forked from mindspore-Ecosystem/mindspore
!1265 add cpu transpose
Merge pull request !1265 from baihuawei/transpose
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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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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*/
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#include "kernel/cpu/transpose_cpu_kernel.h"
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#include "device/cpu/cpu_device_address.h"
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namespace mindspore {
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namespace kernel {
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const size_t kMaxDim = 100;
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void TransposeCPUFwdKernel::InitKernel(const CNodePtr &kernel_node) {
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MS_EXCEPTION_IF_NULL(kernel_node);
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shape_ = AnfAlgo::GetInputDeviceShape(kernel_node, 0);
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axis_ = AnfAlgo::GetNodeAttr<std::vector<int>>(kernel_node, "perm");
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if (shape_.size() != axis_.size()) {
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MS_LOG(EXCEPTION) << "The size of input shape and transpose axis shape must be equal.";
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}
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}
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bool TransposeCPUFwdKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
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const std::vector<kernel::AddressPtr> & /*workspace*/,
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const std::vector<kernel::AddressPtr> &outputs) {
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auto input = reinterpret_cast<float *>(inputs[0]->addr);
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auto output = reinterpret_cast<float *>(outputs[0]->addr);
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size_t size = IntToSize(inputs[0]->size / sizeof(float));
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size_t shape_size = IntToSize(shape_.size());
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if (shape_size > kMaxDim) {
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MS_LOG(EXCEPTION) << "Input is " << shape_size << "-D, but transpose supports max " << kMaxDim << "-D inputs.";
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}
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size_t pos_array[kMaxDim];
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size_t size_offset[kMaxDim];
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size_offset[0] = size / shape_[0];
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for (size_t i = 1; i < shape_size; i++) {
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size_offset[i] = size_offset[SizeToInt(i) - 1] / shape_[i];
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}
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for (size_t position = 0; position < size; position += 1) {
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size_t temp_position = position;
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pos_array[0] = temp_position / size_offset[0];
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for (size_t i = 1; i < shape_size; i++) {
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temp_position -= pos_array[SizeToInt(i) - 1] * size_offset[i - 1];
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pos_array[i] = temp_position / size_offset[i];
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}
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size_t new_position = pos_array[axis_[SizeToInt(shape_size) - 1]];
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size_t new_position_size = 1;
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for (int j = shape_size - 2; j >= 0; j--) {
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new_position_size *= shape_[axis_[j + 1]];
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new_position += pos_array[axis_[j]] * new_position_size;
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}
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output[new_position] = input[position];
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}
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return true;
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}
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} // namespace kernel
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} // namespace mindspore
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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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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*/
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#ifndef MINDSPORE_CCSRC_KERNEL_CPU_TRANSPOSE_CPU_KERNEL_H_
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#define MINDSPORE_CCSRC_KERNEL_CPU_TRANSPOSE_CPU_KERNEL_H_
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#include <vector>
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#include <memory>
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#include <string>
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#include "kernel/cpu/cpu_kernel.h"
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#include "kernel/cpu/cpu_kernel_factory.h"
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namespace mindspore {
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namespace kernel {
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class TransposeCPUFwdKernel : public CPUKernel {
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public:
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TransposeCPUFwdKernel() = default;
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~TransposeCPUFwdKernel() override = default;
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void InitKernel(const CNodePtr &kernel_node) override;
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bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
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const std::vector<AddressPtr> &outputs) override;
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private:
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std::vector<size_t> shape_;
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std::vector<int> axis_;
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};
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MS_REG_CPU_KERNEL(Transpose, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
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TransposeCPUFwdKernel);
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} // namespace kernel
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} // namespace mindspore
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#endif // MINDSPORE_CCSRC_KERNEL_CPU_TRANSPOSE_CPU_KERNEL_H_
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@ -0,0 +1,145 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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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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# ============================================================================
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import pytest
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import numpy as np
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from mindspore import Tensor
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from mindspore.ops import operations as P
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from mindspore.common.api import ms_function
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from mindspore.common.initializer import initializer
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from mindspore.common.parameter import Parameter
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import mindspore.nn as nn
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import mindspore.context as context
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context.set_context(device_target='CPU')
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class Transpose(nn.Cell):
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def __init__(self):
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super(Transpose, self).__init__()
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self.transpose = P.Transpose()
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self.x_2D = Parameter(initializer(Tensor(np.arange(5 * 6).reshape(5, 6).astype(np.float32)), [5, 6]),
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name='x_2D')
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self.perm_2D = (1, 0)
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self.x_3D = Parameter(initializer(Tensor(np.arange(2 * 2 * 4).reshape(2, 2, 4).astype(np.float32)), [2, 2, 4]),
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name='x_3D')
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self.perm_3D = (1, 0, 2)
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self.x_4D = Parameter(
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initializer(Tensor(np.arange(2 * 3 * 4 * 5).reshape(2, 3, 4, 5).astype(np.float32)), [2, 3, 4, 5]),
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name='x_4D')
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self.perm_4D = (0, 1, 2, 3)
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self.x_5D = Parameter(
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initializer(Tensor(np.arange(1 * 2 * 3 * 4 * 5).reshape(1, 2, 3, 4, 5).astype(np.float32)),
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[1, 2, 3, 4, 5]), name='x_5D')
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self.perm_5D = (1, 0, 3, 4, 2)
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@ms_function
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def construct(self):
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return (self.transpose(self.x_2D, self.perm_2D), self.transpose(self.x_3D, self.perm_3D),
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self.transpose(self.x_4D, self.perm_4D), self.transpose(self.x_5D, self.perm_5D))
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_transpose():
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transpose = Transpose()
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output = transpose()
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expect0 = np.array([[[0, 6, 12, 18, 24],
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[1, 7, 13, 19, 25],
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[2, 8, 14, 20, 26],
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[3, 9, 15, 21, 27],
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[4, 10, 16, 22, 28],
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[5, 11, 17, 23, 29]]]).astype(np.float32)
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expect1 = np.array([[[[0, 1, 2, 3],
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[8, 9, 10, 11]],
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[[4, 5, 6, 7],
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[12, 13, 14, 15]]]]).astype(np.float32)
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expect2 = np.array([[[[[0, 1, 2, 3, 4],
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[5, 6, 7, 8, 9],
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[10, 11, 12, 13, 14],
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[15, 16, 17, 18, 19]],
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[[20, 21, 22, 23, 24],
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[25, 26, 27, 28, 29],
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[30, 31, 32, 33, 34],
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[35, 36, 37, 38, 39]],
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[[40, 41, 42, 43, 44],
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[45, 46, 47, 48, 49],
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[50, 51, 52, 53, 54],
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[55, 56, 57, 58, 59]]],
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[[[60, 61, 62, 63, 64],
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[65, 66, 67, 68, 69],
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[70, 71, 72, 73, 74],
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[75, 76, 77, 78, 79]],
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[[80, 81, 82, 83, 84],
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[85, 86, 87, 88, 89],
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[90, 91, 92, 93, 94],
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[95, 96, 97, 98, 99]],
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[[100, 101, 102, 103, 104],
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[105, 106, 107, 108, 109],
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[110, 111, 112, 113, 114],
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[115, 116, 117, 118, 119]]]]]).astype(np.float32)
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expect3 = np.array([[[[[[0, 20, 40],
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[1, 21, 41],
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[2, 22, 42],
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[3, 23, 43],
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[4, 24, 44]],
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[[5, 25, 45],
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[6, 26, 46],
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[7, 27, 47],
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[8, 28, 48],
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[9, 29, 49]],
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[[10, 30, 50],
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[11, 31, 51],
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[12, 32, 52],
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[13, 33, 53],
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[14, 34, 54]],
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[[15, 35, 55],
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[16, 36, 56],
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[17, 37, 57],
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[18, 38, 58],
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[19, 39, 59]]]],
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[[[[60, 80, 100],
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[61, 81, 101],
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[62, 82, 102],
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[63, 83, 103],
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[64, 84, 104]],
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[[65, 85, 105],
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[66, 86, 106],
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[67, 87, 107],
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[68, 88, 108],
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[69, 89, 109]],
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[[70, 90, 110],
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[71, 91, 111],
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[72, 92, 112],
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[73, 93, 113],
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[74, 94, 114]],
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[[75, 95, 115],
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[76, 96, 116],
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[77, 97, 117],
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[78, 98, 118],
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[79, 99, 119]]]]]]).astype(np.float32)
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assert (output[0].asnumpy() == expect0).all()
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assert (output[1].asnumpy() == expect1).all()
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assert (output[2].asnumpy() == expect2).all()
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assert (output[3].asnumpy() == expect3).all()
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test_transpose()
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