wehub-resource-sync
211 lines
7.9 KiB
Python
211 lines
7.9 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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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 TestSparseSum(unittest.TestCase):
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"""
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Test the API paddle.sparse.sum on some sparse tensors.
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x: sparse tensor, out: sparse tensor
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"""
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def to_sparse(self, x, format, sparse_dim=None):
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if format == 'coo':
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if sparse_dim:
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return x.detach().to_sparse_coo(sparse_dim=sparse_dim)
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else:
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return x.detach().to_sparse_coo(sparse_dim=x.ndim)
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elif format == 'csr':
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return x.detach().to_sparse_csr()
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def check_result(
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self, x_shape, dims, keepdim, format, sparse_dim=None, dtype=None
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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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if sparse_dim:
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mask_shape = [*x_shape[:sparse_dim]] + [1] * (
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len(x_shape) - sparse_dim
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)
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mask = paddle.randint(0, 2, mask_shape)
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else:
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mask = paddle.randint(0, 2, x_shape)
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while paddle.sum(mask) == 0:
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if sparse_dim:
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mask_shape = [*x_shape[:sparse_dim]] + [1] * (
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len(x_shape) - sparse_dim
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)
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mask = paddle.randint(0, 2, mask_shape)
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else:
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mask = paddle.randint(0, 2, x_shape)
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# "+ 1" to make sure that all zero elements in "origin_x" is caused by multiplying by "mask",
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# or the backward checks may fail.
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origin_x = (
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paddle.rand(x_shape, dtype='float64') + 1
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) * mask.astype('float64')
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dense_x = origin_x.detach()
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dense_x.stop_gradient = False
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dense_out = paddle.sum(dense_x, dims, keepdim=keepdim, dtype=dtype)
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sp_x = self.to_sparse(origin_x, format, sparse_dim)
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sp_x.stop_gradient = False
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sp_out = paddle.sparse.sum(sp_x, dims, keepdim=keepdim, dtype=dtype)
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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 * mask.astype(dense_x.grad.dtype)).numpy(),
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rtol=1e-05,
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)
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def test_sum_1d(self):
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self.check_result([5], None, False, 'coo')
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self.check_result([5], None, True, 'coo')
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self.check_result([5], 0, False, 'coo')
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self.check_result([5], 0, True, 'coo')
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def test_sum_2d(self):
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self.check_result([2, 5], None, False, 'coo', dtype="float32")
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self.check_result([2, 5], None, True, 'coo')
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self.check_result([2, 5], 0, True, 'coo', dtype="float32")
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self.check_result([2, 5], 0, False, 'coo')
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self.check_result([2, 5], 1, False, 'coo')
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self.check_result([2, 5], None, True, 'csr', dtype="float32")
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self.check_result([2, 5], -1, True, 'csr', dtype="float32")
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self.check_result([2, 5], 0, False, 'coo')
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self.check_result([2, 5], -1, True, 'csr')
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def test_sum_3d(self):
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self.check_result([6, 2, 3], -1, True, 'csr')
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for i in [0, 1, -2, None]:
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self.check_result([6, 2, 3], i, False, 'coo')
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self.check_result([6, 2, 3], i, True, 'coo')
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def test_sum_nd(self):
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for i in range(6):
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self.check_result([8, 3, 4, 4, 5, 3], i, False, 'coo')
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self.check_result([8, 3, 4, 4, 5, 3], i, True, 'coo')
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# Randint now only supports access to dimension 0 to 9.
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self.check_result([2, 3, 4, 2, 3, 4, 2, 3, 4], i, False, 'coo')
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def test_sum_sparse_dim(self):
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for i in range(6):
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self.check_result([8, 3, 4, 4, 5, 3], i, False, 'coo', sparse_dim=3)
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self.check_result([8, 3, 4, 4, 5, 3], i, True, 'coo', sparse_dim=3)
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class TestSparseSumStatic(unittest.TestCase):
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def check_result_coo(self, x_shape, dims, keepdim, dtype=None):
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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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while paddle.sum(mask) == 0:
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mask = paddle.randint(0, 2, x_shape)
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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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sparse_data = origin_data.detach().to_sparse_coo(
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sparse_dim=len(x_shape)
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)
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indices_data = sparse_data.indices()
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values_data = sparse_data.values()
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dense_x = origin_data
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dense_out = paddle.sum(dense_x, dims, keepdim=keepdim, dtype=dtype)
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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=origin_data.shape,
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dtype=origin_data.dtype,
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)
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sp_out = paddle.sparse.sum(
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sp_x, dims, keepdim=keepdim, dtype=dtype
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)
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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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@compare_legacy_with_pt
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def test_sum(self):
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# 1d
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self.check_result_coo([5], None, False)
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self.check_result_coo([5], None, True)
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self.check_result_coo([5], 0, True)
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self.check_result_coo([5], 0, False)
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# 2d
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self.check_result_coo([2, 5], None, False, dtype="float32")
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self.check_result_coo([2, 5], None, True)
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self.check_result_coo([2, 5], 0, True, dtype="float32")
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self.check_result_coo([2, 5], 0, False)
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self.check_result_coo([2, 5], 1, False)
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self.check_result_coo([2, 5], 0, False)
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# 3d
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for i in [0, 1, -2, None]:
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self.check_result_coo([6, 2, 3], i, False)
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self.check_result_coo([6, 2, 3], i, True)
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# nd
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for i in range(6):
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self.check_result_coo([8, 3, 4, 4, 5, 3], i, False)
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self.check_result_coo([8, 3, 4, 4, 5, 3], i, True)
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# Randint now only supports access to dimension 0 to 9.
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self.check_result_coo([2, 3, 4, 2, 3, 4, 2, 3, 4], i, False)
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if __name__ == "__main__":
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unittest.main()
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