Paddle/paddle/fluid/operators/distributed/communicator.cc

/* Copyright (c) 2019 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/communicator.h"

#include <gflags/gflags.h>
#include <chrono>  // NOLINT
#include <thread>  // NOLINT

#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/framework/variable_helper.h"
#include "paddle/fluid/operators/distributed/parameter_recv.h"
#include "paddle/fluid/operators/distributed/parameter_send.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/selected_rows_functor.h"
#include "paddle/fluid/platform/device_context.h"

DEFINE_bool(communicator_independent_recv_thread, true,
            "use an independent to recv vars from parameter server");
DEFINE_int32(communicator_send_queue_size, 20,
             "queue size to recv gradient before send");
DEFINE_int32(communicator_recv_wait_ms, 200, "wait time between each recv");
DEFINE_int32(communicator_thread_pool_size, 5, "thread num to do send or recv");
DEFINE_int32(communicator_max_merge_var_num, 20,
             "max var num to merge and send");
DEFINE_bool(communicator_fake_rpc, false,
            "fake mode does not really send any thing");

namespace paddle {
namespace operators {
namespace distributed {

template <typename T, int MajorType = Eigen::RowMajor,
          typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;

inline double GetCurrentUS() {
  struct timeval time;
  gettimeofday(&time, NULL);
  return 1e+6 * time.tv_sec + time.tv_usec;
}

static inline void MergeVars(const std::string &var_name,
                             const std::vector<std::shared_ptr<Variable>> &vars,
                             Scope *scope) {
  PADDLE_ENFORCE(!vars.empty(), "should have value to merge!");
  auto cpu_place = platform::CPUPlace();
  auto &var0 = vars[0];
  auto *out_var = scope->Var(var_name);
  if (var0->IsType<framework::LoDTensor>()) {
    VLOG(3) << "merge " << var_name << " LoDTensor"
            << var0->Get<framework::LoDTensor>().dims();

    // init output tensor
    auto *out_t = out_var->GetMutable<framework::LoDTensor>();
    auto numel = out_t->numel();

    // check the input dims
    for (auto &var : vars) {
      auto &var_t = var->Get<framework::LoDTensor>();
      PADDLE_ENFORCE_EQ(var_t.numel(), numel, "should have the same dims");
    }

    // set output tensor to 0.
    auto cpu_ctx = paddle::platform::CPUDeviceContext();
    math::SetConstant<paddle::platform::CPUDeviceContext, float>
        constant_functor;
    constant_functor(cpu_ctx, out_t, static_cast<float>(0));

    // sum all vars to out
    auto result = EigenVector<float>::Flatten(*out_t);
    for (auto &var : vars) {
      auto &in_t = var->Get<framework::LoDTensor>();
      auto in = EigenVector<float>::Flatten(in_t);
      result.device(*cpu_ctx.eigen_device()) = result + in;
    }
  } else if (var0->IsType<framework::SelectedRows>()) {
    auto &slr0 = var0->Get<framework::SelectedRows>();
    auto *out_slr = out_var->GetMutable<framework::SelectedRows>();
    out_slr->mutable_rows()->clear();
    out_slr->mutable_value()->mutable_data<float>({{}}, cpu_place);
    std::vector<const paddle::framework::SelectedRows *> inputs;
    inputs.reserve(vars.size());
    for (auto &var : vars) {
      inputs.push_back(&var->Get<framework::SelectedRows>());
    }
    math::scatter::MergeAdd<paddle::platform::CPUDeviceContext, float>
        merge_add;
    auto dev_ctx = paddle::platform::CPUDeviceContext();
    merge_add(dev_ctx, inputs, out_slr, false);
    VLOG(3) << "merge " << var_name << " SelectedRows height: " << slr0.height()
            << " dims: " << slr0.value().dims();
  } else {
    PADDLE_THROW("unsupported var type!");
  }
}

std::unique_ptr<Communicator> Communicator::communicator_(nullptr);
std::once_flag Communicator::init_flag_;

Communicator::Communicator(const RpcCtxMap &send_varname_to_ctx,
                           const RpcCtxMap &recv_varname_to_ctx,
                           Scope *recv_scope)
    : send_varname_to_ctx_(send_varname_to_ctx),
      recv_varname_to_ctx_(recv_varname_to_ctx),
      recv_scope_(recv_scope) {
  // get all send information from graph, build vars_to_send
  VLOG(0) << "communicator_independent_recv_thread: "
          << FLAGS_communicator_independent_recv_thread;
  VLOG(0) << "communicator_send_queue_size: "
          << FLAGS_communicator_send_queue_size;
  VLOG(0) << "communicator_recv_wait_ms: " << FLAGS_communicator_recv_wait_ms;
  VLOG(0) << "communicator_thread_pool_size: "
          << FLAGS_communicator_thread_pool_size;
  VLOG(0) << "communicator_max_merge_var_num: "
          << FLAGS_communicator_max_merge_var_num;
  VLOG(0) << "communicator_fake_rpc: " << FLAGS_communicator_fake_rpc;
  send_scope_.reset(new Scope());
  for (auto &iter : send_varname_to_ctx_) {
    send_varname_to_queue_[iter.first] =
        std::make_shared<BlockingQueue<std::shared_ptr<Variable>>>(
            FLAGS_communicator_send_queue_size);
  }
  send_threadpool_.reset(new ::ThreadPool(FLAGS_communicator_thread_pool_size));
  recv_threadpool_.reset(new ::ThreadPool(FLAGS_communicator_thread_pool_size));
}

Communicator::~Communicator() {
  VLOG(3) << "~Communicator";
  running_ = false;
  if (send_thread_) send_thread_->join();
  if (recv_thread_) recv_thread_->join();
  VLOG(3) << "~Communicator done";
}

void Communicator::SendThread() {
  VLOG(3) << "SendThread start!";
  while (running_) {
    std::vector<std::future<void>> task_futures;
    task_futures.reserve(send_varname_to_ctx_.size());
    VLOG(3) << "run send graph";
    auto before_run_send_graph = GetCurrentUS();
    for (auto &iter : send_varname_to_queue_) {
      auto &var_name = iter.first;
      auto &var_queue = iter.second;
      if (var_queue->Size() > 0) {
        auto send_task = [this, &var_name, &var_queue] {
          VLOG(3) << var_name << " merge and send";
          std::vector<std::shared_ptr<Variable>> vars;
          size_t merged_var_num = 0;
          while (var_queue->Size() > 0 &&
                 merged_var_num < FLAGS_communicator_max_merge_var_num) {
            vars.push_back(var_queue->Pop());
            merged_var_num++;
          }
          auto before_merge = GetCurrentUS();
          MergeVars(var_name, vars, send_scope_.get());
          auto after_merge = GetCurrentUS();
          VLOG(3) << "merge " << var_name << " use time "
                  << after_merge - before_merge;
          auto send_functor = distributed::ParameterSend<float>();
          auto &ctx = send_varname_to_ctx_.at(var_name);
          if (!FLAGS_communicator_fake_rpc) {
            send_functor(ctx, *send_scope_, true);
          }
          auto after_send = GetCurrentUS();
          VLOG(3) << "send " << var_name << " use time "
                  << after_send - after_merge;
        };
        task_futures.emplace_back(
            send_threadpool_->enqueue(std::move(send_task)));
      } else {
        VLOG(3) << var_name << " queue empty";
      }
    }
    for (auto &task_f : task_futures) {
      task_f.wait();
    }
    auto after_run_send_graph = GetCurrentUS();
    VLOG(3) << "run send graph use time "
            << after_run_send_graph - before_run_send_graph;
    if (!FLAGS_communicator_independent_recv_thread) {
      RecvAll();
    }
  }
}

void Communicator::RecvAll() {
  VLOG(3) << "parallel run recv graph";
  auto before_send = GetCurrentUS();
  std::vector<std::future<void>> task_futures;
  task_futures.reserve(recv_varname_to_ctx_.size());
  for (auto &iter : recv_varname_to_ctx_) {
    auto recv_task = [this, &iter] {
      auto &var_name = iter.first;
      VLOG(3) << "recv var " << var_name;
      auto recv_functor = distributed::ParameterRecv<float>();
      if (!FLAGS_communicator_fake_rpc) {
        recv_functor(iter.second, *recv_scope_);
      }
    };
    task_futures.emplace_back(recv_threadpool_->enqueue(std::move(recv_task)));
  }
  for (auto &task : task_futures) {
    task.wait();
  }
  auto after_recv = GetCurrentUS();
  VLOG(3) << "run recv graph use time " << after_recv - before_send;
}

void Communicator::RecvThread() {
  VLOG(3) << "RecvThread start!";
  while (running_) {
    RecvAll();
    std::this_thread::sleep_for(
        std::chrono::milliseconds(FLAGS_communicator_recv_wait_ms));
  }
}

void Communicator::Send(const std::string &var_name,
                        const framework::Scope &scope) {
  VLOG(3) << "communicator send " << var_name;
  // push var into send queue by var_name
  auto *grad_var = scope.FindVar(var_name);
  PADDLE_ENFORCE(grad_var->IsInitialized(), "grad var should be inited");
  auto tmp_grad_var = std::make_shared<Variable>();
  framework::CopyVariable(*grad_var, tmp_grad_var.get());
  auto &queue = send_varname_to_queue_.at(var_name);
  VLOG(3) << "send " << var_name << " queue size " << queue->Size();
  queue->Push(tmp_grad_var);
}

Communicator *Communicator::GetInstance() { return communicator_.get(); }

void Communicator::Start() {
  running_ = true;
  // start send and recv thread
  send_thread_.reset(
      new std::thread(std::bind(&Communicator::SendThread, this)));
  if (FLAGS_communicator_independent_recv_thread) {
    recv_thread_.reset(
        new std::thread(std::bind(&Communicator::RecvThread, this)));
  }
}

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