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LokeZhouLokeZhou
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Upsample (#115)
* add nn.Upsample,CUDAExtension,CppExtension,SequentialSampler,is_sparse * fix cpp_extension ut * fix cpp_extension ut * fix UtilsCppExtensionMatcher ci * fix UtilsCppExtensionMatcher * fix cpp_extension ut * UtilsCppExtensionMatcher bug * UtilsCppExtensionMatcher bug * fix UtilsCppExtensionMatcher pop * fix cpp test * add cpp test * add Attribute2Func
1 parent 748524a commit fafdd56

8 files changed

+420
-1
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‎paconvert/api_mapping.json‎

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"unflattened_size": "shape"
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}
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},
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"torch.nn.Upsample": {
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"Matcher": "GenericMatcher",
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"paddle_api": "paddle.nn.Upsample",
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"args_list": [
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"size",
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"scale_factor",
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"mode",
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"align_corners"
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],
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"unsupport_args": [
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"recompute_scale_factor"
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]
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},
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"torch.nn.UpsamplingBilinear2d": {
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"Matcher": "UpsampleMatcher",
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"paddle_api": "paddle.nn.UpsamplingBilinear2D",
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"Matcher": "GenericMatcher",
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"paddle_api": "paddle.utils.cpp_extension.BuildExtension.with_options"
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},
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"torch.utils.cpp_extension.CUDAExtension": {
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"Matcher": "GenericMatcher",
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"paddle_api": "paddle.utils.cpp_extension.CUDAExtension",
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"args_list": [
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"name",
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"sources"
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],
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"kwargs_change": {
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"name": ""
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}
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},
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"torch.utils.cpp_extension.CUDA_HOME": {
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"Matcher": "GenericMatcher",
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"paddle_api": "paddle.utils.cpp_extension.cpp_extension.CUDA_HOME"
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},
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"torch.utils.cpp_extension.CppExtension": {
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"Matcher": "GenericMatcher",
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"paddle_api": "paddle.utils.cpp_extension.CppExtension",
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"args_list": [
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"name",
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"sources"
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],
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"kwargs_change": {
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"name": ""
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}
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},
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"torch.utils.data.BatchSampler": {
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"Matcher": "TorchUtilDataBatchSampler",
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"args_list": [
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"data_source"
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]
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},
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"torch.utils.data.SequentialSampler": {
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"Matcher": "GenericMatcher",
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"paddle_api": "paddle.io.SequenceSampler",
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"args_list": [
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"data_source"
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]
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},
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"torch.utils.data.default_collate": {
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"Matcher": "GenericMatcher",
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"paddle_api": "paddle.io.dataloader.collate.default_collate_fn",

‎paconvert/api_matcher.py‎

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@@ -3572,6 +3572,13 @@ def generate_code(self, kwargs):
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return GenericMatcher.generate_code(self, kwargs)
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class Attribute2Func(BaseMatcher):
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def get_paddle_class_attribute_nodes(self, node):
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self.parse_func(node)
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code = "{}()".format(self.paddle_api)
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return ast.parse(code).body[0].value
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class LuMatcher(BaseMatcher):
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def generate_code(self, kwargs):
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out_v = kwargs.pop("out") if "out" in kwargs else None

‎paconvert/attribute_mapping.json‎

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@@ -22,7 +22,10 @@
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},
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"torch.Tensor.is_meta": {},
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"torch.Tensor.is_quantized": {},
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"torch.Tensor.is_sparse": {},
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"torch.Tensor.is_sparse": {
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"Matcher": "Attribute2Func",
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"paddle_api": "paddle.Tensor.is_sparse"
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},
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"torch.Tensor.mH": {},
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"torch.Tensor.mT": {},
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"torch.Tensor.names": {},

‎tests/test_Tensor_is_sparse.py‎

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# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# 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.Tensor.is_sparse")
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def test_case_1():
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pytorch_code = textwrap.dedent(
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"""
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import torch
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a = torch.tensor([[ 0.9254, -0.6213]])
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result = a.is_sparse
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"""
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)
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obj.run(pytorch_code, ["result"])

‎tests/test_nn_Upsample.py‎

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# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# 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.nn.Upsample")
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def test_case_1():
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pytorch_code = textwrap.dedent(
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"""
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import torch
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input = torch.tensor([[[[ 1.1524, 0.4714, 0.2857],
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[-1.2533, -0.9829, -1.0981],
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[ 0.1507, -1.1431, -2.0361]],
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[[ 0.1024, -0.4482, 0.4137],
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[ 0.9385, 0.4565, 0.7702],
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[ 0.4135, -0.2587, 0.0482]]]])
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m = torch.nn.Upsample(scale_factor=2, mode='nearest')
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result = m(input)
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"""
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)
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obj.run(pytorch_code, ["result"])
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def test_case_2():
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pytorch_code = textwrap.dedent(
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"""
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import torch
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input = torch.tensor([[[[ 1.1524, 0.4714, 0.2857],
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[-1.2533, -0.9829, -1.0981],
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[ 0.1507, -1.1431, -2.0361]],
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[[ 0.1024, -0.4482, 0.4137],
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[ 0.9385, 0.4565, 0.7702],
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[ 0.4135, -0.2587, 0.0482]]]])
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m = torch.nn.Upsample(scale_factor=2, mode='bilinear')
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result = m(input)
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"""
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)
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obj.run(pytorch_code, ["result"])
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def test_case_3():
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pytorch_code = textwrap.dedent(
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"""
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import torch
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input = torch.tensor([[[[ 1.1524, 0.4714, 0.2857],
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[-1.2533, -0.9829, -1.0981],
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[ 0.1507, -1.1431, -2.0361]],
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[[ 0.1024, -0.4482, 0.4137],
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[ 0.9385, 0.4565, 0.7702],
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[ 0.4135, -0.2587, 0.0482]]]])
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m = torch.nn.Upsample(scale_factor=2, mode='bilinear',align_corners=True)
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result = m(input)
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"""
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)
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obj.run(pytorch_code, ["result"])
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def test_case_4():
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pytorch_code = textwrap.dedent(
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"""
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import torch
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input = torch.tensor([[[[ 1.1524, 0.4714, 0.2857],
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[-1.2533, -0.9829, -1.0981],
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[ 0.1507, -1.1431, -2.0361]],
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[[ 0.1024, -0.4482, 0.4137],
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[ 0.9385, 0.4565, 0.7702],
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[ 0.4135, -0.2587, 0.0482]]]])
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m = torch.nn.Upsample(size=(2,2))
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result = m(input)
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"""
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)
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obj.run(pytorch_code, ["result"])
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def test_case_5():
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pytorch_code = textwrap.dedent(
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"""
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import torch
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input = torch.tensor([[[[ 1.1524, 0.4714, 0.2857],
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[-1.2533, -0.9829, -1.0981],
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[ 0.1507, -1.1431, -2.0361]],
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[[ 0.1024, -0.4482, 0.4137],
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[ 0.9385, 0.4565, 0.7702],
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[ 0.4135, -0.2587, 0.0482]]]])
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m = torch.nn.Upsample(scale_factor=2, mode='bilinear',align_corners=False)
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result = m(input)
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"""
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)
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obj.run(pytorch_code, ["result"])
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def test_case_6():
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pytorch_code = textwrap.dedent(
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"""
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import torch
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input = torch.tensor([[[[ 1.1524, 0.4714, 0.2857],
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[-1.2533, -0.9829, -1.0981],
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[ 0.1507, -1.1431, -2.0361]],
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[[ 0.1024, -0.4482, 0.4137],
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[ 0.9385, 0.4565, 0.7702],
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[ 0.4135, -0.2587, 0.0482]]]])
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m = torch.nn.Upsample(scale_factor=2, mode='bilinear',recompute_scale_factor=True)
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result = m(input)
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"""
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)
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obj.run(
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pytorch_code, unsupport=True, reason="paddle unsupport recompute_scale_factor "
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)

‎tests/test_utils_cpp_extension_CUDAExtension.py‎

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# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# 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.utils.cpp_extension.CUDAExtension")
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# The cuda compile not supports
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def test_case_1():
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pytorch_code = textwrap.dedent(
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"""
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from torch.utils.cpp_extension import CUDAExtension
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CUDAExtension(
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name='cuda_extension',
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sources=['extension.cpp', 'extension_kernel.cu'],
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extra_compile_args={'cxx': ['-g'],
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'nvcc': ['-O2']})
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result = True
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"""
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)
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obj.run(pytorch_code, ["result"])

‎tests/test_utils_cpp_extension_CppExtension.py‎

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# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# 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.utils.cpp_extension.CppExtension")
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# The cpp compile not supports
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def test_case_1():
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pytorch_code = textwrap.dedent(
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"""
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from torch.utils.cpp_extension import CppExtension
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CppExtension(
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name='cuda_extension',
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sources=['extension.cpp'],
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extra_compile_args=['-g'])
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result = True
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"""
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)
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obj.run(pytorch_code, ["result"])

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