wehub-resource-sync
200 lines
7.3 KiB
Python
200 lines
7.3 KiB
Python
# 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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import paddle
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from paddle.compat import split
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class TestCompatSplit(unittest.TestCase):
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def _compare_with_origin(self, input_tensor, size, axis=0):
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pd_results = split(input_tensor, size, dim=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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self.assertEqual(len(origin_results), len(pd_results))
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# check shape and output section size of the output
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for origin_ts, pd_ts in zip(origin_results, pd_results):
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np.testing.assert_allclose(origin_ts.numpy(), pd_ts.numpy())
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def test_basic_split(self):
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"""Test basic splitting with integer size"""
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data = paddle.arange(12).reshape([3, 4]).astype('float32')
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self._compare_with_origin(data, 1, 0)
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self._compare_with_origin(data, 2, 1)
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def test_split_with_list_sections(self):
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"""Test splitting with list of section sizes"""
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data = paddle.rand([10, 5])
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self._compare_with_origin(data, [3, 2, 5], 0)
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self._compare_with_origin(data, [1, 4], -1)
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def test_chained_operations(self):
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"""Test split with complex operation chain"""
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x = paddle.rand([8, 12])
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y = paddle.sin(x) * 2.0 + paddle.exp(x) / 3.0
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z = paddle.nn.functional.relu(y)
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z1, z2 = split(z, 7, dim=1)
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self.assertEqual(z1.shape, [8, 7])
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self.assertEqual(z2.shape, [8, 5])
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z_np = z.numpy()
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np.testing.assert_allclose(z_np[:, :7], z1.numpy())
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np.testing.assert_allclose(z_np[:, 7:], z2.numpy())
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def test_split_grad(self):
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"""Test backprop for split, in1 and in2 are computed by
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compat.split and original split"""
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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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def computation_graph(in_tensor):
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y = in_tensor * 2.3 + 3.0
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y = paddle.maximum(y, paddle.to_tensor([0], dtype=paddle.float32))
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return y.mean(axis=0)
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out1 = computation_graph(in1)
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out2 = computation_graph(in2)
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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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res1.backward()
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res2.backward()
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np.testing.assert_allclose(in1.grad.numpy(), in2.grad.numpy())
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def test_empty_dim(self):
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"""Split with empty dim"""
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in_tensor = paddle.arange(72, dtype=paddle.int64).reshape([3, 12, 2])
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self._compare_with_origin(in_tensor, [5, 0, 7], axis=1)
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def test_split_with_one_block(self):
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"""Resulting tuple should be of length 1"""
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in_tensor = paddle.arange(60, dtype=paddle.float32).reshape([3, 4, 5])
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self._compare_with_origin(in_tensor, 5, paddle.to_tensor([-1]))
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self._compare_with_origin(in_tensor, [5], paddle.to_tensor(2))
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def test_edge_cases(self):
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"""Test edge cases and error handling"""
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x = paddle.arange(5)
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s1, s2 = split(x, [3, 2])
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np.testing.assert_allclose(s1.numpy(), [0, 1, 2])
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np.testing.assert_allclose(s2.numpy(), [3, 4])
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x = paddle.rand([2, 2, 2])
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a, b = split(x, 1, 2)
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self.assertEqual(a.shape, [2, 2, 1])
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# invalid split sections
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with self.assertRaises(ValueError):
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split(x, [3, 1], 1)
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# invalid split axis
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with self.assertRaises(ValueError):
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split(x, 2, 3)
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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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x = paddle.randn([3, 9, 5])
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msg_gt_1 = (
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"paddle.split() received unexpected keyword arguments 'dim', 'split_size_or_sections', 'tensor'. "
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"\nDid you mean to use paddle.compat.split() instead?"
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)
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msg_gt_2 = (
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"paddle.compat.split() received unexpected keyword argument 'num_or_sections'. "
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"\nDid you mean to use paddle.split() instead?"
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)
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msg_gt_3 = "(InvalidArgument) The dim is expected to be in range of [-3, 3), but got 3"
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msg_gt_4 = "paddle.compat.split expects split_sizes have only non-negative entries, but got size = -5 on dim 2"
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split_size = paddle.to_tensor([3])
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msg_gt_5 = (
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"The type of 'split_size_or_sections' in split must be int, list or tuple in imperative mode, but "
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f"received {type(split_size)}."
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)
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with self.assertRaises(TypeError) as cm:
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tensors = paddle.split(tensor=x, split_size_or_sections=3, dim=0)
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self.assertEqual(str(cm.exception), msg_gt_1)
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with self.assertRaises(TypeError) as cm:
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tensors = split(x, num_or_sections=3, dim=0)
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self.assertEqual(str(cm.exception), msg_gt_2)
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with self.assertRaises(ValueError) as cm:
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tensors = split(x, 3, dim=3)
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self.assertEqual(str(cm.exception), msg_gt_3)
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with self.assertRaises(ValueError) as cm:
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tensors = split(x, [3, 3, -5], -2)
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self.assertEqual(str(cm.exception), msg_gt_4)
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with self.assertRaises(TypeError) as cm:
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tensors = split(x, split_size, 1)
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self.assertEqual(str(cm.exception), msg_gt_5)
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class TestFunctionalSplit(unittest.TestCase):
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def test_functional_split(self):
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x = paddle.rand([3, 9, 5])
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out_expect = paddle.compat.split(
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x, split_size_or_sections=[2, 3, 4], dim=1
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)
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out_res = paddle.functional.split(
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x, split_size_or_sections=[2, 3, 4], dim=1
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)
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for expect, res in zip(out_expect, out_res):
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np.testing.assert_allclose(
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expect.numpy(), res.numpy(), atol=1e-8, rtol=1e-8
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)
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out_expect = paddle.compat.split(x, split_size_or_sections=3, dim=-2)
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out_res = paddle.functional.split(x, split_size_or_sections=3, dim=-2)
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for expect, res in zip(out_expect, out_res):
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np.testing.assert_allclose(
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expect.numpy(), res.numpy(), atol=1e-8, rtol=1e-8
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)
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if __name__ == '__main__':
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unittest.main()
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