multi_devices_graph_builder.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/multi_devices_graph_builder.h"
#include "paddle/fluid/framework/details/computation_op_handle.h"
#include "paddle/fluid/framework/details/scale_loss_grad_op_handle.h"
#include "paddle/fluid/framework/details/send_op_handle.h"
#include "paddle/fluid/framework/scope.h"

#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/framework/details/nccl_all_reduce_op_handle.h"
#endif

#include <string>
#include <vector>

namespace paddle {
namespace framework {
namespace details {

#ifdef PADDLE_WITH_CUDA
MultiDevSSAGraphBuilder::MultiDevSSAGraphBuilder(
    const std::vector<platform::Place> &places,
    const std::string &loss_var_name,
    const std::unordered_set<std::string> &params,
    const std::vector<Scope *> &local_scopes, bool skip_scale_loss,
    platform::NCCLContextMap *nccl_ctxs)
    : loss_var_name_(loss_var_name),
      places_(places),
      local_scopes_(local_scopes),
      nccl_ctxs_(nccl_ctxs) {
#else
MultiDevSSAGraphBuilder::MultiDevSSAGraphBuilder(
    const std::vector<platform::Place> &places,
    const std::string &loss_var_name,
    const std::unordered_set<std::string> &params,
    const std::vector<Scope *> &local_scopes, bool skip_scale_loss)
    : loss_var_name_(loss_var_name),
      places_(places),
      local_scopes_(local_scopes) {
#endif
  for (auto &p : params) {
    grad_names_.insert(GradVarName(p));
  }
  skip_scale_loss_ = skip_scale_loss;
}

void MultiDevSSAGraphBuilder::CreateOpHandleIOs(SSAGraph *result,
                                                const OpDesc &op,
                                                const platform::Place &p,
                                                const size_t &i) const {
  auto *op_handle = result->ops_.back().get();
  op_handle->SetDeviceContext(p,
                              platform::DeviceContextPool::Instance().Get(p));

  auto var_names = op.InputArgumentNames();

  for (auto &each_var_name : var_names) {
    VarHandle *var = CreateOrGetLatestVarHandle(result, each_var_name, p, i);
    op_handle->AddInput(var);
  }

  var_names = op.OutputArgumentNames();

  for (auto &each_var_name : var_names) {
    CreateOpOutput(result, op_handle, each_var_name, p, i);
  }
}

std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
    const ProgramDesc &program) const {
  auto graph = new SSAGraph();
  SSAGraph &result = *graph;
  std::unordered_set<std::string> og_has_been_broadcast;

  // We cannot invoke resize. It is a bug of GCC 4.8
  result.vars_ = std::vector<
      std::unordered_map<std::string, std::vector<std::unique_ptr<VarHandle>>>>(
      places_.size());

  bool is_forwarding = true;
  for (auto *op : program.Block(0).AllOps()) {
    if (op->Type() == "send") {
      // append send op if program is distributed trainer main program.
      // always use the first device
      CreateSendOp(&result, *op);
    } else if (IsScaleLossOp(*op)) {
      if (!skip_scale_loss_) {
        CreateScaleLossGradOp(&result);
      }
      is_forwarding = false;
    } else {
      CreateComputationalOps(&result, *op);
      if (!is_forwarding) {
        // Currently, we assume that once gradient is generated, it can be
        // broadcast, and each gradient is only broadcast once. But there are no
        // other cases, for example, we need to adjust the gradient according to
        // the input when we get the gradient, which is not considered at
        // present.
        for (auto &og : op->OutputArgumentNames()) {
          if (IsParameterGradientOnce(og, &og_has_been_broadcast)) {
            InsertNCCLAllReduceOp(&result, og);
          }
        }
      }
    }
  }

  /*
    Dependency graph has been constructed. However, there are still data
    harzaeds need to be handled.
   */
  PolishGraphToSupportDataHazards(&result);

  /*
   * Only variables should be the leaves of graph.
   */
  AddOutputToLeafOps(&result);

  if (VLOG_IS_ON(10)) {
    std::ostringstream sout;
    PrintGraphviz(*graph, sout);
    VLOG(10) << sout.str();
  }

  return std::unique_ptr<SSAGraph>(graph);
}

void MultiDevSSAGraphBuilder::InsertNCCLAllReduceOp(
    SSAGraph *result, const std::string &og) const {
#ifdef PADDLE_WITH_CUDA
  result->ops_.emplace_back(
      new NCCLAllReduceOpHandle(local_scopes_, places_, *nccl_ctxs_));
  auto *op_handle = result->ops_.back().get();

  for (size_t i = 0; i < places_.size(); ++i) {
    auto &p = places_[i];
    auto &vars = result->vars_[i][og];
    PADDLE_ENFORCE(!vars.empty());
    auto &prev_grad = vars.back();
    op_handle->AddInput(prev_grad.get());

    auto var = new VarHandle(vars.size() - 1, i, og, p);
    vars.emplace_back(var);
    op_handle->AddOutput(var);
  }
#else
  PADDLE_ENFORCE("Not implemented");
#endif
}

bool MultiDevSSAGraphBuilder::IsParameterGradientOnce(
    const std::string &og,
    std::unordered_set<std::string> *og_has_been_broadcast) const {
  bool is_pg_once =
      grad_names_.count(og) != 0 && og_has_been_broadcast->count(og) == 0;
  if (is_pg_once) {
    // Insert NCCL AllReduce Op
    og_has_been_broadcast->insert(og);
  }
  return is_pg_once;
}

void MultiDevSSAGraphBuilder::CreateScaleLossGradOp(SSAGraph *result) const {
  for (size_t i = 0; i < places_.size(); ++i) {
// Insert ScaleCost OpHandle
#ifdef PADDLE_WITH_CUDA
    auto *communication_dev_ctx = nccl_ctxs_->DevCtx(places_[i]);
#else
    auto *communication_dev_ctx =
        platform::DeviceContextPool::Instance().Get(platform::CPUPlace());
#endif

    auto *op_handle =
        new ScaleLossGradOpHandle(local_scopes_.size(), local_scopes_[i],
                                  places_[i], communication_dev_ctx);
    result->ops_.emplace_back(op_handle);

    // FIXME: Currently ScaleLossGradOp only use device_count as scale
    // factor. So it does not depend on any other operators.
    // VarHandle *loss = GetVarHandle(loss_var_name, place);
    // loss->pending_ops_.emplace_back(op_handle);
    // op_handle->inputs_.emplace_back(loss);

    CreateOpOutput(result, op_handle, GradVarName(loss_var_name_), places_[i],
                   i);
  }
}

void MultiDevSSAGraphBuilder::CreateComputationalOps(SSAGraph *result,
                                                     const OpDesc &op) const {
  for (size_t scope_idx = 0; scope_idx < places_.size(); ++scope_idx) {
    auto p = places_[scope_idx];
    auto s = local_scopes_[scope_idx];
    result->ops_.emplace_back(new ComputationOpHandle(op, s, p));
    CreateOpHandleIOs(result, op, p, scope_idx);
  }
}

void MultiDevSSAGraphBuilder::CreateSendOp(SSAGraph *result,
                                           const OpDesc &op) const {
  auto &p = places_[0];
  auto *s = local_scopes_[0];
  // FIXME(wuyi): send op always copy from GPU 0
  result->ops_.emplace_back(new SendOpHandle(op, s, p));
  // Create inputs for output on original place and no ssa output
  // is created for send op.
  CreateOpHandleIOs(result, op, p, 0);
}

bool MultiDevSSAGraphBuilder::IsScaleLossOp(const OpDesc &op) const {
  // FIXME(yy): Do not hard code like this
  return op.OutputArgumentNames().size() == 1 &&
         op.OutputArgumentNames()[0] == GradVarName(loss_var_name_);
}
}  // namespace details
}  // namespace framework
}  // namespace paddle