# 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. import unittest import numpy as np import paddle from paddle.compat import split class TestCompatSplit(unittest.TestCase): def _compare_with_origin(self, input_tensor, size, axis=0): pd_results = split(input_tensor, size, dim=axis) if isinstance(size, int): shape_on_axis = input_tensor.shape[axis] remaining_num = shape_on_axis % size num_sections = shape_on_axis // size if remaining_num == 0: size = num_sections else: size = [size for _ in range(num_sections)] size.append(remaining_num) origin_results = paddle.split( input_tensor, num_or_sections=size, axis=axis ) self.assertEqual(len(origin_results), len(pd_results)) # check shape and output section size of the output for origin_ts, pd_ts in zip(origin_results, pd_results): np.testing.assert_allclose(origin_ts.numpy(), pd_ts.numpy()) def test_basic_split(self): """Test basic splitting with integer size""" data = paddle.arange(12).reshape([3, 4]).astype('float32') self._compare_with_origin(data, 1, 0) self._compare_with_origin(data, 2, 1) def test_split_with_list_sections(self): """Test splitting with list of section sizes""" data = paddle.rand([10, 5]) self._compare_with_origin(data, [3, 2, 5], 0) self._compare_with_origin(data, [1, 4], -1) def test_chained_operations(self): """Test split with complex operation chain""" x = paddle.rand([8, 12]) y = paddle.sin(x) * 2.0 + paddle.exp(x) / 3.0 z = paddle.nn.functional.relu(y) z1, z2 = split(z, 7, dim=1) self.assertEqual(z1.shape, [8, 7]) self.assertEqual(z2.shape, [8, 5]) z_np = z.numpy() np.testing.assert_allclose(z_np[:, :7], z1.numpy()) np.testing.assert_allclose(z_np[:, 7:], z2.numpy()) def test_split_grad(self): """Test backprop for split, in1 and in2 are computed by compat.split and original split""" def get_tensors(): np.random.seed(114514) np_arr = np.random.normal(0, 1, [2, 3, 4, 5]) return paddle.to_tensor(np_arr), paddle.to_tensor(np_arr) in1, in2 = get_tensors() in1.stop_gradient = False in2.stop_gradient = False def computation_graph(in_tensor): y = in_tensor * 2.3 + 3.0 y = paddle.maximum(y, paddle.to_tensor([0], dtype=paddle.float32)) return y.mean(axis=0) out1 = computation_graph(in1) out2 = computation_graph(in2) packs1 = paddle.compat.split(out1, 2, dim=2) packs2 = paddle.split(out2, [2, 2, 1], axis=2) res1 = packs1[0] + packs1[1] + packs1[2] res2 = packs2[0] + packs2[1] + packs2[2] res1.backward() res2.backward() np.testing.assert_allclose(in1.grad.numpy(), in2.grad.numpy()) def test_empty_dim(self): """Split with empty dim""" in_tensor = paddle.arange(72, dtype=paddle.int64).reshape([3, 12, 2]) self._compare_with_origin(in_tensor, [5, 0, 7], axis=1) def test_split_with_one_block(self): """Resulting tuple should be of length 1""" in_tensor = paddle.arange(60, dtype=paddle.float32).reshape([3, 4, 5]) self._compare_with_origin(in_tensor, 5, paddle.to_tensor([-1])) self._compare_with_origin(in_tensor, [5], paddle.to_tensor(2)) def test_edge_cases(self): """Test edge cases and error handling""" x = paddle.arange(5) s1, s2 = split(x, [3, 2]) np.testing.assert_allclose(s1.numpy(), [0, 1, 2]) np.testing.assert_allclose(s2.numpy(), [3, 4]) x = paddle.rand([2, 2, 2]) a, b = split(x, 1, 2) self.assertEqual(a.shape, [2, 2, 1]) # invalid split sections with self.assertRaises(ValueError): split(x, [3, 1], 1) # invalid split axis with self.assertRaises(ValueError): split(x, 2, 3) def test_error_hint(self): """Test whether there will be correct exception when users pass paddle.split kwargs in paddle.compat.split, vice versa.""" x = paddle.randn([3, 9, 5]) msg_gt_1 = ( "paddle.split() received unexpected keyword arguments 'dim', 'split_size_or_sections', 'tensor'. " "\nDid you mean to use paddle.compat.split() instead?" ) msg_gt_2 = ( "paddle.compat.split() received unexpected keyword argument 'num_or_sections'. " "\nDid you mean to use paddle.split() instead?" ) msg_gt_3 = "(InvalidArgument) The dim is expected to be in range of [-3, 3), but got 3" msg_gt_4 = "paddle.compat.split expects split_sizes have only non-negative entries, but got size = -5 on dim 2" split_size = paddle.to_tensor([3]) msg_gt_5 = ( "The type of 'split_size_or_sections' in split must be int, list or tuple in imperative mode, but " f"received {type(split_size)}." ) with self.assertRaises(TypeError) as cm: tensors = paddle.split(tensor=x, split_size_or_sections=3, dim=0) self.assertEqual(str(cm.exception), msg_gt_1) with self.assertRaises(TypeError) as cm: tensors = split(x, num_or_sections=3, dim=0) self.assertEqual(str(cm.exception), msg_gt_2) with self.assertRaises(ValueError) as cm: tensors = split(x, 3, dim=3) self.assertEqual(str(cm.exception), msg_gt_3) with self.assertRaises(ValueError) as cm: tensors = split(x, [3, 3, -5], -2) self.assertEqual(str(cm.exception), msg_gt_4) with self.assertRaises(TypeError) as cm: tensors = split(x, split_size, 1) self.assertEqual(str(cm.exception), msg_gt_5) class TestFunctionalSplit(unittest.TestCase): def test_functional_split(self): x = paddle.rand([3, 9, 5]) out_expect = paddle.compat.split( x, split_size_or_sections=[2, 3, 4], dim=1 ) out_res = paddle.functional.split( x, split_size_or_sections=[2, 3, 4], dim=1 ) for expect, res in zip(out_expect, out_res): np.testing.assert_allclose( expect.numpy(), res.numpy(), atol=1e-8, rtol=1e-8 ) out_expect = paddle.compat.split(x, split_size_or_sections=3, dim=-2) out_res = paddle.functional.split(x, split_size_or_sections=3, dim=-2) for expect, res in zip(out_expect, out_res): np.testing.assert_allclose( expect.numpy(), res.numpy(), atol=1e-8, rtol=1e-8 ) if __name__ == '__main__': unittest.main()