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/*
* Copyright (C) 2018 The Android Open Source Project
*
* 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.
*/
#define LOG_TAG "Operations"
#include "OperationResolver.h"
#include "OperationsUtils.h"
#include "Tracing.h"
namespace android {
namespace nn {
namespace gather {
constexpr char kOperationName[] = "GATHER";
constexpr uint32_t kNumInputs = 3;
constexpr uint32_t kInputTensor = 0;
constexpr uint32_t kInputAxis = 1;
constexpr uint32_t kInputIndices = 2;
constexpr uint32_t kNumOutputs = 1;
constexpr uint32_t kOutputTensor = 0;
namespace {
template <typename T>
inline bool eval(const T* inputData, const Shape& inputShape, int32_t axis,
const int32_t* indicesData, const Shape& indicesShape, T* outputData) {
const auto outerSize = getNumberOfElements(inputShape, 0, axis);
const auto axisSize = getSizeOfDimension(inputShape, axis);
const auto innerSize =
getNumberOfElements(inputShape, axis + 1, getNumberOfDimensions(inputShape));
const auto indicesCount = getNumberOfElements(indicesShape);
for (uint32_t outer = 0; outer < outerSize; ++outer) {
for (uint32_t outputIndex = 0; outputIndex < indicesCount; ++outputIndex) {
const auto inputIndex = static_cast<uint32_t>(indicesData[outputIndex]);
NN_RET_CHECK_LE(0u, inputIndex);
NN_RET_CHECK_LT(inputIndex, axisSize);
std::memcpy(outputData + (outer * indicesCount + outputIndex) * innerSize,
inputData + (outer * axisSize + inputIndex) * innerSize,
sizeof(T) * innerSize);
}
}
return true;
}
} // namespace
Result<Version> validate(const IOperationValidationContext* context) {
NN_RET_CHECK_EQ(context->getNumInputs(), kNumInputs);
NN_RET_CHECK_EQ(context->getNumOutputs(), kNumOutputs);
OperandType inputType = context->getInputType(kInputTensor);
NN_RET_CHECK(inputType == OperandType::TENSOR_FLOAT16 ||
inputType == OperandType::TENSOR_FLOAT32 ||
inputType == OperandType::TENSOR_INT32 ||
inputType == OperandType::TENSOR_QUANT8_ASYMM ||
inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED)
<< "Unsupported tensor type for operation " << kOperationName;
NN_RET_CHECK(validateInputTypes(context,
{inputType, OperandType::INT32, OperandType::TENSOR_INT32}));
NN_RET_CHECK(validateOutputTypes(context, {inputType}));
if (inputType == OperandType::TENSOR_QUANT8_ASYMM_SIGNED) {
return Version::ANDROID_R;
} else {
return Version::ANDROID_Q;
}
}
bool prepare(IOperationExecutionContext* context) {
Shape input = context->getInputShape(kInputTensor);
int32_t axis = context->getInputValue<int32_t>(kInputAxis);
NN_RET_CHECK(handleNegativeAxis(input, &axis));
Shape indices = context->getInputShape(kInputIndices);
Shape output = context->getOutputShape(kOutputTensor);
output.dimensions.clear();
output.dimensions.reserve(getNumberOfDimensions(input) + getNumberOfDimensions(indices) - 1);
output.dimensions.insert(output.dimensions.end(), input.dimensions.begin(),
input.dimensions.begin() + axis);
output.dimensions.insert(output.dimensions.end(), indices.dimensions.begin(),
indices.dimensions.end());
output.dimensions.insert(output.dimensions.end(), input.dimensions.begin() + axis + 1,
input.dimensions.end());
return context->setOutputShape(kOutputTensor, output);
}
bool execute(IOperationExecutionContext* context) {
int32_t axis = context->getInputValue<int32_t>(kInputAxis);
NN_RET_CHECK(handleNegativeAxis(context->getInputShape(kInputTensor), &axis));
switch (context->getInputType(kInputTensor)) {
case OperandType::TENSOR_FLOAT16:
return eval(context->getInputBuffer<_Float16>(kInputTensor),
context->getInputShape(kInputTensor), axis,
context->getInputBuffer<int32_t>(kInputIndices),
context->getInputShape(kInputIndices),
context->getOutputBuffer<_Float16>(kOutputTensor));
case OperandType::TENSOR_FLOAT32:
return eval(context->getInputBuffer<float>(kInputTensor),
context->getInputShape(kInputTensor), axis,
context->getInputBuffer<int32_t>(kInputIndices),
context->getInputShape(kInputIndices),
context->getOutputBuffer<float>(kOutputTensor));
case OperandType::TENSOR_INT32:
return eval(context->getInputBuffer<int32_t>(kInputTensor),
context->getInputShape(kInputTensor), axis,
context->getInputBuffer<int32_t>(kInputIndices),
context->getInputShape(kInputIndices),
context->getOutputBuffer<int32_t>(kOutputTensor));
case OperandType::TENSOR_QUANT8_ASYMM:
return eval(context->getInputBuffer<uint8_t>(kInputTensor),
context->getInputShape(kInputTensor), axis,
context->getInputBuffer<int32_t>(kInputIndices),
context->getInputShape(kInputIndices),
context->getOutputBuffer<uint8_t>(kOutputTensor));
case OperandType::TENSOR_QUANT8_ASYMM_SIGNED:
return eval(context->getInputBuffer<int8_t>(kInputTensor),
context->getInputShape(kInputTensor), axis,
context->getInputBuffer<int32_t>(kInputIndices),
context->getInputShape(kInputIndices),
context->getOutputBuffer<int8_t>(kOutputTensor));
default:
NN_RET_CHECK_FAIL() << "Unsupported tensor type for operation " << kOperationName;
}
}
} // namespace gather
NN_REGISTER_OPERATION(GATHER, gather::kOperationName, gather::validate, gather::prepare,
gather::execute);
} // namespace nn
} // namespace android
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