-
Notifications
You must be signed in to change notification settings - Fork 56
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
【Hackathon 7th No.39】为 Paddle 代码转换工具新增 API 转换规则（第 6 组） (#477)
* 【Hackathon 7th No.39】为 Paddle 代码转换工具新增 API 转换规则（第 6 组） * add skip * update Softmin * update * update * fix * fix codestyle check * fix * fix * fix
- Loading branch information
1 parent
785f24b
commit 3c73e2e
Showing
15 changed files
with
1,766 additions
and
5 deletions.
There are no files selected for viewing
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.
Learn more about bidirectional Unicode characters
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.
Learn more about bidirectional Unicode characters
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.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,99 @@ | ||
| # 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 | ||
|
|
||
| from apibase import APIBase | ||
|
|
||
| obj = APIBase("torch.nn.AdaptiveLogSoftmaxWithLoss") | ||
|
|
||
|
|
||
| def test_case_1(): | ||
| pytorch_code = textwrap.dedent( | ||
| """ | ||
| import torch | ||
| import torch.nn as nn | ||
| input = torch.tensor([[ 0.9368637 , -0.0361056 , -0.98917043, 0.06605113, 1.5254455 ], | ||
| [-1.0518035 , -1.0024613 , 0.18699688, -0.35807893, 0.25628588], | ||
| [-0.900478 , -0.41495147, 0.84707606, -1.7883497 , 1.3243382 ]]) | ||
| target = torch.tensor([1, 1, 1]) | ||
| asfm = torch.nn.AdaptiveLogSoftmaxWithLoss(5, 4, [2]) | ||
| out, loss = asfm(input,target) | ||
| """ | ||
| ) | ||
| obj.run(pytorch_code, ["out", "loss"], check_value=False) | ||
|
|
||
|
|
||
| def test_case_2(): | ||
| pytorch_code = textwrap.dedent( | ||
| """ | ||
| import torch | ||
| import torch.nn as nn | ||
| input = torch.tensor([[ 0.9368637 , -0.0361056 , -0.98917043, 0.06605113, 1.5254455 ], | ||
| [-1.0518035 , -1.0024613 , 0.18699688, -0.35807893, 0.25628588], | ||
| [-0.900478 , -0.41495147, 0.84707606, -1.7883497 , 1.3243382 ]]) | ||
| target = torch.tensor([1, 1, 1]) | ||
| asfm = torch.nn.AdaptiveLogSoftmaxWithLoss(5, 4, [3], div_value=2.0) | ||
| out, loss = asfm(input,target) | ||
| """ | ||
| ) | ||
| obj.run(pytorch_code, ["out", "loss"], check_value=False) | ||
|
|
||
|
|
||
| def test_case_3(): | ||
| pytorch_code = textwrap.dedent( | ||
| """ | ||
| import torch | ||
| import torch.nn as nn | ||
| input = torch.tensor([[ 0.9368637 , -0.0361056 , -0.98917043, 0.06605113, 1.5254455 ], | ||
| [-1.0518035 , -1.0024613 , 0.18699688, -0.35807893, 0.25628588], | ||
| [-0.900478 , -0.41495147, 0.84707606, -1.7883497 , 1.3243382 ]]) | ||
| target = torch.tensor([1, 1, 1]) | ||
| asfm = torch.nn.AdaptiveLogSoftmaxWithLoss(5, 4, [1], div_value=3.8, head_bias=True) | ||
| out, loss = asfm(input,target) | ||
| """ | ||
| ) | ||
| obj.run(pytorch_code, ["out", "loss"], check_value=False) | ||
|
|
||
|
|
||
| def test_case_4(): | ||
| pytorch_code = textwrap.dedent( | ||
| """ | ||
| import torch | ||
| import torch.nn as nn | ||
| input = torch.tensor([[ 0.9368637 , -0.0361056 , -0.98917043, 0.06605113, 1.5254455 ], | ||
| [-1.0518035 , -1.0024613 , 0.18699688, -0.35807893, 0.25628588], | ||
| [-0.900478 , -0.41495147, 0.84707606, -1.7883497 , 1.3243382 ]]) | ||
| target = torch.tensor([1, 1, 1]) | ||
| asfm = torch.nn.AdaptiveLogSoftmaxWithLoss(in_features=5, n_classes=8, cutoffs=[5], div_value=3.8, head_bias=True) | ||
| out, loss = asfm(input,target) | ||
| """ | ||
| ) | ||
| obj.run(pytorch_code, ["out", "loss"], check_value=False) | ||
|
|
||
|
|
||
| def test_case_5(): | ||
| pytorch_code = textwrap.dedent( | ||
| """ | ||
| import torch | ||
| import torch.nn as nn | ||
| input = torch.tensor([[ 0.9368637 , -0.0361056 , -0.98917043, 0.06605113, 1.5254455 ], | ||
| [-1.0518035 , -1.0024613 , 0.18699688, -0.35807893, 0.25628588], | ||
| [-0.900478 , -0.41495147, 0.84707606, -1.7883497 , 1.3243382 ]]) | ||
| target = torch.tensor([1, 1, 1]) | ||
| asfm = torch.nn.AdaptiveLogSoftmaxWithLoss(n_classes=8, in_features=5, div_value=3.8, cutoffs=[5], head_bias=True) | ||
| out, loss = asfm(input,target) | ||
| """ | ||
| ) | ||
| obj.run(pytorch_code, ["out", "loss"], check_value=False) |
Oops, something went wrong.