wehub-resource-sync
285 lines
10 KiB
Python
285 lines
10 KiB
Python
# Copyright (c) 2022 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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class TestReshape(unittest.TestCase):
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"""
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Test the API paddle.sparse.reshape on some sparse tensors.
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x: sparse, out: sparse
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"""
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def check_result(self, x_shape, new_shape, format):
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"""
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x_shape: original shape
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new_shape: new shape
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format: "coo" or "csr"
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Transform a sparse tensor with shape "x_shape" to
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a sparse tensor with shape "new_shape".
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Compare the output of paddle.reshape and the output of
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paddle.sparse.reshape.
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"""
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mask = np.random.randint(0, 2, x_shape)
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while np.sum(mask) == 0:
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mask = paddle.randint(0, 2, x_shape)
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np_x = np.random.randint(-100, 100, x_shape) * mask
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# check cpu kernel
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dense_x = paddle.to_tensor(np_x, place=paddle.CPUPlace())
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dense_x.stop_gradient = False
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dense_out = paddle.reshape(dense_x, new_shape)
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if format == "coo":
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sp_x = paddle.to_tensor(
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np_x, place=paddle.CPUPlace()
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).to_sparse_coo(len(x_shape))
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else:
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sp_x = paddle.to_tensor(
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np_x, place=paddle.CPUPlace()
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).to_sparse_csr()
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sp_x.stop_gradient = False
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sp_out = paddle.sparse.reshape(sp_x, new_shape)
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np.testing.assert_allclose(
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sp_out.to_dense().numpy(), dense_out.numpy(), rtol=1e-05
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)
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dense_out.backward()
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sp_out.backward()
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np.testing.assert_allclose(
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sp_x.grad.to_dense().numpy(),
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dense_x.grad.numpy() * np_x.astype('bool').astype('int'),
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rtol=1e-05,
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)
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# check gpu kernel
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if paddle.device.is_compiled_with_cuda() or is_custom_device():
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dense_x = paddle.to_tensor(np_x, place=get_device_place())
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dense_x.stop_gradient = False
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dense_out = paddle.reshape(dense_x, new_shape)
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if format == "coo":
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sp_x = paddle.to_tensor(
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np_x, place=get_device_place()
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).to_sparse_coo(len(x_shape))
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else:
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sp_x = paddle.to_tensor(
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np_x, place=get_device_place()
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).to_sparse_csr()
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sp_x.stop_gradient = False
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sp_out = paddle.sparse.reshape(sp_x, new_shape)
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np.testing.assert_allclose(
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sp_out.to_dense().numpy(), dense_out.numpy(), rtol=1e-05
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)
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dense_out.backward()
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sp_out.backward()
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np.testing.assert_allclose(
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sp_x.grad.to_dense().numpy(),
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dense_x.grad.numpy() * np_x.astype('bool').astype('int'),
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rtol=1e-05,
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)
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def test_reshape_2d(self):
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self.check_result(
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[2, 5],
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[
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10,
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],
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'coo',
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)
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self.check_result([12, 5], [15, 4], 'coo')
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self.check_result([10, 5], [2, 25], 'csr')
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self.check_result([9, 8], [18, 4], 'csr')
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def test_reshape_3d(self):
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self.check_result([6, 2, 3], [6, 2, 3], 'coo')
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self.check_result([6, 2, 3], [2, 3, 3, 2], 'coo')
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self.check_result([6, 2, 3], [1, 18, 2], 'coo')
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self.check_result([6, 2, 3], [2, 9, 2], 'coo')
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self.check_result([6, 2, 3], [2, 1, 18], 'coo')
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self.check_result([6, 2, 3], [1, 2, 2, 3, 3], 'coo')
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self.check_result([6, 2, 3], [6, 2, 3], 'csr')
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self.check_result([6, 2, 3], [6, 3, 2], 'csr')
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self.check_result([6, 2, 3], [2, 6, 3], 'csr')
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self.check_result([6, 2, 3], [3, 6, 2], 'csr')
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self.check_result([6, 2, 3], [4, 9, 1], 'csr')
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self.check_result([6, 2, 3], [12, 1, 3], 'csr')
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def test_reshape_nd(self):
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self.check_result([8, 3, 4, 4, 5, 3], [24, 8, 10, 3], 'coo')
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self.check_result([3, 4, 4, 5, 7], [1, 12, 2, 5, 14], 'coo')
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def test_reshape_with_zero_or_minus_one_in_new_shape(self):
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self.check_result([6, 2, 3], [-1, 0, 3], 'coo')
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self.check_result([6, 2, 3], [2, 3, 0, -1], 'coo')
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self.check_result([6, 2, 3], [1, -1, 2], 'coo')
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self.check_result([6, 2, 3], [-1, 9, 2], 'coo')
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self.check_result([6, 2, 3], [2, -1, 18], 'coo')
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self.check_result([6, 2, 3], [1, 0, 2, -1, 3], 'coo')
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self.check_result([6, 2, 3], [0, 0, -1], 'csr')
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self.check_result([6, 2, 3], [-1, 3, 2], 'csr')
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self.check_result([6, 2, 3], [2, -1, 0], 'csr')
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self.check_result([6, 2, 3], [-1, 6, 2], 'csr')
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self.check_result([6, 2, 3], [-1, 9, 1], 'csr')
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self.check_result([6, 2, 3], [-1, 1, 3], 'csr')
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devices = []
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if paddle.device.get_device() != "cpu":
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devices.append(paddle.device.get_device())
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else:
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devices.append('cpu')
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class TestSparseReshapeStatic(unittest.TestCase):
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"""
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Test the API paddle.sparse.reshape on some sparse tensors. static graph
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x: sparse, out: sparse
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"""
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def check_result_coo(self, x_shape, new_shape):
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"""
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x_shape: original shape
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new_shape: new shape
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static graph only supports coo format.
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Transform a sparse tensor with shape "x_shape" to
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a sparse tensor with shape "new_shape".
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Compare the output of paddle.reshape and the output of
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paddle.sparse.reshape.
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"""
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for device in devices:
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paddle.device.set_device(device)
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mask = paddle.randint(0, 2, x_shape)
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n = 0
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while paddle.sum(mask) == 0:
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mask = paddle.randint(0, 2, x_shape)
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n += 1
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if n > 10000:
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mask[0] = 1
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break
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origin_data = (
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paddle.rand(x_shape, dtype='float32') + 1
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) * mask.astype('float32')
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indices_data, values_data = (
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origin_data.detach()
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.to_sparse_coo(sparse_dim=len(x_shape))
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.indices(),
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origin_data.detach()
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.to_sparse_coo(sparse_dim=len(x_shape))
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.values(),
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)
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dense_x = origin_data
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dense_x.stop_gradient = False
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dense_out = paddle.reshape(dense_x, new_shape)
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paddle.enable_static()
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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indices = paddle.static.data(
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name='indices',
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shape=indices_data.shape,
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dtype=indices_data.dtype,
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)
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values = paddle.static.data(
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name='values',
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shape=values_data.shape,
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dtype=values_data.dtype,
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)
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sp_x = paddle.sparse.sparse_coo_tensor(
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indices,
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values,
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shape=dense_x.shape,
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dtype=dense_x.dtype,
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)
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sp_out = paddle.sparse.reshape(sp_x, new_shape)
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sp_dense_out = sp_out.to_dense()
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sparse_exe = paddle.static.Executor()
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sparse_fetch = sparse_exe.run(
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feed={
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'indices': indices_data.numpy(),
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"values": values_data.numpy(),
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},
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fetch_list=[sp_dense_out],
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return_numpy=True,
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)
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np.testing.assert_allclose(
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dense_out.numpy(), sparse_fetch[0], rtol=1e-5
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)
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paddle.disable_static()
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def test_reshape_2d(self):
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self.check_result_coo(
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[2, 5],
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[
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10,
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],
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)
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self.check_result_coo([12, 5], [15, 4])
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self.check_result_coo([10, 5], [2, 25])
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self.check_result_coo([9, 8], [18, 4])
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def test_reshape_3d(self):
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self.check_result_coo([6, 2, 3], [6, 2, 3])
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self.check_result_coo([6, 2, 3], [2, 3, 3, 2])
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self.check_result_coo([6, 2, 3], [1, 18, 2])
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self.check_result_coo([6, 2, 3], [2, 9, 2])
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self.check_result_coo([6, 2, 3], [2, 1, 18])
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self.check_result_coo([6, 2, 3], [1, 2, 2, 3, 3])
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self.check_result_coo([6, 2, 3], [6, 2, 3])
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self.check_result_coo([6, 2, 3], [6, 3, 2])
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self.check_result_coo([6, 2, 3], [2, 6, 3])
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self.check_result_coo([6, 2, 3], [3, 6, 2])
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self.check_result_coo([6, 2, 3], [4, 9, 1])
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self.check_result_coo([6, 2, 3], [12, 1, 3])
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def test_reshape_nd(self):
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self.check_result_coo([8, 3, 4, 4, 5, 3], [24, 8, 10, 3])
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self.check_result_coo([3, 4, 4, 5, 7], [1, 12, 2, 5, 14])
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def test_reshape_with_zero_or_minus_one_in_new_shape(self):
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self.check_result_coo([6, 2, 3], [-1, 0, 3])
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self.check_result_coo([6, 2, 3], [2, 3, 0, -1])
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self.check_result_coo([6, 2, 3], [1, -1, 2])
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self.check_result_coo([6, 2, 3], [-1, 9, 2])
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self.check_result_coo([6, 2, 3], [2, -1, 18])
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self.check_result_coo([6, 2, 3], [1, 0, 2, -1, 3])
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self.check_result_coo([6, 2, 3], [0, 0, -1])
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self.check_result_coo([6, 2, 3], [-1, 3, 2])
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self.check_result_coo([6, 2, 3], [2, -1, 0])
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self.check_result_coo([6, 2, 3], [-1, 6, 2])
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self.check_result_coo([6, 2, 3], [-1, 9, 1])
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self.check_result_coo([6, 2, 3], [-1, 1, 3])
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if __name__ == "__main__":
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unittest.main()
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