send_recv_op_test.cc

/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.

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 <unistd.h>
#include <string>
#include <thread>

#include "gtest/gtest.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
#include "paddle/framework/program_desc.h"
#include "paddle/string/printf.h"

USE_NO_KERNEL_OP(send);
USE_NO_KERNEL_OP(recv);
USE_OP(sum);

// global for simplicity.
std::unique_ptr<paddle::framework::OperatorBase> recv_op;

void InitTensorsInScope(paddle::framework::Scope &scope,
                        paddle::platform::CPUPlace &place) {
  paddle::platform::CPUDeviceContext ctx(place);
  for (int i = 0; i < 2; ++i) {
    auto var_name = paddle::string::Sprintf("x%d", i);
    auto var = scope.Var(var_name);
    auto tensor = var->GetMutable<paddle::framework::LoDTensor>();
    tensor->Resize({10, 10});
    float *expect = tensor->mutable_data<float>(place);
    for (int64_t i = 0; i < tensor->numel(); ++i) {
      expect[i] = static_cast<float>(i);
    }
  }

  auto out_var = scope.Var("Out");
  auto out_tensor = out_var->GetMutable<paddle::framework::LoDTensor>();
  out_tensor->Resize({10, 10});
  out_tensor->mutable_data<float>(place);  // allocate
}

void AddOp(const std::string &type,
           const paddle::framework::VariableNameMap &inputs,
           const paddle::framework::VariableNameMap &outputs,
           paddle::framework::AttributeMap attrs,
           paddle::framework::BlockDesc *block) {
  // insert output
  for (auto kv : outputs) {
    for (auto v : kv.second) {
      auto var = block->Var(v);
      var->SetDataType(paddle::framework::proto::DataType::FP32);
    }
  }

  // insert op
  auto op = block->AppendOp();
  op->SetType(type);
  for (auto &kv : inputs) {
    op->SetInput(kv.first, kv.second);
  }
  for (auto &kv : outputs) {
    op->SetOutput(kv.first, kv.second);
  }
  op->SetAttrMap(attrs);
}

void StartServerNet() {
  paddle::framework::Scope scope;
  paddle::platform::CPUPlace place;
  InitTensorsInScope(scope, place);

  // sub program run in recv_op, for simple test we use sum
  paddle::framework::ProgramDesc program;
  paddle::framework::BlockDesc *block = program.MutableBlock(0);
  // X for server side tensors, RX for received tensers, must be of same shape.
  AddOp("sum", {{"X", {"x0", "x1"}}}, {{"Out", {"x0"}}}, {}, block);

  paddle::framework::AttributeMap attrs;
  attrs.insert({"endpoint", std::string("127.0.0.1:6174")});
  attrs.insert({"ParamList", std::vector<std::string>({"x0"})});
  attrs.insert({"GradList", std::vector<std::string>({"x1"})});
  std::string program_proto;
  PADDLE_ENFORCE(program.Proto()->SerializeToString(&program_proto));

  attrs.insert({"OptimizeProgram", program_proto});
  recv_op = paddle::framework::OpRegistry::CreateOp("recv", {{"RX", {"x1"}}},
                                                    {}, attrs);
  recv_op->Run(scope, place);
}

TEST(SendRecvOp, CPU) {
  std::thread server_thread(StartServerNet);
  sleep(5);  // wait server to start
  // local net
  paddle::framework::Scope scope;
  paddle::platform::CPUPlace place;
  InitTensorsInScope(scope, place);

  paddle::framework::AttributeMap attrs;
  attrs.insert({"endpoints", std::vector<std::string>({"127.0.0.1:6174"})});
  attrs.insert({"epmap", std::vector<std::string>({"127.0.0.1:6174"})});
  auto send_op = paddle::framework::OpRegistry::CreateOp(
      "send", {{"X", {"x1"}}}, {{"Out", {"x0"}}}, attrs);
  send_op->Run(scope, place);

  auto in_var = scope.Var("x1");
  auto tensor = in_var->GetMutable<paddle::framework::LoDTensor>();
  float *expected = tensor->data<float>();
  auto out_var = scope.Var("x0");
  auto target = out_var->GetMutable<paddle::framework::LoDTensor>();
  // x1 * 2 == x0
  EXPECT_NE(target->memory_size(), size_t(0));
  float *actual = target->data<float>();
  for (int64_t i = 0; i < target->numel(); ++i) {
    EXPECT_EQ(expected[i] * 2, actual[i]);
  }

  recv_op->Stop();
  server_thread.join();
  // recv_op.reset();
}