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* fix docs api * fix docs api * fix docs api * fix docs api * fix docs api * fix docs api * fix docs api * fix docs api * fix docs api * fix docs api
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,154 @@ | ||
| # Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
| # | ||
| import textwrap | ||
|
|
||
| import numpy as np | ||
| import paddle | ||
| import pytest | ||
| from apibase import APIBase | ||
|
|
||
|
|
||
| class AmpAutocastBase(APIBase): | ||
| def compare( | ||
| self, | ||
| name, | ||
| pytorch_result, | ||
| paddle_result, | ||
| check_value=True, | ||
| check_dtype=True, | ||
| check_stop_gradient=True, | ||
| rtol=1.0e-6, | ||
| atol=0.0, | ||
| ): | ||
| ( | ||
| pytorch_numpy, | ||
| paddle_numpy, | ||
| ) = pytorch_result.float().cpu().detach().numpy(), paddle_result.astype( | ||
| "float32" | ||
| ).numpy( | ||
| False | ||
| ) | ||
| assert ( | ||
| pytorch_numpy.shape == paddle_numpy.shape | ||
| ), "API ({}): shape mismatch, torch shape is {}, paddle shape is {}".format( | ||
| name, pytorch_numpy.shape, paddle_numpy.shape | ||
| ) | ||
| assert ( | ||
| pytorch_numpy.dtype == paddle_numpy.dtype | ||
| ), "API ({}): dtype mismatch, torch dtype is {}, paddle dtype is {}".format( | ||
| name, pytorch_numpy.dtype, paddle_numpy.dtype | ||
| ) | ||
| if check_value: | ||
| assert np.allclose( | ||
| pytorch_numpy, paddle_numpy, rtol=rtol, atol=atol | ||
| ), "API ({}): paddle result has diff with pytorch result".format(name) | ||
|
|
||
|
|
||
| obj = AmpAutocastBase("torch.amp.autocast") | ||
|
|
||
|
|
||
| @pytest.mark.skipif( | ||
| condition=not paddle.device.is_compiled_with_cuda(), | ||
| reason="can only run on paddle with CUDA", | ||
| ) | ||
| def test_case_1(): | ||
| pytorch_code = textwrap.dedent( | ||
| """ | ||
| import torch | ||
| model = torch.nn.Linear(10, 5, device="cuda") | ||
| input = torch.randn(4, 10, device="cuda") | ||
| with torch.amp.autocast(device_type="cuda", dtype=torch.float16, enabled=False, cache_enabled=True): | ||
| result = model(input) | ||
| """ | ||
| ) | ||
| obj.run(pytorch_code, ["result"], check_value=False) | ||
|
|
||
|
|
||
| @pytest.mark.skipif( | ||
| condition=not paddle.device.is_compiled_with_cuda(), | ||
| reason="can only run on paddle with CUDA", | ||
| ) | ||
| def test_case_2(): | ||
| pytorch_code = textwrap.dedent( | ||
| """ | ||
| import torch | ||
| model = torch.nn.Linear(10, 5, device="cuda") | ||
| input = torch.randn(4, 10, device="cuda") | ||
| with torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=False, cache_enabled=True): | ||
| result = model(input) | ||
| """ | ||
| ) | ||
| obj.run(pytorch_code, ["result"], check_value=False) | ||
|
|
||
|
|
||
| @pytest.mark.skipif( | ||
| condition=not paddle.device.is_compiled_with_cuda(), | ||
| reason="can only run on paddle with CUDA", | ||
| ) | ||
| def test_case_3(): | ||
| pytorch_code = textwrap.dedent( | ||
| """ | ||
| import torch | ||
| model = torch.nn.Linear(10, 5, device="cuda") | ||
| input = torch.randn(4, 10, device="cuda") | ||
| with torch.amp.autocast(device_type="cuda"): | ||
| result = model(input) | ||
| """ | ||
| ) | ||
| obj.run(pytorch_code, ["result"], check_value=False) | ||
|
|
||
|
|
||
| @pytest.mark.skipif( | ||
| condition=not paddle.device.is_compiled_with_cuda(), | ||
| reason="can only run on paddle with CUDA", | ||
| ) | ||
| def test_case_4(): | ||
| pytorch_code = textwrap.dedent( | ||
| """ | ||
| import torch | ||
| model = torch.nn.Linear(10, 5, device="cuda") | ||
| input = torch.randn(4, 10, device="cuda") | ||
| with torch.amp.autocast(enabled=True, dtype=torch.bfloat16, device_type="cuda", cache_enabled=True): | ||
| result = model(input) | ||
| """ | ||
| ) | ||
| obj.run(pytorch_code, ["result"], check_value=False) | ||
|
|
||
|
|
||
| @pytest.mark.skipif( | ||
| condition=not paddle.device.is_compiled_with_cuda(), | ||
| reason="can only run on paddle with CUDA", | ||
| ) | ||
| def test_case_5(): | ||
| pytorch_code = textwrap.dedent( | ||
| """ | ||
| import torch | ||
| model = torch.nn.Linear(10, 5, device="cuda") | ||
| input = torch.randn(4, 10, device="cuda") | ||
| with torch.amp.autocast("cuda", torch.float16, False, True): | ||
| result = model(input) | ||
| """ | ||
| ) | ||
| obj.run(pytorch_code, ["result"], check_value=False) |
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