wehub-resource-sync
298 lines
10 KiB
Python
298 lines
10 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 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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from paddle.base import core
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def coo_sparse_dim_ref(indices):
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return len(indices)
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def csr_sparse_dim_ref():
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return 2
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def dense_sparse_dim_ref():
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return 0
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class TestSparseDimAPI(unittest.TestCase):
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def setUp(self):
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self.dtype = "float32"
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self.coo_indices = [[0, 0, 0, 1], [0, 0, 1, 2]]
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coo_values = [1.0, 2.0, 3.0, 4.0]
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coo_tensor = paddle.sparse.sparse_coo_tensor(
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self.coo_indices, coo_values, dtype=self.dtype
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)
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csr_crows = [0, 2, 3, 5]
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csr_cols = [1, 3, 2, 0, 1]
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csr_values = [1, 2, 3, 4, 5]
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csr_shape = [3, 4]
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csr_tensor = paddle.sparse.sparse_csr_tensor(
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csr_crows, csr_cols, csr_values, csr_shape, dtype=self.dtype
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)
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other_tensor = paddle.to_tensor([1, 2, 3, 4], dtype=self.dtype)
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self.tensors = [coo_tensor, csr_tensor, other_tensor]
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def test_sparse_dim(self):
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expected_result = [
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coo_sparse_dim_ref(self.coo_indices),
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csr_sparse_dim_ref(),
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dense_sparse_dim_ref(),
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]
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places = [core.CPUPlace()]
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if core.is_compiled_with_cuda() or is_custom_device():
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places.append(get_device_place())
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for place in places:
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paddle.disable_static(place)
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for i, t in enumerate(self.tensors):
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self.assertEqual(t.sparse_dim(), expected_result[i])
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class TestSparseDimAPI1(TestSparseDimAPI):
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def setUp(self):
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self.dtype = "float64"
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self.coo_indices = [[0, 0, 1, 2], [0, 1, 1, 2], [0, 1, 1, 2]]
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coo_values = paddle.to_tensor([1.0, 2.0, 3.0, 4.0])
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coo_tensor = paddle.sparse.sparse_coo_tensor(
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self.coo_indices, coo_values, dtype=self.dtype
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)
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csr_crows = [0, 2, 3, 5]
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csr_cols = [1, 3, 2, 0, 1]
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csr_values = [1, 2, 3, 4, 5]
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csr_shape = [3, 4]
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csr_tensor = paddle.sparse.sparse_csr_tensor(
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csr_crows, csr_cols, csr_values, csr_shape, dtype=self.dtype
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)
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other_tensor = paddle.to_tensor([1, 2, 3, 4], dtype=self.dtype)
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self.tensors = [coo_tensor, csr_tensor, other_tensor]
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class TestSparseDimAPI2(TestSparseDimAPI):
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def setUp(self):
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self.dtype = "int16"
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self.coo_indices = [
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[0, 0, 1, 2],
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[0, 2, 0, 2],
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[0, 1, 1, 0],
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[0, 1, 1, 0],
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]
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coo_values = paddle.to_tensor([1.0, 2.0, 3.0, 4.0])
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coo_tensor = paddle.sparse.sparse_coo_tensor(
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self.coo_indices, coo_values, dtype=self.dtype
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)
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csr_crows = [0, 2, 3, 5]
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csr_cols = [1, 3, 2, 0, 1]
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csr_values = [1, 2, 3, 4, 5]
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csr_shape = [3, 4]
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csr_tensor = paddle.sparse.sparse_csr_tensor(
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csr_crows, csr_cols, csr_values, csr_shape, dtype=self.dtype
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)
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other_tensor = paddle.to_tensor([1, 2, 3, 4], dtype=self.dtype)
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self.tensors = [coo_tensor, csr_tensor, other_tensor]
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class TestSparseDimAPI3(TestSparseDimAPI):
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def setUp(self):
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self.dtype = "int32"
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self.coo_indices = [[0, 0, 0], [0, 1, 2]]
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coo_values = paddle.to_tensor(
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[[[1, 2], [3, 4]], [[1, 2], [0, 0]], [[0, 2], [0, 4]]]
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)
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coo_tensor = paddle.sparse.sparse_coo_tensor(
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self.coo_indices, coo_values, dtype=self.dtype
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)
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csr_crows = [0, 2, 4, 0, 2, 2, 0, 1, 2]
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csr_cols = [0, 1, 0, 1, 0, 1, 1, 1]
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csr_values = [1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 2.0, 4.0]
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csr_shape = [3, 2, 2]
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csr_tensor = paddle.sparse.sparse_csr_tensor(
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csr_crows, csr_cols, csr_values, csr_shape, dtype=self.dtype
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)
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other_tensor = paddle.to_tensor(
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[[[[1, 2], [3, 4]], [[1, 2], [0, 0]], [[0, 2], [0, 4]]]],
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dtype=self.dtype,
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)
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self.tensors = [coo_tensor, csr_tensor, other_tensor]
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class TestSparseDimAPI4(TestSparseDimAPI):
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def setUp(self):
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self.dtype = "int64"
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self.coo_indices = [[0, 0, 1, 2], [0, 1, 1, 2]]
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coo_values = paddle.to_tensor([1.0, 2.0, 3.0, 4.0])
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coo_tensor = paddle.sparse.sparse_coo_tensor(
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self.coo_indices, coo_values, dtype=self.dtype
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)
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csr_crows = [0, 2, 4, 0, 2, 2, 0, 1, 2]
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csr_cols = [0, 1, 0, 1, 0, 1, 1, 1]
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csr_values = [1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 2.0, 4.0]
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csr_shape = [3, 2, 2]
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csr_tensor = paddle.sparse.sparse_csr_tensor(
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csr_crows, csr_cols, csr_values, csr_shape, dtype=self.dtype
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)
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other_tensor = paddle.to_tensor(
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[[[[1, 2], [3, 4]], [[1, 2], [0, 0]], [[0, 2], [0, 4]]]],
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dtype=self.dtype,
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)
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self.tensors = [coo_tensor, csr_tensor, other_tensor]
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class TestSparseDimAPI5(TestSparseDimAPI):
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def setUp(self):
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self.dtype = "uint8"
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self.coo_indices = [
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[0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 1, 1, 2, 2],
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[0, 0, 1, 1, 0, 0, 0, 1],
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[0, 1, 0, 1, 0, 1, 1, 1],
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]
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coo_values = paddle.to_tensor([1, 2, 3, 4, 1, 2, 2, 4])
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coo_tensor = paddle.sparse.sparse_coo_tensor(
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self.coo_indices, coo_values, dtype=self.dtype
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)
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csr_crows = [0, 2, 4, 0, 2, 2, 0, 1, 2]
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csr_cols = [0, 1, 0, 1, 0, 1, 1, 1]
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csr_values = [1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 2.0, 4.0]
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csr_shape = [3, 2, 2]
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csr_tensor = paddle.sparse.sparse_csr_tensor(
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csr_crows, csr_cols, csr_values, csr_shape, dtype=self.dtype
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)
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other_tensor = paddle.to_tensor(
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[[[[1, 2], [3, 4]], [[1, 2], [0, 0]], [[0, 2], [0, 4]]]],
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dtype=self.dtype,
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)
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self.tensors = [coo_tensor, csr_tensor, other_tensor]
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class TestSparseDimAPIStatic(unittest.TestCase):
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def setUp(self):
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self.dtype = "float32"
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self.coo_indices = np.array([[0, 0, 0, 1], [0, 0, 1, 2]]).astype(
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'int64'
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)
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self.coo_values = np.array([1.0, 2.0, 3.0, 4.0]).astype(self.dtype)
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self.coo_shape = [2, 3]
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self.other_tensor_arr = np.array([1, 2, 3, 4]).astype(self.dtype)
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def test_sparse_dim(self):
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expected_result = [
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coo_sparse_dim_ref(self.coo_indices),
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dense_sparse_dim_ref(),
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]
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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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coo_indices = paddle.static.data(
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name='coo_indices',
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shape=self.coo_indices.shape,
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dtype='int64',
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)
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coo_values = paddle.static.data(
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name='coo_values',
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shape=self.coo_indices.shape,
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dtype=self.dtype,
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)
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coo = paddle.sparse.sparse_coo_tensor(
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coo_indices,
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coo_values,
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shape=self.coo_shape,
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dtype=self.dtype,
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)
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other = paddle.static.data(
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name='other',
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shape=self.other_tensor_arr.shape,
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dtype=self.dtype,
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)
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exe = paddle.static.Executor()
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exe.run(
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feed={
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'coo_indices': self.coo_indices,
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'coo_values': self.coo_values,
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'other': self.other_tensor_arr,
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}
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)
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self.assertEqual(coo.sparse_dim(), expected_result[0])
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self.assertEqual(other.sparse_dim(), expected_result[1])
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paddle.disable_static()
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class TestSparseDimAPIStatic1(TestSparseDimAPIStatic):
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def setUp(self):
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self.dtype = "float64"
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self.coo_indices = np.array(
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[[0, 1, 0, 1], [0, 0, 1, 2], [0, 0, 1, 2]]
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).astype('int64')
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self.coo_values = np.array([1.0, 2.0, 3.0, 4.0]).astype(self.dtype)
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self.coo_shape = [2, 3, 3]
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self.other_tensor_arr = np.array([1, 2, 3, 4]).astype(self.dtype)
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class TestSparseDimAPIStatic2(TestSparseDimAPIStatic):
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def setUp(self):
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self.dtype = "int16"
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self.coo_indices = np.array([[0, 0, 0, 1], [0, 0, 1, 2]]).astype(
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'int64'
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)
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self.coo_values = np.array([1.0, 2.0, 3.0, 4.0]).astype(self.dtype)
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self.coo_shape = [2, 3]
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self.other_tensor_arr = np.array([[[1, 2, 3, 4]]]).astype(self.dtype)
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class TestSparseDimAPIStatic3(TestSparseDimAPIStatic):
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def setUp(self):
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self.dtype = "int32"
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self.coo_indices = np.array(
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[[0, 1, 0, 1], [0, 0, 1, 2], [0, 0, 1, 2]]
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).astype('int64')
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self.coo_values = np.array([1.0, 2.0, 3.0, 4.0]).astype(self.dtype)
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self.coo_shape = [2, 3, 3]
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self.other_tensor_arr = np.array([[1, 2, 3, 4]]).astype(self.dtype)
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class TestSparseDimAPIStatic4(TestSparseDimAPIStatic):
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def setUp(self):
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self.dtype = "int64"
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self.coo_indices = np.array([[0, 0, 0, 1], [0, 2, 1, 2]]).astype(
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'int64'
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)
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self.coo_values = np.array([1.0, 2.0, 3.0, 4.0]).astype(self.dtype)
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self.coo_shape = [2, 3]
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self.other_tensor_arr = np.array([[1, 2, 3, 4]]).astype(self.dtype)
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class TestSparseDimAPIStatic5(TestSparseDimAPIStatic):
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def setUp(self):
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self.dtype = "uint8"
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self.coo_indices = np.array([[0, 0, 1, 2, 2], [0, 1, 0, 0, 1]]).astype(
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'int64'
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)
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self.coo_values = np.array(
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[[1.0, 2.0], [3.0, 4.0], [1.0, 2.0], [0.0, 4.0], [2.0, 4.0]]
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).astype(self.dtype)
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self.coo_shape = [3, 2, 2]
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self.other_tensor_arr = np.array([1, 2, 3, 4]).astype(self.dtype)
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if __name__ == "__main__":
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unittest.main()
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