recv_op.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 <future>  // NOLINT
#include <ostream>

#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/framework.pb.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/distributed/distributed.h"
#include "paddle/fluid/operators/distributed/parameter_recv.h"
#include "paddle/fluid/operators/distributed/rpc_common.h"
#include "paddle/fluid/platform/profiler.h"

namespace paddle {
namespace operators {

class RecvOp : public framework::OperatorBase {
 public:
  RecvOp(const std::string &type, const framework::VariableNameMap &inputs,
         const framework::VariableNameMap &outputs,
         const framework::AttributeMap &attrs)
      : OperatorBase(type, inputs, outputs, attrs) {}

  void RunImpl(const framework::Scope &scope,
               const platform::Place &place) const override {
    int do_not_run = Attr<int>("do_not_run");
    if (do_not_run) {
      VLOG(3) << "recv do not run!";
      return;
    }
    std::vector<std::string> epmap = Attr<std::vector<std::string>>("epmap");
    std::vector<std::string> varnames =
        Attr<std::vector<std::string>>("varnames");
    int sync_mode = Attr<int>("sync_mode");
    auto outs = Outputs("Out");
    bool with_barrier = Attr<bool>("with_barrier");

    platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
    auto &ctx = *pool.Get(place);
    auto trainer_id = Attr<int>("trainer_id");

    distributed::RPCClient *rpc_client =
        distributed::RPCClient::GetInstance<RPCCLIENT_T>(trainer_id);

    std::vector<std::string> recv_varnames =
        Attr<std::vector<std::string>>("recv_varnames");

    if (recv_varnames.size() > 0) {
      auto recv_functor = distributed::ParameterRecv<float>();
      auto rpc_ctx = distributed::RpcContext(outs[0], recv_varnames, epmap, {},
                                             trainer_id);
      recv_functor(rpc_ctx, scope);
    } else {
      if (with_barrier) {
        std::vector<distributed::VarHandlePtr> rets;
        for (size_t i = 0; i < outs.size(); i++) {
          std::string varname = varnames.size() == 0 ? outs[i] : varnames[i];
          VLOG(4) << "recv " << outs[i] << " from " << epmap[i] << " with "
                  << varname << " and with AsyncGetVar";
          rets.push_back(
              rpc_client->AsyncGetVar(epmap[i], ctx, scope, varname, outs[i]));
        }
        if (sync_mode) {
          for (size_t i = 0; i < rets.size(); i++) {
            PADDLE_ENFORCE(rets[i]->Wait(), "internal error in RPCClient");
          }
        }
      } else {
        std::vector<distributed::VarHandlePtr> rets;
        for (size_t i = 0; i < outs.size(); i++) {
          std::string varname = varnames.size() == 0 ? outs[i] : varnames[i];
          VLOG(4) << "recv " << outs[i] << " from " << epmap[i] << " with "
                  << varname << " and with AsyncGetVarNoBarrier";
          rets.push_back(rpc_client->AsyncGetVarNoBarrier(epmap[i], ctx, scope,
                                                          varname, outs[i]));
        }
        for (size_t i = 0; i < rets.size(); i++) {
          PADDLE_ENFORCE(rets[i]->Wait(), "internal error in RPCClient");
        }
      }
    }
  }
};

class RecvOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() {
    AddInput("X", "(Any) Dummy inputs, used for control dependency")
        .AsDuplicable();
    AddOutput("Out", "(Tensor) Variables to get from server.").AsDuplicable();
    AddComment(R"DOC(
Recv operator

This operator can get variables from server side.
)DOC");
    AddAttr<std::vector<std::string>>("epmap",
                                      "(string vector, default 127.0.0.1:6164)"
                                      "Server endpoints in the order of input "
                                      "variables for mapping")
        .SetDefault({});
    AddAttr<int>("trainer_id", "trainer id from 0 ~ worker_num.").SetDefault(0);
    AddAttr<int>("sync_mode",
                 "(int, default 0)"
                 "sync recv or async recv.")
        .SetDefault(0);
    AddAttr<bool>("with_barrier",
                  "(bool, default True) if with_barrier=False, will use "
                  "AsyncGetVarNoBarrier get variable from pserver immediately")
        .SetDefault(true);
    AddAttr<std::vector<std::string>>(
        "varnames",
        "(string vector, default {}) "
        "sometimes we need to put received var in another name "
        "for example: we need var named 'moment_1@127.0.0.1:1001', "
        "and it real name on parameter server is 'moment_1'. ")
        .SetDefault({});
    AddAttr<std::vector<std::string>>(
        "recv_varnames",
        "(vector<string>) "
        "the splited parameter varnames to be recved from pserver")
        .SetDefault(std::vector<std::string>{});
    AddAttr<int>("do_not_run", "if recv need to really run").SetDefault(0);
  }
};

class RecvOpShapeInference : public framework::InferShapeBase {
 public:
  void operator()(framework::InferShapeContext *ctx) const override {}
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;

REGISTER_OPERATOR(recv, ops::RecvOp, paddle::framework::EmptyGradOpMaker,
                  ops::RecvOpMaker, ops::RecvOpShapeInference);