| /* 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. |
| ==============================================================================*/ |
| |
| #if defined(VISION_P6) |
| |
| #include <cstdint> |
| |
| #include "tensorflow/lite/c/builtin_op_data.h" |
| #include "tensorflow/lite/c/common.h" |
| #include "tensorflow/lite/kernels/internal/common.h" |
| #include "tensorflow/lite/kernels/internal/tensor_ctypes.h" |
| #include "tensorflow/lite/kernels/kernel_util.h" |
| #include "tensorflow/lite/micro/kernels/add.h" |
| #include "tensorflow/lite/micro/kernels/kernel_util.h" |
| #include "tensorflow/lite/micro/kernels/xtensa/xtensa.h" |
| #include "tensorflow/lite/micro/kernels/xtensa/xtensa_add.h" |
| |
| namespace tflite { |
| |
| TfLiteStatus AddPrepareVision(TfLiteContext* context, TfLiteNode* node) { |
| TFLITE_DCHECK(node->user_data != nullptr); |
| TFLITE_DCHECK(node->builtin_data != nullptr); |
| |
| XtensaAddOpData* data = reinterpret_cast<XtensaAddOpData*>(node->user_data); |
| |
| MicroContext* micro_context = GetMicroContext(context); |
| TfLiteTensor* output = |
| micro_context->AllocateTempOutputTensor(node, kAddOutputTensor); |
| TF_LITE_ENSURE(context, output != nullptr); |
| TfLiteTensor* input1 = |
| micro_context->AllocateTempInputTensor(node, kAddInputTensor1); |
| TF_LITE_ENSURE(context, input1 != nullptr); |
| TfLiteTensor* input2 = |
| micro_context->AllocateTempInputTensor(node, kAddInputTensor2); |
| TF_LITE_ENSURE(context, input2 != nullptr); |
| |
| uint32_t context_size = 0; |
| uint32_t status = xiAddGetMemReqd_Context(&context_size); |
| TFLITE_DCHECK(status == 0); |
| if (context_size) { |
| void* context_data = |
| context->AllocatePersistentBuffer(context, context_size); |
| if (context_data == nullptr) { |
| return kTfLiteError; |
| } |
| data->p_context = reinterpret_cast<uint8_t*>(context_data); |
| data->context_size = context_size; |
| } |
| |
| uint32_t input1_dims[4] = {1, 1, 1, 1}; |
| uint32_t input2_dims[4] = {1, 1, 1, 1}; |
| uint32_t output_dims[4] = {1, 1, 1, 1}; |
| for (int i = 0; i < NumDimensions(input1); i++) { |
| input1_dims[i] = |
| std::max(1, SizeOfDimension(input1, NumDimensions(input1) - 1 - i)); |
| } |
| for (int i = 0; i < NumDimensions(input2); i++) { |
| input2_dims[i] = |
| std::max(1, SizeOfDimension(input2, NumDimensions(input2) - 1 - i)); |
| } |
| for (int i = 0; i < NumDimensions(output); i++) { |
| output_dims[i] = |
| std::max(1, SizeOfDimension(output, NumDimensions(output) - 1 - i)); |
| } |
| |
| status = xiAddSetContext( |
| data->p_context, data->context_size, input1_dims[0], input1_dims[1], |
| input1_dims[2], input1_dims[3], input2_dims[0], input2_dims[1], |
| input2_dims[2], input2_dims[3], output_dims[0], output_dims[1], |
| output_dims[2], output_dims[3], input1->params.zero_point, |
| input2->params.zero_point, output->params.zero_point, |
| data->reference_op_data.input1_multiplier, |
| data->reference_op_data.input2_multiplier, |
| data->reference_op_data.output_multiplier, |
| data->reference_op_data.input1_shift, |
| data->reference_op_data.input2_shift, |
| data->reference_op_data.output_shift, |
| data->reference_op_data.output_activation_min, |
| data->reference_op_data.output_activation_max); |
| if (status) { |
| return kTfLiteError; |
| } |
| |
| micro_context->DeallocateTempTfLiteTensor(output); |
| micro_context->DeallocateTempTfLiteTensor(input1); |
| micro_context->DeallocateTempTfLiteTensor(input2); |
| |
| return kTfLiteOk; |
| } |
| |
| TfLiteStatus AddEvalQuantizedVision(TfLiteContext* context, TfLiteNode* node, |
| const TfLiteAddParams& params, |
| const XtensaAddOpData& data, |
| const TfLiteEvalTensor* input1, |
| const TfLiteEvalTensor* input2, |
| TfLiteEvalTensor* output) { |
| const uint32_t input1_size = NumElements(input1->dims); |
| const uint32_t input2_size = NumElements(input2->dims); |
| const uint32_t output_size = NumElements(output->dims); |
| |
| xiAdd(data.p_context, data.context_size, |
| const_cast<int8_t*>(tflite::micro::GetTensorData<int8_t>(input1)), |
| input1_size, |
| const_cast<int8_t*>(tflite::micro::GetTensorData<int8_t>(input2)), |
| input2_size, tflite::micro::GetTensorData<int8_t>(output), output_size); |
| return kTfLiteOk; |
| } |
| } // namespace tflite |
| #endif // defined(VISION_P6) |