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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,77 @@ | ||
| # 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. | ||
| # | ||
|
|
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| import textwrap | ||
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| from apibase import APIBase | ||
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| obj = APIBase("torch.autograd.backward") | ||
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| def test_case_1(): | ||
| pytorch_code = textwrap.dedent( | ||
| """ | ||
| import torch | ||
| x = torch.tensor([[1, 2], [3, 4]], dtype=torch.float32, requires_grad=True) | ||
| y = torch.tensor([[3, 2], [3, 4]], dtype=torch.float32) | ||
| grad_tensor1 = torch.tensor([[1,2], [2, 3]], dtype=torch.float32) | ||
| grad_tensor2 = torch.tensor([[1,1], [1, 1]], dtype=torch.float32) | ||
| z1 = torch.matmul(x, y) | ||
| z2 = torch.matmul(x, y) | ||
| torch.autograd.backward([z1, z2], [grad_tensor1, grad_tensor2], True) | ||
| x.grad.requires_grad=False | ||
| result = x.grad | ||
| """ | ||
| ) | ||
| obj.run(pytorch_code, ["result"]) | ||
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| def test_case_2(): | ||
| pytorch_code = textwrap.dedent( | ||
| """ | ||
| import torch | ||
| x = torch.tensor([[1, 2], [3, 4]], dtype=torch.float32, requires_grad=True) | ||
| y = torch.tensor([[3, 2], [3, 4]], dtype=torch.float32) | ||
| grad_tensor1 = torch.tensor([[1,2], [2, 3]], dtype=torch.float32) | ||
| grad_tensor2 = torch.tensor([[1,1], [1, 1]], dtype=torch.float32) | ||
| z1 = torch.matmul(x, y) | ||
| z2 = torch.matmul(x, y) | ||
| torch.autograd.backward([z1, z2], [grad_tensor1, grad_tensor2], retain_graph=False) | ||
| x.grad.requires_grad=False | ||
| result = x.grad | ||
| """ | ||
| ) | ||
| obj.run(pytorch_code, ["result"]) | ||
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| def test_case_3(): | ||
| pytorch_code = textwrap.dedent( | ||
| """ | ||
| import torch | ||
| x = torch.tensor([[1, 2], [3, 4]], dtype=torch.float32, requires_grad=True) | ||
| z1 = x.sum() | ||
| torch.autograd.backward([z1]) | ||
| x.grad.requires_grad=False | ||
| result = x.grad | ||
| """ | ||
| ) | ||
| obj.run(pytorch_code, ["result"]) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,70 @@ | ||
| # 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 | ||
|
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| from apibase import APIBase | ||
|
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| obj = APIBase("torch.autograd.functional.hessian") | ||
|
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|
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| def test_case_1(): | ||
| pytorch_code = textwrap.dedent( | ||
| """ | ||
| import torch | ||
| def func(x): | ||
| return torch.sum(x * x) | ||
| x = torch.rand(2, 2) | ||
| h = torch.autograd.functional.hessian(func, x) | ||
| result = h[:] | ||
| result.requires_grad = False | ||
| result = torch.flatten(result) | ||
| """ | ||
| ) | ||
| obj.run(pytorch_code, ["result"]) | ||
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|
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| def test_case_2(): | ||
| pytorch_code = textwrap.dedent( | ||
| """ | ||
| import torch | ||
| def func(x): | ||
| return 2 * torch.sum(x * x + 3 * x) | ||
| x = torch.rand(2, 2) | ||
| h = torch.autograd.functional.hessian(func, x) | ||
| result = h[:] | ||
| result.requires_grad = False | ||
| result = torch.flatten(result) | ||
| """ | ||
| ) | ||
| obj.run(pytorch_code, ["result"]) | ||
|
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|
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| def test_case_3(): | ||
| pytorch_code = textwrap.dedent( | ||
| """ | ||
| import torch | ||
| def func(x): | ||
| return torch.sum(x) | ||
| x = torch.tensor([1.0, 2.0]) | ||
| h = torch.autograd.functional.hessian(func, x) | ||
| result = h[:] | ||
| result.requires_grad = False | ||
| result = torch.flatten(result) | ||
| """ | ||
| ) | ||
| obj.run(pytorch_code, ["result"]) |
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