BufferStore.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 <map>
 
#include <torch/csrc/jit/frontend/tracer.h>
 
#include "neml2/misc/types.h"
 
namespace neml2
{
class NEML2Object;
template <typename T>
struct TensorName;
class TensorValueBase;
template <typename T>
class TensorBase;
 
namespace jit
{
using namespace torch::jit;
}

class BufferStore
{
public:
  BufferStore(NEML2Object * object);
 
  BufferStore(const BufferStore &) = delete;
  BufferStore(BufferStore &&) = delete;
  BufferStore & operator=(const BufferStore &) = delete;
  BufferStore & operator=(BufferStore &&) = delete;
  virtual ~BufferStore() = default;

  const std::map<std::string, std::unique_ptr<TensorValueBase>> & named_buffers() const
  {
    // NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
    return const_cast<BufferStore *>(this)->named_buffers();
  }
  std::map<std::string, std::unique_ptr<TensorValueBase>> & named_buffers();

  const TensorValueBase & get_buffer(const std::string & name) const
  {
    // NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
    return const_cast<BufferStore *>(this)->get_buffer(name);
  }

  TensorValueBase & get_buffer(const std::string & name);
 
protected:
  virtual void send_buffers_to(const TensorOptions & options);

  template <typename T, typename = typename std::enable_if_t<std::is_base_of_v<TensorBase<T>, T>>>
  const T & declare_buffer(const std::string & name, const T & rawval);

  template <typename T, typename = typename std::enable_if_t<std::is_base_of_v<TensorBase<T>, T>>>
  const T & declare_buffer(const std::string & name, const TensorName<T> & tensorname);

  template <typename T, typename = typename std::enable_if_t<std::is_base_of_v<TensorBase<T>, T>>>
  const T & declare_buffer(const std::string & name, const std::string & input_option_name);

  void assign_buffer_stack(jit::Stack & stack);

  jit::Stack collect_buffer_stack() const;
 
private:
  NEML2Object * _object;

  std::map<std::string, std::unique_ptr<TensorValueBase>> _buffer_values;

  std::map<std::string, Size> _buffer_intmd_dims;
};
 
} // namespace neml2