TensorMethods.h

#pragma once

#include <ATen/core/Tensor.h>
#include <c10/core/Scalar.h>
#include <c10/macros/Macros.h>
#include <ATen/core/SparseTensorRef.h>
#include <ATen/core/Type.h>
#include <c10/core/TensorOptions.h>

namespace at {

inline Tensor Tensor::toType(const Type & t, bool non_blocking) const {
  if(type() == t)
    return *this;
  return t.copy(*this, non_blocking);
}

inline Tensor Tensor::cpu() const {
  return toType(type().cpu());
}

inline Tensor Tensor::cuda() const {
  return toType(type().cuda());
}

inline Tensor Tensor::hip() const {
  return toType(type().hip());
}

inline Tensor & Tensor::copy_(const Tensor & src, bool non_blocking) {
  return type().copy_(*this, src, non_blocking);
}

inline Tensor Tensor::toType(ScalarType t) const {
  return toType(type().toScalarType(t));
}

inline Tensor Tensor::toBackend(Backend b) const {
  return toType(type().toBackend(b));
}

inline TensorOptions Tensor::options() const {
  return TensorOptions().dtype(dtype())
                        .device(device())
                        .layout(layout())
                        .is_variable(is_variable());
}

inline void Tensor::backward(
    c10::optional<Tensor> gradient,
    bool keep_graph,
    bool create_graph) {
  type().backward(*this, std::move(gradient), keep_graph, create_graph);
}

inline void Tensor::set_data(Tensor new_data) {
  type().set_data(*this, new_data);
}

// all static inline to allow for inlining of the non-dynamic part of dispatch
${tensor_method_definitions}

inline bool Tensor::is_variable() const noexcept {
  return impl_->is_variable();
}

inline caffe2::TypeMeta Tensor::dtype() const noexcept {
  return impl_->dtype();
}

inline Layout Tensor::layout() const noexcept {
  return impl_->layout();
}

inline Device Tensor::device() const {
  return impl_->device();
}

inline int64_t Tensor::get_device() const {
  // NB: this is not a native function to avoid dispatching overhead.
  return impl_->get_device();
}

inline int64_t get_device(Tensor self) {
  return self.get_device();
}

inline bool Tensor::is_cuda() const {
  // NB: this is not a native function to avoid dispatching overhead.
  return impl_->is_cuda();
}

inline bool is_cuda(Tensor self) {
  return self.is_cuda();
}

inline bool Tensor::is_hip() const {
  // NB: this is not a native function to avoid dispatching overhead.
  return impl_->is_hip();
}

inline bool is_hip(Tensor self) {
  return self.is_hip();
}

inline bool Tensor::is_sparse() const {
  // NB: this is not a native function to avoid dispatching overhead.
  return impl_->is_sparse();
}

inline bool is_sparse(Tensor self) {
  return self.is_sparse();
}

#define DEFINE_CAST(T, name, _)                  \
  template <>                                    \
  inline T* Tensor::data() const {               \
    AT_CHECK(                                    \
        scalar_type() == ScalarType::name,       \
        "expected scalar type ",                 \
        #name,                                   \
        " but found ",                           \
        c10::toString(scalar_type()));           \
    return static_cast<T*>(this->data_ptr());    \
  }

AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_EXCEPT_COMPLEX_HALF(DEFINE_CAST)
#undef DEFINE_CAST

#define DEFINE_ITEM(T, name, _)   \
  template <>                     \
  inline T Tensor::item() const { \
    return item().to##name();     \
  }

AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_EXCEPT_COMPLEX_HALF(DEFINE_ITEM)
#undef DEFINE_ITEM

} //namespace at