| 158 | |
| 159 | template <KernelType kernel_type> |
| 160 | TfLiteStatus EvalQuantized(TfLiteContext* context, TfLiteNode* node, |
| 161 | TfLiteMulParams* params, const OpData* data, |
| 162 | const TfLiteTensor* input1, |
| 163 | const TfLiteTensor* input2, TfLiteTensor* output) { |
| 164 | if (input1->type == input2->type && input1->type == output->type && |
| 165 | (input1->type == kTfLiteUInt8 || input1->type == kTfLiteInt8)) { |
| 166 | tflite::ArithmeticParams op_params; |
| 167 | SetActivationParams(data->output_activation_min, |
| 168 | data->output_activation_max, &op_params); |
| 169 | op_params.input1_offset = -input1->params.zero_point; |
| 170 | op_params.input2_offset = -input2->params.zero_point; |
| 171 | op_params.output_offset = output->params.zero_point; |
| 172 | op_params.output_multiplier = data->output_multiplier; |
| 173 | op_params.output_shift = data->output_shift; |
| 174 | bool need_broadcast = optimized_ops::ProcessBroadcastShapes( |
| 175 | GetTensorShape(input1), GetTensorShape(input2), &op_params); |
| 176 | #define TF_LITE_MUL(type, opname, dtype) \ |
| 177 | type::opname(op_params, GetTensorShape(input1), \ |
| 178 | GetTensorData<dtype>(input1), GetTensorShape(input2), \ |
| 179 | GetTensorData<dtype>(input2), GetTensorShape(output), \ |
| 180 | GetTensorData<dtype>(output)) |
| 181 | if (input1->type == kTfLiteInt8) { |
| 182 | if (kernel_type == kReference) { |
| 183 | if (need_broadcast) { |
| 184 | TF_LITE_MUL(reference_integer_ops, BroadcastMul4DSlow, int8_t); |
| 185 | } else { |
| 186 | TF_LITE_MUL(reference_integer_ops, Mul, int8_t); |
| 187 | } |
| 188 | } else { |
| 189 | if (need_broadcast) { |
| 190 | TF_LITE_MUL(optimized_integer_ops, BroadcastMulFivefold, int8_t); |
| 191 | } else { |
| 192 | TF_LITE_MUL(optimized_integer_ops, Mul, int8_t); |
| 193 | } |
| 194 | } |
| 195 | } else { |
| 196 | // type == kTfLiteUInt8 |
| 197 | if (kernel_type == kReference) { |
| 198 | if (need_broadcast) { |
| 199 | TF_LITE_MUL(reference_ops, BroadcastMul4DSlow, uint8_t); |
| 200 | } else { |
| 201 | TF_LITE_MUL(reference_ops, Mul, uint8_t); |
| 202 | } |
| 203 | } else { |
| 204 | if (need_broadcast) { |
| 205 | TF_LITE_MUL(optimized_ops, BroadcastMulFivefold, uint8_t); |
| 206 | } else { |
| 207 | TF_LITE_MUL(optimized_ops, Mul, uint8_t); |
| 208 | } |
| 209 | } |
| 210 | } |
| 211 | #undef TF_LITE_MUL |
| 212 | } else if (input1->type == kTfLiteInt16 && input2->type == kTfLiteInt16 && |
| 213 | output->type == kTfLiteInt16) { |
| 214 | #define TF_LITE_MUL(type, opname) \ |
| 215 | tflite::ArithmeticParams op_params; \ |
| 216 | type::opname(op_params, GetTensorShape(input1), \ |
| 217 | GetTensorData<int16_t>(input1), GetTensorShape(input2), \ |
nothing calls this directly
no test coverage detected