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25ade36 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 | /* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include "edge-impulse-sdk/tensorflow/lite/kernels/internal/reference/div.h"
#include "edge-impulse-sdk/tensorflow/lite/c/common.h"
#include "edge-impulse-sdk/tensorflow/lite/kernels/internal/quantization_util.h"
#include "edge-impulse-sdk/tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h"
#include "edge-impulse-sdk/tensorflow/lite/kernels/internal/types.h"
#include "edge-impulse-sdk/tensorflow/lite/kernels/kernel_util.h"
#include "edge-impulse-sdk/tensorflow/lite/micro/kernels/kernel_util.h"
#include "edge-impulse-sdk/tensorflow/lite/micro/micro_log.h"
namespace tflite {
namespace {
constexpr int kInputTensor1 = 0;
constexpr int kInputTensor2 = 1;
constexpr int kOutputTensor = 0;
struct OpDataDiv {
// Parameters used in the quantized paths where the output is 8bit
int32_t input1_zero_point;
int32_t input2_zero_point;
int32_t output_zero_point;
int32_t output_activation_min;
int32_t output_activation_max;
// Parameters used in all quantized paths
int32_t output_multiplier;
int output_shift;
};
TfLiteStatus CalculateOpDataDiv(TfLiteContext* context, TfLiteTensor* input1,
TfLiteTensor* input2, TfLiteTensor* output,
TfLiteDivParams* params, OpDataDiv* data) {
TF_LITE_ENSURE_TYPES_EQ(context, input1->type, input2->type);
TF_LITE_ENSURE_TYPES_EQ(context, input1->type, output->type);
if (output->type == kTfLiteInt8) {
TF_LITE_ENSURE_STATUS(CalculateActivationRangeQuantized(
context, params->activation, output, &data->output_activation_min,
&data->output_activation_max));
const double real_multiplier = static_cast<double>(
input1->params.scale / (input2->params.scale * output->params.scale));
QuantizeMultiplier(real_multiplier, &data->output_multiplier,
&data->output_shift);
data->input1_zero_point = input1->params.zero_point;
data->input2_zero_point = input2->params.zero_point;
data->output_zero_point = output->params.zero_point;
}
return kTfLiteOk;
}
void* Init(TfLiteContext* context, const char* buffer, size_t length) {
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
return context->AllocatePersistentBuffer(context, sizeof(OpDataDiv));
}
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
TFLITE_DCHECK(node->user_data != nullptr);
TFLITE_DCHECK(node->builtin_data != nullptr);
MicroContext* micro_context = GetMicroContext(context);
TfLiteTensor* input1 =
micro_context->AllocateTempInputTensor(node, kInputTensor1);
TF_LITE_ENSURE(context, input1 != nullptr);
TfLiteTensor* input2 =
micro_context->AllocateTempInputTensor(node, kInputTensor2);
TF_LITE_ENSURE(context, input2 != nullptr);
TfLiteTensor* output =
micro_context->AllocateTempOutputTensor(node, kOutputTensor);
TF_LITE_ENSURE(context, output != nullptr);
OpDataDiv* data = static_cast<OpDataDiv*>(node->user_data);
auto* params = reinterpret_cast<TfLiteDivParams*>(node->builtin_data);
TF_LITE_ENSURE_STATUS(
CalculateOpDataDiv(context, input1, input2, output, params, data));
micro_context->DeallocateTempTfLiteTensor(input1);
micro_context->DeallocateTempTfLiteTensor(input2);
micro_context->DeallocateTempTfLiteTensor(output);
return kTfLiteOk;
}
void EvalDiv(TfLiteContext* context, TfLiteNode* node, TfLiteDivParams* params,
const OpDataDiv* data, const TfLiteEvalTensor* input1,
const TfLiteEvalTensor* input2, TfLiteEvalTensor* output) {
tflite::ArithmeticParams op_params = {};
#define TF_LITE_DIV(type, opname, data_type) \
data_type output_activation_min, output_activation_max; \
CalculateActivationRange(params->activation, &output_activation_min, \
&output_activation_max); \
SetActivationParams(output_activation_min, output_activation_max, \
&op_params); \
type::opname(op_params, tflite::micro::GetTensorShape(input1), \
tflite::micro::GetTensorData<data_type>(input1), \
tflite::micro::GetTensorShape(input2), \
tflite::micro::GetTensorData<data_type>(input2), \
tflite::micro::GetTensorShape(output), \
tflite::micro::GetTensorData<data_type>(output))
bool requires_broadcast = reference_ops::ProcessBroadcastShapes(
tflite::micro::GetTensorShape(input1),
tflite::micro::GetTensorShape(input2), &op_params);
if (requires_broadcast) {
TF_LITE_DIV(reference_ops, BroadcastDivSlow, float);
} else {
TF_LITE_DIV(reference_ops, Div, float);
}
#undef TF_LITE_DIV
}
TfLiteStatus EvalQuantized(TfLiteContext* context, TfLiteNode* node,
TfLiteDivParams* params, const OpDataDiv* data,
const TfLiteEvalTensor* input1,
const TfLiteEvalTensor* input2,
TfLiteEvalTensor* output) {
tflite::ArithmeticParams op_params = {};
#define TF_LITE_DIV(type, opname, dtype) \
type::opname(op_params, tflite::micro::GetTensorShape(input1), \
tflite::micro::GetTensorData<dtype>(input1), \
tflite::micro::GetTensorShape(input2), \
tflite::micro::GetTensorData<dtype>(input2), \
tflite::micro::GetTensorShape(output), \
tflite::micro::GetTensorData<dtype>(output))
if (input1->type == kTfLiteInt8 && input2->type == kTfLiteInt8 &&
output->type == kTfLiteInt8) {
SetActivationParams(data->output_activation_min,
data->output_activation_max, &op_params);
op_params.input1_offset = -data->input1_zero_point;
op_params.input2_offset = -data->input2_zero_point;
op_params.output_offset = data->output_zero_point;
op_params.output_multiplier = data->output_multiplier;
op_params.output_shift = data->output_shift;
bool requires_broadcast = reference_ops::ProcessBroadcastShapes(
tflite::micro::GetTensorShape(input1),
tflite::micro::GetTensorShape(input2), &op_params);
if (requires_broadcast) {
TF_LITE_DIV(reference_ops, BroadcastDivSlow, int8_t);
} else {
TF_LITE_DIV(reference_ops, Div, int8_t);
}
#undef TF_LITE_DIV
} else {
MicroPrintf("Unsupported combination of input and output types in DIV.");
return kTfLiteError;
}
return kTfLiteOk;
}
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
TFLITE_DCHECK(node->builtin_data != nullptr);
auto* params = static_cast<TfLiteDivParams*>(node->builtin_data);
TFLITE_DCHECK(node->user_data != nullptr);
auto* data = static_cast<OpDataDiv*>(node->user_data);
const TfLiteEvalTensor* input1 =
tflite::micro::GetEvalInput(context, node, kInputTensor1);
const TfLiteEvalTensor* input2 =
tflite::micro::GetEvalInput(context, node, kInputTensor2);
TfLiteEvalTensor* output =
tflite::micro::GetEvalOutput(context, node, kOutputTensor);
if (output->type == kTfLiteFloat32) {
EvalDiv(context, node, params, data, input1, input2, output);
} else if (output->type == kTfLiteInt8) {
TF_LITE_ENSURE_OK(context, EvalQuantized(context, node, params, data,
input1, input2, output));
} else {
MicroPrintf(
"DIV only supports FLOAT32, quantized INT8 "
"now, got type %s (%d).",
TfLiteTypeGetName(output->type), output->type);
return kTfLiteError;
}
return kTfLiteOk;
}
} // namespace
TfLiteRegistration Register_DIV() {
return tflite::micro::RegisterOp(Init, Prepare, Eval);
}
} // namespace tflite
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