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107 lines
3.9 KiB
107 lines
3.9 KiB
/**
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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/concat_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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void ConcatCPUKernel::InitKernel(const CNodePtr &kernel_node) {
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CheckParam(kernel_node);
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axis_ = AnfAlgo::GetNodeAttr<int>(kernel_node, AXIS);
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auto input_1_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
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if (axis_ < 0) {
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axis_ = axis_ + SizeToInt(input_1_shape.size());
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}
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axis_ += 4 - input_1_shape.size();
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auto input_num = AnfAlgo::GetInputTensorNum(kernel_node);
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for (size_t i = 0; i < input_num; i++) {
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auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, i);
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CPUKernelUtils::ExpandDimsTo4(&input_shape);
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input_shape_list_.push_back(input_shape);
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}
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output_shape_ = AnfAlgo::GetOutputInferShape(kernel_node, 0);
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CPUKernelUtils::ExpandDimsTo4(&output_shape_);
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}
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bool ConcatCPUKernel::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 output_addr = reinterpret_cast<float *>(outputs[0]->addr);
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auto buff_size = outputs[0]->size;
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size_t dim0 = output_shape_[0];
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size_t dim1 = output_shape_[1];
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size_t dim2 = output_shape_[2];
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if (axis_ == 3) {
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for (size_t i = 0; i < dim0; ++i) {
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for (size_t j = 0; j < dim1; ++j) {
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for (size_t k = 0; k < dim2; ++k) {
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CopyDataToOutput(inputs, i, j, k, &output_addr, &buff_size);
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}
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}
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}
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} else if (axis_ == 2) {
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for (size_t i = 0; i < dim0; ++i) {
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for (size_t j = 0; j < dim1; ++j) {
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CopyDataToOutput(inputs, i, j, 0, &output_addr, &buff_size);
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}
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}
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} else if (axis_ == 1) {
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for (size_t i = 0; i < dim0; ++i) {
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CopyDataToOutput(inputs, i, 0, 0, &output_addr, &buff_size);
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}
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} else if (axis_ == 0) {
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CopyDataToOutput(inputs, 0, 0, 0, &output_addr, &buff_size);
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}
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return true;
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}
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void ConcatCPUKernel::CopyDataToOutput(const std::vector<kernel::AddressPtr> &inputs, size_t dim0, size_t dim1,
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size_t dim2, float **output_addr, size_t *buff_size) {
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for (size_t i = 0; i < input_shape_list_.size(); ++i) {
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auto input_i_shape = input_shape_list_[i];
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auto input_i_addr = reinterpret_cast<float *>(inputs[i]->addr);
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size_t num = CPUKernelUtils::GetElementNumOnAxis(input_i_shape, axis_);
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num *= input_i_shape[axis_];
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auto pos = CPUKernelUtils::CalcOffset(input_i_shape, dim0, dim1, dim2, 0);
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auto ret = memcpy_s(*output_addr, *buff_size, input_i_addr + pos, num * sizeof(float));
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if (ret != EOK) {
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MS_LOG(EXCEPTION) << "memcpy failed.";
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}
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*output_addr += num;
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*buff_size -= num * sizeof(float);
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}
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}
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void ConcatCPUKernel::CheckParam(const CNodePtr &kernel_node) {
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auto input_shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
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if (input_shape.size() > 4) {
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MS_LOG(EXCEPTION) << "Input dims is " << input_shape.size() << ", but ConcatCPUKernel olny support 4d or lower.";
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}
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size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
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if (output_num != 1) {
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MS_LOG(EXCEPTION) << "Output number is " << output_num << ", but ConcatCPUKernel needs 1 output.";
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}
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}
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} // namespace kernel
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} // namespace mindspore
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