111 lines
4.1 KiB
C++
111 lines
4.1 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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#include "runtime/framework/graph_compiler.h"
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#include "runtime/framework/graph_scheduler.h"
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namespace mindspore {
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namespace runtime {
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void GraphCompiler::set_device_context(device::DeviceContext *device_context) {
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MS_EXCEPTION_IF_NULL(device_context);
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device_context_ = device_context;
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// The member variable 'session_' will be removed after removing session module.
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if (session_ == nullptr) {
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session_ = std::make_shared<session::SessionBasic>();
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}
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}
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GraphId GraphCompiler::CompileGraph(const AnfNodePtrList &nodes, const AnfNodePtrList &outputs) {
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MS_EXCEPTION_IF_NULL(session_);
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// Generate kernel graph.
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auto graph = session_->ConstructKernelGraph(nodes, outputs);
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MS_EXCEPTION_IF_NULL(graph);
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return CompileGraphImpl(graph);
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}
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GraphId GraphCompiler::CompileGraphImpl(const KernelGraphPtr &graph) {
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MS_EXCEPTION_IF_NULL(device_context_);
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// Optimization pass which is irrelevant to device type or format.
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device_context_->OptimizeGraphWithoutDeviceInfo(graph);
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device_context_->SetOperatorInfo(graph->execution_order());
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// Optimization pass which is relevant to device type or format.
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device_context_->OptimizeGraphWithDeviceInfo(graph);
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// Generate 'KernelMod' for all kernels and set 'KernelMod' into kernel,
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// 'KernelMod' is real executive object of kernel.
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device_context_->CreateKernel(graph->execution_order());
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// Transform graph to actor DAG, contains build and link.
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GraphScheduler::GetInstance().Transform(graph, device_context_);
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return graph->graph_id();
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}
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void GraphCompiler::RunGraph(const GraphId &graph_id, const std::vector<tensor::TensorPtr> &inputs,
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VectorRef *outputs) {
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MS_EXCEPTION_IF_NULL(session_);
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auto graph = session_->GetGraph(graph_id);
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MS_EXCEPTION_IF_NULL(graph);
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auto actor_set = GraphScheduler::GetInstance().Fetch(graph);
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MS_EXCEPTION_IF_NULL(actor_set);
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GraphScheduler::GetInstance().Run(actor_set);
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}
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void GraphCompiler::CompileAndRunGraph(session::OpRunInfo *op_run_info, const GraphInfo &graph_info,
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std::vector<tensor::TensorPtr> *input_tensors,
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const std::vector<int64_t> &tensors_mask, VectorRef *outputs) {
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// Check if the graph cache exists.
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if (run_op_graphs_.find(graph_info) == run_op_graphs_.end()) {
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// Prepare the graph
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MS_EXCEPTION_IF_NULL(session_);
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auto graph = session_->ConstructSingleOpGraph(*op_run_info, *input_tensors, tensors_mask);
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MS_EXCEPTION_IF_NULL(graph);
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MS_EXCEPTION_IF_NULL(device_context_);
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device_context_->SetOperatorInfo(graph->execution_order());
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device_context_->OptimizeSingleOpGraph(graph);
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MS_EXCEPTION_IF_NULL(session_);
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session_->RunOpHideNopNode(graph);
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device_context_->CreateKernel(graph->execution_order());
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run_op_graphs_[graph_info] = graph;
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}
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session_->EraseValueNodeTensor(tensors_mask, input_tensors);
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// wait for allreduce
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for (auto &tensor : *input_tensors) {
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if (tensor->NeedWaitDevice()) {
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tensor->WaitDevice();
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}
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}
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// run op
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auto graph = run_op_graphs_[graph_info];
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MS_EXCEPTION_IF_NULL(graph);
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session_->RunOpRemoveNopNode(graph);
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GraphScheduler::GetInstance().Transform(graph, device_context_, input_tensors, GraphExecutionStrategy::kStep);
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auto actor_set = GraphScheduler::GetInstance().Fetch(graph);
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MS_EXCEPTION_IF_NULL(actor_set);
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GraphScheduler::GetInstance().Run(actor_set, GraphExecutionStrategy::kStep);
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}
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} // namespace runtime
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} // namespace mindspore
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