Paddle/paddle/fluid/operators/distributed_ops/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 "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/distributed/communicator.h"
#include "paddle/fluid/operators/distributed/distributed.h"

namespace paddle {
namespace framework {
class InferShapeContext;
class OpDesc;
class Scope;
template <typename T>
class EmptyGradOpMaker;
}  // namespace framework
namespace imperative {
class OpBase;
}  // namespace imperative
}  // namespace paddle

namespace paddle {
namespace operators {

namespace distributed {
class RPCClient;
}  // namespace distributed

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 {
    std::vector<std::string> epmap = Attr<std::vector<std::string>>("epmap");
    std::vector<std::string> varnames =
        Attr<std::vector<std::string>>("varnames");

    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 *communicator = distributed::Communicator::GetInstance();

      if (communicator != nullptr) {
        PADDLE_THROW(platform::errors::InvalidArgument(
            "execute startup program must before fleet.init_worker"));
      }
    } else {
      std::vector<distributed::VarHandlePtr> rets;
      if (with_barrier) {
        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]));
        }
      } else {
        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++) {
        VLOG(7) << "before sync_recv " << outs[i] << "from " << epmap[i];
        PADDLE_ENFORCE_NE(
            rets[i]->Wait(), 0U,
            platform::errors::ExecutionTimeout("internal error in RPCClient"));
        VLOG(7) << "after sync_recv " << outs[i] << "from " << epmap[i];
      }
    }
  }
};

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<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 split 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<paddle::framework::OpDesc>,
    paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>,
    ops::RecvOpMaker, ops::RecvOpShapeInference);