cuda_device_api.cc

/*!
 *  Copyright (c) 2017-2022 by Contributors
 * \file cuda_device_api.cc
 * \brief GPU specific API
 */
#include <dgl/runtime/device_api.h>
#include <dgl/runtime/tensordispatch.h>
#include <dmlc/thread_local.h>
#include <dgl/runtime/registry.h>
#include <cuda_runtime.h>
#include "cuda_common.h"

namespace dgl {
namespace runtime {

class CUDADeviceAPI final : public DeviceAPI {
 public:
  CUDADeviceAPI() {
    int count;
    auto err = cudaGetDeviceCount(&count);
    switch (err) {
      case cudaSuccess:
        break;
      default:
        count = 0;
        cudaGetLastError();
    }
    is_available_ = count > 0;
  }

  bool IsAvailable() final {
    return is_available_;
  }

  void SetDevice(DGLContext ctx) final {
    CUDA_CALL(cudaSetDevice(ctx.device_id));
  }
  void GetAttr(DGLContext ctx, DeviceAttrKind kind, DGLRetValue* rv) final {
    int value = 0;
    switch (kind) {
      case kExist:
        value = (
            cudaDeviceGetAttribute(
                &value, cudaDevAttrMaxThreadsPerBlock, ctx.device_id)
            == cudaSuccess);
        break;
      case kMaxThreadsPerBlock: {
        CUDA_CALL(cudaDeviceGetAttribute(
            &value, cudaDevAttrMaxThreadsPerBlock, ctx.device_id));
        break;
      }
      case kWarpSize: {
        CUDA_CALL(cudaDeviceGetAttribute(
            &value, cudaDevAttrWarpSize, ctx.device_id));
        break;
      }
      case kMaxSharedMemoryPerBlock: {
        CUDA_CALL(cudaDeviceGetAttribute(
            &value, cudaDevAttrMaxSharedMemoryPerBlock, ctx.device_id));
        break;
      }
      case kComputeVersion: {
        std::ostringstream os;
        CUDA_CALL(cudaDeviceGetAttribute(
            &value, cudaDevAttrComputeCapabilityMajor, ctx.device_id));
        os << value << ".";
        CUDA_CALL(cudaDeviceGetAttribute(
            &value, cudaDevAttrComputeCapabilityMinor, ctx.device_id));
        os << value;
        *rv = os.str();
        return;
      }
      case kDeviceName: {
        cudaDeviceProp props;
        CUDA_CALL(cudaGetDeviceProperties(&props, ctx.device_id));
        *rv = std::string(props.name);
        return;
      }
      case kMaxClockRate: {
        CUDA_CALL(cudaDeviceGetAttribute(
            &value, cudaDevAttrClockRate, ctx.device_id));
        break;
      }
      case kMultiProcessorCount: {
        CUDA_CALL(cudaDeviceGetAttribute(
            &value, cudaDevAttrMultiProcessorCount, ctx.device_id));
        break;
      }
      case kMaxThreadDimensions: {
        int dims[3];
        CUDA_CALL(cudaDeviceGetAttribute(
            &dims[0], cudaDevAttrMaxBlockDimX, ctx.device_id));
        CUDA_CALL(cudaDeviceGetAttribute(
            &dims[1], cudaDevAttrMaxBlockDimY, ctx.device_id));
        CUDA_CALL(cudaDeviceGetAttribute(
            &dims[2], cudaDevAttrMaxBlockDimZ, ctx.device_id));

        std::stringstream ss;  // use json string to return multiple int values;
        ss << "[" << dims[0] <<", " << dims[1] << ", " << dims[2] << "]";
        *rv = ss.str();
        return;
      }
    }
    *rv = value;
  }
  void* AllocDataSpace(DGLContext ctx,
                       size_t nbytes,
                       size_t alignment,
                       DGLDataType type_hint) final {
    SetDevice(ctx);
    // Redirect to PyTorch's allocator when available.
    TensorDispatcher* td = TensorDispatcher::Global();
    if (td->IsAvailable())
      return td->CUDAAllocWorkspace(nbytes, getCurrentCUDAStream());

    CHECK_EQ(256 % alignment, 0U)
        << "CUDA space is aligned at 256 bytes";
    void *ret;
    CUDA_CALL(cudaMalloc(&ret, nbytes));
    return ret;
  }

  void FreeDataSpace(DGLContext ctx, void* ptr) final {
    SetDevice(ctx);
    TensorDispatcher* td = TensorDispatcher::Global();
    if (td->IsAvailable())
      return td->CUDAFreeWorkspace(ptr);

    CUDA_CALL(cudaFree(ptr));
  }

  void CopyDataFromTo(const void* from,
                      size_t from_offset,
                      void* to,
                      size_t to_offset,
                      size_t size,
                      DGLContext ctx_from,
                      DGLContext ctx_to,
                      DGLDataType type_hint,
                      DGLStreamHandle stream) {
    cudaStream_t cu_stream = static_cast<cudaStream_t>(stream);
    from = static_cast<const char*>(from) + from_offset;
    to = static_cast<char*>(to) + to_offset;
    if (ctx_from.device_type == kDGLCUDA && ctx_to.device_type == kDGLCUDA) {
      CUDA_CALL(cudaSetDevice(ctx_from.device_id));
      if (ctx_from.device_id == ctx_to.device_id) {
        GPUCopy(from, to, size, cudaMemcpyDeviceToDevice, cu_stream);
      } else {
        CUDA_CALL(cudaMemcpyPeerAsync(to, ctx_to.device_id,
                                      from, ctx_from.device_id,
                                      size, cu_stream));
      }
    } else if (ctx_from.device_type == kDGLCUDA && ctx_to.device_type == kDGLCPU) {
      CUDA_CALL(cudaSetDevice(ctx_from.device_id));
      GPUCopy(from, to, size, cudaMemcpyDeviceToHost, cu_stream);
    } else if (ctx_from.device_type == kDGLCPU && ctx_to.device_type == kDGLCUDA) {
      CUDA_CALL(cudaSetDevice(ctx_to.device_id));
      GPUCopy(from, to, size, cudaMemcpyHostToDevice, cu_stream);
    } else {
      LOG(FATAL) << "expect copy from/to GPU or between GPU";
    }
  }

  void CopyDataFromTo(const void* from,
                      size_t from_offset,
                      void* to,
                      size_t to_offset,
                      size_t size,
                      DGLContext ctx_from,
                      DGLContext ctx_to,
                      DGLDataType type_hint) final {
    auto stream = GetStream();
    CopyDataFromTo(from, from_offset, to, to_offset, size, ctx_from, ctx_to, type_hint, stream);
  }

  DGLStreamHandle CreateStream(DGLContext ctx) {
    CUDA_CALL(cudaSetDevice(ctx.device_id));
    cudaStream_t retval;
    // make sure the legacy default stream won't block on this stream
    CUDA_CALL(cudaStreamCreateWithFlags(&retval, cudaStreamNonBlocking));
    return static_cast<DGLStreamHandle>(retval);
  }

  void FreeStream(DGLContext ctx, DGLStreamHandle stream) {
    CUDA_CALL(cudaSetDevice(ctx.device_id));
    cudaStream_t cu_stream = static_cast<cudaStream_t>(stream);
    CUDA_CALL(cudaStreamDestroy(cu_stream));
  }

  void SyncStreamFromTo(DGLContext ctx, DGLStreamHandle event_src, DGLStreamHandle event_dst) {
    CUDA_CALL(cudaSetDevice(ctx.device_id));
    cudaStream_t src_stream = static_cast<cudaStream_t>(event_src);
    cudaStream_t dst_stream = static_cast<cudaStream_t>(event_dst);
    cudaEvent_t evt;
    CUDA_CALL(cudaEventCreate(&evt));
    CUDA_CALL(cudaEventRecord(evt, src_stream));
    CUDA_CALL(cudaStreamWaitEvent(dst_stream, evt, 0));
    CUDA_CALL(cudaEventDestroy(evt));
  }

  void StreamSync(DGLContext ctx, DGLStreamHandle stream) final {
    CUDA_CALL(cudaSetDevice(ctx.device_id));
    CUDA_CALL(cudaStreamSynchronize(static_cast<cudaStream_t>(stream)));
  }

  /*! NOTE: If the backend is PyTorch, we will use PyTorch's stream management,
   *        so just avoid calling our SetStream/CreateStream unless
   *        you really need advanced stream control.
   * TODO(Xin): Redirect this to PyTorch or remove it.
   * PyTorch allows external CUDA streams to be set as current since v1.11.
   */
  void SetStream(DGLContext ctx, DGLStreamHandle stream) final {}

  DGLStreamHandle GetStream() const final {
    return static_cast<DGLStreamHandle>(getCurrentCUDAStream());
  }

  /*! NOTE: cudaHostRegister can be called from an arbitrary GPU device,
   *        so we don't need to specify a ctx.
   *        The pinned memory can be seen by all CUDA contexts,
   *        not just the one that performed the allocation
   */
  void PinData(void* ptr, size_t nbytes) {
    // prevent users from pinning empty tensors or graphs
    if (ptr == nullptr || nbytes == 0)
      return;
    CUDA_CALL(cudaHostRegister(ptr, nbytes, cudaHostRegisterDefault));
  }

  void UnpinData(void* ptr) {
    if (ptr == nullptr)
      return;
    CUDA_CALL(cudaHostUnregister(ptr));
  }

  bool IsPinned(const void* ptr) override {
    // can't be a pinned tensor if CUDA context is unavailable.
    if (!is_available_)
      return false;

    cudaPointerAttributes attr;
    cudaError_t status = cudaPointerGetAttributes(&attr, ptr);
    bool result = false;

    switch (status) {
    case cudaErrorInvalidValue:
      // might be a normal CPU tensor in CUDA 10.2-
      cudaGetLastError();   // clear error
      break;
    case cudaSuccess:
      result = (attr.type == cudaMemoryTypeHost);
      break;
    case cudaErrorInitializationError:
    case cudaErrorNoDevice:
    case cudaErrorInsufficientDriver:
    case cudaErrorInvalidDevice:
      // We don't want to fail in these particular cases since this function can be called
      // when users only want to run on CPU even if CUDA API is enabled, or in a forked
      // subprocess where CUDA context cannot be initialized.  So we just mark the CUDA
      // context to unavailable and return.
      is_available_ = false;
      cudaGetLastError();   // clear error
      break;
    default:
      LOG(FATAL) << "error while determining memory status: " << cudaGetErrorString(status);
      break;
    }

    return result;
  }

  void* AllocWorkspace(DGLContext ctx, size_t size, DGLDataType type_hint) final {
    SetDevice(ctx);
    // Redirect to PyTorch's allocator when available.
    TensorDispatcher* td = TensorDispatcher::Global();
    if (td->IsAvailable())
      return td->CUDAAllocWorkspace(size, getCurrentCUDAStream());

    return CUDAThreadEntry::ThreadLocal()->pool.AllocWorkspace(ctx, size);
  }

  void FreeWorkspace(DGLContext ctx, void* data) final {
    SetDevice(ctx);
    TensorDispatcher* td = TensorDispatcher::Global();
    if (td->IsAvailable())
      return td->CUDAFreeWorkspace(data);

    CUDAThreadEntry::ThreadLocal()->pool.FreeWorkspace(ctx, data);
  }

  static const std::shared_ptr<CUDADeviceAPI>& Global() {
    static std::shared_ptr<CUDADeviceAPI> inst =
        std::make_shared<CUDADeviceAPI>();
    return inst;
  }

 private:
  static void GPUCopy(const void* from,
                      void* to,
                      size_t size,
                      cudaMemcpyKind kind,
                      cudaStream_t stream) {
    CUDA_CALL(cudaMemcpyAsync(to, from, size, kind, stream));
    if (stream == 0 && kind == cudaMemcpyDeviceToHost) {
      // only wait for the copy, when it's on the default stream, and it's to host memory
      CUDA_CALL(cudaStreamSynchronize(stream));
    }
  }

  bool is_available_ = true;
};

typedef dmlc::ThreadLocalStore<CUDAThreadEntry> CUDAThreadStore;

CUDAThreadEntry::CUDAThreadEntry()
    : pool(kDGLCUDA, CUDADeviceAPI::Global()) {
}

CUDAThreadEntry* CUDAThreadEntry::ThreadLocal() {
  return CUDAThreadStore::Get();
}

cudaStream_t getCurrentCUDAStream() {
  TensorDispatcher* td = TensorDispatcher::Global();
  if (td->IsAvailable())
    return td->CUDAGetCurrentStream();
  else  // return the default stream when TA is not available
    return nullptr;
}

DGL_REGISTER_GLOBAL("device_api.cuda")
.set_body([](DGLArgs args, DGLRetValue* rv) {
    DeviceAPI* ptr = CUDADeviceAPI::Global().get();
    *rv = static_cast<void*>(ptr);
  });

}  // namespace runtime
}  // namespace dgl