🤖 Adds tensorflow lite micro lib

2024-07-22 13:46:43 +02:00
parent 2827b7c1b5
commit c9c6125462
174 changed files with 57470 additions and 0 deletions
@@ -0,0 +1,150 @@
+/* Copyright 2019 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/integer_ops/logistic.h"
+
+#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/quantization_util.h"
+#include "tensorflow/lite/kernels/internal/reference/logistic.h"
+#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
+#include "tensorflow/lite/kernels/kernel_util.h"
+#include "tensorflow/lite/kernels/op_macros.h"
+#include "tensorflow/lite/micro/kernels/kernel_util.h"
+
+namespace tflite {
+namespace ops {
+namespace micro {
+namespace activations {
+namespace {
+constexpr int kInputTensor = 0;
+constexpr int kOutputTensor = 0;
+
+struct OpData {
+  int32_t input_zero_point;
+  int32_t input_range_radius;
+  int32_t input_multiplier;
+  int input_left_shift;
+};
+
+TfLiteStatus CalculateArithmeticOpData(TfLiteContext* context, TfLiteNode* node,
+                                       OpData* data) {
+  const TfLiteTensor* input = GetInput(context, node, kInputTensor);
+  TF_LITE_ENSURE(context, input != nullptr);
+  TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
+  TF_LITE_ENSURE(context, output != nullptr);
+
+  TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
+  if (input->type == kTfLiteInt8) {
+    TF_LITE_ENSURE_EQ(context, output->params.zero_point,
+                      std::numeric_limits<int8_t>::min());
+
+    static constexpr int kInputIntegerBits = 4;
+    const double input_real_multiplier =
+        static_cast<double>(input->params.scale) *
+        static_cast<double>(1 << (31 - kInputIntegerBits));
+
+    data->input_zero_point = input->params.zero_point;
+
+    const double q = std::frexp(input_real_multiplier, &data->input_left_shift);
+    data->input_multiplier = static_cast<int32_t>(TfLiteRound(q * (1ll << 31)));
+
+    data->input_range_radius =
+        CalculateInputRadius(kInputIntegerBits, data->input_left_shift, 31);
+  }
+  return kTfLiteOk;
+}
+}  // namespace
+
+void* LogisticInit(TfLiteContext* context, const char* buffer, size_t length) {
+  TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
+  return context->AllocatePersistentBuffer(context, sizeof(OpData));
+}
+
+TfLiteStatus LogisticPrepare(TfLiteContext* context, TfLiteNode* node) {
+  TFLITE_DCHECK(node->user_data != nullptr);
+  OpData* data = static_cast<OpData*>(node->user_data);
+
+  return CalculateArithmeticOpData(context, node, data);
+}
+
+TfLiteStatus LogisticEval(TfLiteContext* context, TfLiteNode* node) {
+  const TfLiteEvalTensor* input =
+      tflite::micro::GetEvalInput(context, node, kInputTensor);
+  TfLiteEvalTensor* output =
+      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
+
+  TFLITE_DCHECK(node->user_data != nullptr);
+  OpData* data = static_cast<OpData*>(node->user_data);
+
+  if (input->type == kTfLiteFloat32) {
+    switch (output->type) {
+      case kTfLiteFloat32: {
+        reference_ops::Logistic(tflite::micro::GetTensorShape(input),
+                                tflite::micro::GetTensorData<float>(input),
+                                tflite::micro::GetTensorShape(output),
+                                tflite::micro::GetTensorData<float>(output));
+        return kTfLiteOk;
+      }
+      default:
+        TF_LITE_KERNEL_LOG(context, "Input %s, output %s not supported.",
+                           TfLiteTypeGetName(input->type),
+                           TfLiteTypeGetName(output->type));
+        return kTfLiteError;
+    }
+  } else if (input->type == kTfLiteInt8) {
+    switch (output->type) {
+      case kTfLiteInt8: {
+        reference_integer_ops::Logistic(
+            data->input_zero_point, data->input_range_radius,
+            data->input_multiplier, data->input_left_shift,
+            NumElements(input->dims),
+            tflite::micro::GetTensorData<int8_t>(input),
+            tflite::micro::GetTensorData<int8_t>(output));
+        return kTfLiteOk;
+      }
+      default:
+        TF_LITE_KERNEL_LOG(context, "Input %s, output %s not supported.",
+                           TfLiteTypeGetName(input->type),
+                           TfLiteTypeGetName(output->type));
+        return kTfLiteError;
+    }
+  } else {
+    // TODO(b/141211002): Also support other data types once we have supported
+    // temporary tensors in TFLM.
+    TF_LITE_KERNEL_LOG(context, "Input %s, output %s not supported.",
+                       TfLiteTypeGetName(input->type),
+                       TfLiteTypeGetName(output->type));
+    return kTfLiteError;
+  }
+  return kTfLiteOk;
+}
+
+}  // namespace activations
+
+TfLiteRegistration Register_LOGISTIC() {
+  return {/*init=*/activations::LogisticInit,
+          /*free=*/nullptr,
+          /*prepare=*/activations::LogisticPrepare,
+          /*invoke=*/activations::LogisticEval,
+          /*profiling_string=*/nullptr,
+          /*builtin_code=*/0,
+          /*custom_name=*/nullptr,
+          /*version=*/0};
+}
+}  // namespace micro
+}  // namespace ops
+}  // namespace tflite