cudnn_wrappers.h

// Copyright 2004-present Facebook. All Rights Reserved.

#ifndef CAFFE2_CORE_CUDNN_WRAPPERS_H_
#define CAFFE2_CORE_CUDNN_WRAPPERS_H_

#include "caffe2/core/common_cudnn.h"
#include "caffe2/core/context_gpu.h"

// Note [What is CuDNNWrapper good for?]
// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
// Suppose you are writing a kernel that calls into CuDNN, and
// you need a cudnnHandle_t to pass to the kernel call.  How should
// you go about getting one of those handles?  You'd prefer not
// to make a new cudnnHandle_t every call; this can be somewhat
// expensive (1-2%, according to some measurements in TensorFlow.)
// But cudnnHandle_t is not thread-safe, so we can't just have
// a single global cudnnHandle_t that everyone uses.
//
// Thus, the most common method in Caffe2 for getting a CuDNN handle
// is to get a per-thread, per-stream CuDNN handle from CUDAContext
// (which knows what the current thread and stream are).  The idiomatic
// way to do this in Caffe2 today is to make a CuDNNWrapper and then call
// inline_cudnn_handle(), although you didn't really need the
// CuDNNWrapper at all (you could have gotten it directly from
// CUDAContext.)
//
// So, what's all this business about CuDNNWrapper?  In theory, it was
// designed with a more specialized use-case in mind, where you need to
// make multiple calls to CuDNN in parallel; e.g., when manually
// computing group convolution.  By using with_cudnn_state(), you can
// get separate cudnnHandle_t and CUDA stream per parallel thread of
// execution, and run all of the cuDNN calls in parallel.  CuDNNWrapper
// handles the business of synchronizing with the stream prior to this
// call.
//
// (By the way, this is why no such CUBLASWrapper exists; there isn't
// ever any reason you need to call cublas in parallel, since most
// cublas operations have batched variants.)
//
// Now, that's the theory... in practice, this is only ever used when
// multiple operators are run in parallel, and not to actually
// parallelize multiple CuDNN calls (for example, group convolution is
// now supported natively in CuDNN.)  So... while the kit provided here
// might be useful for someone else in the future, it's not really used
// now.  So we might consider deleting it, or unifying this mechanism
// with PyTorch's own CuDNN handle pool.  (which is it's own thing.)

namespace caffe2 {

class CuDNNWrapper;

struct CuDNNWorkspace {
  ~CuDNNWorkspace() noexcept {}

  void* get(size_t nbytes) {
    if (nbytes_ < nbytes) {
      reset();
      data_ = CUDAContext::New(nbytes);
      nbytes_ = nbytes;
    }
    CAFFE_ENFORCE_GE(nbytes_, nbytes);
    return data_.get();
  }

  void reset() {
    data_.clear();
    nbytes_ = 0;
  }

 private:
  at::DataPtr data_{nullptr, nullptr, &NoDelete, at::Device(CUDA)};
  size_t nbytes_{0};
};

// CuDNNState is the owner of the CuDNNWorkspace, and serializes all
// executions of operations that use the state onto it's own stream
// (so multiple Net workers can reuse the same workspace from
// different threads and CUDA streams).
class CuDNNState {
 public:
  explicit CuDNNState(size_t gpu_id) : gpu_id_(gpu_id) {
    CUDAGuard g(gpu_id_);
    CUDNN_ENFORCE(cudnnCreate(&cudnn_handle_));
    CUDA_ENFORCE(cudaEventCreate(&before_));
    CUDA_ENFORCE(cudaEventCreate(&after_));
    CUDA_ENFORCE(cudaStreamCreate(&stream_));
    CUDNN_ENFORCE(cudnnSetStream(cudnn_handle_, stream_));
  }

  ~CuDNNState() noexcept {
    CUDAGuard g(gpu_id_);
    CUDNN_CHECK(cudnnDestroy(cudnn_handle_));
    CUDA_CHECK(cudaStreamDestroy(stream_));
    CUDA_CHECK(cudaEventDestroy(after_));
    CUDA_CHECK(cudaEventDestroy(before_));
  }

  cudnnHandle_t& cudnn_handle() {
    return cudnn_handle_;
  }

  CuDNNWorkspace& workspace() {
    return workspace_;
  }

  template <typename F>
  void execute(cudaStream_t stream, F&& f) {
    CUDA_ENFORCE(cudaEventRecord(before_, stream));
    CUDA_ENFORCE(cudaStreamWaitEvent(stream_, before_, 0));
    f(this);
    CUDA_ENFORCE(cudaEventRecord(after_, stream_));
    CUDA_ENFORCE(cudaStreamWaitEvent(stream, after_, 0));
  }

 private:
  cudnnHandle_t cudnn_handle_{nullptr};
  cudaEvent_t before_{nullptr};
  cudaEvent_t after_{nullptr};
  cudaStream_t stream_{nullptr};
  CuDNNWorkspace workspace_;
  size_t gpu_id_{0};
  C10_DISABLE_COPY_AND_ASSIGN(CuDNNState);
};

class CuDNNWrapper {
 public:
  explicit CuDNNWrapper(CUDAContext* context) : context_(context) {}

  cudnnHandle_t inline_cudnn_handle() {
    return context_->cudnn_handle();
  }

  // Executes the closure F on the CuDNNState associated with state_idx
  template <typename F>
  void with_cudnn_state(size_t state_idx, F&& f) {
    CAFFE_ENFORCE(
        state_idx < CAFFE2_COMPILE_TIME_MAX_CUDNN_STATES, "Invalid state_idx");
    auto& sync_state = cudnn_states()[context_->device_id()][state_idx];

    CUDAGuard dg(context_->device_id());

    // We need to serialize execution on the CuDNNState as we can't
    // allow multiple threads to race through the cudaEventRecord
    // calls (so a worker thread might wait on another worker thread's
    // execution)
    std::lock_guard<std::mutex> g(sync_state.mutex);
    if (!sync_state.state.get()) {
      sync_state.state.reset(new CuDNNState(context_->device_id()));
    }
    CHECK_NOTNULL(sync_state.state.get())->execute(context_->cuda_stream(), f);
  }

 protected:
  // Pointer to an external cuda context that the cudnn wrapper will use.
  CUDAContext* context_;

  static constexpr size_t CAFFE2_COMPILE_TIME_MAX_CUDNN_STATES = 4;

  struct SyncedCuDNNState {
    std::mutex mutex;
    std::unique_ptr<CuDNNState> state;
  };

  using PerGPUCuDNNStates = std::array<
      std::array<SyncedCuDNNState, CAFFE2_COMPILE_TIME_MAX_CUDNN_STATES>,
      C10_COMPILE_TIME_MAX_GPUS>;
  static PerGPUCuDNNStates& cudnn_states();

  C10_DISABLE_COPY_AND_ASSIGN(CuDNNWrapper);
};

}; // namespace caffe2

#endif