Tests for linear op (#965)

Test for torch linear op

Closes [#260](#260)

forge/test/operators/pytorch/nn/test_linear.py

@@ -0,0 +1,345 @@
+# SPDX-FileCopyrightText: © 2024 Tenstorrent AI ULC
+
+# SPDX-License-Identifier: Apache-2.0
+#
+# Tests for testing of embedding operators
+#
+# In this test we test pytorch embedding operator
+
+# GENERAL OP SUPPORT TEST PLAN:
+# 1. Operand type - any supported type
+# 2. Operand source(s):
+# (+)  2.1 From another op
+#       - Operator -> input
+# (+)  2.2 From DRAM queue
+#       - Operator is first node in network
+#       - Input_queue flag = false
+# (+)  2.3 Const Inputs (const eval pass)
+#       - Operator where all inputs are constants.
+# (+)  2.4 From host
+#       - Input tensor as input of network
+#       - Operator is first node in network
+#       - Input_queue flag = true
+# 3 Operand shapes type(s):
+# (+)  3.1 Full tensor (i.e. full expected shape)
+#       - 3-4 by default P1 (high prioriy)
+#       - 2, 5, ++ include P2 (lower prioriy)
+# (+)  3.2 Tensor reduce on one or more dims to 1
+#       - Vector
+#       - Only one dim is not equal to 1
+# (+)  3.3 Scalar P2
+#       - Create tensor of dimension equal to 0 (tensor from scalar) or just to use scalar as simple value
+# 4. Operand / output size of dimensions (few examples of each, 10 values total)
+# (+)  4.1 Divisible by 32
+# (+)  4.2 Prime numbers
+# (+)  4.3 Very large (thousands, 10s of thousands)
+#       - 100x100, 100x1000
+#       - maybe nightly only
+# (+)  4.4 Extreme ratios between height/width
+#      4.5 ...probably many more interesting combinations here
+# 5. Data format - all supported formats
+# (/)  5.1 Output DF
+# (/)  5.2 Intermediate DF
+# (/)  5.3 Accumulation DF
+# (+)  5.4 Operand DFs
+#       - Fix HiFi4 for math fidelity value
+# (+) 6. Math fidelity - LoFi, HiFi2a, Hifi2b, Hifi3, Hifi4
+#       - Fix fp16b (default) for data format value
+# (/) 7. Special attributes - if applicable.. like approx_mode for Exp, for example
+# (/) 8. Special cases - if applicable
+# 9. Variable number of operands - if applicable
+# (/) Few representative values
+# (/) Reuse inputs for selected operators
+
+
+from functools import reduce
+import random
+import pytest
+
+from typing import List, Dict, Type, Optional, Any
+from loguru import logger
+
+import torch
+import forge
+import forge.op
+
+from forge.verify.config import VerifyConfig
+from forge.verify.value_checkers import AllCloseValueChecker
+
+from test.operators.utils import InputSourceFlags, VerifyUtils, ValueRanges
+from test.operators.utils import InputSource
+from test.operators.utils import TestVector
+from test.operators.utils import TestPlan
+from test.operators.utils import FailingReasons
+from test.operators.utils.compat import TestDevice
+from test.operators.utils.compat import TestTensorsUtils
+from test.operators.utils import TestCollection
+from test.operators.utils import TestCollectionCommon
+
+
+class ModelFromAnotherOp(torch.nn.Module):
+
+    model_name = "model_op_src_from_another_op"
+
+    def __init__(self, operator, opname, shape, kwargs):
+        super(ModelFromAnotherOp, self).__init__()
+        self.testname = "Embedding_pytorch_operator_" + opname + "_test_op_src_from_another_op"
+        self.operator = operator
+        self.opname = opname
+        self.shape = shape
+        self.kwargs = {
+            "in_features": kwargs["in_features"],
+            "out_features": kwargs["out_features"],
+        }
+
+        self.l1 = self.operator(**self.kwargs)
+
+    def forward(self, x: torch.Tensor):
+        # we use Add operator to create one operands which is input for the embedding operator
+        add = torch.add(x, x)
+        output = self.l1(add)
+        return output
+
+
+class ModelDirect(torch.nn.Module):
+
+    model_name = "model_op_src_from_host"
+
+    def __init__(self, operator, opname, shape, kwargs):
+        super(ModelDirect, self).__init__()
+        self.testname = "Embedding_pytorch_operator_" + opname + "_test_op_src_from_host"
+        self.operator = operator
+        self.opname = opname
+        self.shape = shape
+        self.kwargs = {
+            "in_features": kwargs["in_features"],
+            "out_features": kwargs["out_features"],
+        }
+
+        self.l1 = self.operator(**self.kwargs)
+
+    def forward(self, x: torch.Tensor):
+        output = self.l1(x)
+        return output
+
+
+class ModelConstEvalPass(torch.nn.Module):
+
+    model_name = "model_op_src_const_eval_pass"
+
+    def __init__(self, operator, opname, shape, kwargs, dtype):
+        super(ModelConstEvalPass, self).__init__()
+        self.testname = "Embedding_pytorch_operator_" + opname + "_test_op_src_const_eval_pass"
+        self.operator = operator
+        self.opname = opname
+        self.shape = shape
+        self.kwargs = {
+            "in_features": kwargs["in_features"],
+            "out_features": kwargs["out_features"],
+        }
+
+        self.constant = torch.rand(self.shape, dtype=dtype)
+        self.l1 = self.operator(**self.kwargs)
+
+    def forward(self, x: torch.Tensor):
+        v1 = self.l1(self.constant)
+        # v2 = torch.add(x, x)
+        v2 = self.l1(x)
+        # add consume inputs
+        add = torch.add(v1, v2)
+        return add
+
+
+class TestVerification:
+
+    MODEL_TYPES = {
+        InputSource.FROM_ANOTHER_OP: ModelFromAnotherOp,
+        InputSource.FROM_HOST: ModelDirect,
+        InputSource.FROM_DRAM_QUEUE: ModelDirect,
+        InputSource.CONST_EVAL_PASS: ModelConstEvalPass,
+    }
+
+    @classmethod
+    def verify(
+        cls,
+        test_device: TestDevice,
+        test_vector: TestVector,
+        input_params: List[Dict] = [],
+        number_of_operands: int = 1,
+        warm_reset: bool = False,
+    ):
+        """Common verification function for all tests"""
+
+        input_source_flag: InputSourceFlags = None
+        if test_vector.input_source in (InputSource.FROM_DRAM_QUEUE,):
+            input_source_flag = InputSourceFlags.FROM_DRAM
+
+        operator = getattr(torch.nn, test_vector.operator)
+
+        kwargs = test_vector.kwargs if test_vector.kwargs else {}
+
+        model_type = cls.MODEL_TYPES[test_vector.input_source]
+        if test_vector.input_source == InputSource.CONST_EVAL_PASS:
+            pytorch_model = model_type(
+                operator=operator,
+                opname=test_vector.operator,
+                shape=test_vector.input_shape,
+                kwargs=kwargs,
+                dtype=TestTensorsUtils.get_dtype_for_df(test_vector.dev_data_format),
+            )
+        else:
+            pytorch_model = model_type(
+                operator=operator,
+                opname=test_vector.operator,
+                shape=test_vector.input_shape,
+                kwargs=kwargs,
+            )
+
+        input_shapes = tuple([test_vector.input_shape for _ in range(number_of_operands)])
+        logger.trace(f"***input_shapes: {input_shapes}")
+
+        VerifyUtils.verify(
+            model=pytorch_model,
+            test_device=test_device,
+            input_shapes=input_shapes,
+            input_params=input_params,
+            input_source_flag=input_source_flag,
+            dev_data_format=test_vector.dev_data_format,
+            math_fidelity=test_vector.math_fidelity,
+            pcc=test_vector.pcc,
+            warm_reset=warm_reset,
+            deprecated_verification=False,
+            verify_config=VerifyConfig(value_checker=AllCloseValueChecker(rtol=1e-2, atol=1e-2)),
+            value_range=ValueRanges.SMALL,
+        )
+
+
+class TestParamsData:
+
+    __test__ = False  # Avoid collecting TestParamsData as a pytest test
+
+    test_plan: TestPlan = None
+
+    @classmethod
+    def get_out_features(cls, input_shape: List[int]):
+        treshold = 10000
+        out_features = []
+        rng = random.Random(sum(input_shape))
+        for _ in range(2):
+            out_features.append(rng.randint(1, 1000))
+        out_features.append(sum(input_shape) % treshold)
+        return out_features
+
+    @classmethod
+    def generate_kwargs(cls, test_vector: TestVector):
+        kwarg_list = []
+        in_features = test_vector.input_shape[-1]
+        out_features_list = TestParamsData.get_out_features(test_vector.input_shape)
+        bias_list = [True, False]
+        for out_features in out_features_list:
+            for bias in bias_list:
+                kwarg_list.append(
+                    {
+                        "in_features": in_features,
+                        "out_features": out_features,
+                        "bias": bias,
+                    }
+                )
+        return kwarg_list
+
+
+class TestCollectionData:
+
+    __test__ = False  # Avoid collecting TestCollectionData as a pytest test
+
+    all = TestCollection(
+        operators=[
+            "Linear",  # 00
+        ],
+        input_sources=TestCollectionCommon.all.input_sources,
+        input_shapes=TestCollectionCommon.all.input_shapes,
+        dev_data_formats=TestCollectionCommon.all.dev_data_formats,
+        math_fidelities=TestCollectionCommon.all.math_fidelities,
+    )
+
+    single = TestCollection(
+        input_sources=TestCollectionCommon.single.input_sources,
+        input_shapes=TestCollectionCommon.single.input_shapes,
+        dev_data_formats=TestCollectionCommon.single.dev_data_formats,
+        math_fidelities=TestCollectionCommon.single.math_fidelities,
+    )
+
+
+TestParamsData.test_plan = TestPlan(
+    verify=lambda test_device, test_vector: TestVerification.verify(
+        test_device,
+        test_vector,
+    ),
+    collections=[
+        # Test plan:
+        # 2. Operand source(s):
+        # 3. Operand shapes type(s):
+        # 4. Operand / output size of dimensions
+        TestCollection(
+            operators=TestCollectionData.all.operators,
+            input_sources=TestCollectionData.all.input_sources,
+            input_shapes=TestCollectionData.all.input_shapes,
+            kwargs=lambda test_vector: TestParamsData.generate_kwargs(test_vector),
+        ),
+        # Test plan:
+        # 5. Data format
+        TestCollection(
+            operators=TestCollectionData.all.operators,
+            input_sources=TestCollectionData.single.input_sources,
+            input_shapes=TestCollectionData.single.input_shapes,
+            kwargs=lambda test_vector: TestParamsData.generate_kwargs(test_vector),
+            dev_data_formats=TestCollectionCommon.float.dev_data_formats,
+            math_fidelities=TestCollectionData.single.math_fidelities,
+        ),
+        # Test plan:
+        # 6. Math fidelity
+        TestCollection(
+            operators=TestCollectionData.all.operators,
+            input_sources=TestCollectionData.single.input_sources,
+            input_shapes=TestCollectionData.single.input_shapes,
+            kwargs=lambda test_vector: TestParamsData.generate_kwargs(test_vector),
+            dev_data_formats=TestCollectionData.single.dev_data_formats,
+            math_fidelities=TestCollectionData.all.math_fidelities,
+        ),
+    ],
+    failing_rules=[
+        # E   RuntimeError: The expanded size of the tensor (x) must match the existing size (y) at non-singleton dimension 0.  Target sizes: [x].  Tensor sizes: [y]
+        TestCollection(
+            input_sources=TestCollectionData.all.input_sources,
+            criteria=lambda test_vector: len(test_vector.input_shape) == 4 and test_vector.input_shape[0] > 1,
+            failing_reason=FailingReasons.MICROBATCHING_UNSUPPORTED,
+        ),
+        # E   ValueError: Data mismatch -> AllCloseValueChecker (all_close):
+        TestCollection(
+            input_shapes=[
+                (1, 10000),
+            ],
+            failing_reason=FailingReasons.DATA_MISMATCH,
+        ),
+        # # THIS ERROR OCCURES WHEN USING DEPRICATED VERIFICATION METHOD (NOT ALLCLOSE VALUE CHECKER)
+        # # E   AssertionError: PCC check failed
+        # # this also happens for other 2 dim ipnut shapes where microbatch size is 1 and out_features is 1 - not all cases are failing
+        # TestCollection(
+        #     input_shapes=[
+        #         (1, 10000),
+        #     ],
+        #     kwargs=[
+        #         {
+        #             "out_features": 1,
+        #         },
+        #     ],
+        #     failing_reason=FailingReasons.DATA_MISMATCH,
+        # ),
+    ],
+)
+
+
+def get_test_plans() -> List[TestPlan]:
+    return [
+        TestParamsData.test_plan,
+    ]

forge/test/operators/utils/failing_reasons.py

@@ -177,6 +177,11 @@ def validate_exception_message(
             lambda ex: isinstance(ex, RuntimeError)
             and "293 unique+common runtime args targeting kernel reader_concat_stick_layout_interleaved_start_id on (x=0,y=0) are too large. Max allowable is 256"
             in f"{ex}",
+            lambda ex: isinstance(ex, RuntimeError)
+            and "mat1 and mat2 must have the same dtype, but got Int and Float" in f"{ex}",
+        ],
+        FailingReasons.MICROBATCHING_UNSUPPORTED: [
+            lambda ex: isinstance(ex, RuntimeError) and "The expanded size of the tensor" in f"{ex}",
         ],
     }