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#13408: Pytorch sweeps set 1
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tests/sweep_framework/sweeps/eltwise/binary/add/add_all_pytorch2.py
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tests/sweep_framework/sweeps/eltwise/binary/eq/eq_scalar_pytorch2.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,89 @@ | ||
| # SPDX-FileCopyrightText: © 2024 Tenstorrent Inc. | ||
|
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| # SPDX-License-Identifier: Apache-2.0 | ||
|
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| from typing import Optional, Tuple | ||
| from functools import partial | ||
|
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| import torch | ||
| import random | ||
| import ttnn | ||
| from tests.sweep_framework.utils import gen_shapes | ||
| from tests.tt_eager.python_api_testing.sweep_tests.generation_funcs import gen_func_with_cast_tt | ||
|
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| from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time | ||
| from models.utility_functions import torch_random | ||
|
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| # Override the default timeout in seconds for hang detection. | ||
| TIMEOUT = 30 | ||
|
|
||
| random.seed(0) | ||
|
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||
|
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| # Parameters provided to the test vector generator are defined here. | ||
| # They are defined as dict-type suites that contain the arguments to the run function as keys, and lists of possible inputs as values. | ||
| # Each suite has a key name (in this case "suite_1" and "suite_2") which will associate the test vectors to this specific suite of inputs. | ||
| # Developers can create their own generator functions and pass them to the parameters as inputs. | ||
| parameters = { | ||
| "nightly": { | ||
| "input_shape": [ | ||
| [1, 1, 256], | ||
| [1, 16], | ||
| [1, 7], | ||
| [1, 7], | ||
| [16, 49, 49], | ||
| [16, 64, 64], | ||
| [1], | ||
| [1], | ||
| [4, 49, 49], | ||
| [4, 64, 64], | ||
| [64, 49, 49], | ||
| [64, 64, 64], | ||
| ], | ||
| "scalar": [1, 0, 1, 50256, 0, 0, 1, 50256, 0, 0, 0, 0], | ||
| "input_a_dtype": [ttnn.bfloat16], | ||
| "input_a_layout": [ttnn.TILE_LAYOUT], | ||
| "input_a_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG], | ||
| "output_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG], | ||
| }, | ||
| } | ||
|
|
||
|
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| # This is the run instructions for the test, defined by the developer. | ||
| # The run function must take the above-defined parameters as inputs. | ||
| # The runner will call this run function with each test vector, and the returned results from this function will be stored. | ||
| # If you defined a mesh_device_fixture above, the object you yielded will be passed into this function as 'device'. Otherwise, it will be the default ttnn device opened by the infra. | ||
| def run( | ||
| input_shape, | ||
| scalar, | ||
| input_a_dtype, | ||
| input_a_layout, | ||
| input_a_memory_config, | ||
| output_memory_config, | ||
| *, | ||
| device, | ||
| ) -> list: | ||
| data_seed = random.randint(0, 20000000) | ||
| torch.manual_seed(data_seed) | ||
|
|
||
| torch_input_tensor_a = gen_func_with_cast_tt( | ||
| partial(torch_random, low=-100, high=100, dtype=torch.float32), input_a_dtype | ||
| )(input_shape) | ||
|
|
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| golden_function = ttnn.get_golden_function(ttnn.eq) | ||
| torch_output_tensor = golden_function(torch_input_tensor_a, scalar) | ||
|
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| input_tensor_a = ttnn.from_torch( | ||
| torch_input_tensor_a, | ||
| dtype=input_a_dtype, | ||
| layout=input_a_layout, | ||
| device=device, | ||
| memory_config=input_a_memory_config, | ||
| ) | ||
|
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| start_time = start_measuring_time() | ||
| output_tensor = ttnn.eq(input_tensor_a, scalar, memory_config=output_memory_config) | ||
| output_tensor = ttnn.to_torch(output_tensor) | ||
| e2e_perf = stop_measuring_time(start_time) | ||
|
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| return [check_with_pcc(torch_output_tensor, output_tensor, 0.999), e2e_perf] |
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tests/sweep_framework/sweeps/eltwise/binary/floor_divide/floor_divide_pytorch2.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,97 @@ | ||
| # SPDX-FileCopyrightText: © 2024 Tenstorrent Inc. | ||
|
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| # SPDX-License-Identifier: Apache-2.0 | ||
|
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| from typing import Optional, Tuple | ||
| from functools import partial | ||
|
|
||
| import torch | ||
| import random | ||
| import ttnn | ||
| from tests.sweep_framework.utils import gen_shapes | ||
| from tests.tt_eager.python_api_testing.sweep_tests.generation_funcs import gen_func_with_cast_tt | ||
|
|
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| from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time | ||
| from models.utility_functions import torch_random | ||
|
|
||
| # Override the default timeout in seconds for hang detection. | ||
| TIMEOUT = 30 | ||
|
|
||
| random.seed(0) | ||
|
|
||
|
|
||
| # Parameters provided to the test vector generator are defined here. | ||
| # They are defined as dict-type suites that contain the arguments to the run function as keys, and lists of possible inputs as values. | ||
| # Each suite has a key name (in this case "suite_1" and "suite_2") which will associate the test vectors to this specific suite of inputs. | ||
| # Developers can create their own generator functions and pass them to the parameters as inputs. | ||
| parameters = { | ||
| "nightly": { | ||
| "input_shape": [ | ||
| [128], | ||
| ], | ||
| "scalar": [ | ||
| 2, | ||
| ], | ||
| "input_a_dtype": [ttnn.bfloat16], | ||
| "input_a_layout": [ttnn.TILE_LAYOUT], | ||
| "input_a_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG], | ||
| "output_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG], | ||
| }, | ||
| } | ||
|
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| def mesh_device_fixture(): | ||
| device = ttnn.open_device(device_id=0) | ||
| assert ttnn.device.is_wormhole_b0(device), "This op is available for Wormhole_B0 only" | ||
| yield (device, "Wormhole_B0") | ||
| ttnn.close_device(device) | ||
| del device | ||
|
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||
|
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| # This is the run instructions for the test, defined by the developer. | ||
| # The run function must take the above-defined parameters as inputs. | ||
| # The runner will call this run function with each test vector, and the returned results from this function will be stored. | ||
| # If you defined a mesh_device_fixture above, the object you yielded will be passed into this function as 'device'. Otherwise, it will be the default ttnn device opened by the infra. | ||
| def run( | ||
| input_shape, | ||
| scalar, | ||
| input_a_dtype, | ||
| input_a_layout, | ||
| input_a_memory_config, | ||
| output_memory_config, | ||
| *, | ||
| device, | ||
| ) -> list: | ||
| data_seed = random.randint(0, 20000000) | ||
| torch.manual_seed(data_seed) | ||
|
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| torch_input_tensor_a = gen_func_with_cast_tt( | ||
| partial(torch_random, low=-100, high=100, dtype=torch.float32), input_a_dtype | ||
| )(input_shape) | ||
|
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| torch_input_tensor_b = torch.tensor(scalar, dtype=torch.float32) | ||
|
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| golden_function = ttnn.get_golden_function(ttnn.floor_div) | ||
| torch_output_tensor = golden_function(torch_input_tensor_a, torch_input_tensor_b) | ||
|
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| input_tensor_a = ttnn.from_torch( | ||
| torch_input_tensor_a, | ||
| dtype=input_a_dtype, | ||
| layout=input_a_layout, | ||
| device=device, | ||
| memory_config=input_a_memory_config, | ||
| ) | ||
| input_tensor_b = ttnn.from_torch( | ||
| torch_input_tensor_b, | ||
| dtype=input_a_dtype, | ||
| layout=input_a_layout, | ||
| device=device, | ||
| memory_config=input_a_memory_config, | ||
| ) | ||
|
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| start_time = start_measuring_time() | ||
| output_tensor = ttnn.floor_div(input_tensor_a, input_tensor_b, memory_config=output_memory_config) | ||
| output_tensor = ttnn.to_torch(output_tensor) | ||
| e2e_perf = stop_measuring_time(start_time) | ||
|
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| return [check_with_pcc(torch_output_tensor, output_tensor, 0.999), e2e_perf] |
80 changes: 80 additions & 0 deletions
80
tests/sweep_framework/sweeps/eltwise/binary/gt/gt_scalar_pytorch2.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,80 @@ | ||
| # SPDX-FileCopyrightText: © 2024 Tenstorrent Inc. | ||
|
|
||
| # SPDX-License-Identifier: Apache-2.0 | ||
|
|
||
| from typing import Optional, Tuple | ||
| from functools import partial | ||
|
|
||
| import torch | ||
| import random | ||
| import ttnn | ||
| from tests.sweep_framework.utils import gen_shapes | ||
| from tests.tt_eager.python_api_testing.sweep_tests.generation_funcs import gen_func_with_cast_tt | ||
|
|
||
| from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time | ||
| from models.utility_functions import torch_random | ||
|
|
||
| # Override the default timeout in seconds for hang detection. | ||
| TIMEOUT = 30 | ||
|
|
||
| random.seed(0) | ||
|
|
||
|
|
||
| # Parameters provided to the test vector generator are defined here. | ||
| # They are defined as dict-type suites that contain the arguments to the run function as keys, and lists of possible inputs as values. | ||
| # Each suite has a key name (in this case "suite_1" and "suite_2") which will associate the test vectors to this specific suite of inputs. | ||
| # Developers can create their own generator functions and pass them to the parameters as inputs. | ||
| parameters = { | ||
| "nightly": { | ||
| "input_shape": [ | ||
| [10, 10], | ||
| [15, 15], | ||
| [], | ||
| ], | ||
| "scalar": [0, 0], | ||
| "input_a_dtype": [ttnn.bfloat16], | ||
| "input_a_layout": [ttnn.TILE_LAYOUT], | ||
| "input_a_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG], | ||
| "output_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG], | ||
| }, | ||
| } | ||
|
|
||
|
|
||
| # This is the run instructions for the test, defined by the developer. | ||
| # The run function must take the above-defined parameters as inputs. | ||
| # The runner will call this run function with each test vector, and the returned results from this function will be stored. | ||
| # If you defined a mesh_device_fixture above, the object you yielded will be passed into this function as 'device'. Otherwise, it will be the default ttnn device opened by the infra. | ||
| def run( | ||
| input_shape, | ||
| scalar, | ||
| input_a_dtype, | ||
| input_a_layout, | ||
| input_a_memory_config, | ||
| output_memory_config, | ||
| *, | ||
| device, | ||
| ) -> list: | ||
| data_seed = random.randint(0, 20000000) | ||
| torch.manual_seed(data_seed) | ||
|
|
||
| torch_input_tensor_a = gen_func_with_cast_tt( | ||
| partial(torch_random, low=-100, high=100, dtype=torch.float32), input_a_dtype | ||
| )(input_shape) | ||
|
|
||
| golden_function = ttnn.get_golden_function(ttnn.gt) | ||
| torch_output_tensor = golden_function(torch_input_tensor_a, scalar) | ||
|
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| input_tensor_a = ttnn.from_torch( | ||
| torch_input_tensor_a, | ||
| dtype=input_a_dtype, | ||
| layout=input_a_layout, | ||
| device=device, | ||
| memory_config=input_a_memory_config, | ||
| ) | ||
|
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| start_time = start_measuring_time() | ||
| output_tensor = ttnn.gt(input_tensor_a, scalar, memory_config=output_memory_config) | ||
| output_tensor = ttnn.to_torch(output_tensor) | ||
| e2e_perf = stop_measuring_time(start_time) | ||
|
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| return [check_with_pcc(torch_output_tensor, output_tensor, 0.999), e2e_perf] |
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