brpc_serde_test.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 <unistd.h>
#include <string>
#include <thread>  // NOLINT

#include "brpc/channel.h"
#include "google/protobuf/text_format.h"
#include "gtest/gtest.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/framework/variable.h"
#include "paddle/fluid/operators/distributed/brpc/brpc_sendrecvop_utils.h"
#include "paddle/fluid/operators/distributed/brpc/brpc_variable_response.h"
#include "paddle/fluid/operators/distributed/sendrecvop_utils.h"
#include "paddle/fluid/operators/distributed/variable_response.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/string/printf.h"

namespace framework = paddle::framework;
namespace platform = paddle::platform;
namespace operators = paddle::operators;
namespace math = paddle::operators::math;
namespace memory = paddle::memory;

void RunSerdeTestSelectedRows(platform::Place place) {
  platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
  auto& ctx = *pool.Get(place);

  butil::IOBuf iobuf;
  sendrecv::VariableMessage msg;
  int tensor_numel = 564 * 128;

  // serialize var to IOBuf
  {
    framework::Variable var;
    auto* slr = var.GetMutable<framework::SelectedRows>();
    slr->set_height(1000);
    auto* tensor = slr->mutable_value();
    auto* rows = slr->mutable_rows();
    tensor->Resize(framework::make_ddim({564, 128}));
    tensor->mutable_data<float>(place);
    math::set_constant(ctx, tensor, 32.7);
    for (int i = 0; i < 564; ++i) rows->push_back(i);

    operators::distributed::SerializeToIOBuf("myvar", &var, ctx, &msg, &iobuf,
                                             "", false);
  }

  // desrialize
  {
    framework::Scope scope;
    scope.Var("myvar");
    operators::distributed::BRPCVariableResponse resp(&scope, &ctx);
    EXPECT_EQ(resp.Parse(iobuf, msg), 0);

    framework::Variable* var2 = resp.GetVar();

    auto* slr2 = var2->GetMutable<framework::SelectedRows>();
    auto* tensor2 = slr2->mutable_value();
    auto* rows2 = slr2->mutable_rows();
    float* tensor_data2 = nullptr;
    framework::Tensor tmp_tensor;

    if (platform::is_gpu_place(ctx.GetPlace())) {
      platform::CPUPlace cpu;
      framework::TensorCopy(*tensor2, cpu, &tmp_tensor);
      tensor_data2 = tmp_tensor.data<float>();
    } else {
      tensor_data2 = const_cast<float*>(tensor2->data<float>());
    }
    const int64_t* rows_data2 = rows2->data();

    for (int i = 0; i < tensor_numel; ++i) {
      EXPECT_FLOAT_EQ(tensor_data2[i], 32.7);
    }
    for (size_t i = 0; i < rows2->size(); ++i) {
      EXPECT_EQ(rows_data2[i], static_cast<int64_t>(i));
    }
    EXPECT_EQ(slr2->height(), 1000);
  }
}

void RunTestLodTensor(platform::Place place) {
  platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
  auto& ctx = *pool.Get(place);

  // serialize var to ByteBuffer
  butil::IOBuf iobuf;
  sendrecv::VariableMessage msg;
  int tensor_numel = 512 * 8 * 4 * 2;
  {
    framework::Variable var;
    auto* tensor = var.GetMutable<framework::LoDTensor>();
    tensor->Resize(framework::make_ddim({512, 8, 4, 2}));
    framework::LoD lod;
    lod.push_back(framework::Vector<size_t>({1, 3, 8}));
    tensor->set_lod(lod);
    tensor->mutable_data<float>(place);
    math::set_constant(ctx, tensor, 31.9);

    operators::distributed::SerializeToIOBuf("myvar", &var, ctx, &msg, &iobuf,
                                             "", false);
  }

  // check sendrecv::VariableMessage meta data
  {
    EXPECT_EQ(msg.varname(), "myvar");
    EXPECT_EQ(msg.type(), 0);
    EXPECT_EQ(msg.dims()[0], 512);
    EXPECT_EQ(msg.dims()[1], 8);
    EXPECT_EQ(msg.dims()[2], 4);
    EXPECT_EQ(msg.dims()[3], 2);
    EXPECT_EQ(msg.lod_level(), 1);
    EXPECT_EQ(msg.lod(0).lod_data(0), 1);
    EXPECT_EQ(msg.lod(0).lod_data(1), 3);
    EXPECT_EQ(msg.lod(0).lod_data(2), 8);
  }

  // deserialize
  {
    framework::Scope scope;
    scope.Var("myvar");
    operators::distributed::BRPCVariableResponse resp(&scope, &ctx);
    EXPECT_EQ(resp.Parse(iobuf, msg), 0);

    framework::Variable* var2 = resp.GetVar();

    auto tensor2 = var2->Get<framework::LoDTensor>();
    float* tensor_data2 = nullptr;
    framework::Tensor tmp_tensor;

    if (platform::is_gpu_place(ctx.GetPlace())) {
      platform::CPUPlace cpu;
      framework::TensorCopy(tensor2, cpu, &tmp_tensor);
      tensor_data2 = tmp_tensor.data<float>();
    } else {
      tensor_data2 = const_cast<float*>(tensor2.data<float>());
    }

    for (int i = 0; i < tensor_numel; ++i)
      EXPECT_FLOAT_EQ(tensor_data2[i], 31.9);
  }
}

TEST(LodTensor, Run) {
  platform::CPUPlace place;
  RunTestLodTensor(place);
#ifdef PADDLE_WITH_CUDA
  platform::CUDAPlace gpu(0);
  RunTestLodTensor(gpu);
#endif
}

TEST(SelectedRows, Run) {
  platform::CPUPlace place;
  RunSerdeTestSelectedRows(place);
#ifdef PADDLE_WITH_CUDA
  platform::CUDAPlace gpu;
  RunSerdeTestSelectedRows(gpu);
#endif
}