4565291a98
Converter support will be added in a follow-up CL. PiperOrigin-RevId: 340563780 Change-Id: I4ea49fef309518b6447cb117365653b9b1d7d6a1
137 lines
5.0 KiB
C++
137 lines
5.0 KiB
C++
/* Copyright 2020 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 "tensorflow/lite/kernels/internal/reference/broadcast_to.h"
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#include <string.h>
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#include <cstdint>
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#include <memory>
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#include "tensorflow/lite/c/common.h"
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#include "tensorflow/lite/kernels/internal/tensor.h"
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#include "tensorflow/lite/kernels/kernel_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 broadcastto {
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constexpr int kInputTensor = 0;
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constexpr int kShapeTensor = 1;
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constexpr int kOutputTensor = 0;
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constexpr int kMaxDims = 8;
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struct BroadcastToContext {
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BroadcastToContext(TfLiteContext* context, TfLiteNode* node) {
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input = GetInput(context, node, kInputTensor);
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shape = GetInput(context, node, kShapeTensor);
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output = GetOutput(context, node, kOutputTensor);
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}
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const TfLiteTensor* input;
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const TfLiteTensor* shape;
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TfLiteTensor* output;
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};
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TfLiteStatus ResizeOutputTensor(TfLiteContext* context,
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BroadcastToContext* op_context) {
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// Ensures the shape is 1D tensor.
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TF_LITE_ENSURE_EQ(context, NumDimensions(op_context->shape), 1);
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// Ensure output dims is not less than input dims.
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int input_num_dims = NumDimensions(op_context->input);
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int output_num_dims = SizeOfDimension(op_context->shape, 0);
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TF_LITE_ENSURE_MSG(context, input_num_dims <= output_num_dims,
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"Output shape must be broadcastable from input shape.");
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TF_LITE_ENSURE_MSG(context, output_num_dims <= kMaxDims,
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"BroadcastTo only supports 1-8D tensor.");
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// Check if output shape is broadcastable from input shape.
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auto get_shape_data = [op_context](int i) -> int32_t {
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if (op_context->shape->type == kTfLiteInt32) {
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return GetTensorData<int32_t>(op_context->shape)[i];
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} else {
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return GetTensorData<int64_t>(op_context->shape)[i];
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}
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};
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int extending_dims = output_num_dims - input_num_dims;
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for (int idx = 0; idx < input_num_dims; ++idx) {
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TF_LITE_ENSURE_MSG(context,
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(SizeOfDimension(op_context->input, idx) == 1 ||
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SizeOfDimension(op_context->input, idx) ==
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get_shape_data(extending_dims + idx)),
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"Output shape must be broadcastable from input shape.");
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}
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// Resizing the shape of the output tensor.
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TfLiteIntArray* output_shape = TfLiteIntArrayCreate(output_num_dims);
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std::unique_ptr<TfLiteIntArray, void (*)(TfLiteIntArray*)>
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scoped_output_shape(output_shape, TfLiteIntArrayFree);
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for (int idx = 0; idx < output_num_dims; ++idx) {
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output_shape->data[idx] = get_shape_data(idx);
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}
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return context->ResizeTensor(context, op_context->output,
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scoped_output_shape.release());
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}
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TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
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TF_LITE_ENSURE(context, NumInputs(node) == 2);
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TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
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TF_LITE_ENSURE_MSG(context,
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(NumDimensions(GetInput(context, node, 0)) <= kMaxDims),
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"BroadcastTo only supports 1-8D tensor.");
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BroadcastToContext op_context(context, node);
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TF_LITE_ENSURE(context, op_context.shape->type == kTfLiteInt32 ||
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op_context.shape->type == kTfLiteInt64);
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TF_LITE_ENSURE_EQ(context, op_context.input->type, op_context.output->type);
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// Not yet support string type due to the use of memcopy with fixed size.
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TF_LITE_ENSURE(context, op_context.input->type != kTfLiteString);
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if (IsConstantTensor(op_context.shape)) {
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return ResizeOutputTensor(context, &op_context);
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}
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SetTensorToDynamic(op_context.output);
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return kTfLiteOk;
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}
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TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
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BroadcastToContext op_context(context, node);
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if (IsDynamicTensor(op_context.output)) {
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TF_LITE_ENSURE_OK(context, ResizeOutputTensor(context, &op_context));
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}
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// BroadcastTo op support upto 8 dims, matching the support of Tensorflow.
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reference_ops::BroadcastTo<kMaxDims>(
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GetTensorShape(op_context.input), op_context.input->data.raw,
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GetTensorShape(op_context.output), op_context.output->data.raw,
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op_context.input->type);
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return kTfLiteOk;
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}
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} // namespace broadcastto
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TfLiteRegistration* Register_BROADCAST_TO() {
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static TfLiteRegistration r = {nullptr, nullptr, broadcastto::Prepare,
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broadcastto::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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