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