wehub-resource-sync
447 lines
18 KiB
C++
447 lines
18 KiB
C++
/* Copyright 2018 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/comparisons.h"
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#include <stdint.h>
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#include "Eigen/Core" // from @eigen_archive
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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/quantization_util.h"
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#include "tensorflow/lite/kernels/internal/reference/string_comparisons.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/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 comparisons {
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namespace {
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constexpr int kInputTensor1 = 0;
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constexpr int kInputTensor2 = 1;
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constexpr int kOutputTensor = 0;
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TfLiteStatus ComparisonPrepareCommon(TfLiteContext* context, TfLiteNode* node,
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bool is_string_allowed) {
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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* input1;
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TF_LITE_ENSURE_OK(context,
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GetInputSafe(context, node, kInputTensor1, &input1));
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const TfLiteTensor* input2;
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TF_LITE_ENSURE_OK(context,
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GetInputSafe(context, node, kInputTensor2, &input2));
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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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// Don't support string.
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if (!is_string_allowed) {
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TF_LITE_ENSURE(context, input1->type != kTfLiteString);
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}
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// Currently only support tensors have the same type.
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TF_LITE_ENSURE_TYPES_EQ(context, input1->type, input2->type);
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output->type = kTfLiteBool;
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bool requires_broadcast = !HaveSameShapes(input1, input2);
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TfLiteIntArray* output_size = nullptr;
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if (requires_broadcast) {
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TF_LITE_ENSURE_OK(context, CalculateShapeForBroadcast(
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context, input1, input2, &output_size));
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} else {
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output_size = TfLiteIntArrayCopy(input1->dims);
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}
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return context->ResizeTensor(context, output, output_size);
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}
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TfLiteStatus ComparisonPrepare(TfLiteContext* context, TfLiteNode* node) {
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return ComparisonPrepareCommon(context, node, false);
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}
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TfLiteStatus ComparisonPrepareStringAllowed(TfLiteContext* context,
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TfLiteNode* node) {
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return ComparisonPrepareCommon(context, node, true);
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}
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void QuantizeMultiplier(double double_multiplier, int32_t* quantized_multiplier,
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int* left_shift) {
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if (double_multiplier < 1.0) {
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QuantizeMultiplierSmallerThanOneExp(double_multiplier, quantized_multiplier,
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left_shift);
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} else {
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QuantizeMultiplierGreaterThanOne(double_multiplier, quantized_multiplier,
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left_shift);
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}
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}
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template <typename input_dtype, reference_ops::ComparisonFn<int32> opname>
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void ComparisonQuantized(const TfLiteTensor* input1, const TfLiteTensor* input2,
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TfLiteTensor* output, bool requires_broadcast) {
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if (input1->type == kTfLiteUInt8 || input1->type == kTfLiteInt8 ||
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input1->type == kTfLiteInt16) {
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auto input1_offset = -input1->params.zero_point;
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auto input2_offset = -input2->params.zero_point;
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const int left_shift = 8;
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int32 input1_multiplier;
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int32 input2_multiplier;
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int input1_shift;
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int input2_shift;
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QuantizeMultiplier(input1->params.scale, &input1_multiplier, &input1_shift);
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QuantizeMultiplier(input2->params.scale, &input2_multiplier, &input2_shift);
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ComparisonParams op_params;
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op_params.left_shift = left_shift;
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op_params.input1_offset = input1_offset;
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op_params.input1_multiplier = input1_multiplier;
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op_params.input1_shift = input1_shift;
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op_params.input2_offset = input2_offset;
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op_params.input2_multiplier = input2_multiplier;
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op_params.input2_shift = input2_shift;
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if (requires_broadcast) {
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reference_ops::BroadcastComparison4DSlowWithScaling<input_dtype, opname>(
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op_params, GetTensorShape(input1), GetTensorData<input_dtype>(input1),
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GetTensorShape(input2), GetTensorData<input_dtype>(input2),
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GetTensorShape(output), GetTensorData<bool>(output));
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} else {
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reference_ops::ComparisonWithScaling<input_dtype, opname>(
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op_params, GetTensorShape(input1), GetTensorData<input_dtype>(input1),
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GetTensorShape(input2), GetTensorData<input_dtype>(input2),
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GetTensorShape(output), GetTensorData<bool>(output));
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}
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}
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}
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template <typename T, reference_ops::ComparisonFn<T> opname>
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void Comparison(const TfLiteTensor* input1, const TfLiteTensor* input2,
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TfLiteTensor* output, bool requires_broadcast) {
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ComparisonParams op_params;
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requires_broadcast
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? reference_ops::BroadcastComparison4DSlowImpl<T, opname>(
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op_params, GetTensorShape(input1), GetTensorData<T>(input1),
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GetTensorShape(input2), GetTensorData<T>(input2),
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GetTensorShape(output), GetTensorData<bool>(output))
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: reference_ops::ComparisonImpl<T, opname>(
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op_params, GetTensorShape(input1), GetTensorData<T>(input1),
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GetTensorShape(input2), GetTensorData<T>(input2),
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GetTensorShape(output), GetTensorData<bool>(output));
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}
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void ComparisonString(bool (*opname)(const StringRef&, const StringRef&),
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const TfLiteTensor* input1, const TfLiteTensor* input2,
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TfLiteTensor* output, bool requires_broadcast) {
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bool* output_data = GetTensorData<bool>(output);
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if (requires_broadcast) {
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reference_ops::BroadcastComparison4DSlowStringImpl(
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opname, GetTensorShape(input1), input1, GetTensorShape(input2), input2,
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GetTensorShape(output), output_data);
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} else {
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reference_ops::ComparisonStringImpl(opname, GetTensorShape(input1), input1,
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GetTensorShape(input2), input2,
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GetTensorShape(output), output_data);
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}
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}
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TfLiteStatus EqualEval(TfLiteContext* context, TfLiteNode* node) {
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const TfLiteTensor* input1;
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TF_LITE_ENSURE_OK(context,
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GetInputSafe(context, node, kInputTensor1, &input1));
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const TfLiteTensor* input2;
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TF_LITE_ENSURE_OK(context,
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GetInputSafe(context, node, kInputTensor2, &input2));
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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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bool requires_broadcast = !HaveSameShapes(input1, input2);
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switch (input1->type) {
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case kTfLiteBool:
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Comparison<bool, reference_ops::EqualFn>(input1, input2, output,
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requires_broadcast);
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break;
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case kTfLiteFloat32:
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Comparison<float, reference_ops::EqualFn>(input1, input2, output,
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requires_broadcast);
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break;
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case kTfLiteFloat16:
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Comparison<Eigen::half, reference_ops::EqualFn>(input1, input2, output,
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requires_broadcast);
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break;
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case kTfLiteInt16:
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if (input1->quantization.type == kTfLiteNoQuantization) {
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Comparison<int16_t, reference_ops::EqualFn>(input1, input2, output,
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requires_broadcast);
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} else {
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ComparisonQuantized<int16_t, reference_ops::EqualFn>(
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input1, input2, output, requires_broadcast);
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}
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break;
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case kTfLiteInt32:
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Comparison<int32_t, reference_ops::EqualFn>(input1, input2, output,
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requires_broadcast);
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break;
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case kTfLiteInt64:
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Comparison<int64_t, reference_ops::EqualFn>(input1, input2, output,
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requires_broadcast);
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break;
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case kTfLiteUInt8:
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ComparisonQuantized<uint8_t, reference_ops::EqualFn>(
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input1, input2, output, requires_broadcast);
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break;
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case kTfLiteInt8:
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ComparisonQuantized<int8_t, reference_ops::EqualFn>(
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input1, input2, output, requires_broadcast);
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break;
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case kTfLiteString:
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ComparisonString(reference_ops::StringRefEqualFn, input1, input2, output,
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requires_broadcast);
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break;
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default:
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TF_LITE_KERNEL_LOG(context,
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"Does not support type %d, requires "
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"bool|float|int|uint8|int16|string",
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input1->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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TfLiteStatus NotEqualEval(TfLiteContext* context, TfLiteNode* node) {
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const TfLiteTensor* input1;
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TF_LITE_ENSURE_OK(context,
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GetInputSafe(context, node, kInputTensor1, &input1));
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const TfLiteTensor* input2;
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TF_LITE_ENSURE_OK(context,
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GetInputSafe(context, node, kInputTensor2, &input2));
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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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bool requires_broadcast = !HaveSameShapes(input1, input2);
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switch (input1->type) {
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case kTfLiteBool:
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Comparison<bool, reference_ops::NotEqualFn>(input1, input2, output,
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requires_broadcast);
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break;
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case kTfLiteFloat32:
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Comparison<float, reference_ops::NotEqualFn>(input1, input2, output,
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requires_broadcast);
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break;
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case kTfLiteFloat16:
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Comparison<Eigen::half, reference_ops::NotEqualFn>(input1, input2, output,
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requires_broadcast);
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break;
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case kTfLiteInt32:
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Comparison<int32_t, reference_ops::NotEqualFn>(input1, input2, output,
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requires_broadcast);
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break;
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case kTfLiteInt64:
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Comparison<int64_t, reference_ops::NotEqualFn>(input1, input2, output,
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requires_broadcast);
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break;
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case kTfLiteUInt8:
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ComparisonQuantized<uint8_t, reference_ops::NotEqualFn>(
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input1, input2, output, requires_broadcast);
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break;
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case kTfLiteInt8:
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ComparisonQuantized<int8_t, reference_ops::NotEqualFn>(
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input1, input2, output, requires_broadcast);
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break;
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case kTfLiteInt16:
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if (input1->quantization.type != kTfLiteNoQuantization) {
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ComparisonQuantized<int16_t, reference_ops::NotEqualFn>(
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input1, input2, output, requires_broadcast);
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}
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break;
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case kTfLiteString:
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ComparisonString(reference_ops::StringRefNotEqualFn, input1, input2,
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output, requires_broadcast);
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break;
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default:
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TF_LITE_KERNEL_LOG(context,
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"Does not support type %d, requires "
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"bool|float|int|uint8|qint16|string",
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input1->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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TfLiteStatus LessEval(TfLiteContext* context, TfLiteNode* node, int lhs,
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int rhs) {
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const TfLiteTensor* input1;
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TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, lhs, &input1));
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const TfLiteTensor* input2;
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TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, rhs, &input2));
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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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bool requires_broadcast = !HaveSameShapes(input1, input2);
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switch (input1->type) {
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case kTfLiteFloat32:
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Comparison<float, reference_ops::LessFn>(input1, input2, output,
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requires_broadcast);
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break;
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case kTfLiteFloat16:
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Comparison<Eigen::half, reference_ops::LessFn>(input1, input2, output,
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requires_broadcast);
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break;
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case kTfLiteBFloat16:
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Comparison<Eigen::bfloat16, reference_ops::LessFn>(input1, input2, output,
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requires_broadcast);
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break;
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case kTfLiteInt16:
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Comparison<int16_t, reference_ops::LessFn>(input1, input2, output,
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requires_broadcast);
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break;
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case kTfLiteInt32:
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Comparison<int32_t, reference_ops::LessFn>(input1, input2, output,
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requires_broadcast);
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break;
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case kTfLiteInt64:
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Comparison<int64_t, reference_ops::LessFn>(input1, input2, output,
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requires_broadcast);
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break;
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case kTfLiteUInt8:
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ComparisonQuantized<uint8_t, reference_ops::LessFn>(
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input1, input2, output, requires_broadcast);
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break;
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case kTfLiteInt8:
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ComparisonQuantized<int8_t, reference_ops::LessFn>(input1, input2, output,
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requires_broadcast);
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break;
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default:
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TF_LITE_KERNEL_LOG(context,
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"Does not support type %d, requires float|int|uint8",
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input1->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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TfLiteStatus LessEqualEval(TfLiteContext* context, TfLiteNode* node, int lhs,
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int rhs) {
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const TfLiteTensor* input1;
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TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, lhs, &input1));
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const TfLiteTensor* input2;
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TF_LITE_ENSURE_OK(context, GetInputSafe(context, node, rhs, &input2));
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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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bool requires_broadcast = !HaveSameShapes(input1, input2);
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switch (input1->type) {
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case kTfLiteFloat32:
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Comparison<float, reference_ops::LessEqualFn>(input1, input2, output,
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requires_broadcast);
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break;
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case kTfLiteFloat16:
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Comparison<Eigen::half, reference_ops::LessEqualFn>(
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input1, input2, output, requires_broadcast);
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break;
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case kTfLiteInt16:
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Comparison<int16_t, reference_ops::LessEqualFn>(input1, input2, output,
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requires_broadcast);
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break;
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case kTfLiteInt32:
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Comparison<int32_t, reference_ops::LessEqualFn>(input1, input2, output,
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requires_broadcast);
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break;
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case kTfLiteInt64:
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Comparison<int64_t, reference_ops::LessEqualFn>(input1, input2, output,
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requires_broadcast);
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break;
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case kTfLiteUInt8:
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ComparisonQuantized<uint8_t, reference_ops::LessEqualFn>(
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input1, input2, output, requires_broadcast);
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break;
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case kTfLiteInt8:
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ComparisonQuantized<int8_t, reference_ops::LessEqualFn>(
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input1, input2, output, requires_broadcast);
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break;
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default:
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TF_LITE_KERNEL_LOG(context,
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"Does not support type %d, requires float|int|uint8",
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input1->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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TfLiteStatus LessEval(TfLiteContext* context, TfLiteNode* node) {
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return LessEval(context, node, kInputTensor1, kInputTensor2);
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}
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TfLiteStatus LessEqualEval(TfLiteContext* context, TfLiteNode* node) {
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return LessEqualEval(context, node, kInputTensor1, kInputTensor2);
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}
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TfLiteStatus GreaterEval(TfLiteContext* context, TfLiteNode* node) {
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return LessEval(context, node, kInputTensor2, kInputTensor1);
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}
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TfLiteStatus GreaterEqualEval(TfLiteContext* context, TfLiteNode* node) {
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return LessEqualEval(context, node, kInputTensor2, kInputTensor1);
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}
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} // namespace
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} // namespace comparisons
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TfLiteRegistration* Register_EQUAL() {
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static TfLiteRegistration r = {nullptr, nullptr,
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comparisons::ComparisonPrepareStringAllowed,
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comparisons::EqualEval};
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return &r;
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}
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TfLiteRegistration* Register_NOT_EQUAL() {
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static TfLiteRegistration r = {nullptr, nullptr,
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comparisons::ComparisonPrepareStringAllowed,
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comparisons::NotEqualEval};
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return &r;
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}
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TfLiteRegistration* Register_GREATER() {
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static TfLiteRegistration r = {nullptr, nullptr,
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comparisons::ComparisonPrepare,
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comparisons::GreaterEval};
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return &r;
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}
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TfLiteRegistration* Register_GREATER_EQUAL() {
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static TfLiteRegistration r = {nullptr, nullptr,
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comparisons::ComparisonPrepare,
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comparisons::GreaterEqualEval};
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return &r;
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}
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TfLiteRegistration* Register_LESS() {
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static TfLiteRegistration r = {
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nullptr, nullptr, comparisons::ComparisonPrepare, comparisons::LessEval};
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return &r;
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}
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TfLiteRegistration* Register_LESS_EQUAL() {
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static TfLiteRegistration r = {nullptr, nullptr,
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comparisons::ComparisonPrepare,
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comparisons::LessEqualEval};
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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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