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115 lines
3.9 KiB
115 lines
3.9 KiB
/*
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* Copyright (C) 2019 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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namespace android {
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namespace nn {
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namespace fill_op {
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constexpr uint32_t kNumInputs = 2;
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constexpr uint32_t kDimsTensor = 0;
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constexpr uint32_t kValueScalar = 1;
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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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bool executeTyped(IOperationExecutionContext* context) {
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T* output = context->getOutputBuffer<T>(kOutputTensor);
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const int numElements = getNumberOfElements(context->getOutputShape(kOutputTensor));
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const T value = context->getInputValue<T>(kValueScalar);
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for (int i = 0; i < numElements; ++i) {
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output[i] = value;
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}
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return true;
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}
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bool getValueType(OperandType outputType, OperandType* valueType) {
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switch (outputType) {
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case OperandType::TENSOR_FLOAT16:
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*valueType = OperandType::FLOAT16;
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return true;
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case OperandType::TENSOR_FLOAT32:
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*valueType = OperandType::FLOAT32;
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return true;
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case OperandType::TENSOR_INT32:
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*valueType = OperandType::INT32;
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return true;
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default:
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NN_RET_CHECK_FAIL() << "Unsupported value type for fill op: " << outputType;
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}
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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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// Check output type first because input value type is dependent on the
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// output type.
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OperandType outputType = context->getOutputType(kOutputTensor);
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NN_RET_CHECK(outputType == OperandType::TENSOR_FLOAT16 ||
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outputType == OperandType::TENSOR_FLOAT32 ||
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outputType == OperandType::TENSOR_INT32)
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<< "Unsupported output type for fill op: " << outputType;
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NN_RET_CHECK(validateOutputTypes(context, {outputType}));
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OperandType valueType;
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NN_RET_CHECK(getValueType(outputType, &valueType));
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NN_RET_CHECK(validateInputTypes(context, {OperandType::TENSOR_INT32, valueType}));
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return Version::ANDROID_R;
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}
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bool prepare(IOperationExecutionContext* context) {
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Shape dimsShape = context->getInputShape(kDimsTensor);
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NN_RET_CHECK_EQ(getNumberOfDimensions(dimsShape), 1);
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Shape outputShape = context->getOutputShape(kOutputTensor);
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outputShape.dimensions.resize(dimsShape.dimensions[0]);
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const int32_t* dims = context->getInputBuffer<int32_t>(kDimsTensor);
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for (int i = 0; i < dimsShape.dimensions[0]; ++i) {
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outputShape.dimensions[i] = dims[i];
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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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switch (context->getInputType(kValueScalar)) {
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case OperandType::FLOAT16:
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return executeTyped<_Float16>(context);
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case OperandType::FLOAT32:
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return executeTyped<float>(context);
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case OperandType::INT32:
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return executeTyped<int32_t>(context);
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default:
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NN_RET_CHECK_FAIL() << "Unsupported value type for fill op.";
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}
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}
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} // namespace fill_op
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NN_REGISTER_OPERATION(FILL, "FILL", fill_op::validate, fill_op::prepare, fill_op::execute);
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} // namespace nn
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} // namespace android
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