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@ -20,43 +20,121 @@ limitations under the License. */
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#include "paddle/fluid/operators/detail/grpc_client.h"
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#include "paddle/fluid/operators/detail/grpc_server.h"
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#include "paddle/fluid/framework/block_desc.h"
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/framework/operator.h"
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namespace framework = paddle::framework;
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namespace platform = paddle::platform;
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namespace detail = paddle::operators::detail;
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USE_OP(lookup_table);
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std::unique_ptr<detail::AsyncGRPCServer> rpc_service_;
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framework::BlockDesc* AppendPrefetchBlcok(framework::ProgramDesc* program) {
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auto root_block = program->MutableBlock(0);
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auto* block = program->AppendBlock(*root_block);
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framework::VariableNameMap input({{"W", {"w"}}, {"Ids", {"ids"}}});
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framework::VariableNameMap output({{"Output", {"out"}}});
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auto op = block->AppendOp();
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op->SetType("lookup_table");
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op->SetInput("W", {"w"});
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op->SetInput("Ids", {"ids"});
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op->SetOutput("Out", {"out"});
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auto& out = *root_block->Var("out");
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out.SetType(framework::proto::VarType::SELECTED_ROWS);
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out.SetShape({10, 10});
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return block;
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}
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void CreateVarsOnScope(framework::Scope* scope, platform::CPUPlace* place) {
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auto w_var = scope->Var("w");
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w_var->GetMutable<framework::SelectedRows>();
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auto out_var = scope->Var("out");
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out_var->GetMutable<framework::SelectedRows>();
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auto ids_var = scope->Var("ids");
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ids_var->GetMutable<framework::SelectedRows>();
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}
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void InitTensorsOnClient(framework::Scope* scope, platform::CPUPlace* place,
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int64_t rows_numel) {
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CreateVarsOnScope(scope, place);
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auto ids_var = scope->Var("ids")->GetMutable<framework::SelectedRows>();
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auto rows = ids_var->mutable_rows();
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for (int64_t i = 0; i < rows_numel; ++i) rows->push_back(i * 2);
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ids_var->mutable_value()->Resize({rows_numel, 1});
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ids_var->mutable_value()->mutable_data<float>(*place);
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}
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void InitTensorsOnServer(framework::Scope* scope, platform::CPUPlace* place,
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int64_t rows_numel) {
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CreateVarsOnScope(scope, place);
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auto w = scope->Var("w")->GetMutable<framework::SelectedRows>();
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auto rows = w->mutable_rows();
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for (int64_t i = 0; i < rows_numel; ++i) rows->push_back(i);
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auto w_value = w->mutable_value();
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w_value->Resize({rows_numel, 10});
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auto ptr = w_value->mutable_data<float>(*place);
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for (int64_t i = 0; i < w_value->numel(); ++i) {
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ptr[i] = static_cast<float>(i / 10);
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}
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}
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void StartServer(const std::string& endpoint) {
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rpc_service_.reset(new detail::AsyncGRPCServer(endpoint));
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framework::ProgramDesc program;
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framework::Scope scope;
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platform::CPUPlace place;
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framework::Executor exe(place);
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platform::CPUDeviceContext ctx(place);
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auto* block = AppendPrefetchBlcok(&program);
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auto prepared = exe.Prepare(program, block->ID());
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InitTensorsOnServer(&scope, &place, 10);
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rpc_service_->SetProgram(&program);
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rpc_service_->SetPrefetchPreparedCtx(prepared.get());
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rpc_service_->SetDevCtx(&ctx);
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rpc_service_->SetScope(&scope);
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rpc_service_->SetExecutor(&exe);
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rpc_service_->RunSyncUpdate();
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}
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TEST(PREFETCH, CPU) {
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// start up a server instance backend
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// TODO(Yancey1989): Need to start a server with optimize blocks and
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// prefetch blocks.
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std::thread server_thread(StartServer, "127.0.0.1:8889");
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sleep(2);
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framework::Scope scope;
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platform::CPUPlace place;
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platform::CPUDeviceContext ctx(place);
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// create var on local scope
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std::string in_var_name("in");
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int64_t rows_numel = 5;
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InitTensorsOnClient(&scope, &place, rows_numel);
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std::string in_var_name("ids");
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std::string out_var_name("out");
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auto* in_var = scope.Var(in_var_name);
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auto* in_tensor = in_var->GetMutable<framework::LoDTensor>();
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in_tensor->Resize({10, 10});
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VLOG(3) << "before mutable_data";
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in_tensor->mutable_data<int>(place);
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scope.Var(out_var_name);
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VLOG(3) << "before fetch";
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detail::RPCClient client;
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client.AsyncPrefetchVariable("127.0.0.1:8889", ctx, scope, in_var_name,
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out_var_name);
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client.Wait();
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auto var = scope.Var(out_var_name);
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auto value = var->GetMutable<framework::SelectedRows>()->value();
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auto ptr = value.mutable_data<float>(place);
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rpc_service_->ShutDown();
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server_thread.join();
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rpc_service_.reset(nullptr);
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for (int64_t i = 0; i < rows_numel; ++i) {
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EXPECT_EQ(ptr[0 + i * value.dims()[1]], static_cast<float>(i * 2));
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}
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}
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