changes to optimize max_pool for kelvin
Change-Id: Id7a7d12c4a341deaa106c3a6ac00167dc237ec91
diff --git a/tensorflow/lite/micro/kernels/kelvin/pooling.cc b/tensorflow/lite/micro/kernels/kelvin/pooling.cc
new file mode 100644
index 0000000..47f1362
--- /dev/null
+++ b/tensorflow/lite/micro/kernels/kelvin/pooling.cc
@@ -0,0 +1,120 @@
+/* Copyright 2024 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 "tensorflow/lite/kernels/internal/reference/pooling.h"
+
+#include "tensorflow/lite/c/builtin_op_data.h"
+#include "tensorflow/lite/kernels/kernel_util.h"
+#include "tensorflow/lite/micro/kernels/kernel_util.h"
+#include "tensorflow/lite/micro/kernels/pooling.h"
+#include "tensorflow/lite/micro/micro_log.h"
+#include "tflm/opt/opt.h"
+
+namespace tflite {
+
+namespace {
+
+TfLiteStatus AverageEval(TfLiteContext* context, TfLiteNode* node) {
+ TFLITE_DCHECK(node->builtin_data != nullptr);
+ auto* params = reinterpret_cast<TfLitePoolParams*>(node->builtin_data);
+
+ TFLITE_DCHECK(node->user_data != nullptr);
+ const OpDataPooling* data =
+ static_cast<const OpDataPooling*>(node->user_data);
+
+ const TfLiteEvalTensor* input =
+ micro::GetEvalInput(context, node, kPoolingInputTensor);
+ TfLiteEvalTensor* output =
+ micro::GetEvalOutput(context, node, kPoolingOutputTensor);
+
+ // Inputs and outputs share the same type, guaranteed by the converter.
+ switch (input->type) {
+ case kTfLiteFloat32:
+ AveragePoolingEvalFloat(context, node, params, data, input, output);
+ break;
+ case kTfLiteInt8:
+ AveragePoolingEvalQuantized<int8_t>(context, node, params, data, input,
+ output);
+ break;
+ case kTfLiteInt16:
+ AveragePoolingEvalQuantized<int16_t>(context, node, params, data, input,
+ output);
+ break;
+ default:
+ MicroPrintf("Input type %s is not currently supported",
+ TfLiteTypeGetName(input->type));
+ return kTfLiteError;
+ }
+ return kTfLiteOk;
+}
+
+TfLiteStatus MaxEval(TfLiteContext* context, TfLiteNode* node) {
+ TFLITE_DCHECK(node->builtin_data != nullptr);
+ auto* params = reinterpret_cast<TfLitePoolParams*>(node->builtin_data);
+
+ TFLITE_DCHECK(node->user_data != nullptr);
+ const OpDataPooling* data =
+ static_cast<const OpDataPooling*>(node->user_data);
+
+ const TfLiteEvalTensor* input =
+ micro::GetEvalInput(context, node, kPoolingInputTensor);
+ TfLiteEvalTensor* output =
+ micro::GetEvalOutput(context, node, kPoolingOutputTensor);
+
+ switch (input->type) {
+ case kTfLiteFloat32:
+ MaxPoolingEvalFloat(context, node, params, data, input, output);
+ break;
+ case kTfLiteInt8:
+ tflite::PoolParams op_params;
+ op_params.stride_height = params->stride_height;
+ op_params.stride_width = params->stride_width;
+ op_params.filter_height = params->filter_height;
+ op_params.filter_width = params->filter_width;
+ op_params.padding_values.height = data->padding.height;
+ op_params.padding_values.width = data->padding.width;
+ op_params.quantized_activation_min = data->activation_min;
+ op_params.quantized_activation_max = data->activation_max;
+ kelvin::opt::MaxPoolGeneric(op_params, tflite::micro::GetTensorShape(input),
+ input->data.int8, tflite::micro::GetTensorShape(output),
+ output->data.int8);
+ break;
+ case kTfLiteInt16:
+ MaxPoolingEvalQuantized<int16_t>(context, node, params, data, input,
+ output);
+ break;
+ default:
+ MicroPrintf("Type %s not currently supported.",
+ TfLiteTypeGetName(input->type));
+ return kTfLiteError;
+ }
+ return kTfLiteOk;
+}
+
+void* Init(TfLiteContext* context, const char* buffer, size_t length) {
+ TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
+ return context->AllocatePersistentBuffer(context, sizeof(OpDataPooling));
+}
+
+} // namespace
+
+TFLMRegistration Register_AVERAGE_POOL_2D() {
+ return tflite::micro::RegisterOp(Init, PoolingPrepare, AverageEval);
+}
+
+TFLMRegistration Register_MAX_POOL_2D() {
+ return tflite::micro::RegisterOp(Init, PoolingPrepare, MaxEval);
+}
+
+} // namespace tflite
