forked from huawei/mindspore2022
98 lines
4.2 KiB
C++
98 lines
4.2 KiB
C++
/**
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* Copyright 2021 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_FL_SERVER_KERNEL_OPTIMIZER_KERNEL_H_
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#define MINDSPORE_CCSRC_FL_SERVER_KERNEL_OPTIMIZER_KERNEL_H_
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#include <memory>
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#include <string>
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#include <vector>
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#include <functional>
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#include "backend/kernel_compiler/common_utils.h"
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#include "backend/kernel_compiler/cpu/cpu_kernel.h"
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#include "fl/server/common.h"
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#include "fl/server/memory_register.h"
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#include "fl/server/kernel/params_info.h"
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namespace mindspore {
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namespace fl {
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namespace server {
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namespace kernel {
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using mindspore::kernel::IsSameShape;
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using mindspore::kernel::USE_NESTEROV;
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// OptimizerKernel is the kernel in server for weights' optimizing.
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// Normally server's optimizer kernels should be inherited from CPU's optimzier kernels to reuse the implementation.
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class OptimizerKernel : public CPUKernel {
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public:
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OptimizerKernel() = default;
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virtual ~OptimizerKernel() = default;
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// InitKernel and Launch methods are inherited from pure virtual function of CPUKernel so it must have implementation.
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virtual void InitKernel(const CNodePtr &kernel_node) {}
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virtual bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
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const std::vector<AddressPtr> &outputs) {
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return true;
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}
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// Server kernel's memory allocation method, which is different from the workflow in
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// Session(GPUSession/CPUSession/AscendSession).
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// virtual void AssignMemory(const CNodePtr &kernel_node, std::shared_ptr<MemoryRegister> memory_register) = 0;
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// Setter and getter of kernels parameters information.
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void set_params_info(const ParamsInfo ¶ms_info) { params_info_ = params_info; }
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const std::vector<std::string> &input_names() { return params_info_.inputs_names(); }
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const std::vector<std::string> &workspace_names() { return params_info_.workspace_names(); }
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const std::vector<std::string> &output_names() { return params_info_.outputs_names(); }
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// Returns information about whether some inputs should reuse kernel node inputs memory.
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const ReuseKernelNodeInfo &reuse_kernel_node_inputs_info() { return reuse_kernel_node_inputs_info_; }
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protected:
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virtual void GenerateReuseKernelNodeInfo() = 0;
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void InitServerKernelInputOutputSize(const CNodePtr &kernel_node) {
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MS_EXCEPTION_IF_NULL(kernel_node);
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size_t input_num = AnfAlgo::GetInputTensorNum(kernel_node);
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size_t type_size = sizeof(float);
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for (size_t input_index = 0; input_index < input_num; ++input_index) {
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std::vector<size_t> shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, input_index);
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size_t tensor_size =
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shape.empty() ? type_size : std::accumulate(shape.begin(), shape.end(), type_size, std::multiplies<size_t>());
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input_size_list_.emplace_back(tensor_size);
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}
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size_t output_num = AnfAlgo::GetOutputTensorNum(kernel_node);
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for (size_t output_index = 0; output_index < output_num; ++output_index) {
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std::vector<size_t> shape = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, output_index);
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size_t tensor_size =
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shape.empty() ? type_size : std::accumulate(shape.begin(), shape.end(), type_size, std::multiplies<size_t>());
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output_size_list_.emplace_back(tensor_size);
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}
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}
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// Parameters information used for kernel register, memory assignment, etc.
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ParamsInfo params_info_;
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// Information about server kernel reusing kernel node inputs memory from the front end.
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// Key refers to the server kernel's input index. Value refers to the kernel node's input index.
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ReuseKernelNodeInfo reuse_kernel_node_inputs_info_;
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};
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} // namespace kernel
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} // namespace server
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} // namespace fl
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} // namespace mindspore
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#endif // MINDSPORE_CCSRC_FL_SERVER_KERNEL_OPTIMIZER_KERNEL_H_
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