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【Hackathon 5th No.27】为 Paddle 新增 select_scatter API -part (#59343)
* support select_scatter op * fix example code * fix sc * update example * remove unused files * add name * fix conflict * update * remove * update * add type * update type
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| Original file line number | Diff line number | Diff line change |
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| # Copyright (c) 2021 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 copy | ||
| import unittest | ||
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| import numpy as np | ||
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| import paddle | ||
| from paddle.framework import core | ||
| from paddle.pir_utils import test_with_pir_api | ||
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| paddle.enable_static() | ||
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| class TestSelectScatterAPI(unittest.TestCase): | ||
| def setUp(self): | ||
| np.random.seed(0) | ||
| self.shape = [2, 3, 4] | ||
| self.type = np.float32 | ||
| self.x_np = np.random.random(self.shape).astype(self.type) | ||
| self.place = [paddle.CPUPlace()] | ||
| self.axis = 1 | ||
| self.index = 1 | ||
| self.value_shape = [2, 4] | ||
| self.value_np = np.random.random(self.value_shape).astype(self.type) | ||
| self.x_feed = copy.deepcopy(self.x_np) | ||
| if core.is_compiled_with_cuda(): | ||
| self.place.append(paddle.CUDAPlace(0)) | ||
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| def get_out_ref(self, out_ref, index, value_np): | ||
| for i in range(2): | ||
| for j in range(4): | ||
| out_ref[i, index, j] = value_np[i, j] | ||
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| @test_with_pir_api | ||
| def test_api_static(self): | ||
| paddle.enable_static() | ||
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| def run(place): | ||
| with paddle.static.program_guard(paddle.static.Program()): | ||
| x = paddle.static.data('Src', self.shape, self.type) | ||
| value = paddle.static.data( | ||
| 'Values', self.value_shape, self.type | ||
| ) | ||
| out = paddle.select_scatter(x, value, self.axis, self.index) | ||
| exe = paddle.static.Executor(place) | ||
| res = exe.run( | ||
| feed={ | ||
| 'Src': self.x_feed, | ||
| 'Values': self.value_np, | ||
| }, | ||
| fetch_list=[out], | ||
| ) | ||
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| out_ref = copy.deepcopy(self.x_np) | ||
| self.get_out_ref(out_ref, self.index, self.value_np) | ||
| for out in res: | ||
| np.testing.assert_allclose(out, out_ref, rtol=0.001) | ||
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| for place in self.place: | ||
| run(place) | ||
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| def test_api_dygraph(self): | ||
| def run(place): | ||
| paddle.disable_static(place) | ||
| x_tensor = paddle.to_tensor(self.x_np) | ||
| value_tensor = paddle.to_tensor(self.value_np) | ||
| out = paddle.select_scatter( | ||
| x_tensor, value_tensor, self.axis, self.index | ||
| ) | ||
| out_ref = copy.deepcopy(self.x_np) | ||
| self.get_out_ref(out_ref, self.index, self.value_np) | ||
| np.testing.assert_allclose(out.numpy(), out_ref, rtol=0.001) | ||
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| paddle.enable_static() | ||
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| for place in self.place: | ||
| run(place) | ||
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| class TestSelectScatterAPICase2(TestSelectScatterAPI): | ||
| def setUp(self): | ||
| np.random.seed(0) | ||
| self.shape = [2, 3, 4, 5] | ||
| self.type = np.float64 | ||
| self.x_np = np.random.random(self.shape).astype(self.type) | ||
| self.place = [paddle.CPUPlace()] | ||
| self.axis = 2 | ||
| self.index = 1 | ||
| self.value_shape = [2, 3, 5] | ||
| self.value_np = np.random.random(self.value_shape).astype(self.type) | ||
| self.x_feed = copy.deepcopy(self.x_np) | ||
| if core.is_compiled_with_cuda(): | ||
| self.place.append(paddle.CUDAPlace(0)) | ||
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| def get_out_ref(self, out_ref, index, value_np): | ||
| for i in range(2): | ||
| for j in range(3): | ||
| for k in range(5): | ||
| out_ref[i, j, index, k] = value_np[i, j, k] | ||
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| class TestSelectScatterAPICase3(TestSelectScatterAPI): | ||
| def setUp(self): | ||
| np.random.seed(0) | ||
| self.shape = [2, 3, 4, 5, 6] | ||
| self.type = np.int32 | ||
| self.x_np = np.random.random(self.shape).astype(self.type) | ||
| self.place = [paddle.CPUPlace()] | ||
| self.axis = 2 | ||
| self.index = 1 | ||
| self.value_shape = [2, 3, 5, 6] | ||
| self.value_np = np.random.random(self.value_shape).astype(self.type) | ||
| self.x_feed = copy.deepcopy(self.x_np) | ||
| if core.is_compiled_with_cuda(): | ||
| self.place.append(paddle.CUDAPlace(0)) | ||
|
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| def get_out_ref(self, out_ref, index, value_np): | ||
| for i in range(2): | ||
| for j in range(3): | ||
| for k in range(5): | ||
| for w in range(6): | ||
| out_ref[i, j, index, k, w] = value_np[i, j, k, w] | ||
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| class TestSelectScatterAPIError(unittest.TestCase): | ||
| def setUp(self): | ||
| np.random.seed(0) | ||
| self.shape = [2, 3, 4] | ||
| self.x_np = np.random.random(self.shape).astype(np.float32) | ||
| self.place = [paddle.CPUPlace()] | ||
| self.axis = 1 | ||
| self.index = 1 | ||
| self.value_shape = [2, 4] | ||
| self.value_np = np.random.random(self.value_shape).astype(np.float32) | ||
| self.x_feed = copy.deepcopy(self.x_np) | ||
| if core.is_compiled_with_cuda(): | ||
| self.place.append(paddle.CUDAPlace(0)) | ||
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| def test_len_of_shape_not_equal_error(self): | ||
| with self.assertRaises(RuntimeError): | ||
| x_tensor = paddle.to_tensor(self.x_np) | ||
| value_tensor = paddle.to_tensor(self.value_np).reshape((2, 2, 2)) | ||
| res = paddle.select_scatter(x_tensor, value_tensor, 1, 1) | ||
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| def test_one_of_size_not_equal_error(self): | ||
| with self.assertRaises(RuntimeError): | ||
| x_tensor = paddle.to_tensor(self.x_np) | ||
| value_tensor = paddle.to_tensor([[2, 2], [2, 2]]).astype(np.float32) | ||
| res = paddle.select_scatter(x_tensor, value_tensor, 1, 1) | ||
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| if __name__ == "__main__": | ||
| paddle.enable_static() | ||
| unittest.main() |