wehub-resource-sync
296 lines
12 KiB
C++
296 lines
12 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 "tensorflow/lite/kernels/internal/reference/concatenation.h"
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#include <stdint.h>
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#include <cstddef>
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#include <cstring>
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#include <limits>
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#include "Eigen/Core" // from @eigen_archive
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#include "tensorflow/lite/core/c/builtin_op_data.h"
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#include "tensorflow/lite/core/c/common.h"
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#include "tensorflow/lite/kernels/internal/compatibility.h"
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#include "tensorflow/lite/kernels/internal/optimized/optimized_ops.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/internal/types.h"
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#include "tensorflow/lite/kernels/kernel_util.h"
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#include "tensorflow/lite/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 concatenation {
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// This file has two implementation of Concatenation.
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enum KernelType {
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kReference,
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kGenericOptimized,
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};
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template <KernelType kernel_type>
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TfLiteStatus EvalImpl(TfLiteContext* context, TfLiteNode* node, int axis,
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TfLiteTensor* output) {
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// TODO(ahentz): Creating 'all_inputs' below is not very efficient. We should
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// allocate and populate these during Prepare().
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// TODO(ycling): Activation function parameter is ignored. For now we don't have
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// a model with a Concatenation with fused activation function.
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#define TF_LITE_CONCATENATION(scalar) \
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{ \
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VectorOfTensors<scalar> all_inputs(*context, *node->inputs); \
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tflite::ConcatenationParams op_params; \
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op_params.axis = axis; \
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op_params.inputs_count = node->inputs->size; \
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if (kernel_type == kReference) { \
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reference_ops::Concatenation(op_params, all_inputs.shapes(), \
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all_inputs.data(), GetTensorShape(output), \
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GetTensorData<scalar>(output)); \
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} else { \
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optimized_ops::Concatenation(op_params, all_inputs.shapes(), \
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all_inputs.data(), GetTensorShape(output), \
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GetTensorData<scalar>(output)); \
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} \
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}
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#define TF_LITE_CONCATENATION_QUANTIZED() \
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{ \
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VectorOfQuantizedTensors all_inputs(*context, *node->inputs); \
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tflite::ConcatenationParams op_params; \
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op_params.axis = axis; \
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op_params.input_zeropoint = all_inputs.zero_point(); \
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op_params.input_scale = all_inputs.scale(); \
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op_params.inputs_count = node->inputs->size; \
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op_params.output_zeropoint = output->params.zero_point; \
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op_params.output_scale = output->params.scale; \
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if (kernel_type == kReference) { \
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reference_ops::ConcatenationWithScaling( \
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op_params, all_inputs.shapes(), all_inputs.data(), \
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GetTensorShape(output), GetTensorData<uint8>(output)); \
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} else { \
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optimized_ops::ConcatenationWithScaling( \
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op_params, all_inputs.shapes(), all_inputs.data(), \
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GetTensorShape(output), GetTensorData<uint8>(output)); \
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} \
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}
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switch (output->type) {
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case kTfLiteUInt8:
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TF_LITE_CONCATENATION_QUANTIZED();
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return kTfLiteOk;
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case kTfLiteInt4:
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TF_LITE_CONCATENATION(Int4);
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return kTfLiteOk;
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default:
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break;
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}
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switch (TfLiteTypeGetSizeBits(output->type)) {
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case 8:
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TF_LITE_CONCATENATION(int8_t);
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break;
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case 16:
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TF_LITE_CONCATENATION(int16_t);
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break;
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case 32:
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TF_LITE_CONCATENATION(int32_t);
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break;
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case 64:
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TF_LITE_CONCATENATION(int64_t);
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break;
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default:
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TF_LITE_KERNEL_LOG(context, "Type '%s' is not supported currently.",
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TfLiteTypeGetName(output->type));
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return kTfLiteError;
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}
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#undef TF_LITE_CONCATENATION_QUANTIZED
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#undef TF_LITE_CONCATENATION
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return kTfLiteOk;
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}
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TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
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auto* params =
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reinterpret_cast<TfLiteConcatenationParams*>(node->builtin_data);
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int axis = params->axis;
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int num_inputs = node->inputs->size;
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// The number of dimensions of the input tensors must match, and all
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// dimensions except 'axis' must be equal.
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const TfLiteTensor* t0;
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TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, 0, &t0));
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TfLiteType input_type = t0->type;
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if (axis < 0) axis += t0->dims->size;
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TF_LITE_ENSURE(context, axis >= 0);
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TF_LITE_ENSURE(context,
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axis < t0->dims->size || (t0->dims->size == 0 && axis == 0));
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TF_LITE_ENSURE_EQ(context, params->activation, kTfLiteActNone);
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TF_LITE_ENSURE(context,
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input_type == kTfLiteFloat32 || input_type == kTfLiteFloat16 ||
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input_type == kTfLiteBFloat16 ||
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input_type == kTfLiteUInt8 || input_type == kTfLiteInt8 ||
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input_type == kTfLiteInt16 || input_type == kTfLiteInt32 ||
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input_type == kTfLiteInt64 || input_type == kTfLiteBool ||
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input_type == kTfLiteUInt32 || input_type == kTfLiteInt4);
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// Check to see if we can calculate the output now.
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bool all_inputs_at_prepare = true;
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for (int i = 0; i < num_inputs; ++i) {
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const TfLiteTensor* t;
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TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, i, &t));
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if (!IsConstantOrPersistentTensor(t)) {
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all_inputs_at_prepare = false;
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break;
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}
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}
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// Output dimensions will match input dimensions, except 'axis', which
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// will be the sum of inputs
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int sum_axis = t0->dims->size > 0 ? t0->dims->data[axis] : 1;
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// Check if we are concatenating constant scalars.
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if (all_inputs_at_prepare && t0->dims->size == 0 && axis == 0 &&
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input_type != kTfLiteInt4) {
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for (int i = 1; i < num_inputs; ++i) {
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const TfLiteTensor* t;
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TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, i, &t));
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TF_LITE_ENSURE_EQ(context, t->dims->size, t0->dims->size);
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}
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TfLiteTensor* output;
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TF_LITE_ENSURE_OK(context, GetOutputSafe(context, node, 0, &output));
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TfLiteIntArray* output_size = TfLiteIntArrayCreate(1);
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output_size->data[0] = num_inputs;
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SetTensorToPersistentRo(output);
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context->ResizeTensor(context, output, output_size);
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size_t input_type_size;
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TF_LITE_ENSURE_STATUS(GetSizeOfType(context, t0->type, &input_type_size));
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void* o_data = output->data.data;
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for (int i = 0; i < num_inputs; ++i) {
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const TfLiteTensor* t;
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TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, i, &t));
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const void* i_data = t->data.data;
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memcpy(o_data, i_data, input_type_size);
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o_data = (void*)((uintptr_t)o_data + input_type_size);
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}
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return kTfLiteOk;
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} else {
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// Scalar concatenation is only supported for constant tensors
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// (handled above).
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TF_LITE_ENSURE(context, t0->dims->size > 0);
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for (int i = 1; i < num_inputs; ++i) {
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const TfLiteTensor* t;
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TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, i, &t));
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TF_LITE_ENSURE_EQ(context, t->dims->size, t0->dims->size);
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TF_LITE_ENSURE_EQ(context, t->type, input_type);
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for (int d = 0; d < t0->dims->size; ++d) {
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if (d == axis) {
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// Avoid integer overflow in sum_axis below
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TF_LITE_ENSURE(context, t->dims->data[axis] >= 0);
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TF_LITE_ENSURE(context,
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t->dims->data[axis] <=
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std::numeric_limits<int>::max() - sum_axis);
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sum_axis += t->dims->data[axis];
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} else {
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TF_LITE_ENSURE_EQ(context, t->dims->data[d], t0->dims->data[d]);
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}
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}
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}
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}
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TfLiteIntArray* output_size = TfLiteIntArrayCreate(t0->dims->size);
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for (int d = 0; d < t0->dims->size; ++d) {
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output_size->data[d] = (d == axis) ? sum_axis : t0->dims->data[d];
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}
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TfLiteTensor* output;
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TF_LITE_ENSURE_OK(context, GetOutputSafe(context, node, 0, &output));
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TF_LITE_ENSURE_TYPES_EQ(context, output->type, input_type);
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if (input_type == kTfLiteInt8) {
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// Make sure there is no re-scaling needed for Int8 quantized kernel. This
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// is a restriction we introduced to Int8 kernels.
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VectorOfTensors<int8_t> all_inputs(*context, *node->inputs);
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for (int i = 0; i < node->inputs->size; ++i) {
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const TfLiteTensor* t;
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TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, i, &t));
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TF_LITE_ENSURE_EQ(context, t->params.scale, output->params.scale);
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TF_LITE_ENSURE_EQ(context, t->params.zero_point,
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output->params.zero_point);
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}
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}
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if (input_type == kTfLiteInt16) {
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// Make sure that all Int16 inputs have a null zero-point.
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for (int i = 0; i < node->inputs->size; ++i) {
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const TfLiteTensor* t = GetInput(context, node, i);
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TF_LITE_ENSURE_EQ(context, t->params.zero_point, 0);
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}
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TF_LITE_ENSURE_EQ(context, output->params.zero_point, 0);
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}
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if (all_inputs_at_prepare) {
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SetTensorToPersistentRo(output);
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context->ResizeTensor(context, output, output_size);
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return EvalImpl<kReference>(context, node, axis, output);
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}
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return context->ResizeTensor(context, output, output_size);
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}
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template <KernelType kernel_type>
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TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
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auto* params =
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reinterpret_cast<TfLiteConcatenationParams*>(node->builtin_data);
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int axis = params->axis;
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TfLiteTensor* output;
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TF_LITE_ENSURE_OK(context, GetOutputSafe(context, node, 0, &output));
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if (IsConstantOrPersistentTensor(output)) {
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// Output is computed in Prepare.
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return kTfLiteOk;
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}
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if (axis < 0) axis += output->dims->size;
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return EvalImpl<kernel_type>(context, node, axis, output);
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}
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#undef TF_LITE_MACRO_DISPATCH
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} // namespace concatenation
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TfLiteRegistration* Register_CONCATENATION_REF() {
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static TfLiteRegistration r = {
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nullptr, nullptr, concatenation::Prepare,
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concatenation::Eval<concatenation::kReference>};
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return &r;
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}
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TfLiteRegistration* Register_CONCATENATION_GENERIC_OPT() {
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static TfLiteRegistration r = {
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nullptr, nullptr, concatenation::Prepare,
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concatenation::Eval<concatenation::kGenericOptimized>};
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return &r;
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}
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TfLiteRegistration* Register_CONCATENATION() {
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// TODO(ahentz): It turns out the two versions of Concatenation are almost
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// identical, so we should consider removing one.
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return Register_CONCATENATION_GENERIC_OPT();
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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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