymmetric and signed-symmetric
+            qmin, qmax = _derive_qmin_qmax(bitwidth=bitwidth, signed=symmetric)
+            encoding_analyzer = kwargs.pop('encoding_analyzer', args[2])
+            block_size = kwargs.pop('block_size', args[3])
+
+        assert qmin is not None
+        assert qmax is not None
+
+        if kwargs:
+            cls = type(self).__qualname__
+            unexpected_keys = ', '.join(kwargs.keys())
+            raise TypeError(f"{cls}.__init__ got unexpected keyword argument: {unexpected_keys}")
+
+        if qmin >= qmax:
+            raise ValueError(f"qmax should be strictly larger than qmin. Got qmax={qmax}, qmin={qmin}")
+
+        self.qmin = qmin
+        self.qmax = qmax
+        self._symmetric = symmetric
+        self.block_size = block_size
+
+        self.encoding_analyzer = encoding_analyzer or \
+                                 MinMaxEncodingAnalyzer(torch_builtins.get_encoding_shape_with_blocks(self.shape,
+                                                                                                      self.block_size))
+
+        if self.block_size is None and not _is_expandable(self.encoding_analyzer.observer.shape, self.shape):
+            raise RuntimeError(f'Encoding analyzer of shape {self.encoding_analyzer.observer.shape} '
+                               f'is incompatible with quantizer of shape {self.shape}.')
+
+    @abc.abstractmethod
+    def get_min(self, dtype=None) -> torch.Tensor:
+        """
+        Compute quantization min to be used for forward pass.
+        Return None f the quantizer is not initialized yet.
+
+        Args:
+            dtype (torch.dtype): dtype of the computed min
+
+        Returns:
+            Quantization min
+
+        """
+
+    @abc.abstractmethod
+    def get_max(self, dtype=None) -> torch.Tensor:
+        """
+        Compute quantization max to be used for forward pass.
+        Return None f the quantizer is not initialized yet.
+
+        Args:
+            dtype (torch.dtype): dtype of the computed max
+
+        Returns:
+            Quantization max
+
+        """
+
+    @abc.abstractmethod
+    def get_scale(self, dtype=None) -> torch.Tensor:
+        """
+        Compute quantization scale to be used for forward pass.
+        Return None f the quantizer is not initialized yet.
+
+        Args:
+            dtype (torch.dtype): dtype of the computed scale
+
+        Returns:
+            Quantization scale
+
+        """
+
+    @abc.abstractmethod
+    def get_offset(self, dtype=None) -> torch.Tensor:
+        """
+        Compute quantization offset to be used for forward pass.
+        Return None f the quantizer is not initialized yet.
+
+        Args:
+            dtype (torch.dtype): dtype of the computed offset
+
+        Returns:
+            Quantization offset
+
+        """
+
+    @abc.abstractmethod
+    def set_range(self, min: torch.Tensor, max: torch.Tensor):
+        """
+        Set quantization parameters to the given min-max range
+        """
+
+    def get_encodings(self) -> Optional[AffineEncoding]:
+        """
+        Return the quantizer's encodings as an AffineEncoding object
+        """
+        if self.is_initialized():
+            return AffineEncoding(self.get_scale(dtype=torch.float32),
+                                  self.get_offset(dtype=torch.float32),
+                                  self.qmin, self.qmax, self._symmetric, self.block_size)
+        return None
+
+    @torch.no_grad()
+    def get_legacy_encodings(self) -> Optional[List[Dict]]:
+        """
+        Returns a list of encodings, each represented as a List of Dicts
+        """
+        # pylint: disable=redefined-builtin, protected-access
+
+        if not self.is_initialized():
+            return None
+
+        return self.get_encodings()._to_legacy_format()
+
+    @torch.no_grad()
+    def set_legacy_encodings(self, encodings: List[Dict]):
+        """
+        Set encodings represented in the same format as the output of get_legacy_encodings as below:
+
+        [
+            {'min': float, 'max': float, 'scale': float, 'offset': float,
+                     'bitwidth': int, 'dtype': str, 'is_symmetric': str},
+            {'min': float, 'max': float, 'scale': float, 'offset': float,
+                     'bitwidth': int, 'dtype': str, 'is_symmetric': str},
+            ...
+        ]
+        """
+        def str_to_bool(s: str):
+            s = s.lower()
+            if s == "false":
+                return False
+            if s == "true":
+                return True
+            raise ValueError
+
+        bitwidth = encodings[0]['bitwidth']
+        symmetric = str_to_bool(encodings[0]['is_symmetric'])
+        # We support two quantization modes: (unsigned) asymmetric and signed-symmetric
+        self.qmin, self.qmax = _derive_qmin_qmax(bitwidth=bitwidth, signed=symmetric)
+        self.symmetric = symmetric
+        # Note: We can only accurately infer signed-ness in the symmetric case, but AIMET uses unsigned for asymmetric
+        min_ = torch.tensor([e['min'] for e in encodings]).view(self.shape)
+        max_ = torch.tensor([e['max'] for e in encodings]).view(self.shape)
+        self.set_range(min_, max_)
+
+    def extra_repr(self) -> str:
+        extra_repr = f'shape={self.shape}'
+
+        if self.block_size is not None:
+            extra_repr += f", block_size={self.block_size}"
+
+        extra_repr += f', qmin={self.qmin}, qmax={self.qmax}, symmetric={self.symmetric}'
+        return extra_repr
+
+    @property
+    def symmetric(self) -> bool:
+        """
+        Indicates whether this quantizer uses symmetric quantization
+        """
+        return self._symmetric
+
+    @symmetric.setter
+    def symmetric(self, symmetric: bool):
+        """
+        Set the quantizer symmetry
+
+        :param symmetric: If True, use symmetric encodings. Else, use asymmetric encodings
+        """
+        self._symmetric = symmetric
+
+    @property
+    @docstring(_GridMixin._get_bitwidth.__doc__)
+    def bitwidth(self) -> int: # pylint: disable=missing-function-docstring
+        return self._get_bitwidth()
+
+    @bitwidth.setter
+    def bitwidth(self, bitwidth: int):
+        self._set_bitwidth(bitwidth)
+
+    @property
+    @docstring(_GridMixin._get_signed.__doc__)
+    def signed(self) -> bool: # pylint: disable=missing-function-docstring
+        return self._get_signed()
+
+    @signed.setter
+    def signed(self, signed: bool):
+        self._set_signed(signed)
+
+
+class MinMaxQuantizer(AffineQuantizerBase): # pylint: disable=abstract-method
+    """
+    Affine quantizer with min-max as trainable parameters
+    """
+
+    min: torch.nn.Parameter
+    max: torch.nn.Parameter
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        self.register_quantization_parameter('min', nn.Parameter(-torch.ones(self.shape)))
+        self.register_quantization_parameter('max', nn.Parameter(torch.ones(self.shape)))
+
+    @contextlib.contextmanager
+    def compute_encodings(self):
+        """
+        Observe inputs and update quantization parameters based on the input statistics.
+        During ``compute_encodings`` is enabled, the quantizer forward pass performs
+        dynamic quantization using the batch statistics.
+        """
+        if not self._allow_overwrite:
+            yield
+            return
+
+        original_forward = self.forward
+
+        @functools.wraps(original_forward)
+        def forward_wrapper(input):
+            input = input.as_subclass(torch.Tensor)
+            expanded_input = torch_builtins.reshape_tensor_for_blocks(input, self.shape, self.block_size)
+            batch_statistics = self.encoding_analyzer.update_stats(expanded_input)
+            num_steps = self.qmax - self.qmin
+            dynamic_min, dynamic_max =\
+                    self.encoding_analyzer.compute_encodings_from_stats(batch_statistics,
+                                                                        num_steps,
+                                                                        self.symmetric)
+            if self.block_size is not None:
+                dynamic_min = dynamic_min.view(self.min.shape)
+                dynamic_max = dynamic_max.view(self.max.shape)
+            dynamic_min = dynamic_min.to(dtype=self.min.dtype,
+                                         device=self.min.device).expand_as(self.min)
+            dynamic_max = dynamic_max.to(dtype=self.max.dtype,
+                                         device=self.max.device).expand_as(self.max)
+
+            with patch_attr(self, 'min', dynamic_min),\
+                    patch_attr(self, 'max', dynamic_max):
+                return original_forward(input)
+
+        self.encoding_analyzer.reset_stats()
+
+        try:
+            with patch_attr(self, 'forward', forward_wrapper):
+                yield
+        except: # pylint: disable=try-except-raise
+            raise
+        else:
+            try:
+                num_steps = self.qmax - self.qmin
+                enc_min, enc_max = self.encoding_analyzer.compute_encodings(num_steps, self.symmetric)
+                if self.block_size is not None:
+                    enc_min = enc_min.view(self.min.shape)
+                    enc_max = enc_max.view(self.max.shape)
+                _flag_extreme_min_max(enc_min, enc_max)
+
+            except StatisticsNotFoundError:
+                return
+
+            if enc_min is None or enc_max is None:
+                return
+
+            self.set_range(enc_min, enc_max)
+
+    def get_min(self, dtype=None) -> Optional[torch.Tensor]:
+        """
+        Compute quantization min to be used for forward pass.
+
+        NOTE: self.min may not be equal to self.get_min().
+              self.get_min() returns slightly recalibrated version of self.min.
+
+        :param dtype: dtype of the computed min. Use of self.min.dtype by default.
+        :return: Quantization min
+        """
+        if not self.is_initialized():
+            return None
+        return self.get_scale(dtype) * (self.get_offset(dtype) + self.qmin)
+
+    def get_max(self, dtype=None) -> Optional[torch.Tensor]:
+        """
+        Compute quantization max to be used for forward pass.
+
+        NOTE: self.max may not be equal to self.get_max()
+              self.get_max() returns slightly recalibrated version of self.max.
+
+        :param dtype: dtype of the computed max. Use of self.min.dtype by default.
+        :return: Quantization max
+        """
+        if not self.is_initialized():
+            return None
+        return self.get_scale(dtype) * (self.get_offset(dtype) + self.qmax)
+
+    def get_scale(self, dtype=None) -> Optional[torch.Tensor]:
+        """
+        Compute quantization scale to be used for forward pass.
+
+        :param dtype: dtype of the computed scale. Use of self.min.dtype by default.
+        :return: Quantization scale
+        """
+        if not self.is_initialized():
+            return None
+
+        dtype = dtype or torch.float32
+        num_steps = self.qmax - self.qmin
+
+        scale = (self.max.to(dtype) - self.min.to(dtype)) / num_steps
+        return scale.to(dtype)
+
+    def get_offset(self, dtype=None) -> Optional[torch.Tensor]:
+        """
+        Compute quantization offset to be used for forward pass.
+
+        :param dtype: dtype of the computed offset. Use of self.min.dtype by default.
+        :return: Quantization offset
+        """
+        if not self.is_initialized():
+            return None
+
+        dtype = dtype or torch.float32
+
+        if self.symmetric:
+            offset = torch.full_like(self.min,
+                                     fill_value=-round((self.qmin + self.qmax) / 2),
+                                     requires_grad=False,
+                                     dtype=dtype)
+        else:
+            offset = ste_round(self.min.to(dtype) / self.get_scale(dtype)) - self.qmin
+
+        return offset.to(dtype)
+
+    def set_range(self, min: torch.Tensor, max: torch.Tensor):
+        """
+        Set quantization parameters to the given min-max range
+        """
+        with torch.no_grad(), SafeGatheredParameters(self.parameters(recurse=False), modifier_rank=0):
+            self.min.copy_(min)
+            self.max.copy_(max)
+
+
+
    
+[docs]
+class Quantize(MinMaxQuantizer):
+    r"""Applies quantization to the input.
+
+    Precisely,
+
+    .. math::
+        out = clamp\left(\left\lceil\frac{input}{scale}\right\rfloor - offset, qmin, qmax\right)
+
+    where :math:`scale` and :math:`offset` are derived from learnable parameters
+    :math:`\theta_{min}` and :math:`\theta_{max}`.
+
+    If block size :math:`B = \begin{pmatrix} B_0  & B_1  & \cdots & B_{D-1} \end{pmatrix}` is specified,
+    this equation will be further generalized as
+
+    .. math::
+        out_{j_0 \cdots j_{D-1}} & = clamp\left(
+            \left\lceil\frac{input_{j_0 \cdots j_{D-1}}}{scale_{i_0 \cdots i_{D-1}}}\right\rfloor
+            - offset_{i_0 \cdots i_{D-1}}, qmin, qmax\right)\\
+
+        \text{where} \quad \forall_{0 \leq d < D} \quad i_d = \left\lfloor \frac{j_d}{B_d} \right\rfloor
+
+    Args:
+        shape (tuple): Shape of the quantization parameters
+        bitwidth (int): Quantization bitwidth
+        symmetric (bool): If True, performs symmetric quantization;
+                          otherwise, performs asymmetric quantization
+        encoding_analyzer (EncodingAnalyzer, optional): Encoding analyzer for calibrating quantization encodings
+                                                        (default: absolute min-max encoding analyzer)
+        block_size (Tuple[int, ...], optional): Block size
+
+    :ivar Tensor min: :math:`\theta_{min}` from which scale and offset will be derived.
+    :ivar Tensor max: :math:`\theta_{max}` from which scale and offset will be derived.
+
+    .. note::
+        :class:`Quantize` cannot run :meth:`forward` until :attr:`min` and :attr:`max` are properly initialized,
+        which can be done based on input statistics using :meth:`compute_encodings` or
+        by manually assigning a new value to :attr:`min` and :attr:`max`.
+        See the examples below.
+
+    Examples:
+
+        >>> import aimet_torch.v2.quantization as Q
+        >>> input = torch.randn(5, 10)
+        >>> q = Q.affine.Quantize(shape=(5, 1), bitwidth=8, symmetric=False, block_size=(1, 5))
+        >>> q.is_initialized()
+        False
+        >>> with q.compute_encodings():
+        ...     _ = q(input)
+        ...
+        >>> q.is_initialized()
+        True
+        >>> q(input)
+        QuantizedTensor([[129.,  64., 255., 122.,   0., 192., 106.,  94., 255.,   0.],
+                         [  0., 145., 181., 255., 144., 255., 194.,   0.,  74.,  86.],
+                         [122.,   0., 255., 150.,  33., 103., 103.,   0.,  37., 255.],
+                         [255., 111., 237., 218.,   0.,  49., 155., 255.,   0., 179.],
+                         [  0.,  66., 255.,  89., 110.,  17.,  36.,  83., 255.,   0.]],
+                        grad_fn=<AliasBackward0>)
+
+
+        >>> import aimet_torch.v2.quantization as Q
+        >>> input = torch.randn(5, 10)
+        >>> q = Q.affine.Quantize(shape=(5, 1), bitwidth=8, symmetric=False, block_size=(1, 5))
+        >>> q.is_initialized()
+        False
+        >>> q.min = torch.nn.Parameter(-torch.ones_like(q.min))
+        >>> q.max = torch.nn.Parameter(torch.ones_like(q.max))
+        >>> q.is_initialized()
+        True
+        >>> q(input)
+        QuantizedTensor([[187., 186., 131.,   0., 203.,  64.,  80.,   0., 143., 152.],
+                         [ 16.,   0., 255.,   0.,   0., 150.,   0., 255.,  32., 255.],
+                         [255., 226.,   0., 255.,  55., 172.,   0., 255., 145., 255.],
+                         [207., 146., 216., 238.,   0.,   0., 141., 178., 255., 188.],
+                         [ 63.,  59.,  19., 162.,  30., 255., 109., 255.,   0., 255.]],
+                        grad_fn=<AliasBackward0>)
+    """
+    # NOTE: Deepspeed has a bug where it will inadvertently patch __init__ method permanently
+    #       unless each leaf class explicitly defines its own __init__ separately.
+    #       As a temporary workaround, we define __init__ to avoid triggering this bug.
+    # pylint: disable=useless-super-delegation
+    def __init__(self, shape, *args, **kwargs):
+        super().__init__(shape, *args, **kwargs)
+
+
    
+[docs]
+    def forward(self, input: torch.Tensor) -> QuantizedTensor:
+        """Quantizes the input tensor
+
+        Args:
+            input (torch.Tensor): Input to quantize
+
+        Returns:
+            Quantized output
+
+        """
+        if not self.is_initialized():
+            raise RuntimeError(
+                'Failed to run Quantize since quantization parameters are not initialized.'
+                ' Please initialize the quantization parameters using `compute_encodings()`.'
+            )
+
+        encoding = self.get_encodings()
+
+        # Subclasses of torch.Tensor with custom __torch_function__ (in our case, QuantizedTensorBase)
+        # is known to introduce substantial CPU overhead.
+        # Cast types of the inputs to plain torch.Tensor for faster execution.
+        input = input.as_subclass(torch.Tensor)
+
+        output = quantize(input,
+                          encoding.scale,
+                          encoding.offset,
+                          encoding.qmin,
+                          encoding.qmax,
+                          block_size=self.block_size)
+        output = output.as_subclass(QuantizedTensor)
+        output.encoding = encoding
+        return output
    
+
    
+
+
+
+
    
+[docs]
+class QuantizeDequantize(MinMaxQuantizer):
+    r"""Applies fake-quantization by quantizing and dequantizing the input.
+
+    Precisely,
+
+    .. math::
+        out = (\overline{input} + offset) * scale
+
+    where
+
+    .. math::
+        \overline{input} = clamp\left(\left\lceil\frac{input}{scale}\right\rfloor - offset, qmin, qmax\right)
+
+    and :math:`scale` and :math:`offset` are derived from learnable parameters
+    :math:`\theta_{min}` and :math:`\theta_{max}`.
+
+    If block size :math:`B = \begin{pmatrix} B_0  & B_1  & \cdots & B_{D-1} \end{pmatrix}` is specified,
+    this equation will be further generalized as
+
+    .. math::
+        out_{j_0 \cdots j_{D-1}} &= (\overline{input}_{j_0 \cdots j_{D-1}} + offset_{i_0 \cdots i_{D-1}}) * scale_{i_0 \cdots i_{D-1}}\\
+        \overline{input}_{j_0 \cdots j_{D-1}} &= clamp\left(
+            \left\lceil\frac{input_{j_0 \cdots j_{D-1}}}{scale_{i_0 \cdots i_{D-1}}}\right\rfloor
+            - offset_{i_0 \cdots i_{D-1}}, qmin, qmax\right)\\
+
+        \text{where} \quad \forall_{0 \leq d < D} \quad i_d = \left\lfloor \frac{j_d}{B_d} \right\rfloor
+
+    Args:
+        shape (tuple): Shape of the quantization parameters
+        bitwidth (int): Quantization bitwidth
+        symmetric (bool): If True, performs symmetric quantization;
+                          otherwise, performs asymmetric quantization
+        encoding_analyzer (EncodingAnalyzer, optional): Encoding analyzer for calibrating quantization encodings
+                                                        (default: absolute min-max encoding analyzer)
+        block_size (Tuple[int, ...], optional): Block size
+
+    :ivar Tensor min: :math:`\theta_{min}` from which scale and offset will be derived.
+    :ivar Tensor max: :math:`\theta_{max}` from which scale and offset will be derived.
+
+    .. note::
+        :class:`QuantizeDequantize` cannot run :meth:`forward` until :attr:`min` and :attr:`max` are properly initialized,
+        which can be done based on input statistics using :meth:`compute_encodings` or
+        by manually assigning a new value to :attr:`min` and :attr:`max`.
+        See the examples below.
+
+    Examples:
+
+        >>> import aimet_torch.v2.quantization as Q
+        >>> input = torch.randn(5, 10)
+        >>> qdq = Q.affine.QuantizeDequantize(shape=(5, 2), bitwidth=8, symmetric=False, block_size=(1, 5))
+        >>> qdq.is_initialized()
+        False
+        >>> with qdq.compute_encodings():
+        ...     _ = qdq(input)
+        ...
+        >>> qdq.is_initialized()
+        True
+        >>> qdq(input)
+        DequantizedTensor([[-0.2771,  0.3038,  1.0819,  0.9700,  0.9487, -0.1307,
+                            -1.7894, -0.1709, -0.2212,  0.7741],
+                           [-1.0295, -1.2265, -1.0295,  1.0564,  0.6177, -1.0386,
+                            -0.0176, -2.6054,  1.8836, -0.1232],
+                           [-0.8229,  0.5540,  0.3992, -0.2363,  1.2546, -1.0036,
+                             0.2355,  0.1741,  1.6079,  0.6247],
+                           [-1.0115,  1.2458,  0.9157, -1.4694, -0.0639, -0.2568,
+                             0.0680,  1.6695,  0.7932, -0.1889],
+                           [ 0.0158,  0.5695,  0.5220,  0.1977, -1.4475, -0.0424,
+                            -1.1128, -0.8796, -0.1060,  1.5897]],
+                          grad_fn=<AliasBackward0>)
+
+
+        >>> import aimet_torch.v2.quantization as Q
+        >>> input = torch.randn(5, 10)
+        >>> qdq = Q.affine.QuantizeDequantize(shape=(5, 2), bitwidth=8, symmetric=False, block_size=(1, 5))
+        >>> qdq.is_initialized()
+        False
+        >>> qdq.min = torch.nn.Parameter(-torch.ones_like(qdq.min))
+        >>> qdq.max = torch.nn.Parameter(torch.ones_like(qdq.max))
+        >>> qdq.is_initialized()
+        True
+        >>> qdq(input)
+        DequantizedTensor([[-0.6196, -0.9961,  0.0549, -0.6431,  1.0039, -0.8706,
+                             1.0039,  0.4706, -0.2353,  0.8078],
+                           [ 0.3451, -0.1176, -0.9961, -0.4549, -0.0549, -0.0471,
+                            -0.5255, -0.2353,  1.0039, -0.9961],
+                           [-0.4157,  0.0784,  0.5333,  0.1647, -0.9961, -0.9961,
+                            -0.2118, -0.2196,  0.9176,  0.9490],
+                           [ 1.0039, -0.7765,  0.4784, -0.8706,  1.0039,  0.6039,
+                            -0.4157, -0.2118, -0.9961,  0.3137],
+                           [ 1.0039,  0.3216, -0.2353, -0.7765, -0.9961,  0.8000,
+                             1.0039,  0.4157,  0.4392,  0.4863]],
+                          grad_fn=<AliasBackward0>)
+    """
+    # NOTE: Deepspeed has a bug where it will inadvertently patch __init__ method permanently
+    #       unless each leaf class explicitly defines its own __init__ separately.
+    #       As a temporary workaround, we define __init__ to avoid triggering this bug.
+    # pylint: disable=useless-super-delegation
+    def __init__(self, shape, *args, **kwargs):
+        super().__init__(shape, *args, **kwargs)
+
+
    
+[docs]
+    def forward(self, input: torch.Tensor) -> DequantizedTensor:
+        """Quantizes and dequantizes the input tensor
+
+        Args:
+            input (torch.Tensor): Input to quantize and dequantize
+
+        Returns:
+            Quantize-dequantized output
+
+        """
+        if not self.is_initialized():
+            raise RuntimeError(
+                'Failed to run QuantizeDequantize since quantization parameters are not initialized.'
+                ' Please initialize the quantization parameters using `compute_encodings()`.'
+            )
+
+        encoding = self.get_encodings()
+
+        # Subclasses of torch.Tensor with custom __torch_function__ (in our case, QuantizedTensorBase)
+        # is known to introduce substantial CPU overhead.
+        # Cast types of the inputs to plain torch.Tensor for faster execution.
+        input = input.as_subclass(torch.Tensor)
+
+        output = quantize_dequantize(input,
+                                     encoding.scale,
+                                     encoding.offset,
+                                     encoding.qmin,
+                                     encoding.qmax,
+                                     block_size=self.block_size)
+        output = output.as_subclass(DequantizedTensor)
+        output.encoding = encoding
+        return output
    
+
    
+
+
+
+class GroupedBlockQuantizeDequantize(QuantizeDequantize): # pylint: disable=too-many-ancestors
+    """ Class for performing Grouped Block Quantize Dequantize """
+    def __init__(self, shape, bitwidth: int, symmetric: bool, decompressed_bw: int,
+                 encoding_analyzer: EncodingAnalyzer = None, block_size: Optional[Tuple[int, ...]] = None,
+                 block_grouping: Optional[Tuple[int, ...]] = None):
+        """
+        Grouped Block Quantize Dequantize constructor.
+
+        :param shape: Shape of the quantization parameters
+        :type shape: tuple
+        :param bitwidth: Quantization bitwidth
+        :type bitwidth: int
+        :param symmetric: If True, performs symmetric quantization;
+                          otherwise, performs asymmetric quantization
+        :type symmetric: bool
+        :param decompressed_bw: Bitwidth used for decompression
+        :type decompressed_bw: int
+        :param encoding_analyzer: Encoding analyzer for calibrating quantization encodings
+                                  (default: absolute min-max encoding analyzer)
+        :type encoding_analyzer: EncodingAnalyzer, optional
+        :param block_size: Block size per dimension.
+        :type block_size: Tuple
+        :param block_grouping: Block grouping per dimension. If provided, every set of block_group scales will be
+                               grouped together, and the maximum scale for all blocks in the group will be used to find
+                               the scale in the decompressed_grid to be shared by all blocks in the group.
+                               If no block_grouping is provided, default behavior uses a block group of 1 for all dims,
+                               equivalent to Blockwise Quantization.
+                               A value of -1 for a block group for a dimension is equivalent to grouping all blocks in
+                               the dimension in one group. This is also equivalent to a block group value equal to the
+                               number of blocks for that dimension.
+        :type block_grouping: Tuple
+        """
+        super().__init__(shape, bitwidth, symmetric, encoding_analyzer, block_size)
+        self.decompressed_bw = decompressed_bw
+        self.block_grouping = block_grouping
+        if self.block_grouping is None:
+            # Default to BQ behavior with 1 for all block grouping dims if not provided
+            self.block_grouping = tuple(1 for _ in enumerate(self.shape))
+
+        if block_grouping is not None:
+            if len(block_grouping) != len(shape):
+                raise RuntimeError(f'Length of block grouping {block_grouping} must equal length of shape {shape}.')
+            for idx, block_group in enumerate(block_grouping):
+                if block_group != -1 and shape[idx] % block_group != 0:
+                    raise RuntimeError(f'Quantizer shape dimensions must divide evenly with corresponding block '
+                                       f'grouping values for shapes {shape} and block grouping {block_grouping}.')
+
+        if self.decompressed_bw < self.bitwidth:
+            raise RuntimeError(f'Decompressed bitwidth {decompressed_bw} cannot be smaller than self.bitwidth '
+                               f'{bitwidth}')
+
+        if not symmetric:
+            raise RuntimeError('GroupedBlockQuantizeDequantize only supports symmetric quantization.')
+
+    def get_scale(self, dtype=None) -> torch.Tensor:
+        """
+        Compute quantization scale to be used for forward pass.
+        Overrides QuantizeDequantize self.get_scale() to apply the grouped block algorithm for calculating modified
+        scales.
+
+        :param dtype: dtype of the computed scale. Use of self.min.dtype by default.
+        :return: Updated scale
+        """
+        orig_scale = super().get_scale(dtype)
+        orig_scale_shape = orig_scale.shape
+        reshaped_scale = orig_scale.view(self.get_expanded_scale_shape())
+        max_scale = torch.amax(reshaped_scale, list(range(1, len(orig_scale_shape) * 2, 2)), keepdim=True)
+        per_channel_scale = max_scale / 2 ** (self.decompressed_bw - self.bitwidth)
+        updated_scale = quantize_dequantize(reshaped_scale,
+                                            scale=per_channel_scale,
+                                            offset=torch.zeros_like(per_channel_scale),
+                                            qmin=1,
+                                            qmax=2 ** (self.decompressed_bw - self.bitwidth))
+        return updated_scale.view(orig_scale_shape)
+
+    def get_expanded_scale_shape(self) -> Tuple[int, ...]:
+        """
+        Get expanded scale shape which breaks each scale dimension into a pair of dimensions with sizes
+        (original_shape / block_grouping, block_grouping).
+
+        :return: Expanded scale shape
+        """
+        expanded_shape = []
+        for idx, block_group in enumerate(self.block_grouping):
+            # Block group of -1 is equivalent to grouping all blocks together
+            if block_group == -1:
+                expanded_shape.append(1)
+                expanded_shape.append(self.shape[idx])
+            else:
+                expanded_shape.append(self.shape[idx] // block_group)
+                expanded_shape.append(block_group)
+        return expanded_shape
+
+    def get_per_channel_scale(self, dtype=None) -> torch.Tensor:
+        """
+        Get per channel scale.
+
+        :return: Per channel scale
+        """
+        orig_scale = super().get_scale(dtype)
+        orig_scale_shape = orig_scale.shape
+        reshaped_scale = orig_scale.view(self.get_expanded_scale_shape())
+        max_scale = torch.amax(reshaped_scale, list(range(1, len(orig_scale_shape) * 2, 2)), keepdim=True)
+        per_channel_scale = max_scale / 2 ** (self.decompressed_bw - self.bitwidth)
+        return per_channel_scale
+
+    def get_per_block_integer_scale(self) -> torch.Tensor:
+        """
+        Get per block integer scale.
+
+        :return: Per block integer scale
+        """
+        per_channel_scale = self.get_per_channel_scale()
+        expanded_scale = self.get_scale().view(self.get_expanded_scale_shape())
+        integer_scale = torch.round(expanded_scale / per_channel_scale).int().view(self.get_scale().shape)
+        return integer_scale
+
+    def get_encodings(self) -> Optional[GroupedBlockEncoding]:
+        """
+        Return the quantizer's encodings as an EncodingBase object
+        """
+        if self.is_initialized():
+            return GroupedBlockEncoding(scale=self.get_scale(dtype=torch.float32),
+                                        offset=self.get_offset(dtype=torch.float32),
+                                        bitwidth=self.bitwidth,
+                                        signed=self.signed,
+                                        symmetry=self.symmetric,
+                                        block_size=self.block_size,
+                                        block_grouping=self.block_grouping,
+                                        decompressed_bw=self.decompressed_bw,
+                                        per_channel_scale=self.get_per_channel_scale(dtype=torch.float32),
+                                        per_block_int_scale=self.get_per_block_integer_scale())
+        return None
+
    +
    +
    +
    + +
    + + +
    +
    +
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    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
    + Made with Sphinx and @pradyunsg's + + Furo + +
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    + + + + + + + + + \ No newline at end of file diff --git a/releases/2.0.0/_modules/aimet_torch/v2/quantization/float/quantizer.html b/releases/2.0.0/_modules/aimet_torch/v2/quantization/float/quantizer.html new file mode 100644 index 0000000..92c52f7 --- /dev/null +++ b/releases/2.0.0/_modules/aimet_torch/v2/quantization/float/quantizer.html @@ -0,0 +1,690 @@ + + + + + + + + aimet_torch.v2.quantization.float.quantizer - AIMET + + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    +

    Source code for aimet_torch.v2.quantization.float.quantizer

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2024, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+# pylint: disable=redefined-builtin
+""" Float quantizers """
+
+import contextlib
+import functools
+from typing import Optional, List, Dict
+import math
+
+import torch
+from aimet_torch.v2.quantization.encoding_analyzer import EncodingAnalyzer, _flag_extreme_min_max
+from aimet_torch.v2.quantization.base import QuantizerBase
+from aimet_torch.v2.quantization.float import FloatEncoding
+from aimet_torch.v2.utils import StatisticsNotFoundError, patch_attr
+from aimet_torch.fp_quantization import fake_cast_to_ieee_float
+
+
+__all__ = ['QuantizeDequantize', 'FloatQuantizeDequantize']
+
+
+def _ieee_float_max_representable_value(exponent_bits, mantissa_bits):
+    exponent_max = 2 ** exponent_bits - 1
+    exponent_bias = exponent_max // 2
+    return (2 - 2**-mantissa_bits) * 2 ** (exponent_max - exponent_bias - 1)
+
+
+_IEEE_FLOAT16_EXPONENT_BITS = 5
+_IEEE_FLOAT16_MANTISSA_BITS = 10
+assert _ieee_float_max_representable_value(_IEEE_FLOAT16_EXPONENT_BITS, _IEEE_FLOAT16_MANTISSA_BITS) == \
+        torch.finfo(torch.float16).max
+
+_BFLOAT16_EXPONENT_BITS = 8
+_BFLOAT16_MANTISSA_BITS = 7
+assert _ieee_float_max_representable_value(_BFLOAT16_EXPONENT_BITS, _BFLOAT16_MANTISSA_BITS) == \
+        torch.finfo(torch.bfloat16).max
+
+
+
    
+[docs]
+class FloatQuantizeDequantize(QuantizerBase): # pylint: disable=abstract-method
+    r"""
+    Simulates quantization by fake-casting the input
+
+    If dtype is provided, this is equivalent to
+
+    .. math::
+        out = x.to(dtype).to(x.dtype) \\
+
+
+    If the exponent and mantissa bits are provided, this is equivalent to
+
+    .. math::
+        out = \left\lceil\frac{x_c}{scale}\right\rfloor * scale
+
+    where
+
+    .. math::
+        x_c   &= clamp(x, -max, max) \\
+        bias  &= 2^{exponent} - \log_2(max) + \log_2(2 - 2^{-mantissa}) - 1 \\
+        scale &= 2 ^ {\left\lfloor \log_2 |x_c| + bias \right\rfloor - mantissa - bias} \\
+
+
+    The IEEE standard computes the maximum representable value by
+
+    .. math::
+        max = (2 - 2^{-mantissa}) * 2^{(\left\lfloor 0.5 * exponent\_max \right\rfloor)} \\
+
+    where
+
+    .. math::
+        exponent\_max = 2^{exponent} - 1 \\
+
+    Args:
+        exponent_bits (int): Number of exponent bits to simulate
+        mantissa_bits (int):  Number of mantissa bits to simulate
+        dtype (torch.dtype): torch.dtype to simulate. This argument is mutually exclusive with exponent_bits and mantissa_bits.
+        encoding_analyzer (EncodingAnalyzer): If specified, the maximum value to represent will be determined dynamically based on the input statistics for finer precision.
+
+    Examples:
+
+        >>> import aimet_torch.v2.quantization as Q
+        >>> input = torch.tensor([[ 1.8998, -0.0947],[-1.0891, -0.1727]])
+        >>> qdq = Q.float.FloatQuantizeDequantize(mantissa_bits=7, exponent_bits=8)
+        >>> # Unlike AffineQuantizer, FloatQuantizer is initialized without calling compute_encodings()
+        >>> qdq.is_initialized()
+        True
+        >>> qdq.is_bfloat16()
+        True
+        >>> qdq.bitwidth
+        16
+        >>> qdq(input)
+        tensor([[ 1.8984, -0.0947], [-1.0859, -0.1729]])
+
+        >>> from aimet_torch.v2.quantization.encoding_analyzer import MinMaxEncodingAnalyzer
+        >>> encoding_analyzer = MinMaxEncodingAnalyzer(shape=[])
+        >>> qdq = Q.float.FloatQuantizeDequantize(dtype=torch.float16, encoding_analyzer=encoding_analyzer)
+        >>> qdq.is_float16()
+        True
+        >>> qdq.bitwidth
+        16
+        >>> qdq(input)
+        tensor([[ 1.8994, -0.0947], [-1.0889, -0.1727]])
+    """
+
+    maxval: torch.Tensor
+
+    def __init__(self,
+                 exponent_bits: int = None,
+                 mantissa_bits: int = None,
+                 dtype: torch.dtype = None,
+                 encoding_analyzer: EncodingAnalyzer = None):
+        super().__init__()
+
+        if dtype is None:
+            if exponent_bits is None or mantissa_bits is None:
+                raise ValueError('Neither "dtype" nor "exponent/mantissa_bits" was specified.')
+
+        if dtype is not None:
+            if exponent_bits is not None or mantissa_bits is not None:
+                raise ValueError(
+                    'Argument "dtype" is mutually exclusive with "exponent/mantissa_bits".')
+
+            if dtype not in (torch.half, torch.float16, torch.bfloat16):
+                raise ValueError(
+                    f"Float quantizer only supports torch.float16 and torch.bfloat16. Got {dtype}.")
+
+            if dtype in (torch.half, torch.float16):
+                exponent_bits = _IEEE_FLOAT16_EXPONENT_BITS
+                mantissa_bits = _IEEE_FLOAT16_MANTISSA_BITS
+            else:
+                exponent_bits = _BFLOAT16_EXPONENT_BITS
+                mantissa_bits = _BFLOAT16_MANTISSA_BITS
+
+        self.exponent_bits = exponent_bits
+        self.mantissa_bits = mantissa_bits
+        self.encoding_analyzer = encoding_analyzer
+
+        if self.encoding_analyzer:
+            shape = self.encoding_analyzer.observer.shape
+            maxval = _ieee_float_max_representable_value(exponent_bits, mantissa_bits)
+            self.register_buffer('maxval', torch.full(shape, maxval))
+        else:
+            self.register_buffer('maxval', None)
+
+
    
+[docs]
+    def get_extra_state(self):
+        extra_state_dict = {}
+        extra_state_dict['exponent_bits'] = torch.tensor(self.exponent_bits)
+        extra_state_dict['mantissa_bits'] = torch.tensor(self.mantissa_bits)
+        super_extra_state = super().get_extra_state()
+        extra_state_dict.update(super_extra_state)
+        return extra_state_dict
    
+
+
+
    
+[docs]
+    def set_extra_state(self, state):
+        self.exponent_bits = state['exponent_bits'].item()
+        self.mantissa_bits = state['mantissa_bits'].item()
+        super().set_extra_state(state)
    
+
+
+
    
+[docs]
+    def load_state_dict(self, state_dict, strict: bool = True):
+        if 'maxval' in state_dict:
+            if self.maxval is None:
+                del self.maxval
+                self.register_buffer('maxval', state_dict['maxval'])
+        elif self.maxval is not None:
+            del self.maxval
+            self.register_buffer('maxval', None)
+
+        ret = super().load_state_dict(state_dict, strict)
+        return ret
    
+
+
+    @property
+    def bitwidth(self):
+        """
+        Returns bitwidth of the quantizer
+        """
+        return self.exponent_bits + self.mantissa_bits + 1
+
+
    
+[docs]
+    def is_float16(self):
+        """
+        Returns true if current configuration simulates IEEE float16
+        """
+        return self.exponent_bits == _IEEE_FLOAT16_EXPONENT_BITS and \
+               self.mantissa_bits == _IEEE_FLOAT16_MANTISSA_BITS
    
+
+
+
    
+[docs]
+    def is_bfloat16(self):
+        """
+        Returns true if current configuration simulates bfloat16
+        """
+        return self.exponent_bits == _BFLOAT16_EXPONENT_BITS and \
+               self.mantissa_bits == _BFLOAT16_MANTISSA_BITS
    
+
+
+    def get_legacy_encodings(self) -> Optional[List[Dict]]:
+        """
+        :meta private:
+        """
+        return [{'bitwidth': self.bitwidth, 'dtype': 'float'}]
+
+    def set_legacy_encodings(self, encodings: List[Dict]):
+        """
+        :meta private:
+        Set encodings represented in the same format as the output of get_legacy_encodings as below:
+
+        [
+            {'bitwidth': int, 'dtype': str},
+            ...
+        ]
+        """
+        if encodings[0]['bitwidth'] != 16:
+            raise RuntimeError(f"{self.__class__} can only import 16-bit legay encodings.")
+        self.exponent_bits = 5
+        self.mantissa_bits = 10
+
+
    
+[docs]
+    def get_encodings(self) -> Optional[FloatEncoding]:
+        if self.is_initialized():
+            return FloatEncoding(self.mantissa_bits, self.exponent_bits, self.maxval)
+        return None
    
+
+
+
    
+[docs]
+    @contextlib.contextmanager
+    def compute_encodings(self):
+        """
+        Observe inputs and update quantization parameters based on the input statistics.
+        During ``compute_encodings`` is enabled, the quantizer forward pass performs
+        dynamic quantization using the batch statistics.
+        """
+        if not self.encoding_analyzer or not self._allow_overwrite:
+            yield
+            return
+
+        original_forward = self.forward
+
+        @functools.wraps(original_forward)
+        def forward_wrapper(input):
+            input = input.as_subclass(torch.Tensor)
+            batch_statistics = self.encoding_analyzer.update_stats(input)
+            num_steps = math.pow(2, self.bitwidth) - 1
+            dynamic_min, dynamic_max =\
+                    self.encoding_analyzer.compute_encodings_from_stats(batch_statistics,
+                                                                        num_steps,
+                                                                        is_symmetric=False)
+            dynamic_absmax = torch.maximum(dynamic_min.abs(), dynamic_max.abs())
+            dynamic_absmax = dynamic_absmax.to(dtype=self.maxval.dtype,
+                                               device=self.maxval.device).expand_as(self.maxval)
+
+            with patch_attr(self, 'maxval', dynamic_absmax):
+                return original_forward(input)
+
+        self.encoding_analyzer.reset_stats()
+
+        try:
+            with patch_attr(self, 'forward', forward_wrapper):
+                yield
+        except: # pylint: disable=try-except-raise
+            raise
+        else:
+            try:
+                num_steps = math.pow(2, self.bitwidth) - 1
+                min, max = self.encoding_analyzer.compute_encodings(num_steps,
+                                                                    is_symmetric=False)
+                _flag_extreme_min_max(min, max)
+            except StatisticsNotFoundError:
+                return
+
+            if min is None or max is None:
+                return
+
+            absmax = torch.maximum(min.abs(), max.abs()).expand_as(self.maxval)
+            with torch.no_grad():
+                self.maxval.copy_(absmax)
    
+
+
+
    
+[docs]
+    def forward(self, input: torch.Tensor):
+        """
+        :param input: Input to quantize and dequantize
+        :return: Quantize-dequantized output
+        """
+        maxval = self.maxval
+        exponent_bits = self.exponent_bits
+        mantissa_bits = self.mantissa_bits
+
+        if maxval is None:
+            if self.is_float16() or self.is_bfloat16():
+                # Fast forward using type casting
+                orig_dtype = input.dtype
+                dtype = torch.float16 if self.is_float16() else torch.bfloat16
+                return input.to(dtype).to(orig_dtype)
+
+            maxval = _ieee_float_max_representable_value(exponent_bits, mantissa_bits)
+
+        # Subclasses of torch.Tensor with custom __torch_function__ (in our case, QuantizedTensorBase)
+        # is known to introduce substantial CPU overhead.
+        # Cast types of the inputs to plain torch.Tensor for faster execution.
+        return fake_cast_to_ieee_float(input.as_subclass(torch.Tensor), maxval, exponent_bits, mantissa_bits)
    
+
+
+    def extra_repr(self):
+        """
+        :meta private:
+        """
+        return f'exponent_bits={self.exponent_bits}, mantissa_bits={self.mantissa_bits}'
    
+
+
+class QuantizeDequantize(FloatQuantizeDequantize):
+    r"""
+    Alias of FloatQuantizeDequantize
+    """
+
    +
    +
    +
    + +
    + + +
    +
    +
    +
    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
    + Made with Sphinx and @pradyunsg's + + Furo + +
    +
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    +
    + +
    +
    + + + + + + + + + \ No newline at end of file diff --git a/releases/2.0.0/_modules/aimet_torch/v2/quantization/tensor.html b/releases/2.0.0/_modules/aimet_torch/v2/quantization/tensor.html new file mode 100644 index 0000000..004bb73 --- /dev/null +++ b/releases/2.0.0/_modules/aimet_torch/v2/quantization/tensor.html @@ -0,0 +1,873 @@ + + + + + + + + aimet_torch.v2.quantization.tensor - AIMET + + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    +

    Source code for aimet_torch.v2.quantization.tensor

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2024, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+""" Quantized tensor class implementation """
+
+import abc
+import copy
+
+import torch
+from torch.utils._pytree import tree_map
+
+from aimet_torch.v2.quantization.base import EncodingBase
+
+
+__all__ = ['QuantizedTensorBase', 'QuantizedTensor', 'DequantizedTensor', 'EncodingError']
+
+
+HANDLED_FUNCTIONS = {}
+def implements(torch_function):
+    """
+    Register an override for QuantizedTensorBase
+    """
+    def decorator(func):
+        HANDLED_FUNCTIONS[torch_function] = func
+        return func
+
+    return decorator
+
+
+
    
+[docs]
+class QuantizedTensorBase(torch.Tensor):
+    """
+    Abstract base class for quantized tensors.
+    Represents a quantized or dequantized tensor as a subclass of :class:`torch.Tensor` which also holds the quantization encodings.
+    This object can be safely quantized or dequantized through the :meth:`quantize` and :meth:`dequantize` methods without
+    changing the represented data values.
+
+    Example:
+
+        >>> from aimet_torch.v2 import quantization as Q
+        >>> quantizer = Q.affine.Quantize(shape=(2, 1), bitwidth=8, symmetric=True)
+        >>> x = torch.tensor([[-1.20, 4.1, -0.21, 2.3],
+        ...                   [0.2, 5.6, -1.0, -.1]])
+        >>> with quantizer.compute_encodings():
+        ...     x_q = quantizer(x)
+        >>> torch.equal(x_q.encoding.scale, quantizer.get_scale())
+        True
+        >>> x_q
+        QuantizedTensor([[-37., 127.,  -7.,  71.],
+                         [  5., 127., -23.,  -2.]])
+        >>> x_q.quantized_repr()
+        tensor([[-37, 127,  -7,  71],
+                [  5, 127, -23,  -2]], dtype=torch.int8)
+        >>> x_q.dequantize()
+        DequantizedTensor([[-1.1945,  4.1000, -0.2260,  2.2921],
+                           [ 0.2205,  5.6000, -1.0142, -0.0882]])
+    """
+
+    encoding: EncodingBase
+
+    _attr_descriptors = {
+        torch.Tensor.dtype.__get__,
+        torch.Tensor.device.__get__,
+        torch.Tensor.layout.__get__,
+        torch.Tensor.shape.__get__,
+        torch.Tensor.size,
+    }
+
+    _cast_ops = {
+        torch.Tensor.half,
+        torch.Tensor.float,
+        torch.Tensor.double,
+        torch.Tensor.char,
+        torch.Tensor.short,
+        torch.Tensor.int,
+        torch.Tensor.long,
+        torch.Tensor.cuda,
+        torch.Tensor.cpu,
+        torch.Tensor.to,
+        torch.Tensor.type,
+        torch.Tensor.type_as,
+    }
+
+    # Operations that an encoding can always pass through
+    _passthrough_ops = {
+        torch.Tensor.contiguous,
+        torch.Tensor.requires_grad_,
+        torch.Tensor.share_memory_,
+    }
+
+    # Operations that a per-tensor encoding can pass through
+    _pertensor_passthrough_ops = {
+        torch.Tensor.__getitem__,
+        torch.Tensor.as_strided,
+        torch.Tensor.broadcast_to,
+        torch.Tensor.chunk,
+        torch.Tensor.dsplit,
+        torch.Tensor.expand,
+        torch.Tensor.expand_as,
+        torch.Tensor.flatten,
+        torch.Tensor.flip,
+        torch.Tensor.fliplr,
+        torch.Tensor.flipud,
+        torch.Tensor.gather,
+        torch.Tensor.H.__get__,
+        torch.Tensor.hsplit,
+        torch.Tensor.index_select,
+        torch.Tensor.kthvalue,
+        torch.Tensor.masked_select,
+        torch.Tensor.mH.__get__,
+        torch.Tensor.movedim,
+        torch.Tensor.moveaxis,
+        torch.Tensor.msort,
+        torch.Tensor.mT.__get__,
+        torch.Tensor.narrow,
+        torch.Tensor.permute,
+        torch.Tensor.repeat,
+        torch.Tensor.reshape,
+        torch.Tensor.reshape_as,
+        torch.Tensor.resize,
+        torch.Tensor.resize_,
+        torch.Tensor.resize_as,
+        torch.Tensor.resize_as_,
+        torch.Tensor.select,
+        torch.Tensor.split,
+        torch.Tensor.squeeze,
+        torch.Tensor.squeeze_,
+        torch.Tensor.swapaxes,
+        torch.Tensor.swapdims,
+        torch.Tensor.T.__get__,
+        torch.Tensor.t,
+        torch.Tensor.t_,
+        torch.Tensor.take,
+        torch.Tensor.take_along_dim,
+        torch.Tensor.tensor_split,
+        torch.Tensor.tile,
+        torch.Tensor.transpose,
+        torch.Tensor.unflatten,
+        torch.Tensor.unsqueeze,
+        torch.Tensor.unsqueeze_,
+        torch.Tensor.view,
+        torch.Tensor.view_as,
+        torch.as_strided,
+        torch.as_strided_copy,
+        torch.chunk,
+        torch.dsplit,
+        torch.expand_copy,
+        torch.flatten,
+        torch.flip,
+        torch.fliplr,
+        torch.flipud,
+        torch.gather,
+        torch.hsplit,
+        torch.index_select,
+        torch.masked_select,
+        torch.moveaxis,
+        torch.movedim,
+        torch.narrow,
+        torch.narrow_copy,
+        torch.permute,
+        torch.permute_copy,
+        torch.reshape,
+        torch.select,
+        torch.split,
+        torch.squeeze,
+        torch.squeeze_copy,
+        torch.swapaxes,
+        torch.swapdims,
+        torch.t,
+        torch.take,
+        torch.take_along_dim,
+        torch.tensor_split,
+        torch.tile,
+        torch.t_copy,
+        torch.unbind,
+        torch.unflatten,
+        torch.unsqueeze,
+        torch.unsqueeze_copy,
+        torch.vsplit,
+        torch.view_copy,
+    }
+
+
    
+[docs]
+    @abc.abstractmethod
+    def quantize(self) -> "QuantizedTensor":
+        """
+        Quantizes ``self`` with the associated encoding
+
+        .. note::
+            This method must be an IDEMPOTENT function.
+            The result of calling this method multiple times should be equal to calling it only once.
+            In other words, calling this method multiple times should not result in duplicate quantization.
+        """
+        raise NotImplementedError
    
+
+
+
    
+[docs]
+    @abc.abstractmethod
+    def dequantize(self) -> "DequantizedTensor":
+        """
+        Dequantizes ``self`` with the associated encoding
+
+        .. note::
+            This method must be an IDEMPOTENT function.
+            The result of calling this method multiple times should be equal to calling it only once.
+            In other words, calling this method multiple times should not result in duplicate dequantization.
+        """
+        raise NotImplementedError
    
+
+
+
    
+[docs]
+    @abc.abstractmethod
+    def quantized_repr(self) -> torch.Tensor:
+        """
+        Return the quantized representation of ``self`` as a :class:`torch.Tensor` with data type :attr:`self.encoding.dtype`
+
+        .. note::
+            The result of this function may not be able to carry a gradient depending on the quantized data type.
+            Thus, it may be necessary to call this only within an autograd function to allow for backpropagation.
+
+        Example:
+
+            >>> from aimet_torch.v2 import quantization as Q
+            >>> quantizer = Q.affine.Quantize(shape=(2, 1), bitwidth=8, symmetric=True)
+            >>> x = torch.randn((2, 4), requires_grad=True)
+            >>> with quantizer.compute_encodings():
+            ...     x_q = quantizer(x)
+            >>> x_q
+            QuantizedTensor([[  11.,  -57., -128.,   38.],
+                             [  28.,   -0., -128.,  -40.]], grad_fn=<AliasBackward0>)
+            >>> x_q.quantized_repr()
+            tensor([[  11,  -57, -128,   38],
+                    [  28,    0, -128,  -40]], dtype=torch.int8)
+        """
+        raise NotImplementedError
    
+
+
+    @classmethod
+    def __new__(cls, *args, **kwargs):
+        encoding = kwargs.pop('encoding', None)
+        ret = super().__new__(*args, **kwargs)
+        if not ret.is_floating_point():
+            raise RuntimeError(f"Non-floating point dtype `{ret.dtype}` is not allowed for quantized tensors.")
+        ret.encoding = encoding
+        return ret
+
+
    
+[docs]
+    def new_empty(self, size, *, dtype=None, device=None, requires_grad=False,
+                  layout=torch.strided, pin_memory=False, **kwargs) -> "QuantizedTensorBase":
+        # PyTorch requires subclasses of torch.Tensor to override this method such that
+        # it returns an instance of the subclass, not a plain torch.Tensor,
+        # for the subclass to be deep-copyable
+        encoding = kwargs.pop('encoding', None)
+        t = super().new_empty(size, dtype=dtype, device=device, requires_grad=requires_grad,
+                              layout=layout, pin_memory=pin_memory, **kwargs).as_subclass(type(self))
+        t.encoding = encoding
+        return t
    
+
+
+
    
+[docs]
+    @implements(torch.clone)
+    def clone(self, *, memory_format=torch.preserve_format):
+        """
+        Returns a copy of self
+
+        :param memory_format: Desired memory format of the returned tensor (default=torch.preserve_format)
+        """
+        # Note: use encoding.clone() here instead of deepcopy to propagate gradient through operation
+        encoding_clone = self.encoding and self.encoding._clone() # pylint:disable = protected-access
+        self_clone = super().clone(memory_format=memory_format).as_subclass(self.__class__)
+        self_clone.encoding = encoding_clone
+        return self_clone
    
+
+
+
    
+[docs]
+    @implements(torch.detach)
+    def detach(self) -> "QuantizedTensorBase":
+        """
+        Returns a new QuantizedTensorBase with data and encoding detached from the current graph
+        """
+        self_detached = super().detach().as_subclass(self.__class__)
+        self_detached.encoding = self.encoding and self.encoding._detach() # pylint:disable = protected-access
+        return self_detached
    
+
+
+    @classmethod
+    def __torch_function__(cls, func, types, args=(), kwargs=None): # pylint: disable=too-many-return-statements
+        if func in HANDLED_FUNCTIONS:
+            kwargs = kwargs if kwargs is not None else {}
+            return HANDLED_FUNCTIONS[func](*args, **kwargs)
+        ret = super().__torch_function__(func, types, args, kwargs)
+
+        if func in cls._attr_descriptors:
+            return ret
+
+        self, *_ = args
+
+        if not isinstance(self, QuantizedTensorBase):
+            # If self is not a subclass of QuantizedTensorBase, return a plain torch.Tensor
+            return tree_map(lambda t: t.as_subclass(torch.Tensor) if isinstance(t, QuantizedTensorBase) else t, ret)
+
+        if func in cls._cast_ops:
+            if not ret.dtype.is_floating_point:
+                raise RuntimeError(
+                    f"Type casting to non-floating point dtype `{ret.dtype}` is not allowed for quantized tensors. "
+                    "To cast quantized tensors to integer, use `qtensor.quantzed_repr()`."
+                )
+
+            # Outputs of cast ops can inherit the same encoding as its parents
+            ret.encoding = self.encoding and self.encoding.to(device=ret.device)
+            return ret
+
+        def propagate_encoding(qtensor, encoding):
+            if isinstance(qtensor, QuantizedTensorBase):
+                qtensor.encoding = copy.copy(encoding)
+
+        if func in cls._passthrough_ops:
+            if self is ret:
+                return ret
+
+            tree_map(lambda t: propagate_encoding(t, self.encoding), ret)
+            return ret
+
+        if func in cls._pertensor_passthrough_ops:
+            if self is ret:
+                return ret
+
+            if self.encoding and self.encoding.granularity == "pertensor":
+                # Return a cls object with the same encoding which can later be quantized or dequantized
+                tree_map(lambda t: propagate_encoding(t, self.encoding), ret)
+            else:
+                # Return a cls object with no encoding
+                # If the user later tries to quantize or dequantize this, an error will be thrown
+                tree_map(lambda t: propagate_encoding(t, None), ret)
+            return ret
+
+        if self is ret:
+            # Non-passthrough in-place functions invalidate the encoding of the input tensor.
+            # Discard the stale encoding.
+            ret.encoding = None
+            return ret
+
+        def set_encoding(qtensor):
+            if not hasattr(qtensor, 'encoding'):
+                qtensor.encoding = None
+
+            if qtensor.encoding is None:
+                # If encoding does not exist, return a plain torch.Tensor
+                return qtensor.as_subclass(torch.Tensor)
+
+            return qtensor
+
+        return tree_map(lambda t: set_encoding(t) if isinstance(t, cls) else t, ret)
    
+
+
+
+
    
+[docs]
+class QuantizedTensor(QuantizedTensorBase):
+    """
+    Represents a quantized tensor object. The object holds quantized values stored in a floating-point tensor along with
+    an :class:`EncodingBase` object which holds the information necessary to map the quantized values back to the
+    real/represented values.
+    """
+
+
    
+[docs]
+    def quantize(self) -> "QuantizedTensor":
+        """
+        Returns ``self``
+        """
+        return self
    
+
+
+
    
+[docs]
+    def dequantize(self) -> "DequantizedTensor":
+        """
+        Dequantizes ``self`` using :attr:`self.encoding` to produce a :class:`DequantizedTensor` with the same encoding
+        information.
+
+        Example:
+
+            >>> from aimet_torch.v2.quantization as Q
+            >>> x = torch.tensor([[2.57, -2.312],
+            ...                   [0.153, 0.205]])
+            >>> quantizer = Q.affine.Quantize(shape=(), bitwidth=8, symmetric=True)
+            >>> quantizer.set_range(-128 * 0.1, 127 * 0.1)
+            >>> x_q = quantizer(x)
+            >>> x_q
+            QuantizedTensor([[ 26., -23.],
+                             [  2.,   2.]], grad_fn=<AliasBackward0>)
+            >>> x_dq = x_q.dequantize()
+            >>> x_dq
+            DequantizedTensor([[ 2.6000, -2.3000],
+                               [ 0.2000,  0.2000]], grad_fn=<AliasBackward0>)
+            >>> torch.equal(x_dq.encoding.scale, x_q.encoding.scale)
+            True
+        """
+        if self.encoding is None:
+            raise EncodingError("Encoding does not exist")
+
+        qtensor = self.encoding.dequantize(self.as_subclass(torch.Tensor))
+        qtensor = qtensor.as_subclass(DequantizedTensor)
+        qtensor.encoding = copy.copy(self.encoding)
+        return qtensor
    
+
+
+
    
+[docs]
+    def quantized_repr(self) -> torch.Tensor:
+        # FIXME(kyunggeu): This only works for affine encodings.
+        #                  Needs to be generalized for any kind of encodings
+        if self.encoding is None:
+            raise EncodingError("Encoding does not exist")
+        return self.quantize().as_subclass(torch.Tensor).to(self.encoding.dtype)
    
+
    
+
+
+
+
    
+[docs]
+class DequantizedTensor(QuantizedTensorBase):
+    """
+    Represents a tensor which has been quantized and subsequently dequantized. This object contains real floating point
+    data as well as an :class:`EncodingBase` object which holds information about the quantization parameters with which
+    the data was quantized. With this, a :class:`DequantizedTensor` can be converted back to its quantized representation
+    without further loss in information.
+    """
+
+
    
+[docs]
+    def quantize(self) -> QuantizedTensor:
+        """
+        Quantizes ``self`` using :attr:`self.encoding` to produce a :class:`QuantizedTensor` with the same encoding
+        information.
+
+        Example:
+
+            >>> import aimet_torch.v2.quantization as Q
+            >>> x = torch.tensor([[0.39, 51.0], [3.521, 9.41]])
+            >>> quant_dequant = Q.affine.QuantizeDequantize(shape=(), bitwidth=8, symmetric=False)
+            >>> quant_dequant.set_range(-10, 41)
+            >>> x_qdq = quant_dequant(x)
+            >>> x_qdq
+            DequantizedTensor([[ 0.4000, 41.0000],
+                               [ 3.6000,  9.4000]], grad_fn=<AliasBackward0>)
+            >>> x_qdq.quantize()
+            QuantizedTensor([[ 52., 255.],
+                             [ 68.,  97.]], grad_fn=<AliasBackward0>)
+        """
+        if self.encoding is None:
+            raise EncodingError("Encoding does not exist")
+
+        qtensor = self.encoding.quantize(self.as_subclass(torch.Tensor))
+        qtensor = qtensor.as_subclass(QuantizedTensor)
+        qtensor.encoding = copy.copy(self.encoding)
+        return qtensor
    
+
+
+
    
+[docs]
+    def dequantize(self) -> "DequantizedTensor":
+        """
+        Returns ``self``
+        """
+        return self
    
+
+
+
    
+[docs]
+    def quantized_repr(self) -> torch.Tensor:
+        """
+        Return the quantized representation of ``self`` as a :class:`torch.Tensor` with data type :attr:`self.encoding.dtype`.
+
+        .. note::
+            The result of this function may not be able to carry a gradient depending on the quantized data type.
+            Thus, it may be necessary to call this only within an autograd function to allow for backpropagation.
+
+        Example:
+
+            >>> import aimet_torch.v2.quantization as Q
+            >>> x = torch.tensor([[0.39, 51.0], [3.521, 9.41]])
+            >>> quant_dequant = Q.affine.QuantizeDequantize(shape=(), bitwidth=8, symmetric=False)
+            >>> quant_dequant.set_range(-10, 41)
+            >>> x_qdq = quant_dequant(x)
+            >>> x_qdq
+            DequantizedTensor([[ 0.4000, 41.0000],
+                               [ 3.6000,  9.4000]], grad_fn=<AliasBackward0>)
+            >>> x_qdq.quantized_repr()
+            tensor([[ 52, 255],
+                    [ 68,  97]], dtype=torch.uint8)
+        """
+        # FIXME(kyunggeu): This only works for affine encodings.
+        #                  Needs to be generalized for any kind of encodings
+        if self.encoding is None:
+            raise EncodingError("Encoding does not exist")
+        return self.quantize().as_subclass(torch.Tensor).to(self.encoding.dtype)
    
+
    
+
+
+
+class EncodingError(RuntimeError):
+    """Error that indicates an encoding is missing or invalid"""
+
    +
    +
    +
    + +
    + + +
    +
    +
    +
    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
    + Made with Sphinx and @pradyunsg's + + Furo + +
    +
    +
    + + + + + + +
    +
    +
    + +
    +
    + +
    +
    + + + + + + + + + \ No newline at end of file diff --git a/releases/2.0.0/_modules/aimet_torch/v2/quantsim/config_utils.html b/releases/2.0.0/_modules/aimet_torch/v2/quantsim/config_utils.html new file mode 100644 index 0000000..342d924 --- /dev/null +++ b/releases/2.0.0/_modules/aimet_torch/v2/quantsim/config_utils.html @@ -0,0 +1,790 @@ + + + + + + + + aimet_torch.v2.quantsim.config_utils - AIMET + + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    +

    Source code for aimet_torch.v2.quantsim.config_utils

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2024, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+""" Commonly used utilities for configuring quantsim """
+
+from typing import overload, Callable, List, Optional, Tuple, Type, Union
+import torch
+from aimet_common.utils import AimetLogger
+from aimet_torch.v2.quantsim.quantsim import QuantizationSimModel
+from aimet_torch.v2.quantization.affine import QuantizeDequantize, GroupedBlockQuantizeDequantize
+from aimet_torch.v2.quantization.float import FloatQuantizeDequantize
+from aimet_torch.utils import get_device
+
+logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.Quant)
+
+modules_with_in_out_channels = (torch.nn.Linear,
+                                torch.nn.Conv1d,
+                                torch.nn.Conv2d,
+                                torch.nn.Conv3d,
+                                torch.nn.ConvTranspose1d,
+                                torch.nn.ConvTranspose2d,
+                                torch.nn.ConvTranspose3d)
+
+def _get_quantizer_device_or_default(quantizer: torch.nn.Module, default_device: Union[str, torch.device]):
+    """ Get device from quantizer if possible, else return default_device """
+    try:
+        device = get_device(quantizer)
+    except StopIteration:
+        device = default_device
+    return device
+
+def _get_in_channels_dim(module: torch.nn.Module):
+    """ Get input channels dimension for the given module """
+    if not isinstance(module, modules_with_in_out_channels):
+        raise AssertionError(f'In channels not defined for module of type {type(module)}')
+    if isinstance(module, (torch.nn.ConvTranspose1d,
+                           torch.nn.ConvTranspose2d,
+                           torch.nn.ConvTranspose3d)):
+        return 0
+    return 1
+
+def _get_out_channels_dim(module: torch.nn.Module):
+    """ Get input channels dimension for the given module """
+    if not isinstance(module, modules_with_in_out_channels):
+        raise AssertionError(f'Out channels not defined for module of type {type(module)}')
+    if isinstance(module, (torch.nn.ConvTranspose1d,
+                           torch.nn.ConvTranspose2d,
+                           torch.nn.ConvTranspose3d)):
+        return 1
+    return 0
+
+def _get_block_size_array_for_module(module: torch.nn.Module, block_size: Union[int, Tuple[int, ...]]):
+    """
+    Return block size array for a module given a single block size value, assuming that the block size value is to be
+    applied to the in_channels dimension, and the out_channels dimension is per channel.
+    """
+    if isinstance(block_size, (tuple, list)):
+        return block_size
+
+    assert isinstance(block_size, int)
+    if not isinstance(module, modules_with_in_out_channels):
+        error_msg = f'Single value block size is only supported for modules of types in ' \
+                    f'config_utils.modules_with_in_out_channels, but got module of type {type(module)}. Update ' \
+                    f'the argument for identifying modules to set to exclude unsupported types.'
+        raise RuntimeError(error_msg)
+    assert hasattr(module, 'weight')
+
+    # Initialize block sizes with -1, meaning blocks will span the entire dimension for each axis
+    block_size_array = [-1] * len(module.weight.shape)
+
+    # Set in channels dimension block size to block_size, and set out channels dimension block size to 1 (per channel)
+    block_size_array[_get_in_channels_dim(module)] = block_size
+    block_size_array[_get_out_channels_dim(module)] = 1
+    return block_size_array
+
+
+def _get_block_grouping_array_for_module(module: torch.nn.Module, block_grouping: Union[int, Tuple[int, ...]]):
+    """
+    Return block grouping array for a module given a single block grouping value, assuming that the block grouping value
+    is to be applied to the in_channels dimension.
+    """
+    if isinstance(block_grouping, (tuple, list)):
+        return block_grouping
+
+    assert isinstance(block_grouping, int)
+    if not isinstance(module, modules_with_in_out_channels):
+        error_msg = f'Single value block grouping is only supported for modules of types in ' \
+                    f'config_utils.modules_with_in_out_channels, but got module of type {type(module)}. Update ' \
+                    f'the argument for identifying modules to set to exclude unsupported types.'
+        raise RuntimeError(error_msg)
+    assert hasattr(module, 'weight')
+
+    # Initialize block grouping with 1, meaning no blocks will be grouped together in each dimension
+    block_grouping_array = [1] * len(module.weight.shape)
+
+    # Set in channels dimension block size to block_grouping
+    block_grouping_array[_get_in_channels_dim(module)] = block_grouping
+    return block_grouping_array
+
+
+def _parse_arg_for_condition(arg):
+    """ Transform the given arg into a corresponding condition expression """
+    if isinstance(arg, List) and isinstance(arg[0], type) and issubclass(arg[0], torch.nn.Module):
+        module_type = arg
+        condition = lambda module: isinstance(module, tuple(module_type))
+    elif isinstance(arg, List):
+        if not isinstance(arg[0], torch.nn.Module):
+            raise RuntimeError('List given as arg must contain either torch.nn.Module types or specific '
+                               'torch.nn.Module objects.')
+        qmodules = arg
+        condition = lambda module: module in qmodules
+    else:
+        condition = arg
+    return condition
+
+
+def _get_weight_quantizer_shape_from_block_size(quant_layer: torch.nn.Module, block_size: Tuple[int, ...]) \
+        -> Optional[List[int]]:
+    """ Given a block size, get the corresponding weight quantizer shape """
+    weight_shape = quant_layer.weight.shape
+    block_size = _get_block_size_array_for_module(quant_layer, block_size)
+    assert len(block_size) == len(weight_shape)
+    quantizer_shape = []
+    for idx, shape in enumerate(weight_shape):
+        if block_size[idx] != -1:
+            if not shape % block_size[idx] == 0:
+                return None
+
+            quantizer_shape.append(shape // block_size[idx])
+        else:
+            quantizer_shape.append(1)
+    return quantizer_shape
+
+
+@overload
+def set_activation_quantizers_to_float(sim: QuantizationSimModel, module_type: List[Type[torch.nn.Module]],
+                                       exponent_bits: int = None, mantissa_bits: int = None,
+                                       dtype: torch.dtype = None):
+    """ Set activation quantizers of the given module type to float """
+
+
+@overload
+def set_activation_quantizers_to_float(sim: QuantizationSimModel, qmodules: List[torch.nn.Module],
+                                       exponent_bits: int = None, mantissa_bits: int = None, dtype: torch.dtype = None):
+    """ Set activation quantizers of the given qmodules to float """
+
+
+@overload
+def set_activation_quantizers_to_float(sim: QuantizationSimModel, condition: Callable[[torch.nn.Module], bool],
+                                       exponent_bits: int = None, mantissa_bits: int = None, dtype: torch.dtype = None):
+    """ Set activation quantizers of all the modules that satisfy the given condition to float. """
+
+
+
    
+[docs]
+def set_activation_quantizers_to_float(sim: QuantizationSimModel, arg, exponent_bits: int = None,
+                                       mantissa_bits: int = None, dtype: torch.dtype = None):
+    """
+    Set activation quantizers of modules to float.
+
+    :param sim: Quantsim to set activation quantizers for
+    :param arg: Argument determining which modules to set. This can consist of either:
+
+        1. A list of torch.nn.Module types, in which case all modules whose type is in the list will be set
+
+        2. A list of torch.nn.Modules, in which case all modules in the list will be set
+
+        3. A callable function which takes a torch.nn.Module as input and returns True if the module is to be set, False
+           otherwise
+    :param exponent_bits: Number of exponent bits to simulate
+    :param mantissa_bits: Number of mantissa bits to simulate
+    :param dtype: torch.dtype to simulate. This argument is mutually exclusive with exponent_bits and mantissa_bits.
+
+    Examples:
+
+        >>> # Assume 'sim' is a QuantizationSimModel object imported from aimet_torch.v2.quantsim
+        >>> # Allows setting of all Linear and Conv output quantizers to floating point activation quantization:
+        >>> set_activation_quantizers_to_float(sim=sim,
+        ...                                    arg=[torch.nn.Linear, torch.nn.Conv2d],
+        ...                                    dtype=torch.float16)
+        >>> # Allows setting of specific model layers' output quantizers to floating point activation quantization:
+        >>> set_activation_quantizers_to_float(sim=sim,
+        ...                                    arg=[sim.model.conv2, sim.model.linear1],
+        ...                                    dtype=torch.float16)
+        >>> # Allows setting of only Convolution layers with input channels dim == 128 to floating point activation quantization:
+        >>> set_activation_quantizers_to_float(sim=sim,
+        ...                                    arg=lambda module: isinstance(module, torch.nn.Conv2d) and module.weight.shape[1] == 128,
+        ...                                    dtype=torch.float16)
+    """
+
+    condition = _parse_arg_for_condition(arg)
+    _set_activation_quantizers_to_float(sim, condition, exponent_bits, mantissa_bits, dtype)
    
+
+
+
+def _set_activation_quantizers_to_float(sim: QuantizationSimModel, condition: Callable[[torch.nn.Module], bool],
+                                        exponent_bits: int = None, mantissa_bits: int = None,
+                                        dtype: torch.dtype = None):
+    """ Set activation quantizers of all the modules that satisfy the given condition to float. """
+    model_device = get_device(sim.model)
+    for _, quant_layer in sim.named_qmodules():
+        if condition(quant_layer):
+            for idx, quantizer in enumerate(quant_layer.input_quantizers):
+                if quantizer is not None:
+                    device = _get_quantizer_device_or_default(quantizer, model_device)
+                    quant_layer.input_quantizers[idx] = FloatQuantizeDequantize(exponent_bits, mantissa_bits, dtype).to(device)
+
+            for idx, quantizer in enumerate(quant_layer.output_quantizers):
+                if quantizer is not None:
+                    device = _get_quantizer_device_or_default(quantizer, model_device)
+                    quant_layer.output_quantizers[idx] = FloatQuantizeDequantize(exponent_bits, mantissa_bits, dtype).to(device)
+
+
+@overload
+def set_blockwise_quantization_for_weights(sim: QuantizationSimModel, module_type: List[Type[torch.nn.Module]],
+                                           bitwidth: int, symmetric: bool, block_size: Union[int, Tuple[int, ...]]):
+    """ Set weight parameter quantizers of the given module type to blockwise """
+
+
+@overload
+def set_blockwise_quantization_for_weights(sim: QuantizationSimModel, qmodules: List[torch.nn.Module], bitwidth: int,
+                                           symmetric: bool, block_size: Union[int, Tuple[int, ...]]):
+    """ Set weight parameter quantizers of the given modules to blockwise """
+
+
+@overload
+def set_blockwise_quantization_for_weights(sim: QuantizationSimModel, condition: Callable[[torch.nn.Module], bool],
+                                           bitwidth: int, symmetric: bool, block_size: Union[int, Tuple[int, ...]]):
+    """ Set weight parameter quantizers of modules that satisfy the given condition to blockwise """
+
+
+
    
+[docs]
+def set_blockwise_quantization_for_weights(sim: QuantizationSimModel, arg, bitwidth: int, symmetric: bool,
+                                           block_size: Union[int, Tuple[int, ...]]):
+    """
+    Set weight parameter quantizers of modules to blockwise.
+
+    :param sim: Quantsim to set weight quantizers for
+    :param arg: Argument determining which modules to set. This can consist of either:
+
+        1. A list of torch.nn.Module types, in which case all modules whose type is in the list will be set
+
+        2. A list of torch.nn.Modules, in which case all modules in the list will be set
+
+        3. A callable function which takes a torch.nn.Module as input and returns True if the module is to be set, False
+           otherwise
+    :param bitwidth: Bitwidth for affine quantization
+    :param symmetric: True if affine quantization is symmetric, False otherwise
+    :param block_size: Block size for affine quantization. This can be an array in which case all layers identified
+        by arg must have weight shapes compatible with the array length, or can be an integer value, in which case the
+        block size will be applied to the weight's in_channels dimension, and per channel will be used for the weight's
+        out_channels dimension.
+
+        A block size value of -1 for a particular dimension is equivalent to a block size equal
+        to the size of that particular dimension.
+
+    Examples:
+
+        >>> # Assume 'sim' is a QuantizationSimModel object imported from aimet_torch.v2.quantsim
+        >>> # Allows setting of all Linear and Conv weight quantizers to block_size 64 in the input_channels dimension:
+        >>> set_blockwise_quantization_for_weights(sim=sim,
+        ...                                        arg=[torch.nn.Linear, torch.nn.Conv2d],
+        ...                                        bitwidth=4,
+        ...                                        symmetric=True,
+        ...                                        block_size=64)
+        >>> # Allows setting of specific model layers' weight quantizer block_size to 64 in the input_channels dimension:
+        >>> set_blockwise_quantization_for_weights(sim=sim,
+        ...                                        arg=[sim.model.conv2, sim.model.linear1],
+        ...                                        bitwidth=4,
+        ...                                        symmetric=True,
+        ...                                        block_size=64)
+        >>> # Allows setting of only Convolution layers with input channels dim == 128 to block_size 64 in the input_channels dimension
+        >>> set_blockwise_quantization_for_weights(sim=sim,
+        ...                                        arg=lambda module: isinstance(module, torch.nn.Conv2d) and module.weight.shape[1] == 128,
+        ...                                        bitwidth=4,
+        ...                                        symmetric=True,
+        ...                                        block_size=64)
+    """
+    condition = _parse_arg_for_condition(arg)
+    _set_blockwise_quantization_for_weights(sim, condition, bitwidth, symmetric, block_size)
    
+
+
+
+def _get_layers_to_quantizer_shapes_for_block_size(sim, condition, block_size):
+    layer_to_quantizer_shape_dict = {}
+    invalid_layers_for_block_size = []
+    for name, quant_layer in sim.named_qmodules():
+        if condition(quant_layer) and 'weight' in quant_layer.param_quantizers and \
+                quant_layer.param_quantizers['weight'] is not None:
+            assert hasattr(quant_layer, 'weight')
+            layer_to_quantizer_shape_dict[quant_layer] = \
+                _get_weight_quantizer_shape_from_block_size(quant_layer, block_size)
+            if layer_to_quantizer_shape_dict[quant_layer] is None:
+                invalid_layers_for_block_size.append((name, quant_layer.weight.shape))
+
+    if invalid_layers_for_block_size:
+        for name, shape in invalid_layers_for_block_size:
+            error_str = f"Quant layer {name} has shape {shape} which does not align with block_size {block_size}. " \
+                        f"Each dimension's shape must divide evenly with the corresponding block size."
+            logger.error(error_str)
+            raise RuntimeError('Quant layers found whose weights do not align with block size.')
+
+    return layer_to_quantizer_shape_dict
+
+
+def _set_blockwise_quantization_for_weights(sim: QuantizationSimModel, condition: Callable[[torch.nn.Module], bool],
+                                            bitwidth: int, symmetric: bool, block_size: Union[int, Tuple[int, ...]]):
+    """ Set weight parameter quantizers of modules that satisfy the given condition to blockwise """
+    model_device = get_device(sim.model)
+    layer_to_quantizer_shape_dict = _get_layers_to_quantizer_shapes_for_block_size(sim, condition, block_size)
+    for layer, quantizer_shape in layer_to_quantizer_shape_dict.items():
+        layer_block_size = _get_block_size_array_for_module(layer, block_size)
+        device = _get_quantizer_device_or_default(layer.param_quantizers['weight'], model_device)
+        layer.param_quantizers['weight'] = QuantizeDequantize(quantizer_shape, bitwidth, symmetric, None,
+                                                              layer_block_size).to(device)
+
+
+@overload
+def set_grouped_blockwise_quantization_for_weights(sim: QuantizationSimModel, module_type: List[Type[torch.nn.Module]],
+                                                   bitwidth: int, symmetric: bool, decompressed_bw: int,
+                                                   block_size: Union[int, Tuple[int, ...]],
+                                                   block_grouping: Union[int, Tuple[int, ...]] = -1):
+    """ Set weight parameter quantizers of the given module type to grouped blockwise """
+
+
+@overload
+def set_grouped_blockwise_quantization_for_weights(sim: QuantizationSimModel, qmodules: List[torch.nn.Module],
+                                                   bitwidth: int, symmetric: bool, decompressed_bw: int,
+                                                   block_size: Union[int, Tuple[int, ...]],
+                                                   block_grouping: Union[int, Tuple[int, ...]] = -1):
+    """ Set weight parameter quantizers of the given modules to grouped blockwise """
+
+
+@overload
+def set_grouped_blockwise_quantization_for_weights(sim: QuantizationSimModel,
+                                                   condition: Callable[[torch.nn.Module], bool],
+                                                   bitwidth: int, symmetric: bool, decompressed_bw: int,
+                                                   block_size: Union[int, Tuple[int, ...]],
+                                                   block_grouping: Union[int, Tuple[int, ...]] = -1):
+    """ Set weight parameter quantizers of modules that satisfy the given condition to grouped blockwise """
+
+
+
    
+[docs]
+def set_grouped_blockwise_quantization_for_weights(sim: QuantizationSimModel, arg,
+                                                   bitwidth: int, symmetric: bool, decompressed_bw: int,
+                                                   block_size: Union[int, Tuple[int, ...]],
+                                                   block_grouping: Union[int, Tuple[int, ...]] = -1):
+    """
+    Set weight parameter quantizers of modules to grouped blockwise.
+
+    :param sim: Quantsim to set weight quantizers for
+    :param arg: Argument determining which modules to set. This can consist of either:
+
+        1. A list of torch.nn.Module types, in which case all modules whose type is in the list will be set
+
+        2. A list of torch.nn.Modules, in which case all modules in the list will be set
+
+        3. A callable function which takes a torch.nn.Module as input and returns True if the module is to be set, False
+           otherwise
+    :param bitwidth: Bitwidth for affine quantization
+    :param symmetric: True if affine quantization is symmetric, False otherwise
+    :param decompressed_bw: Decompressed bw for grouped block quantization
+    :param block_size: Block size for affine quantization. This can be an array in which case all layers identified
+        by arg must have weight shapes compatible with the array length, or can be an integer value, in which case the
+        block size will be applied to the weight's in_channels dimension and per channel will be used for the weight's
+        out_channels dimension.
+
+        A block size value of -1 for a particular dimension is equivalent to a block size equal
+        to the size of that particular dimension.
+    :param block_grouping: Block grouping for grouped block quantization. This can be an array in which case all layers
+        identified by arg must have weight shapes compatible with the array length, or can be an integer value, in which
+        case the block grouping will be applied to the weight's in_channels dimension, and no other dimensions will
+        experience block grouping.
+
+        A block grouping value of -1 for a particular dimension is equivalent to a block
+        grouping equal to the number of blocks for that particular dimension.
+
+    Examples:
+
+        >>> # Assume 'sim' is a QuantizationSimModel object imported from aimet_torch.v2.quantsim
+        >>> # Allows setting of all Linear and Conv weight quantizers to LPBQ with block_size 64 in the input_channels dimension:
+        >>> set_grouped_blockwise_quantization_for_weights(sim=sim,
+        ...                                                arg=[torch.nn.Linear, torch.nn.Conv2d],
+        ...                                                bitwidth=4,
+        ...                                                symmetric=True,
+        ...                                                decompressed_bw=8,
+        ...                                                block_size=64,
+        ...                                                block_grouping=-1)
+        >>> # Allows setting of specific model layers' weight quantizer to LPBQ with block_size 64 in the input_channels dimension:
+        >>> set_grouped_blockwise_quantization_for_weights(sim=sim,
+        ...                                                arg=[sim.model.conv2, sim.model.linear1],
+        ...                                                bitwidth=4,
+        ...                                                symmetric=True,
+        ...                                                decompressed_bw=8,
+        ...                                                block_size=64,
+        ...                                                block_grouping=-1)
+        >>> # Allows setting of only Convolution layers with input channels dim == 128 to LPBQ with block_size 64 in the input_channels dimension:
+        >>> set_grouped_blockwise_quantization_for_weights(sim=sim,
+        ...                                                arg=lambda module: isinstance(module, torch.nn.Conv2d) and module.weight.shape[1] == 128,
+        ...                                                bitwidth=4,
+        ...                                                symmetric=True,
+        ...                                                decompressed_bw=8,
+        ...                                                block_size=64,
+        ...                                                block_grouping=-1)
+    """
+    condition = _parse_arg_for_condition(arg)
+    _set_grouped_blockwise_quantization_for_weights(sim, condition, bitwidth, symmetric, decompressed_bw, block_size,
+                                                    block_grouping)
    
+
+
+
+def _set_grouped_blockwise_quantization_for_weights(sim: QuantizationSimModel,
+                                                    condition: Callable[[torch.nn.Module], bool],
+                                                    bitwidth: int, symmetric: bool, decompressed_bw: int,
+                                                    block_size: Union[int, Tuple[int, ...]],
+                                                    block_grouping: Union[int, Tuple[int, ...]]):
+    """ Set weight parameter quantizers of modules that satisfy the given condition to grouped blockwise """
+    model_device = get_device(sim.model)
+    layer_to_quantizer_shape_dict = _get_layers_to_quantizer_shapes_for_block_size(sim, condition, block_size)
+    for layer, quantizer_shape in layer_to_quantizer_shape_dict.items():
+        layer_block_size = _get_block_size_array_for_module(layer, block_size)
+        layer_block_grouping = _get_block_grouping_array_for_module(layer, block_grouping)
+        device = _get_quantizer_device_or_default(layer.param_quantizers['weight'], model_device)
+        layer.param_quantizers['weight'] = GroupedBlockQuantizeDequantize(quantizer_shape, bitwidth, symmetric,
+                                                                          decompressed_bw, None, layer_block_size,
+                                                                          layer_block_grouping).to(device)
+
    +
    +
    +
    + +
    + + +
    +
    +
    +
    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
    + Made with Sphinx and @pradyunsg's + + Furo + +
    +
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    +
    +
    + +
    +
    + +
    +
    + + + + + + + + + \ No newline at end of file diff --git a/releases/2.0.0/_modules/aimet_torch/v2/quantsim/quantsim.html b/releases/2.0.0/_modules/aimet_torch/v2/quantsim/quantsim.html new file mode 100644 index 0000000..708f562 --- /dev/null +++ b/releases/2.0.0/_modules/aimet_torch/v2/quantsim/quantsim.html @@ -0,0 +1,938 @@ + + + + + + + + aimet_torch.v2.quantsim.quantsim - AIMET + + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    +

    Source code for aimet_torch.v2.quantsim.quantsim

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2024, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+""" Top level API for performing quantization simulation of a pytorch model """
+
+import copy
+from typing import Union, Tuple, Optional, Sequence, TypeVar, Any, Callable, overload
+import warnings
+import itertools
+import io
+import contextlib
+import torch
+
+from aimet_common.defs import QuantScheme, QuantizationDataType
+from aimet_torch._base.quantsim import (
+    _QuantizationSimModelBase,
+    logger,
+    unquantizable_modules,
+    QuantParams,
+    ExportableQuantModule,
+    save_checkpoint,
+    load_checkpoint,
+    check_accumulator_overflow,
+)
+from aimet_torch.v2 import nn as aimet_nn
+from aimet_torch.v2.nn import BaseQuantizationMixin, QuantizationMixin
+from aimet_torch.v2.nn.fake_quant import _legacy_impl
+from aimet_torch.v2._builder import _V2LazyQuantizeWrapper
+from aimet_torch.v2.quantization.base import QuantizerBase
+from aimet_torch.v2.quantization.affine import AffineQuantizerBase
+from aimet_torch.v2.quantization.encoding_analyzer import PercentileEncodingAnalyzer
+from aimet_torch.v2.utils import patch_attr
+from aimet_torch import utils
+from aimet_torch.utils import deprecated, _red
+from aimet_torch.v2.deepspeed_utils import _register_zero3_forward_hooks
+
+__all__ = [
+    'QuantizationSimModel',
+    'QuantParams',
+    'ExportableQuantModule',
+    'save_checkpoint',
+    'load_checkpoint',
+    'check_accumulator_overflow',
+    'load_encodings_to_sim',
+    'compute_encodings_for_sims',
+]
+
+unquantizable_modules = (QuantizerBase, *unquantizable_modules)
+quantized_modules = (BaseQuantizationMixin, _V2LazyQuantizeWrapper)
+containers = (
+    torch.nn.Container,
+    torch.nn.Sequential,
+    torch.nn.ModuleList,
+    torch.nn.ModuleDict,
+    torch.nn.ParameterList,
+    torch.nn.ParameterDict,
+)
+
+
+class _NOT_SPECIFIED:
+    pass
+
+
+def _convert_to_qmodule(module: torch.nn.Module):
+    """
+    Helper function to convert all modules to quantized aimet.nn modules.
+    """
+    if not isinstance(module, (*quantized_modules, *unquantizable_modules, *containers)):
+        try:
+            module = QuantizationMixin.from_module(module)
+        except RuntimeError as e:
+            try:
+                module = _legacy_impl.FakeQuantizationMixin.from_module(module)
+            except RuntimeError:
+                if not tuple(module.children()):
+                    raise e # pylint: disable=raise-missing-from
+
+    for name, child in module.named_children():
+        setattr(module, name, _convert_to_qmodule(child))
+
+    return module
+
+
+
    
+[docs]
+class QuantizationSimModel(_QuantizationSimModelBase):
+    """
+    Class that simulates the quantized model execution on a target hardware backend.
+
+    QuantizationSimModel simulates quantization of a given model by converting
+    all PyTorch modules into :ref:`quantized modules<api-torch-quantized-modules>`
+    with input/output/parameter :ref:`quantizers<api-torch-quantizers>` as necessary.
+
+    Example:
+
+        >>> model = torchvision.models.resnet18()
+        >>> dummy_input = torch.randn(1, 3, 224, 224)
+        >>> sim = QuantizationSimModel(model, dummy_input)
+        >>> print(model)
+        ResNet(
+          (conv1): Conv2d(
+            3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False
+          )
+          ...
+        )
+        >>> print(sim.model)
+        ResNet(
+          (conv1): QuantizedConv2d(
+            3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False
+            (param_quantizers): ModuleDict(
+              (weight): QuantizeDequantize(shape=(), qmin=-128, qmax=127, symmetric=True)
+            )
+            (input_quantizers): ModuleList(
+              (0): QuantizeDequantize(shape=(), qmin=0, qmax=255, symmetric=False)
+            )
+            (output_quantizers): ModuleList(
+              (0): None
+            )
+          )
+          ...
+        )
+    """
+    _quantized_modules = quantized_modules
+
+    def __init__(self, # pylint: disable=too-many-arguments, too-many-locals, too-many-branches
+                 model: torch.nn.Module,
+                 dummy_input: Union[torch.Tensor, Sequence[torch.Tensor]],
+                 quant_scheme: Union[str, QuantScheme] = None, # NOTE: Planned to be deprecated
+                 rounding_mode: Optional[str] = None, # NOTE: Planned to be deprecated
+                 default_output_bw: int = 8,
+                 default_param_bw: int = 8,
+                 in_place: bool = False,
+                 config_file: Optional[str] = None,
+                 default_data_type: QuantizationDataType = QuantizationDataType.int):
+        """
+        .. warning::
+           `rounding_mode` parameter is deprecated.
+           Passing `rounding_mode` will throw runtime error in >=1.35.
+
+        .. warning::
+           The default value of `quant_scheme` will change
+           from `QuantScheme.post_training_tf_enhanced` to `QuantScheme.training_range_learning_with_tf_init`
+           in the future versions, and will be deprecated in the longer term.
+
+        Args:
+            model (torch.nn.Module): Model to simulate the quantized execution of
+            dummy_input (Tensor | Sequence[Tensor]): Dummy input to be used to capture
+                the computational graph of the model. All input tensors are expected to be
+                already placed on the appropriate devices to run forward pass of the model.
+            quant_scheme (QuantScheme, optional): Quantization scheme that indicates
+                how to observe and calibrate the quantization encodings (Default: `QuantScheme.post_training_tf_enhanced`)
+            rounding_mode: Deprecated
+            default_output_bw (int, optional): Default bitwidth (4-31) to use for quantizing all layer inputs and outputs
+                unless otherwise specified in the config file. (Default: 8)
+            default_param_bw (int, optional): Default bitwidth (4-31) to use for quantizing all layer parameters
+                unless otherwise specified in the config file. (Default: 8)
+            in_place (bool, optional): If True, then the given model is modified in-place into a quantized model. (Default: `False`)
+            config_file (str, optional): Path to the quantization simulation config file (Default: `None`)
+            default_data_type (QuantizationDataType, optional): Default data type to use for quantizing all
+                inputs, outputs and parameters unless otherwise specified in the config file.
+                Possible options are QuantizationDataType.int and QuantizationDataType.float.
+                Note that the mode default_data_type=QuantizationDataType.float is only supported with
+                default_output_bw=16 or 32 and default_param_bw=16 or 32. (Default: `QuantizationDataType.int`)
+        """
+        if not quant_scheme:
+            old_default = QuantScheme.post_training_tf_enhanced
+            new_default = QuantScheme.training_range_learning_with_tf_init
+            msg = _red(f"The default value of 'quant_scheme' has changed from '{old_default}' "
+                       f"to '{new_default}' since aimet-torch==2.0.0. "
+                       "If you wish to maintain the legacy default behavior, "
+                       f"please explicitly pass 'quant_scheme={old_default}'")
+            warnings.warn(msg, DeprecationWarning, stacklevel=2)
+            quant_scheme = new_default
+
+        if rounding_mode:
+            if rounding_mode == 'nearest':
+                warnings.warn(_red("Passing rounding_mode='nearest' is no longer needed "\
+                                   "and will be deprecated soon in the later versions."),
+                              DeprecationWarning, stacklevel=2)
+            else:
+                raise TypeError("'rounding_mode' parameter is no longer supported.")
+
+        qmodules = {
+            name: module for name, module in model.named_modules()
+            if isinstance(module, BaseQuantizationMixin)
+        }
+        quantizers = {
+            name: module for name, module in model.named_modules()
+            if isinstance(module, QuantizerBase)
+        }
+
+        if isinstance(model, BaseQuantizationMixin):
+            problem = f"the model itself is already a quantized module of type {type(model)}."
+        elif isinstance(model, QuantizerBase):
+            problem = f"the model itself is already a quantizer object of type {type(model)}."
+        elif qmodules:
+            problem = f"the model already contains quantized modules: {', '.join(qmodules.keys())}."
+        elif quantizers:
+            problem = f"the model already contains quantizers: {', '.join(quantizers.keys())}."
+        else:
+            problem = None
+
+        if problem:
+            raise RuntimeError(
+                "QuantizationSimModel can only take base models WITHOUT quantized modules or quantizers, "
+                "but " + problem
+            )
+
+        if not in_place:
+            model = copy.deepcopy(model)
+            in_place = True
+
+        model = _convert_to_qmodule(model)
+
+        with _register_zero3_forward_hooks(model, use_dummy_params=True):
+            # NOTE: Register for the model is pre-partitioned by deepspeed zero3 or zero3-offload.
+            #       Pre-partitioned models aren't runnable as-is, but are needed to to be initialized
+            #       with `deepspeed.initialize` before running forward pass.
+            #       However, `deepspeed.initialize` can only come after quantsim is created, since
+            #       quantsim will add additional learnable parameters to the model which also need
+            #       to be initialized by deepspeed.
+            #       Since quantsim constructor relies on torch.jit tracing which involves running
+            #       forward pass of the model, here we register a temporary hook to make
+            #       uninitialized but pre-partitioned models runnable.
+            super().__init__(model, dummy_input, quant_scheme,
+                             rounding_mode='nearest',
+                             default_output_bw=default_output_bw,
+                             default_param_bw=default_param_bw,
+                             in_place=in_place,
+                             config_file=config_file,
+                             default_data_type=default_data_type)
+
+        # Quantization parameters are placed on cpu by default.
+        # Move them to cuda device as necessary
+
+        default_device = torch.device('cpu')
+
+        for param_or_buffer in itertools.chain(self.model.parameters(), self.model.buffers()):
+            if param_or_buffer.device.type != 'cpu':
+                # Use the first non-cpu device as default device.
+                # Default device is necessary for the input/output quantizers of
+                # modules without any parameters such as ReLU
+                default_device = param_or_buffer.device
+                break
+
+        for module in self.model.modules():
+            if not isinstance(module, BaseQuantizationMixin):
+                continue
+
+            try:
+                # Find the device of the first parameter of the orignal module
+                param_or_buffer = next(iter(itertools.chain(module.parameters(recurse=False),
+                                                            module.buffers(recurse=False))))
+                device = param_or_buffer.device
+            except StopIteration:
+                # If the original module has no parameter, use default device
+                device = default_device
+
+            # Set quantization parameters to the device of the original module
+            module.to(device=device)
+
+    @overload
+    def compute_encodings(self, forward_pass_callback: Callable[[torch.nn.Module], Any]): # pylint: disable=arguments-differ
+        ...
+
+    T = TypeVar('T')
+
+    @overload
+    def compute_encodings(self, # pylint: disable=arguments-differ
+                          forward_pass_callback: Callable[[torch.nn.Module, T], Any],
+                          forward_pass_callback_args: T):
+        ...
+
+    del T
+
+    # pylint: disable=arguments-differ
+
    
+[docs]
+    def compute_encodings(self, forward_pass_callback, forward_pass_callback_args=_NOT_SPECIFIED):
+        r"""
+        Computes encodings for all quantizers in the model.
+
+        This API will invoke `forward_pass_callback`, a function written by the user that runs
+        forward pass(es) of the quantized model with a small, representative subset of the training dataset.
+        By doing so, the quantizers in the quantized model will observe the inputs and initialize
+        their quantization encodings according to the observed input statistics.
+
+        This function is overloaded with the following signatures:
+
+        .. function:: compute_encodings(forward_pass_callback)
+           :noindex:
+
+           :param forward_pass_callback_: A function that takes a quantized model and runs forward passes
+               with a small, representative subset of training dataset
+           :type forward_pass_callback_: Callable[[torch.nn.Module], Any]
+
+        .. function:: compute_encodings(forward_pass_callback, forward_pass_callback_args)
+           :noindex:
+
+           :param forward_pass_callback_: A function that takes a quantized model and runs forward passes
+               with a small, representative subset of training dataset
+           :type forward_pass_callback_: Callable[[torch.nn.Module, T], Any]
+           :param T forward_pass_callback_args: The second argument to `forward_pass_callback`.
+
+        Example:
+
+            >>> sim = QuantizationSimModel(...)
+            >>> _ = sim.model(input) # Can't run forward until quantizer encodings are initialized
+            RuntimeError: Failed to run QuantizeDequantize since quantization parameters are not initialized.
+            Please initialize the quantization parameters using `compute_encodings()`.
+            >>> def run_forward_pass(quantized_model: torch.nn.Module):
+            ...     for input in train_dataloader:
+            ...         with torch.no_grad():
+            ...             _ = quantized_model(input)
+            ...
+            >>> sim.compute_encodings(run_forward_pass)
+            >>> _ = sim.model(input) # Now runs successfully!
+        """
+
+        if forward_pass_callback_args is _NOT_SPECIFIED:
+            args = (self.model,)
+        else:
+            args = (self.model, forward_pass_callback_args)
+
+        # Run forward iterations so we can collect statistics to compute the appropriate encodings
+        with utils.in_eval_mode(self.model), torch.no_grad():
+            with aimet_nn.compute_encodings(self.model):
+                _ = forward_pass_callback(*args)
    
+
+
+
    
+[docs]
+    def export(self, path: str, filename_prefix: str, dummy_input: Union[torch.Tensor, Tuple],
+               *args, **kwargs):
+        if isinstance(dummy_input, torch.Tensor):
+            dummy_input = (dummy_input,)
+
+        @torch.no_grad()
+        def concretize_block_size(qtzr, inp):
+            """
+            Fill in block sizes for dimensions with block size -1
+            """
+            inp, = inp
+            dims = len(qtzr.block_size)
+            input_shape = inp.shape[-dims:]
+            scale_shape = qtzr.get_scale().shape[-dims:]
+            block_size = qtzr.block_size
+
+            concrete_block_size = tuple(inp_size//scale_size if blk_size == -1 else blk_size
+                                        for inp_size, scale_size, blk_size
+                                        in zip(input_shape, scale_shape, block_size))
+            ctx = patch_attr(qtzr, 'block_size', concrete_block_size)
+            stack.enter_context(ctx)
+
+        handles = []
+
+        try:
+            with contextlib.ExitStack() as stack:
+                for qtzr in self.model.modules():
+                    if not isinstance(qtzr, AffineQuantizerBase):
+                        continue
+
+                    if qtzr.block_size and any(size == -1 for size in qtzr.block_size):
+                        h = qtzr.register_forward_pre_hook(concretize_block_size)
+                        handles.append(h)
+
+                if handles:
+                    with utils.in_eval_mode(self.model), torch.no_grad():
+                        _ = self.model(*dummy_input)
+
+                return super().export(path, filename_prefix, dummy_input, *args, **kwargs)
+
+        finally:
+            for h in handles:
+                h.remove()
    
+
+
+    def set_percentile_value(self, percentile_value: float):
+        """
+        Set the percentile value to be used while computing encodings
+        """
+        self._percentile_value = percentile_value
+        for module in self.model.modules():
+            if isinstance(module, QuantizerBase):
+                if isinstance(module.encoding_analyzer, PercentileEncodingAnalyzer):
+                    module.encoding_analyzer.set_percentile(percentile_value)
+
+    def __str__(self):
+        stream = io.StringIO(newline='\n')
+        stream.write("-------------------------\n")
+        stream.write("Quantized Model Report\n")
+        stream.write("-------------------------\n")
+        stream.write(f"{self.model}\n")
+        return stream.getvalue()
+
+    def exclude_param_from_quantization(self, param_name_to_exclude: str):
+        """
+        Excludes all parameters matching 'param_name' from quantization
+        :param param_name_to_exclude: Name of the parameter to exclude
+        :return: None
+        """
+        super().exclude_param_from_quantization(param_name_to_exclude)
+        for module in self.model.modules():
+            if isinstance(module, BaseQuantizationMixin):
+                if param_name_to_exclude in module.param_quantizers:
+                    module.param_quantizers[param_name_to_exclude] = None
+
+    # pylint: disable=missing-function-docstring
+    @staticmethod
+    def compute_layer_encodings_for_sim(sim: 'QuantizationSimModel'):
+        raise NotImplementedError("QuantizationSimModel.compute_layer_encodings_for_sim has been removed.")
+
+    # pylint: disable=missing-function-docstring, unused-argument
+    @staticmethod
+    def prepare_sim_for_compute_encodings(sim: 'QuantizationSimModel'):
+        logger.warning("QuantizationSimModel.prepare_sim_for_compute_encodings has been deprecated and is no longer necessary. "
+                       "Any calls can be safely removed.")
+
+    # pylint: disable=missing-function-docstring
+    @classmethod
+    def set_mode_for_recurrent_module(cls, layer, name: str):
+        raise NotImplementedError("QuantizationSimModel.set_mode_for_recurrent_module has been removed.")
+
+    @staticmethod
+    def save_model_with_embedded_quantization_nodes(sim_model, path: str, filename_prefix: str,
+                                                    dummy_input, onnx_export_args=None,
+                                                    export_to_torchscript=False, is_conditional=False):
+        raise NotImplementedError("QuantizationSimModel.save_model_with_embedded_quantization_nodes has been removed.")
+
+    @staticmethod
+    def _replace_quantization_wrapper_with_native_torch_quantization_nodes(quant_sim_model, device: torch.device):
+        raise NotImplementedError()
+
+    @classmethod
+    @torch.no_grad()
+    def _apply_qdq_to_model_parameters(cls, model: torch.nn.Module):
+        """
+        Applies quant-dequant to the parameters of a PyTorch model
+        to avoid rounding error during weight quantization.
+
+        :param model: The PyTorch model whose parameters will be quant-dequantized.
+        """
+        for module in model.modules():
+            if isinstance(module, BaseQuantizationMixin):
+                # pylint: disable=protected-access
+                module._patch_quantized_parameters()
+                if isinstance(module, QuantizationMixin):
+                    module._patch_dequantized_parameters()
+                cls._update_parameters_by_attr(module)
+
+    def named_qmodules(self):
+        """Generator that yields all quantized modules in the model and their names
+        """
+        for name, module in self.model.named_modules():
+            if isinstance(module, (BaseQuantizationMixin, _V2LazyQuantizeWrapper)):
+                yield name, module
+
+    @deprecated(f'Use {named_qmodules.__qualname__} instead.')
+    def quant_wrappers(self): # pylint: disable=missing-docstring
+        return self.named_qmodules()
+
+    # Overrides V1QuantizationSimModel._add_quantization_wrappers
+    def _add_quantization_wrappers(self, module, num_inout_tensors, default_data_type):
+        # pylint: disable=protected-access
+        for name, child in module.named_children():
+            if isinstance(child, BaseQuantizationMixin):
+                child_wrapper = self._create_quantizer_module(child, num_inout_tensors, default_data_type)
+                setattr(module, name, child_wrapper)
+                child = child_wrapper._module_to_wrap
+            self._add_quantization_wrappers(child, num_inout_tensors, default_data_type)
+
+    def _realize_quant_wrappers_in_model(self, model: torch.nn.Module):
+        for name, child in model.named_children():
+            if isinstance(child, _V2LazyQuantizeWrapper):
+                child = child.realize()
+                setattr(model, name, child)
+            self._realize_quant_wrappers_in_model(child)
+
+    def _create_quantizer_module(self, module_to_quantize: torch.nn.Module, num_inout_tensors,
+                                 data_type: QuantizationDataType) -> torch.nn.Module:
+        """Instantiates wrapper based on quant scheme
+        """
+        # We lookup the number of input and output tensors already determined
+        # Special case, we are adding a wrapper for a module not in the forward pass: Use default of 1, 1
+        num_in_tensors, num_out_tensors = num_inout_tensors.get(module_to_quantize, (1, 1))
+        quantized_module = _V2LazyQuantizeWrapper(module_to_quantize, self._default_param_bw, self._default_output_bw,
+                                                  self._rounding_mode, self._quant_scheme, num_inputs=num_in_tensors,
+                                                  num_outputs=num_out_tensors, data_type=data_type)
+        return quantized_module
+
+    @classmethod
+    def _remove_quantization_wrappers(cls, starting_module, list_of_modules_to_exclude):
+        """
+        Recursively remove quantization wrappers from all appropriate modules starting with a given module
+        :param starting_module: Module to recursive search downstream from
+        :param list_of_modules_to_exclude: List of torch modules to remove quantization wrappers from (if present)
+        :return: None
+        """
+        for name, module in starting_module.named_children():
+            if module in list_of_modules_to_exclude:
+                if isinstance(module, BaseQuantizationMixin):
+                    orig_module = module.get_original_module()
+                    setattr(starting_module, name, orig_module)
+                    module = orig_module
+            # Recursively call children modules if present
+            if not utils.is_leaf_module(module):
+                cls._remove_quantization_wrappers(module, list_of_modules_to_exclude)
    
+
+
+
+@deprecated("""
+Use QuantizationSimModel.load_encodings with the following keyword arguments instead:
+```
+sim.load_encodings(encoding_path
+                   strict=True,
+                   partial=False,
+                   requires_grad=None,
+                   allow_overwrite=None)
+```
+"""
+)
+def load_encodings_to_sim(quant_sim_model: _QuantizationSimModelBase, pytorch_encoding_path: str):
+    """
+    Loads the saved encodings to quant sim model. The encoding filename to load should end in _torch.encodings,
+    generated as part of quantsim export.
+
+    :param quant_sim_model: Quantized model to load encodings for. Note: The model configuration should be the same as
+        when encodings were exported.
+    :param pytorch_encoding_path: Path of the encodings file to load.
+    """
+    quant_sim_model.load_encodings(pytorch_encoding_path,
+                                   strict=True,
+                                   partial=False,
+                                   requires_grad=None,
+                                   allow_overwrite=None)
+
+
+@deprecated(r"""
+Use aimet_torch.nn.compute_encodings contextmanager on each sim.model instead. For example:
+```
+with torch.no_grad(), \
+        aimet_torch.v2.nn.compute_encodings(sim_0.model), \
+        aimet_torch.v2.nn.compute_encodings(sim_1.model), \
+        aimet_torch.v2.nn.compute_encodings(sim_2.model):
+    # Run forward pass with calibration dataset
+```
+"""
+)
+def compute_encodings_for_sims(sim_list: Sequence[QuantizationSimModel], forward_pass_callback: Callable,
+                               forward_pass_callback_args: Any):
+    """
+    Compute encodings for a list of QuantSims.
+
+    :param sim_list: List of QuantSims to compute encodings for.
+    :param forward_pass_callback: A callback function that simply runs forward passes on the models. This callback
+        function should use representative data for the forward pass, so the calculated encodings work for all
+        data samples. This callback internally chooses the number of data samples it wants to use for calculating
+        encodings.
+        The callback expects exactly two inputs:
+            - List of models which are involved in the forward pass. The models are taken directly from calling
+            sim.model for each sim in sim_list, passed in the same order in which the sims appear in sim_list.
+            - Forward pass callback args
+    :param forward_pass_callback_args: These argument(s) are passed to the forward_pass_callback as-is. Up to
+        the user to determine the type of this parameter. E.g. could be simply an integer representing the number
+        of data samples to use. Or could be a tuple of parameters or an object representing something more complex.
+        If set to None, forward_pass_callback will be invoked with no parameters.
+    """
+    ctx_managers = [torch.no_grad()]
+    for sim in sim_list:
+        ctx_managers.append(utils.in_eval_mode(sim.model))
+        ctx_managers.append(aimet_nn.compute_encodings(sim.model))
+
+    with contextlib.ExitStack() as stack:
+        for mgr in ctx_managers:
+            stack.enter_context(mgr)
+        _ = forward_pass_callback([sim.model for sim in sim_list], forward_pass_callback_args)
+
    +
    +
    +
    + +
    + + +
    +
    +
    +
    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
    + Made with Sphinx and @pradyunsg's + + Furo + +
    +
    +
    + + + + + + +
    +
    +
    + +
    +
    + +
    +
    + + + + + + + + + \ No newline at end of file diff --git a/releases/2.0.0/_modules/aimet_torch/v2/visualization_tools/quant_stats_visualization.html b/releases/2.0.0/_modules/aimet_torch/v2/visualization_tools/quant_stats_visualization.html new file mode 100644 index 0000000..1c4c352 --- /dev/null +++ b/releases/2.0.0/_modules/aimet_torch/v2/visualization_tools/quant_stats_visualization.html @@ -0,0 +1,1173 @@ + + + + + + + + aimet_torch.v2.visualization_tools.quant_stats_visualization - AIMET + + + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    +

    Source code for aimet_torch.v2.visualization_tools.quant_stats_visualization

    +# -*- mode: python -*-
+# =============================================================================
+#  @@-COPYRIGHT-START-@@
+#
+#  Copyright (c) 2023-2023, Qualcomm Innovation Center, Inc. All rights reserved.
+#
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#  3. Neither the name of the copyright holder nor the names of its contributors
+#     may be used to endorse or promote products derived from this software
+#     without specific prior written permission.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+#
+#  SPDX-License-Identifier: BSD-3-Clause
+#
+#  @@-COPYRIGHT-END-@@
+# =============================================================================
+
+""" Tool to visualize min and max activations/weights of quantized modules in a given model"""
+
+
+import os
+from pathlib import Path
+import torch
+from bokeh.events import DocumentReady, Reset
+from bokeh.layouts import row, column
+from bokeh.models import ColumnDataSource, TextInput, CustomJS, Range1d, HoverTool, CustomJSHover, Div, \
+    BooleanFilter, CDSView, Spacer, DataTable, StringFormatter, ScientificFormatter, TableColumn, Tooltip, Select, \
+    Whisker, FactorRange
+from bokeh.models.tools import ResetTool
+from bokeh.models.dom import HTML
+from bokeh.plotting import figure, save, curdoc
+from aimet_torch.v2.quantsim import QuantizationSimModel
+from aimet_torch.utils import get_ordered_list_of_modules
+from aimet_torch.v2.quantization.base import QuantizerBase
+from aimet_torch.v2.quantization.encoding_analyzer import _MinMaxObserver, _HistogramObserver
+
+
+def _visualize(sim: QuantizationSimModel, dummy_input, mode: str, save_path: str = "./quant_stats_visualization.html", percentile_list: list = None) -> None:
+    """
+    Helper function for the visualization APIs.
+
+    :param sim: Calibrated QuantSim Object.
+    :param dummy_input: Dummy Input.
+    :param mode: Whether to plot basic or advanced stats.
+    :param save_path: Path for saving the visualization. Format is 'path_to_dir/file_name.html'. Default is './quant_stats_visualization.html'.
+    :param percentile_list: Percentiles to be extracted and used.
+    """
+
+    # Ensure that sim is an instance of QuantizationSimModel
+    if not isinstance(sim, QuantizationSimModel):
+        raise TypeError(f"Expected type 'aimet_torch.v2.quantsim.QuantizationSimModel', got '{type(sim)}'.")
+
+    if percentile_list is None:
+        raise ValueError("percentile_list cannot be None. Consider providing an empty percentile_list if needed.")
+
+    # Ensure that the save path is valid
+    _check_path(save_path)
+
+    # Topologically sort the quantized modules into an ordered list for easier indexing in the plots
+    ordered_list = get_ordered_list_of_modules(sim.model, dummy_input)
+    stats_list = []
+
+    # Collect stats from observers
+    for i in ordered_list:
+        module_stats = _get_observer_stats(i, percentile_list=percentile_list)
+        if module_stats is not None:
+            stats_list.append(module_stats)
+
+    # Raise an error if no stats were found
+    if len(stats_list) == 0:
+        raise RuntimeError(
+            "No stats found to plot. Either there were no quantized modules, or calibration was not performed before calling this function, or no observers of type _MinMaxObserver or _HistogramObserver were present.")
+
+
+    stats_dict = dict()
+    keys_list = ["name", 0, 100] + percentile_list
+    stats_dict["idx"] = list(range(len(stats_list)))
+    for i in keys_list:
+        stats_dict[i] = [None] * len(stats_list)
+    for idx, stats in enumerate(stats_list):
+        for i in keys_list:
+            stats_dict[i][idx] = stats[i]
+
+    if mode == "advanced":
+        _get_additional_boxplot_stats(stats_dict)
+
+    visualizer = QuantStatsVisualizer(stats_dict, percentile_list)
+
+    # Save an interactive bokeh plot as a standalone html
+    visualizer.export_plot_as_html(save_path, mode)
+
+
+
    
+[docs]
+def visualize_stats(sim: QuantizationSimModel, dummy_input, save_path: str = "./quant_stats_visualization.html") -> None:
+    """Produces an interactive html to view the stats collected by each quantizer during calibration
+
+    .. note::
+
+        The QuantizationSimModel input is expected to have been calibrated before using this function. Stats will only
+        be plotted for activations/parameters with quantizers containing calibration statistics.
+
+    Creates an interactive visualization of min and max activations/weights of all quantized modules in the input
+    QuantSim object. The features include:
+
+        - Adjustable threshold values to flag layers whose min or max activations/weights exceed the set thresholds
+        - Tables containing names and ranges for layers exceeding threshold values
+
+    Saves the visualization as a .html at the given path.
+
+    Example:
+
+        >>> sim = aimet_torch.v2.quantsim.QuantizationSimModel(model, dummy_input, quant_scheme=QuantScheme.post_training_tf)
+        >>> with aimet_torch.v2.nn.compute_encodings(sim.model):
+        ...     for data, _ in data_loader:
+        ...         sim.model(data)
+        ...
+        >>> visualize_stats(sim, dummy_input, save_path="./quant_stats_visualization.html")
+
+    :param sim: Calibrated QuantizationSimModel
+    :param dummy_input: Sample input used to trace the model
+    :param save_path: Path for saving the visualization. Default is "./quant_stats_visualization.html"
+    """
+
+    percentile_list = []
+    _visualize(sim, dummy_input, mode="basic", save_path=save_path, percentile_list=percentile_list)
    
+
+
+
+def visualize_advanced_stats(sim: QuantizationSimModel, dummy_input, save_path: str = "./quant_advanced_stats_visualization.html", additional_percentiles: tuple = (1, 99)) -> None:
+    """Produces an interactive html to view the advanced stats collected by each quantizer during calibration
+
+    .. note::
+
+        The QuantizationSimModel input is expected to have been calibrated before using this function. Stats will only
+        be plotted for activations/parameters with quantizers containing calibration statistics.
+
+    .. note::
+
+        For plotting advanced stats, the quantizer encoding analyzers should have observers of type
+        :class:`_HistogramObserver`. If observers are of the type
+        :class:`_MinMaxObserver`, then advanced stats cannot be extracted and only the min and max values are
+        shown in the boxplots.
+
+    .. note::
+
+        In case of Per-channel or Blockwise quantizers,
+        percentiles are not extracted due to the presence of multiple histograms. For these
+        quantizers, only min and max values are shown in the boxplots.
+
+    Creates an interactive visualization of min and max activations/weights of all quantized modules in the input
+    QuantSim object. The features include:
+
+        - Adjustable threshold values to flag layers whose min or max activations/weights exceed the set thresholds
+        - Table containing names and ranges for layers exceeding threshold values
+        - Select different views of the table to group layers exceeding threshold values
+        - Filter layers listed in the table by name
+        - Select one or more layers from the table for viewing their boxplots and highlighting them in the main plot
+
+    Saves the visualization as a .html at the given path.
+
+    Example:
+
+        >>> sim = aimet_torch.v2.quantsim.QuantizationSimModel(model, dummy_input, quant_scheme=QuantScheme.post_training_tf_enhanced)
+        >>> with aimet_torch.v2.nn.compute_encodings(sim.model):
+        ...     for data, _ in data_loader:
+        ...         sim.model(data)
+        ...
+        >>> visualize_advanced_stats(sim, dummy_input, save_path="./quant_advanced_stats_visualization.html", additional_percentiles=(1, 99))
+
+    :param sim: Calibrated QuantizationSimModel
+    :param dummy_input: Sample input used to trace the model
+    :param save_path: Path for saving the visualization. Default is "./quant_advanced_stats_visualization.html"
+    :param additional_percentiles: Percentiles other than those related to the boxplot (25, 50, 75) to be shown.
+    """
+
+    percentile_list = _add_key_percentiles(additional_percentiles)
+    percentile_list = sorted(percentile_list)
+    _visualize(sim, dummy_input, mode="advanced", save_path=save_path, percentile_list=percentile_list)
+
+
+def _check_path(path: str):
+    """ Function for sanity check on the given path """
+    path_to_directory = os.path.dirname(path)
+    if path_to_directory != '' and not os.path.exists(path_to_directory):
+        raise NotADirectoryError(f"'{path_to_directory}' is not a directory.")
+    if not path.endswith('.html'):
+        raise ValueError("'save_path' must end with '.html'.")
+
+
+def _get_observer_stats(module, percentile_list):
+    """
+    Function to extract stats from an observer.
+    Handles observers of types _MinMaxObserver, _HistogramObserver.
+    """
+    module_name, module_quantizer = module[0], module[1]
+    if isinstance(module_quantizer, QuantizerBase):
+        if isinstance(module_quantizer.encoding_analyzer.observer, _MinMaxObserver):
+            rng = module_quantizer.encoding_analyzer.observer.get_stats()
+            if rng.min is not None:
+                stats = dict()
+                stats["name"] = module_name
+                stats[0] = torch.min(rng.min).item()
+                stats[100] = torch.max(rng.max).item()
+                for p in percentile_list:
+                    stats[p] = None
+                return stats
+
+        elif isinstance(module_quantizer.encoding_analyzer.observer, _HistogramObserver):
+            histogram_list = module_quantizer.encoding_analyzer.observer.get_stats()
+            if len(histogram_list) == 1:
+                histogram = histogram_list[0]
+                if histogram.min is not None:
+                    stats = dict()
+                    stats["name"] = module_name
+                    stats[0] = histogram.min.item()
+                    stats[100] = histogram.max.item()
+                    _get_advanced_stats_from_histogram(histogram, stats, percentile_list)
+                    return stats
+            elif len(histogram_list) > 1:
+                stats = dict()
+                stats["name"] = module_name
+                curmin = float("inf")
+                curmax = float("-inf")
+                for histogram in histogram_list:
+                    if histogram.min is not None:
+                        curmin = min(curmin, histogram.min.item())
+                        curmax = max(curmax, histogram.max.item())
+                if curmin < float("inf"):
+                    stats[0] = curmin
+                    stats[100] = curmax
+                    for p in percentile_list:
+                        stats[p] = None
+                    return stats
+
+    return None
+
+
+def _add_key_percentiles(percentiles: tuple):
+    """ Add percentiles required for boxplot if not already present """
+    percentile_list = list(percentiles)
+    for p in [25, 50, 75]:
+        if p not in percentile_list:
+            percentile_list.append(p)
+    return percentile_list
+
+
+def _get_advanced_stats_from_histogram(histogram, stats, percentile_list):
+    """ High level function to extract advanced stats from a histogram object """
+    if len(percentile_list) > 0:
+        percentile_stats = _get_percentile_stats_from_histogram(histogram, percentile_list)
+        for i, percentile in enumerate(percentile_list):
+            stats[percentile] = percentile_stats[i]
+
+
+def _get_percentile_stats_from_histogram(histogram, percentile_list):
+    """ Function to extract percentile stats from a histogram object """
+    if len(percentile_list) == 0:
+        raise RuntimeError("'percentile_list' cannot be empty.'")
+    if not _is_sorted(percentile_list):
+        raise RuntimeError("'percentile_list' must be sorted before calling this function.")
+
+    n = torch.sum(histogram.histogram).item()
+    cum_f = 0
+    idx = 0
+    percentile_stats = []
+    for i in range(len(histogram.histogram)):
+        f = histogram.histogram[i].item()
+        if f > 0:
+            bin_low = histogram.bin_edges[i].item()
+            bin_high = histogram.bin_edges[i + 1].item()
+            while True:
+                if (cum_f + f) / n >= percentile_list[idx] / 100:
+                    percentile_stats.append(bin_low + ((n * percentile_list[idx] / 100 - cum_f) / f) * (bin_high - bin_low))
+                    idx += 1
+                    if idx == len(percentile_list):
+                        return percentile_stats
+                else:
+                    break
+        cum_f += f
+
+    return None
+
+
+def _get_additional_boxplot_stats(stats_dict: dict):
+    """ Get additional values required to plot a boxplot"""
+    stats_dict["stridx"] = []
+    stats_dict["boxplot_upper"] = []
+    stats_dict["boxplot_lower"] = []
+    for i in range(len(stats_dict["idx"])):
+        stats_dict["stridx"].append(str(stats_dict["idx"][i]))
+        if (stats_dict[25][i] is not None) and (stats_dict[75][i] is not None):
+            inter_quantile_range = stats_dict[75][i] - stats_dict[25][i]
+            stats_dict["boxplot_upper"].append(stats_dict[75][i] + 1.5 * inter_quantile_range)
+            stats_dict["boxplot_lower"].append(stats_dict[25][i] - 1.5 * inter_quantile_range)
+        else:
+            stats_dict["boxplot_upper"].append(None)
+            stats_dict["boxplot_lower"].append(None)
+
+
+def _is_sorted(arr: list):
+    for i in range(len(arr) - 1):
+        if arr[i] > arr[i + 1]:
+            return False
+    return True
+
+
+class DataSources:
+    """
+    Class to hold the Bokeh ColumnDataSource objects needed in the visualization.
+    """
+
+    def __init__(self,
+                 stats_dict: dict,
+                 plot: figure,
+                 default_values: dict,
+                 percentiles: list
+                 ):
+        self.data_source = ColumnDataSource(
+            data=dict(idx=stats_dict["idx"],
+                      namelist=stats_dict["name"],
+                      minlist=stats_dict[0],
+                      min_namelist=["Min"] * len(stats_dict["idx"]),
+                      maxlist=stats_dict[100],
+                      max_namelist=["Max"] * len(stats_dict["idx"]),
+                      marker_yminlist=[default_values['default_ymin']] * len(stats_dict["idx"]),
+                      marker_ymaxlist=[default_values['default_ymax']] * len(stats_dict["idx"]),
+                      selected=[False] * len(stats_dict["idx"])))
+        if "stridx" in stats_dict.keys():
+            self.data_source.add(data=stats_dict["stridx"], name="stridx")
+        if "boxplot_upper" in stats_dict.keys():
+            self.data_source.add(data=stats_dict["boxplot_upper"], name="boxplot_upper_list")
+        if "boxplot_lower" in stats_dict.keys():
+            self.data_source.add(data=stats_dict["boxplot_lower"], name="boxplot_lower_list")
+        for key in [25, 50, 75]:
+            if key in stats_dict:
+                self.data_source.add(data=stats_dict[key], name=str(key) + "%ilelist")
+        for key in percentiles:
+            self.data_source.add(data=stats_dict[key], name=str(key) + "%ilelist")
+            self.data_source.add(data=[str(key)+" %ile" for _ in range(len(stats_dict["idx"]))], name=str(key) + "%ile_namelist")
+
+        self.default_values_source = ColumnDataSource(
+            data=dict(default_ymax=[default_values['default_ymax']],
+                      default_ymin=[default_values['default_ymin']],
+                      default_maxclip=[default_values['default_maxclip']],
+                      default_minclip=[default_values['default_minclip']],
+                      default_xmax=[default_values['default_xmax']],
+                      default_xmin=[default_values['default_xmin']]))
+
+        self.limits_source = ColumnDataSource(
+            data=dict(ymax=[default_values['default_ymax']], ymin=[default_values['default_ymin']],
+                      xmin=[plot.x_range.start], xmax=[plot.x_range.end],
+                      minclip=[default_values['default_minclip']],
+                      maxclip=[default_values['default_maxclip']]))
+
+        self.table_data_source = ColumnDataSource(
+            data=dict(idx=[], namelist=[], minlist=[], maxlist=[]))
+
+        self.selected_data_source = ColumnDataSource(
+            data=dict(idx=[], namelist=[], floor=[], ceil=[], minlist=[], min_namelist=[], maxlist=[], max_namelist=[])
+        )
+        if "stridx" in stats_dict.keys():
+            self.selected_data_source.add(data=[], name="stridx")
+        if "boxplot_upper" in stats_dict.keys():
+            self.selected_data_source.add(data=[], name="boxplot_upper_list")
+        if "boxplot_lower" in stats_dict.keys():
+            self.selected_data_source.add(data=[], name="boxplot_lower_list")
+        for key in [25, 50, 75]:
+            if key in stats_dict:
+                self.selected_data_source.add(data=[], name=str(key) + "%ilelist")
+        for key in percentiles:
+            self.selected_data_source.add(data=[], name=str(key) + "%ilelist")
+            self.selected_data_source.add(data=[], name=str(key) + "%ile_namelist")
+
+
+class TableFilters:
+    """
+    Class for holding data filters.
+    """
+
+    def __init__(self, data_sources: DataSources):
+        self.name_filter = BooleanFilter()
+        self.name_filter.booleans = [True for _ in range(len(data_sources.data_source.data['idx']))]
+        self.min_thresh_filter = BooleanFilter()
+        self.min_thresh_filter.booleans = [True for _ in range(len(data_sources.data_source.data['idx']))]
+        self.max_thresh_filter = BooleanFilter()
+        self.max_thresh_filter.booleans = [True for _ in range(len(data_sources.data_source.data['idx']))]
+
+
+class TableViews:
+    """
+    Class for holding views of the data sources.
+    """
+
+    def __init__(self, tablefilters: TableFilters):
+        self.min_thresh_view = CDSView(filter=tablefilters.min_thresh_filter)
+        self.max_thresh_view = CDSView(filter=tablefilters.max_thresh_filter)
+
+
+class TableObjects:
+    """
+    Class for holding various objects related to the table elements in the visualization.
+    """
+
+    def __init__(self, datasources: DataSources):
+        self.filters = TableFilters(datasources)
+        self.views = TableViews(self.filters)
+
+        columns = [
+            TableColumn(field="idx", title="Layer Index",
+                        width=QuantStatsVisualizer.table_column_widths["Layer Index"]),
+            TableColumn(field="namelist", title="Layer Name",
+                        formatter=StringFormatter(font_style="bold"),
+                        width=QuantStatsVisualizer.table_column_widths["Layer Name"]),
+            TableColumn(field="minlist", title="Min Activation",
+                        formatter=ScientificFormatter(precision=3),
+                        width=QuantStatsVisualizer.table_column_widths["Min Activation"]),
+            TableColumn(field="maxlist", title="Max Activation",
+                        formatter=ScientificFormatter(precision=3),
+                        width=QuantStatsVisualizer.table_column_widths["Max Activation"]),
+        ]
+
+        self.data_table = DataTable(source=datasources.table_data_source, columns=columns,
+                                    sortable=True, width=QuantStatsVisualizer.plot_dims["table_width"],
+                                    selectable="checkbox",
+                                    index_position=None,
+                                    )
+
+
+class InputWidgets:
+    """
+    Class to hold various input widgets.
+    """
+
+    def __init__(self, default_values: dict):
+        self.ymin_input = TextInput(value=str(default_values['default_ymin']),
+                                    title="Enter lower display limit of the plot")
+        self.ymax_input = TextInput(value=str(default_values['default_ymax']),
+                                    title="Enter upper display limit of the plot")
+        self.minclip_input = TextInput(value=str(default_values['default_minclip']),
+                                       title="Enter lower threshold value for activations/weights")
+        self.maxclip_input = TextInput(value=str(default_values['default_maxclip']),
+                                       title="Enter upper threshold value for activations/weights")
+
+        self.name_input = TextInput(value="", title="Enter Name Filter")
+
+        tooltip_table_mode = Tooltip(content=HTML("""
+                                                <h3> Select Table View </h3>
+                                                <p> Following table views are available </p>
+                                                <ol>
+                                                <li> <b> All: </b> All quantized layers </li>
+                                                <li> <b> Min: </b> Quantized layers with min activation below lower threshold value </li>
+                                                <li> <b> Max: </b> Quantized layers with max activation above upper threshold value </li>
+                                                <li> <b> Min | Max: </b> Union of Min and Max </li>
+                                                <li> <b> Min & Max: </b> Intersection of Min and Max </li>
+                                                </ol>
+                                            """),
+                                     position="right")
+        self.table_view_select = Select(title="Select Table View",
+                                        value="Min | Max",
+                                        options=["All", "Min", "Max", "Min | Max", "Min & Max"],
+                                        width=200,
+                                        description=tooltip_table_mode
+                                        )
+
+
+class CustomCallbacks:
+    """
+    Class to hold Custom JavaScript Callbacks for interactivity in the visualization.
+    """
+
+    def __init__(self):
+        self.limit_change_callback = None
+        self.reset_callback = None
+        self.name_filter_callback = None
+        self.select_table_view_callback = None
+        self.table_selection_callback = None
+
+
+class QuantStatsVisualizer:
+    """
+    Class for constructing the visualization with functionality to export the plot as
+
+    :param stats_dict: Dictionary containing the module names, indices, and other extracted statistics
+    """
+
+    # Class level constants
+    plot_dims = {"plot_width": 700,
+                 "plot_height": 400,
+                 "table_width": 800,
+                 "boxplot_unit_width": 150,
+                 "boxplot_height": 400,
+                 "whisker_head": 20,
+                 "boxplot_vbar_width": 0.7,
+                 "boxplot_text_font_size": "10px"}
+    initial_vals = {"default_ymin": -1e5, "default_ymax": 1e5}
+    spacer_dims = {"sp1_width": 50, "sp1_height": 40, "sp2_width": 100}
+    table_column_widths = {"Layer Index": 100,
+                           "Layer Name": 400,
+                           "Min Activation": 100,
+                           "Max Activation": 100}
+
+    def __init__(self, stats_dict: dict, percentile_list: list):
+        self.stats_dict = stats_dict
+        self.plot = figure(
+            title="Min Max Activations/Weights of quantized modules for given model",
+            x_axis_label="Layer index",
+            y_axis_label="Activation/Weight",
+            tools="pan,wheel_zoom,box_zoom")
+        self.boxplot = figure(
+            x_range=FactorRange(),
+            title="Boxplots of selected layers",
+            x_axis_label="Layer index",
+            y_axis_label="Activation/Weight",
+            tools="pan,wheel_zoom,box_zoom")
+        self.default_values = dict()
+        self.percentiles = []
+        for percentile in percentile_list:
+            if percentile not in [25, 50, 75]:
+                self.percentiles.append(percentile)
+
+    def _add_plot_lines(self, datasources: DataSources):
+        self.plot.segment(x0='xmin', x1='xmax', y0='ymin', y1='ymin', line_width=4, line_color='black',
+                          source=datasources.limits_source)
+        self.plot.segment(x0='xmin', x1='xmax', y0='ymax', y1='ymax', line_width=4, line_color='black',
+                          source=datasources.limits_source)
+        self.plot.segment(x0='xmin', x1='xmax', y0='minclip', y1='minclip', line_width=2, line_color='black',
+                          line_dash='dashed',
+                          source=datasources.limits_source)
+        self.plot.segment(x0='xmin', x1='xmax', y0='maxclip', y1='maxclip', line_width=2, line_color='black',
+                          line_dash='dashed',
+                          source=datasources.limits_source)
+        self.plot.line('idx', 'maxlist', source=datasources.data_source, legend_label="Max Activation", line_width=2,
+                       line_color="red")
+        self.plot.line('idx', 'minlist', source=datasources.data_source, legend_label="Min Activation", line_width=2,
+                       line_color="blue")
+        selections = self.plot.segment(x0='idx', x1='idx', y0='floor', y1='ceil', line_width=2, line_color='goldenrod',
+                                       line_alpha=0.5, source=datasources.selected_data_source)
+
+        return selections
+
+    def _add_min_max_markers(self, datasources: DataSources, tableobjects: TableObjects):
+        min_markers = self.plot.circle_x('idx', 'marker_yminlist', source=datasources.data_source, size=10,
+                                         color='orange',
+                                         line_color="navy")
+        min_markers.view = tableobjects.views.min_thresh_view
+        max_markers = self.plot.circle_x('idx', 'marker_ymaxlist', source=datasources.data_source, size=10,
+                                         color='orange',
+                                         line_color="navy")
+        max_markers.view = tableobjects.views.max_thresh_view
+
+        return min_markers, max_markers
+
+    def _add_boxplots(self, datasources: DataSources):
+        whisker = Whisker(base="stridx", upper="boxplot_upper_list", lower="boxplot_lower_list", source=datasources.selected_data_source)
+        whisker.upper_head.size = whisker.lower_head.size = QuantStatsVisualizer.plot_dims["whisker_head"]
+        self.boxplot.add_layout(whisker)
+        self.boxplot.vbar("stridx", QuantStatsVisualizer.plot_dims["boxplot_vbar_width"], "50%ilelist", "75%ilelist", source=datasources.selected_data_source, line_color="black")
+        self.boxplot.vbar("stridx", QuantStatsVisualizer.plot_dims["boxplot_vbar_width"], "25%ilelist", "50%ilelist", source=datasources.selected_data_source, line_color="black")
+        self.boxplot.circle(x="stridx", y="minlist", source=datasources.selected_data_source,
+                            name="min_points", color="orange")
+        self.boxplot.text(x="stridx", y="minlist", text="min_namelist",
+                          source=datasources.selected_data_source,
+                          x_offset=5, y_offset=5,
+                          text_font_size=QuantStatsVisualizer.plot_dims["boxplot_text_font_size"],
+                          name="min_labels")
+        self.boxplot.circle(x="stridx", y="maxlist", source=datasources.selected_data_source,
+                            name="max_points", color="orange")
+        self.boxplot.text(x="stridx", y="maxlist", text="max_namelist",
+                          source=datasources.selected_data_source,
+                          x_offset=5, y_offset=5,
+                          text_font_size=QuantStatsVisualizer.plot_dims["boxplot_text_font_size"],
+                          name="max_labels")
+        for percentile in self.percentiles:
+            self.boxplot.circle(x="stridx", y=str(percentile)+"%ilelist", source=datasources.selected_data_source, name=str(percentile) + "_" + "points", color="orange")
+            self.boxplot.text(x="stridx", y=str(percentile)+"%ilelist", text=str(percentile)+"%ile_namelist", source=datasources.selected_data_source,
+                                         x_offset=5, y_offset=5, text_font_size=QuantStatsVisualizer.plot_dims["boxplot_text_font_size"], name=str(percentile) + "_" + "labels")
+
+    @staticmethod
+    def _get_marker_hovertool(min_markers, max_markers):
+        format_code = """
+                    if (Math.abs(value) < 1e-3 || Math.abs(value) > 1e5) {
+                    return value.toExponential(3);
+                    } else {
+                    return value.toFixed(3);
+                    }
+                """
+
+        format_hover = CustomJSHover(code=format_code)
+
+        marker_hover = HoverTool(renderers=[min_markers, max_markers], tooltips=[
+            ("Layer Index", "@idx"),
+            ("Name", "@namelist"),
+            ("Max Activation", "@maxlist{custom}"),
+            ("Min Activation", "@minlist{custom}"),
+        ], formatters={
+            "@minlist": format_hover,
+            "@maxlist": format_hover,
+        })
+
+        return marker_hover
+
+    @staticmethod
+    def _get_selection_hovertool(selections):
+        format_code = """
+                    if (Math.abs(value) < 1e-3 || Math.abs(value) > 1e5) {
+                    return value.toExponential(3);
+                    } else {
+                    return value.toFixed(3);
+                    }
+                """
+
+        format_hover = CustomJSHover(code=format_code)
+
+        selection_hover = HoverTool(renderers=[selections], tooltips=[
+            ("Layer Index", "@idx"),
+            ("Name", "@namelist"),
+            ("Max Activation", "@maxlist{custom}"),
+            ("Min Activation", "@minlist{custom}"),
+        ], formatters={
+            "@minlist": format_hover,
+            "@maxlist": format_hover,
+        })
+
+        return selection_hover
+
+    def _define_callbacks(self, datasources, tableobjects, inputwidgets, mode):
+        customcallbacks = CustomCallbacks()
+
+        table_columns = ["idx", "namelist", "minlist", "maxlist"]
+        selection_columns = []
+
+        if mode == "basic":
+            selection_columns += ["idx", "namelist", "minlist", "min_namelist", "maxlist", "max_namelist"]
+        elif mode == "advanced":
+            selection_columns += ["idx", "namelist", "minlist", "min_namelist", "maxlist", "max_namelist", "stridx", "boxplot_upper_list", "boxplot_lower_list", "25%ilelist", "50%ilelist", "75%ilelist"]
+            for percentile in self.percentiles:
+                selection_columns.append(str(percentile) + "%ilelist")
+                selection_columns.append(str(percentile) + "%ile_namelist")
+
+        customcallbacks.limit_change_callback = CustomJS(args=dict(
+            limits_source=datasources.limits_source,
+            data_source=datasources.data_source,
+            table_data_source=datasources.table_data_source,
+            selected_data_source=datasources.selected_data_source,
+            min_marker_source=datasources.data_source,
+            max_marker_source=datasources.data_source,
+            ymax_input=inputwidgets.ymax_input,
+            ymin_input=inputwidgets.ymin_input,
+            maxclip_input=inputwidgets.maxclip_input,
+            minclip_input=inputwidgets.minclip_input,
+            plot=self.plot,
+            min_thresh_filter=tableobjects.filters.min_thresh_filter,
+            max_thresh_filter=tableobjects.filters.max_thresh_filter,
+            name_filter=tableobjects.filters.name_filter,
+            select=inputwidgets.table_view_select,
+            table_columns=table_columns,
+        ), code=(Path(__file__).parent / "quant_stats_visualization_JS_code/utils.js").read_text("utf8") + (Path(__file__).parent / "quant_stats_visualization_JS_code/limit_change_callback.js").read_text("utf8"))
+
+        customcallbacks.reset_callback = CustomJS(args=dict(
+            limits_source=datasources.limits_source,
+            data_source=datasources.data_source,
+            table_data_source=datasources.table_data_source,
+            selected_data_source=datasources.selected_data_source,
+            default_values_source=datasources.default_values_source,
+            min_marker_source=datasources.data_source,
+            max_marker_source=datasources.data_source,
+            ymax_input=inputwidgets.ymax_input,
+            ymin_input=inputwidgets.ymin_input,
+            maxclip_input=inputwidgets.maxclip_input,
+            minclip_input=inputwidgets.minclip_input,
+            select=inputwidgets.table_view_select,
+            name_input=inputwidgets.name_input,
+            plot=self.plot,
+            boxplot=self.boxplot,
+            min_thresh_filter=tableobjects.filters.min_thresh_filter,
+            max_thresh_filter=tableobjects.filters.max_thresh_filter,
+            name_filter=tableobjects.filters.name_filter,
+            selection_columns=selection_columns,
+            table_columns=table_columns,
+            mode=mode,
+            boxplot_unit_width=QuantStatsVisualizer.plot_dims["boxplot_unit_width"],
+        ), code=(Path(__file__).parent / "quant_stats_visualization_JS_code/utils.js").read_text("utf8") + (Path(__file__).parent / "quant_stats_visualization_JS_code/reset_callback.js").read_text("utf8"))
+
+        customcallbacks.name_filter_callback = CustomJS(args=dict(
+            data_source=datasources.data_source,
+            table_data_source=datasources.table_data_source,
+            limits_source=datasources.limits_source,
+            min_thresh_filter=tableobjects.filters.min_thresh_filter,
+            max_thresh_filter=tableobjects.filters.max_thresh_filter,
+            name_filter=tableobjects.filters.name_filter,
+            select=inputwidgets.table_view_select,
+            table_columns=table_columns,
+        ), code=(Path(__file__).parent / "quant_stats_visualization_JS_code/utils.js").read_text("utf8") + (Path(__file__).parent / "quant_stats_visualization_JS_code/name_filter_callback.js").read_text("utf8"))
+
+        customcallbacks.select_table_view_callback = CustomJS(args=dict(
+            data_source=datasources.data_source,
+            table_data_source=datasources.table_data_source,
+            select=inputwidgets.table_view_select,
+            min_thresh_filter=tableobjects.filters.min_thresh_filter,
+            max_thresh_filter=tableobjects.filters.max_thresh_filter,
+            name_filter=tableobjects.filters.name_filter,
+            table=tableobjects.data_table,
+            table_columns=table_columns,
+        ), code=(Path(__file__).parent / "quant_stats_visualization_JS_code/utils.js").read_text("utf8") + (Path(__file__).parent / "quant_stats_visualization_JS_code/select_table_view_callback.js").read_text("utf8"))
+
+        customcallbacks.table_selection_callback = CustomJS(args=dict(
+            data_source=datasources.data_source,
+            table_data_source=datasources.table_data_source,
+            selected_data_source=datasources.selected_data_source,
+            limits_source=datasources.limits_source,
+            boxplot=self.boxplot,
+            selection_columns=selection_columns,
+            mode=mode,
+            boxplot_unit_width=QuantStatsVisualizer.plot_dims["boxplot_unit_width"],
+        ), code=(Path(__file__).parent / "quant_stats_visualization_JS_code/utils.js").read_text("utf8") + (Path(__file__).parent / "quant_stats_visualization_JS_code/table_selection_callback.js").read_text("utf8"))
+
+        return customcallbacks
+
+    def _attach_callbacks(self, datasources, inputwidgets, customcallbacks):
+        self.plot.js_on_event(Reset, customcallbacks.reset_callback)
+        inputwidgets.ymax_input.js_on_change('value', customcallbacks.limit_change_callback)
+        inputwidgets.ymin_input.js_on_change('value', customcallbacks.limit_change_callback)
+        inputwidgets.maxclip_input.js_on_change('value', customcallbacks.limit_change_callback)
+        inputwidgets.minclip_input.js_on_change('value', customcallbacks.limit_change_callback)
+        inputwidgets.name_input.js_on_change("value", customcallbacks.name_filter_callback)
+        inputwidgets.table_view_select.js_on_change('value', customcallbacks.select_table_view_callback)
+        datasources.table_data_source.selected.js_on_change('indices', customcallbacks.table_selection_callback)
+
+    def _create_layout(self, inputwidgets, tableobjects, mode):
+        heading_1 = Div(text="<h2>Quant Stats Visualizer</h2>")
+        heading_2 = Div(text="<h2>Quant Stats Data Table</h2>")
+
+        sp1 = Spacer(width=QuantStatsVisualizer.spacer_dims["sp1_width"],
+                     height=QuantStatsVisualizer.spacer_dims["sp1_height"])
+        row1 = row(inputwidgets.ymin_input, inputwidgets.ymax_input)
+        row2 = row(inputwidgets.minclip_input, inputwidgets.maxclip_input)
+        inputs1 = column(row1, row2)
+        if mode == "basic":
+            layout = column(heading_1, inputs1, sp1, self.plot,
+                            column(heading_2, row(inputwidgets.table_view_select, inputwidgets.name_input),
+                                   tableobjects.data_table))
+        elif mode == "advanced":
+            sp2 = Spacer(width=QuantStatsVisualizer.spacer_dims["sp2_width"],
+                         height=QuantStatsVisualizer.plot_dims["boxplot_height"])
+            layout = column(heading_1, inputs1, sp1, row(self.plot, sp2, self.boxplot),
+                            column(heading_2, row(inputwidgets.table_view_select, inputwidgets.name_input),
+                                   tableobjects.data_table))
+        else:
+            raise ValueError(f"Expected mode to be 'basic' or 'advanced', got '{mode}'.")
+
+        return layout
+
+    def export_plot_as_html(self, save_path: str, mode: str) -> None:
+        """
+        Method for constructing the visualization and saving it to the given path.
+
+        :param save_path: Path for saving the visualization.
+        :param mode: Whether to plot basic or advanced stats.
+        """
+
+        curdoc().theme = 'light_minimal'
+
+        self.plot.width = QuantStatsVisualizer.plot_dims["plot_width"]
+        self.plot.height = QuantStatsVisualizer.plot_dims["plot_height"]
+
+        # Defining the default values of plotting parameters
+        self.default_values['default_ymax'] = QuantStatsVisualizer.initial_vals["default_ymax"]
+        self.default_values['default_ymin'] = QuantStatsVisualizer.initial_vals["default_ymin"]
+        self.default_values['default_xmax'] = len(self.stats_dict["idx"]) - 1
+        self.default_values['default_xmin'] = 0
+        self.default_values['default_maxclip'] = self.default_values['default_ymax'] / 2
+        self.default_values['default_minclip'] = self.default_values['default_ymin'] / 2
+
+        self.plot.x_range = Range1d(0, len(self.stats_dict["idx"]))
+        self.plot.y_range = Range1d(self.default_values['default_ymax'] * 1.05,
+                                    self.default_values['default_ymin'] * 1.05)
+
+        # Creating and adding a reset tool
+        rt = ResetTool()
+        self.plot.add_tools(rt)
+
+        # Defining Bokeh ColumnDataSources
+        datasources = DataSources(stats_dict=self.stats_dict,
+                                  plot=self.plot,
+                                  default_values=self.default_values,
+                                  percentiles=self.percentiles
+                                  )
+
+        # Creating plot objects
+        selections = self._add_plot_lines(datasources)
+
+        if mode == "advanced":
+            self.boxplot.width = 5 * QuantStatsVisualizer.plot_dims["boxplot_unit_width"]
+            self.boxplot.height = QuantStatsVisualizer.plot_dims["boxplot_height"]
+            self.boxplot.y_range = Range1d()
+            self._add_boxplots(datasources)
+
+        # Defining the table objects and name filter views
+        tableobjects = TableObjects(datasources)
+
+        # Marker points to see which layers cross the thresholds
+        min_markers, max_markers = self._add_min_max_markers(datasources, tableobjects)
+
+        # Defining a hover functionality to see layer details on hovering on the marker points and selections
+        marker_hover = self._get_marker_hovertool(min_markers, max_markers)
+        selection_hover = self._get_selection_hovertool(selections)
+        self.plot.add_tools(marker_hover, selection_hover)
+
+        # Creating the input widgets
+        inputwidgets = InputWidgets(self.default_values)
+
+        # Defining Custom JavaScript callbacks
+        customcallbacks = self._define_callbacks(datasources, tableobjects, inputwidgets, mode)
+
+        # Attach events to corresponding callbacks
+        curdoc().js_on_event(DocumentReady, customcallbacks.reset_callback)
+        self._attach_callbacks(datasources, inputwidgets, customcallbacks)
+
+        # Define the formatting
+        layout = self._create_layout(inputwidgets, tableobjects, mode)
+
+        # Save as standalone html
+        save(layout, save_path)
+
    +
    +
    +
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    + + +
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    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
    + Made with Sphinx and @pradyunsg's + + Furo + +
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    All modules for which code is available

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    + Copyright © 2020, Qualcomm Innovation Center, Inc. +
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    + + + + + + + + + \ No newline at end of file diff --git a/releases/2.0.0/_sphinx_design_static/design-tabs.js b/releases/2.0.0/_sphinx_design_static/design-tabs.js new file mode 100644 index 0000000..b25bd6a --- /dev/null +++ b/releases/2.0.0/_sphinx_design_static/design-tabs.js @@ -0,0 +1,101 @@ +// @ts-check + +// Extra JS capability for selected tabs to be synced +// The selection is stored in local storage so that it persists across page loads. + +/** + * @type {Record} + */ +let sd_id_to_elements = {}; +const storageKeyPrefix = "sphinx-design-tab-id-"; + +/** + * Create a key for a tab element. + * @param {HTMLElement} el - The tab element. + * @returns {[string, string, string] | null} - The key. + * + */ +function create_key(el) { + let syncId = el.getAttribute("data-sync-id"); + let syncGroup = el.getAttribute("data-sync-group"); + if (!syncId || !syncGroup) return null; + return [syncGroup, syncId, syncGroup + "--" + syncId]; +} + +/** + * Initialize the tab selection. + * + */ +function ready() { + // Find all tabs with sync data + + /** @type {string[]} */ + let groups = []; + + document.querySelectorAll(".sd-tab-label").forEach((label) => { + if (label instanceof HTMLElement) { + let data = create_key(label); + if (data) { + let [group, id, key] = data; + + // add click event listener + // @ts-ignore + label.onclick = onSDLabelClick; + + // store map of key to elements + if (!sd_id_to_elements[key]) { + sd_id_to_elements[key] = []; + } + sd_id_to_elements[key].push(label); + + if (groups.indexOf(group) === -1) { + groups.push(group); + // Check if a specific tab has been selected via URL parameter + const tabParam = new URLSearchParams(window.location.search).get( + group + ); + if (tabParam) { + console.log( + "sphinx-design: Selecting tab id for group '" + + group + + "' from URL parameter: " + + tabParam + ); + window.sessionStorage.setItem(storageKeyPrefix + group, tabParam); + } + } + + // Check is a specific tab has been selected previously + let previousId = window.sessionStorage.getItem( + storageKeyPrefix + group + ); + if (previousId === id) { + // console.log( + // "sphinx-design: Selecting tab from session storage: " + id + // ); + // @ts-ignore + label.previousElementSibling.checked = true; + } + } + } + }); +} + +/** + * Activate other tabs with the same sync id. + * + * @this {HTMLElement} - The element that was clicked. + */ +function onSDLabelClick() { + let data = create_key(this); + if (!data) return; + let [group, id, key] = data; + for (const label of sd_id_to_elements[key]) { + if (label === this) continue; + // @ts-ignore + label.previousElementSibling.checked = true; + } + window.sessionStorage.setItem(storageKeyPrefix + group, id); +} + +document.addEventListener("DOMContentLoaded", ready, false); diff --git a/releases/2.0.0/_sphinx_design_static/sphinx-design.min.css b/releases/2.0.0/_sphinx_design_static/sphinx-design.min.css new file mode 100644 index 0000000..860c36d --- /dev/null +++ b/releases/2.0.0/_sphinx_design_static/sphinx-design.min.css @@ -0,0 +1 @@ +.sd-bg-primary{background-color:var(--sd-color-primary) !important}.sd-bg-text-primary{color:var(--sd-color-primary-text) !important}button.sd-bg-primary:focus,button.sd-bg-primary:hover{background-color:var(--sd-color-primary-highlight) !important}a.sd-bg-primary:focus,a.sd-bg-primary:hover{background-color:var(--sd-color-primary-highlight) !important}.sd-bg-secondary{background-color:var(--sd-color-secondary) !important}.sd-bg-text-secondary{color:var(--sd-color-secondary-text) !important}button.sd-bg-secondary:focus,button.sd-bg-secondary:hover{background-color:var(--sd-color-secondary-highlight) !important}a.sd-bg-secondary:focus,a.sd-bg-secondary:hover{background-color:var(--sd-color-secondary-highlight) !important}.sd-bg-success{background-color:var(--sd-color-success) !important}.sd-bg-text-success{color:var(--sd-color-success-text) 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padding-right: 0.5em; +} +aside.footnote > p { + margin-left: 2em; +} +div.citation > p { + margin-left: 4em; +} +aside.footnote > p:last-of-type, +div.citation > p:last-of-type { + margin-bottom: 0em; +} +aside.footnote > p:last-of-type:after, +div.citation > p:last-of-type:after { + content: ""; + clear: both; +} + +dl.field-list { + display: grid; + grid-template-columns: fit-content(30%) auto; +} + +dl.field-list > dt { + font-weight: bold; + word-break: break-word; + padding-left: 0.5em; + padding-right: 5px; +} + +dl.field-list > dd { + padding-left: 0.5em; + margin-top: 0em; + margin-left: 0em; + margin-bottom: 0em; +} + +dl { + margin-bottom: 15px; +} + +dd > :first-child { + margin-top: 0px; +} + +dd ul, dd table { + margin-bottom: 10px; +} + +dd { + margin-top: 3px; + margin-bottom: 10px; + margin-left: 30px; +} + +.sig dd { + margin-top: 0px; + margin-bottom: 0px; +} + +.sig dl { + margin-top: 0px; + margin-bottom: 0px; +} + +dl > dd:last-child, +dl > dd:last-child > :last-child { + margin-bottom: 0; +} + +dt:target, span.highlighted { + background-color: #fbe54e; +} + +rect.highlighted { + fill: #fbe54e; +} + +dl.glossary dt { + font-weight: bold; + font-size: 1.1em; +} + +.versionmodified { + font-style: italic; +} + +.system-message { + background-color: #fda; + padding: 5px; + border: 3px solid red; +} + +.footnote:target { + background-color: #ffa; +} + +.line-block { + display: block; + margin-top: 1em; + margin-bottom: 1em; +} + +.line-block .line-block { + margin-top: 0; + margin-bottom: 0; + margin-left: 1.5em; +} + +.guilabel, .menuselection { + font-family: sans-serif; +} + +.accelerator { + text-decoration: underline; +} + +.classifier { + font-style: oblique; +} + +.classifier:before { + font-style: normal; + margin: 0 0.5em; + content: ":"; + display: inline-block; +} + +abbr, acronym { + border-bottom: dotted 1px; + cursor: help; +} + +.translated { + background-color: rgba(207, 255, 207, 0.2) +} + +.untranslated { + background-color: rgba(255, 207, 207, 0.2) +} + +/* -- code displays --------------------------------------------------------- */ + +pre { + overflow: auto; + overflow-y: hidden; /* fixes display issues on Chrome browsers */ +} + +pre, div[class*="highlight-"] { + clear: both; +} + +span.pre { + -moz-hyphens: none; + -ms-hyphens: none; + -webkit-hyphens: none; + hyphens: none; + white-space: nowrap; +} + +div[class*="highlight-"] { + margin: 1em 0; +} + +td.linenos pre { + border: 0; + background-color: transparent; + color: #aaa; +} + +table.highlighttable { + display: block; +} + +table.highlighttable tbody { + display: block; +} + +table.highlighttable tr { + display: flex; +} + +table.highlighttable td { + margin: 0; + padding: 0; +} + +table.highlighttable td.linenos { + padding-right: 0.5em; +} + +table.highlighttable td.code { + flex: 1; + overflow: hidden; +} + +.highlight .hll { + display: block; +} + +div.highlight pre, +table.highlighttable pre { + margin: 0; +} + +div.code-block-caption + div { + margin-top: 0; +} + +div.code-block-caption { + margin-top: 1em; + padding: 2px 5px; + font-size: small; +} + +div.code-block-caption code { + background-color: transparent; +} + +table.highlighttable td.linenos, +span.linenos, +div.highlight span.gp { /* gp: Generic.Prompt */ + user-select: none; + -webkit-user-select: text; /* Safari fallback only */ + -webkit-user-select: none; /* Chrome/Safari */ + -moz-user-select: none; /* Firefox */ + -ms-user-select: none; /* IE10+ */ +} + +div.code-block-caption span.caption-number { + padding: 0.1em 0.3em; + font-style: italic; +} + +div.code-block-caption span.caption-text { +} + +div.literal-block-wrapper { + margin: 1em 0; +} + +code.xref, a code { + background-color: transparent; + font-weight: bold; +} + +h1 code, h2 code, h3 code, h4 code, h5 code, h6 code { + background-color: transparent; +} + +.viewcode-link { + float: right; +} + +.viewcode-back { + float: right; + font-family: sans-serif; +} + +div.viewcode-block:target { + margin: -1px -10px; + padding: 0 10px; +} + +/* -- math display ---------------------------------------------------------- */ + +img.math { + vertical-align: middle; +} + +div.body div.math p { + text-align: center; +} + +span.eqno { + float: right; +} + +span.eqno a.headerlink { + position: absolute; + z-index: 1; +} + +div.math:hover a.headerlink { + visibility: visible; +} + +/* -- printout stylesheet --------------------------------------------------- */ + +@media print { + div.document, + div.documentwrapper, + div.bodywrapper { + margin: 0 !important; + width: 100%; + } + + div.sphinxsidebar, + div.related, + div.footer, + #top-link { + display: none; + } +} \ No newline at end of file diff --git a/releases/2.0.0/_static/check-solid.svg b/releases/2.0.0/_static/check-solid.svg new file mode 100644 index 0000000..92fad4b --- /dev/null +++ b/releases/2.0.0/_static/check-solid.svg @@ -0,0 +1,4 @@ + + + + diff --git a/releases/2.0.0/_static/clipboard.min.js b/releases/2.0.0/_static/clipboard.min.js new file mode 100644 index 0000000..54b3c46 --- /dev/null +++ b/releases/2.0.0/_static/clipboard.min.js @@ -0,0 +1,7 @@ +/*! + * clipboard.js v2.0.8 + * https://clipboardjs.com/ + * + * Licensed MIT © Zeno Rocha + */ +!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return o}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),c=n.n(e);function a(t){try{return document.execCommand(t)}catch(t){return}}var f=function(t){t=c()(t);return a("cut"),t};var l=function(t){var e,n,o,r=1 + + + + diff --git a/releases/2.0.0/_static/copybutton.css b/releases/2.0.0/_static/copybutton.css new file mode 100644 index 0000000..f1916ec --- /dev/null +++ b/releases/2.0.0/_static/copybutton.css @@ -0,0 +1,94 @@ +/* Copy buttons */ +button.copybtn { + position: absolute; + display: flex; + top: .3em; + right: .3em; + width: 1.7em; + height: 1.7em; + opacity: 0; + transition: opacity 0.3s, border .3s, background-color .3s; + user-select: none; + padding: 0; + border: none; + outline: none; + border-radius: 0.4em; + /* The colors that GitHub uses */ + border: #1b1f2426 1px solid; + background-color: #f6f8fa; + color: #57606a; +} + +button.copybtn.success { + border-color: #22863a; + color: #22863a; +} + +button.copybtn svg { + stroke: currentColor; + width: 1.5em; + height: 1.5em; + padding: 0.1em; +} + +div.highlight { + position: relative; +} + +/* Show the copybutton */ +.highlight:hover button.copybtn, button.copybtn.success { + opacity: 1; +} + +.highlight button.copybtn:hover { + background-color: rgb(235, 235, 235); +} + +.highlight button.copybtn:active { + background-color: rgb(187, 187, 187); +} + +/** + * A minimal CSS-only tooltip copied from: + * https://codepen.io/mildrenben/pen/rVBrpK + * + * To use, write HTML like the following: + * + *

    Short

    + */ + .o-tooltip--left { + position: relative; + } + + .o-tooltip--left:after { + opacity: 0; + visibility: hidden; + position: absolute; + content: attr(data-tooltip); + padding: .2em; + font-size: .8em; + left: -.2em; + background: grey; + color: white; + white-space: nowrap; + z-index: 2; + border-radius: 2px; + transform: translateX(-102%) translateY(0); + transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); +} + +.o-tooltip--left:hover:after { + display: block; + opacity: 1; + visibility: visible; + transform: translateX(-100%) translateY(0); + transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); + transition-delay: .5s; +} + +/* By default the copy button shouldn't show up when printing a page */ +@media print { + button.copybtn { + display: none; + } +} diff --git a/releases/2.0.0/_static/copybutton.js b/releases/2.0.0/_static/copybutton.js new file mode 100644 index 0000000..2ea7ff3 --- /dev/null +++ b/releases/2.0.0/_static/copybutton.js @@ -0,0 +1,248 @@ +// Localization support +const messages = { + 'en': { + 'copy': 'Copy', + 'copy_to_clipboard': 'Copy to clipboard', + 'copy_success': 'Copied!', + 'copy_failure': 'Failed to copy', + }, + 'es' : { + 'copy': 'Copiar', + 'copy_to_clipboard': 'Copiar al portapapeles', + 'copy_success': '¡Copiado!', + 'copy_failure': 'Error al copiar', + }, + 'de' : { + 'copy': 'Kopieren', + 'copy_to_clipboard': 'In die Zwischenablage kopieren', + 'copy_success': 'Kopiert!', + 'copy_failure': 'Fehler beim Kopieren', + }, + 'fr' : { + 'copy': 'Copier', + 'copy_to_clipboard': 'Copier dans le presse-papier', + 'copy_success': 'Copié !', + 'copy_failure': 'Échec de la copie', + }, + 'ru': { + 'copy': 'Скопировать', + 'copy_to_clipboard': 'Скопировать в буфер', + 'copy_success': 'Скопировано!', + 'copy_failure': 'Не удалось скопировать', + }, + 'zh-CN': { + 'copy': '复制', + 'copy_to_clipboard': '复制到剪贴板', + 'copy_success': '复制成功!', + 'copy_failure': '复制失败', + }, + 'it' : { + 'copy': 'Copiare', + 'copy_to_clipboard': 'Copiato negli appunti', + 'copy_success': 'Copiato!', + 'copy_failure': 'Errore durante la copia', + } +} + +let locale = 'en' +if( document.documentElement.lang !== undefined + && messages[document.documentElement.lang] !== undefined ) { + locale = document.documentElement.lang +} + +let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT; +if (doc_url_root == '#') { + doc_url_root = ''; +} + +/** + * SVG files for our copy buttons + */ +let iconCheck = ` + ${messages[locale]['copy_success']} + + +` + +// If the user specified their own SVG use that, otherwise use the default +let iconCopy = ``; +if (!iconCopy) { + iconCopy = ` + ${messages[locale]['copy_to_clipboard']} + + + +` +} + +/** + * Set up copy/paste for code blocks + */ + +const runWhenDOMLoaded = cb => { + if (document.readyState != 'loading') { + cb() + } else if (document.addEventListener) { + document.addEventListener('DOMContentLoaded', cb) + } else { + document.attachEvent('onreadystatechange', function() { + if (document.readyState == 'complete') cb() + }) + } +} + +const codeCellId = index => `codecell${index}` + +// Clears selected text since ClipboardJS will select the text when copying +const clearSelection = () => { + if (window.getSelection) { + window.getSelection().removeAllRanges() + } else if (document.selection) { + document.selection.empty() + } +} + +// Changes tooltip text for a moment, then changes it back +// We want the timeout of our `success` class to be a bit shorter than the +// tooltip and icon change, so that we can hide the icon before changing back. +var timeoutIcon = 2000; +var timeoutSuccessClass = 1500; + +const temporarilyChangeTooltip = (el, oldText, newText) => { + el.setAttribute('data-tooltip', newText) + el.classList.add('success') + // Remove success a little bit sooner than we change the tooltip + // So that we can use CSS to hide the copybutton first + setTimeout(() => el.classList.remove('success'), timeoutSuccessClass) + setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon) +} + +// Changes the copy button icon for two seconds, then changes it back +const temporarilyChangeIcon = (el) => { + el.innerHTML = iconCheck; + setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon) +} + +const addCopyButtonToCodeCells = () => { + // If ClipboardJS hasn't loaded, wait a bit and try again. This + // happens because we load ClipboardJS asynchronously. + if (window.ClipboardJS === undefined) { + setTimeout(addCopyButtonToCodeCells, 250) + return + } + + // Add copybuttons to all of our code cells + const COPYBUTTON_SELECTOR = 'div.highlight pre'; + const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR) + codeCells.forEach((codeCell, index) => { + const id = codeCellId(index) + codeCell.setAttribute('id', id) + + const clipboardButton = id => + `` + codeCell.insertAdjacentHTML('afterend', clipboardButton(id)) + }) + +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string +} + +/** + * Removes excluded text from a Node. + * + * @param {Node} target Node to filter. + * @param {string} exclude CSS selector of nodes to exclude. + * @returns {DOMString} Text from `target` with text removed. + */ +function filterText(target, exclude) { + const clone = target.cloneNode(true); // clone as to not modify the live DOM + if (exclude) { + // remove excluded nodes + clone.querySelectorAll(exclude).forEach(node => node.remove()); + } + return clone.innerText; +} + +// Callback when a copy button is clicked. Will be passed the node that was clicked +// should then grab the text and replace pieces of text that shouldn't be used in output +function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { + var regexp; + var match; + + // Do we check for line continuation characters and "HERE-documents"? + var useLineCont = !!lineContinuationChar + var useHereDoc = !!hereDocDelim + + // create regexp to capture prompt and remaining line + if (isRegexp) { + regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') + } else { + regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') + } + + const outputLines = []; + var promptFound = false; + var gotLineCont = false; + var gotHereDoc = false; + const lineGotPrompt = []; + for (const line of textContent.split('\n')) { + match = line.match(regexp) + if (match || gotLineCont || gotHereDoc) { + promptFound = regexp.test(line) + lineGotPrompt.push(promptFound) + if (removePrompts && promptFound) { + outputLines.push(match[2]) + } else { + outputLines.push(line) + } + gotLineCont = line.endsWith(lineContinuationChar) & useLineCont + if (line.includes(hereDocDelim) & useHereDoc) + gotHereDoc = !gotHereDoc + } else if (!onlyCopyPromptLines) { + outputLines.push(line) + } else if (copyEmptyLines && line.trim() === '') { + outputLines.push(line) + } + } + + // If no lines with the prompt were found then just use original lines + if (lineGotPrompt.some(v => v === true)) { + textContent = outputLines.join('\n'); + } + + // Remove a trailing newline to avoid auto-running when pasting + if (textContent.endsWith("\n")) { + textContent = textContent.slice(0, -1) + } + return textContent +} + + +var copyTargetText = (trigger) => { + var target = document.querySelector(trigger.attributes['data-clipboard-target'].value); + + // get filtered text + let exclude = '.linenos'; + + let text = filterText(target, exclude); + return formatCopyText(text, '', false, true, true, true, '', '') +} + + // Initialize with a callback so we can modify the text before copy + const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText}) + + // Update UI with error/success messages + clipboard.on('success', event => { + clearSelection() + temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success']) + temporarilyChangeIcon(event.trigger) + }) + + clipboard.on('error', event => { + temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure']) + }) +} + +runWhenDOMLoaded(addCopyButtonToCodeCells) \ No newline at end of file diff --git a/releases/2.0.0/_static/copybutton_funcs.js b/releases/2.0.0/_static/copybutton_funcs.js new file mode 100644 index 0000000..dbe1aaa --- /dev/null +++ b/releases/2.0.0/_static/copybutton_funcs.js @@ -0,0 +1,73 @@ +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string +} + +/** + * Removes excluded text from a Node. + * + * @param {Node} target Node to filter. + * @param {string} exclude CSS selector of nodes to exclude. + * @returns {DOMString} Text from `target` with text removed. + */ +export function filterText(target, exclude) { + const clone = target.cloneNode(true); // clone as to not modify the live DOM + if (exclude) { + // remove excluded nodes + clone.querySelectorAll(exclude).forEach(node => node.remove()); + } + return clone.innerText; +} + +// Callback when a copy button is clicked. Will be passed the node that was clicked +// should then grab the text and replace pieces of text that shouldn't be used in output +export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { + var regexp; + var match; + + // Do we check for line continuation characters and "HERE-documents"? + var useLineCont = !!lineContinuationChar + var useHereDoc = !!hereDocDelim + + // create regexp to capture prompt and remaining line + if (isRegexp) { + regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') + } else { + regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') + } + + const outputLines = []; + var promptFound = false; + var gotLineCont = false; + var gotHereDoc = false; + const lineGotPrompt = []; + for (const line of textContent.split('\n')) { + match = line.match(regexp) + if (match || gotLineCont || gotHereDoc) { + promptFound = regexp.test(line) + lineGotPrompt.push(promptFound) + if (removePrompts && promptFound) { + outputLines.push(match[2]) + } else { + outputLines.push(line) + } + gotLineCont = line.endsWith(lineContinuationChar) & useLineCont + if (line.includes(hereDocDelim) & useHereDoc) + gotHereDoc = !gotHereDoc + } else if (!onlyCopyPromptLines) { + outputLines.push(line) + } else if (copyEmptyLines && line.trim() === '') { + outputLines.push(line) + } + } + + // If no lines with the prompt were found then just use original lines + if (lineGotPrompt.some(v => v === true)) { + textContent = outputLines.join('\n'); + } + + // Remove a trailing newline to avoid auto-running when pasting + if (textContent.endsWith("\n")) { + textContent = textContent.slice(0, -1) + } + return textContent +} diff --git a/releases/2.0.0/_static/debug.css b/releases/2.0.0/_static/debug.css new file mode 100644 index 0000000..74d4aec --- /dev/null +++ b/releases/2.0.0/_static/debug.css @@ -0,0 +1,69 @@ +/* + This CSS file should be overridden by the theme authors. It's + meant for debugging and developing the skeleton that this theme provides. +*/ +body { + font-family: -apple-system, "Segoe UI", Roboto, Helvetica, Arial, sans-serif, + "Apple Color Emoji", "Segoe UI Emoji"; + background: lavender; +} +.sb-announcement { + background: rgb(131, 131, 131); +} +.sb-announcement__inner { + background: black; + color: white; +} +.sb-header { + background: lightskyblue; +} +.sb-header__inner { + background: royalblue; + color: white; +} +.sb-header-secondary { + background: lightcyan; +} +.sb-header-secondary__inner { + background: cornflowerblue; + color: white; +} +.sb-sidebar-primary { + background: lightgreen; +} +.sb-main { + background: blanchedalmond; +} +.sb-main__inner { + background: antiquewhite; +} +.sb-header-article { + background: lightsteelblue; +} +.sb-article-container { + background: snow; +} +.sb-article-main { + background: white; +} +.sb-footer-article { + background: lightpink; +} +.sb-sidebar-secondary { + background: lightgoldenrodyellow; +} +.sb-footer-content { + background: plum; +} +.sb-footer-content__inner { + background: palevioletred; +} +.sb-footer { + background: pink; +} +.sb-footer__inner { + background: salmon; +} +.sb-article { + background: white; +} diff --git a/releases/2.0.0/_static/design-tabs.js b/releases/2.0.0/_static/design-tabs.js new file mode 100644 index 0000000..b25bd6a --- /dev/null +++ b/releases/2.0.0/_static/design-tabs.js @@ -0,0 +1,101 @@ +// @ts-check + +// Extra JS capability for selected tabs to be synced +// The selection is stored in local storage so that it persists across page loads. + +/** + * @type {Record} + */ +let sd_id_to_elements = {}; +const storageKeyPrefix = "sphinx-design-tab-id-"; + +/** + * Create a key for a tab element. + * @param {HTMLElement} el - The tab element. + * @returns {[string, string, string] | null} - The key. + * + */ +function create_key(el) { + let syncId = el.getAttribute("data-sync-id"); + let syncGroup = el.getAttribute("data-sync-group"); + if (!syncId || !syncGroup) return null; + return [syncGroup, syncId, syncGroup + "--" + syncId]; +} + +/** + * Initialize the tab selection. + * + */ +function ready() { + // Find all tabs with sync data + + /** @type {string[]} */ + let groups = []; + + document.querySelectorAll(".sd-tab-label").forEach((label) => { + if (label instanceof HTMLElement) { + let data = create_key(label); + if (data) { + let [group, id, key] = data; + + // add click event listener + // @ts-ignore + label.onclick = onSDLabelClick; + + // store map of key to elements + if (!sd_id_to_elements[key]) { + sd_id_to_elements[key] = []; + } + sd_id_to_elements[key].push(label); + + if (groups.indexOf(group) === -1) { + groups.push(group); + // Check if a specific tab has been selected via URL parameter + const tabParam = new URLSearchParams(window.location.search).get( + group + ); + if (tabParam) { + console.log( + "sphinx-design: Selecting tab id for group '" + + group + + "' from URL parameter: " + + tabParam + ); + window.sessionStorage.setItem(storageKeyPrefix + group, tabParam); + } + } + + // Check is a specific tab has been selected previously + let previousId = window.sessionStorage.getItem( + storageKeyPrefix + group + ); + if (previousId === id) { + // console.log( + // "sphinx-design: Selecting tab from session storage: " + id + // ); + // @ts-ignore + label.previousElementSibling.checked = true; + } + } + } + }); +} + +/** + * Activate other tabs with the same sync id. + * + * @this {HTMLElement} - The element that was clicked. + */ +function onSDLabelClick() { + let data = create_key(this); + if (!data) return; + let [group, id, key] = data; + for (const label of sd_id_to_elements[key]) { + if (label === this) continue; + // @ts-ignore + label.previousElementSibling.checked = true; + } + window.sessionStorage.setItem(storageKeyPrefix + group, id); +} + +document.addEventListener("DOMContentLoaded", ready, false); diff --git a/releases/2.0.0/_static/doctools.js b/releases/2.0.0/_static/doctools.js new file mode 100644 index 0000000..0398ebb --- /dev/null +++ b/releases/2.0.0/_static/doctools.js @@ -0,0 +1,149 @@ +/* + * Base JavaScript utilities for all Sphinx HTML documentation. + */ +"use strict"; + +const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", +]); + +const _ready = (callback) => { + if (document.readyState !== "loading") { + callback(); + } else { + document.addEventListener("DOMContentLoaded", callback); + } +}; + +/** + * Small JavaScript module for the documentation. + */ +const Documentation = { + init: () => { + Documentation.initDomainIndexTable(); + Documentation.initOnKeyListeners(); + }, + + /** + * i18n support + */ + TRANSLATIONS: {}, + PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), + LOCALE: "unknown", + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext: (string) => { + const translated = Documentation.TRANSLATIONS[string]; + switch (typeof translated) { + case "undefined": + return string; // no translation + case "string": + return translated; // translation exists + default: + return translated[0]; // (singular, plural) translation tuple exists + } + }, + + ngettext: (singular, plural, n) => { + const translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated !== "undefined") + return translated[Documentation.PLURAL_EXPR(n)]; + return n === 1 ? singular : plural; + }, + + addTranslations: (catalog) => { + Object.assign(Documentation.TRANSLATIONS, catalog.messages); + Documentation.PLURAL_EXPR = new Function( + "n", + `return (${catalog.plural_expr})` + ); + Documentation.LOCALE = catalog.locale; + }, + + /** + * helper function to focus on search bar + */ + focusSearchBar: () => { + document.querySelectorAll("input[name=q]")[0]?.focus(); + }, + + /** + * Initialise the domain index toggle buttons + */ + initDomainIndexTable: () => { + const toggler = (el) => { + const idNumber = el.id.substr(7); + const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); + if (el.src.substr(-9) === "minus.png") { + el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; + toggledRows.forEach((el) => (el.style.display = "none")); + } else { + el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; + toggledRows.forEach((el) => (el.style.display = "")); + } + }; + + const togglerElements = document.querySelectorAll("img.toggler"); + togglerElements.forEach((el) => + el.addEventListener("click", (event) => toggler(event.currentTarget)) + ); + togglerElements.forEach((el) => (el.style.display = "")); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); + }, + + initOnKeyListeners: () => { + // only install a listener if it is really needed + if ( + !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && + !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS + ) + return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.altKey || event.ctrlKey || event.metaKey) return; + + if (!event.shiftKey) { + switch (event.key) { + case "ArrowLeft": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const prevLink = document.querySelector('link[rel="prev"]'); + if (prevLink && prevLink.href) { + window.location.href = prevLink.href; + event.preventDefault(); + } + break; + case "ArrowRight": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const nextLink = document.querySelector('link[rel="next"]'); + if (nextLink && nextLink.href) { + window.location.href = nextLink.href; + event.preventDefault(); + } + break; + } + } + + // some keyboard layouts may need Shift to get / + switch (event.key) { + case "/": + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; + Documentation.focusSearchBar(); + event.preventDefault(); + } + }); + }, +}; + +// quick alias for translations +const _ = Documentation.gettext; + +_ready(Documentation.init); diff --git a/releases/2.0.0/_static/documentation_options.js b/releases/2.0.0/_static/documentation_options.js new file mode 100644 index 0000000..f38abe9 --- /dev/null +++ b/releases/2.0.0/_static/documentation_options.js @@ -0,0 +1,13 @@ +const DOCUMENTATION_OPTIONS = { + VERSION: '', + LANGUAGE: 'en', + COLLAPSE_INDEX: false, + BUILDER: 'html', + FILE_SUFFIX: '.html', + LINK_SUFFIX: '.html', + HAS_SOURCE: true, + SOURCELINK_SUFFIX: '.txt', + NAVIGATION_WITH_KEYS: true, + SHOW_SEARCH_SUMMARY: true, + ENABLE_SEARCH_SHORTCUTS: true, +}; \ No newline at end of file diff --git a/releases/2.0.0/_static/file.png b/releases/2.0.0/_static/file.png new file mode 100644 index 0000000000000000000000000000000000000000..a858a410e4faa62ce324d814e4b816fff83a6fb3 GIT binary patch literal 286 zcmV+(0pb3MP)s`hMrGg#P~ix$^RISR_I47Y|r1 z_CyJOe}D1){SET-^Amu_i71Lt6eYfZjRyw@I6OQAIXXHDfiX^GbOlHe=Ae4>0m)d(f|Me07*qoM6N<$f}vM^LjV8( literal 0 HcmV?d00001 diff --git a/releases/2.0.0/_static/language_data.js b/releases/2.0.0/_static/language_data.js new file mode 100644 index 0000000..c7fe6c6 --- /dev/null +++ b/releases/2.0.0/_static/language_data.js @@ -0,0 +1,192 @@ +/* + * This script contains the language-specific data used by searchtools.js, + * namely the list of stopwords, stemmer, scorer and splitter. + */ + +var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"]; + + +/* Non-minified version is copied as a separate JS file, if available */ + +/** + * Porter Stemmer + */ +var Stemmer = function() { + + var step2list = { + ational: 'ate', + tional: 'tion', + enci: 'ence', + anci: 'ance', + izer: 'ize', + bli: 'ble', + alli: 'al', + entli: 'ent', + eli: 'e', + ousli: 'ous', + ization: 'ize', + ation: 'ate', + ator: 'ate', + alism: 'al', + iveness: 'ive', + fulness: 'ful', + ousness: 'ous', + aliti: 'al', + iviti: 'ive', + biliti: 'ble', + logi: 'log' + }; + + var step3list = { + icate: 'ic', + ative: '', + alize: 'al', + iciti: 'ic', + ical: 'ic', + ful: '', + ness: '' + }; + + var c = "[^aeiou]"; // consonant + var v = "[aeiouy]"; // vowel + var C = c + "[^aeiouy]*"; // consonant sequence + var V = v + "[aeiou]*"; // vowel sequence + + var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0 + var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1 + var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 + var s_v = "^(" + C + ")?" + v; // vowel in stem + + this.stemWord = function (w) { + var stem; + var suffix; + var firstch; + var origword = w; + + if (w.length < 3) + return w; + + var re; + var re2; + var re3; + var re4; + + firstch = w.substr(0,1); + if (firstch == "y") + w = firstch.toUpperCase() + w.substr(1); + + // Step 1a + re = /^(.+?)(ss|i)es$/; + re2 = /^(.+?)([^s])s$/; + + if (re.test(w)) + w = w.replace(re,"$1$2"); + else if (re2.test(w)) + w = w.replace(re2,"$1$2"); + + // Step 1b + re = /^(.+?)eed$/; + re2 = /^(.+?)(ed|ing)$/; + if (re.test(w)) { + var fp = re.exec(w); + re = new RegExp(mgr0); + if (re.test(fp[1])) { + re = /.$/; + w = w.replace(re,""); + } + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = new RegExp(s_v); + if (re2.test(stem)) { + w = stem; + re2 = /(at|bl|iz)$/; + re3 = new RegExp("([^aeiouylsz])\\1$"); + re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re2.test(w)) + w = w + "e"; + else if (re3.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + else if (re4.test(w)) + w = w + "e"; + } + } + + // Step 1c + re = /^(.+?)y$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(s_v); + if (re.test(stem)) + w = stem + "i"; + } + + // Step 2 + re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step2list[suffix]; + } + + // Step 3 + re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step3list[suffix]; + } + + // Step 4 + re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; + re2 = /^(.+?)(s|t)(ion)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + if (re.test(stem)) + w = stem; + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1] + fp[2]; + re2 = new RegExp(mgr1); + if (re2.test(stem)) + w = stem; + } + + // Step 5 + re = /^(.+?)e$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + re2 = new RegExp(meq1); + re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) + w = stem; + } + re = /ll$/; + re2 = new RegExp(mgr1); + if (re.test(w) && re2.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + + // and turn initial Y back to y + if (firstch == "y") + w = firstch.toLowerCase() + w.substr(1); + return w; + } +} + diff --git a/releases/2.0.0/_static/minus.png b/releases/2.0.0/_static/minus.png new file mode 100644 index 0000000000000000000000000000000000000000..d96755fdaf8bb2214971e0db9c1fd3077d7c419d GIT binary patch literal 90 zcmeAS@N?(olHy`uVBq!ia0vp^+#t*WBp7;*Yy1LIik>cxAr*|t7R?Mi>2?kWtu=nj kDsEF_5m^0CR;1wuP-*O&G^0G}KYk!hp00i_>zopr08q^qX#fBK literal 0 HcmV?d00001 diff --git a/releases/2.0.0/_static/nbsphinx-broken-thumbnail.svg b/releases/2.0.0/_static/nbsphinx-broken-thumbnail.svg new file mode 100644 index 0000000..4919ca8 --- /dev/null +++ b/releases/2.0.0/_static/nbsphinx-broken-thumbnail.svg @@ -0,0 +1,9 @@ + + + + diff --git a/releases/2.0.0/_static/nbsphinx-code-cells.css b/releases/2.0.0/_static/nbsphinx-code-cells.css new file mode 100644 index 0000000..a3fb27c --- /dev/null +++ b/releases/2.0.0/_static/nbsphinx-code-cells.css @@ -0,0 +1,259 @@ +/* remove conflicting styling from Sphinx themes */ +div.nbinput.container div.prompt *, +div.nboutput.container div.prompt *, +div.nbinput.container div.input_area pre, +div.nboutput.container div.output_area pre, +div.nbinput.container div.input_area .highlight, +div.nboutput.container div.output_area .highlight { + border: none; 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false,\n nestedClass: \"active\",\n\n // Offset & reflow\n offset: 0,\n reflow: false,\n\n // Event support\n events: true,\n };\n\n //\n // Methods\n //\n\n /**\n * Merge two or more objects together.\n * @param {Object} objects The objects to merge together\n * @returns {Object} Merged values of defaults and options\n */\n var extend = function () {\n var merged = {};\n Array.prototype.forEach.call(arguments, function (obj) {\n for (var key in obj) {\n if (!obj.hasOwnProperty(key)) return;\n merged[key] = obj[key];\n }\n });\n return merged;\n };\n\n /**\n * Emit a custom event\n * @param {String} type The event type\n * @param {Node} elem The element to attach the event to\n * @param {Object} detail Any details to pass along with the event\n */\n var emitEvent = function (type, elem, detail) {\n // Make sure events are enabled\n if (!detail.settings.events) return;\n\n // Create a new event\n var event = new CustomEvent(type, {\n bubbles: true,\n cancelable: true,\n detail: detail,\n });\n\n // Dispatch the event\n elem.dispatchEvent(event);\n };\n\n /**\n * Get an element's distance from the top of the Document.\n * @param {Node} elem The element\n * @return {Number} Distance from the top in pixels\n */\n var getOffsetTop = function (elem) {\n var location = 0;\n if (elem.offsetParent) {\n while (elem) {\n location += elem.offsetTop;\n elem = elem.offsetParent;\n }\n }\n return location >= 0 ? location : 0;\n };\n\n /**\n * Sort content from first to last in the DOM\n * @param {Array} contents The content areas\n */\n var sortContents = function (contents) {\n if (contents) {\n contents.sort(function (item1, item2) {\n var offset1 = getOffsetTop(item1.content);\n var offset2 = getOffsetTop(item2.content);\n if (offset1 < offset2) return -1;\n return 1;\n });\n }\n };\n\n /**\n * Get the offset to use for calculating position\n * @param {Object} settings The settings for this instantiation\n * @return {Float} The number of pixels to offset the calculations\n */\n var getOffset = function (settings) {\n // if the offset is a function run it\n if (typeof settings.offset === \"function\") {\n return parseFloat(settings.offset());\n }\n\n // Otherwise, return it as-is\n return parseFloat(settings.offset);\n };\n\n /**\n * Get the document element's height\n * @private\n * @returns {Number}\n */\n var getDocumentHeight = function () {\n return Math.max(\n document.body.scrollHeight,\n document.documentElement.scrollHeight,\n document.body.offsetHeight,\n document.documentElement.offsetHeight,\n document.body.clientHeight,\n document.documentElement.clientHeight,\n );\n };\n\n /**\n * Determine if an element is in view\n * @param {Node} elem The element\n * @param {Object} settings The settings for this instantiation\n * @param {Boolean} bottom If true, check if element is above bottom of viewport instead\n * @return {Boolean} Returns true if element is in the viewport\n */\n var isInView = function (elem, settings, bottom) {\n var bounds = elem.getBoundingClientRect();\n var offset = getOffset(settings);\n if (bottom) {\n return (\n parseInt(bounds.bottom, 10) <\n (window.innerHeight || document.documentElement.clientHeight)\n );\n }\n return parseInt(bounds.top, 10) <= offset;\n };\n\n /**\n * Check if at the bottom of the viewport\n * @return {Boolean} If true, page is at the bottom of the viewport\n */\n var isAtBottom = function () {\n if (\n Math.ceil(window.innerHeight + window.pageYOffset) >=\n getDocumentHeight()\n )\n return true;\n return false;\n };\n\n /**\n * Check if the last item should be used (even if not at the top of the page)\n * @param {Object} item The last item\n * @param {Object} settings The settings for this instantiation\n * @return {Boolean} If true, use the last item\n */\n var useLastItem = function (item, settings) {\n if (isAtBottom() && isInView(item.content, settings, true)) return true;\n return false;\n };\n\n /**\n * Get the active content\n * @param {Array} contents The content areas\n * @param {Object} settings The settings for this instantiation\n * @return {Object} The content area and matching navigation link\n */\n var getActive = function (contents, settings) {\n var last = contents[contents.length - 1];\n if (useLastItem(last, settings)) return last;\n for (var i = contents.length - 1; i >= 0; i--) {\n if (isInView(contents[i].content, settings)) return contents[i];\n }\n };\n\n /**\n * Deactivate parent navs in a nested navigation\n * @param {Node} nav The starting navigation element\n * @param {Object} settings The settings for this instantiation\n */\n var deactivateNested = function (nav, settings) {\n // If nesting isn't activated, bail\n if (!settings.nested || !nav.parentNode) return;\n\n // Get the parent navigation\n var li = nav.parentNode.closest(\"li\");\n if (!li) return;\n\n // Remove the active class\n li.classList.remove(settings.nestedClass);\n\n // Apply recursively to any parent navigation elements\n deactivateNested(li, settings);\n };\n\n /**\n * Deactivate a nav and content area\n * @param {Object} items The nav item and content to deactivate\n * @param {Object} settings The settings for this instantiation\n */\n var deactivate = function (items, settings) {\n // Make sure there are items to deactivate\n if (!items) return;\n\n // Get the parent list item\n var li = items.nav.closest(\"li\");\n if (!li) return;\n\n // Remove the active class from the nav and content\n li.classList.remove(settings.navClass);\n items.content.classList.remove(settings.contentClass);\n\n // Deactivate any parent navs in a nested navigation\n deactivateNested(li, settings);\n\n // Emit a custom event\n emitEvent(\"gumshoeDeactivate\", li, {\n link: items.nav,\n content: items.content,\n settings: settings,\n });\n };\n\n /**\n * Activate parent navs in a nested navigation\n * @param {Node} nav The starting navigation element\n * @param {Object} settings The settings for this instantiation\n */\n var activateNested = function (nav, settings) {\n // If nesting isn't activated, bail\n if (!settings.nested) return;\n\n // Get the parent navigation\n var li = nav.parentNode.closest(\"li\");\n if (!li) return;\n\n // Add the active class\n li.classList.add(settings.nestedClass);\n\n // Apply recursively to any parent navigation elements\n activateNested(li, settings);\n };\n\n /**\n * Activate a nav and content area\n * @param {Object} items The nav item and content to activate\n * @param {Object} settings The settings for this instantiation\n */\n var activate = function (items, settings) {\n // Make sure there are items to activate\n if (!items) return;\n\n // Get the parent list item\n var li = items.nav.closest(\"li\");\n if (!li) return;\n\n // Add the active class to the nav and content\n li.classList.add(settings.navClass);\n items.content.classList.add(settings.contentClass);\n\n // Activate any parent navs in a nested navigation\n activateNested(li, settings);\n\n // Emit a custom event\n emitEvent(\"gumshoeActivate\", li, {\n link: items.nav,\n content: items.content,\n settings: settings,\n });\n };\n\n /**\n * Create the Constructor object\n * @param {String} selector The selector to use for navigation items\n * @param {Object} options User options and settings\n */\n var Constructor = function (selector, options) {\n //\n // Variables\n //\n\n var publicAPIs = {};\n var navItems, contents, current, timeout, settings;\n\n //\n // Methods\n //\n\n /**\n * Set variables from DOM elements\n */\n publicAPIs.setup = function () {\n // Get all nav items\n navItems = document.querySelectorAll(selector);\n\n // Create contents array\n contents = [];\n\n // Loop through each item, get it's matching content, and push to the array\n Array.prototype.forEach.call(navItems, function (item) {\n // Get the content for the nav item\n var content = document.getElementById(\n decodeURIComponent(item.hash.substr(1)),\n );\n if (!content) return;\n\n // Push to the contents array\n contents.push({\n nav: item,\n content: content,\n });\n });\n\n // Sort contents by the order they appear in the DOM\n sortContents(contents);\n };\n\n /**\n * Detect which content is currently active\n */\n publicAPIs.detect = function () {\n // Get the active content\n var active = getActive(contents, settings);\n\n // if there's no active content, deactivate and bail\n if (!active) {\n if (current) {\n deactivate(current, settings);\n current = null;\n }\n return;\n }\n\n // If the active content is the one currently active, do nothing\n if (current && active.content === current.content) return;\n\n // Deactivate the current content and activate the new content\n deactivate(current, settings);\n activate(active, settings);\n\n // Update the currently active content\n current = active;\n };\n\n /**\n * Detect the active content on scroll\n * Debounced for performance\n */\n var scrollHandler = function (event) {\n // If there's a timer, cancel it\n if (timeout) {\n window.cancelAnimationFrame(timeout);\n }\n\n // Setup debounce callback\n timeout = window.requestAnimationFrame(publicAPIs.detect);\n };\n\n /**\n * Update content sorting on resize\n * Debounced for performance\n */\n var resizeHandler = function (event) {\n // If there's a timer, cancel it\n if (timeout) {\n window.cancelAnimationFrame(timeout);\n }\n\n // Setup debounce callback\n timeout = window.requestAnimationFrame(function () {\n sortContents(contents);\n publicAPIs.detect();\n });\n };\n\n /**\n * Destroy the current instantiation\n */\n publicAPIs.destroy = function () {\n // Undo DOM changes\n if (current) {\n deactivate(current, settings);\n }\n\n // Remove event listeners\n window.removeEventListener(\"scroll\", scrollHandler, false);\n if (settings.reflow) {\n window.removeEventListener(\"resize\", resizeHandler, false);\n }\n\n // Reset variables\n contents = null;\n navItems = null;\n current = null;\n timeout = null;\n settings = null;\n };\n\n /**\n * Initialize the 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+ +/** + * Simple result scoring code. + */ +if (typeof Scorer === "undefined") { + var Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename, kind] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +// Global search result kind enum, used by themes to style search results. +class SearchResultKind { + static get index() { return "index"; } + static get object() { return "object"; } + static get text() { return "text"; } + static get title() { return "title"; } +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms, highlightTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + const contentRoot = document.documentElement.dataset.content_root; + + const [docName, title, anchor, descr, score, _filename, kind] = item; + + let listItem = document.createElement("li"); + // Add a class representing the item's type: + // can be used by a theme's CSS selector for styling + // See SearchResultKind for the class names. + listItem.classList.add(`kind-${kind}`); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = contentRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = contentRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) { + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + // highlight search terms in the description + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + } + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms, anchor) + ); + // highlight search terms in the summary + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = Documentation.ngettext( + "Search finished, found one page matching the search query.", + "Search finished, found ${resultCount} pages matching the search query.", + resultCount, + ).replace('${resultCount}', resultCount); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms, + highlightTerms, +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms, highlightTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; +// Helper function used by query() to order search results. +// Each input is an array of [docname, title, anchor, descr, score, filename, kind]. +// Order the results by score (in opposite order of appearance, since the +// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically. +const _orderResultsByScoreThenName = (a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString, anchor) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + for (const removalQuery of [".headerlink", "script", "style"]) { + htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() }); + } + if (anchor) { + const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`); + if (anchorContent) return anchorContent.textContent; + + console.warn( + `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.` + ); + } + + // if anchor not specified or not found, fall back to main content + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent) return docContent.textContent; + + console.warn( + "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.setAttribute("role", "list"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + _parseQuery: (query) => { + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + return [query, searchTerms, excludedTerms, highlightTerms, objectTerms]; + }, + + /** + * execute search (requires search index to be loaded) + */ + _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // Collect multiple result groups to be sorted separately and then ordered. + // Each is an array of [docname, title, anchor, descr, score, filename, kind]. + const normalResults = []; + const nonMainIndexResults = []; + + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase().trim(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + const score = Math.round(Scorer.title * queryLower.length / title.length); + const boost = titles[file] === title ? 1 : 0; // add a boost for document titles + normalResults.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score + boost, + filenames[file], + SearchResultKind.title, + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id, isMain] of foundEntries) { + const score = Math.round(100 * queryLower.length / entry.length); + const result = [ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + SearchResultKind.index, + ]; + if (isMain) { + normalResults.push(result); + } else { + nonMainIndexResults.push(result); + } + } + } + } + + // lookup as object + objectTerms.forEach((term) => + normalResults.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) { + normalResults.forEach((item) => (item[4] = Scorer.score(item))); + nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item))); + } + + // Sort each group of results by score and then alphabetically by name. + normalResults.sort(_orderResultsByScoreThenName); + nonMainIndexResults.sort(_orderResultsByScoreThenName); + + // Combine the result groups in (reverse) order. + // Non-main index entries are typically arbitrary cross-references, + // so display them after other results. + let results = [...nonMainIndexResults, ...normalResults]; + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + return results.reverse(); + }, + + query: (query) => { + const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query); + const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms, highlightTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + SearchResultKind.object, + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + if (!terms.hasOwnProperty(word)) { + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + } + if (!titleTerms.hasOwnProperty(word)) { + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: titleTerms[term], score: Scorer.partialTitle }); + }); + } + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (!fileMap.has(file)) fileMap.set(file, [word]); + else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + SearchResultKind.text, + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords, anchor) => { + const text = Search.htmlToText(htmlText, anchor); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); diff --git a/releases/2.0.0/_static/skeleton.css b/releases/2.0.0/_static/skeleton.css new file mode 100644 index 0000000..467c878 --- /dev/null +++ b/releases/2.0.0/_static/skeleton.css @@ -0,0 +1,296 @@ +/* Some sane resets. */ +html { + height: 100%; +} + +body { + margin: 0; + min-height: 100%; +} + +/* All the flexbox magic! */ +body, +.sb-announcement, +.sb-content, +.sb-main, +.sb-container, +.sb-container__inner, +.sb-article-container, +.sb-footer-content, +.sb-header, +.sb-header-secondary, +.sb-footer { + display: flex; +} + +/* These order things vertically */ +body, +.sb-main, +.sb-article-container { + flex-direction: column; +} + +/* Put elements in the center */ +.sb-header, +.sb-header-secondary, +.sb-container, +.sb-content, +.sb-footer, +.sb-footer-content { + justify-content: center; +} +/* Put elements at the ends */ +.sb-article-container { + justify-content: space-between; +} + +/* These elements grow. */ +.sb-main, +.sb-content, +.sb-container, +article { + flex-grow: 1; +} + +/* Because padding making this wider is not fun */ +article { + box-sizing: border-box; +} + +/* The announcements element should never be wider than the page. */ +.sb-announcement { + max-width: 100%; +} + +.sb-sidebar-primary, +.sb-sidebar-secondary { + flex-shrink: 0; + width: 17rem; +} + +.sb-announcement__inner { + justify-content: center; + + box-sizing: border-box; + height: 3rem; + + overflow-x: auto; + white-space: nowrap; +} + +/* Sidebars, with checkbox-based toggle */ +.sb-sidebar-primary, +.sb-sidebar-secondary { + position: fixed; + height: 100%; + top: 0; +} + +.sb-sidebar-primary { + left: -17rem; + transition: left 250ms ease-in-out; +} +.sb-sidebar-secondary { + right: -17rem; + transition: right 250ms ease-in-out; +} + +.sb-sidebar-toggle { + display: none; +} +.sb-sidebar-overlay { + position: fixed; + top: 0; + width: 0; + height: 0; + + transition: width 0ms ease 250ms, height 0ms ease 250ms, opacity 250ms ease; + + opacity: 0; + background-color: rgba(0, 0, 0, 0.54); +} + +#sb-sidebar-toggle--primary:checked + ~ .sb-sidebar-overlay[for="sb-sidebar-toggle--primary"], +#sb-sidebar-toggle--secondary:checked + ~ .sb-sidebar-overlay[for="sb-sidebar-toggle--secondary"] { + width: 100%; + height: 100%; + opacity: 1; + transition: width 0ms ease, height 0ms ease, opacity 250ms ease; +} + +#sb-sidebar-toggle--primary:checked ~ .sb-container .sb-sidebar-primary { + left: 0; +} +#sb-sidebar-toggle--secondary:checked ~ .sb-container .sb-sidebar-secondary { + right: 0; +} + +/* Full-width mode */ +.drop-secondary-sidebar-for-full-width-content + .hide-when-secondary-sidebar-shown { + display: none !important; +} +.drop-secondary-sidebar-for-full-width-content .sb-sidebar-secondary { + display: none !important; +} + +/* Mobile views */ +.sb-page-width { + width: 100%; +} + +.sb-article-container, +.sb-footer-content__inner, +.drop-secondary-sidebar-for-full-width-content .sb-article, +.drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 100vw; +} + +.sb-article, +.match-content-width { + padding: 0 1rem; + box-sizing: border-box; +} + +@media (min-width: 32rem) { + .sb-article, + .match-content-width { + padding: 0 2rem; + } +} + +/* Tablet views */ +@media (min-width: 42rem) { + .sb-article-container { + width: auto; + } + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 42rem; + } + .sb-article, + .match-content-width { + width: 42rem; + } +} +@media (min-width: 46rem) { + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 46rem; + } + .sb-article, + .match-content-width { + width: 46rem; + } +} +@media (min-width: 50rem) { + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 50rem; + } + .sb-article, + .match-content-width { + width: 50rem; + } +} + +/* Tablet views */ +@media (min-width: 59rem) { + .sb-sidebar-secondary { + position: static; + } + .hide-when-secondary-sidebar-shown { + display: none !important; + } + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 59rem; + } + .sb-article, + .match-content-width { + width: 42rem; + } +} +@media (min-width: 63rem) { + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 63rem; + } + .sb-article, + .match-content-width { + width: 46rem; + } +} +@media (min-width: 67rem) { + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 67rem; + } + .sb-article, + .match-content-width { + width: 50rem; + } +} + +/* Desktop views */ +@media (min-width: 76rem) { + .sb-sidebar-primary { + position: static; + } + .hide-when-primary-sidebar-shown { + display: none !important; + } + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 59rem; + } + .sb-article, + .match-content-width { + width: 42rem; + } +} + +/* Full desktop views */ +@media (min-width: 80rem) { + .sb-article, + .match-content-width { + width: 46rem; + } + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 63rem; + } +} + +@media (min-width: 84rem) { + .sb-article, + .match-content-width { + width: 50rem; + } + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 67rem; + } +} + +@media (min-width: 88rem) { + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 67rem; + } + .sb-page-width { + width: 88rem; + } +} diff --git a/releases/2.0.0/_static/sphinx-design.min.css b/releases/2.0.0/_static/sphinx-design.min.css new file mode 100644 index 0000000..860c36d --- /dev/null +++ b/releases/2.0.0/_static/sphinx-design.min.css @@ -0,0 +1 @@ +.sd-bg-primary{background-color:var(--sd-color-primary) !important}.sd-bg-text-primary{color:var(--sd-color-primary-text) 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b/releases/2.0.0/_static/sphinx_highlight.js @@ -0,0 +1,154 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + const rest = document.createTextNode(val.substr(pos + text.length)); + parent.insertBefore( + span, + parent.insertBefore( + rest, + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + /* There may be more occurrences of search term in this node. So call this + * function recursively on the remaining fragment. + */ + _highlight(rest, addItems, text, className); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '

    ' + + '' + + _("Hide Search Matches") + + "

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highlightSearchWords() when we are on the search page. + * It will highlight words from the *previous* search query. + */ + if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); + SphinxHighlight.initEscapeListener(); +}); diff --git a/releases/2.0.0/_static/styles/furo-extensions.css b/releases/2.0.0/_static/styles/furo-extensions.css new file mode 100644 index 0000000..8229587 --- /dev/null +++ b/releases/2.0.0/_static/styles/furo-extensions.css @@ -0,0 +1,2 @@ +#furo-sidebar-ad-placement{padding:var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)}#furo-sidebar-ad-placement .ethical-sidebar{background:var(--color-background-secondary);border:none;box-shadow:none}#furo-sidebar-ad-placement .ethical-sidebar:hover{background:var(--color-background-hover)}#furo-sidebar-ad-placement .ethical-sidebar a{color:var(--color-foreground-primary)}#furo-sidebar-ad-placement .ethical-callout 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Remove the inheritance of text transform in Firefox.\n */\n\nbutton,\nselect { /* 1 */\n text-transform: none;\n}\n\n/**\n * Correct the inability to style clickable types in iOS and Safari.\n */\n\nbutton,\n[type=\"button\"],\n[type=\"reset\"],\n[type=\"submit\"] {\n -webkit-appearance: button;\n}\n\n/**\n * Remove the inner border and padding in Firefox.\n */\n\nbutton::-moz-focus-inner,\n[type=\"button\"]::-moz-focus-inner,\n[type=\"reset\"]::-moz-focus-inner,\n[type=\"submit\"]::-moz-focus-inner {\n border-style: none;\n padding: 0;\n}\n\n/**\n * Restore the focus styles unset by the previous rule.\n */\n\nbutton:-moz-focusring,\n[type=\"button\"]:-moz-focusring,\n[type=\"reset\"]:-moz-focusring,\n[type=\"submit\"]:-moz-focusring {\n outline: 1px dotted ButtonText;\n}\n\n/**\n * Correct the padding in Firefox.\n */\n\nfieldset {\n padding: 0.35em 0.75em 0.625em;\n}\n\n/**\n * 1. Correct the text wrapping in Edge and IE.\n * 2. Correct the color inheritance from `fieldset` elements in IE.\n * 3. Remove the padding so developers are not caught out when they zero out\n * `fieldset` elements in all browsers.\n */\n\nlegend {\n box-sizing: border-box; /* 1 */\n color: inherit; /* 2 */\n display: table; /* 1 */\n max-width: 100%; /* 1 */\n padding: 0; /* 3 */\n white-space: normal; /* 1 */\n}\n\n/**\n * Add the correct vertical alignment in Chrome, Firefox, and Opera.\n */\n\nprogress {\n vertical-align: baseline;\n}\n\n/**\n * Remove the default vertical scrollbar in IE 10+.\n */\n\ntextarea {\n overflow: auto;\n}\n\n/**\n * 1. Add the correct box sizing in IE 10.\n * 2. Remove the padding in IE 10.\n */\n\n[type=\"checkbox\"],\n[type=\"radio\"] {\n box-sizing: border-box; /* 1 */\n padding: 0; /* 2 */\n}\n\n/**\n * Correct the cursor style of increment and decrement buttons in Chrome.\n */\n\n[type=\"number\"]::-webkit-inner-spin-button,\n[type=\"number\"]::-webkit-outer-spin-button {\n height: auto;\n}\n\n/**\n * 1. Correct the odd appearance in Chrome and Safari.\n * 2. Correct the outline style in Safari.\n */\n\n[type=\"search\"] {\n -webkit-appearance: textfield; /* 1 */\n outline-offset: -2px; /* 2 */\n}\n\n/**\n * Remove the inner padding in Chrome and Safari on macOS.\n */\n\n[type=\"search\"]::-webkit-search-decoration {\n -webkit-appearance: none;\n}\n\n/**\n * 1. Correct the inability to style clickable types in iOS and Safari.\n * 2. Change font properties to `inherit` in Safari.\n */\n\n::-webkit-file-upload-button {\n -webkit-appearance: button; /* 1 */\n font: inherit; /* 2 */\n}\n\n/* Interactive\n ========================================================================== */\n\n/*\n * Add the correct display in Edge, IE 10+, and Firefox.\n */\n\ndetails {\n display: block;\n}\n\n/*\n * Add the correct display in all browsers.\n */\n\nsummary {\n display: list-item;\n}\n\n/* Misc\n ========================================================================== */\n\n/**\n * Add the correct display in IE 10+.\n */\n\ntemplate {\n display: none;\n}\n\n/**\n * Add the correct display in IE 10.\n */\n\n[hidden] {\n display: none;\n}\n","// This file contains styles for managing print media.\n\n////////////////////////////////////////////////////////////////////////////////\n// Hide elements not relevant to print media.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n // Hide icon container.\n .content-icon-container\n display: none !important\n\n // Hide showing header links if hovering over when printing.\n .headerlink\n display: none !important\n\n // Hide mobile header.\n .mobile-header\n display: none !important\n\n // Hide navigation links.\n .related-pages\n display: none !important\n\n////////////////////////////////////////////////////////////////////////////////\n// Tweaks related to decolorization.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n // Apply a border around code which no longer have a color background.\n .highlight\n border: 0.1pt solid var(--color-foreground-border)\n\n////////////////////////////////////////////////////////////////////////////////\n// Avoid page break in some relevant cases.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n ul, ol, dl, a, table, pre, blockquote, p\n page-break-inside: avoid\n\n h1, h2, h3, h4, h5, h6, img, figure, caption\n page-break-inside: avoid\n page-break-after: avoid\n\n ul, ol, dl\n page-break-before: avoid\n",".visually-hidden\n position: absolute !important\n width: 1px !important\n height: 1px !important\n padding: 0 !important\n margin: -1px !important\n overflow: hidden !important\n clip: rect(0,0,0,0) !important\n white-space: nowrap !important\n border: 0 !important\n color: var(--color-foreground-primary)\n background: var(--color-background-primary)\n\n:-moz-focusring\n outline: auto\n","// This file serves as the \"skeleton\" of the theming logic.\n//\n// This contains the bulk of the logic for handling dark mode, color scheme\n// toggling and the handling of color-scheme-specific hiding of elements.\n\nbody\n @include fonts\n @include spacing\n @include icons\n @include admonitions\n @include default-admonition(#651fff, \"abstract\")\n @include default-topic(#14B8A6, \"pencil\")\n\n @include colors\n\n.only-light\n display: block !important\nhtml body .only-dark\n display: none !important\n\n// Ignore dark-mode hints if print media.\n@media not print\n // Enable dark-mode, if requested.\n body[data-theme=\"dark\"]\n @include colors-dark\n\n html & .only-light\n display: none !important\n .only-dark\n display: block !important\n\n // Enable dark mode, unless explicitly told to avoid.\n @media (prefers-color-scheme: dark)\n body:not([data-theme=\"light\"])\n @include colors-dark\n\n html & .only-light\n display: none !important\n .only-dark\n display: block !important\n\n//\n// Theme toggle presentation\n//\nbody[data-theme=\"auto\"]\n .theme-toggle svg.theme-icon-when-auto-light\n display: block\n\n @media (prefers-color-scheme: dark)\n .theme-toggle svg.theme-icon-when-auto-dark\n display: block\n .theme-toggle svg.theme-icon-when-auto-light\n display: none\n\nbody[data-theme=\"dark\"]\n .theme-toggle svg.theme-icon-when-dark\n display: block\n\nbody[data-theme=\"light\"]\n .theme-toggle svg.theme-icon-when-light\n display: block\n","// Fonts used by this theme.\n//\n// There are basically two things here -- using the system font stack and\n// defining sizes for various elements in %ages. We could have also used `em`\n// but %age is easier to reason about for me.\n\n@mixin fonts {\n // These are adapted from https://systemfontstack.com/\n --font-stack: -apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial,\n sans-serif, Apple Color Emoji, Segoe UI Emoji;\n --font-stack--monospace: \"SFMono-Regular\", Menlo, Consolas, Monaco,\n Liberation Mono, Lucida Console, monospace;\n --font-stack--headings: var(--font-stack);\n\n --font-size--normal: 100%;\n --font-size--small: 87.5%;\n --font-size--small--2: 81.25%;\n --font-size--small--3: 75%;\n --font-size--small--4: 62.5%;\n\n // Sidebar\n --sidebar-caption-font-size: var(--font-size--small--2);\n --sidebar-item-font-size: var(--font-size--small);\n --sidebar-search-input-font-size: var(--font-size--small);\n\n // Table of Contents\n --toc-font-size: var(--font-size--small--3);\n --toc-font-size--mobile: var(--font-size--normal);\n --toc-title-font-size: var(--font-size--small--4);\n\n // Admonitions\n //\n // These aren't defined in terms of %ages, since nesting these is permitted.\n --admonition-font-size: 0.8125rem;\n --admonition-title-font-size: 0.8125rem;\n\n // Code\n --code-font-size: var(--font-size--small--2);\n\n // API\n --api-font-size: var(--font-size--small);\n}\n","// Spacing for various elements on the page\n//\n// If the user wants to tweak things in a certain way, they are permitted to.\n// They also have to deal with the consequences though!\n\n@mixin spacing {\n // Header!\n --header-height: calc(\n var(--sidebar-item-line-height) + 4 * #{var(--sidebar-item-spacing-vertical)}\n );\n --header-padding: 0.5rem;\n\n // Sidebar\n --sidebar-tree-space-above: 1.5rem;\n --sidebar-caption-space-above: 1rem;\n\n --sidebar-item-line-height: 1rem;\n --sidebar-item-spacing-vertical: 0.5rem;\n --sidebar-item-spacing-horizontal: 1rem;\n --sidebar-item-height: calc(\n var(--sidebar-item-line-height) + 2 *#{var(--sidebar-item-spacing-vertical)}\n );\n\n --sidebar-expander-width: var(--sidebar-item-height); // be square\n\n --sidebar-search-space-above: 0.5rem;\n --sidebar-search-input-spacing-vertical: 0.5rem;\n --sidebar-search-input-spacing-horizontal: 0.5rem;\n --sidebar-search-input-height: 1rem;\n --sidebar-search-icon-size: var(--sidebar-search-input-height);\n\n // Table of Contents\n --toc-title-padding: 0.25rem 0;\n --toc-spacing-vertical: 1.5rem;\n --toc-spacing-horizontal: 1.5rem;\n --toc-item-spacing-vertical: 0.4rem;\n --toc-item-spacing-horizontal: 1rem;\n}\n","// Expose theme icons as CSS variables.\n\n$icons: (\n // Adapted from tabler-icons\n // url: https://tablericons.com/\n \"search\":\n url('data:image/svg+xml;charset=utf-8,'),\n // Factored out from mkdocs-material on 24-Aug-2020.\n // url: https://squidfunk.github.io/mkdocs-material/reference/admonitions/\n \"pencil\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"abstract\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"info\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"flame\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"question\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"warning\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"failure\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"spark\":\n url('data:image/svg+xml;charset=utf-8,')\n);\n\n@mixin icons {\n @each $name, $glyph in $icons {\n --icon-#{$name}: #{$glyph};\n }\n}\n","// Admonitions\n\n// Structure of these is:\n// admonition-class: color \"icon-name\";\n//\n// The colors are translated into CSS variables below. The icons are\n// used directly in the main declarations to set the `mask-image` in\n// the title.\n\n// prettier-ignore\n$admonitions: (\n // Each of these has an reST directives for it.\n \"caution\": #ff9100 \"spark\",\n \"warning\": #ff9100 \"warning\",\n \"danger\": #ff5252 \"spark\",\n \"attention\": #ff5252 \"warning\",\n \"error\": #ff5252 \"failure\",\n \"hint\": #00c852 \"question\",\n \"tip\": #00c852 \"info\",\n \"important\": #00bfa5 \"flame\",\n \"note\": #00b0ff \"pencil\",\n \"seealso\": #448aff \"info\",\n \"admonition-todo\": #808080 \"pencil\"\n);\n\n@mixin default-admonition($color, $icon-name) {\n --color-admonition-title: #{$color};\n --color-admonition-title-background: #{rgba($color, 0.2)};\n\n --icon-admonition-default: var(--icon-#{$icon-name});\n}\n\n@mixin default-topic($color, $icon-name) {\n --color-topic-title: #{$color};\n --color-topic-title-background: #{rgba($color, 0.2)};\n\n --icon-topic-default: var(--icon-#{$icon-name});\n}\n\n@mixin admonitions {\n @each $name, $values in $admonitions {\n --color-admonition-title--#{$name}: #{nth($values, 1)};\n --color-admonition-title-background--#{$name}: #{rgba(\n nth($values, 1),\n 0.2\n )};\n }\n}\n","// Colors used throughout this theme.\n//\n// The aim is to give the user more control. Thus, instead of hard-coding colors\n// in various parts of the stylesheet, the approach taken is to define all\n// colors as CSS variables and reusing them in all the places.\n//\n// `colors-dark` depends on `colors` being included at a lower specificity.\n\n@mixin colors {\n --color-problematic: #b30000;\n\n // Base Colors\n --color-foreground-primary: black; // for main text and headings\n --color-foreground-secondary: #5a5c63; // for secondary text\n --color-foreground-muted: #6b6f76; // for muted text\n --color-foreground-border: #878787; // for content borders\n\n --color-background-primary: white; // for content\n --color-background-secondary: #f8f9fb; // for navigation + ToC\n --color-background-hover: #efeff4ff; // for navigation-item hover\n --color-background-hover--transparent: #efeff400;\n --color-background-border: #eeebee; // for UI borders\n --color-background-item: #ccc; // for \"background\" items (eg: copybutton)\n\n // Announcements\n --color-announcement-background: #000000dd;\n --color-announcement-text: #eeebee;\n\n // Brand colors\n --color-brand-primary: #0a4bff;\n --color-brand-content: #2757dd;\n --color-brand-visited: #872ee0;\n\n // API documentation\n --color-api-background: var(--color-background-hover--transparent);\n --color-api-background-hover: var(--color-background-hover);\n --color-api-overall: var(--color-foreground-secondary);\n --color-api-name: var(--color-problematic);\n --color-api-pre-name: var(--color-problematic);\n --color-api-paren: var(--color-foreground-secondary);\n --color-api-keyword: var(--color-foreground-primary);\n\n --color-api-added: #21632c;\n --color-api-added-border: #38a84d;\n --color-api-changed: #046172;\n --color-api-changed-border: #06a1bc;\n --color-api-deprecated: #605706;\n --color-api-deprecated-border: #f0d90f;\n --color-api-removed: #b30000;\n --color-api-removed-border: #ff5c5c;\n\n --color-highlight-on-target: #ffffcc;\n\n // Inline code background\n --color-inline-code-background: var(--color-background-secondary);\n\n // Highlighted text (search)\n --color-highlighted-background: #ddeeff;\n --color-highlighted-text: var(--color-foreground-primary);\n\n // GUI Labels\n --color-guilabel-background: #ddeeff80;\n --color-guilabel-border: #bedaf580;\n --color-guilabel-text: var(--color-foreground-primary);\n\n // Admonitions!\n --color-admonition-background: transparent;\n\n //////////////////////////////////////////////////////////////////////////////\n // Everything below this should be one of:\n // - var(...)\n // - *-gradient(...)\n // - special literal values (eg: transparent, none)\n //////////////////////////////////////////////////////////////////////////////\n\n // Tables\n --color-table-header-background: var(--color-background-secondary);\n --color-table-border: var(--color-background-border);\n\n // Cards\n --color-card-border: var(--color-background-secondary);\n --color-card-background: transparent;\n --color-card-marginals-background: var(--color-background-secondary);\n\n // Header\n --color-header-background: var(--color-background-primary);\n --color-header-border: var(--color-background-border);\n --color-header-text: var(--color-foreground-primary);\n\n // Sidebar (left)\n --color-sidebar-background: var(--color-background-secondary);\n --color-sidebar-background-border: var(--color-background-border);\n\n --color-sidebar-brand-text: var(--color-foreground-primary);\n --color-sidebar-caption-text: var(--color-foreground-muted);\n --color-sidebar-link-text: var(--color-foreground-secondary);\n --color-sidebar-link-text--top-level: var(--color-brand-primary);\n\n --color-sidebar-item-background: var(--color-sidebar-background);\n --color-sidebar-item-background--current: var(\n --color-sidebar-item-background\n );\n --color-sidebar-item-background--hover: linear-gradient(\n 90deg,\n var(--color-background-hover--transparent) 0%,\n var(--color-background-hover) var(--sidebar-item-spacing-horizontal),\n var(--color-background-hover) 100%\n );\n\n --color-sidebar-item-expander-background: transparent;\n --color-sidebar-item-expander-background--hover: var(\n --color-background-hover\n );\n\n --color-sidebar-search-text: var(--color-foreground-primary);\n --color-sidebar-search-background: var(--color-background-secondary);\n --color-sidebar-search-background--focus: var(--color-background-primary);\n --color-sidebar-search-border: var(--color-background-border);\n --color-sidebar-search-icon: var(--color-foreground-muted);\n\n // Table of Contents (right)\n --color-toc-background: var(--color-background-primary);\n --color-toc-title-text: var(--color-foreground-muted);\n --color-toc-item-text: var(--color-foreground-secondary);\n --color-toc-item-text--hover: var(--color-foreground-primary);\n --color-toc-item-text--active: var(--color-brand-primary);\n\n // Actual page contents\n --color-content-foreground: var(--color-foreground-primary);\n --color-content-background: transparent;\n\n // Links\n --color-link: var(--color-brand-content);\n --color-link-underline: var(--color-background-border);\n --color-link--hover: var(--color-brand-content);\n --color-link-underline--hover: var(--color-foreground-border);\n\n --color-link--visited: var(--color-brand-visited);\n --color-link-underline--visited: var(--color-background-border);\n --color-link--visited--hover: var(--color-brand-visited);\n --color-link-underline--visited--hover: var(--color-foreground-border);\n}\n\n@mixin colors-dark {\n --color-problematic: #ee5151;\n\n // Base Colors\n --color-foreground-primary: #cfd0d0; // for main text and headings\n --color-foreground-secondary: #9ca0a5; // for secondary text\n --color-foreground-muted: #81868d; // for muted text\n --color-foreground-border: #666666; // for content borders\n\n --color-background-primary: #131416; // for content\n --color-background-secondary: #1a1c1e; // for navigation + ToC\n --color-background-hover: #1e2124ff; // for navigation-item hover\n --color-background-hover--transparent: #1e212400;\n --color-background-border: #303335; // for UI borders\n --color-background-item: #444; // for \"background\" items (eg: copybutton)\n\n // Announcements\n --color-announcement-background: #000000dd;\n --color-announcement-text: #eeebee;\n\n // Brand colors\n --color-brand-primary: #3d94ff;\n --color-brand-content: #5ca5ff;\n --color-brand-visited: #b27aeb;\n\n // Highlighted text (search)\n --color-highlighted-background: #083563;\n\n // GUI Labels\n --color-guilabel-background: #08356380;\n --color-guilabel-border: #13395f80;\n\n // API documentation\n --color-api-keyword: var(--color-foreground-secondary);\n --color-highlight-on-target: #333300;\n\n --color-api-added: #3db854;\n --color-api-added-border: #267334;\n --color-api-changed: #09b0ce;\n --color-api-changed-border: #056d80;\n --color-api-deprecated: #b1a10b;\n --color-api-deprecated-border: #6e6407;\n --color-api-removed: #ff7575;\n --color-api-removed-border: #b03b3b;\n\n // Admonitions\n --color-admonition-background: #18181a;\n\n // Cards\n --color-card-border: var(--color-background-secondary);\n --color-card-background: #18181a;\n --color-card-marginals-background: var(--color-background-hover);\n}\n","// This file contains the styling for making the content throughout the page,\n// including fonts, paragraphs, headings and spacing among these elements.\n\nbody\n font-family: var(--font-stack)\npre,\ncode,\nkbd,\nsamp\n font-family: var(--font-stack--monospace)\n\n// Make fonts look slightly nicer.\nbody\n -webkit-font-smoothing: antialiased\n -moz-osx-font-smoothing: grayscale\n\n// Line height from Bootstrap 4.1\narticle\n line-height: 1.5\n\n//\n// Headings\n//\nh1,\nh2,\nh3,\nh4,\nh5,\nh6\n line-height: 1.25\n font-family: var(--font-stack--headings)\n font-weight: bold\n\n border-radius: 0.5rem\n margin-top: 0.5rem\n margin-bottom: 0.5rem\n margin-left: -0.5rem\n margin-right: -0.5rem\n padding-left: 0.5rem\n padding-right: 0.5rem\n\n + p\n margin-top: 0\n\nh1\n font-size: 2.5em\n margin-top: 1.75rem\n margin-bottom: 1rem\nh2\n font-size: 2em\n margin-top: 1.75rem\nh3\n font-size: 1.5em\nh4\n font-size: 1.25em\nh5\n font-size: 1.125em\nh6\n font-size: 1em\n\nsmall\n opacity: 75%\n font-size: 80%\n\n// Paragraph\np\n margin-top: 0.5rem\n margin-bottom: 0.75rem\n\n// Horizontal rules\nhr.docutils\n height: 1px\n padding: 0\n margin: 2rem 0\n background-color: var(--color-background-border)\n border: 0\n\n.centered\n text-align: center\n\n// Links\na\n text-decoration: underline\n\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline)\n\n &:visited\n color: var(--color-link--visited)\n text-decoration-color: var(--color-link-underline--visited)\n &:hover\n color: var(--color-link--visited--hover)\n text-decoration-color: var(--color-link-underline--visited--hover)\n\n &:hover\n color: var(--color-link--hover)\n text-decoration-color: var(--color-link-underline--hover)\n &.muted-link\n color: inherit\n &:hover\n color: var(--color-link--hover)\n text-decoration-color: var(--color-link-underline--hover)\n &:visited\n color: var(--color-link--visited--hover)\n text-decoration-color: var(--color-link-underline--visited--hover)\n","// This file contains the styles for the overall layouting of the documentation\n// skeleton, including the responsive changes as well as sidebar toggles.\n//\n// This is implemented as a mobile-last design, which isn't ideal, but it is\n// reasonably good-enough and I got pretty tired by the time I'd finished this\n// to move the rules around to fix this. Shouldn't take more than 3-4 hours,\n// if you know what you're doing tho.\n\n// HACK: Not all browsers account for the scrollbar width in media queries.\n// This results in horizontal scrollbars in the breakpoint where we go\n// from displaying everything to hiding the ToC. We accomodate for this by\n// adding a bit of padding to the TOC drawer, disabling the horizontal\n// scrollbar and allowing the scrollbars to cover the padding.\n// https://www.456bereastreet.com/archive/201301/media_query_width_and_vertical_scrollbars/\n\n// HACK: Always having the scrollbar visible, prevents certain browsers from\n// causing the content to stutter horizontally between taller-than-viewport and\n// not-taller-than-viewport pages.\n\nhtml\n overflow-x: hidden\n overflow-y: scroll\n scroll-behavior: smooth\n\n.sidebar-scroll, .toc-scroll, article[role=main] *\n // Override Firefox scrollbar style\n scrollbar-width: thin\n scrollbar-color: var(--color-foreground-border) transparent\n\n // Override Chrome scrollbar styles\n &::-webkit-scrollbar\n width: 0.25rem\n height: 0.25rem\n &::-webkit-scrollbar-thumb\n background-color: var(--color-foreground-border)\n border-radius: 0.125rem\n\n//\n// Overalls\n//\nhtml,\nbody\n height: 100%\n color: var(--color-foreground-primary)\n background: var(--color-background-primary)\n\n.skip-to-content\n position: fixed\n padding: 1rem\n border-radius: 1rem\n left: 0.25rem\n top: 0.25rem\n z-index: 40\n background: var(--color-background-primary)\n color: var(--color-foreground-primary)\n\n transform: translateY(-200%)\n transition: transform 300ms ease-in-out\n\n &:focus-within\n transform: translateY(0%)\n\narticle\n color: var(--color-content-foreground)\n background: var(--color-content-background)\n overflow-wrap: break-word\n\n.page\n display: flex\n // fill the viewport for pages with little content.\n min-height: 100%\n\n.mobile-header\n width: 100%\n height: var(--header-height)\n background-color: var(--color-header-background)\n color: var(--color-header-text)\n border-bottom: 1px solid var(--color-header-border)\n\n // Looks like sub-script/super-script have this, and we need this to\n // be \"on top\" of those.\n z-index: 10\n\n // We don't show the header on large screens.\n display: none\n\n // Add shadow when scrolled\n &.scrolled\n border-bottom: none\n box-shadow: 0 0 0.2rem rgba(0, 0, 0, 0.1), 0 0.2rem 0.4rem rgba(0, 0, 0, 0.2)\n\n .header-center\n a\n color: var(--color-header-text)\n text-decoration: none\n\n.main\n display: flex\n flex: 1\n\n// Sidebar (left) also covers the entire left portion of screen.\n.sidebar-drawer\n box-sizing: border-box\n\n border-right: 1px solid var(--color-sidebar-background-border)\n background: var(--color-sidebar-background)\n\n display: flex\n justify-content: flex-end\n // These next two lines took me two days to figure out.\n width: calc((100% - #{$full-width}) / 2 + #{$sidebar-width})\n min-width: $sidebar-width\n\n// Scroll-along sidebars\n.sidebar-container,\n.toc-drawer\n box-sizing: border-box\n width: $sidebar-width\n\n.toc-drawer\n background: var(--color-toc-background)\n // See HACK described on top of this document\n padding-right: 1rem\n\n.sidebar-sticky,\n.toc-sticky\n position: sticky\n top: 0\n height: min(100%, 100vh)\n height: 100vh\n\n display: flex\n flex-direction: column\n\n.sidebar-scroll,\n.toc-scroll\n flex-grow: 1\n flex-shrink: 1\n\n overflow: auto\n scroll-behavior: smooth\n\n// Central items.\n.content\n padding: 0 $content-padding\n width: $content-width\n\n display: flex\n flex-direction: column\n justify-content: space-between\n\n.icon\n display: inline-block\n height: 1rem\n width: 1rem\n svg\n width: 100%\n height: 100%\n\n//\n// Accommodate announcement banner\n//\n.announcement\n background-color: var(--color-announcement-background)\n color: var(--color-announcement-text)\n\n height: var(--header-height)\n display: flex\n align-items: center\n overflow-x: auto\n & + .page\n min-height: calc(100% - var(--header-height))\n\n.announcement-content\n box-sizing: border-box\n padding: 0.5rem\n min-width: 100%\n white-space: nowrap\n text-align: center\n\n a\n color: var(--color-announcement-text)\n text-decoration-color: var(--color-announcement-text)\n\n &:hover\n color: var(--color-announcement-text)\n text-decoration-color: var(--color-link--hover)\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for theme\n////////////////////////////////////////////////////////////////////////////////\n.no-js .theme-toggle-container // don't show theme toggle if there's no JS\n display: none\n\n.theme-toggle-container\n display: flex\n\n.theme-toggle\n display: flex\n cursor: pointer\n border: none\n padding: 0\n background: transparent\n\n.theme-toggle svg\n height: 1.25rem\n width: 1.25rem\n color: var(--color-foreground-primary)\n display: none\n\n.theme-toggle-header\n display: flex\n align-items: center\n justify-content: center\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for elements\n////////////////////////////////////////////////////////////////////////////////\n.toc-overlay-icon, .nav-overlay-icon\n display: none\n cursor: pointer\n\n .icon\n color: var(--color-foreground-secondary)\n height: 1.5rem\n width: 1.5rem\n\n.toc-header-icon, .nav-overlay-icon\n // for when we set display: flex\n justify-content: center\n align-items: center\n\n.toc-content-icon\n height: 1.5rem\n width: 1.5rem\n\n.content-icon-container\n float: right\n display: flex\n margin-top: 1.5rem\n margin-left: 1rem\n margin-bottom: 1rem\n gap: 0.5rem\n\n .edit-this-page, .view-this-page\n svg\n color: inherit\n height: 1.25rem\n width: 1.25rem\n\n.sidebar-toggle\n position: absolute\n display: none\n// \n.sidebar-toggle[name=\"__toc\"]\n left: 20px\n.sidebar-toggle:checked\n left: 40px\n// \n\n.overlay\n position: fixed\n top: 0\n width: 0\n height: 0\n\n transition: width 0ms, height 0ms, opacity 250ms ease-out\n\n opacity: 0\n background-color: rgba(0, 0, 0, 0.54)\n.sidebar-overlay\n z-index: 20\n.toc-overlay\n z-index: 40\n\n// Keep things on top and smooth.\n.sidebar-drawer\n z-index: 30\n transition: left 250ms ease-in-out\n.toc-drawer\n z-index: 50\n transition: right 250ms ease-in-out\n\n// Show the Sidebar\n#__navigation:checked\n & ~ .sidebar-overlay\n width: 100%\n height: 100%\n opacity: 1\n & ~ .page\n .sidebar-drawer\n top: 0\n left: 0\n // Show the toc sidebar\n#__toc:checked\n & ~ .toc-overlay\n width: 100%\n height: 100%\n opacity: 1\n & ~ .page\n .toc-drawer\n top: 0\n right: 0\n\n////////////////////////////////////////////////////////////////////////////////\n// Back to top\n////////////////////////////////////////////////////////////////////////////////\n.back-to-top\n text-decoration: none\n\n display: none\n position: fixed\n left: 0\n top: 1rem\n padding: 0.5rem\n padding-right: 0.75rem\n border-radius: 1rem\n font-size: 0.8125rem\n\n background: var(--color-background-primary)\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), #6b728080 0px 0px 1px 0px\n\n z-index: 10\n\n margin-left: 50%\n transform: translateX(-50%)\n svg\n height: 1rem\n width: 1rem\n fill: currentColor\n display: inline-block\n\n span\n margin-left: 0.25rem\n\n .show-back-to-top &\n display: flex\n align-items: center\n\n////////////////////////////////////////////////////////////////////////////////\n// Responsive layouting\n////////////////////////////////////////////////////////////////////////////////\n// Make things a bit bigger on bigger screens.\n@media (min-width: $full-width + $sidebar-width)\n html\n font-size: 110%\n\n@media (max-width: $full-width)\n // Collapse \"toc\" into the icon.\n .toc-content-icon\n display: flex\n .toc-drawer\n position: fixed\n height: 100vh\n top: 0\n right: -$sidebar-width\n border-left: 1px solid var(--color-background-muted)\n .toc-tree\n border-left: none\n font-size: var(--toc-font-size--mobile)\n\n // Accomodate for a changed content width.\n .sidebar-drawer\n width: calc((100% - #{$full-width - $sidebar-width}) / 2 + #{$sidebar-width})\n\n@media (max-width: $content-padded-width + $sidebar-width)\n // Center the page\n .content\n margin-left: auto\n margin-right: auto\n padding: 0 $content-padding--small\n\n@media (max-width: $content-padded-width--small + $sidebar-width)\n // Collapse \"navigation\".\n .nav-overlay-icon\n display: flex\n .sidebar-drawer\n position: fixed\n height: 100vh\n width: $sidebar-width\n\n top: 0\n left: -$sidebar-width\n\n // Swap which icon is visible.\n .toc-header-icon, .theme-toggle-header\n display: flex\n .toc-content-icon, .theme-toggle-content\n display: none\n\n // Show the header.\n .mobile-header\n position: sticky\n top: 0\n display: flex\n justify-content: space-between\n align-items: center\n\n .header-left,\n .header-right\n display: flex\n height: var(--header-height)\n padding: 0 var(--header-padding)\n label\n height: 100%\n width: 100%\n user-select: none\n\n .nav-overlay-icon .icon,\n .theme-toggle svg\n height: 1.5rem\n width: 1.5rem\n\n // Add a scroll margin for the content\n :target\n scroll-margin-top: calc(var(--header-height) + 2.5rem)\n\n // Show back-to-top below the header\n .back-to-top\n top: calc(var(--header-height) + 0.5rem)\n\n // Accommodate for the header.\n .page\n flex-direction: column\n justify-content: center\n\n@media (max-width: $content-width + 2* $content-padding--small)\n // Content should respect window limits.\n .content\n width: 100%\n overflow-x: auto\n\n@media (max-width: $content-width)\n article[role=main] aside.sidebar\n float: none\n width: 100%\n margin: 1rem 0\n","// Overall Layout Variables\n//\n// Because CSS variables can't be used in media queries. The fact that this\n// makes the layout non-user-configurable is a good thing.\n$content-padding: 3em;\n$content-padding--small: 1em;\n$content-width: 46em;\n$sidebar-width: 15em;\n$content-padded-width: $content-width + 2 * $content-padding;\n$content-padded-width--small: $content-width + 2 * $content-padding--small;\n$full-width: $content-padded-width + 2 * $sidebar-width;\n","//\n// The design here is strongly inspired by mkdocs-material.\n.admonition, .topic\n margin: 1rem auto\n padding: 0 0.5rem 0.5rem 0.5rem\n\n background: var(--color-admonition-background)\n\n border-radius: 0.2rem\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n font-size: var(--admonition-font-size)\n\n overflow: hidden\n page-break-inside: avoid\n\n // First element should have no margin, since the title has it.\n > :nth-child(2)\n margin-top: 0\n\n // Last item should have no margin, since we'll control that w/ padding\n > :last-child\n margin-bottom: 0\n\n.admonition p.admonition-title,\np.topic-title\n position: relative\n margin: 0 -0.5rem 0.5rem\n padding-left: 2rem\n padding-right: .5rem\n padding-top: .4rem\n padding-bottom: .4rem\n\n font-weight: 500\n font-size: var(--admonition-title-font-size)\n line-height: 1.3\n\n // Our fancy icon\n &::before\n content: \"\"\n position: absolute\n left: 0.5rem\n width: 1rem\n height: 1rem\n\n// Default styles\np.admonition-title\n background-color: var(--color-admonition-title-background)\n &::before\n background-color: var(--color-admonition-title)\n mask-image: var(--icon-admonition-default)\n mask-repeat: no-repeat\n\np.topic-title\n background-color: var(--color-topic-title-background)\n &::before\n background-color: var(--color-topic-title)\n mask-image: var(--icon-topic-default)\n mask-repeat: no-repeat\n\n//\n// Variants\n//\n.admonition\n border-left: 0.2rem solid var(--color-admonition-title)\n\n @each $type, $value in $admonitions\n &.#{$type}\n border-left-color: var(--color-admonition-title--#{$type})\n > .admonition-title\n background-color: var(--color-admonition-title-background--#{$type})\n &::before\n background-color: var(--color-admonition-title--#{$type})\n mask-image: var(--icon-#{nth($value, 2)})\n\n.admonition-todo > .admonition-title\n text-transform: uppercase\n","// This file stylizes the API documentation (stuff generated by autodoc). It's\n// deeply nested due to how autodoc structures the HTML without enough classes\n// to select the relevant items.\n\n// API docs!\ndl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple)\n // Tweak the spacing of all the things!\n dd\n margin-left: 2rem\n > :first-child\n margin-top: 0.125rem\n > :last-child\n margin-bottom: 0.75rem\n\n // This is used for the arguments\n .field-list\n margin-bottom: 0.75rem\n\n // \"Headings\" (like \"Parameters\" and \"Return\")\n > dt\n text-transform: uppercase\n font-size: var(--font-size--small)\n\n dd:empty\n margin-bottom: 0.5rem\n dd > ul\n margin-left: -1.2rem\n > li\n > p:nth-child(2)\n margin-top: 0\n // When the last-empty-paragraph follows a paragraph, it doesn't need\n // to augument the existing spacing.\n > p + p:last-child:empty\n margin-top: 0\n margin-bottom: 0\n\n // Colorize the elements\n > dt\n color: var(--color-api-overall)\n\n.sig:not(.sig-inline)\n font-weight: bold\n\n font-size: var(--api-font-size)\n font-family: var(--font-stack--monospace)\n\n margin-left: -0.25rem\n margin-right: -0.25rem\n padding-top: 0.25rem\n padding-bottom: 0.25rem\n padding-right: 0.5rem\n\n // These are intentionally em, to properly match the font size.\n padding-left: 3em\n text-indent: -2.5em\n\n border-radius: 0.25rem\n\n background: var(--color-api-background)\n transition: background 100ms ease-out\n\n &:hover\n background: var(--color-api-background-hover)\n\n // adjust the size of the [source] link on the right.\n a.reference\n .viewcode-link\n font-weight: normal\n width: 4.25rem\n\nem.property\n font-style: normal\n &:first-child\n color: var(--color-api-keyword)\n.sig-name\n color: var(--color-api-name)\n.sig-prename\n font-weight: normal\n color: var(--color-api-pre-name)\n.sig-paren\n color: var(--color-api-paren)\n.sig-param\n font-style: normal\n\ndiv.versionadded,\ndiv.versionchanged,\ndiv.deprecated,\ndiv.versionremoved\n border-left: 0.1875rem solid\n border-radius: 0.125rem\n\n padding-left: 0.75rem\n\n p\n margin-top: 0.125rem\n margin-bottom: 0.125rem\n\ndiv.versionadded\n border-color: var(--color-api-added-border)\n .versionmodified\n color: var(--color-api-added)\n\ndiv.versionchanged\n border-color: var(--color-api-changed-border)\n .versionmodified\n color: var(--color-api-changed)\n\ndiv.deprecated\n border-color: var(--color-api-deprecated-border)\n .versionmodified\n color: var(--color-api-deprecated)\n\ndiv.versionremoved\n border-color: var(--color-api-removed-border)\n .versionmodified\n color: var(--color-api-removed)\n\n// Align the [docs] and [source] to the right.\n.viewcode-link, .viewcode-back\n float: right\n text-align: right\n",".line-block\n margin-top: 0.5rem\n margin-bottom: 0.75rem\n .line-block\n margin-top: 0rem\n margin-bottom: 0rem\n padding-left: 1rem\n","// Captions\narticle p.caption,\ntable > caption,\n.code-block-caption\n font-size: var(--font-size--small)\n text-align: center\n\n// Caption above a TOCTree\n.toctree-wrapper.compound\n .caption, :not(.caption) > .caption-text\n font-size: var(--font-size--small)\n text-transform: uppercase\n\n text-align: initial\n margin-bottom: 0\n\n > ul\n margin-top: 0\n margin-bottom: 0\n","// Inline code\ncode.literal, .sig-inline\n background: var(--color-inline-code-background)\n border-radius: 0.2em\n // Make the font smaller, and use padding to recover.\n font-size: var(--font-size--small--2)\n padding: 0.1em 0.2em\n\n pre.literal-block &\n font-size: inherit\n padding: 0\n\n p &\n border: 1px solid var(--color-background-border)\n\n.sig-inline\n font-family: var(--font-stack--monospace)\n\n// Code and Literal Blocks\n$code-spacing-vertical: 0.625rem\n$code-spacing-horizontal: 0.875rem\n\n// Wraps every literal block + line numbers.\ndiv[class*=\" highlight-\"],\ndiv[class^=\"highlight-\"]\n margin: 1em 0\n display: flex\n\n .table-wrapper\n margin: 0\n padding: 0\n\npre\n margin: 0\n padding: 0\n overflow: auto\n\n // Needed to have more specificity than pygments' \"pre\" selector. :(\n article[role=\"main\"] .highlight &\n line-height: 1.5\n\n &.literal-block,\n .highlight &\n font-size: var(--code-font-size)\n padding: $code-spacing-vertical $code-spacing-horizontal\n\n // Make it look like all the other blocks.\n &.literal-block\n margin-top: 1rem\n margin-bottom: 1rem\n\n border-radius: 0.2rem\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n\n// All code is always contained in this.\n.highlight\n width: 100%\n border-radius: 0.2rem\n\n // Make line numbers and prompts un-selectable.\n .gp, span.linenos\n user-select: none\n pointer-events: none\n\n // Expand the line-highlighting.\n .hll\n display: block\n margin-left: -$code-spacing-horizontal\n margin-right: -$code-spacing-horizontal\n padding-left: $code-spacing-horizontal\n padding-right: $code-spacing-horizontal\n\n/* Make code block captions be nicely integrated */\n.code-block-caption\n display: flex\n padding: $code-spacing-vertical $code-spacing-horizontal\n\n border-radius: 0.25rem\n border-bottom-left-radius: 0\n border-bottom-right-radius: 0\n font-weight: 300\n border-bottom: 1px solid\n\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n border-color: var(--color-background-border)\n\n + div[class]\n margin-top: 0\n pre\n border-top-left-radius: 0\n border-top-right-radius: 0\n\n// When `html_codeblock_linenos_style` is table.\n.highlighttable\n width: 100%\n display: block\n tbody\n display: block\n\n tr\n display: flex\n\n // Line numbers\n td.linenos\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n padding: $code-spacing-vertical $code-spacing-horizontal\n padding-right: 0\n border-top-left-radius: 0.2rem\n border-bottom-left-radius: 0.2rem\n\n .linenodiv\n padding-right: $code-spacing-horizontal\n font-size: var(--code-font-size)\n box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n\n // Actual code\n td.code\n padding: 0\n display: block\n flex: 1\n overflow: hidden\n\n .highlight\n border-top-left-radius: 0\n border-bottom-left-radius: 0\n\n// When `html_codeblock_linenos_style` is inline.\n.highlight\n span.linenos\n display: inline-block\n padding-left: 0\n padding-right: $code-spacing-horizontal\n margin-right: $code-spacing-horizontal\n box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n","// Inline Footnote Reference\n.footnote-reference\n font-size: var(--font-size--small--4)\n vertical-align: super\n\n// Definition list, listing the content of each note.\n// docutils <= 0.17\ndl.footnote.brackets\n font-size: var(--font-size--small)\n color: var(--color-foreground-secondary)\n\n display: grid\n grid-template-columns: max-content auto\n dt\n margin: 0\n > .fn-backref\n margin-left: 0.25rem\n\n &:after\n content: \":\"\n\n .brackets\n &:before\n content: \"[\"\n &:after\n content: \"]\"\n\n dd\n margin: 0\n padding: 0 1rem\n\n// docutils >= 0.18\naside.footnote\n font-size: var(--font-size--small)\n color: var(--color-foreground-secondary)\n\naside.footnote > span,\ndiv.citation > span\n float: left\n font-weight: 500\n padding-right: 0.25rem\n\naside.footnote > *:not(span),\ndiv.citation > p\n margin-left: 2rem\n","//\n// Figures\n//\nimg\n box-sizing: border-box\n max-width: 100%\n height: auto\n\narticle\n figure, .figure\n border-radius: 0.2rem\n\n margin: 0\n :last-child\n margin-bottom: 0\n\n .align-left\n float: left\n clear: left\n margin: 0 1rem 1rem\n\n .align-right\n float: right\n clear: right\n margin: 0 1rem 1rem\n\n .align-default,\n .align-center\n display: block\n text-align: center\n margin-left: auto\n margin-right: auto\n\n // WELL, table needs to be stylised like a table.\n table.align-default\n display: table\n text-align: initial\n",".genindex-jumpbox, .domainindex-jumpbox\n border-top: 1px solid var(--color-background-border)\n border-bottom: 1px solid var(--color-background-border)\n padding: 0.25rem\n\n.genindex-section, .domainindex-section\n h2\n margin-top: 0.75rem\n margin-bottom: 0.5rem\n ul\n margin-top: 0\n margin-bottom: 0\n","ul,\nol\n padding-left: 1.2rem\n\n // Space lists out like paragraphs\n margin-top: 1rem\n margin-bottom: 1rem\n // reduce margins within li.\n li\n > p:first-child\n margin-top: 0.25rem\n margin-bottom: 0.25rem\n\n > p:last-child\n margin-top: 0.25rem\n\n > ul,\n > ol\n margin-top: 0.5rem\n margin-bottom: 0.5rem\n\nol\n &.arabic\n list-style: decimal\n &.loweralpha\n list-style: lower-alpha\n &.upperalpha\n list-style: upper-alpha\n &.lowerroman\n list-style: lower-roman\n &.upperroman\n list-style: upper-roman\n\n// Don't space lists out when they're \"simple\" or in a `.. toctree::`\n.simple,\n.toctree-wrapper\n li\n > ul,\n > ol\n margin-top: 0\n margin-bottom: 0\n\n// Definition Lists\n.field-list,\n.option-list,\ndl:not([class]),\ndl.simple,\ndl.footnote,\ndl.glossary\n dt\n font-weight: 500\n margin-top: 0.25rem\n + dt\n margin-top: 0\n\n .classifier::before\n content: \":\"\n margin-left: 0.2rem\n margin-right: 0.2rem\n\n dd\n > p:first-child,\n ul\n margin-top: 0.125rem\n\n ul\n margin-bottom: 0.125rem\n",".math-wrapper\n width: 100%\n overflow-x: auto\n\ndiv.math\n position: relative\n text-align: center\n\n .headerlink,\n &:focus .headerlink\n display: none\n\n &:hover .headerlink\n display: inline-block\n\n span.eqno\n position: absolute\n right: 0.5rem\n top: 50%\n transform: translate(0, -50%)\n z-index: 1\n","// Abbreviations\nabbr[title]\n cursor: help\n\n// \"Problematic\" content, as identified by Sphinx\n.problematic\n color: var(--color-problematic)\n\n// Keyboard / Mouse \"instructions\"\nkbd:not(.compound)\n margin: 0 0.2rem\n padding: 0 0.2rem\n border-radius: 0.2rem\n border: 1px solid var(--color-foreground-border)\n color: var(--color-foreground-primary)\n vertical-align: text-bottom\n\n font-size: var(--font-size--small--3)\n display: inline-block\n\n box-shadow: 0 0.0625rem 0 rgba(0, 0, 0, 0.2), inset 0 0 0 0.125rem var(--color-background-primary)\n\n background-color: var(--color-background-secondary)\n\n// Blockquote\nblockquote\n border-left: 4px solid var(--color-background-border)\n background: var(--color-background-secondary)\n\n margin-left: 0\n margin-right: 0\n padding: 0.5rem 1rem\n\n .attribution\n font-weight: 600\n text-align: right\n\n &.pull-quote,\n &.highlights\n font-size: 1.25em\n\n &.epigraph,\n &.pull-quote\n border-left-width: 0\n border-radius: 0.5rem\n\n &.highlights\n border-left-width: 0\n background: transparent\n\n// Center align embedded-in-text images\np .reference img\n vertical-align: middle\n","p.rubric\n line-height: 1.25\n font-weight: bold\n font-size: 1.125em\n\n // For Numpy-style documentation that's got rubrics within it.\n // https://github.com/pradyunsg/furo/discussions/505\n dd &\n line-height: inherit\n font-weight: inherit\n\n font-size: var(--font-size--small)\n text-transform: uppercase\n","article .sidebar\n float: right\n clear: right\n width: 30%\n\n margin-left: 1rem\n margin-right: 0\n\n border-radius: 0.2rem\n background-color: var(--color-background-secondary)\n border: var(--color-background-border) 1px solid\n\n > *\n padding-left: 1rem\n padding-right: 1rem\n\n > ul, > ol // lists need additional padding, because bullets.\n padding-left: 2.2rem\n\n .sidebar-title\n margin: 0\n padding: 0.5rem 1rem\n border-bottom: var(--color-background-border) 1px solid\n\n font-weight: 500\n\n// TODO: subtitle\n// TODO: dedicated variables?\n","[role=main] .table-wrapper.container\n width: 100%\n overflow-x: auto\n margin-top: 1rem\n margin-bottom: 0.5rem\n padding: 0.2rem 0.2rem 0.75rem\n\ntable.docutils\n border-radius: 0.2rem\n border-spacing: 0\n border-collapse: collapse\n\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n th\n background: var(--color-table-header-background)\n\n td,\n th\n // Space things out properly\n padding: 0 0.25rem\n\n // Get the borders looking just-right.\n border-left: 1px solid var(--color-table-border)\n border-right: 1px solid var(--color-table-border)\n border-bottom: 1px solid var(--color-table-border)\n\n p\n margin: 0.25rem\n\n &:first-child\n border-left: none\n &:last-child\n border-right: none\n\n // MyST-parser tables set these classes for control of column alignment\n &.text-left\n text-align: left\n &.text-right\n text-align: right\n &.text-center\n text-align: center\n",":target\n scroll-margin-top: 2.5rem\n\n@media (max-width: $full-width - $sidebar-width)\n :target\n scroll-margin-top: calc(2.5rem + var(--header-height))\n\n // When a heading is selected\n section > span:target\n scroll-margin-top: calc(2.8rem + var(--header-height))\n\n// Permalinks\n.headerlink\n font-weight: 100\n user-select: none\n\nh1,\nh2,\nh3,\nh4,\nh5,\nh6,\ndl dt,\np.caption,\nfigcaption p,\ntable > caption,\n.code-block-caption\n > .headerlink\n margin-left: 0.5rem\n visibility: hidden\n &:hover > .headerlink\n visibility: visible\n\n // Don't change to link-like, if someone adds the contents directive.\n > .toc-backref\n color: inherit\n text-decoration-line: none\n\n// Figure and table captions are special.\nfigure:hover > figcaption > p > .headerlink,\ntable:hover > caption > .headerlink\n visibility: visible\n\n:target >, // Regular section[id] style anchors\nspan:target ~ // Non-regular span[id] style \"extra\" anchors\n h1,\n h2,\n h3,\n h4,\n h5,\n h6\n &:nth-of-type(1)\n background-color: var(--color-highlight-on-target)\n // .headerlink\n // visibility: visible\n code.literal\n background-color: transparent\n\ntable:target > caption,\nfigure:target\n background-color: var(--color-highlight-on-target)\n\n// Inline page contents\n.this-will-duplicate-information-and-it-is-still-useful-here li :target\n background-color: var(--color-highlight-on-target)\n\n// Code block permalinks\n.literal-block-wrapper:target .code-block-caption\n background-color: var(--color-highlight-on-target)\n\n// When a definition list item is selected\n//\n// There isn't really an alternative to !important here, due to the\n// high-specificity of API documentation's selector.\ndt:target\n background-color: var(--color-highlight-on-target) !important\n\n// When a footnote reference is selected\n.footnote > dt:target + dd,\n.footnote-reference:target\n background-color: var(--color-highlight-on-target)\n",".guilabel\n background-color: var(--color-guilabel-background)\n border: 1px solid var(--color-guilabel-border)\n color: var(--color-guilabel-text)\n\n padding: 0 0.3em\n border-radius: 0.5em\n font-size: 0.9em\n","// This file contains the styles used for stylizing the footer that's shown\n// below the content.\n\nfooter\n font-size: var(--font-size--small)\n display: flex\n flex-direction: column\n\n margin-top: 2rem\n\n// Bottom of page information\n.bottom-of-page\n display: flex\n align-items: center\n justify-content: space-between\n\n margin-top: 1rem\n padding-top: 1rem\n padding-bottom: 1rem\n\n color: var(--color-foreground-secondary)\n border-top: 1px solid var(--color-background-border)\n\n line-height: 1.5\n\n @media (max-width: $content-width)\n text-align: center\n flex-direction: column-reverse\n gap: 0.25rem\n\n .left-details\n font-size: var(--font-size--small)\n\n .right-details\n display: flex\n flex-direction: column\n gap: 0.25rem\n text-align: right\n\n .icons\n display: flex\n justify-content: flex-end\n gap: 0.25rem\n font-size: 1rem\n\n a\n text-decoration: none\n\n svg,\n img\n font-size: 1.125rem\n height: 1em\n width: 1em\n\n// Next/Prev page information\n.related-pages\n a\n display: flex\n align-items: center\n\n text-decoration: none\n &:hover .page-info .title\n text-decoration: underline\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline)\n\n svg.furo-related-icon,\n svg.furo-related-icon > use\n flex-shrink: 0\n\n color: var(--color-foreground-border)\n\n width: 0.75rem\n height: 0.75rem\n margin: 0 0.5rem\n\n &.next-page\n max-width: 50%\n\n float: right\n clear: right\n text-align: right\n\n &.prev-page\n max-width: 50%\n\n float: left\n clear: left\n\n svg\n transform: rotate(180deg)\n\n.page-info\n display: flex\n flex-direction: column\n overflow-wrap: anywhere\n\n .next-page &\n align-items: flex-end\n\n .context\n display: flex\n align-items: center\n\n padding-bottom: 0.1rem\n\n color: var(--color-foreground-muted)\n font-size: var(--font-size--small)\n text-decoration: none\n","// This file contains the styles for the contents of the left sidebar, which\n// contains the navigation tree, logo, search etc.\n\n////////////////////////////////////////////////////////////////////////////////\n// Brand on top of the scrollable tree.\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-brand\n display: flex\n flex-direction: column\n flex-shrink: 0\n\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n text-decoration: none\n\n.sidebar-brand-text\n color: var(--color-sidebar-brand-text)\n overflow-wrap: break-word\n margin: var(--sidebar-item-spacing-vertical) 0\n font-size: 1.5rem\n\n.sidebar-logo-container\n margin: var(--sidebar-item-spacing-vertical) 0\n\n.sidebar-logo\n margin: 0 auto\n display: block\n max-width: 100%\n\n////////////////////////////////////////////////////////////////////////////////\n// Search\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-search-container\n display: flex\n align-items: center\n margin-top: var(--sidebar-search-space-above)\n\n position: relative\n\n background: var(--color-sidebar-search-background)\n &:hover,\n &:focus-within\n background: var(--color-sidebar-search-background--focus)\n\n &::before\n content: \"\"\n position: absolute\n left: var(--sidebar-item-spacing-horizontal)\n width: var(--sidebar-search-icon-size)\n height: var(--sidebar-search-icon-size)\n\n background-color: var(--color-sidebar-search-icon)\n mask-image: var(--icon-search)\n\n.sidebar-search\n box-sizing: border-box\n\n border: none\n border-top: 1px solid var(--color-sidebar-search-border)\n border-bottom: 1px solid var(--color-sidebar-search-border)\n\n padding-top: var(--sidebar-search-input-spacing-vertical)\n padding-bottom: var(--sidebar-search-input-spacing-vertical)\n padding-right: var(--sidebar-search-input-spacing-horizontal)\n padding-left: calc(var(--sidebar-item-spacing-horizontal) + var(--sidebar-search-input-spacing-horizontal) + var(--sidebar-search-icon-size))\n\n width: 100%\n\n color: var(--color-sidebar-search-foreground)\n background: transparent\n z-index: 10\n\n &:focus\n outline: none\n\n &::placeholder\n font-size: var(--sidebar-search-input-font-size)\n\n//\n// Hide Search Matches link\n//\n#searchbox .highlight-link\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal) 0\n margin: 0\n text-align: center\n\n a\n color: var(--color-sidebar-search-icon)\n font-size: var(--font-size--small--2)\n\n////////////////////////////////////////////////////////////////////////////////\n// Structure/Skeleton of the navigation tree (left)\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-tree\n font-size: var(--sidebar-item-font-size)\n margin-top: var(--sidebar-tree-space-above)\n margin-bottom: var(--sidebar-item-spacing-vertical)\n\n ul\n padding: 0\n margin-top: 0\n margin-bottom: 0\n\n display: flex\n flex-direction: column\n\n list-style: none\n\n li\n position: relative\n margin: 0\n\n > ul\n margin-left: var(--sidebar-item-spacing-horizontal)\n\n .icon\n color: var(--color-sidebar-link-text)\n\n .reference\n box-sizing: border-box\n color: var(--color-sidebar-link-text)\n\n // Fill the parent.\n display: inline-block\n line-height: var(--sidebar-item-line-height)\n text-decoration: none\n\n // Don't allow long words to cause wrapping.\n overflow-wrap: anywhere\n\n height: 100%\n width: 100%\n\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n &:hover\n color: var(--color-sidebar-link-text)\n background: var(--color-sidebar-item-background--hover)\n\n // Add a nice little \"external-link\" arrow here.\n &.external::after\n content: url('data:image/svg+xml,')\n margin: 0 0.25rem\n vertical-align: middle\n color: var(--color-sidebar-link-text)\n\n // Make the current page reference bold.\n .current-page > .reference\n font-weight: bold\n\n label\n position: absolute\n top: 0\n right: 0\n height: var(--sidebar-item-height)\n width: var(--sidebar-expander-width)\n\n cursor: pointer\n user-select: none\n\n display: flex\n justify-content: center\n align-items: center\n\n .caption, :not(.caption) > .caption-text\n font-size: var(--sidebar-caption-font-size)\n color: var(--color-sidebar-caption-text)\n\n font-weight: bold\n text-transform: uppercase\n\n margin: var(--sidebar-caption-space-above) 0 0 0\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n // If it has children, add a bit more padding to wrap the content to avoid\n // overlapping with the

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