FM / core

/usr/local/lib64/python3.6/site-packages/torch/include/ATen/core/

/usr/local/lib64/python3.6/site-packages/torch/include/ATen/core

Name	Size	Mode	Actions
boxing/	-	0755	rm
dispatch/	-	0755	rm
op_registration/	-	0755	rm
alias_info.h	2986	0644	edit dl rm
Array.h	768	0644	edit dl rm
ATenGeneral.h	45	0644	edit dl rm
ATenOpList.h	246	0644	edit dl rm
aten_interned_strings.h	25389	0644	edit dl rm
Backtrace.h	59	0644	edit dl rm
blob.h	5422	0644	edit dl rm
builtin_function.h	3649	0644	edit dl rm
DeprecatedTypeProperties.h	3773	0644	edit dl rm
DeprecatedTypePropertiesRegistry.h	795	0644	edit dl rm
Dict.h	13195	0644	edit dl rm
Dict_inl.h	7996	0644	edit dl rm
Dimname.h	1188	0644	edit dl rm
DimVector.h	247	0644	edit dl rm
DistributionsHelper.h	12594	0644	edit dl rm
Formatting.h	959	0644	edit dl rm
function.h	2145	0644	edit dl rm
functional.h	1460	0644	edit dl rm
function_schema.h	13577	0644	edit dl rm
function_schema_inl.h	9319	0644	edit dl rm
Generator.h	4935	0644	edit dl rm
grad_mode.h	210	0644	edit dl rm
interned_strings.h	25332	0644	edit dl rm
interned_strings_class.h	770	0644	edit dl rm
ivalue.h	38823	0644	edit dl rm
ivalue_inl.h	59963	0644	edit dl rm
ivalue_to.h	756	0644	edit dl rm
jit_type.h	75971	0644	edit dl rm
jit_type_base.h	6508	0644	edit dl rm
LegacyTypeDispatch.h	4626	0644	edit dl rm
List.h	15667	0644	edit dl rm
List_inl.h	11012	0644	edit dl rm
Macros.h	44	0644	edit dl rm
MT19937RNGEngine.h	6410	0644	edit dl rm
NamedTensor.h	5050	0644	edit dl rm
operator_name.h	3018	0644	edit dl rm
PhiloxRNGEngine.h	6496	0644	edit dl rm
PythonModeTLS.h	403	0644	edit dl rm
qualified_name.h	4358	0644	edit dl rm
QuantizerBase.h	2443	0644	edit dl rm
Range.h	418	0644	edit dl rm
Reduction.h	461	0644	edit dl rm
rref_interface.h	1144	0644	edit dl rm
Scalar.h	29	0644	edit dl rm
ScalarType.h	33	0644	edit dl rm
stack.h	6034	0644	edit dl rm
Tensor.h	1756	0644	edit dl rm
TensorAccessor.h	10296	0644	edit dl rm
TensorBase.h	32767	0644	edit dl rm
TensorBody.h	247555	0644	edit dl rm
TransformationHelper.h	6911	0644	edit dl rm
typeid.h	29	0644	edit dl rm
UndefinedTensorImpl.h	42	0644	edit dl rm
UnsafeFromTH.h	708	0644	edit dl rm
VariableHooksInterface.h	3312	0644	edit dl rm
Variadic.h	2257	0644	edit dl rm
Vitals.h	2305	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/torch/include/ATen/core/TensorAccessor.h (10296B)

#pragma once

#include <c10/macros/Macros.h>
#include <c10/util/Deprecated.h>
#include <c10/util/Exception.h>
#include <stdint.h>
#include <cstddef>

namespace at {

// The PtrTraits argument to the TensorAccessor/GenericPackedTensorAccessor
// is used to enable the __restrict__ keyword/modifier for the data
// passed to cuda.
template <typename T>
struct DefaultPtrTraits {
  typedef T* PtrType;
};

#if defined(__CUDACC__) || defined(__HIPCC__)
template <typename T>
struct RestrictPtrTraits {
  typedef T* __restrict__ PtrType;
};
#endif

// TensorAccessorBase and TensorAccessor are used for both CPU and CUDA tensors.
// For CUDA tensors it is used in device code (only). This means that we restrict ourselves
// to functions and types available there (e.g. IntArrayRef isn't).

// The PtrTraits argument is only relevant to cuda to support `__restrict__` pointers.
template<typename T, size_t N, template <typename U> class PtrTraits = DefaultPtrTraits, typename index_t = int64_t>
class TensorAccessorBase {
public:
  typedef typename PtrTraits<T>::PtrType PtrType;

C10_HOST_DEVICE TensorAccessorBase(
      PtrType data_,
      const index_t* sizes_,
      const index_t* strides_)
      : data_(data_), sizes_(sizes_), strides_(strides_) {}
  C10_HOST IntArrayRef sizes() const {
    return IntArrayRef(sizes_,N);
  }
  C10_HOST IntArrayRef strides() const {
    return IntArrayRef(strides_,N);
  }
  C10_HOST_DEVICE index_t stride(index_t i) const {
    return strides_[i];
  }
  C10_HOST_DEVICE index_t size(index_t i) const {
    return sizes_[i];
  }
  C10_HOST_DEVICE PtrType data() {
    return data_;
  }
  C10_HOST_DEVICE const PtrType data() const {
    return data_;
  }
protected:
  PtrType data_;
  const index_t* sizes_;
  const index_t* strides_;
};

// The `TensorAccessor` is typically instantiated for CPU `Tensor`s using
// `Tensor.accessor<T, N>()`.
// For CUDA `Tensor`s, `GenericPackedTensorAccessor` is used on the host and only
// indexing on the device uses `TensorAccessor`s.
template<typename T, size_t N, template <typename U> class PtrTraits = DefaultPtrTraits, typename index_t = int64_t>
class TensorAccessor : public TensorAccessorBase<T,N,PtrTraits,index_t> {
public:
  typedef typename PtrTraits<T>::PtrType PtrType;

C10_HOST_DEVICE TensorAccessor(
      PtrType data_,
      const index_t* sizes_,
      const index_t* strides_)
      : TensorAccessorBase<T, N, PtrTraits, index_t>(data_,sizes_,strides_) {}

C10_HOST_DEVICE TensorAccessor<T, N - 1, PtrTraits, index_t> operator[](index_t i) {
    return TensorAccessor<T,N-1,PtrTraits,index_t>(this->data_ + this->strides_[0]*i,this->sizes_+1,this->strides_+1);
  }

C10_HOST_DEVICE const TensorAccessor<T, N-1, PtrTraits, index_t> operator[](index_t i) const {
    return TensorAccessor<T,N-1,PtrTraits,index_t>(this->data_ + this->strides_[0]*i,this->sizes_+1,this->strides_+1);
  }
};

template<typename T, template <typename U> class PtrTraits, typename index_t>
class TensorAccessor<T,1,PtrTraits,index_t> : public TensorAccessorBase<T,1,PtrTraits,index_t> {
public:
  typedef typename PtrTraits<T>::PtrType PtrType;

C10_HOST_DEVICE TensorAccessor(
      PtrType data_,
      const index_t* sizes_,
      const index_t* strides_)
      : TensorAccessorBase<T, 1, PtrTraits, index_t>(data_,sizes_,strides_) {}
  C10_HOST_DEVICE T & operator[](index_t i) {
    // NOLINTNEXTLINE(clang-analyzer-core.NullDereference)
    return this->data_[this->strides_[0]*i];
  }
  C10_HOST_DEVICE const T & operator[](index_t i) const {
    return this->data_[this->strides_[0]*i];
  }
};

// GenericPackedTensorAccessorBase and GenericPackedTensorAccessor are used on for CUDA `Tensor`s on the host
// and as
// In contrast to `TensorAccessor`s, they copy the strides and sizes on instantiation (on the host)
// in order to transfer them on the device when calling kernels.
// On the device, indexing of multidimensional tensors gives to `TensorAccessor`s.
// Use RestrictPtrTraits as PtrTraits if you want the tensor's data pointer to be marked as __restrict__.
// Instantiation from data, sizes, strides is only needed on the host and std::copy isn't available
// on the device, so those functions are host only.
template<typename T, size_t N, template <typename U> class PtrTraits = DefaultPtrTraits, typename index_t = int64_t>
class GenericPackedTensorAccessorBase {
public:
  typedef typename PtrTraits<T>::PtrType PtrType;
  C10_HOST GenericPackedTensorAccessorBase(
      PtrType data_,
      const index_t* sizes_,
      const index_t* strides_)
      : data_(data_) {
    std::copy(sizes_, sizes_ + N, std::begin(this->sizes_));
    std::copy(strides_, strides_ + N, std::begin(this->strides_));
  }

// if index_t is not int64_t, we want to have an int64_t constructor
  template <typename source_index_t, class = typename std::enable_if<std::is_same<source_index_t, int64_t>::value>::type>
  C10_HOST GenericPackedTensorAccessorBase(
      PtrType data_,
      const source_index_t* sizes_,
      const source_index_t* strides_)
      : data_(data_) {
    for (int i = 0; i < N; i++) {
      this->sizes_[i] = sizes_[i];
      this->strides_[i] = strides_[i];
    }
  }

C10_HOST_DEVICE index_t stride(index_t i) const {
    return strides_[i];
  }
  C10_HOST_DEVICE index_t size(index_t i) const {
    return sizes_[i];
  }
  C10_HOST_DEVICE PtrType data() {
    return data_;
  }
  C10_HOST_DEVICE const PtrType data() const {
    return data_;
  }
protected:
  PtrType data_;
  index_t sizes_[N];
  index_t strides_[N];
  C10_HOST void bounds_check_(index_t i) const {
    TORCH_CHECK_INDEX(
        0 <= i && i < N,
        "Index ",
        i,
        " is not within bounds of a tensor of dimension ",
        N);
  }
};

template<typename T, size_t N, template <typename U> class PtrTraits = DefaultPtrTraits, typename index_t = int64_t>
class GenericPackedTensorAccessor : public GenericPackedTensorAccessorBase<T,N,PtrTraits,index_t> {
public:
  typedef typename PtrTraits<T>::PtrType PtrType;

C10_HOST GenericPackedTensorAccessor(
      PtrType data_,
      const index_t* sizes_,
      const index_t* strides_)
      : GenericPackedTensorAccessorBase<T, N, PtrTraits, index_t>(data_, sizes_, strides_) {}

// if index_t is not int64_t, we want to have an int64_t constructor
  template <typename source_index_t, class = typename std::enable_if<std::is_same<source_index_t, int64_t>::value>::type>
  C10_HOST GenericPackedTensorAccessor(
      PtrType data_,
      const source_index_t* sizes_,
      const source_index_t* strides_)
      : GenericPackedTensorAccessorBase<T, N, PtrTraits, index_t>(data_, sizes_, strides_) {}

C10_DEVICE TensorAccessor<T, N - 1, PtrTraits, index_t> operator[](index_t i) {
    index_t* new_sizes = this->sizes_ + 1;
    index_t* new_strides = this->strides_ + 1;
    return TensorAccessor<T,N-1,PtrTraits,index_t>(this->data_ + this->strides_[0]*i, new_sizes, new_strides);
  }

C10_DEVICE const TensorAccessor<T, N - 1, PtrTraits, index_t> operator[](index_t i) const {
    const index_t* new_sizes = this->sizes_ + 1;
    const index_t* new_strides = this->strides_ + 1;
    return TensorAccessor<T,N-1,PtrTraits,index_t>(this->data_ + this->strides_[0]*i, new_sizes, new_strides);
  }

/// Returns a PackedTensorAccessor of the same dimension after transposing the
  /// two dimensions given. Does not actually move elements; transposition is
  /// made by permuting the size/stride arrays. If the dimensions are not valid,
  /// asserts.
  C10_HOST GenericPackedTensorAccessor<T, N, PtrTraits, index_t> transpose(
      index_t dim1,
      index_t dim2) const {
    this->bounds_check_(dim1);
    this->bounds_check_(dim2);
    GenericPackedTensorAccessor<T, N, PtrTraits, index_t> result(
        this->data_, this->sizes_, this->strides_);
    std::swap(result.strides_[dim1], result.strides_[dim2]);
    std::swap(result.sizes_[dim1], result.sizes_[dim2]);
    return result;
  }
};

template<typename T, template <typename U> class PtrTraits, typename index_t>
class GenericPackedTensorAccessor<T,1,PtrTraits,index_t> : public GenericPackedTensorAccessorBase<T,1,PtrTraits,index_t> {
public:
  typedef typename PtrTraits<T>::PtrType PtrType;
  C10_HOST GenericPackedTensorAccessor(
      PtrType data_,
      const index_t* sizes_,
      const index_t* strides_)
      : GenericPackedTensorAccessorBase<T, 1, PtrTraits, index_t>(data_, sizes_, strides_) {}

// if index_t is not int64_t, we want to have an int64_t constructor
  template <typename source_index_t, class = typename std::enable_if<std::is_same<source_index_t, int64_t>::value>::type>
  C10_HOST GenericPackedTensorAccessor(
      PtrType data_,
      const source_index_t* sizes_,
      const source_index_t* strides_)
      : GenericPackedTensorAccessorBase<T, 1, PtrTraits, index_t>(data_, sizes_, strides_) {}

C10_DEVICE T & operator[](index_t i) {
    return this->data_[this->strides_[0] * i];
  }
  C10_DEVICE const T& operator[](index_t i) const {
    return this->data_[this->strides_[0]*i];
  }

// Same as in the general N-dimensional case, but note that in the
  // 1-dimensional case the returned PackedTensorAccessor will always be an
  // identical copy of the original
  C10_HOST GenericPackedTensorAccessor<T, 1, PtrTraits, index_t> transpose(
      index_t dim1,
      index_t dim2) const {
    this->bounds_check_(dim1);
    this->bounds_check_(dim2);
    return GenericPackedTensorAccessor<T, 1, PtrTraits, index_t>(
        this->data_, this->sizes_, this->strides_);
  }
};

// Can't put this directly into the macro function args because of commas
#define AT_X GenericPackedTensorAccessor<T, N, PtrTraits, index_t>

// Old name for `GenericPackedTensorAccessor`
template <typename T, size_t N, template <typename U> class PtrTraits = DefaultPtrTraits, typename index_t = int64_t>
C10_DEFINE_DEPRECATED_USING(PackedTensorAccessor, AT_X)

#undef AT_X

template <typename T, size_t N, template <typename U> class PtrTraits = DefaultPtrTraits>
using PackedTensorAccessor32 = GenericPackedTensorAccessor<T, N, PtrTraits, int32_t>;

template <typename T, size_t N, template <typename U> class PtrTraits = DefaultPtrTraits>
using PackedTensorAccessor64 = GenericPackedTensorAccessor<T, N, PtrTraits, int64_t>;
} // namespace at

cmd: