request_handler_impl.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/operators/distributed/request_handler_impl.h"
#include <iostream>
#include <string>
#include <vector>

#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/variable_helper.h"
#include "paddle/fluid/operators/distributed/rpc_server.h"
#include "paddle/fluid/string/piece.h"
#include "paddle/fluid/string/printf.h"
#include "paddle/fluid/string/split.h"

#include "paddle/fluid/operators/distributed/async_sparse_param_update_recorder.h"
#include "paddle/fluid/operators/distributed/heart_beat_monitor.h"
#include "paddle/fluid/operators/distributed/large_scale_kv.h"

namespace paddle {
namespace operators {
namespace distributed {

// define LOOKUP_TABLE_PATH for checkpoint notify to save lookup table variables
// to directory specified.
constexpr char LOOKUP_TABLE_PATH[] = "kLookupTablePath";

bool RequestSendHandler::Handle(const std::string &varname,
                                framework::Scope *scope,
                                framework::Variable *invar,
                                framework::Variable **outvar,
                                const int trainer_id,
                                const std::string &out_var_name,
                                const std::string &table_name) {
  VLOG(4) << "RequestSendHandler:" << varname;

  // Sync
  if (varname == BATCH_BARRIER_MESSAGE) {
    VLOG(3) << "sync: recv BATCH_BARRIER_MESSAGE";
    rpc_server_->IncreaseBatchBarrier(kRequestSend);
  } else if (varname == COMPLETE_MESSAGE) {
    VLOG(3) << "sync: recv complete message";

    if (HeartBeatMonitor::GetInstance() != nullptr) {
      HeartBeatMonitor::GetInstance()->Update(trainer_id, "", COMPLETED);
    }

    rpc_server_->Complete();
  } else {
    // Async
    if (distributed_mode_ != DistributedMode::kSync) {
      VLOG(3) << "async process var: " << varname;
      if (varname == BATCH_BARRIER_MESSAGE) {
        PADDLE_THROW(
            "async mode should not recv BATCH_BARRIER_MESSAGE or "
            "COMPLETE_MESSAGE");
      }
      HeartBeatMonitor::GetInstance()->Update(trainer_id, varname, RUNNING);

      std::string run_varname = varname;

      string::Piece part_piece("@PIECE");
      string::Piece var_name_piece = string::Piece(varname);

      if (string::Contains(var_name_piece, part_piece)) {
        auto varname_splits = paddle::string::Split(varname, '@');
        PADDLE_ENFORCE_EQ(varname_splits.size(), 3);
        run_varname = varname_splits[0];
        scope->Rename(varname, run_varname);
      }

      auto *var = scope->FindVar(run_varname);

      // for sparse ids
      if (var->IsType<framework::SelectedRows>()) {
        if (distributed_mode_ == DistributedMode::kAsync ||
            distributed_mode_ == DistributedMode::kHalfAsync) {
          auto *ins = distributed::LargeScaleKV::GetInstance();
          if (ins->GradInLargeScale(run_varname)) {
            auto *large_scale_var = ins->GetByGrad(run_varname);

            for (auto name : large_scale_var->CachedVarnames()) {
              scope->Var(name);
            }
          }
        }
        if (distributed_mode_ == DistributedMode::kGeo) {
          if (AsyncSparseParamUpdateRecorder::GetInstance()->HasGrad(
                  run_varname)) {
            auto &grad_slr =
                scope->FindVar(run_varname)->Get<framework::SelectedRows>();
            AsyncSparseParamUpdateRecorder::GetInstance()->Update(
                run_varname, grad_slr.rows());
          }
        }
      }

      executor_->RunPreparedContext((*grad_to_prepared_ctx_)[run_varname].get(),
                                    scope);
      return true;
    } else {  // sync
      rpc_server_->WaitCond(kRequestSend);
      VLOG(3) << "sync: processing received var: " << varname;
      PADDLE_ENFORCE_NOT_NULL(
          invar, platform::errors::NotFound(
                     "sync: Can not find server side var %s.", varname));
    }
  }
  return true;
}

bool RequestGetHandler::Handle(const std::string &varname,
                               framework::Scope *scope,
                               framework::Variable *invar,
                               framework::Variable **outvar,
                               const int trainer_id,
                               const std::string &out_var_name,
                               const std::string &table_name) {
  VLOG(3) << "RequestGetHandler:" << varname
          << " out_var_name: " << out_var_name << " trainer_id: " << trainer_id
          << " table_name: " << table_name;

  if (distributed_mode_ == DistributedMode::kSync) {
    if (varname == FETCH_BARRIER_MESSAGE) {
      VLOG(3) << "sync: recv fetch barrier message";
      rpc_server_->IncreaseBatchBarrier(kRequestGet);
    } else {
      rpc_server_->WaitCond(kRequestGet);
      *outvar = scope_->FindVar(varname);
    }
  } else {
    if (varname != FETCH_BARRIER_MESSAGE && varname != COMPLETE_MESSAGE) {
      if (enable_dc_asgd_) {
        // NOTE: the format is determined by distribute_transpiler.py
        std::string param_bak_name =
            string::Sprintf("%s.trainer_%d_bak", varname, trainer_id);
        VLOG(3) << "getting " << param_bak_name << " trainer_id " << trainer_id;
        auto var = scope_->FindVar(varname);
        auto t_orig = var->Get<framework::LoDTensor>();
        auto param_bak = scope_->Var(param_bak_name);
        auto t = param_bak->GetMutable<framework::LoDTensor>();
        t->mutable_data(dev_ctx_->GetPlace(), t_orig.type());
        VLOG(3) << "copying " << varname << " to " << param_bak_name;
        framework::TensorCopy(t_orig, dev_ctx_->GetPlace(), t);
      }

      if (distributed_mode_ == DistributedMode::kGeo &&
          AsyncSparseParamUpdateRecorder::GetInstance()->HasParam(varname) &&
          !table_name.empty()) {
        VLOG(3) << "AsyncSparseParamUpdateRecorder " << varname << " exist ";

        std::vector<int64_t> updated_rows;
        AsyncSparseParamUpdateRecorder::GetInstance()->GetAndClear(
            varname, trainer_id, &updated_rows);

        if (VLOG_IS_ON(3)) {
          std::ostringstream sstream;
          sstream << "[";
          for (auto &row_id : updated_rows) {
            sstream << row_id << ", ";
          }
          sstream << "]";
          VLOG(3) << "updated_rows size: " << updated_rows.size() << " "
                  << sstream.str();
        }

        auto &origin_tensor =
            scope_->FindVar(varname)->Get<framework::LoDTensor>();
        auto *origin_tensor_data = origin_tensor.data<float>();
        auto &dims = origin_tensor.dims();
        *outvar = scope->Var();
        auto *out_slr = (*outvar)->GetMutable<framework::SelectedRows>();
        out_slr->set_rows(updated_rows);
        out_slr->set_height(dims[0]);
        auto out_dims = framework::make_ddim(
            {static_cast<int64_t>(updated_rows.size()), dims[1]});
        auto *data = out_slr->mutable_value()->mutable_data<float>(
            out_dims, origin_tensor.place());
        auto width = dims[1];
        for (size_t i = 0; i < updated_rows.size(); ++i) {
          PADDLE_ENFORCE_LT(updated_rows[i], dims[0]);
          memcpy(data + i * width, origin_tensor_data + updated_rows[i] * width,
                 sizeof(float) * width);
        }
      } else {
        *outvar = scope_->FindVar(varname);
      }
    }
  }
  return true;
}

bool RequestGetNoBarrierHandler::Handle(const std::string &varname,
                                        framework::Scope *scope,
                                        framework::Variable *invar,
                                        framework::Variable **outvar,
                                        const int trainer_id,
                                        const std::string &out_var_name,
                                        const std::string &table_name) {
  VLOG(4) << "RequestGetNoBarrierHandler:" << varname
          << " out_var_name: " << out_var_name;

  // get var from pserver immediately without barriers
  string::Piece without_barrier_piece(WITHOUT_BARRIER_MESSAGE);
  string::Piece var_name_piece = string::Piece(varname);

  if (string::Contains(var_name_piece, without_barrier_piece)) {
    var_name_piece = string::TrimSuffix(var_name_piece, without_barrier_piece);
    VLOG(4) << "Get var " << var_name_piece << " with "
            << WITHOUT_BARRIER_MESSAGE;
    *outvar = scope_->FindVar(var_name_piece.ToString());
    return true;
  } else {
    PADDLE_THROW("GetNoBarrier must contain %s", WITHOUT_BARRIER_MESSAGE);
  }
  return true;
}

bool RequestPrefetchHandler::Handle(const std::string &varname,
                                    framework::Scope *scope,
                                    framework::Variable *invar,
                                    framework::Variable **outvar,
                                    const int trainer_id,
                                    const std::string &out_var_name,
                                    const std::string &table_name) {
  VLOG(4) << "RequestPrefetchHandler " << varname;

  (*outvar)->GetMutable<framework::LoDTensor>();

  VLOG(1) << "Prefetch "
          << "tablename: " << table_name << " ids:" << varname
          << " out: " << out_var_name;
  paddle::platform::CPUPlace cpu_place;
  auto *ins = distributed::LargeScaleKV::GetInstance();

  if (ins->ParamInLargeScale(table_name)) {
    auto lookup_table_op = PullLargeScaleOp(table_name, varname, out_var_name);
    lookup_table_op->Run(*scope, cpu_place);
  } else {
    auto lookup_table_op =
        BuildLookupTableOp(table_name, varname, out_var_name);
    lookup_table_op->Run(*scope, cpu_place);
  }

  return true;
}

bool RequestCheckpointHandler::Handle(const std::string &varname,
                                      framework::Scope *scope,
                                      framework::Variable *invar,
                                      framework::Variable **outvar,
                                      const int trainer_id,
                                      const std::string &out_var_name,
                                      const std::string &table_name) {
  VLOG(4) << "receive save var " << varname << " with path " << out_var_name;

  auto *ins = distributed::LargeScaleKV::GetInstance();
  ins->Get(varname)->Save(out_var_name);
  //  auto checkpoint_op = BuildCheckpointOp(varname, out_var_name);
  //  paddle::platform::CPUPlace cpu_place;
  //  checkpoint_op->Run(*scope_, cpu_place);
  return true;
}

bool RequestNotifyHandler::Handle(const std::string &varname,
                                  framework::Scope *scope,
                                  framework::Variable *invar,
                                  framework::Variable **outvar,
                                  const int trainer_id,
                                  const std::string &out_var_name,
                                  const std::string &table_name) {
  VLOG(3) << "RequestNotifyHandler: " << varname
          << ", trainer_id: " << trainer_id;

  string::Piece decay_piece(STEP_COUNTER);
  string::Piece var_name_piece = string::Piece(varname);
  if (string::Contains(var_name_piece, decay_piece)) {
    VLOG(3) << "LearningRate Decay Counter Update";

    auto *send_var = scope->FindVar(varname);
    auto send_var_tensor = send_var->Get<framework::LoDTensor>();
    auto *send_value =
        send_var_tensor.mutable_data<int64_t>(send_var_tensor.place());

    auto counter = decay_counters.at(trainer_id);
    counter += send_value[0];
    decay_counters.at(trainer_id) = counter;

    auto *global_step_var = this->scope()->FindVar(LEARNING_RATE_DECAY_COUNTER);
    if (global_step_var == nullptr) {
      PADDLE_THROW(platform::errors::InvalidArgument(
          "can not find LEARNING_RATE_DECAY_COUNTER "));
    }

    auto *tensor = global_step_var->GetMutable<framework::LoDTensor>();
    auto *value = tensor->mutable_data<int64_t>(platform::CPUPlace());

    auto global_counter = 0;
    for (auto &trainer_counter : decay_counters) {
      global_counter += trainer_counter.second;
    }
    value[0] = global_counter;

    if (lr_decay_prepared_ctx_.get() == nullptr) {
      PADDLE_THROW(platform::errors::InvalidArgument(
          "can not find decay block for executor"));
    }

    executor_->RunPreparedContext(lr_decay_prepared_ctx_.get(), scope_);
  }
  return true;
}

}  // namespace distributed
}  // namespace operators
}  // namespace paddle