wehub-resource-sync
105 lines
3.6 KiB
C++
105 lines
3.6 KiB
C++
/* 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 <cstdint>
|
|
|
|
#include "tensorflow/lite/c/c_api_types.h"
|
|
#include "tensorflow/lite/c/common.h"
|
|
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
|
|
#include "tensorflow/lite/kernels/kernel_util.h"
|
|
|
|
namespace tflite {
|
|
namespace ops {
|
|
namespace builtin {
|
|
namespace stablehlo_shift_left {
|
|
namespace {
|
|
|
|
constexpr int kInputTensor1 = 0;
|
|
constexpr int kInputTensor2 = 1;
|
|
constexpr int kOutputTensor = 0;
|
|
|
|
template <typename DataType>
|
|
TfLiteStatus EvalImpl(const TfLiteTensor* operand1,
|
|
const TfLiteTensor* operand2, TfLiteTensor* result) {
|
|
const int num_elements = NumElements(result);
|
|
const DataType* input1 = GetTensorData<DataType>(operand1);
|
|
const DataType* input2 = GetTensorData<DataType>(operand2);
|
|
DataType* output = GetTensorData<DataType>(result);
|
|
|
|
for (int i = 0; i < num_elements; ++i) {
|
|
output[i] = input1[i] << input2[i];
|
|
}
|
|
|
|
return kTfLiteOk;
|
|
}
|
|
|
|
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
|
|
TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
|
|
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
|
|
const TfLiteTensor* input1;
|
|
TF_LITE_ENSURE_OK(context,
|
|
GetInputSafe(context, node, kInputTensor1, &input1));
|
|
const TfLiteTensor* input2;
|
|
TF_LITE_ENSURE_OK(context,
|
|
GetInputSafe(context, node, kInputTensor2, &input2));
|
|
TfLiteTensor* output;
|
|
TF_LITE_ENSURE_OK(context,
|
|
GetOutputSafe(context, node, kOutputTensor, &output));
|
|
TF_LITE_ENSURE_TYPES_EQ(context, input1->type, input2->type);
|
|
output->type = input1->type;
|
|
return context->ResizeTensor(context, output,
|
|
TfLiteIntArrayCopy(input1->dims));
|
|
}
|
|
|
|
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
|
|
const TfLiteTensor* input1;
|
|
TF_LITE_ENSURE_OK(context,
|
|
GetInputSafe(context, node, kInputTensor1, &input1));
|
|
const TfLiteTensor* input2;
|
|
TF_LITE_ENSURE_OK(context,
|
|
GetInputSafe(context, node, kInputTensor2, &input2));
|
|
TfLiteTensor* output;
|
|
TF_LITE_ENSURE_OK(context,
|
|
GetOutputSafe(context, node, kOutputTensor, &output));
|
|
|
|
TfLiteType data_type = input1->type;
|
|
|
|
if (data_type == kTfLiteInt8) {
|
|
return EvalImpl<int8_t>(input1, input2, output);
|
|
} else if (data_type == kTfLiteInt16) {
|
|
return EvalImpl<int16_t>(input1, input2, output);
|
|
} else if (data_type == kTfLiteInt32) {
|
|
return EvalImpl<int32_t>(input1, input2, output);
|
|
} else {
|
|
TF_LITE_KERNEL_LOG(context, "(Index Type: %s) currently not supported.\n",
|
|
TfLiteTypeGetName(data_type));
|
|
return kTfLiteError;
|
|
}
|
|
}
|
|
|
|
} // namespace
|
|
} // namespace stablehlo_shift_left
|
|
|
|
TfLiteRegistration* Register_STABLEHLO_SHIFT_LEFT() {
|
|
static TfLiteRegistration r = {nullptr, nullptr,
|
|
stablehlo_shift_left::Prepare,
|
|
stablehlo_shift_left::Eval};
|
|
return &r;
|
|
}
|
|
|
|
} // namespace builtin
|
|
} // namespace ops
|
|
} // namespace tflite
|