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support sparse tensor dense matmul fot CPU

This commit is contained in:
xuguoyang 2021-03-22 16:14:30 +08:00
parent e8cb23e35e
commit 7df6bfe7dd
7 changed files with 493 additions and 4 deletions
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75
mindspore/ccsrc/backend/kernel_compiler/cpu/sparse_tensor_dense_matmul_cpu_kernel.cc Normal file
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@ -0,0 +1,75 @@
/**
* Copyright 2020-2021 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "backend/kernel_compiler/cpu/sparse_tensor_dense_matmul_cpu_kernel.h"
namespace mindspore {
namespace kernel {
template <typename I, typename T>
void SparseTensorDenseMatmulCPUKernel<I, T>::InitKernel(const CNodePtr &kernel_node) {
output_size_ = 1;
auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0);
for (auto &dim : output_shape) {
output_size_ *= dim;
}
aValues_size_ = 1;
auto aValues_shape = AnfAlgo::GetInputDeviceShape(kernel_node, 1);
for (auto &dim : aValues_shape) {
aValues_size_ *= dim;
}
b_shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 3);
output_shape_ = AnfAlgo::GetOutputDeviceShape(kernel_node, 0);
}
template <typename I, typename T>
bool SparseTensorDenseMatmulCPUKernel<I, T>::Launch(const std::vector<kernel::AddressPtr> &inputs,
const std::vector<kernel::AddressPtr> & /*workspace*/,
const std::vector<kernel::AddressPtr> &outputs) {
auto a_indices = reinterpret_cast<I *>(inputs[0]->addr);
auto a_values = reinterpret_cast<T *>(inputs[1]->addr);
auto b = reinterpret_cast<T *>(inputs[3]->addr);
auto out = reinterpret_cast<T *>(outputs[0]->addr);
memset(out, 0, output_size_);
const size_t nnz = aValues_size_;
const size_t rhs_right = b_shape_[1];
const size_t lhs_right = b_shape_[0];
for (size_t i = 0; i < nnz; ++i) {
const size_t m = a_indices[i * 2];
const size_t k = a_indices[i * 2 + 1];
if (k > lhs_right) {
MS_LOG(ERROR) << "Invalid value: k: " << k << ", lhs_right: " << lhs_right;
return false;
}
if (m > output_shape_[0]) {
MS_LOG(ERROR) << "Invalid value: m: " << m << ", output_shape: " << output_shape_[0];
return false;
}
for (size_t n = 0; n < rhs_right; ++n) {
const float b_value = b[k * lhs_right + n];
out[m * output_shape_[0] + n] += a_values[i] * b_value;
}
}
return true;
}
} // namespace kernel
} // namespace mindspore

243
mindspore/ccsrc/backend/kernel_compiler/cpu/sparse_tensor_dense_matmul_cpu_kernel.h Normal file
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@ -0,0 +1,243 @@
/**
* Copyright 2020-2021 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_SPARSE_TENSOR_DENSE_MATMUL_CPU_KERNEL_H_
#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_SPARSE_TENSOR_DENSE_MATMUL_CPU_KERNEL_H_
#include <vector>
#include "backend/kernel_compiler/cpu/cpu_kernel.h"
#include "backend/kernel_compiler/cpu/cpu_kernel_factory.h"
namespace mindspore {
namespace kernel {
template <typename I, typename T>
class SparseTensorDenseMatmulCPUKernel : public CPUKernel {
public:
SparseTensorDenseMatmulCPUKernel() = default;
~SparseTensorDenseMatmulCPUKernel() override = default;
void InitKernel(const CNodePtr &kernel_node) override;
bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
const std::vector<AddressPtr> &outputs) override;
private:
std::vector<size_t> output_shape_;
std::vector<size_t> b_shape_;
size_t output_size_;
size_t aValues_size_;
};
MS_REG_CPU_KERNEL_T_S(SparseTensorDenseMatmul,
KernelAttr()
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeBool)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeBool)
.AddOutputAttr(kNumberTypeBool),
SparseTensorDenseMatmulCPUKernel, int32_t, bool);
MS_REG_CPU_KERNEL_T_S(SparseTensorDenseMatmul,
KernelAttr()
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeUInt8)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeUInt8)
.AddOutputAttr(kNumberTypeUInt8),
SparseTensorDenseMatmulCPUKernel, int32_t, uint8_t);
MS_REG_CPU_KERNEL_T_S(SparseTensorDenseMatmul,
KernelAttr()
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeUInt16)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeUInt16)
.AddOutputAttr(kNumberTypeUInt16),
SparseTensorDenseMatmulCPUKernel, int32_t, uint16_t);
MS_REG_CPU_KERNEL_T_S(SparseTensorDenseMatmul,
KernelAttr()
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeUInt32)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeUInt32)
.AddOutputAttr(kNumberTypeUInt32),
SparseTensorDenseMatmulCPUKernel, int32_t, uint32_t);
MS_REG_CPU_KERNEL_T_S(SparseTensorDenseMatmul,
KernelAttr()
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeUInt64)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeUInt64)
.AddOutputAttr(kNumberTypeUInt64),
SparseTensorDenseMatmulCPUKernel, int32_t, uint64_t);
MS_REG_CPU_KERNEL_T_S(SparseTensorDenseMatmul,
KernelAttr()
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeInt8)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeInt8)
.AddOutputAttr(kNumberTypeInt8),
SparseTensorDenseMatmulCPUKernel, int32_t, int8_t);
MS_REG_CPU_KERNEL_T_S(SparseTensorDenseMatmul,
KernelAttr()
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeInt16)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeInt16)
.AddOutputAttr(kNumberTypeInt16),
SparseTensorDenseMatmulCPUKernel, int32_t, int16_t);
MS_REG_CPU_KERNEL_T_S(SparseTensorDenseMatmul,
KernelAttr()
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeInt32)
.AddOutputAttr(kNumberTypeInt32),
SparseTensorDenseMatmulCPUKernel, int32_t, int32_t);
MS_REG_CPU_KERNEL_T_S(SparseTensorDenseMatmul,
KernelAttr()
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeInt64)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeInt64)
.AddOutputAttr(kNumberTypeInt64),
SparseTensorDenseMatmulCPUKernel, int32_t, int64_t);
MS_REG_CPU_KERNEL_T_S(SparseTensorDenseMatmul,
KernelAttr()
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
SparseTensorDenseMatmulCPUKernel, int32_t, float);
MS_REG_CPU_KERNEL_T_S(SparseTensorDenseMatmul,
KernelAttr()
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeFloat64)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeFloat64)
.AddOutputAttr(kNumberTypeFloat64),
SparseTensorDenseMatmulCPUKernel, int32_t, double);
MS_REG_CPU_KERNEL_T_S(SparseTensorDenseMatmul,
KernelAttr()
.AddInputAttr(kNumberTypeInt64)
.AddInputAttr(kNumberTypeBool)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeBool)
.AddOutputAttr(kNumberTypeBool),
SparseTensorDenseMatmulCPUKernel, int64_t, bool);
MS_REG_CPU_KERNEL_T_S(SparseTensorDenseMatmul,
KernelAttr()
.AddInputAttr(kNumberTypeInt64)
.AddInputAttr(kNumberTypeUInt8)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeUInt8)
.AddOutputAttr(kNumberTypeUInt8),
SparseTensorDenseMatmulCPUKernel, int64_t, uint8_t);
MS_REG_CPU_KERNEL_T_S(SparseTensorDenseMatmul,
KernelAttr()
.AddInputAttr(kNumberTypeInt64)
.AddInputAttr(kNumberTypeUInt16)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeUInt16)
.AddOutputAttr(kNumberTypeUInt16),
SparseTensorDenseMatmulCPUKernel, int64_t, uint16_t);
MS_REG_CPU_KERNEL_T_S(SparseTensorDenseMatmul,
KernelAttr()
.AddInputAttr(kNumberTypeInt64)
.AddInputAttr(kNumberTypeUInt32)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeUInt32)
.AddOutputAttr(kNumberTypeUInt32),
SparseTensorDenseMatmulCPUKernel, int64_t, uint32_t);
MS_REG_CPU_KERNEL_T_S(SparseTensorDenseMatmul,
KernelAttr()
.AddInputAttr(kNumberTypeInt64)
.AddInputAttr(kNumberTypeUInt64)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeUInt64)
.AddOutputAttr(kNumberTypeUInt64),
SparseTensorDenseMatmulCPUKernel, int64_t, uint64_t);
MS_REG_CPU_KERNEL_T_S(SparseTensorDenseMatmul,
KernelAttr()
.AddInputAttr(kNumberTypeInt64)
.AddInputAttr(kNumberTypeInt8)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeInt8)
.AddOutputAttr(kNumberTypeInt8),
SparseTensorDenseMatmulCPUKernel, int64_t, int8_t);
MS_REG_CPU_KERNEL_T_S(SparseTensorDenseMatmul,
KernelAttr()
.AddInputAttr(kNumberTypeInt64)
.AddInputAttr(kNumberTypeInt16)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeInt16)
.AddOutputAttr(kNumberTypeInt16),
SparseTensorDenseMatmulCPUKernel, int64_t, int16_t);
MS_REG_CPU_KERNEL_T_S(SparseTensorDenseMatmul,
KernelAttr()
.AddInputAttr(kNumberTypeInt64)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeInt32)
.AddOutputAttr(kNumberTypeInt32),
SparseTensorDenseMatmulCPUKernel, int64_t, int32_t);
MS_REG_CPU_KERNEL_T_S(SparseTensorDenseMatmul,
KernelAttr()
.AddInputAttr(kNumberTypeInt64)
.AddInputAttr(kNumberTypeInt64)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeInt64)
.AddOutputAttr(kNumberTypeInt64),
SparseTensorDenseMatmulCPUKernel, int64_t, int64_t);
MS_REG_CPU_KERNEL_T_S(SparseTensorDenseMatmul,
KernelAttr()
.AddInputAttr(kNumberTypeInt64)
.AddInputAttr(kNumberTypeFloat32)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
SparseTensorDenseMatmulCPUKernel, int64_t, float);
MS_REG_CPU_KERNEL_T_S(SparseTensorDenseMatmul,
KernelAttr()
.AddInputAttr(kNumberTypeInt64)
.AddInputAttr(kNumberTypeFloat64)
.AddInputAttr(kNumberTypeInt32)
.AddInputAttr(kNumberTypeFloat64)
.AddOutputAttr(kNumberTypeFloat64),
SparseTensorDenseMatmulCPUKernel, int64_t, double);
} // namespace kernel
} // namespace mindspore
#endif // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_RMSPROP_CPU_KERNEL_H_

3
mindspore/nn/sparse/__init__.py
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@ -15,8 +15,9 @@
"""
Sparse related transformation.
"""
from .sparse import SparseToDense
from .sparse import (SparseToDense, SparseTensorDenseMatmul)
__all__ = [
"SparseToDense",
"SparseTensorDenseMatmul",
]

49
mindspore/nn/sparse/sparse.py
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@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -52,3 +52,50 @@ class SparseToDense(Cell):
return self.sparse_to_dense(sparse_tensor.indices,
sparse_tensor.values,
sparse_tensor.dense_shape)
class SparseTensorDenseMatmul(Cell):
"""
Multiply SparseTensor(of rank 2) "A" by dense tensor.
The shape of sparse tensor is :math:`(N, C)`, and the shape of dense tensor is :math:`(C, M)`, then the shape of
output tensor is :math:`(N, M)`.The output data type is the same as "values".
Args:
- *adjoint_st** (Bool) - If true, SparseTensor is transposed before multiplication. Default: False.
- *adjoint_dt** (Bool) - If true, DenseTensor is transposed before multiplication. Default: False.
Inputs:
- **indices** (Tensor) - The indices of sparse representation, support int32/int64.
- **values** (Tensor) - Values corresponding to each row of indices.
- **dense_shape** (tuple) - An int tuple which specifies the shape of dense tensor. The dense_shape is :
math:`(N, C)`. If `adjoint_st` is True, its shape must be :math:`(N, C)` after transpose.
- **dense** (Tensor) - Dense Matrix. The shape of the tensor is :math:`(C, M)`. If
`adjoint_dt` is True, its shape must be :math:`(C, M)` after transpose.
Returns:
Tensor, the shape of tensor is :math:`(N, M)`.The output data type is the same as "values".
Examples:
>>> class NetSparseDenseMatmul(nn.Cell):
... def __init__(self):
... super(NetSparseDenseMatmul, self).__init__()
... self.matmul = nn.SparseTensorDenseMatmul()
...
... def construct(self, indices, values, dens_shape, dt):
... return self.matmul(indices, values, dens_shape, dt)
...
>>> indices = Tensor([[0, 1], [1, 2]], dtype=ms.int32)
>>> values = Tensor([1, 2], dtype=ms.float32)
>>> dense_shape = (3, 4)
>>> dsMatrix = Tensor([[1, 1], [2, 2], [3, 3], [4, 4]], dtype=ms.float32)
>>> test_SparseDenseMatmul = NetSparseDenseMatmul()
>>> out = test_SparseDenseMatmul(indices, values, dens_shape, dsMatrix)
"""
def __init__(self, adjoint_st=False, adjoint_dt=False):
"""Initialize SparseTensorDenseMatmul"""
super(SparseTensorDenseMatmul, self).__init__()
self.adjst = adjoint_st
self.adjdt = adjoint_dt
self.matmul = P.SparseTensorDenseMatmul(adjoint_st=self.adjst, adjoint_dt=self.adjdt)
def construct(self, indices, values, dense_shape, dense):
return self.matmul(indices, values, dense_shape, dense)

3
mindspore/ops/operations/__init__.py
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@ -94,7 +94,7 @@ from ._thor_ops import (CusBatchMatMul, CusCholeskyTrsm, CusFusedAbsMax1, CusImg
CusMatMulCubeDenseRight,
CusMatMulCubeFraczLeftCast, Im2Col, UpdateThorGradient, Cholesky, CholeskyTrsm, DetTriangle,
ProdForceSeA)
from .sparse_ops import SparseToDense
from .sparse_ops import (SparseToDense, SparseTensorDenseMatmul)
from ._embedding_cache_ops import (CacheSwapHashmap, SearchCacheIdx, CacheSwapTable, UpdateCache, MapCacheIdx,
SubAndFilter,
MapUniform, DynamicAssign, PadAndShift)
@ -428,6 +428,7 @@ __all__ = [
"Pull",
"ReLUV2",
"SparseToDense",
"SparseTensorDenseMatmul",
"MatrixInverse",
"Range",
"IndexAdd",

71
mindspore/ops/operations/sparse_ops.py
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@ -1,6 +1,6 @@
# coding: utf-8
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -53,3 +53,72 @@ class SparseToDense(PrimitiveWithInfer):
'dtype': values['dtype'],
'value': None}
return out
class SparseTensorDenseMatmul(PrimitiveWithInfer):
"""
Multiply SparseTensor(of rank 2) "A" by dense tensor.
The shape of sparse tensor is :math:`(N, C)`, and the shape of dense tensor is :math:`(C, M)`, then the shape of
output tensor is :math:`(N, M)`.The output data type is the same as "values".
tensors.
Args:
- *adjoint_st** (Bool) - If true, SparseTensor is transposed before multiplication. Default: False.
- *adjoint_dt** (Bool) - If true, DenseTensor is transposed before multiplication. Default: False.
Inputs:
- **indices** (Tensor) - The indices of sparse representation, support int32/int64.
- **values** (Tensor) - Values corresponding to each row of indices.
- **dense_shape** (tuple) - An int tuple which specifies the shape of dense tensor. The dense_shape is :
math:`(N, C)`. If `adjoint_st` is True, its shape must be :math:`(N, C)` after transpose.
- **dense** (Tensor) - Dense Matrix. The shape of the tensor is :math:`(C, M)`. If
`adjoint_dt` is True, its shape must be :math:`(C, M)` after transpose.
Outputs:
Tensor, the shape of tensor is :math:`(N, M)`. The output data type is the same as "values".
Raises:
TypeError: If `indices` is neither int32 nor int64.
TypeError: If 'values' is not boot, uint8-64, int8-64, float16-64.
TypeError: If 'dense' is not boot, uint8-64, int8-64, float16-64.
ValueError: If length of shape of `SparseTensor` or `DenseTensor` is not equal to 2
Supported Platforms:
``CPU``
Examples:
>>> indices = Tensor([[0, 1], [1, 2]], dtype=ms.int32)
>>> values = Tensor([1, 2], dtype=ms.float32)
>>> dense_shape = (3, 4)
>>> dsMatrix = Tensor([[1,1], [2,2], [3,3 ], [4, 4]], dtype=ms.float32)
>>> out = ops.SparseTensorDenseMatmul(indices, values, dense_shape, dsMatrix)
"""
@prim_attr_register
def __init__(self, adjoint_st=False, adjoint_dt=False):
"""Initialize SparseTensorDenseMatmul"""
self.adjoint_st = adjoint_st
self.adjoint_dt = adjoint_dt
self.init_prim_io_names(inputs=['indices', 'values', 'dense_shape', 'dense'],
outputs=['output'])
self.add_prim_attr('adjoint_st', self.adjoint_st)
self.add_prim_attr('adjoint_dt', self.adjoint_dt)
validator.check_value_type("adjoint_st", adjoint_st, [bool], self.name)
validator.check_value_type("adjoint_dt", adjoint_dt, [bool], self.name)
def __infer__(self, indices, values, dense_shape, dense):
validator.check_tensor_dtype_valid('indices', indices['dtype'], [mstype.int32, mstype.int64], self.name)
valid_types = mstype.number_type + (mstype.bool_,)
args = {'values': values['dtype'], 'dense': dense['dtype']}
validator.check_tensors_dtypes_same_and_valid(args, valid_types, self.name)
a_shape = dense_shape['value']
b_shape = dense['shape']
if len(a_shape) != 2 or len(b_shape) != 2:
raise ValueError('SparseTensorDenseMatmul SparseTensor, DenseTensor should have the same dimension size '
+ f'and equal to 2, while SparseTensor size is ({len(a_shape)}) and DenseTensor size is '
+ f'({len(b_shape)}).')
out_shape = []
out_shape.append(a_shape[0])
out_shape.append(b_shape[1])
out = {'shape': tuple(out_shape),
'dtype': values['dtype'],
'value': None}
return out

53
tests/st/ops/cpu/test_sparse_tensor_dense_matmul_op.py Normal file
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@ -0,0 +1,53 @@
# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
import numpy as np
import mindspore as ms
import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore import SparseTensor
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
class NetSparseDenseMatmul(nn.Cell):
def __init__(self):
super(NetSparseDenseMatmul, self).__init__()
self.matmul = nn.SparseTensorDenseMatmul()
def construct(self, indices, values, dens_shape, dt):
return self.matmul(indices, values, dens_shape, dt)
class NetSparseTensor(nn.Cell):
def __init__(self, dense_shape):
super(NetSparseTensor, self).__init__()
self.dense_shape = dense_shape
def construct(self, indices, values):
x = SparseTensor(indices, values, self.dense_shape)
return x.values, x.indices, x.dense_shape
def test_sparse_tensor_dense_matmul():
indices = Tensor([[0, 1], [1, 1]])
values = Tensor([5, 5], dtype=ms.float32)
dens_shape = (3, 3)
spMatrix = np.array([[5, 0, 0], [0, 5, 0], [0, 0, 5]], dtype=np.float32)
dsMatrix = np.array([[1, 1, 1], [1, 1, 1], [1, 1, 1]], dtype=np.float32)
test_SparseDenseMatmul = NetSparseDenseMatmul()
out_ms = test_SparseDenseMatmul(indices, values, dens_shape, Tensor(dsMatrix))
out_np = np.matmul(spMatrix, dsMatrix)
error = np.ones(shape=dsMatrix.shape) * 10e-6
diff = out_ms.asnumpy() - out_np
assert np.all(diff < error)