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204 lines
9.0 KiB
204 lines
9.0 KiB
4 months ago
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/*
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* Copyright (C) 2018 The Android Open Source Project
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#define LOG_TAG "Operations"
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#include <vector>
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#include "IndexedShapeWrapper.h"
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#include "OperationResolver.h"
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#ifdef NN_INCLUDE_CPU_IMPLEMENTATION
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#include "CpuOperationUtils.h"
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#endif // NN_INCLUDE_CPU_IMPLEMENTATION
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namespace android {
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namespace nn {
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namespace slice {
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constexpr char kOperationName[] = "SLICE";
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constexpr uint32_t kNumInputs = 3;
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constexpr uint32_t kInputTensor = 0;
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constexpr uint32_t kBeginTensor = 1;
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constexpr uint32_t kSizeTensor = 2;
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constexpr uint32_t kNumOutputs = 1;
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constexpr uint32_t kOutputTensor = 0;
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#ifdef NN_INCLUDE_CPU_IMPLEMENTATION
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namespace {
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template <typename T>
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void addVectors(const std::vector<T>& a, const std::vector<T>& b, std::vector<T>* res) {
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for (int i = 0; i < res->size(); ++i) {
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res->at(i) = a[i] + b[i];
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}
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}
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template <typename T>
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bool evalGeneric(const T* inputData, const Shape& inputShape, const int32_t* beginData,
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const Shape& beginShape, const int32_t* sizeData, const Shape& sizeShape,
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T* outputData, const Shape& outputShape) {
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const int outputSize = getNumberOfElements(outputShape);
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const IndexedShapeWrapper indexedOutput = IndexedShapeWrapper(outputShape);
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const IndexedShapeWrapper indexedInput = IndexedShapeWrapper(inputShape);
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std::vector<uint32_t> outputIndex(getNumberOfDimensions(outputShape), 0);
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std::vector<uint32_t> beginIndex(getSizeOfDimension(beginShape, 0));
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std::vector<uint32_t> inputIndex(getNumberOfDimensions(inputShape));
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for (int i = 0; i < beginIndex.size(); ++i) {
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beginIndex[i] = static_cast<uint32_t>(beginData[i]);
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}
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bool lastIndex = false;
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uint32_t outputOffset;
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uint32_t inputOffset;
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do {
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addVectors(outputIndex, beginIndex, &inputIndex);
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NN_RET_CHECK(indexedOutput.indexToFlatIndex(outputIndex, &outputOffset));
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NN_RET_CHECK(indexedInput.indexToFlatIndex(inputIndex, &inputOffset));
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outputData[outputOffset] = inputData[inputOffset];
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NN_RET_CHECK(indexedOutput.nextIndexInplace(&outputIndex, &lastIndex));
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} while (!lastIndex);
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return true;
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}
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} // namespace
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#endif // NN_INCLUDE_CPU_IMPLEMENTATION
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Result<Version> validate(const IOperationValidationContext* context) {
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NN_RET_CHECK_EQ(context->getNumInputs(), kNumInputs);
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NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs);
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const OperandType inputType = context->getInputType(kInputTensor);
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NN_RET_CHECK(inputType == OperandType::TENSOR_FLOAT16 ||
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inputType == OperandType::TENSOR_FLOAT32 ||
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inputType == OperandType::TENSOR_INT32 ||
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inputType == OperandType::TENSOR_QUANT8_ASYMM ||
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inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED)
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<< "Unsupported tensor type for operation " << kOperationName;
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auto minSupportedVersion = Version::ANDROID_OC_MR1;
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if (inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) {
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minSupportedVersion = Version::ANDROID_R;
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} else {
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minSupportedVersion = Version::ANDROID_Q;
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}
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NN_RET_CHECK(validateInputTypes(
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context, {inputType, OperandType::TENSOR_INT32, OperandType::TENSOR_INT32}));
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NN_RET_CHECK(validateOutputTypes(context, {inputType}));
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return minSupportedVersion;
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}
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#ifdef NN_INCLUDE_CPU_IMPLEMENTATION
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bool prepare(IOperationExecutionContext* context) {
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const Shape& inputShape = context->getInputShape(kInputTensor);
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const int32_t n_dims = getNumberOfDimensions(inputShape);
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NN_RET_CHECK(n_dims > 0);
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const Shape& beginShape = context->getInputShape(kBeginTensor);
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NN_RET_CHECK_EQ(getNumberOfDimensions(beginShape), 1);
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NN_RET_CHECK_EQ(getSizeOfDimension(beginShape, 0), n_dims);
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const Shape& sizeShape = context->getInputShape(kSizeTensor);
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NN_RET_CHECK_EQ(getNumberOfDimensions(sizeShape), 1);
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NN_RET_CHECK_EQ(getSizeOfDimension(sizeShape, 0), n_dims);
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const int32_t* beginData = context->getInputBuffer<int32_t>(kBeginTensor);
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const int32_t* sizeData = context->getInputBuffer<int32_t>(kSizeTensor);
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Shape outputShape = context->getOutputShape(kOutputTensor);
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outputShape.dimensions.resize(n_dims);
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for (int i = 0; i < n_dims; ++i) {
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const int32_t sliceBegin = beginData[i];
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int32_t sliceSize = sizeData[i];
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if (sliceSize == -1) {
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sliceSize = getSizeOfDimension(inputShape, i) - sliceBegin;
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}
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NN_RET_CHECK_LE(beginData[i], getSizeOfDimension(inputShape, i));
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NN_RET_CHECK_GE(sliceSize, 0);
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NN_RET_CHECK_LE(sliceBegin + sliceSize, getSizeOfDimension(inputShape, i));
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outputShape.dimensions[i] = sliceSize;
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}
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return context->setOutputShape(kOutputTensor, outputShape);
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}
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bool execute(IOperationExecutionContext* context) {
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// Bypass execution in the case of zero-sized input.
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if (getNumberOfElements(context->getOutputShape(kOutputTensor)) == 0) return true;
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switch (context->getInputType(kInputTensor)) {
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case OperandType::TENSOR_FLOAT16:
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return evalGeneric(context->getInputBuffer<_Float16>(kInputTensor),
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context->getInputShape(kInputTensor),
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context->getInputBuffer<int32_t>(kBeginTensor),
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context->getInputShape(kBeginTensor),
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context->getInputBuffer<int32_t>(kSizeTensor),
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context->getInputShape(kSizeTensor),
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context->getOutputBuffer<_Float16>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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case OperandType::TENSOR_FLOAT32:
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return evalGeneric(context->getInputBuffer<float>(kInputTensor),
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context->getInputShape(kInputTensor),
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context->getInputBuffer<int32_t>(kBeginTensor),
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context->getInputShape(kBeginTensor),
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context->getInputBuffer<int32_t>(kSizeTensor),
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context->getInputShape(kSizeTensor),
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context->getOutputBuffer<float>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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case OperandType::TENSOR_INT32:
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return evalGeneric(context->getInputBuffer<int32_t>(kInputTensor),
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context->getInputShape(kInputTensor),
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context->getInputBuffer<int32_t>(kBeginTensor),
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context->getInputShape(kBeginTensor),
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context->getInputBuffer<int32_t>(kSizeTensor),
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context->getInputShape(kSizeTensor),
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context->getOutputBuffer<int32_t>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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case OperandType::TENSOR_QUANT8_ASYMM:
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return evalGeneric(context->getInputBuffer<uint8_t>(kInputTensor),
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context->getInputShape(kInputTensor),
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context->getInputBuffer<int32_t>(kBeginTensor),
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context->getInputShape(kBeginTensor),
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context->getInputBuffer<int32_t>(kSizeTensor),
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context->getInputShape(kSizeTensor),
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context->getOutputBuffer<uint8_t>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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case OperandType::TENSOR_QUANT8_ASYMM_SIGNED:
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return evalGeneric(context->getInputBuffer<int8_t>(kInputTensor),
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context->getInputShape(kInputTensor),
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context->getInputBuffer<int32_t>(kBeginTensor),
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context->getInputShape(kBeginTensor),
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context->getInputBuffer<int32_t>(kSizeTensor),
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context->getInputShape(kSizeTensor),
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context->getOutputBuffer<int8_t>(kOutputTensor),
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context->getOutputShape(kOutputTensor));
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default:
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NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName;
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}
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}
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#endif // NN_INCLUDE_CPU_IMPLEMENTATION
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} // namespace slice
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NN_REGISTER_OPERATION(SLICE, slice::kOperationName, slice::validate, slice::prepare, slice::execute,
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.allowZeroSizedInput = true);
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} // namespace nn
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} // namespace android
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