FM / ATen

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

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

Name	Size	Mode	Actions
core/	-	0755	rm
cpu/	-	0755	rm
cuda/	-	0755	rm
cudnn/	-	0755	rm
detail/	-	0755	rm
hip/	-	0755	rm
native/	-	0755	rm
quantized/	-	0755	rm
AccumulateType.h	4438	0644	edit dl rm
ArrayRef.h	44	0644	edit dl rm
ATen.h	998	0644	edit dl rm
autocast_mode.h	6716	0644	edit dl rm
Backend.h	43	0644	edit dl rm
Backtrace.h	46	0644	edit dl rm
BatchedFallback.h	965	0644	edit dl rm
BatchedTensorImpl.h	5383	0644	edit dl rm
CompositeExplicitAutogradFunctions.h	1622	0644	edit dl rm
CompositeExplicitAutogradFunctions_inl.h	54075	0644	edit dl rm
CompositeImplicitAutogradFunctions.h	1622	0644	edit dl rm
CompositeImplicitAutogradFunctions_inl.h	142082	0644	edit dl rm
Config.h	734	0644	edit dl rm
Context.h	12767	0644	edit dl rm
cpp_custom_type_hack.h	5326	0644	edit dl rm
CPUApplyUtils.h	12582	0644	edit dl rm
CPUFixedAllocator.h	830	0644	edit dl rm
CPUFunctions.h	1600	0644	edit dl rm
CPUFunctions_inl.h	171924	0644	edit dl rm
CPUGeneratorImpl.h	1431	0644	edit dl rm
CUDAFunctions.h	1601	0644	edit dl rm
CUDAFunctions_inl.h	185696	0644	edit dl rm
CUDAGeneratorImpl.h	4695	0644	edit dl rm
Device.h	42	0644	edit dl rm
DeviceGuard.h	1134	0644	edit dl rm
Dimname.h	31	0644	edit dl rm
DimVector.h	46	0644	edit dl rm
Dispatch.h	52137	0644	edit dl rm
div_rtn.h	204	0644	edit dl rm
DLConvertor.h	576	0644	edit dl rm
dlpack.h	5244	0644	edit dl rm
DynamicLibrary.h	369	0644	edit dl rm
ExpandUtils.h	14506	0644	edit dl rm
Formatting.h	34	0644	edit dl rm
Functions.h	846326	0644	edit dl rm
Generator.h	46	0644	edit dl rm
InferSize.h	2143	0644	edit dl rm
InitialTensorOptions.h	445	0644	edit dl rm
Layout.h	42	0644	edit dl rm
MapAllocator.h	2999	0644	edit dl rm
MatrixRef.h	3016	0644	edit dl rm
MemoryOverlap.h	1117	0644	edit dl rm
MetaFunctions.h	1601	0644	edit dl rm
MetaFunctions_inl.h	84006	0644	edit dl rm
NamedTensor.h	35	0644	edit dl rm
NamedTensorUtils.h	5747	0644	edit dl rm
NativeFunctions.h	354651	0644	edit dl rm
NativeMetaFunctions.h	35445	0644	edit dl rm
NumericUtils.h	2787	0644	edit dl rm
OpaqueTensorImpl.h	6080	0644	edit dl rm
Operators.h	1707199	0644	edit dl rm
OpMathType.h	460	0644	edit dl rm
Parallel.h	4875	0644	edit dl rm
ParallelNative.h	2443	0644	edit dl rm
ParallelNativeTBB.h	2934	0644	edit dl rm
ParallelOpenMP.h	3049	0644	edit dl rm
PTThreadPool.h	394	0644	edit dl rm
record_function.h	24044	0644	edit dl rm
RedispatchFunctions.h	1112886	0644	edit dl rm
RegistrationDeclarations.h	545777	0644	edit dl rm
SavedTensorHooks.h	328	0644	edit dl rm
Scalar.h	44	0644	edit dl rm
ScalarOps.h	2272	0644	edit dl rm
ScalarType.h	129	0644	edit dl rm
SequenceNumber.h	373	0644	edit dl rm
SmallVector.h	47	0644	edit dl rm
SparseCsrTensorImpl.h	2045	0644	edit dl rm
SparseCsrTensorUtils.h	523	0644	edit dl rm
SparseTensorImpl.h	12417	0644	edit dl rm
SparseTensorUtils.h	4219	0644	edit dl rm
Storage.h	43	0644	edit dl rm
Tensor.h	48	0644	edit dl rm
TensorAccessor.h	51	0644	edit dl rm
TensorGeometry.h	1855	0644	edit dl rm
TensorIndexing.h	21923	0644	edit dl rm
TensorIterator.h	29962	0644	edit dl rm
TensorIteratorInternal.h	1862	0644	edit dl rm
TensorMeta.h	2917	0644	edit dl rm
TensorNames.h	2519	0644	edit dl rm
TensorOperators.h	3275	0644	edit dl rm
TensorOptions.h	49	0644	edit dl rm
TensorUtils.h	5687	0644	edit dl rm
ThreadLocalState.h	3289	0644	edit dl rm
TracerMode.h	5576	0644	edit dl rm
TypeDefault.h	680	0644	edit dl rm
Utils.h	5993	0644	edit dl rm
Version.h	340	0644	edit dl rm
VmapMode.h	952	0644	edit dl rm
VmapTransforms.h	7654	0644	edit dl rm
WrapDimUtils.h	3438	0644	edit dl rm
WrapDimUtilsMulti.h	768	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/torch/include/ATen/Utils.h (5993B)

#pragma once

#include <ATen/core/ATenGeneral.h>
#include <ATen/core/Generator.h>
#include <ATen/Formatting.h>
#include <c10/core/ScalarType.h>
#include <c10/core/StorageImpl.h>
#include <c10/core/UndefinedTensorImpl.h>
#include <c10/util/accumulate.h>
#include <c10/util/ArrayRef.h>
#include <c10/util/Exception.h>
#include <c10/util/irange.h>

#include <algorithm>
#include <sstream>
#include <typeinfo>
#include <numeric>
#include <memory>

#define AT_DISALLOW_COPY_AND_ASSIGN(TypeName) \
  TypeName(const TypeName&) = delete; \
  void operator=(const TypeName&) = delete

namespace at {

TORCH_API int _crash_if_asan(int);

// TODO: This unwrapping code is ONLY used for TH bindings; once TH goes
// away, we can delete this function
static inline TensorImpl* checked_dense_tensor_unwrap(const Tensor& expr, const char * name, int pos, const char * api, bool allowNull, DeviceType device_type, ScalarType scalar_type) {
  if(allowNull && !expr.defined()) {
    return nullptr;
  }
  if (expr.layout() != Layout::Strided) {
    AT_ERROR("Expected dense tensor but got ", expr.layout(),
             " for argument #", pos, " '", name, "' in call to ", api);
  }
  if (expr.device().type() != device_type) {
    AT_ERROR("Expected object of device type ", device_type, " but got device type ", expr.device().type(),
             " for argument #", pos, " '", name, "' in call to ", api);
  }
  if (expr.scalar_type() != scalar_type) {
    AT_ERROR("Expected object of scalar type ", scalar_type, " but got scalar type ", expr.scalar_type(),
             " for argument #", pos, " '", name, "' in call to ", api);
  }
  return expr.unsafeGetTensorImpl();
}

// Converts a TensorList (i.e. ArrayRef<Tensor> to vector of TensorImpl*)
// NB: This is ONLY used by legacy TH bindings, and ONLY used by cat.
// Once cat is ported entirely to ATen this can be deleted!
static inline std::vector<TensorImpl*> checked_dense_tensor_list_unwrap(ArrayRef<Tensor> tensors, const char * name, int pos, DeviceType device_type, ScalarType scalar_type) {
  std::vector<TensorImpl*> unwrapped;
  unwrapped.reserve(tensors.size());
  for (const auto i : c10::irange(tensors.size())) {
    const auto& expr = tensors[i];
    if (expr.layout() != Layout::Strided) {
      AT_ERROR("Expected dense tensor but got ", expr.layout(),
               " for sequence element ", i , " in sequence argument at position #", pos, " '", name, "'");
    }
    if (expr.device().type() != device_type) {
      AT_ERROR("Expected object of device type ", device_type, " but got device type ", expr.device().type(),
               " for sequence element ", i , " in sequence argument at position #", pos, " '", name, "'");
    }
    if (expr.scalar_type() != scalar_type) {
      AT_ERROR("Expected object of scalar type ", scalar_type, " but got scalar type ", expr.scalar_type(),
               " for sequence element ", i , " in sequence argument at position #", pos, " '", name, "'");
    }
    unwrapped.emplace_back(expr.unsafeGetTensorImpl());
  }
  return unwrapped;
}

template <size_t N>
std::array<int64_t, N> check_intlist(ArrayRef<int64_t> list, const char * name, int pos) {
  if (list.empty()) {
    // TODO: is this necessary?  We used to treat nullptr-vs-not in IntList differently
    // with strides as a way of faking optional.
    list = {};
  }
  auto res = std::array<int64_t, N>();
  if (list.size() == 1 && N > 1) {
    res.fill(list[0]);
    return res;
  }
  if (list.size() != N) {
    AT_ERROR("Expected a list of ", N, " ints but got ", list.size(), " for argument #", pos, " '", name, "'");
  }
  std::copy_n(list.begin(), N, res.begin());
  return res;
}

/**
 * Utility function to static cast input Generator* to
 * the backend generator type (CPU/CUDAGeneratorImpl etc.)
 */
template <typename T>
static inline T * check_generator(c10::optional<Generator> gen) {
  TORCH_CHECK(gen.has_value(), "Expected Generator but received nullopt");
  TORCH_CHECK(gen->defined(), "Generator with undefined implementation is not allowed");
  TORCH_CHECK(T::device_type() == gen->device().type(), "Expected a '", T::device_type(), "' device type for generator but found '", gen->device().type(), "'");
  return gen->get<T>();
}

/**
 * Utility function used in tensor implementations, which
 * supplies the default generator to tensors, if an input generator
 * is not supplied. The input Generator* is also static casted to
 * the backend generator type (CPU/CUDAGeneratorImpl etc.)
 */
template <typename T>
static inline T* get_generator_or_default(const c10::optional<Generator>& gen, const Generator& default_gen) {
  return gen.has_value() && gen->defined() ? check_generator<T>(gen) : check_generator<T>(default_gen);
}

inline void check_size_nonnegative(IntArrayRef size) {
  for (auto x: size) {
    TORCH_CHECK(x >= 0, "Trying to create tensor with negative dimension ", x, ": ", size);
  }
}

namespace detail {
TORCH_API
Tensor empty_cpu(IntArrayRef size, c10::optional<ScalarType> dtype_opt, c10::optional<Layout> layout_opt,
                 c10::optional<Device> device_opt, c10::optional<bool> pin_memory_opt, c10::optional<c10::MemoryFormat> memory_format_opt);

TORCH_API
Tensor empty_generic(
  IntArrayRef size,
  c10::Allocator* allocator,
  // technically this can be inferred from the device, but usually the
  // correct setting is obvious from the call site so just make callers
  // pass it in
  c10::DispatchKey dispatch_key,
  ScalarType dtype,
  Device device,
  c10::optional<c10::MemoryFormat> memory_format
);

template <typename T>
TORCH_API
Tensor tensor_cpu(ArrayRef<T> values, const TensorOptions& options);

template <typename T>
TORCH_API
Tensor tensor_backend(ArrayRef<T> values, const TensorOptions& options);

template <typename T>
TORCH_API
Tensor tensor_complex_cpu(ArrayRef<T> values, const TensorOptions& options);

template <typename T>
TORCH_API
Tensor tensor_complex_backend(ArrayRef<T> values, const TensorOptions& options);
} // namespace detail

} // at

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