[ControlFlow] replace executor in run method of control flow ops with standalone_executor #45696
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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@@ -16,6 +16,8 @@ | |
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| #include <unordered_set> | ||
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| #include "gflags/gflags.h" | ||
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| #include "paddle/fluid/framework/details/nan_inf_utils.h" | ||
| #include "paddle/fluid/framework/details/share_tensor_buffer_functor.h" | ||
| #include "paddle/fluid/framework/new_executor/interpreter/interpreter_util.h" | ||
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@@ -47,6 +49,12 @@ PADDLE_DEFINE_EXPORTED_bool(new_executor_use_local_scope, | |
| true, | ||
| "Use local_scope in new executor(especially used " | ||
| "in UT), can turn off for better performance"); | ||
| PADDLE_DEFINE_EXPORTED_bool(control_flow_use_new_executor, | ||
| true, | ||
| "Use new executor in control flow op"); | ||
| PADDLE_DEFINE_EXPORTED_bool(control_flow_use_new_executor_cache, | ||
| true, | ||
| "Cache new executor in control flow op"); | ||
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| DECLARE_bool(check_nan_inf); | ||
| DECLARE_bool(benchmark); | ||
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@@ -107,7 +115,8 @@ InterpreterCore::InterpreterCore(const platform::Place& place, | |
| const BlockDesc& block, | ||
| const std::set<std::string>& skip_gc_vars, | ||
| framework::Scope* scope, | ||
| bool used_for_jit, | ||
| bool used_for_control_flow_op) | ||
| : place_(place), | ||
| block_(block), | ||
| execution_config_(place, block.OpSize()), | ||
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@@ -119,8 +128,10 @@ InterpreterCore::InterpreterCore(const platform::Place& place, | |
| completion_notifier_ = main_thread_blocker_.RegisterEvent(kTaskCompletion); | ||
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| execution_config_.used_for_jit = used_for_jit; | ||
| execution_config_.used_for_control_flow_op = used_for_control_flow_op; | ||
| execution_config_.create_local_scope = !used_for_jit && | ||
| FLAGS_new_executor_use_local_scope && | ||
| !used_for_control_flow_op; | ||
| execution_config_.skip_gc_vars = skip_gc_vars; | ||
| execution_config_.Log(/*log_level=*/8); | ||
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@@ -224,7 +235,7 @@ paddle::framework::FetchList InterpreterCore::Run( | |
| } | ||
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| paddle::framework::FetchList InterpreterCore::Run( | ||
| const std::vector<std::string>& feed_names, bool need_fetch) { | ||
|
There was a problem hiding this comment. Since there is no fetch var in the running Program of conditional_block_op, whether the need_fetch parameter may not be required? There was a problem hiding this comment. For There was a problem hiding this comment. Maybe we can decide by whether the block has the |
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| SetDeviceId(place_); | ||
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| #ifdef PADDLE_WITH_MKLDNN | ||
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@@ -243,12 +254,12 @@ paddle::framework::FetchList InterpreterCore::Run( | |
| execution_config_.skip_gc_vars, | ||
| &op_func_nodes, | ||
| &var_scope_, | ||
| execution_config_, | ||
| HasLocalScope()); | ||
| SetFeedVarsInplaceSkip(feed_names); | ||
| // convert vec func_list to graph | ||
| Convert(&op_func_nodes); | ||
| is_build_ = true; | ||
| } else { | ||
| // For the program that only run once, it is no need to | ||
| // create work_queue, so the async_work_queue_ is created | ||
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@@ -281,7 +292,7 @@ paddle::framework::FetchList InterpreterCore::Run( | |
| Scope* inner_scope = | ||
| HasLocalScope() ? local_scope_ : var_scope_.GetMutableScope(); | ||
| auto* fetch_var = inner_scope->FindVar(interpreter::kFetchVarName); | ||
| if (fetch_var && need_fetch) { | ||
| return std::move(*fetch_var->GetMutable<framework::FetchList>()); | ||
| } else { | ||
| return {}; | ||
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@@ -311,9 +322,18 @@ void InterpreterCore::reset_scope(Scope* new_scope) { | |
| var_scope_.SetScope(new_scope); | ||
| auto& var_list = var_scope_.MutableVarList(); | ||
| for (size_t i = 0; i < var_list.size(); i++) { | ||
| const auto& var_name = var_scope_.GetNameById(i); | ||
| var_list[i] = new_scope->FindVar(var_name); | ||
| } | ||
| // The index should assured valid, cause the InterpreterCore may not be fully | ||
| // built, but was still cached and used. For example, see unit test | ||
| // `test_assert.py`, it may exit before `InterpreterCore::Convert`, but still | ||
| // was cached and used by later tests. | ||
| for (size_t i = 0; i < std::min(refs_.size(), var_list.size()); i++) { | ||
| refs_[i]->ResetVariable(var_list[i]); | ||
| } | ||
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| for (size_t i = 0; i < vec_instruction_.size(); i++) { | ||
| BuildAndCacheInstructionCtx(&vec_instruction_[i]); | ||
| } | ||
| } | ||
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@@ -540,6 +560,10 @@ void InterpreterCore::Convert( | |
| if (var_desc && ins.count(item.first) && | ||
| !info.IsInArgBufferNeeded(var_desc->Name())) { | ||
| continue; | ||
| } else if (!block_.HasVar(var_scope_.GetNameById(id))) { | ||
| VLOG(10) << "[gc_check_inputs] skip gc: " | ||
| << var_scope_.GetNameById(id); | ||
| continue; | ||
| } | ||
| gc_check_vars.insert(id); | ||
| } | ||
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@@ -661,9 +685,9 @@ void InterpreterCore::RunInstruction(const Instruction& instr_node) { | |
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| #ifdef PADDLE_WITH_ASCEND_CL | ||
| if (platform::is_npu_place(place)) { | ||
| // NOTE(wangxi): nan/inf cannot be detected on NPU by checking the | ||
| // variable values, but only through special `float_status` to checks | ||
| // whether the operation is overflow. More about `float_status`, see: | ||
| // https://gitee.com/ascend/modelzoo/issues/I3NF8V?from=project-issue | ||
| if (FLAGS_check_nan_inf) { | ||
| framework::details::NPUAllocAndClearFloatStatus(*op, *local_scope, place); | ||
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@@ -734,7 +758,7 @@ void InterpreterCore::RunInstruction(const Instruction& instr_node) { | |
| } | ||
| } | ||
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| VLOG(4) << "End run " << place << " " << op->DebugStringEx(local_scope); | ||
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| if (!instr_node.InplaceBackMap().empty()) { | ||
| platform::RecordEvent inplaceback_event( | ||
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@@ -965,9 +989,9 @@ void InterpreterCore::RecordStreamForGC(const Instruction& instr) { | |
| if (platform::is_gpu_place(place)) { | ||
| memory::RecordStream(allocation, stream); | ||
| } else if (platform::is_cuda_pinned_place(place)) { | ||
| // TODO(Ruibiao): Here should do something to make sure that the tensor | ||
| // is not freed until the H2D copies done. However, simplely launch a | ||
| // CUDA runtime callback to the H2D stream may lead a high performance | ||
| // overhead. As all the cases we meet in H2D are copies from CPUPlace at | ||
| // present, we just log a WARNING here. A better design is required. | ||
| LOG(WARNING) << "Copy data from a CUDAPinned tensor in an asynchronous " | ||
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@@ -984,8 +1008,8 @@ void InterpreterCore::RecordStreamForGC(const Instruction& instr) { | |
| * instr.GCCheckVars. | ||
| * 2. The stream which initializes this tensor is different from the stream | ||
| * which the instruction run in. | ||
| * 3. The tensor is the instruction's input, cause we assume that | ||
| * instruction will initialize all output tensors with its running stream. | ||
| * 4. In the OP function of this instruction, the tensor is an input of a | ||
| * async CUDA kernel. | ||
| * | ||
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@@ -995,8 +1019,8 @@ void InterpreterCore::RecordStreamForGC(const Instruction& instr) { | |
| * initialized this tensor has less time overhead. Conversely, it may take | ||
| * more time if we try to extract those cross-stream input vars from | ||
| * instr.GCCheckVars. | ||
| * 2. Now the instruction has no idea of which vars involving async running | ||
| * in OP function, and thus we can not recognize condition 4. It should be | ||
| * supported later. | ||
| */ | ||
| for (int var_id : instr.GCCheckVars()) { | ||
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@@ -1099,12 +1123,12 @@ void InterpreterCore::Prepare(const std::vector<std::string>& feed_names, | |
| execution_config_.skip_gc_vars, | ||
| &op_func_nodes, | ||
| &var_scope_, | ||
| execution_config_, | ||
| HasLocalScope()); | ||
| SetFeedVarsInplaceSkip(feed_names); | ||
| // convert vec func_list to graph | ||
| Convert(&op_func_nodes); | ||
| is_build_ = true; | ||
| } | ||
| // NOTE: Because feed_tensor will be GC after | ||
| // paddle::framework::BuildOpFuncList, so we should | ||
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