types.h

// Copyright 2024, UChicago Argonne, LLC
// All Rights Reserved
// Software Name: NEML2 -- the New Engineering material Model Library, version 2
// By: Argonne National Laboratory
// OPEN SOURCE LICENSE (MIT)
//
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to deal
// in the Software without restriction, including without limitation the rights
// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
// copies of the Software, and to permit persons to whom the Software is
// furnished to do so, subject to the following conditions:
//
// The above copyright notice and this permission notice shall be included in
// all copies or substantial portions of the Software.
//
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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// THE SOFTWARE.
 
#pragma once
 
#include <c10/util/ArrayRef.h>
#include <c10/core/TensorOptions.h>
 
namespace at
{
class Tensor;
namespace indexing
{
struct Slice;
struct TensorIndex;
}
namespace indexing {…}
} // namespace at
namespace at {…}
 
namespace neml2
{
using ATensor = at::Tensor;

// torch also provide unsigned integer types after version 2.2.1, but for backward compatibility
// reasons we don't include them here
constexpr auto kInt8 = c10::kChar;
constexpr auto kInt16 = c10::kShort;
constexpr auto kInt32 = c10::kInt;
constexpr auto kInt64 = c10::kLong;
constexpr auto kFloat16 = c10::kHalf;
constexpr auto kFloat32 = c10::kFloat;
constexpr auto kFloat64 = c10::kDouble;
 
// Device types (mirroring the definition in <c10/core/DeviceType.h>)
constexpr auto kCPU = c10::DeviceType::CPU;
constexpr auto kCUDA = c10::DeviceType::CUDA;
 
template <typename T, unsigned N>
using SmallVector = c10::SmallVector<T, N>;
template <typename T>
using ArrayRef = c10::ArrayRef<T>;
using TensorOptions = c10::TensorOptions;
using Dtype = c10::ScalarType;
using DeviceIndex = c10::DeviceIndex;
using Device = c10::Device;
 
using Real = double;
using Size = int64_t;
using Integer = int64_t;
using TensorShape = c10::SmallVector<Size, 8>;
using TensorShapeRef = c10::ArrayRef<Size>;
 
namespace indexing
{
using namespace at::indexing;
using TensorIndices = c10::SmallVector<TensorIndex, 8>;
using TensorIndicesRef = c10::ArrayRef<TensorIndex>;
} // namespace indexing
namespace indexing {…}

enum class FType : int8_t
{
  NONE = 0,
  INPUT = 1 << 0,
  OUTPUT = 1 << 1,
  PARAMETER = 1 << 2,
  BUFFER = 1 << 3
};
enum class FType : int8_t {…};
std::ostream & operator<<(std::ostream & os, FType f);

constexpr auto eps = std::numeric_limits<Real>::epsilon();
constexpr Real sqrt2 = 1.4142135623730951;
constexpr Real invsqrt2 = 0.7071067811865475;
} // namespace neml2