chromium/third_party/tflite/src/tensorflow/lite/core/async/async_subgraph.h

/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#ifndef TENSORFLOW_LITE_CORE_ASYNC_ASYNC_SUBGRAPH_H_
#define TENSORFLOW_LITE_CORE_ASYNC_ASYNC_SUBGRAPH_H_

#include <atomic>
#include <map>
#include <vector>

#include "tensorflow/lite/core/async/async_kernel_internal.h"
#include "tensorflow/lite/core/async/c/types.h"
#include "tensorflow/lite/core/async/interop/c/types.h"
#include "tensorflow/lite/core/c/c_api_types.h"
#include "tensorflow/lite/core/c/common.h"
#include "tensorflow/lite/core/subgraph.h"

namespace tflite {
namespace async {

// Forward declaration
class AsyncSubgraphTestPeer;

// AsyncSubgraph class manages to dispatch I/O information and
// schedule executions to underlying delegate kernels.
// TODO(b/191883048): Currently we require either `AllocateTensors` or
// `EnsureTensorAllocation` called to ensure the backend kernels are prepared.
// However, we don't need to allocate the CPU memory for input / output tensors.
// We need customize the OpPrepare or memory planner to skip the allocation
// for user provided buffer case.
class AsyncSubgraph { … };

}  // namespace async
}  // namespace tflite

#endif  // TENSORFLOW_LITE_CORE_ASYNC_ASYNC_SUBGRAPH_H_