1970c2158b
As part of ongoing refactoring, `tflite::GetInput`, `tflite::GetOutput`, `tflite::GetTemporary` and `tflite::GetIntermediates` will return `nullptr` in some cases. Hence, we insert the `nullptr` checks on all usages. We also insert `nullptr` checks on usages of `tflite::GetVariableInput` and `tflite::GetOptionalInputTensor` but only in the cases where there is no obvious check that `nullptr` is acceptable (that is, we only insert the check for the output of these two functions if the tensor is accessed as if it is always not `nullptr`). PiperOrigin-RevId: 332521299 Change-Id: I29af455bcb48d0b92e58132d951a3badbd772d56
295 lines
11 KiB
C++
295 lines
11 KiB
C++
/* Copyright 2017 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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#include <stdint.h>
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#include <algorithm>
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#include <tuple>
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#include <utility>
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#include "tensorflow/lite/c/common.h"
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#include "tensorflow/lite/kernels/internal/reference/reference_ops.h"
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#include "tensorflow/lite/kernels/internal/tensor.h"
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#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
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#include "tensorflow/lite/kernels/kernel_util.h"
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#include "tensorflow/lite/string_util.h"
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namespace tflite {
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namespace ops {
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namespace builtin {
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namespace tile {
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constexpr int kInputTensor = 0;
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constexpr int kInputMultipliers = 1;
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constexpr int kOutputTensor = 0;
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namespace {
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template <typename T>
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TfLiteIntArray* MultiplyShapeDims(const TfLiteIntArray& shape,
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const TfLiteTensor* multipliers,
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int num_dimensions) {
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const T* multipliers_v = GetTensorData<T>(multipliers);
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TfLiteIntArray* output_shape = TfLiteIntArrayCreate(num_dimensions);
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for (int i = 0; i < num_dimensions; ++i) {
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output_shape->data[i] = shape.data[i] * multipliers_v[i];
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}
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return output_shape;
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}
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TfLiteStatus ResizeOutput(TfLiteContext* context, TfLiteNode* node) {
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const TfLiteTensor* input;
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TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kInputTensor, &input));
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TfLiteTensor* output;
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TF_LITE_ENSURE_OK(context,
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GetOutputSafe(context, node, kOutputTensor, &output));
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const TfLiteTensor* multipliers;
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TF_LITE_ENSURE_OK(
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context, GetInputSafe(context, node, kInputMultipliers, &multipliers));
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const int num_dimensions = NumDimensions(input);
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const int num_multipliers = NumElements(multipliers);
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TF_LITE_ENSURE_EQ(context, num_dimensions, num_multipliers);
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switch (multipliers->type) {
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case kTfLiteInt32:
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return context->ResizeTensor(
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context, output,
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MultiplyShapeDims<int32_t>(*input->dims, multipliers,
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num_dimensions));
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case kTfLiteInt64:
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return context->ResizeTensor(
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context, output,
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MultiplyShapeDims<int64_t>(*input->dims, multipliers,
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num_dimensions));
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default:
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context->ReportError(
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context, "Multipliers of type '%s' are not supported by tile.",
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TfLiteTypeGetName(multipliers->type));
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return kTfLiteError;
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}
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}
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template <typename T, typename M>
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void CopyMultipleTimes(const T* in_data, int32_t in_size, M multiplier,
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T* out_data) {
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for (M i = 0; i < multiplier; ++i) {
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const T* in_end = in_data + in_size;
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T* new_out_data = std::copy(in_data, in_end, out_data);
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in_data = out_data;
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out_data = new_out_data;
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}
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}
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template <typename M>
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void CopyStringMultipleTimes(const TfLiteTensor* in_data, int in_data_index,
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const int dimension_size, M multiplier,
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DynamicBuffer* buffer) {
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for (M i = 0; i < multiplier; ++i) {
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for (int j = 0; j < dimension_size; ++j) {
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const auto string_ref = GetString(in_data, in_data_index + j);
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buffer->AddString(string_ref.str, string_ref.len);
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}
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}
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}
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template <typename T, typename M>
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std::pair<int, int> TileOneDimension(const TfLiteIntArray& in_dimensions,
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const T* in_data, const M* multipliers,
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T* out_data, int dimension) {
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const int dimension_size = in_dimensions.data[dimension];
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if (dimension == in_dimensions.size - 1) {
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CopyMultipleTimes(in_data, dimension_size, multipliers[dimension],
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out_data);
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return std::make_pair(
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dimension_size,
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dimension_size * static_cast<int>(multipliers[dimension]));
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}
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int total_stride_size = 0, total_tiled_stride_size = 0;
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const T* copy_from_data = in_data;
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T* copy_to_data = out_data;
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for (int i = 0; i < dimension_size; ++i) {
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int stride_size = 0, tiled_stride_size = 0;
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std::tie(stride_size, tiled_stride_size) =
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TileOneDimension(in_dimensions, copy_from_data, multipliers,
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copy_to_data, dimension + 1);
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copy_from_data += stride_size;
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copy_to_data += tiled_stride_size;
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total_stride_size += stride_size;
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total_tiled_stride_size += tiled_stride_size;
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}
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CopyMultipleTimes(out_data, total_tiled_stride_size,
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multipliers[dimension] - 1,
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out_data + total_tiled_stride_size);
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return std::make_pair(
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total_stride_size,
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static_cast<int>(total_tiled_stride_size * multipliers[dimension]));
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}
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template <typename M>
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std::pair<int, int> TileStringOneDimension(
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const TfLiteIntArray& in_dimensions, const TfLiteTensor* in_data,
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int in_data_index, const M* multipliers, DynamicBuffer* buffer,
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int buffer_index, int dimension, TfLiteTensor* out_data) {
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const int dimension_size = in_dimensions.data[dimension];
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if (dimension == in_dimensions.size - 1) {
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CopyStringMultipleTimes(in_data, in_data_index, dimension_size,
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multipliers[dimension], buffer);
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return {dimension_size,
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dimension_size * static_cast<int>(multipliers[dimension])};
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}
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int total_stride_size = 0, total_tiled_stride_size = 0;
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for (int i = 0; i < dimension_size; ++i) {
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int stride_size, tiled_stride_size;
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std::tie(stride_size, tiled_stride_size) = TileStringOneDimension(
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in_dimensions, in_data, in_data_index + total_stride_size, multipliers,
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buffer, buffer_index + total_tiled_stride_size, dimension + 1,
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out_data);
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total_stride_size += stride_size;
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total_tiled_stride_size += tiled_stride_size;
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}
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buffer->WriteToTensor(out_data, /*new_shape=*/nullptr);
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CopyStringMultipleTimes(out_data, buffer_index, total_tiled_stride_size,
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multipliers[dimension] - 1, buffer);
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return {total_stride_size,
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total_tiled_stride_size * static_cast<int>(multipliers[dimension])};
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}
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template <typename T>
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void Tile(const TfLiteIntArray& in_dimensions, const TfLiteTensor* in_data,
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const TfLiteTensor* multipliers, TfLiteTensor* out_data) {
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// Doing recursively tiling from top to down dimension.
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switch (multipliers->type) {
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case kTfLiteInt32:
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TileOneDimension(in_dimensions, GetTensorData<T>(in_data),
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GetTensorData<int32_t>(multipliers),
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GetTensorData<T>(out_data), 0);
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break;
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case kTfLiteInt64:
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TileOneDimension(in_dimensions, GetTensorData<T>(in_data),
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GetTensorData<int64_t>(multipliers),
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GetTensorData<T>(out_data), 0);
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break;
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default:
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break;
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}
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}
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void TileString(const TfLiteIntArray& in_dimensions,
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const TfLiteTensor* in_data, const TfLiteTensor* multipliers,
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DynamicBuffer* buffer, TfLiteTensor* out_data) {
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// Doing recursively tiling from top to down dimension.
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switch (multipliers->type) {
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case kTfLiteInt32:
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TileStringOneDimension(in_dimensions, in_data, 0,
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GetTensorData<int32_t>(multipliers), buffer, 0, 0,
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out_data);
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break;
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case kTfLiteInt64:
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TileStringOneDimension(in_dimensions, in_data, 0,
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GetTensorData<int64_t>(multipliers), buffer, 0, 0,
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out_data);
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break;
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default:
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break;
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}
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}
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} // namespace
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TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
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TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
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TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
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const TfLiteTensor* input;
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TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kInputTensor, &input));
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TfLiteTensor* output;
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TF_LITE_ENSURE_OK(context,
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GetOutputSafe(context, node, kOutputTensor, &output));
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TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
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const TfLiteTensor* multipliers;
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TF_LITE_ENSURE_OK(
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context, GetInputSafe(context, node, kInputMultipliers, &multipliers));
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// Only int32 and int64 multipliers type is supported.
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if (multipliers->type != kTfLiteInt32 && multipliers->type != kTfLiteInt64) {
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context->ReportError(context,
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"Multipliers of type '%s' are not supported by tile.",
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TfLiteTypeGetName(multipliers->type));
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return kTfLiteError;
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}
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if (IsConstantTensor(multipliers)) {
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TF_LITE_ENSURE_OK(context, ResizeOutput(context, node));
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} else {
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SetTensorToDynamic(output);
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}
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return kTfLiteOk;
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}
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TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
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const TfLiteTensor* input;
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TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, kInputTensor, &input));
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TfLiteTensor* output;
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TF_LITE_ENSURE_OK(context,
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GetOutputSafe(context, node, kOutputTensor, &output));
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const TfLiteTensor* multipliers;
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TF_LITE_ENSURE_OK(
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context, GetInputSafe(context, node, kInputMultipliers, &multipliers));
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if (IsDynamicTensor(output)) {
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TF_LITE_ENSURE_OK(context, ResizeOutput(context, node));
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}
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switch (output->type) {
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case kTfLiteFloat32:
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Tile<float>(*(input->dims), input, multipliers, output);
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break;
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case kTfLiteUInt8:
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Tile<uint8_t>(*(input->dims), input, multipliers, output);
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break;
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case kTfLiteInt32:
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Tile<int32_t>(*(input->dims), input, multipliers, output);
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break;
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case kTfLiteInt64:
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Tile<int64_t>(*(input->dims), input, multipliers, output);
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break;
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case kTfLiteString: {
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DynamicBuffer buffer;
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TileString(*(input->dims), input, multipliers, &buffer, output);
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buffer.WriteToTensor(output, /*new_shape=*/nullptr);
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break;
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}
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case kTfLiteBool:
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Tile<bool>(*(input->dims), input, multipliers, output);
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break;
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default:
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context->ReportError(context, "Type '%s' is not supported by tile.",
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TfLiteTypeGetName(output->type));
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return kTfLiteError;
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}
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return kTfLiteOk;
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}
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} // namespace tile
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TfLiteRegistration* Register_TILE() {
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static TfLiteRegistration r = {nullptr, nullptr, tile::Prepare, tile::Eval};
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return &r;
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}
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} // namespace builtin
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} // namespace ops
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} // namespace tflite
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