wehub-resource-sync
349 lines
12 KiB
Python
349 lines
12 KiB
Python
# Copyright (c) 2024 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 logging
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import unittest
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import numpy as np
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from op_test import is_custom_device
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import paddle
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from paddle import sparse
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from paddle.base import core
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logging.basicConfig(
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format='%(asctime)s - %(levelname)s - %(message)s', level=logging.INFO
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)
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logger = logging.getLogger(__name__)
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"only test when CUDA is available",
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)
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class TestSparseConvImplicitGemm(unittest.TestCase):
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def test_SubmConv2D_igemm_forward(self):
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indices = [[0, 0, 0, 0], [0, 0, 1, 2], [1, 3, 2, 3]]
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values = [[1], [2], [3], [4]]
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indices = paddle.to_tensor(indices, dtype='int32')
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values = paddle.to_tensor(values, dtype='float32')
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dense_shape = [1, 3, 4, 1]
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correct_out_values = [[4], [5], [10], [7]]
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sparse_input = paddle.sparse.sparse_coo_tensor(
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indices, values, dense_shape, False
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)
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subm_conv2d = paddle.sparse.nn.SubmConv2D(
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1,
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1,
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3,
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padding=1,
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stride=1,
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data_format='NHWC',
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key='subm_conv_2d',
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backend='igemm',
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)
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# set weight to all ones
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subm_conv2d.weight = paddle.create_parameter(
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(3, 3, 1, 1),
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dtype='float32',
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default_initializer=paddle.nn.initializer.Constant(value=1.0),
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)
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sparse_out = subm_conv2d(sparse_input)
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# the output shape of subm_conv is same as input shape
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np.testing.assert_array_equal(indices, sparse_out.indices().numpy())
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np.testing.assert_array_equal(
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correct_out_values, sparse_out.values().numpy()
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)
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def test_SubmConv3D_igemm_forward(self):
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indices = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 2], [1, 3, 2, 3]]
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values = [[1], [2], [3], [4]]
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indices = paddle.to_tensor(indices, dtype='int32')
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values = paddle.to_tensor(values, dtype='float32')
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dense_shape = [1, 1, 3, 4, 1]
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correct_out_values = [[4], [5], [10], [7]]
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sparse_input = paddle.sparse.sparse_coo_tensor(
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indices, values, dense_shape, False
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)
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subm_conv3d = paddle.sparse.nn.SubmConv3D(
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1,
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1,
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(1, 3, 3),
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padding=1,
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stride=1,
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data_format='NDHWC',
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key='subm_conv',
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backend='igemm',
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)
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# set weight to all ones
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subm_conv3d.weight = paddle.create_parameter(
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(1, 3, 3, 1, 1),
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dtype='float32',
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default_initializer=paddle.nn.initializer.Constant(value=1.0),
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)
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sparse_out = subm_conv3d(sparse_input)
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# the output shape of subm_conv is same as input shape
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np.testing.assert_array_equal(indices, sparse_out.indices().numpy())
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np.testing.assert_array_equal(
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correct_out_values, sparse_out.values().numpy()
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)
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def test_submconv2d_igemm_forward(self):
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indices = [[0, 0, 0, 0], [0, 0, 1, 2], [1, 3, 2, 3]]
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values = [[1], [2], [3], [4]]
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indices = paddle.to_tensor(indices, dtype='int32')
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values = paddle.to_tensor(values, dtype='float32')
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dense_shape = [1, 3, 4, 1]
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correct_out_values = [[5], [6], [11], [8]]
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sparse_input = paddle.sparse.sparse_coo_tensor(
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indices, values, dense_shape, False
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)
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weight = paddle.ones((3, 3, 1, 1), dtype='float32')
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bias = paddle.ones((1), dtype='float32')
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sparse_out = paddle.sparse.nn.functional.subm_conv2d_igemm(
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sparse_input,
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weight,
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bias,
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stride=1,
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padding=1,
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dilation=1,
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groups=1,
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data_format="NHWC",
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key='subm_conv_2d',
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)
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# the output shape of subm_conv is same as input shape
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np.testing.assert_array_equal(indices, sparse_out.indices().numpy())
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np.testing.assert_array_equal(
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correct_out_values, sparse_out.values().numpy()
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)
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def test_submconv3d_igemm_forward(self):
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indices = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 2], [1, 3, 2, 3]]
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values = [[1], [2], [3], [4]]
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indices = paddle.to_tensor(indices, dtype='int32')
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values = paddle.to_tensor(values, dtype='float32')
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dense_shape = [1, 1, 3, 4, 1]
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correct_out_values = [[5], [6], [11], [8]]
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sparse_input = paddle.sparse.sparse_coo_tensor(
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indices, values, dense_shape, False
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)
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weight = paddle.ones((1, 3, 3, 1, 1), dtype='float32')
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bias = paddle.ones((1), dtype='float32')
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sparse_out = paddle.sparse.nn.functional.subm_conv3d_igemm(
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sparse_input,
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weight,
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bias,
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stride=1,
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padding=1,
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dilation=1,
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groups=1,
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data_format="NDHWC",
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key='subm_conv_3d',
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)
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# the output shape of subm_conv is same as input shape
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np.testing.assert_array_equal(indices, sparse_out.indices().numpy())
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np.testing.assert_array_equal(
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correct_out_values, sparse_out.values().numpy()
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)
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def test_multi_input(self):
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indices_1 = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 2], [1, 3, 2, 3]]
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indices_2 = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 2], [0, 3, 2, 3]]
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values = [[1], [2], [3], [4]]
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indices_1 = paddle.to_tensor(indices_1, dtype='int32')
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indices_2 = paddle.to_tensor(indices_2, dtype='int32')
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values = paddle.to_tensor(values, dtype='float32')
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dense_shape = [1, 1, 3, 4, 1]
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correct_out_values_1 = [[4], [5], [10], [7]]
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correct_out_values_2 = [[1], [5], [9], [7]]
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sparse_input_1 = paddle.sparse.sparse_coo_tensor(
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indices_1, values, dense_shape, False
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)
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sparse_input_2 = paddle.sparse.sparse_coo_tensor(
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indices_2, values, dense_shape, False
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)
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subm_conv3d = paddle.sparse.nn.SubmConv3D(
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1,
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1,
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(1, 3, 3),
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padding=1,
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stride=1,
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data_format='NDHWC',
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key='subm_conv',
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backend='igemm',
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)
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# set weight to all ones
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subm_conv3d.weight = paddle.create_parameter(
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(1, 3, 3, 1, 1),
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dtype='float32',
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default_initializer=paddle.nn.initializer.Constant(value=1.0),
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)
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sparse_out = subm_conv3d(sparse_input_1)
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np.testing.assert_array_equal(indices_1, sparse_out.indices().numpy())
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np.testing.assert_array_equal(
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correct_out_values_1, sparse_out.values().numpy()
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)
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sparse_out = subm_conv3d(sparse_input_2)
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# the output shape of subm_conv is same as input shape
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np.testing.assert_array_equal(indices_2, sparse_out.indices().numpy())
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np.testing.assert_array_equal(
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correct_out_values_2, sparse_out.values().numpy()
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)
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class TestStatic(unittest.TestCase):
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def test3d(self):
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paddle.enable_static()
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main = paddle.static.Program()
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with paddle.static.program_guard(main):
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indices = paddle.static.data(
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name='indices', shape=[4, 4], dtype='int32'
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)
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values = paddle.static.data(
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name='values', shape=[4, 1], dtype='float32'
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)
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dense_shape = [1, 1, 3, 4, 1]
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sp_x = sparse.sparse_coo_tensor(indices, values, dense_shape)
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weight_shape = [1, 3, 3, 1, 1]
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weight = paddle.static.data(
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name='weight', shape=weight_shape, dtype='float32'
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)
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bias_shape = [1]
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bias = paddle.static.data(
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name='bias', shape=bias_shape, dtype='float32'
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)
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out = sparse.nn.functional.subm_conv3d_igemm(
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sp_x,
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weight,
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bias,
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stride=1,
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padding=1,
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dilation=1,
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groups=1,
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data_format="NDHWC",
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)
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sp_out = sparse.nn.functional.relu(out)
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out_indices = sp_out.indices()
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out_values = sp_out.values()
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out = sp_out.to_dense()
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exe = paddle.static.Executor()
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indices_data = [
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[0, 0, 0, 0],
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[0, 0, 0, 0],
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[0, 0, 1, 2],
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[1, 3, 2, 3],
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]
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values_data = [[1.0], [2.0], [3.0], [4.0]]
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weight_data = np.array(
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[[[[[1], [1], [1]], [[1], [1], [1]], [[1], [1], [1]]]]]
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).astype('float32')
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weight_data = weight_data.reshape(weight_shape)
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bias_data = np.array([1]).astype('float32')
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fetch = exe.run(
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feed={
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'indices': indices_data,
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'values': values_data,
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'weight': weight_data,
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'bias': bias_data,
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},
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fetch_list=[out, out_indices, out_values],
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return_numpy=True,
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)
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correct_out_values = [[5.0], [6.0], [11.0], [8.0]]
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np.testing.assert_array_equal(correct_out_values, fetch[2])
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paddle.disable_static()
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def test2d(self):
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paddle.enable_static()
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main = paddle.static.Program()
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with paddle.static.program_guard(main):
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indices = paddle.static.data(
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name='indices', shape=[3, 4], dtype='int32'
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)
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values = paddle.static.data(
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name='values', shape=[4, 1], dtype='float32'
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)
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dense_shape = [1, 3, 4, 1]
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sp_x = sparse.sparse_coo_tensor(indices, values, dense_shape)
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weight_shape = [3, 3, 1, 1]
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weight = paddle.static.data(
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name='weight', shape=weight_shape, dtype='float32'
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)
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bias_shape = [1]
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bias = paddle.static.data(
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name='bias', shape=bias_shape, dtype='float32'
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)
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out = sparse.nn.functional.subm_conv2d_igemm(
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sp_x,
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weight,
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bias,
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stride=1,
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padding=1,
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dilation=1,
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groups=1,
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data_format="NHWC",
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)
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sp_out = sparse.nn.functional.relu(out)
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out_indices = sp_out.indices()
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out_values = sp_out.values()
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out = sp_out.to_dense()
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exe = paddle.static.Executor()
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indices_data = [
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[0, 0, 0, 0],
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[0, 0, 1, 2],
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[1, 3, 2, 3],
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]
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values_data = [[1.0], [2.0], [3.0], [4.0]]
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weight_data = np.array(
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[[[[1], [1], [1]], [[1], [1], [1]], [[1], [1], [1]]]]
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).astype('float32')
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weight_data = weight_data.reshape(weight_shape)
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bias_data = np.array([1]).astype('float32')
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fetch = exe.run(
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feed={
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'indices': indices_data,
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'values': values_data,
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'weight': weight_data,
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'bias': bias_data,
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},
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fetch_list=[out, out_indices, out_values],
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return_numpy=True,
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)
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correct_out_values = [[5.0], [6.0], [11.0], [8.0]]
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np.testing.assert_array_equal(correct_out_values, fetch[2])
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paddle.disable_static()
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if __name__ == "__main__":
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unittest.main()
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