chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# Copyright (c) 2021 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.compat import split
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class TestCompatSplitStatic(unittest.TestCase):
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def _compare_with_origin_static(
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self, input_shape, size, axis=0, dim_rank=-1
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):
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"""size_dim: -1 means we input size by int, 0 means 0-size tensor, 1 means tensor with shape [1]"""
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numel = 1
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for v in input_shape:
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numel *= v
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input_axis = axis
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if dim_rank == 0:
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input_axis = paddle.to_tensor(axis)
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elif dim_rank == 1:
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input_axis = paddle.to_tensor([axis])
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program()):
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input_tensor = paddle.arange(numel, dtype=paddle.float32).reshape(
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input_shape
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)
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pd_results = split(input_tensor, size, dim=input_axis)
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if isinstance(size, int):
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shape_on_axis = input_tensor.shape[axis]
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remaining_num = shape_on_axis % size
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num_sections = shape_on_axis // size
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if remaining_num == 0:
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size = num_sections
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else:
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size = [size for _ in range(num_sections)]
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size.append(remaining_num)
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origin_results = paddle.split(
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input_tensor, num_or_sections=size, axis=axis
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)
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assert len(pd_results) == len(origin_results), "length mismatched"
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place = (
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get_device_place()
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if (paddle.is_compiled_with_cuda() or is_custom_device())
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else paddle.CPUPlace()
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)
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exe = paddle.static.Executor(place)
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results = exe.run(fetch_list=[*origin_results, *pd_results])
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length_needed = len(results) // 2
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for i in range(length_needed):
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np.testing.assert_allclose(
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results[i], results[i + length_needed]
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)
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paddle.disable_static()
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def test_split_composite_static(self):
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paddle.seed(114514)
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def get_tensors():
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np.random.seed(114514)
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np_arr = np.random.normal(0, 1, [2, 3, 4, 5])
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return paddle.to_tensor(np_arr), paddle.to_tensor(np_arr)
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in1, in2 = get_tensors()
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in1.stop_gradient = False
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in2.stop_gradient = False
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@paddle.jit.to_static
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def computation_graph(in1: paddle.Tensor, in2: paddle.Tensor):
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y1 = in1 * 1.5 + 1.0
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y1 = paddle.minimum(y1, paddle.to_tensor([0], dtype=paddle.float32))
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out1 = y1.mean(axis=0)
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y2 = in2 * 1.5 + 1.0
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y2 = paddle.minimum(y2, paddle.to_tensor([0], dtype=paddle.float32))
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out2 = y2.mean(axis=0)
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packs1 = paddle.compat.split(out1, 2, dim=2)
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packs2 = paddle.split(out2, [2, 2, 1], axis=2)
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res1 = packs1[0] + packs1[1] + packs1[2]
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res2 = packs2[0] + packs2[1] + packs2[2]
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return res1, res2
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res1, res2 = computation_graph(in1, in2)
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np.testing.assert_allclose(res1.numpy(), res2.numpy())
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def test_static_graph(self):
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"""Test static graph execution"""
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# fixed random seed for reproducibility
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np.random.seed(114514)
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# old static graph mode
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data(name='x', shape=[None, 6], dtype='float32')
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result0, result1 = split(x, split_size_or_sections=[3, 3], dim=1)
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output = result0 * 2.0 + paddle.sin(result1)
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place = (
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get_device_place()
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if (paddle.is_compiled_with_cuda() or is_custom_device())
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else paddle.CPUPlace()
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)
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exe = paddle.static.Executor(place)
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input_data = np.random.rand(3, 6).astype('float32')
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feed = {'x': input_data}
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results = exe.run(feed=feed, fetch_list=[result0, result1, output])
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pd_result0, pd_result1 = results[0], results[1]
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np.testing.assert_allclose(input_data[:, :3], pd_result0)
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np.testing.assert_allclose(input_data[:, 3:], pd_result1)
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expected_output = input_data[:, :3] * 2.0 + np.sin(
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input_data[:, 3:]
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)
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np.testing.assert_allclose(
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expected_output, results[2], rtol=1e-4, atol=1e-4
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)
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paddle.disable_static()
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def test_error_hint(self):
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"""Test whether there will be correct exception when users pass paddle.split kwargs in paddle.compat.split, vice versa."""
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msg_gt_1 = "split_size_or_sections must be greater than 0."
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msg_gt_2 = "len(split_size_or_sections) must not be more than input.shape[dim]."
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msg_gt_3 = "The type of 'split_size_or_sections' in split must be int, list or tuple in imperative mode."
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msg_gt_4 = (
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"'dim' is not allowed to be a pir.Value in a static graph: "
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"\npir.Value can not be used for indexing python lists/tuples."
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)
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paddle.enable_static()
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with self.assertRaises(AssertionError) as cm:
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x = paddle.randn([3, 4, 5])
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tensors = split(x, -2, dim=0)
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self.assertEqual(str(cm.exception), msg_gt_1)
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with self.assertRaises(AssertionError) as cm:
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x = paddle.randn([3, 4, 5])
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tensors = split(x, (1, 1, 1, 1, 2, 2), dim=-1)
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self.assertEqual(str(cm.exception), msg_gt_2)
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with self.assertRaises(TypeError) as cm:
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x = paddle.randn([3, 4, 5])
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tensors = split(x, paddle.to_tensor(2), dim=2)
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self.assertEqual(str(cm.exception), msg_gt_3)
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with self.assertRaises(TypeError) as cm:
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x = paddle.randn([3, 4, 5])
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tensors = split(x, 2, dim=paddle.to_tensor(2))
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paddle.disable_static()
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self.assertEqual(str(cm.exception), msg_gt_4)
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def test_basic_split(self):
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"""Test basic splitting with integer size"""
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input_shape = [3, 6]
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self._compare_with_origin_static(input_shape, 1, 0)
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self._compare_with_origin_static(input_shape, 3, -1)
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self._compare_with_origin_static(input_shape, 4, dim_rank=0)
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self._compare_with_origin_static(input_shape, 3, dim_rank=1)
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if __name__ == '__main__':
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unittest.main()
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