DLConvertor.cpp

#include <ATen/DLConvertor.h>
#include <ATen/Functions.h>

#include <iostream>
#include <sstream>

using namespace std;
namespace at {

DLDataType getDLDataType(const Tensor& t) {
  DLDataType dtype;
  dtype.lanes = 1;
  dtype.bits = t.element_size() * 8;
  switch (t.scalar_type()) {
    case ScalarType::Byte:
      dtype.code = DLDataTypeCode::kDLUInt;
      break;
    case ScalarType::Char:
      dtype.code = DLDataTypeCode::kDLInt;
      break;
    case ScalarType::Double:
      dtype.code = DLDataTypeCode::kDLFloat;
      break;
    case ScalarType::Float:
      dtype.code = DLDataTypeCode::kDLFloat;
      break;
    case ScalarType::Int:
      dtype.code = DLDataTypeCode::kDLInt;
      break;
    case ScalarType::Long:
      dtype.code = DLDataTypeCode::kDLInt;
      break;
    case ScalarType::Short:
      dtype.code = DLDataTypeCode::kDLInt;
      break;
    case ScalarType::Half:
      dtype.code = DLDataTypeCode::kDLFloat;
      break;
    case ScalarType::Bool:
      dtype.code = DLDataTypeCode::kDLUInt;
      break;
    case ScalarType::BFloat16:
      throw std::logic_error("BFloat16 is not supported by dlpack");
      break;
    case ScalarType::QInt8:
      throw std::logic_error("QInt8 is not supported by dlpack");
      break;
    case ScalarType::QUInt8:
      throw std::logic_error("QUInt8 is not supported by dlpack");
      break;
    case ScalarType::QInt32:
      throw std::logic_error("QInt32 is not supported by dlpack");
      break;
    case ScalarType::ComplexHalf:
      throw std::logic_error("ComplexHalf is not supported by dlpack");
      break;
    case ScalarType::ComplexFloat:
      throw std::logic_error("ComplexFloat is not supported by dlpack");
      break;
    case ScalarType::ComplexDouble:
      throw std::logic_error("ComplexDouble is not supported by dlpack");
      break;
    case ScalarType::Undefined:
      throw std::logic_error("Undefined is not a valid ScalarType");
    case ScalarType::NumOptions:
      throw std::logic_error("NumOptions is not a valid ScalarType");
  }
  return dtype;
}

DLContext getDLContext(const Tensor& tensor, const int64_t& device_id) {
  DLContext ctx;
  ctx.device_id = device_id;
  switch (tensor.device().type()) {
    case DeviceType::CPU:
      ctx.device_type = DLDeviceType::kDLCPU;
      break;
    case DeviceType::CUDA:
#ifdef USE_ROCM
      // ROCM, if enabled will look like cuda to PyTorch
      // while everyone else should see HIP
      ctx.device_type = DLDeviceType::kDLROCM;
#else
      ctx.device_type = DLDeviceType::kDLGPU;
#endif
      break;
    case DeviceType::OPENCL:
      ctx.device_type = DLDeviceType::kDLOpenCL;
      break;
    case DeviceType::HIP:
      ctx.device_type = DLDeviceType::kDLROCM;
      break;
    default:
      throw std::logic_error("Cannot pack tensors on " + tensor.device().str());
  }
  return ctx;
}

static Device getATenDevice(const DLContext& ctx) {
  switch (ctx.device_type) {
    case DLDeviceType::kDLCPU:
      return at::Device(DeviceType::CPU);
#ifndef USE_ROCM
    // if we are compiled under HIP, we cannot do cuda
    case DLDeviceType::kDLGPU:
      return at::Device(DeviceType::CUDA, ctx.device_id);
#endif
    case DLDeviceType::kDLOpenCL:
      return at::Device(DeviceType::OPENCL, ctx.device_id);
    case DLDeviceType::kDLROCM:
#ifdef USE_ROCM
      // this looks funny, we need to return CUDA here to masquerade
      return at::Device(DeviceType::CUDA, ctx.device_id);
#else
      return at::Device(DeviceType::HIP, ctx.device_id);
#endif
    default:
      throw std::logic_error(
          "Unsupported device_type: " + c10::to_string(ctx.device_type));
  }
}

ScalarType toScalarType(const DLDataType& dtype) {
  ScalarType stype;
  if (dtype.lanes != 1)
    throw std::logic_error("ATen does not support lanes != 1");
  switch (dtype.code) {
    case DLDataTypeCode::kDLUInt:
      switch (dtype.bits) {
        case 8:
          stype = ScalarType::Byte;
          break;
        default:
          throw std::logic_error(
              "Unsupported kUInt bits " + c10::to_string(dtype.bits));
      }
      break;
    case DLDataTypeCode::kDLInt:
      switch (dtype.bits) {
        case 8:
          stype = ScalarType::Char;
          break;
        case 16:
          stype = ScalarType::Short;
          break;
        case 32:
          stype = ScalarType::Int;
          break;
        case 64:
          stype = ScalarType::Long;
          break;
        default:
          throw std::logic_error(
              "Unsupported kInt bits " + c10::to_string(dtype.bits));
      }
      break;
    case DLDataTypeCode::kDLFloat:
      switch (dtype.bits) {
        case 16:
          stype = ScalarType::Half;
          break;
        case 32:
          stype = ScalarType::Float;
          break;
        case 64:
          stype = ScalarType::Double;
          break;
        default:
          throw std::logic_error(
              "Unsupported kFloat bits " + c10::to_string(dtype.bits));
      }
      break;
    default:
      throw std::logic_error("Unsupported code " + c10::to_string(dtype.code));
  }
  return stype;
}

struct ATenDLMTensor {
  Tensor handle;
  DLManagedTensor tensor;
};

void deleter(DLManagedTensor* arg) {
  delete static_cast<ATenDLMTensor*>(arg->manager_ctx);
}

// This function returns a shared_ptr to memory managed DLpack tensor
// constructed out of ATen tensor
DLManagedTensor* toDLPack(const Tensor& src) {
  ATenDLMTensor* atDLMTensor(new ATenDLMTensor);
  atDLMTensor->handle = src;
  atDLMTensor->tensor.manager_ctx = atDLMTensor;
  atDLMTensor->tensor.deleter = &deleter;
  atDLMTensor->tensor.dl_tensor.data = src.data_ptr();
  int64_t device_id = 0;
  if (src.is_cuda()) {
    device_id = src.get_device();
  }
  atDLMTensor->tensor.dl_tensor.ctx = getDLContext(src, device_id);
  atDLMTensor->tensor.dl_tensor.ndim = src.dim();
  atDLMTensor->tensor.dl_tensor.dtype = getDLDataType(src);
  atDLMTensor->tensor.dl_tensor.shape =
      const_cast<int64_t*>(src.sizes().data());
  atDLMTensor->tensor.dl_tensor.strides =
      const_cast<int64_t*>(src.strides().data());
  atDLMTensor->tensor.dl_tensor.byte_offset = 0;
  return &(atDLMTensor->tensor);
}

Tensor fromDLPack(const DLManagedTensor* src) {
  Device device = getATenDevice(src->dl_tensor.ctx);
  ScalarType stype = toScalarType(src->dl_tensor.dtype);
  auto deleter = [src](void* self) {
    src->deleter(const_cast<DLManagedTensor*>(src));
  };
  if (!src->dl_tensor.strides) {
    return at::from_blob(src->dl_tensor.data,
        IntArrayRef(src->dl_tensor.shape, src->dl_tensor.ndim),
        deleter,
        at::device(device).dtype(stype));
  }
  return at::from_blob(
      src->dl_tensor.data,
      IntArrayRef(src->dl_tensor.shape, src->dl_tensor.ndim),
      IntArrayRef(src->dl_tensor.strides, src->dl_tensor.ndim),
      deleter,
      at::device(device).dtype(stype),
      { device });
}
} // namespace at