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https://github.com/saymrwulf/onnxruntime.git
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PR comments (#3374)
* PR comments * PR comments * PR comments * PR comments * PR comments * PR comments * PR comments Co-authored-by: Ethan Tao <ettao@microsoft.com>
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ytaous
GitHub
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2ce90cff4c
6 changed files with 57 additions and 46 deletions
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@ -796,6 +796,7 @@ if (onnxruntime_USE_DML)
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endif()
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if (onnxruntime_ENABLE_TRAINING)
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add_compile_definitions(ENABLE_TRAINING)
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if (onnxruntime_USE_HOROVOD)
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if (WIN32)
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message( FATAL_ERROR "Horovod is not supported on Windows." )
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@ -30,6 +30,3 @@ if (WIN32)
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set_target_properties(onnxruntime_framework PROPERTIES VS_USER_PROPS ${PROJECT_SOURCE_DIR}/ConfigureVisualStudioCodeAnalysis.props)
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endif()
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if(onnxruntime_ENABLE_TRAINING)
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target_compile_definitions(onnxruntime_framework PUBLIC ENABLE_TRAINING)
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endif()
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@ -187,12 +187,6 @@ Status SequentialExecutor::Execute(const SessionState& session_state, const std:
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return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "Got nullptr from GetKernel for node: ",
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node.Name());
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std::string node_name = node.Name();
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if (node_name.empty()) {
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// Node name field is often blank in execution graph, so derive something meaningful for profile traces and logs.
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node_name = node.OpType() + "_" + std::to_string(node_index);
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}
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#ifdef ONNXRUNTIME_ENABLE_INSTRUMENT
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LARGE_INTEGER kernel_start;
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QueryPerformanceCounter(&kernel_start);
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@ -241,22 +235,26 @@ Status SequentialExecutor::Execute(const SessionState& session_state, const std:
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utils::DumpNodeInputs(op_kernel_context, p_op_kernel->Node());
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#endif
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const std::string node_name_for_profiling = [&]() -> std::string {
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if (!is_profiler_enabled) return {};
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// Derive something meaningful for profile traces and logs if node name field is blank in execution graph
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return node.Name().empty() ? MakeString(node.OpType(), "_", node_index) : node.Name();
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}();
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if (is_profiler_enabled) {
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session_state.Profiler().EndTimeAndRecordEvent(profiling::NODE_EVENT,
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node_name + "_fence_before",
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node_name_for_profiling + "_fence_before",
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sync_time_begin,
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{{"op_name", p_op_kernel->KernelDef().OpName()}});
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// call compute on the kernel
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VLOGS(logger, 1) << "Computing kernel: " << node_name;
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VLOGS(logger, 1) << "Computing kernel: " << node_name_for_profiling;
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kernel_begin_time = session_state.Profiler().StartTime();
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}
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if (is_profiler_enabled) {
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// Calculate total input sizes for this operation.
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CalculateTotalInputSizes(&op_kernel_context, p_op_kernel,
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input_activation_sizes,input_parameter_sizes, node_name);
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input_activation_sizes,input_parameter_sizes, node_name_for_profiling);
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}
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#ifdef CONCURRENCY_VISUALIZER
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@ -286,12 +284,12 @@ Status SequentialExecutor::Execute(const SessionState& session_state, const std:
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if (is_profiler_enabled) {
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// Calculate total output sizes for this operation.
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CalculateTotalOutputSizes(&op_kernel_context, total_output_sizes, node_name);
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CalculateTotalOutputSizes(&op_kernel_context, total_output_sizes, node_name_for_profiling);
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#if defined(TRACE_EXECUTION)
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// Trace execution step.
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const Node& node = p_op_kernel->Node();
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std::cout << "Executed op kernel node " << node_name
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std::cout << "Executed op kernel node " << node_name_for_profiling
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<< " Index=" << node.Index()
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<< " OpType=" << node.OpType()
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<< " Name=" << node.Name()
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@ -302,7 +300,7 @@ Status SequentialExecutor::Execute(const SessionState& session_state, const std:
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#endif
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session_state.Profiler().EndTimeAndRecordEvent(profiling::NODE_EVENT,
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node_name + "_kernel_time",
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node_name_for_profiling + "_kernel_time",
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kernel_begin_time,
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// Log additional operation args / info.
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{
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@ -356,7 +354,7 @@ Status SequentialExecutor::Execute(const SessionState& session_state, const std:
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#endif
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if (is_profiler_enabled) {
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session_state.Profiler().EndTimeAndRecordEvent(profiling::NODE_EVENT,
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node_name + "_fence_after",
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node_name_for_profiling + "_fence_after",
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sync_time_begin,
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{{"op_name", p_op_kernel->KernelDef().OpName()}});
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}
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@ -366,7 +364,7 @@ Status SequentialExecutor::Execute(const SessionState& session_state, const std:
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#endif
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// free ml-values corresponding to this node
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VLOGS(logger, 1) << "Releasing node ML values after computing kernel: " << node_name;
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VLOGS(logger, 1) << "Releasing node ML values.";
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ORT_RETURN_IF_ERROR(ReleaseNodeMLValues(frame, seq_exec_plan, node_exec_plan, logger));
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}
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@ -136,6 +136,7 @@ const std::unordered_map<int, OrtValue>& SessionState::GetConstantInitializedTen
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return constant_initialized_tensors_;
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}
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#ifdef ENABLE_TRAINING
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Status SessionState::GetInitializedTensors(
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const std::unordered_set<std::string>& interested_weights,
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bool allow_missing_weights, NameMLValMap& retrieved_weights) const {
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@ -162,6 +163,7 @@ NameMLValMap SessionState::GetInitializedTensors(const std::unordered_set<std::s
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ORT_ENFORCE(status.IsOK(), status.ErrorMessage());
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return result;
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}
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#endif
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SessionState& SessionState::SetLogger(const logging::Logger& logger) {
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logger_ = &logger;
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@ -186,6 +188,7 @@ static int64_t CalculateMemoryPatternsKey(const std::vector<std::reference_wrapp
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return key;
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}
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#ifdef ENABLE_TRAINING
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namespace {
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Status ResolveDimParams(const GraphViewer& graph, const std::map<std::string, TensorShape>& feeds, std::unordered_map<std::string, int64_t>& out) {
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for (const auto* input : graph.GetInputs()) {
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@ -280,6 +283,7 @@ Status SessionState::GeneratePatternGroupCache(const std::vector<std::reference_
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}
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return Status::OK();
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}
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#endif
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const MemoryPatternGroup* SessionState::GetMemoryPatternGroup(const std::vector<std::reference_wrapper<const TensorShape>>& input_shapes,
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const std::vector<int>& feed_mlvalue_idxs) const {
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@ -459,7 +463,9 @@ const NodeIndexInfo& SessionState::GetNodeIndexInfo() const {
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}
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void SessionState::UpdateToBeExecutedNodes(const std::vector<int>& fetch_mlvalue_idxs) {
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if (to_be_executed_nodes_.find(fetch_mlvalue_idxs) != to_be_executed_nodes_.end())
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std::vector<int> sorted_idxs = fetch_mlvalue_idxs;
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std::sort(sorted_idxs.begin(), sorted_idxs.end());
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if (to_be_executed_nodes_.find(sorted_idxs) != to_be_executed_nodes_.end())
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return;
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const Graph& graph = GetGraphViewer()->GetGraph();
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@ -471,10 +477,8 @@ void SessionState::UpdateToBeExecutedNodes(const std::vector<int>& fetch_mlvalue
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for (auto idx : fetch_mlvalue_idxs) {
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std::string node_arg_name;
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if (!this->GetOrtValueNameIdxMap().GetName(idx, node_arg_name).IsOK()) {
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to_be_executed_nodes_.insert(std::make_pair(fetch_mlvalue_idxs, reachable_nodes));
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return;
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}
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const auto status = this->GetOrtValueNameIdxMap().GetName(idx, node_arg_name);
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ORT_ENFORCE(status.IsOK(), status.ErrorMessage());
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auto ending_node = graph.GetProducerNode(node_arg_name);
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nodes.push_back(ending_node);
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}
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@ -482,12 +486,14 @@ void SessionState::UpdateToBeExecutedNodes(const std::vector<int>& fetch_mlvalue
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// Reversely traverse to get reachable nodes.
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graph.ReverseDFSFrom(
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nodes, {}, [&reachable_nodes](const Node* n) { reachable_nodes.insert(n->Index()); });
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to_be_executed_nodes_.insert(std::make_pair(fetch_mlvalue_idxs, reachable_nodes));
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to_be_executed_nodes_.insert(std::make_pair(sorted_idxs, reachable_nodes));
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}
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const std::unordered_set<NodeIndex>* SessionState::GetToBeExecutedNodes(
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const std::vector<int>& fetch_mlvalue_idxs) const {
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auto it = to_be_executed_nodes_.find(fetch_mlvalue_idxs);
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std::vector<int> sorted_idxs = fetch_mlvalue_idxs;
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std::sort(sorted_idxs.begin(), sorted_idxs.end());
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auto it = to_be_executed_nodes_.find(sorted_idxs);
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return (it != to_be_executed_nodes_.end()) ? &it->second : nullptr;
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}
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@ -116,6 +116,7 @@ class SessionState {
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*/
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const std::unordered_map<int, OrtValue>& GetConstantInitializedTensors() const;
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#ifdef ENABLE_TRAINING
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/**
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Get some initialized tensors (weights).
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@param interested_weights The names of the weights to retrieve.
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@ -133,6 +134,7 @@ class SessionState {
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Any names in interested_weights with no corresponding weight are ignored.
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*/
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NameMLValMap GetInitializedTensors(const std::unordered_set<std::string>& interested_weights) const;
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#endif
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// execution plan
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void SetExecutionPlan(std::unique_ptr<SequentialExecutionPlan> p_seq_exec_plan);
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@ -249,10 +251,12 @@ class SessionState {
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private:
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ORT_DISALLOW_COPY_ASSIGNMENT_AND_MOVE(SessionState);
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#ifdef ENABLE_TRAINING
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Status GeneratePatternGroupCache(
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const std::vector<std::reference_wrapper<const TensorShape>>& input_shape,
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const std::vector<int>& feed_mlvalue_idxs,
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MemoryPatternGroup* output) const;
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#endif
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// cache of the constructed kernels to avoid spending construction
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// time per executor
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@ -10,16 +10,21 @@ using namespace ONNX_NAMESPACE;
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using namespace ::onnxruntime::common;
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namespace onnxruntime {
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static std::pair<bool, Node*> IsInputTranspose(const Graph& graph, NodeArg& node_arg) {
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const auto& trans_node = graph.GetProducerNode(node_arg.Name());
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static Node* GetTransposeNodeFromOutput(Graph& graph, NodeArg& node_arg) {
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Node* trans_node = graph.GetMutableProducerNode(node_arg.Name());
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if (trans_node == nullptr || trans_node->OpType() != "Transpose") {
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return std::make_pair(false, nullptr);
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return nullptr;
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}
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// if the node has Graph output, skip it too
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if (!graph.GetNodeOutputsInGraphOutputs(*trans_node).empty()) {
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return nullptr;
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}
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auto perms = RetrieveValues<int64_t>(trans_node->GetAttributes().at("perm"));
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int64_t rank = perms.size();
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if (rank < 2) {
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return std::make_pair(false, nullptr);
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return nullptr;
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}
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bool is_trans_on_last_two_dims = true;
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@ -31,14 +36,14 @@ static std::pair<bool, Node*> IsInputTranspose(const Graph& graph, NodeArg& node
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}
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if (is_trans_on_last_two_dims) {
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is_trans_on_last_two_dims = (int64_t)perms[rank - 2] == rank - 1 && (int64_t)perms[rank - 1] == rank - 2;
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is_trans_on_last_two_dims = perms[rank - 2] == rank - 1 && perms[rank - 1] == rank - 2;
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}
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if (!is_trans_on_last_two_dims) {
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return std::make_pair(false, nullptr);
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return nullptr;
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}
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return std::make_pair(true, const_cast<Node*>(trans_node));
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return trans_node;
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}
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static size_t UpdateConsumerCount(Graph& graph, NodeArg* target, std::unordered_map<NodeArg*, size_t>& count_map) {
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@ -72,27 +77,27 @@ Status MatmulTransposeFusion::ApplyImpl(Graph& graph, bool& modified, int graph_
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}
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NodeArg* left_input = node.MutableInputDefs()[0];
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auto left = IsInputTranspose(graph, *left_input);
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auto left = GetTransposeNodeFromOutput(graph, *left_input);
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NodeArg* right_input = node.MutableInputDefs()[1];
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auto right = IsInputTranspose(graph, *right_input);
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auto right = GetTransposeNodeFromOutput(graph, *right_input);
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if (!left.first && !right.first) {
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if (!left && !right) {
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continue;
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}
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if (left.first) {
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if (left) {
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size_t left_consumers = UpdateConsumerCount(graph, left_input, consumer_count);
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if (left_consumers == 0)
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removed_nodes.push_front(left.second->Index());
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left_input = left.second->MutableInputDefs()[0];
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removed_nodes.push_front(left->Index());
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left_input = left->MutableInputDefs()[0];
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}
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if (right.first) {
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if (right) {
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size_t right_consumers = UpdateConsumerCount(graph, right_input, consumer_count);
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if (right_consumers == 0)
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removed_nodes.push_front(right.second->Index());
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right_input = right.second->MutableInputDefs()[0];
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removed_nodes.push_front(right->Index());
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right_input = right->MutableInputDefs()[0];
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}
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const std::vector<NodeArg*> input_defs{left_input, right_input};
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@ -103,16 +108,16 @@ Status MatmulTransposeFusion::ApplyImpl(Graph& graph, bool& modified, int graph_
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"fused MatMul and Transpose ",
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input_defs,
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output_defs, {}, kMSDomain);
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bool transpose_left = left.first;
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bool transpose_left = (left != nullptr);
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if (node.OpType() == "TransposeMatMul") {
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transpose_left ^= static_cast<bool>(node.GetAttributes().at("transA").i());
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}
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bool transpose_right = right.first;
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bool transpose_right = (right != nullptr);
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if (node.OpType() == "TransposeMatMul") {
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transpose_right ^= static_cast<bool>(node.GetAttributes().at("transB").i());
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}
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matmul_node.AddAttribute("transA", (int64_t)transpose_left);
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matmul_node.AddAttribute("transB", (int64_t)transpose_right);
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matmul_node.AddAttribute("transA", static_cast<int64_t>(transpose_left));
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matmul_node.AddAttribute("transB", static_cast<int64_t>(transpose_right));
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// Assign provider to this new node. Provider should be same as the provider for old node.
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matmul_node.SetExecutionProviderType(node.GetExecutionProviderType());
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