build_strategy.cc

/* 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 "paddle/fluid/framework/details/build_strategy.h"

#include <glog/logging.h>
#include <memory>

#include "paddle/fluid/framework/details/memory_optimize_helper.h"
#include "paddle/fluid/framework/details/multi_devices_graph_pass.h"
#include "paddle/fluid/framework/details/multi_devices_graph_print_pass.h"
#include "paddle/fluid/framework/details/reduce_op_handle.h"
#include "paddle/fluid/framework/details/sequential_execution_pass.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/framework/ir/graph_to_program_pass.h"
#include "paddle/fluid/framework/ir/graph_viz_pass.h"

namespace paddle {
namespace framework {
namespace details {

static inline bool SeqOnlyAllReduceOps(const BuildStrategy &strategy) {
  // Should fix the allreduce op order if scheduling
  // them in multiple threads or processes to avoid hang.
  // NOTE: ParallelGraph would execute this pass on each graph, so
  // don't need to append it here.
  return (!strategy.enable_sequential_execution_ &&
          strategy.num_trainers_ > 1) &&
         !strategy.enable_parallel_graph_;
}

class ParallelExecutorPassBuilder : public ir::PassBuilder {
 public:
  explicit ParallelExecutorPassBuilder(const BuildStrategy &strategy)
      : ir::PassBuilder(), strategy_(strategy) {
    if (strategy_.enable_sequential_execution_) {
      AppendPass("sequential_execution_pass");
    }

    // Add op fusion.
    if (strategy.fuse_relu_depthwise_conv_) {
      AppendPass("fuse_relu_depthwise_conv_pass");
    }

    // NOTE(dzhwinter): A note for automatical inplace.
    // 1. modify program desc passes should put
    // before inplace pass.
    // 2. manually configured inplace should put
    // before inplace_pass

    // Add automatically inplace.
    if (strategy_.enable_inplace_) {
      AppendPass("inplace_pass");
    }

    // Add a graph viz pass to record a graph.
    if (!strategy_.debug_graphviz_path_.empty()) {
      auto viz_pass = AppendPass("graph_viz_pass");
      const std::string graph_path = string::Sprintf(
          "%s%s", strategy_.debug_graphviz_path_.c_str(), "_original_graph");
      viz_pass->Set<std::string>("graph_viz_path", new std::string(graph_path));
    }

    if (strategy.fuse_elewise_add_act_ops_) {
      auto fuse_elewise_add_act_pass = AppendPass("fuse_elewise_add_act_pass");
      // Add a graph viz pass to record a graph.
      if (!strategy.debug_graphviz_path_.empty()) {
        auto viz_pass = AppendPass("graph_viz_pass");
        const std::string graph_path = string::Sprintf(
            "%s%s", strategy.debug_graphviz_path_.c_str(), "_fused_graph");
        viz_pass->Set<std::string>("graph_viz_path",
                                   new std::string(graph_path));
      }
    }

    CollectiveContext *context = CollectiveContext::GetInstance();
    context->endpoints_ = strategy_.trainers_endpoints_;
    context->trainer_id_ = strategy_.trainer_id_;
    PADDLE_ENFORCE(strategy_.trainer_id_ >= 0, "trainer_id_ >= 0");
    if (strategy_.trainer_id_ > 0 && strategy_.trainers_endpoints_.size() > 0) {
      PADDLE_ENFORCE((unsigned)(strategy_.trainer_id_) <
                         strategy_.trainers_endpoints_.size(),
                     "trainer_id_ < endpoints_ size");
    }
    VLOG(1) << "CollectiveContext:" << context->String();

    // NOTE(dzh): memory optimize should be a runtime pass.
    // However, after multi_devices_pass, VarHandle, OpHandle is
    // the de-fact IR, any reuse on Graph is meaningless.
    // A side-effect of that, memory optimize cannot forsee the fetched vars
    // , so fetchlist should be set persistable before call the Run interface.
    if (strategy.memory_optimize_) {
      auto memory_optimize_pass = AppendPass("memory_optimize_pass");
    }

    AppendMultiDevPass(strategy);

    // Add a graph print pass to record a graph with device info.
    if (!strategy_.debug_graphviz_path_.empty()) {
      auto multi_devices_print_pass = AppendPass("multi_devices_print_pass");
      const std::string graph_path =
          string::Sprintf("%s%s", strategy_.debug_graphviz_path_.c_str(),
                          "_multi_devices_graph");
      multi_devices_print_pass->Set<std::string>(kGraphvizPath,
                                                 new std::string(graph_path));
      multi_devices_print_pass->Set<details::GraphvizSSAGraphPrinter>(
          "graph_printer", new details::GraphvizSSAGraphPrinter);
    }

    // Verify that the graph is correct for multi-device executor.
    AppendPass("multi_devices_check_pass");

    if (SeqOnlyAllReduceOps(strategy)) {
      AppendPass("all_reduce_deps_pass");
    }

    if (strategy_.remove_unnecessary_lock_) {
      AppendPass("modify_op_lock_and_record_event_pass");
    }
  }

  // Convert graph to run on multi-devices.
  void AppendMultiDevPass(const BuildStrategy &strategy) {
    ir::Pass *multi_devices_pass;

    if (strategy_.is_distribution_) {
      VLOG(3) << "multi device parameter server mode";
      multi_devices_pass = AppendPass("dist_multi_devices_pass").get();
    } else if (strategy_.async_mode_) {
      multi_devices_pass = AppendPass("async_multi_devices_pass").get();
    } else {
      if (strategy.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce) {
        VLOG(3) << "multi devices collective mode with allreduce";
        multi_devices_pass =
            AppendPass("allreduce_mode_multi_devices_pass").get();
      } else if (strategy.reduce_ == BuildStrategy::ReduceStrategy::kReduce) {
        VLOG(3) << "multi deivces collective mode with reduce";
        multi_devices_pass = AppendPass("reduce_mode_multi_devices_pass").get();
      } else {
        PADDLE_THROW("Unknown reduce strategy.");
      }
    }
    multi_devices_pass->SetNotOwned<const BuildStrategy>("strategy",
                                                         &strategy_);
  }

 private:
  BuildStrategy strategy_;
};

std::shared_ptr<ir::PassBuilder> BuildStrategy::CreatePassesFromStrategy(
    bool finalize_strategy) const {
  if (is_finalized_) {
    return pass_builder_;
  }
  pass_builder_.reset(new ParallelExecutorPassBuilder(*this));
  if (finalize_strategy) {
    is_finalized_ = true;
  }
  return pass_builder_;
}

bool BuildStrategy::IsMultiDevPass(const std::string &pass_name) const {
  return framework::details::MultiDevSSAGraphBuilder().count(pass_name) > 0;
}

std::unique_ptr<ir::Graph> BuildStrategy::Apply(
    std::unique_ptr<ir::Graph> graph,
    const std::vector<platform::Place> &places,
    const std::string &loss_var_name, const std::vector<Scope *> &local_scopes,
    const size_t &nranks,
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
    const bool use_cuda, platform::NCCLContextMap *nccl_ctxs) const {
#else
    const bool use_cuda) const {
#endif
  // Create a default one if not finalized by user.
  CreatePassesFromStrategy(false);

  for (std::shared_ptr<ir::Pass> &pass : pass_builder_->AllPasses()) {
    if (IsMultiDevPass(pass->Type())) {
      pass->Erase(kPlaces);
      pass->SetNotOwned<const std::vector<platform::Place>>(kPlaces, &places);
      pass->Erase(kLossVarName);
      pass->SetNotOwned<const std::string>(kLossVarName, &loss_var_name);
      pass->Erase(kLocalScopes);
      pass->SetNotOwned<const std::vector<Scope *>>(kLocalScopes,
                                                    &local_scopes);
      pass->Erase(kNRanks);
      pass->Set<size_t>(kNRanks, new size_t(nranks));

#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
      platform::NCCLContextMap *nctx = use_cuda ? nccl_ctxs : nullptr;
      pass->Erase("nccl_ctxs");
      pass->SetNotOwned<platform::NCCLContextMap>("nccl_ctxs", nctx);
#endif
    } else if (pass->Type() == "sequential_execution_pass") {
      LOG(INFO) << "set enable_sequential_execution:"
                << enable_sequential_execution_;
    } else if (pass->Type() == "all_reduce_deps_pass") {
      LOG(INFO) << "SeqOnlyAllReduceOps:" << SeqOnlyAllReduceOps(*this)
                << ", num_trainers:" << num_trainers_;
    } else if (pass->Type() == "fuse_relu_depthwise_conv_pass") {
      if (!use_cuda) {
        LOG(WARNING) << "fuse_relu_depthwise_conv_pass is only supported on "
                        "GPU, skipped.";
        continue;
      }
    }
    VLOG(3) << "Start Apply Pass " << pass->Type();
    graph = pass->Apply(std::move(graph));
    VLOG(3) << "Finish Apply Pass " << pass->Type();
  }
  VLOG(3) << "All Passes Applied";
  return graph;
}

}  // namespace details
}  // namespace framework
}  // namespace paddle

USE_PASS(fuse_relu_depthwise_conv_pass);
USE_PASS(fuse_elewise_add_act_pass);
USE_PASS(graph_viz_pass);
USE_PASS(multi_batch_merge_pass);
USE_PASS(reduce_mode_multi_devices_pass);
USE_PASS(allreduce_mode_multi_devices_pass);
USE_PASS(dist_multi_devices_pass);
USE_PASS(multi_devices_check_pass);
USE_PASS(multi_devices_print_pass);
USE_PASS(memory_optimize_pass);
USE_PASS(sequential_execution_pass);
USE_PASS(all_reduce_deps_pass);
USE_PASS(modify_op_lock_and_record_event_pass);
USE_PASS(inplace_pass);
USE_PASS(lock_free_optimize_pass);
USE_PASS(graph_to_program_pass);