nccl_op_test.cu

/* 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. */

#define EIGEN_USE_GPU

#include <glog/logging.h>
#include <gtest/gtest.h>
#include <algorithm>
#include <memory>
#include <mutex>
#include <thread>
#include <utility>
#include <vector>

#include "paddle/framework/block_desc.h"
#include "paddle/framework/op_desc.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/program_desc.h"
#include "paddle/framework/var_desc.h"
#include "paddle/operators/math/math_function.h"
#include "paddle/operators/nccl/nccl_gpu_common.h"
#include "paddle/platform/device_context.h"
#include "paddle/platform/enforce.h"
#include "paddle/platform/gpu_info.h"
#include "paddle/platform/place.h"

USE_NO_KERNEL_OP(ncclInit);
USE_GPU_ONLY_OP(ncclAllReduce);
USE_GPU_ONLY_OP(ncclReduce);
USE_GPU_ONLY_OP(ncclBcast);

namespace f = paddle::framework;
namespace p = paddle::platform;

static std::vector<int> gpu_list;

// test data amount
const f::DDim kDims = {100, 100};

// nccl op common tester, init communicator.
class NCCLTester : public ::testing::Test {
 public:
  virtual void SetUp() override {
    cpu_ctx = new p::CPUDeviceContext(p::CPUPlace());
    for (size_t i = 0; i < gpu_list.size(); ++i) {
      p::GPUPlace place(i);
      dev_ctxs.emplace_back(new p::CUDADeviceContext(place));
    }

    NCCLInitOp();
  }

  virtual void TearDown() override {
    for (auto &device_context : dev_ctxs) {
      delete device_context;
    }
  }

  void NCCLInitOp() {
    std::unique_ptr<f::OpDescBind> op1(new f::OpDescBind);

    op1->SetType("ncclInit");
    op1->SetOutput("Communicator", {"comm"});
    op1->SetAttr("gpus", {gpu_list});

    auto *var = g_scope.Var("comm");
    var->GetMutable<p::Communicator>();

    auto op = f::OpRegistry::CreateOp(*op1);
    VLOG(1) << "invoke NCCLInitOp.";
    op->Run(g_scope, *cpu_ctx);
    VLOG(1) << "NCCLInitOp finished.";
  }

  template <class T>
  void PerThreadProgram(int gpu_id, const f::OpDescBind &op_desc,
                        f::Scope *scope) {
    std::unique_lock<std::mutex> lk(mu);
    f::ProgramDescBind program;
    f::BlockDescBind *block = program.Block(0);
    f::OpDescBind *op1 = block->AppendOp();
    *op1 = op_desc;

    p::GPUPlace place(gpu_id);
    auto &ctx = dev_ctxs.at(gpu_id);

    auto *send_tensor = scope->Var("st")->GetMutable<f::LoDTensor>();
    auto *recv_tensor = scope->Var("rt")->GetMutable<f::LoDTensor>();
    send_tensor->Resize(kDims);
    send_tensor->mutable_data<T>(kDims, place);

    std::vector<T> send_vector(f::product(kDims), gpu_id);
    send_tensor->CopyFromVector<T>(send_vector, *ctx);
    lk.unlock();
    PADDLE_ENFORCE(send_tensor->numel() == f::product(kDims),
                   "Tensor numel not match!");
    ctx->Wait();

    VLOG(1) << "Send Tensor filled with elements " << send_tensor->numel();

    auto op = f::OpRegistry::CreateOp(*op1);
    VLOG(1) << "Device : " << gpu_id << " invoke " << op_desc.Type();
    op->Run(*scope, *ctx);
    VLOG(1) << "Device : " << gpu_id << " finished " << op_desc.Type();
  }

 public:
  std::vector<p::DeviceContext *> dev_ctxs;
  p::DeviceContext *cpu_ctx;
  f::Scope g_scope;
  std::mutex mu;
};

// ncclInitOp with desc
// TEST(NCCL, ncclInitOp) {
//   std::unique_ptr<f::OpDescBind> op_desc(new f::OpDescBind);

//   op_desc->SetType("ncclInit");
//   op_desc->SetOutput("Communicator", {"x1"});
//   op_desc->SetAttr("gpus", {gpu_list});

//   f::Scope g_scope;
//   std::unique_ptr<p::DeviceContext> ctx(new
//   p::CPUDeviceContext(p::CPUPlace()));

//   auto *var = g_scope.Var("x1");
//   var->GetMutable<p::Communicator>();

//   auto op = f::OpRegistry::CreateOp(*op_desc);
//   VLOG(1) << "invoke NCCLInitOp.";
//   op->Run(g_scope, *ctx.get());
//   VLOG(1) << "NCCLInitOp finished.";
// }

// ncclAllReduceOp with desc
// TEST_F(NCCLTester, ncclAllReduceOp) {
//   std::unique_ptr<f::OpDescBind> op2(new f::OpDescBind);
//   op2->SetType("ncclAllReduce");
//   op2->SetInput("X", {"st"});
//   op2->SetInput("Communicator", {"comm"});
//   op2->SetOutput("Out", {"rt"});

//   std::vector<f::Scope *> dev_scopes;

//   std::vector<std::thread> ths;

//   for (size_t i = 0; i < gpu_list.size(); ++i) {
//     dev_scopes.emplace_back(&g_scope.NewScope());
//     std::thread th(&NCCLTester::PerThreadProgram<float>, this, gpu_list[i],
//                    *op2.get(), dev_scopes[i]);
//     ths.emplace_back(std::move(th));
//   }

//   for (size_t i = 0; i < gpu_list.size(); ++i) {
//     ths[i].join();
//   }

//   // check results
//   float result = std::accumulate(gpu_list.begin(), gpu_list.end(), 0);

//   for (size_t i = 0; i < dev_scopes.size(); ++i) {
//     p::CPUPlace cpu_place;
//     p::GPUPlace gpu_place(gpu_list[i]);

//     auto &recv_tensor = dev_scopes[i]->FindVar("rt")->Get<f::LoDTensor>();
//     auto *rt = recv_tensor.data<float>();
//     auto *result_tensor =
//     dev_scopes[i]->Var("ct")->GetMutable<f::LoDTensor>();
//     result_tensor->Resize(kDims);
//     auto *ct = result_tensor->mutable_data<float>(cpu_place);

//     paddle::memory::Copy(
//         cpu_place, ct, p::GPUPlace(gpu_list[i]), rt,
//         recv_tensor.numel() * sizeof(float),
//         static_cast<p::CUDADeviceContext *>(dev_ctxs[i])->stream());

//     for (size_t j = 0; j < f::product(kDims); ++j) {
//       ASSERT_NEAR(ct[j], result, 1e-5);
//     }
//   }
// }

// ncclAReduceOp with desc
TEST_F(NCCLTester, ncclReduceOp) {
  std::unique_ptr<f::OpDescBind> op2(new f::OpDescBind);
  const int kRoot = 0;
  op2->SetType("ncclReduce");
  op2->SetInput("X", {"st"});
  op2->SetInput("Communicator", {"comm"});
  op2->SetOutput("Out", {"rt"});
  op2->SetAttr("root", {kRoot});

  std::vector<f::Scope *> dev_scopes;

  std::vector<std::thread> ths;

  for (size_t i = 0; i < gpu_list.size(); ++i) {
    dev_scopes.emplace_back(&g_scope.NewScope());
    std::thread th(&NCCLTester::PerThreadProgram<float>, this, gpu_list[i],
                   *op2.get(), dev_scopes[i]);
    ths.emplace_back(std::move(th));
  }

  for (size_t i = 0; i < gpu_list.size(); ++i) {
    ths[i].join();
  }

  // check results on
  float result = std::accumulate(gpu_list.begin(), gpu_list.end(), 0);

  p::CPUPlace cpu_place;
  p::GPUPlace gpu_place(gpu_list[kRoot]);

  auto &recv_tensor = dev_scopes[kRoot]->FindVar("rt")->Get<f::LoDTensor>();
  auto *rt = recv_tensor.data<float>();
  auto *result_tensor =
      dev_scopes[kRoot]->Var("ct")->GetMutable<f::LoDTensor>();
  result_tensor->Resize(kDims);
  auto *ct = result_tensor->mutable_data<float>(cpu_place);

  paddle::memory::Copy(
      cpu_place, ct, p::GPUPlace(gpu_list[kRoot]), rt,
      recv_tensor.numel() * sizeof(float),
      static_cast<p::CUDADeviceContext *>(dev_ctxs[kRoot])->stream());

  for (int j = 0; j < f::product(kDims); ++j) {
    ASSERT_NEAR(ct[j], result, 1e-5);
  }
}

// // ncclBcastOp with desc
TEST_F(NCCLTester, ncclBcastOp) {
  std::unique_ptr<f::OpDescBind> op2(new f::OpDescBind);
  const int kRoot = 0;
  op2->SetType("ncclBcast");
  op2->SetInput("X", {"st"});
  op2->SetInput("Communicator", {"comm"});
  op2->SetOutput("Out", {"rt"});
  op2->SetAttr("root", {kRoot});

  std::vector<f::Scope *> dev_scopes;

  std::vector<std::thread> ths;

  for (size_t i = 0; i < gpu_list.size(); ++i) {
    dev_scopes.emplace_back(&g_scope.NewScope());
    std::thread th(&NCCLTester::PerThreadProgram<float>, this, gpu_list[i],
                   *op2.get(), dev_scopes[i]);
    ths.emplace_back(std::move(th));
  }

  for (size_t i = 0; i < gpu_list.size(); ++i) {
    ths[i].join();
  }

  const int idx = 1;
  // check results on
  float result = std::accumulate(gpu_list.begin(), gpu_list.end(), 0);

  p::CPUPlace cpu_place;
  p::GPUPlace gpu_place(gpu_list[idx]);

  auto &recv_tensor = dev_scopes[idx]->FindVar("rt")->Get<f::LoDTensor>();
  auto *rt = recv_tensor.data<float>();
  auto *result_tensor = dev_scopes[idx]->Var("ct")->GetMutable<f::LoDTensor>();
  result_tensor->Resize(kDims);
  auto *ct = result_tensor->mutable_data<float>(cpu_place);

  paddle::memory::Copy(
      cpu_place, ct, p::GPUPlace(gpu_list[idx]), rt,
      recv_tensor.numel() * sizeof(float),
      static_cast<p::CUDADeviceContext *>(dev_ctxs[idx])->stream());

  for (size_t j = 0; j < f::product(kDims); ++j) {
    ASSERT_NEAR(ct[j], result, 1e-5);
  }
}

// joint ncclBcastOp and ncclReduceOp
TEST_F(NCCLTester, MultipleOp) {
  const int kRoot = 0;
  std::unique_ptr<f::OpDescBind> op1(new f::OpDescBind);
  op1->SetType("ncclReduce");
  op1->SetInput("X", {"rt"});
  op1->SetInput("Communicator", {"comm"});
  op1->SetOutput("Out", {"rt"});
  op2->SetAttr("root", {kRoot});

  std::unique_ptr<f::OpDescBind> op2(new f::OpDescBind);
  op2->SetType("ncclBcast");
  op2->SetInput("X", {"st"});
  op2->SetInput("Communicator", {"comm"});
  op2->SetOutput("Out", {"rt"});
  op2->SetAttr("root", {kRoot});

  std::vector<f::Scope *> dev_scopes;

  std::vector<std::thread> ths;

  // run Bcast
  for (size_t i = 0; i < gpu_list.size(); ++i) {
    dev_scopes.emplace_back(&g_scope.NewScope());
    std::thread th(&NCCLTester::PerThreadProgram<float>, this, gpu_list[i],
                   *op1.get(), dev_scopes[i]);
    ths.emplace_back(std::move(th));
  }

  for (size_t i = 0; i < gpu_list.size(); ++i) {
    ths[i].join();
  }

  ths.clear();

  // run Reduce
  for (size_t i = 0; i < gpu_list.size(); ++i) {
    dev_scopes.emplace_back(&g_scope.NewScope());
    std::thread th(&NCCLTester::PerThreadProgram<float>, this, gpu_list[i],
                   *op2.get(), dev_scopes[i]);
    ths.emplace_back(std::move(th));
  }

  for (size_t i = 0; i < gpu_list.size(); ++i) {
    ths[i].join();
  }

  // check results
  float result = std::accumulate(gpu_list.begin(), gpu_list.end(), 0);

  for (size_t i = 0; i < dev_scopes.size(); ++i) {
    p::CPUPlace cpu_place;
    p::GPUPlace gpu_place(gpu_list[i]);

    auto &recv_tensor = dev_scopes[i]->FindVar("rt")->Get<f::LoDTensor>();
    auto *rt = recv_tensor.data<float>();
    auto *result_tensor = dev_scopes[i]->Var("ct")->GetMutable<f::LoDTensor>();
    result_tensor->Resize(kDims);
    auto *ct = result_tensor->mutable_data<float>(cpu_place);

    paddle::memory::Copy(
        cpu_place, ct, p::GPUPlace(gpu_list[i]), rt,
        recv_tensor.numel() * sizeof(float),
        static_cast<p::CUDADeviceContext *>(dev_ctxs[i])->stream());

    for (int j = 0; j < f::product(kDims); ++j) {
      ASSERT_NEAR(ct[j], result, 1e-5);
    }
  }
}

int main(int argc, char **argv) {
  const int dev_count = p::GetCUDADeviceCount();
  if (dev_count <= 1) {
    LOG(WARNING)
        << "Cannot test multi-gpu nccl, because the CUDA device count is "
        << dev_count;
    return 0;
  }

  for (int i = 0; i < dev_count; ++i) {
    gpu_list.emplace_back(i);
  }
  testing::InitGoogleTest(&argc, argv);

  // device context should be release before scope.
  // otherwise driver will down.
  return RUN_ALL_TESTS();
}