wehub-resource-sync
142 lines
5.1 KiB
Python
142 lines
5.1 KiB
Python
# Copyright (c) 2025 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 unittest
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import numpy as np
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from op_test import get_device_place, is_custom_device
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import paddle
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from paddle import base
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def get_places():
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places = []
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if base.is_compiled_with_cuda() or is_custom_device():
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places.append(get_device_place())
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places.append(paddle.CPUPlace())
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return places
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class TestIndexSelectAPI_Compatibility(unittest.TestCase):
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def setUp(self):
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np.random.seed(123)
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self.places = get_places()
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self.shape = [10, 20]
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self.index_shape = [5]
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self.axis = 1
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self.dtype = "float32"
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self.init_data()
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def init_data(self):
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self.np_input = np.random.rand(*self.shape).astype(self.dtype)
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self.np_index = np.random.randint(
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0, self.shape[self.axis], self.index_shape
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).astype("int64")
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def test_dygraph_Compatibility(self):
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paddle.disable_static()
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x = paddle.to_tensor(self.np_input)
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index = paddle.to_tensor(self.np_index)
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paddle_dygraph_out = []
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# Position args (args)
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out1 = paddle.index_select(x, index, self.axis)
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paddle_dygraph_out.append(out1)
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# Keywords args (kwargs) for paddle
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out2 = paddle.index_select(x=x, index=index, axis=self.axis)
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paddle_dygraph_out.append(out2)
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# Keywords args for torch
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out3 = paddle.index_select(input=x, index=index, dim=self.axis)
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paddle_dygraph_out.append(out3)
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# Combined args and kwargs
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out4 = paddle.index_select(x, index, dim=self.axis)
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paddle_dygraph_out.append(out4)
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# Tensor method args
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out5 = x.index_select(index, self.axis)
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paddle_dygraph_out.append(out5)
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# Tensor method kwargs
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out6 = x.index_select(index=index, dim=self.axis)
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paddle_dygraph_out.append(out6)
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# PyTorch positional args order: (Tensor, int, Tensor)
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out7 = paddle.index_select(x, self.axis, index)
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paddle_dygraph_out.append(out7)
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out8 = paddle.index_select(x, self.axis, index=index)
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paddle_dygraph_out.append(out8)
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# Test out
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ref_out_shape = list(self.np_input.shape)
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ref_out_shape[self.axis] = len(self.np_index)
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out9 = paddle.empty(ref_out_shape, dtype=x.dtype)
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paddle.index_select(input=x, index=index, dim=self.axis, out=out9)
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paddle_dygraph_out.append(out9)
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# Numpy reference out
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ref_out = np.take(self.np_input, self.np_index, axis=self.axis)
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# Check
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for out in paddle_dygraph_out:
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np.testing.assert_allclose(ref_out, out.numpy(), rtol=1e-05)
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paddle.enable_static()
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def test_static_Compatibility(self):
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paddle.enable_static()
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main = paddle.static.Program()
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startup = paddle.static.Program()
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with base.program_guard(main, startup):
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x = paddle.static.data(name="x", shape=self.shape, dtype=self.dtype)
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index = paddle.static.data(
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name="index", shape=self.index_shape, dtype="int64"
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)
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# Position args (args)
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out1 = paddle.index_select(x, index, self.axis)
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# Keywords args (kwargs) for paddle
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out2 = paddle.index_select(x=x, index=index, axis=self.axis)
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# Keywords args for torch
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out3 = paddle.index_select(input=x, index=index, dim=self.axis)
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# Combined args and kwargs
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out4 = paddle.index_select(x, index, dim=self.axis)
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# Tensor method args
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out5 = x.index_select(index, self.axis)
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# Tensor method kwargs
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out6 = x.index_select(index=index, dim=self.axis)
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# PyTorch positional args order: (Tensor, int, Tensor)
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out7 = paddle.index_select(x, self.axis, index)
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out8 = paddle.index_select(x, self.axis, index=index)
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# Do not support out in static
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ref_out = np.take(self.np_input, self.np_index, axis=self.axis)
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fetch_list = [
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out1,
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out2,
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out3,
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out4,
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out5,
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out6,
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out7,
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out8,
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]
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for place in self.places:
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exe = base.Executor(place)
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fetches = exe.run(
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main,
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feed={"x": self.np_input, "index": self.np_index},
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fetch_list=fetch_list,
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)
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for out in fetches:
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np.testing.assert_allclose(out, ref_out, rtol=1e-05)
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if __name__ == "__main__":
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unittest.main()
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