// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. #include #include #include #include #include #include "paddle/fluid/framework/details/all_reduce_deps_pass.h" #include "paddle/fluid/framework/details/all_reduce_op_handle.h" #include "paddle/fluid/framework/details/multi_devices_helper.h" #include "paddle/fluid/framework/details/op_graph_view.h" #include "paddle/fluid/framework/details/var_handle.h" #include "paddle/fluid/framework/ir/graph_helper.h" #include "paddle/fluid/framework/op_proto_maker.h" namespace paddle { namespace framework { namespace details { VarHandle* GetValidInput(const OpHandleBase* a) { for (auto p : a->Inputs()) { VarHandle* b = dynamic_cast(p); if (b) { return b; } } return nullptr; } std::unique_ptr AllReduceDepsPass::ApplyImpl( std::unique_ptr graph) const { auto graph_ops = ir::FilterByNodeWrapper(*graph); // get vars order int order = 0; std::unordered_map vars; // TODO(gongwb): use graph topology sort to find the order of operators. // Note that must assert topology sort is stable auto& ops = Get>(kAllOpDescs); for (auto* op_desc : ops) { auto outputs = op_desc->Outputs(); for (auto& o_it : outputs) { for (auto& v : o_it.second) { // values vars[v] = order; } } order++; } std::vector dist_ops; // get allreduce ops. for (auto& op : graph_ops) { // FIXME(gongwb):add broad cast. if (op->Name() == "all_reduce" || op->Name() == "reduce") { dist_ops.push_back(op); } } VLOG(10) << "dist_ops size:" << dist_ops.size() << std::endl; std::sort(dist_ops.begin(), dist_ops.end(), [&](OpHandleBase* op1, OpHandleBase* op2) { VarHandle* i0 = dynamic_cast(GetValidInput(op1)); VarHandle* i1 = dynamic_cast(GetValidInput(op2)); PADDLE_ENFORCE(i0 != nullptr && i1 != nullptr, "%s convert to %s error", op1->DebugString(), op2->DebugString()); auto l_it = vars.find(i0->name()); auto r_it = vars.find(i1->name()); if (l_it->second < r_it->second) return true; if (l_it->second == r_it->second) { return i0->name() < i1->name(); } return false; }); // add dependency. auto& sorted_ops = dist_ops; for (size_t i = 1; i < sorted_ops.size(); ++i) { auto* dep_var = new DummyVarHandle(graph->CreateControlDepVar()); auto* pre_op = sorted_ops[i - 1]; auto* op = sorted_ops[i]; pre_op->AddOutput(dep_var); op->AddInput(dep_var); graph->Get(kGraphDepVars).emplace(dep_var); VLOG(10) << "add all_reduce sequential dependencies between " << pre_op << " and " << op; VLOG(10) << "pre_op:" << pre_op->DebugString() << ", op:" << op->DebugString(); } return graph; } } // namespace details } // namespace framework } // namespace paddle REGISTER_PASS(all_reduce_deps_pass, paddle::framework::details::AllReduceDepsPass) .RequirePassAttr(paddle::framework::details::kAllOpDescs);