ps_gpu_worker.cc

/* Copyright (c) 2020 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/device_worker.h"
#include "paddle/fluid/framework/device_worker_factory.h"
#include "paddle/fluid/platform/cpu_helper.h"
#include "paddle/fluid/platform/lodtensor_printer.h"
#include "paddle/fluid/string/string_helper.h"

#if (defined PADDLE_WITH_NCCL || defined PADDLE_WITH_RCCL) && \
    (defined PADDLE_WITH_PSLIB)
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/cuda_device_guard.h"
#endif

#if defined _WIN32 || defined __APPLE__
#else
#define _LINUX
#endif

namespace paddle {
namespace framework {

void PSGPUWorker::Initialize(const TrainerDesc& desc) {
  param_ = desc.downpour_param();
  dev_ctx_ = platform::DeviceContextPool::Instance().Get(place_);
  mpi_rank_ = desc.mpi_rank();
  trainer_desc_ = desc;
  for (int i = 0; i < param_.sparse_table_size(); ++i) {
    uint64_t table_id =
        static_cast<uint64_t>(param_.sparse_table(i).table_id());
    TableParameter table = param_.sparse_table(i);
    sparse_key_names_[table_id].resize(table.sparse_key_name_size());
    for (int j = 0; j < table.sparse_key_name_size(); ++j) {
      sparse_key_names_[table_id][j] = table.sparse_key_name(j);
    }
    sparse_value_names_[table_id].resize(table.sparse_value_name_size());
    for (int j = 0; j < table.sparse_value_name_size(); ++j) {
      sparse_value_names_[table_id][j] = table.sparse_value_name(j);
    }
    sparse_grad_names_[table_id].resize(table.sparse_grad_name_size());
    for (int j = 0; j < table.sparse_grad_name_size(); ++j) {
      sparse_grad_names_[table_id][j] = table.sparse_grad_name(j);
    }
    label_var_name_[table_id] = table.label_var_name();
    sparse_push_keys_[table_id] = std::vector<uint64_t>();
  }

  for (int i = 0; i < param_.dense_table_size(); ++i) {
    uint64_t table_id = static_cast<uint64_t>(param_.dense_table(i).table_id());
    auto table = param_.dense_table(i);
    dense_value_names_[table_id].resize(table.dense_value_name_size());
    for (int j = 0; j < table.dense_value_name_size(); ++j) {
      dense_value_names_[table_id][j] = table.dense_value_name(j);
    }
    dense_grad_names_[table_id].resize(table.dense_grad_name_size());
    for (int j = 0; j < table.dense_grad_name_size(); ++j) {
      dense_grad_names_[table_id][j] = table.dense_grad_name(j);
    }
  }

  skip_ops_.resize(param_.skip_ops_size());
  for (int i = 0; i < param_.skip_ops_size(); ++i) {
    skip_ops_[i] = param_.skip_ops(i);
  }
  for (int i = 0; i < param_.stat_var_names_size(); ++i) {
    stat_var_name_map_[param_.stat_var_names(i)] = 1;
  }

  need_to_push_sparse_ = param_.push_sparse();
  need_to_push_dense_ = param_.push_dense();

  fetch_config_ = desc.fetch_config();
  use_cvm_ = desc.use_cvm();
  // for sparse value accessor, embedding only
  no_cvm_ = desc.no_cvm();
  scale_datanorm_ = desc.scale_datanorm();
  dump_slot_ = desc.dump_slot();
  adjust_ins_weight_config_ = desc.adjust_ins_weight_config();
  for (int i = 0; i < desc.check_nan_var_names_size(); ++i) {
    check_nan_var_names_.push_back(desc.check_nan_var_names(i));
  }
  copy_table_config_ = desc.copy_table_config();
  for (int i = 0; i < copy_table_config_.src_sparse_tables_size(); ++i) {
    uint64_t src_table = copy_table_config_.src_sparse_tables(i);
    uint64_t dest_table = copy_table_config_.dest_sparse_tables(i);
    VLOG(3) << "copy_sparse_tables_ push back " << src_table << "->"
            << dest_table;
    copy_sparse_tables_.push_back(std::make_pair(src_table, dest_table));
  }
  for (int i = 0; i < copy_table_config_.src_dense_tables_size(); ++i) {
    uint64_t src_table = copy_table_config_.src_dense_tables(i);
    uint64_t dest_table = copy_table_config_.dest_dense_tables(i);
    VLOG(3) << "copy_dense_tables_ push back " << src_table << "->"
            << dest_table;
    copy_dense_tables_.push_back(std::make_pair(src_table, dest_table));
  }
  for (auto& m : copy_table_config_.table_denpendency_map()) {
    if (sparse_key_names_.find(m.key()) != sparse_key_names_.end()) {
      // currently only support one dependency
      for (auto& value : m.values()) {
        table_dependency_[m.key()] = value;
      }
    }
  }
}

void PSGPUWorker::SetChannelWriter(ChannelObject<std::string>* queue) {
  writer_.Reset(queue);
}

void PSGPUWorker::TrainFiles() {
  VLOG(0) << "Begin to train files";
  platform::SetNumThreads(1);
  platform::Timer timeline;
  timeline.Start();

  int total_ins_num = 0;

  // how to accumulate fetched values here
  device_reader_->Start();
  int cur_batch;
  int batch_cnt = 0;

  platform::SetDeviceId(thread_id_);
  while ((cur_batch = device_reader_->Next()) > 0) {
    total_ins_num += cur_batch;
    for (auto& op : ops_) {
      bool need_skip = false;
      for (auto t = 0u; t < skip_ops_.size(); ++t) {
        if (op->Type().find(skip_ops_[t]) != std::string::npos) {
          need_skip = true;
          break;
        }
      }
      if (!need_skip) {
        op->Run(*thread_scope_, place_);
      }
    }
    if (need_dump_field_) {
      DumpField(*thread_scope_, dump_mode_, dump_interval_);
    }
    if (need_dump_param_ && thread_id_ == 0) {
      DumpParam(*thread_scope_, batch_cnt);
    }

    for (std::string& var_name : check_nan_var_names_) {
      Variable* var = thread_scope_->FindVar(var_name);
      if (var == nullptr) {
        continue;
      }
      LoDTensor* tensor = var->GetMutable<LoDTensor>();
      if (tensor == nullptr || !tensor->IsInitialized()) {
        continue;
      }
      if (framework::TensorContainsInf(*tensor) ||
          framework::TensorContainsNAN(*tensor)) {
        static std::mutex mutex;
        {
          std::lock_guard<std::mutex> lock(mutex);
          VLOG(0) << "worker " << thread_id_ << ": " << var_name
                  << " cantains inf or nan";
          auto all_vars = thread_scope_->LocalVarNames();
          std::stringstream ss;
          ss << "====== worker " << thread_id_ << "======\n";
          for (auto& local_var : all_vars) {
            platform::PrintVar(thread_scope_, local_var, local_var, &ss);
            ss << "\n";
          }
          std::cout << ss.str() << std::endl;
          VLOG(0) << "worker " << thread_id_ << "print nan var done....";
        }
        sleep(600);
        exit(-1);
      }
    }

    dev_ctx_->Wait();
    PrintFetchVars();
    thread_scope_->DropKids();
    ++batch_cnt;
  }
  if (need_dump_field_ || need_dump_param_) {
    writer_.Flush();
  }
  timeline.Pause();
  VLOG(0) << "GpuPs worker " << thread_id_ << " train cost "
          << timeline.ElapsedSec() << " seconds, ins_num: " << total_ins_num;
  return;
}

void PSGPUWorker::TrainFilesWithProfiler() {
  platform::SetNumThreads(1);
  VLOG(0) << "Begin to train files with profiler";
  device_reader_->Start();
  std::vector<double> op_total_time;
  std::vector<std::string> op_name;
  for (auto& op : ops_) {
    bool need_skip = false;
    for (auto t = 0u; t < skip_ops_.size(); ++t) {
      if (op->Type().find(skip_ops_[t]) != std::string::npos) {
        need_skip = true;
        break;
      }
    }
    if (!need_skip) {
      op_name.push_back(op->Type());
    }
  }

  VLOG(3) << "op name size: " << op_name.size();
  op_total_time.resize(op_name.size());
  for (size_t i = 0; i < op_total_time.size(); ++i) {
    op_total_time[i] = 0.0;
  }
  platform::Timer timeline;
  double total_time = 0.0;
  double read_time = 0.0;
  int total_ins_num = 0;
  int cur_batch;
  timeline.Start();
  platform::SetDeviceId(thread_id_);
  while ((cur_batch = device_reader_->Next()) > 0) {
    total_ins_num += cur_batch;
    timeline.Pause();
    read_time += timeline.ElapsedSec();
    total_time += timeline.ElapsedSec();

    int run_op_idx = 0;
    dev_ctx_->Wait();
    for (auto& op : ops_) {
      bool need_skip = false;
      for (auto t = 0u; t < skip_ops_.size(); ++t) {
        if (op->Type().find(skip_ops_[t]) != std::string::npos) {
          need_skip = true;
          break;
        }
      }
      if (!need_skip) {
        timeline.Start();
        VLOG(3) << "Going to run op " << op_name[run_op_idx];
        op->Run(*thread_scope_, place_);
        dev_ctx_->Wait();
        VLOG(3) << "Op " << op_name[run_op_idx] << " Finished";
        timeline.Pause();
        op_total_time[run_op_idx++] += timeline.ElapsedSec();
        total_time += timeline.ElapsedSec();
      }
    }
    timeline.Start();
    PrintFetchVars();
    thread_scope_->DropKids();
    dev_ctx_->Wait();
    timeline.Pause();
    total_time += timeline.ElapsedSec();
    timeline.Start();
  }
  VLOG(0) << "GpuPs worker " << thread_id_ << " train cost " << total_time
          << " seconds, ins_num: " << total_ins_num;
  for (size_t i = 0; i < op_name.size(); ++i) {
    VLOG(0) << "card:" << thread_id_ << ", op: " << op_name[i]
            << ", mean time: " << op_total_time[i] / total_ins_num
            << "s, totol time:" << op_total_time[i] << "sec";
  }
  VLOG(0) << "card: " << thread_id_ << " read time: " << read_time
          << ", percent: " << read_time / total_time * 100;
  return;
}

void PSGPUWorker::ResetStat() {
  total_time_ = 0;
  read_time_ = 0;
  pack_time_ = 0;
  pull_sparse_local_time_ = 0;
  op_all_time_ = 0;
  xpu_op_time_ = 0;
  xpu_wait_time_ = 0;
  cpu_op_time_ = 0;
  collect_label_time_ = 0;
  fill_sparse_time_ = 0;
  push_sparse_time_ = 0;
  gpu_2_cpu_time_ = 0;
  cpu_2_gpu_time_ = 0;
  total_inst_ = 0;
}

void PSGPUWorker::ProduceTasks() { return; }

}  // end namespace framework
}  // end namespace paddle
#endif