wehub-resource-sync
121 lines
4.2 KiB
C++
121 lines
4.2 KiB
C++
/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_GATHER_H_
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#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_GATHER_H_
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#include <cstdint>
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#include <cstring>
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#include "ruy/profiler/instrumentation.h" // from @ruy
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#include "tensorflow/lite/core/c/c_api_types.h"
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#include "tensorflow/lite/kernels/internal/compatibility.h"
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#include "tensorflow/lite/kernels/internal/runtime_shape.h"
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#include "tensorflow/lite/kernels/internal/types.h"
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namespace tflite {
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namespace reference_ops {
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template <typename T, typename CoordsT = int32_t>
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inline TfLiteStatus Gather(const tflite::GatherParams& op_params,
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const RuntimeShape& input_shape, const T* input_data,
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const RuntimeShape& coords_shape,
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const CoordsT* coords_data,
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const RuntimeShape& output_shape, T* output_data,
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bool int4_input = false) {
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ruy::profiler::ScopeLabel label("Gather");
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int axis = op_params.axis;
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if (axis < 0) {
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axis += input_shape.DimensionsCount();
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}
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TFLITE_DCHECK_GE(axis, 0);
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TFLITE_DCHECK_LT(axis, input_shape.DimensionsCount());
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int batch_dims = op_params.batch_dims;
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if (batch_dims < 0) {
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batch_dims += coords_shape.DimensionsCount();
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}
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TFLITE_DCHECK_GE(batch_dims, 0);
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TFLITE_DCHECK_LT(batch_dims, input_shape.DimensionsCount());
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TFLITE_DCHECK_LE(batch_dims, coords_shape.DimensionsCount());
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TFLITE_DCHECK_GE(axis, batch_dims);
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for (int i = 0; i < batch_dims; ++i) {
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TFLITE_DCHECK_EQ(input_shape.Dims(i), coords_shape.Dims(i));
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}
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const int axis_size = input_shape.Dims(axis);
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int batch_size = 1;
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for (int i = 0; i < batch_dims; ++i) {
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batch_size *= input_shape.Dims(i);
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}
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int outer_size = 1;
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for (int i = batch_dims; i < axis; ++i) {
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outer_size *= input_shape.Dims(i);
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}
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int inner_size = 1;
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for (int i = axis + 1; i < input_shape.DimensionsCount(); ++i) {
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inner_size *= input_shape.Dims(i);
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}
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int input_flat_size = input_shape.FlatSize();
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int output_flat_size = output_shape.FlatSize();
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if (int4_input) {
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// TODO(b/298210669) It doesn't handle the case when sizes are not
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// divisible by 2.
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TFLITE_DCHECK_EQ(inner_size % 2, 0);
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inner_size /= 2;
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TFLITE_DCHECK_EQ(input_flat_size % 2, 0);
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input_flat_size /= 2;
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TFLITE_DCHECK_EQ(output_flat_size % 2, 0);
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output_flat_size /= 2;
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}
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int coord_size = 1;
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for (int i = batch_dims; i < coords_shape.DimensionsCount(); ++i) {
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coord_size *= coords_shape.Dims(i);
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}
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for (int batch = 0; batch < batch_size; ++batch) {
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for (int outer = 0; outer < outer_size; ++outer) {
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for (int i = 0; i < coord_size; ++i) {
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// TODO(rsun): replace memcpy with a for loop
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const int64_t coord = coords_data[batch * coord_size + i];
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if (coord < 0 || coord >= axis_size) {
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return kTfLiteError;
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}
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const int64_t from_pos =
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(((batch * outer_size) + outer) * axis_size + coord) * inner_size;
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TFLITE_DCHECK(from_pos >= 0);
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TFLITE_DCHECK(from_pos + inner_size <= input_flat_size);
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const int64_t to_pos =
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(((batch * outer_size) + outer) * coord_size + i) * inner_size;
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TFLITE_DCHECK(to_pos >= 0);
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TFLITE_DCHECK(to_pos + inner_size <= output_flat_size);
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std::memcpy(&output_data[to_pos], &input_data[from_pos],
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sizeof(T) * inner_size);
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}
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}
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}
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return kTfLiteOk;
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}
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} // namespace reference_ops
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} // namespace tflite
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#endif // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_GATHER_H_
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