chromium/third_party/tflite/src/tensorflow/lite/kernels/internal/reference/batch_matmul.h

/* Copyright 2020 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_KERNELS_INTERNAL_REFERENCE_BATCH_MATMUL_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BATCH_MATMUL_H_

#include <algorithm>
#include <cstdint>

#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/compatibility.h"
#include "tensorflow/lite/kernels/internal/portable_tensor_utils.h"
#include "tensorflow/lite/kernels/internal/types.h"

namespace tflite {
namespace reference_ops {
namespace batch_matmul {

// Determine which dimension is the broadcast dimension.
inline int broadcast_dim(int lhs_dim, int rhs_dim) { … }

// Compute the "extent" for iterating on this dimension.
// If we are broadcasting, then don't advance (i.e return 0).
inline int extent(const RuntimeShape& shape, int x) { … }

}  // namespace batch_matmul

template <typename Ta, typename Tb, typename Tout>
inline void BatchMatMul(const RuntimeShape& lhs_shape, const Ta* lhs_data,
                        const RuntimeShape& rhs_shape, const Tb* rhs_data,
                        const RuntimeShape& output_shape, Tout* output_data) { … }

inline void BatchMatMul(const RuntimeShape& lhs_shape, const int8_t* lhs_data,
                        const RuntimeShape& rhs_shape, const int8_t* rhs_data,
                        const float* scaling_factors,
                        const int32_t* input_offset, int32_t* row_sums,
                        const RuntimeShape& output_shape, float* output_data,
                        bool* compute_row_sums) { … }

template <typename T, typename AccumT>
inline void BatchMatMul(const FullyConnectedParams& params,
                        const RuntimeShape& lhs_shape, const T* lhs_data,
                        const RuntimeShape& rhs_shape, const T* rhs_data,
                        const RuntimeShape& output_shape, T* output_data) { … }

}  // namespace reference_ops
}  // namespace tflite

#endif  // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_BATCH_MATMUL_H_