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/stack.h (6034B)

#pragma once

#include <type_traits>

#include <ATen/core/ivalue.h>
#include <c10/util/Deprecated.h>

// TODO move this to c10 namespace

namespace torch {
namespace jit {

using c10::IValue;
using Stack = std::vector<IValue>;

class Operation {
  template <typename F, typename Arg>
  using accepts = std::is_constructible<std::function<void(Arg)>, F&&>;

public:
  template <typename F,
            std::enable_if_t<accepts<F, Stack*>::value, int> = 0>
  C10_DEPRECATED_MESSAGE("Please use void(Stack&) to register operator instead.")
  Operation(F&& raw): op_([raw = std::forward<F>(raw)](Stack& stack) {
    raw(&stack);
  }) {}

template <typename F,
            std::enable_if_t<accepts<F, Stack&>::value &&
                !std::is_same<std::decay_t<F>, Operation>::value, int> = 0>
  Operation(F&& op): op_(std::forward<F>(op)) {}

Operation(std::nullptr_t) noexcept {}

explicit operator bool() const noexcept {
    return op_ ? true : false;
  }

void operator()(Stack& stack) {
    op_(stack);
  }

template <typename T>
  T* target() noexcept {
    return op_.target<T>();
  }

private:
  std::function<void(Stack&)> op_;
};

// An operation with N inputs and M outputs pops the last N inputs off
// the stack and pushes its M inputs onto the stack
// before: <other stack items> I0, I1, ... IN <- stack.back()
// after: <other stack items> O0, O1, ... OM
// operations are defined this way so that ownership of inputs can be
// transferred to the operation and it can incrementally drop ownership of
// tensors when they become unneeded. For large operations, like 'run an entire
// subgraph', this functionality is very important for minimizing gpu memory
// usage return value is the relative 'offset' to jump to for the next
// operation:
//   pc += 1 + offset
// so a return value of 0 goes to the next instruction

// treat the last N elements of the stack as a list, looking up
// element i
static inline IValue& peek(Stack& stack, size_t i, size_t N) {
  return *(stack.end() - N + i);
}
static inline IValue& peek(Stack* stack, size_t i, size_t N) {
  return peek(*stack, i, N);
}
static inline const IValue& peek(const Stack& stack, size_t i, size_t N) {
  return *(stack.end() - N + i);
}
static inline const IValue& peek(const Stack* stack, size_t i, size_t N) {
  return peek(*stack, i, N);
}
// treat the last N elements of the stack as a list, looking up the
// slice starting at index i and having length len
static inline at::ArrayRef<IValue> peekSlice(
    const Stack& stack,
    size_t i,
    size_t len,
    size_t N) {
  return at::ArrayRef<IValue>(stack).slice(stack.size() - N + i, len);
}
static inline at::ArrayRef<IValue> last(const Stack& stack, size_t N) {
  return peekSlice(stack, 0, N, N);
}
static inline at::ArrayRef<IValue> last(const Stack* stack, size_t N) {
  return last(*stack, N);
}
static inline void drop(Stack& stack, size_t n) {
  stack.erase(stack.end() - n, stack.end());
}
static inline void drop(Stack* stack, size_t n) {
  drop(*stack, n);
}
static inline IValue pop(Stack& stack) {
  auto r = std::move(stack.back());
  stack.pop_back();
  return r;
}
static inline IValue pop(Stack* stack) {
  return pop(*stack);
}
static inline std::vector<IValue> pop(Stack& stack, size_t n) {
  std::vector<IValue> result;
  result.reserve(n);
  for (size_t i = 0; i < n; ++i) {
    result.push_back(std::move(peek(stack, i, n)));
  }
  drop(stack, n);
  return result;
}

// variadic pop:
// int64_t a; at::Tensor b;
// pop(stack, a, b);
// equivalent to:
// b = pop(stack).toTensor();
// a = pop(stack).toInt();
template <typename... Types>
static inline void pop(Stack& stack, Types&... args) {
  size_t i = 0;
  constexpr size_t N = sizeof...(args);
  (void)std::initializer_list<int>{
      (args = std::move(peek(stack, i++, N)).template to<Types>(), 0)...};
  drop(stack, N);
}
template <typename... Types>
static inline void pop(Stack* stack, Types&... args) {
  pop(*stack, args...);
}
template <typename Type>
static inline void push_one(Stack& stack, Type&& arg) {
  stack.emplace_back(std::forward<Type>(arg));
}

static inline void push_one(Stack& stack, c10::TensorOptions options) {
  stack.emplace_back(c10::typeMetaToScalarType(options.dtype()));
  stack.emplace_back(options.layout());
  stack.emplace_back(options.device());
  stack.emplace_back(options.pinned_memory());
}

template <typename... Types>
static inline void push(Stack& stack, Types&&... args) {
  (void)std::initializer_list<int>{(push_one(stack, std::forward<Types>(args)), 0)...};
}
template <typename... Types>
static inline void push(Stack* stack, Types&&... args) {
  return push(*stack, std::forward<Types>(args)...);
}
template <class T>
static inline void push_list_elements(Stack& stack, const c10::List<T>& elements) {
  for (T elem : elements) {
    stack.push_back(std::move(elem));
  }
}

// The packer here is carefully written not to make any unnecessary
// copies.

// pack takes the return values of aten functions pushes them onto the stack
template <typename T>
inline void pack(Stack& stack, T&& v) {
  stack.emplace_back(std::forward<T>(v));
}
template <typename T>
inline void pack(Stack* stack, T&& v) {
  pack(*stack, std::forward<T>(v));
}

template <std::size_t remaining, typename... Args>
struct TuplePacker {
  // NB: *Not* a universal reference.
  static void execute(Stack& stack, std::tuple<Args...>&& t) {
    // NB: The move here does not "destroy" the entire tuple, that is
    // not what std::move does; only the particular tuple index
    // processed here gets stolen.
    pack(stack, std::get<sizeof...(Args) - remaining>(std::move(t)));
    TuplePacker<remaining - 1, Args...>::execute(stack, std::move(t));
  }
};

template <typename... Args>
struct TuplePacker<0, Args...> {
  static void execute(Stack& stack, std::tuple<Args...>&& t){};
};

template <typename... Args>
inline void pack(Stack& stack, std::tuple<Args...>&& t) {
  TuplePacker<sizeof...(Args), Args...>::execute(stack, std::move(t));
}

} // namespace jit
} // namespace torch

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