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120 lines
3.9 KiB
120 lines
3.9 KiB
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/operators/distributed/distributed.h"
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namespace paddle {
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namespace framework {
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class InferShapeContext;
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class OpDesc;
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class Scope;
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template <typename T>
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class EmptyGradOpMaker;
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} // namespace framework
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namespace imperative {
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class OpBase;
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} // namespace imperative
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} // namespace paddle
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namespace paddle {
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namespace operators {
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namespace distributed {
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class RPCClient;
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} // namespace distributed
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class PrefetchOp : public framework::OperatorBase {
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public:
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PrefetchOp(const std::string& type, const framework::VariableNameMap& inputs,
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const framework::VariableNameMap& outputs,
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const framework::AttributeMap& attrs)
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: OperatorBase(type, inputs, outputs, attrs) {}
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void RunImpl(const framework::Scope& scope,
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const platform::Place& place) const override {
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auto ins = Inputs("X");
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auto outs = Outputs("Out");
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std::vector<std::string> epmap = Attr<std::vector<std::string>>("epmap");
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platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
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auto& ctx = *pool.Get(place);
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distributed::RPCClient* rpc_client =
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distributed::RPCClient::GetInstance<RPCCLIENT_T>(
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Attr<int>("trainer_id"));
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std::vector<distributed::VarHandlePtr> rets;
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for (size_t i = 0; i < ins.size(); i++) {
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if (NeedSend(scope, ins[i])) {
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VLOG(3) << "sending " << ins[i] << " to " << epmap[i] << " to get "
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<< outs[i] << " back";
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rets.push_back(rpc_client->AsyncPrefetchVar(epmap[i], ctx, scope,
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ins[i], outs[i]));
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} else {
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VLOG(3) << "don't send no-initialied variable: " << ins[i];
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}
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}
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for (size_t i = 0; i < rets.size(); i++) {
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PADDLE_ENFORCE_EQ(
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rets[i]->Wait(), true,
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platform::errors::Fatal(
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"It's a fatal error of RPCClient that RPCClient can't "
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"get the wait result. It may happen when trainers or "
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"parameter servers exit un normally or the network "
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"issue!"));
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}
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}
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};
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class PrefetchOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() {
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AddInput("X", "(LoDTensor) Input Id variables to be sent").AsDuplicable();
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AddOutput("Out",
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"(LoDTensor) result "
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"to be fetched from parameter server")
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.AsDuplicable();
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AddAttr<int>("trainer_id", "trainer id from 0 ~ worker_num.").SetDefault(0);
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AddAttr<std::vector<std::string>>(
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"epmap",
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"(string vector, default 127.0.0.1:6164)"
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"Server endpoints in the order of input variables for mapping")
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.SetDefault({"127.0.0.1:6164"});
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AddComment(R"DOC(
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Prefetch operator
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This operator will send Ids variables to listen_and_serve op at
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the parameter server and fetch result back.
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)DOC");
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}
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};
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class PrefetchOpShapeInference : public framework::InferShapeBase {
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public:
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void operator()(framework::InferShapeContext* ctx) const override {}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OPERATOR(
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prefetch, ops::PrefetchOp,
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paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
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paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>,
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ops::PrefetchOpMaker, ops::PrefetchOpShapeInference);
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