parallel_executor.cc

/* Copyright (c) 2016 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/parallel_executor.h"
#include "lod_tensor.h"
#include "op_registry.h"
#include "threadpool.h"

namespace paddle {
namespace framework {

#ifdef PADDLE_WITH_CUDA

// FIXME: CHECK the return value of x;
#define NCCL_INVOKE(x) x
#endif

struct OpHandle;

struct VarHandleBase {
  virtual ~VarHandleBase() {}
  virtual std::string DebugString() const = 0;

  OpHandle *generated_op_;
  std::vector<OpHandle *> pending_ops_;
};

struct VarHandle : public VarHandleBase {
  std::string DebugString() const override {
    std::stringstream ss;
    ss << name_ << ":" << place_;
    return ss.str();
  }

  size_t version_;
  std::string name_;
  platform::Place place_;
};

struct DependencyVarHandle : public VarHandleBase {
  std::string DebugString() const override { return "Deps var"; }
};

struct OpHandle {
  std::vector<VarHandleBase *> inputs_;
  std::vector<VarHandleBase *> outputs_;
  std::unordered_map<platform::Place, platform::DeviceContext *,
                     platform::PlaceHash>
      dev_ctx_;

  std::string DebugString() {
    std::stringstream ss;
    ss << "(";
    for (auto *var : inputs_) {
      ss << var->DebugString() << ", ";
    }
    ss << ") --> (";
    for (auto *var : outputs_) {
      ss << var->DebugString() << ", ";
    }
    ss << ")\n";
    return ss.str();
  }

  virtual ~OpHandle() {}

  virtual void Run() {}
  virtual void Wait() {}
};

struct ComputationOpHandle : public OpHandle {
  std::unique_ptr<OperatorBase> op_;
  Scope *scope_;
  platform::Place place_;

  explicit ComputationOpHandle(const OpDesc &op_desc, platform::Place place)
      : op_(framework::OpRegistry::CreateOp(op_desc)),
        scope_(nullptr),
        place_(place) {}

  void Run() override {
    // Wait other op if necessary
    LOG(INFO) << DebugString();
    auto *cur_ctx = dev_ctx_[place_];
    for (auto *in : inputs_) {
      if (in->generated_op_ && in->generated_op_->dev_ctx_[place_] != cur_ctx) {
        in->generated_op_->Wait();
      }
    }

    op_->Run(*scope_, place_);
  }
};

struct ScaleLossGradOpHandle : public OpHandle {};

struct NCCLAllReduceOpHandle : public OpHandle {};

class ParallelExecutorPrivate {
 public:
  explicit ParallelExecutorPrivate(size_t num_threads = 12)
      : pool_(num_threads) {}

  std::unordered_map<platform::Place, Scope *, platform::PlaceHash>
      local_scopes_;

#ifdef PADDLE_WITH_CUDA
  struct NCCLContext {
    std::unique_ptr<platform::CUDADeviceContext> ctx_;
    ncclComm_t comm;

    explicit NCCLContext(int dev_id) {
      ctx_.reset(new platform::CUDADeviceContext(platform::CUDAPlace(dev_id)));
    }

    cudaStream_t stream() const { return ctx_->stream(); }

    int device_id() const {
      return boost::get<platform::CUDAPlace>(ctx_->GetPlace()).device;
    }

    static void InitNCCLContext(std::map<int, NCCLContext> &contexts) {
      std::vector<ncclComm_t> comms;
      std::vector<int> devs;
      comms.resize(contexts.size());
      devs.reserve(contexts.size());

      for (auto &ctx : contexts) {
        devs.push_back(ctx.first);
      }

      NCCL_INVOKE(platform::dynload::ncclCommInitAll(
          &comms[0], static_cast<int>(contexts.size()), &devs[0]));

      int i = 0;
      for (auto &ctx : contexts) {
        ctx.second.comm = comms[i++];
      }
    }
  };

  std::map<int, NCCLContext> communication_streams_;

  NCCLContext &GetNCCLCtx(platform::Place p) {
    int dev_id = boost::get<platform::CUDAPlace>(p).device;
    return communication_streams_.at(dev_id);
  }

#endif

  platform::DeviceContext *CommunicationDevCtx(const platform::Place &place) {
    if (platform::is_cpu_place(place) || local_scopes_.size() == 1) {
      return const_cast<platform::DeviceContext *>(
          platform::DeviceContextPool::Instance().Get(place));
    } else {
#ifdef PADDLE_WITH_CUDA
      return GetNCCLCtx(place).ctx_.get();
#else
      PADDLE_THROW("Not compiled with CUDA")
#endif
    }
  }

  platform::Place main_place_;

  std::unordered_map<platform::Place,
                     std::unordered_map<std::string, std::map<int, VarHandle>>,
                     platform::PlaceHash>
      vars_;
  std::unordered_set<std::unique_ptr<VarHandleBase>> dep_vars_;

  std::vector<std::unique_ptr<OpHandle>> ops_;

  ThreadPool pool_;
};

// TODO(yy): Move this function somewhere
ncclDataType_t ToNCCLDataType(std::type_index type) {
  // FIXME!!
  return ncclFloat;
}

ParallelExecutor::ParallelExecutor(
    const std::vector<platform::Place> &places,
    const std::unordered_set<std::string> &params,
    const ProgramDesc &startup_program, const ProgramDesc &main_program,
    const std::string &loss_var_name, Scope *scope)
    : member_(new ParallelExecutorPrivate()) {
  // Step 1. RunStartupProgram and Bcast the params to devs.
  Executor exe(places[0]);
  exe.Run(startup_program, scope, 0);
  // Create local scopes
  for (auto &place : places) {
    member_->local_scopes_[place] = &scope->NewScope();
  }
  member_->main_place_ = places[0];

  // Bcast Parameters to all GPUs
  if (platform::is_gpu_place(member_->main_place_) &&
      member_->local_scopes_.size() != 1) {  // Is CUDA
    BuildNCCLCommunicator();
    BCastParamsToGPUs(startup_program);
  }
  // Startup Program has been run. All local scopes has correct parameters.

  // Step 2. Convert main_program to SSA form and dependency graph. Also, insert
  // ncclOp
  ConstructDependencyGraph(params, main_program, loss_var_name);
}

void ParallelExecutor::ConstructDependencyGraph(
    const std::unordered_set<std::string> &params,
    const ProgramDesc &main_program, const std::string &loss_var_name) const {
  std::unordered_set<std::string> grads;
  for (auto &each_param : params) {
    grads.insert(each_param + "@GRAD");
  }

  bool is_forwarding = true;
  for (auto *op : main_program.Block(0).AllOps()) {
    bool change_forward = false;

    if (!is_forwarding) {
      // FIXME(yy): Do not hard code like this
      if (op->OutputArgumentNames().size() == 1 &&
          op->OutputArgumentNames()[0] == loss_var_name + "@GRAD") {
        continue;  // Drop fill 1. for backward coeff;
      }
    }

    for (auto &pair : member_->local_scopes_) {
      member_->ops_.emplace_back(new ComputationOpHandle(*op, pair.first));
      auto *op_handle = member_->ops_.back().get();
      op_handle->dev_ctx_[pair.first] = const_cast<platform::DeviceContext *>(
          platform::DeviceContextPool::Instance().Get(pair.first));

      auto var_names = op->InputArgumentNames();

      for (auto &each_var_name : var_names) {
        auto &place = pair.first;
        VarHandle *var = GetVarHandle(each_var_name, place);
        op_handle->inputs_.emplace_back(var);
        var->pending_ops_.emplace_back(op_handle);
      }
      var_names = op->OutputArgumentNames();

      for (auto &each_var_name : var_names) {
        auto &place = pair.first;
        GenerateVar(op_handle, each_var_name, place);
      }

      if (is_forwarding) {
        if (var_names.size() == 1 && var_names[0] == loss_var_name) {
          // Insert ScaleCost OpHandle
          member_->ops_.emplace_back(new ScaleLossGradOpHandle());
          op_handle = member_->ops_.back().get();

          op_handle->dev_ctx_[pair.first] =
              member_->CommunicationDevCtx(pair.first);

          auto &place = pair.first;
          VarHandle *loss = GetVarHandle(loss_var_name, place);
          loss->pending_ops_.emplace_back(op_handle);
          op_handle->inputs_.emplace_back(loss);
          GenerateVar(op_handle, loss_var_name + "@GRAD", place);
          change_forward = true;
          LOG(INFO) << "Scale Loss " << op_handle->DebugString();
        }
      }
    }

    if (change_forward) {
      is_forwarding = false;
    }

    if (!is_forwarding) {
      auto var_names = op->OutputArgumentNames();
      for (auto &og : var_names) {
        if (grads.count(og) != 0) {  // is param grad
          // Insert NCCL AllReduce Op
          member_->ops_.emplace_back(new NCCLAllReduceOpHandle());
          auto *op_handle = member_->ops_.back().get();

          for (auto &pair : member_->local_scopes_) {
            auto &place = pair.first;
            auto &vars = member_->vars_[place][og];

            if (vars.empty()) {  // This device has no data. continue.
              continue;
            }
            auto *prev_grad = &vars[vars.size() - 1];
            op_handle->inputs_.emplace_back(prev_grad);
            prev_grad->pending_ops_.emplace_back(op_handle);
            auto &var = vars[vars.size()];
            var.place_ = place;
            var.generated_op_ = op_handle;
            var.name_ = og;
            var.version_ = vars.size() - 1;
            op_handle->outputs_.emplace_back(&var);

            for (auto &pair : member_->local_scopes_) {
              op_handle->dev_ctx_[pair.first] =
                  member_->CommunicationDevCtx(pair.first);
            }
          }
        }
      }
    }
  }

  /**
   * Dependency graph has been constructed. However, there are still data
   * harzaeds need to be handled.
   *
   * We only handle write after read(WAR), since it should not have a write
   * after write in program. If there are write after write operators, we need
   * prune them.
   *
   * https://en.wikipedia.org/wiki/Hazard_(computer_architecture)#Write_after_read_(WAR)
   */

  for (auto &place_pair : member_->vars_) {
    for (auto &name_pair : place_pair.second) {
      if (name_pair.second.size() <= 1) {
        return;
      }
      auto it_new = name_pair.second.rbegin();
      auto it_old = name_pair.second.rbegin();
      ++it_old;
      for (; it_old != name_pair.second.rend(); it_new = it_old, ++it_old) {
        auto *write_op = it_new->second.generated_op_;
        auto &read_ops = it_old->second.pending_ops_;

        for (auto *read_op : read_ops) {
          // Manually add a dependency var from read_op to write_op;

          auto *dep_var = new DependencyVarHandle();
          dep_var->generated_op_ = read_op;
          read_op->outputs_.emplace_back(dep_var);

          dep_var->pending_ops_.emplace_back(write_op);
          write_op->inputs_.emplace_back(dep_var);
          member_->dep_vars_.emplace(dep_var);
        }
      }
    }
  }
}

void ParallelExecutor::GenerateVar(OpHandle *op_handle,
                                   const std::string &each_var_name,
                                   const platform::Place &place) const {
  auto &vars = member_->vars_[place][each_var_name];
  size_t version = vars.size();
  auto &var = vars[version];
  var.version_ = version;
  var.generated_op_ = op_handle;
  var.name_ = each_var_name;
  var.place_ = place;
  op_handle->outputs_.emplace_back(&var);
}

VarHandle *ParallelExecutor::GetVarHandle(const std::string &each_var_name,
                                          const platform::Place &place) const {
  auto &var_holders = member_->vars_[place];
  auto &var_holder = var_holders[each_var_name];
  VarHandle *var = nullptr;
  if (var_holder.empty()) {
    auto &init_var = var_holder[0];
    init_var.place_ = place;
    init_var.name_ = each_var_name;
    init_var.generated_op_ = nullptr;
    init_var.version_ = 0;
    var = &init_var;
  } else {
    var = &var_holder.rbegin()->second;
  }
  return var;
}

void ParallelExecutor::BCastParamsToGPUs(
    const ProgramDesc &startup_program) const {
#ifdef PADDLE_WITH_CUDA
  auto *main_scope = member_->local_scopes_[member_->main_place_];

  for (auto *var_desc : startup_program.Block(0).AllVars()) {
    if (var_desc->GetType() == proto::VarType::LOD_TENSOR) {
      auto &main_tensor =
          main_scope->FindVar(var_desc->Name())->Get<LoDTensor>();
      ncclDataType_t data_type = ToNCCLDataType(main_tensor.type());
      auto &dims = main_tensor.dims();
      size_t numel = main_tensor.numel();
      std::vector<std::pair<void *, ParallelExecutorPrivate::NCCLContext *>>
          mems;
      mems.emplace_back(const_cast<void *>(main_tensor.data<void>()),
                        &member_->GetNCCLCtx(member_->main_place_));

      for (auto &pair : member_->local_scopes_) {
        if (pair.first == member_->main_place_) {
          continue;
        }

        auto local_scope = pair.second;
        auto *t = local_scope->Var(var_desc->Name())->GetMutable<LoDTensor>();
        t->Resize(dims);
        mems.emplace_back(t->mutable_data(pair.first, main_tensor.type()),
                          &member_->GetNCCLCtx(member_->main_place_));
      }

      // TODO(yy): Invoke ncclBCast here. mems, numel, data_type. The mems[0]
      // is the src, rests are dests.

      (void)(data_type);
      (void)(numel);
    }
  }
#else
  PADDLE_THROW("Not compiled with CUDA");
#endif
}

void ParallelExecutor::BuildNCCLCommunicator() const {
#ifdef PADDLE_WITH_CUDA
  for (auto &place_pair : member_->local_scopes_) {
    auto place = place_pair.first;
    int dev_id = boost::get<platform::CUDAPlace>(place).device;

    member_->communication_streams_.emplace(
        dev_id, ParallelExecutorPrivate::NCCLContext(dev_id));
  }

  ParallelExecutorPrivate::NCCLContext::InitNCCLContext(
      member_->communication_streams_);
#endif
}

std::vector<LoDTensor> ParallelExecutor::Run(
    const std::vector<std::string> &fetch_tensors) {
  // Version --> VarHandle

  std::unordered_map<VarHandleBase *, bool> pending_vars;
  std::unordered_map<OpHandle *, size_t> pending_ops;

  for (auto &place_pair : member_->vars_) {
    for (auto &name_pair : place_pair.second) {
      for (auto &version_pair : name_pair.second) {
        pending_vars[&version_pair.second] =
            version_pair.second.generated_op_ == nullptr;
      }
    }
  }

  for (auto &var : member_->dep_vars_) {
    pending_vars[var.get()] = var->generated_op_ == nullptr;
  }

  for (auto &op : member_->ops_) {
    pending_ops.insert({op.get(), op->inputs_.size()});
  }

  while (!pending_ops.empty()) {
    VarHandleBase *ready_var = nullptr;
    for (auto &pair : pending_vars) {
      if (pair.second) {
        ready_var = pair.first;
      }
    }

    if (ready_var == nullptr) {
      member_->pool_.Wait();  // Wait thread pool;
      continue;
    }

    pending_vars.erase(ready_var);

    std::vector<OpHandle *> to_run;

    for (auto *op : ready_var->pending_ops_) {
      auto &deps = pending_ops[op];
      --deps;
      if (deps == 0) {
        to_run.emplace_back(op);
      }
    }

    for (auto *op : to_run) {
      pending_ops.erase(op);

      std::vector<bool *> ready_buffer;
      for (auto *var : op->outputs_) {
        ready_buffer.emplace_back(&pending_vars[var]);
      }

      auto op_run = [ready_buffer, op] {
        // TODO(yy) Check Previous Op has same dev ctx.
        op->Run();
        for (auto *ready : ready_buffer) {
          *ready = true;
        }
      };

      member_->pool_.Run(op_run);
    }
  }
  return std::vector<LoDTensor>();
}
}  // namespace framework
}  // namespace paddle