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add cpu transpose

tags/v0.5.0-beta
baihuawei 5 years ago
parent
650a45b233
commit
eb0f897d31
3 changed files with 253 additions and 0 deletions
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  1. +64
    -0
      mindspore/ccsrc/kernel/cpu/transpose_cpu_kernel.cc
  2. +44
    -0
      mindspore/ccsrc/kernel/cpu/transpose_cpu_kernel.h
  3. +145
    -0
      tests/st/ops/cpu/test_transpose_op.py

+ 64
- 0
mindspore/ccsrc/kernel/cpu/transpose_cpu_kernel.cc View File

@@ -0,0 +1,64 @@
/**
* Copyright 2020 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 "kernel/cpu/transpose_cpu_kernel.h"
#include "device/cpu/cpu_device_address.h"
namespace mindspore {
namespace kernel {
const size_t kMaxDim = 100;
void TransposeCPUFwdKernel::InitKernel(const CNodePtr &kernel_node) {
MS_EXCEPTION_IF_NULL(kernel_node);
shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
axis_ = AnfAlgo::GetNodeAttr<std::vector<int>>(kernel_node, "perm");
if (shape_.size() != axis_.size()) {
MS_LOG(EXCEPTION) << "The size of input shape and transpose axis shape must be equal.";
}
}
bool TransposeCPUFwdKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
const std::vector<kernel::AddressPtr> & /*workspace*/,
const std::vector<kernel::AddressPtr> &outputs) {
auto input = reinterpret_cast<float *>(inputs[0]->addr);
auto output = reinterpret_cast<float *>(outputs[0]->addr);
size_t size = IntToSize(inputs[0]->size / sizeof(float));
size_t shape_size = IntToSize(shape_.size());
if (shape_size > kMaxDim) {
MS_LOG(EXCEPTION) << "Input is " << shape_size << "-D, but transpose supports max " << kMaxDim << "-D inputs.";
}
size_t pos_array[kMaxDim];
size_t size_offset[kMaxDim];
size_offset[0] = size / shape_[0];
for (size_t i = 1; i < shape_size; i++) {
size_offset[i] = size_offset[SizeToInt(i) - 1] / shape_[i];
}
for (size_t position = 0; position < size; position += 1) {
size_t temp_position = position;
pos_array[0] = temp_position / size_offset[0];
for (size_t i = 1; i < shape_size; i++) {
temp_position -= pos_array[SizeToInt(i) - 1] * size_offset[i - 1];
pos_array[i] = temp_position / size_offset[i];
}
size_t new_position = pos_array[axis_[SizeToInt(shape_size) - 1]];
size_t new_position_size = 1;
for (int j = shape_size - 2; j >= 0; j--) {
new_position_size *= shape_[axis_[j + 1]];
new_position += pos_array[axis_[j]] * new_position_size;
}
output[new_position] = input[position];
}
return true;
}
} // namespace kernel
} // namespace mindspore

+ 44
- 0
mindspore/ccsrc/kernel/cpu/transpose_cpu_kernel.h View File

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/**
* Copyright 2020 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_KERNEL_CPU_TRANSPOSE_CPU_KERNEL_H_
#define MINDSPORE_CCSRC_KERNEL_CPU_TRANSPOSE_CPU_KERNEL_H_
#include <vector>
#include <memory>
#include <string>
#include "kernel/cpu/cpu_kernel.h"
#include "kernel/cpu/cpu_kernel_factory.h"
namespace mindspore {
namespace kernel {
class TransposeCPUFwdKernel : public CPUKernel {
public:
TransposeCPUFwdKernel() = default;
~TransposeCPUFwdKernel() 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> shape_;
std::vector<int> axis_;
};
MS_REG_CPU_KERNEL(Transpose, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
TransposeCPUFwdKernel);
} // namespace kernel
} // namespace mindspore
#endif // MINDSPORE_CCSRC_KERNEL_CPU_TRANSPOSE_CPU_KERNEL_H_

+ 145
- 0
tests/st/ops/cpu/test_transpose_op.py View File

@@ -0,0 +1,145 @@
# Copyright 2020 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 pytest
import numpy as np
from mindspore import Tensor
from mindspore.ops import operations as P
from mindspore.common.api import ms_function
from mindspore.common.initializer import initializer
from mindspore.common.parameter import Parameter
import mindspore.nn as nn
import mindspore.context as context
context.set_context(device_target='CPU')
class Transpose(nn.Cell):
def __init__(self):
super(Transpose, self).__init__()
self.transpose = P.Transpose()
self.x_2D = Parameter(initializer(Tensor(np.arange(5 * 6).reshape(5, 6).astype(np.float32)), [5, 6]),
name='x_2D')
self.perm_2D = (1, 0)
self.x_3D = Parameter(initializer(Tensor(np.arange(2 * 2 * 4).reshape(2, 2, 4).astype(np.float32)), [2, 2, 4]),
name='x_3D')
self.perm_3D = (1, 0, 2)
self.x_4D = Parameter(
initializer(Tensor(np.arange(2 * 3 * 4 * 5).reshape(2, 3, 4, 5).astype(np.float32)), [2, 3, 4, 5]),
name='x_4D')
self.perm_4D = (0, 1, 2, 3)
self.x_5D = Parameter(
initializer(Tensor(np.arange(1 * 2 * 3 * 4 * 5).reshape(1, 2, 3, 4, 5).astype(np.float32)),
[1, 2, 3, 4, 5]), name='x_5D')
self.perm_5D = (1, 0, 3, 4, 2)
@ms_function
def construct(self):
return (self.transpose(self.x_2D, self.perm_2D), self.transpose(self.x_3D, self.perm_3D),
self.transpose(self.x_4D, self.perm_4D), self.transpose(self.x_5D, self.perm_5D))
@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_transpose():
transpose = Transpose()
output = transpose()
expect0 = np.array([[[0, 6, 12, 18, 24],
[1, 7, 13, 19, 25],
[2, 8, 14, 20, 26],
[3, 9, 15, 21, 27],
[4, 10, 16, 22, 28],
[5, 11, 17, 23, 29]]]).astype(np.float32)
expect1 = np.array([[[[0, 1, 2, 3],
[8, 9, 10, 11]],
[[4, 5, 6, 7],
[12, 13, 14, 15]]]]).astype(np.float32)
expect2 = np.array([[[[[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19]],
[[20, 21, 22, 23, 24],
[25, 26, 27, 28, 29],
[30, 31, 32, 33, 34],
[35, 36, 37, 38, 39]],
[[40, 41, 42, 43, 44],
[45, 46, 47, 48, 49],
[50, 51, 52, 53, 54],
[55, 56, 57, 58, 59]]],
[[[60, 61, 62, 63, 64],
[65, 66, 67, 68, 69],
[70, 71, 72, 73, 74],
[75, 76, 77, 78, 79]],
[[80, 81, 82, 83, 84],
[85, 86, 87, 88, 89],
[90, 91, 92, 93, 94],
[95, 96, 97, 98, 99]],
[[100, 101, 102, 103, 104],
[105, 106, 107, 108, 109],
[110, 111, 112, 113, 114],
[115, 116, 117, 118, 119]]]]]).astype(np.float32)
expect3 = np.array([[[[[[0, 20, 40],
[1, 21, 41],
[2, 22, 42],
[3, 23, 43],
[4, 24, 44]],
[[5, 25, 45],
[6, 26, 46],
[7, 27, 47],
[8, 28, 48],
[9, 29, 49]],
[[10, 30, 50],
[11, 31, 51],
[12, 32, 52],
[13, 33, 53],
[14, 34, 54]],
[[15, 35, 55],
[16, 36, 56],
[17, 37, 57],
[18, 38, 58],
[19, 39, 59]]]],
[[[[60, 80, 100],
[61, 81, 101],
[62, 82, 102],
[63, 83, 103],
[64, 84, 104]],
[[65, 85, 105],
[66, 86, 106],
[67, 87, 107],
[68, 88, 108],
[69, 89, 109]],
[[70, 90, 110],
[71, 91, 111],
[72, 92, 112],
[73, 93, 113],
[74, 94, 114]],
[[75, 95, 115],
[76, 96, 116],
[77, 97, 117],
[78, 98, 118],
[79, 99, 119]]]]]]).astype(np.float32)
assert (output[0].asnumpy() == expect0).all()
assert (output[1].asnumpy() == expect1).all()
assert (output[2].asnumpy() == expect2).all()
assert (output[3].asnumpy() == expect3).all()
test_transpose()

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