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