wehub-resource-sync
243 lines
7.8 KiB
Python
243 lines
7.8 KiB
Python
# Copyright (c) 2023 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 utils import compare_legacy_with_pt
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import paddle
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data_5d = [
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[[2, 3, 4, 5, 6], [0, 1, 2, 4], [0, 1, 2, -4], [3, 3, 4, -2]],
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]
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data_4d = [
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[[2, 3, 4, 5], [0, 1, 2, 3], [0, 1, 2, -4], [3, 3, 4, -2]],
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]
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data_3d = [
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[[4, 4, 5], [-3, -2, -1], [1, -3, 2], [3, 3, 4]],
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[[4, 4, 5], [0, 1, 2], [0, 1, 2], [3, 3, 4]],
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[[4, 4, 5], [-1], [0], [2]],
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[[4, 4, 5], [0], [1], [2]],
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[[4, 4, 5], [1], [2], [3]],
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[[4, 4, 5], [1, 2], [2, 2], [3, 4]],
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[[4, 4, 5], [0, 2], [2, 2], [3, 4]],
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]
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data_2d = [
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[[3, 4], [0], [0], [2]],
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[[3, 4], [1], [-3], [2]],
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[[3, 4], [-2, -1], [-3, 0], [2, -1]],
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[[78, 78], [0, -1], [32, 58], [-2, -1]],
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]
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devices = ['cpu']
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if paddle.device.get_device() != "cpu":
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devices.append(paddle.device.get_device())
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class TestSparseSlice(unittest.TestCase):
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"""
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Test the API paddle.sparse.slice on some sparse tensors.
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x: sparse, out: sparse
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"""
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def _check_result(self, np_x, axes, starts, ends, format='coo'):
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for device in devices:
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paddle.device.set_device(device)
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self._check_result_with_place(np_x, axes, starts, ends, format)
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def _check_result_with_place(self, np_x, axes, starts, ends, format='coo'):
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x_shape = np_x.shape
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dense_x = paddle.to_tensor(np_x)
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dense_x.stop_gradient = False
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dense_out = paddle.slice(dense_x, axes, starts, ends)
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if format == 'coo':
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sp_x = paddle.to_tensor(np_x).to_sparse_coo(len(x_shape))
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else:
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sp_x = paddle.to_tensor(np_x).to_sparse_csr()
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sp_x.stop_gradient = False
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sp_out = paddle.sparse.slice(sp_x, axes, starts, ends)
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np.testing.assert_allclose(
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sp_out.to_dense().numpy(), dense_out.numpy(), rtol=1e-5
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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-5,
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)
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def check_result_with_shape(
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self, x_shape, axes, starts, ends, format='coo'
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):
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mask = np.random.randint(0, 2, x_shape)
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np_x = np.random.randint(-100, 100, x_shape) * mask
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self._check_result(np_x, axes, starts, ends, format)
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def check_result_with_list(self, x, axes, starts, ends, format='coo'):
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np_x = np.array(x)
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self._check_result(np_x, axes, starts, ends, format)
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def test_coo_5d(self):
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for item in data_5d:
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self.check_result_with_shape(*item, format='coo')
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def test_coo_4d(self):
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for item in data_4d:
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self.check_result_with_shape(*item, format='coo')
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def test_coo_3d(self):
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for item in data_3d:
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self.check_result_with_shape(*item, format='coo')
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def test_coo_2d(self):
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x = [[1, 2, 3, 4], [0, 1, 2, 0]]
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self.check_result_with_list(x, [0, 1], [0, 1], [2, 3], format='coo')
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for item in data_2d:
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self.check_result_with_shape(*item, format='coo')
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def test_coo_1d(self):
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x = [-49, 55, -5, 0, 3, 0, 0, -60, -21, 0, 0, 0]
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self.check_result_with_list(x, [0], [3], [5], format='coo')
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def test_coo_1d_zero(self):
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x = [-49, 55, -5, 0, 3, 0, 0, -60, -21, 0, 0, 0]
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self.check_result_with_list(x, [0], [-3], [-1], format='coo')
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def test_csr_3d(self):
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for item in data_3d:
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self.check_result_with_shape(*item, format='csr')
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def test_csr_3d_zero(self):
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x = [[[0, 0, 1, 2], [0, 0, 0, 2]]]
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self.check_result_with_list(x, [1, 2], [0, 0], [2, 2], format='csr')
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def test_csr_2d(self):
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for item in data_2d:
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self.check_result_with_shape(*item, format='csr')
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def test_csr_2d_zero(self):
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x = [[0, 0, 1, 2], [0, 0, 0, 1]]
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self.check_result_with_list(x, [0, 1], [0, 0], [2, 2], format='csr')
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class TestSparseCooSliceStatic(unittest.TestCase):
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def _check_result_coo(self, np_x, axes, starts, ends):
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for device in devices:
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paddle.device.set_device(device)
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self._check_result_coo_with_place(np_x, axes, starts, ends)
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def _check_result_coo_with_place(self, np_x, axes, starts, ends):
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x_shape = np_x.shape
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dense_x = paddle.to_tensor(np_x)
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dense_x.stop_gradient = False
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dense_out = paddle.slice(dense_x, axes, starts, ends)
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sp_x = paddle.to_tensor(
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np_x,
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).to_sparse_coo(len(x_shape))
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indices_data = sp_x.detach().indices()
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values_data = sp_x.detach().values()
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paddle.enable_static()
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mp = paddle.static.Program()
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sp = paddle.static.Program()
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with paddle.static.program_guard(mp, sp):
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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.slice(sp_x, axes, starts, ends)
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sp_dense_out = sp_out.to_dense()
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exe = paddle.static.Executor()
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res = 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(),
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res[0],
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rtol=1e-5,
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)
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paddle.disable_static()
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def check_result_with_shape(
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self, x_shape, axes, starts, ends, format='coo'
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):
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mask = np.random.randint(0, 2, x_shape)
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np_x = np.random.randint(-100, 100, x_shape) * mask
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if format == 'coo':
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self._check_result_coo(np_x, axes, starts, ends)
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def check_result_with_list(self, x, axes, starts, ends, format='coo'):
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np_x = np.array(x)
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if format == 'coo':
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self._check_result_coo(np_x, axes, starts, ends)
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@compare_legacy_with_pt
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def test_coo_5d(self):
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for item in data_5d:
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self.check_result_with_shape(*item, format='coo')
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@compare_legacy_with_pt
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def test_coo_4d(self):
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for item in data_4d:
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self.check_result_with_shape(*item, format='coo')
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@compare_legacy_with_pt
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def test_coo_3d(self):
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for item in data_3d:
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self.check_result_with_shape(*item, format='coo')
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@compare_legacy_with_pt
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def test_coo_2d(self):
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for item in data_2d:
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self.check_result_with_shape(*item, format='coo')
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@compare_legacy_with_pt
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def test_coo_1d(self):
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x = [-49, 55, -5, 0, 3, 0, 0, -60, -21, 0, 0, 0]
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self.check_result_with_list(x, [0], [3], [5], format='coo')
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@compare_legacy_with_pt
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def test_coo_1d_zero(self):
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x = [-49, 55, -5, 0, 3, 0, 0, -60, -21, 0, 0, 0]
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self.check_result_with_list(x, [0], [-3], [-1], format='coo')
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if __name__ == "__main__":
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unittest.main()
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