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 <unordered_set>
#include <utility>
#include "paddle/fluid/framework/details/reduce_op_handle.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"
#include "paddle/fluid/framework/ir/memory_optimize_pass/memory_optimize_helper.h"
#include "paddle/fluid/framework/ir/memory_optimize_pass/reference_count_pass_helper.h"
#include "paddle/fluid/framework/ir/multi_devices_graph_pass/multi_devices_graph_pass.h"
#include "paddle/fluid/framework/ir/multi_devices_graph_pass/multi_devices_graph_print_pass.h"

DECLARE_bool(use_mkldnn);

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) {
    // Add a graph viz pass to record a graph.
    if (!strategy_.debug_graphviz_path_.empty()) {
      VLOG(1) << "Add graph_viz_pass";
      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));
    }

    // Note(zcd): record_skip_memory_opt_vars_pass should be the first pass.
    VLOG(1) << "Add record_skip_memory_opt_vars_pass";
    AppendPass("record_skip_memory_opt_vars_pass");

#ifdef PADDLE_WITH_MKLDNN
    if (FLAGS_use_mkldnn) {
      VLOG(1) << "Add mkldnn_placement_pass";
      AppendPass("mkldnn_placement_pass");
    } else if (!strategy_.mkldnn_enabled_op_types_.empty()) {
      LOG(WARNING)
          << "mkldnn_enabled_op_types specify the operator type list to "
             "use MKLDNN acceleration. It is null in default, means "
             "that all the operators supported by MKLDNN will be "
             "accelerated. And it should not be set when "
             "FLAGS_use_mkldnn=false.";
    }
#else
    PADDLE_ENFORCE(!FLAGS_use_mkldnn,
                   "Please compile with MKLDNN first to use MKLDNN");
#endif

    if (strategy_.enable_sequential_execution_) {
      VLOG(1) << "Add sequential_execution_pass";
      AppendPass("sequential_execution_pass");
    }

    // Add op fusion.
    if (strategy.sync_batch_norm_) {
      AppendPass("sync_batch_norm_pass");
    }

    // Add op fusion.
    if (strategy.fuse_relu_depthwise_conv_) {
      VLOG(1) << "Add fuse_relu_depthwise_conv_pass";
      AppendPass("fuse_relu_depthwise_conv_pass");
    }

    // TODO(zjl): refactor MemoryOptimizePass to fit
    // new strategy, which does not need to set
    // var.persistable = True
    if (strategy_.use_legacy_memory_optimize_strategy_) {
      if (strategy_.enable_inplace_) {
        VLOG(5) << "Add inplace_pass";
        AppendPass("inplace_pass");
      }
    }

    if (strategy_.fuse_elewise_add_act_ops_) {
      VLOG(1) << "Add fuse_elewise_add_act_pass";
      AppendPass("fuse_elewise_add_act_pass");
    }

    // for single card training, fuse_all_reduce_ops is unnecessary.
    // coalesce_grad_tensor_pass should be before of MultiDevPass.
    if (strategy_.fuse_all_reduce_ops_) {
      VLOG(1) << "Add coalesce_grad_tensor_pass";
      AppendPass("coalesce_grad_tensor_pass");
    }

    // Fuse all the optimization operators.
    if (strategy_.is_distribution_) {
      VLOG(3) << "Currently, fuse_all_optimizer_ops only works under "
                 "Non-distributed mode.";
      strategy_.fuse_all_optimizer_ops_ = false;
    }
    if (strategy_.reduce_ == BuildStrategy::ReduceStrategy::kReduce ||
        strategy_.is_distribution_) {
      VLOG(3) << "Currently, fuse_all_optimizer_ops only works under AllReduce "
                 "mode.";
      strategy_.fuse_all_optimizer_ops_ = false;
    }
    if (strategy_.fuse_all_optimizer_ops_) {
      // NOTE: fuse_all_xx_ops will count the number of xx operator first,
      // if the number is zero, fuse_all_reduce_ops will do nothing.
      // Currently, only one type of optimization algorithm can be fused.
      VLOG(1) << "Add fuse_adam_op_pass";
      AppendPass("fuse_adam_op_pass");
      VLOG(1) << "Add fuse_sgd_op_pass";
      AppendPass("fuse_sgd_op_pass");
      VLOG(1) << "Add fuse_momentum_op_pass";
      AppendPass("fuse_momentum_op_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_.use_legacy_memory_optimize_strategy_) {
      if (strategy_.memory_optimize_) {
        VLOG(5) << "Add memory_optimize_pass";
        AppendPass("memory_optimize_pass");
      }
    }

    // runtime_context_cache pass should be the last pass to enable the attr of
    // all original and fused operators. But no operators can be enabled this
    // attr if putting it after MultiDevPass.
    if (strategy_.cache_runtime_context_) {
      VLOG(1) << "Add runtime_context_cache_pass";
      AppendPass("runtime_context_cache_pass");
    }

    AppendMultiDevPass(strategy_);

    if (strategy_.fuse_all_reduce_ops_) {
      // NOTE: fuse_all_reduce_ops will count the number of all_reduce operator
      // first, if the number is zero, fuse_all_reduce_ops will do nothing.
      VLOG(1) << "Add fuse_all_reduce_op_pass";
      AppendPass("fuse_all_reduce_op_pass");
    }

    // Add a graph print pass to record a graph with device info.
    if (!strategy_.debug_graphviz_path_.empty()) {
      VLOG(1) << "Add multi_devices_print_pass";
      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>(ir::kGraphvizPath,
                                                 new std::string(graph_path));
      multi_devices_print_pass->Set<ir::GraphvizSSAGraphPrinter>(
          "graph_printer", new ir::GraphvizSSAGraphPrinter);
    }

    // experimental shows that the program will be faster if append
    // all_reduce_deps_pass here.
    if (!strategy_.enable_parallel_graph_ &&
        (SeqOnlyAllReduceOps(strategy_) ||
         strategy.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce)) {
      VLOG(1) << "Add all_reduce_deps_pass";
      AppendPass("all_reduce_deps_pass");
    }

    if (strategy_.num_trainers_ > 1 && !strategy_.async_mode_ &&
        !strategy_.is_distribution_ &&
        strategy_.enable_backward_optimizer_op_deps_) {
      VLOG(1) << "Add backward_op_deps_pass";
      AppendPass("backward_optimizer_op_deps_pass");
    }

    if (strategy_.remove_unnecessary_lock_) {
      VLOG(1) << "Add modify_op_lock_and_record_event_pass";
      AppendPass("modify_op_lock_and_record_event_pass");
    }

    // Verify that the graph is correct for multi-device executor.
    VLOG(1) << "Add multi_devices_check_pass";
    AppendPass("multi_devices_check_pass");
  }

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

    if (strategy_.async_mode_) {
      VLOG(1) << "Add async_multi_devices_pass";
      multi_devices_pass = AppendPass("async_multi_devices_pass").get();
    } else if (strategy_.is_distribution_) {
      VLOG(1)
          << "Add dist_multi_devices_pass, multi device parameter server mode";
      multi_devices_pass = AppendPass("dist_multi_devices_pass").get();
    } else {
      if (strategy.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce) {
        VLOG(1) << "Add all_reduce_mode_multi_devices_pass";
        multi_devices_pass =
            AppendPass("all_reduce_mode_multi_devices_pass").get();
      } else if (strategy.reduce_ == BuildStrategy::ReduceStrategy::kReduce) {
        VLOG(1) << "Add reduce_mode_multi_devices_pass";
        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::ir::MultiDevSSAGraphBuilder().count(pass_name) > 0;
}

ir::Graph *BuildStrategy::Apply(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::NCCLCommunicator *nccl_ctxs) const {
#else
                                const bool use_cuda) const {
#endif
  VLOG(3) << "apply all passes";
  // Create a default one if not finalized by user.
  CreatePassesFromStrategy(false);

  for (std::shared_ptr<ir::Pass> &pass : pass_builder_->AllPasses()) {
    VLOG(3) << "BuildStrategy::Apply pass:" << pass->Type();
    if (IsMultiDevPass(pass->Type())) {
      pass->Erase(kPlaces);
      pass->SetNotOwned<const std::vector<platform::Place>>(kPlaces, &places);
      pass->Erase(ir::kLossVarName);
      pass->SetNotOwned<const std::string>(ir::kLossVarName, &loss_var_name);
      pass->Erase(kLocalScopes);
      pass->SetNotOwned<const std::vector<Scope *>>(kLocalScopes,
                                                    &local_scopes);
      pass->Erase(ir::kNRanks);
      pass->Set<size_t>(ir::kNRanks, new size_t(nranks));

#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
      platform::NCCLCommunicator *nctx = use_cuda ? nccl_ctxs : nullptr;
      pass->Erase(kNCCLCtxs);
      pass->SetNotOwned<platform::NCCLCommunicator>(kNCCLCtxs, nctx);
#endif
    } else if (pass->Type() == "coalesce_grad_tensor_pass" ||
               pass->Type() == "fuse_adam_op_pass" ||
               pass->Type() == "fuse_sgd_op_pass" ||
               pass->Type() == "fuse_momentum_op_pass" ||
               pass->Type() == "fuse_all_reduce_op_pass") {
      pass->Erase(kPlaces);
      pass->SetNotOwned<const std::vector<platform::Place>>(kPlaces, &places);
      pass->Erase(kLocalScopes);
      pass->SetNotOwned<const std::vector<Scope *>>(kLocalScopes,
                                                    &local_scopes);
      if (pass->Type() == "fuse_all_reduce_op_pass") {
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
        platform::NCCLCommunicator *nctx = use_cuda ? nccl_ctxs : nullptr;
        pass->Erase(kNCCLCtxs);
        pass->SetNotOwned<platform::NCCLCommunicator>(kNCCLCtxs, nctx);
        pass->Erase(kUseHierarchicalAllReduce);
        pass->Set<bool>(kUseHierarchicalAllReduce,
                        new bool(use_hierarchical_allreduce_));
#endif
      }
    } else if (pass->Type() == "coalesce_grad_tensor_pass") {
      pass->Erase(kPlaces);
      pass->SetNotOwned<const std::vector<platform::Place>>(kPlaces, &places);
      pass->Erase(kLocalScopes);
      pass->SetNotOwned<const std::vector<Scope *>>(kLocalScopes,
                                                    &local_scopes);
    } else if (pass->Type() == "sequential_execution_pass") {
      LOG(INFO) << "set enable_sequential_execution:"
                << enable_sequential_execution_;
    } else if (pass->Type() == "all_reduce_deps_pass") {
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
      platform::NCCLCommunicator *nctx = use_cuda ? nccl_ctxs : nullptr;
      pass->Erase(kNCCLCtxs);
      pass->SetNotOwned<platform::NCCLCommunicator>(kNCCLCtxs, nctx);
      pass->Erase(kUseHierarchicalAllReduce);
      pass->Set<bool>(kUseHierarchicalAllReduce,
                      new bool(use_hierarchical_allreduce_));
#endif
      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;
      }
    } else if (pass->Type() == "inplace_pass") {
      pass->Erase(ir::kUseCuda);
      pass->Set<bool>(ir::kUseCuda, new bool(use_cuda));
    } else if (pass->Type() == "mkldnn_placement_pass") {
      pass->Set("mkldnn_enabled_op_types",
                new std::unordered_set<std::string>(mkldnn_enabled_op_types_));
    } else if (pass->Type() == "backward_optimizer_op_deps_pass") {
      if (!use_cuda) {
        VLOG(1) << "backward_optimizer_op_deps_pass is only supported on "
                   "GPU, skipped.";
        continue;
      }
    }
    VLOG(3) << "Start Apply Pass " << pass->Type();
    graph = pass->Apply(graph);
    VLOG(3) << "Finish Apply Pass " << pass->Type();
  }
  VLOG(3) << "All Passes Applied";
  return graph;
}

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

USE_PASS(sync_batch_norm_pass);
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(all_reduce_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(backward_optimizer_op_deps_pass);
USE_PASS(modify_op_lock_and_record_event_pass);
USE_PASS(inplace_pass);
USE_PASS(lock_free_optimize_pass);
USE_PASS(coalesce_grad_tensor_pass);
USE_PASS(graph_to_program_pass);
USE_PASS(fuse_adam_op_pass);
USE_PASS(fuse_sgd_op_pass);
USE_PASS(fuse_momentum_op_pass);
USE_PASS(fuse_all_reduce_op_pass);
USE_PASS(runtime_context_cache_pass);
USE_PASS(record_skip_memory_opt_vars_pass);
#ifdef PADDLE_WITH_MKLDNN
USE_PASS(mkldnn_placement_pass);
#endif