EVOLUTION-MANAGER

Edit File: cpu_function_runtime.h

Licensed under the Apache License, Version 2.0 (the "License");
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distributed under the License is distributed on an "AS IS" BASIS,
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==============================================================================*/

#ifndef TENSORFLOW_COMPILER_XLA_CPU_FUNCTION_RUNTIME_H_
#define TENSORFLOW_COMPILER_XLA_CPU_FUNCTION_RUNTIME_H_

#include "tensorflow/core/platform/types.h"

#include <cassert>

namespace xla {
namespace cpu_function_runtime {
// Stores information about one buffer used by an XLA:CPU compiled function.
// These buffers are used for holding inputs to the computation, outputs from
// the computation and as temporary scratch space.
class BufferInfo {
 public:
  // Creates a BufferInfo from a serialized encoding generated by `Encode`.
  explicit BufferInfo(
      std::pair<tensorflow::uint64, tensorflow::uint64> encoding)
      : entry_param_number_(encoding.second) {
    Kind kind;
    tensorflow::uint64 size;
    Unpack(encoding.first, &kind, &size);
    kind_ = kind;
    size_ = size;
  }

// Returns true if this buffer stores a constant.  These never need to be
  // allocated by the runtime.
  bool is_constant() const { return kind() == Kind::kConstant; }

// Returns true if this buffer stores an entry parameter.  These may or may
  // not need to be allocated by the runtime, depending on
  // XlaCompiledCpuFunction::AllocMode.
  bool is_entry_parameter() const { return kind() == Kind::kEntryParameter; }

// Returns the entry parameter number of this buffer.
  tensorflow::uint64 entry_parameter_number() const {
    assert(is_entry_parameter());
    return entry_param_number_;
  }

// Returns true if this buffer is temporary scratch space required by the XLA
  // computations.  These are always allocated by the runtime.
  bool is_temp_buffer() const { return kind() == Kind::kTempBuffer; }

// Returns true if this buffer is allocated on the C stack or into registers.
  // These buffers are never allocated by the runtime.
  bool is_on_stack_buffer() const { return kind() == Kind::kOnStackBuffer; }

// Returns the size for this buffer.
  tensorflow::uint64 size() const { return size_; }

// Encodes this BufferInfo into two 64 bit integers that can be used to
  // reconstruct the BufferInfo later using the constructor.  We need this
  // because we use BufferInfo in places where using protocol buffers would
  // negatively impact binary size.
  std::pair<tensorflow::uint64, tensorflow::uint64> Encode() const {
    static_assert(sizeof(*this) == 16, "");
    tensorflow::uint64 upper = Pack(kind(), size_);
    tensorflow::uint64 lower = entry_param_number_;
    return {upper, lower};
  }

bool operator==(const BufferInfo& buffer_info) const {
    if (kind() != buffer_info.kind() || size() != buffer_info.size()) {
      return false;
    }
    return !is_entry_parameter() ||
           entry_parameter_number() == buffer_info.entry_parameter_number();
  }

// Factory methods:

static BufferInfo MakeTempBuffer(tensorflow::uint64 size) {
    return BufferInfo(Kind::kTempBuffer, /*size=*/size,
                      /*entry_param_number=*/-1);
  }
  static BufferInfo MakeConstant(tensorflow::uint64 size) {
    return BufferInfo(Kind::kConstant, /*size=*/size,
                      /*entry_param_number=*/-1);
  }
  static BufferInfo MakeEntryParameter(tensorflow::uint64 size,
                                       tensorflow::uint64 param_number) {
    return BufferInfo(Kind::kEntryParameter, /*size=*/size,
                      /*entry_param_number=*/param_number);
  }
  static BufferInfo MakeOnStackBuffer(tensorflow::uint64 size) {
    return BufferInfo(Kind::kOnStackBuffer, /*size=*/size,
                      /*entry_param_number=*/-1);
  }

private:
  BufferInfo() = default;

enum class Kind : tensorflow::uint64 {
    kConstant,
    kTempBuffer,
    kEntryParameter,
    kOnStackBuffer
  };

Kind kind() const { return static_cast<Kind>(kind_); }

explicit BufferInfo(Kind kind, tensorflow::uint64 size,
                      tensorflow::uint64 entry_param_number)
      : kind_(kind), size_(size), entry_param_number_(entry_param_number) {}

static tensorflow::uint64 Pack(Kind kind, tensorflow::uint64 size) {
    return (static_cast<tensorflow::uint64>(size) << 2) |
           static_cast<tensorflow::uint64>(kind);
  }

static void Unpack(tensorflow::uint64 packed, Kind* kind,
                     tensorflow::uint64* size) {
    *size = packed >> 2;
    *kind = static_cast<Kind>((packed << 62) >> 62);
  }

Kind kind_ : 2;
  tensorflow::uint64 size_ : 62;
  tensorflow::int64 entry_param_number_;
};

// Align to 64-bytes, to mimic tensorflow::Allocator::kAllocatorAlignment.
constexpr size_t kAlign = 64;

// The minimum alignment of buffers passed to XLA:CPU.
constexpr size_t kMinAlign = 16;

// When declaring variables that will be passed to an XLA instance as input via
// set_arg_data(), be it a regular input or a resource variable in the graph,
// the C++ variables must be aligned.
//
// Example usage:
//   XLA_ALIGN std::array<float, 4> arg_x;
//   XLA_ALIGN float arg_y;
//   xla_instance.set_arg_data(0, arg_x.date());
//   xla_instance.set_arg_data(0, &arg_y);
#define XLA_ALIGN alignas(xla::cpu_function_runtime::kAlign)

// AlignedBufferBytes returns the sum of the size of each buffer in
// `buffer_infos`, skipping constants, on-stack buffers and, if
// allocate_entry_params is false, entry parameters.  There are `n` entries in
// `buffer_infos`.  Each buffer is aligned to kAlign byte boundaries.
size_t AlignedBufferBytes(const BufferInfo* buffer_infos, size_t n,
                          bool allocate_entry_params);

// MallocContiguousBuffers allocates buffers for use by the entry point
// generated by tfcompile.  There are `n` entries in `buffer_infos`.  If
// `annotate_initialized` is set, the allocated memory will be annotated as
// having been initialized - this is useful when allocating temporary buffers.
// If allocate_entry_params is true then allocates temp buffers and entry
// parameters, otherwise allocated only temp buffers.  Slots in `bufs`
// corresponding to unallocated buffers are set to nullptr.
//
// A single contiguous block of memory is allocated, and portions of it are
// parceled out into `bufs`, which must have space for `n` entries.  Returns
// the head of the allocated contiguous block, which should be passed to
// FreeContiguous when the buffers are no longer in use.
void* MallocContiguousBuffers(const BufferInfo* buffer_infos, size_t n,
                              bool allocate_entry_params, void** bufs,
                              bool annotate_initialized);

// FreeContiguous frees the contiguous block of memory allocated by
// MallocContiguousBuffers.
void FreeContiguous(void* contiguous);
}  // namespace cpu_function_runtime
}  // namespace xla

#endif  // TENSORFLOW_COMPILER_XLA_CPU_FUNCTION_RUNTIME_H_