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203 lines
8.0 KiB
203 lines
8.0 KiB
4 months ago
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/*
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* Copyright (C) 2017 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 "LSHProjection.h"
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#include <utils/hash/farmhash.h>
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#include <memory>
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#include "CpuExecutor.h"
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#include "LegacyUtils.h"
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#include "Tracing.h"
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#include "nnapi/Types.h"
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namespace android {
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namespace nn {
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LSHProjection::LSHProjection(const Operation& operation, RunTimeOperandInfo* operands) {
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input_ = GetInput(operation, operands, kInputTensor);
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weight_ = GetInput(operation, operands, kWeightTensor);
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hash_ = GetInput(operation, operands, kHashTensor);
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type_ = static_cast<LSHProjectionType>(
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getScalarData<int32_t>(*GetInput(operation, operands, kTypeParam)));
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output_ = GetOutput(operation, operands, kOutputTensor);
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}
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bool LSHProjection::Prepare(const Operation& operation, RunTimeOperandInfo* operands,
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Shape* outputShape) {
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// Check that none of the required inputs are omitted.
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constexpr int requiredInputs[] = {kHashTensor, kInputTensor, kTypeParam};
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for (const int requiredInput : requiredInputs) {
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NN_RET_CHECK(!IsNullInput(GetInput(operation, operands, requiredInput)))
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<< "required input " << requiredInput << " is omitted";
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}
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NN_CHECK_EQ(NumOutputs(operation), 1);
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const RunTimeOperandInfo* hash = GetInput(operation, operands, kHashTensor);
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NN_CHECK_EQ(NumDimensions(hash), 2);
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// Support up to 32 bits.
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NN_CHECK(SizeOfDimension(hash, 1) <= 32);
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const RunTimeOperandInfo* input = GetInput(operation, operands, kInputTensor);
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NN_CHECK(NumDimensions(input) >= 1);
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const auto& typeOperand = operands[operation.inputs[kTypeParam]];
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NN_RET_CHECK(typeOperand.length >= sizeof(int32_t));
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auto type = static_cast<LSHProjectionType>(getScalarData<int32_t>(typeOperand));
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switch (type) {
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case LSHProjectionType_SPARSE:
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case LSHProjectionType_SPARSE_DEPRECATED:
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NN_CHECK(NumInputsWithValues(operation, operands) == 3);
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outputShape->dimensions = {SizeOfDimension(hash, 0)};
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break;
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case LSHProjectionType_DENSE: {
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RunTimeOperandInfo* weight = GetInput(operation, operands, kWeightTensor);
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NN_CHECK_EQ(NumInputsWithValues(operation, operands), 4);
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NN_CHECK_EQ(NumDimensions(weight), 1);
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NN_CHECK_EQ(SizeOfDimension(weight, 0), SizeOfDimension(input, 0));
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outputShape->dimensions = {SizeOfDimension(hash, 0) * SizeOfDimension(hash, 1)};
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break;
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}
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default:
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return false;
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}
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outputShape->type = OperandType::TENSOR_INT32;
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outputShape->offset = 0;
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outputShape->scale = 0.f;
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return true;
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}
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// Compute sign bit of dot product of hash(seed, input) and weight.
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// NOTE: use float as seed, and convert it to double as a temporary solution
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// to match the trained model. This is going to be changed once the new
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// model is trained in an optimized method.
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//
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template <typename T>
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int runningSignBit(const RunTimeOperandInfo* input, const RunTimeOperandInfo* weight, float seed) {
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double score = 0.0;
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int input_item_bytes = nonExtensionOperandSizeOfData(input->type, input->dimensions) /
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SizeOfDimension(input, 0);
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char* input_ptr = (char*)(input->buffer);
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const size_t seed_size = sizeof(seed);
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const size_t key_bytes = seed_size + input_item_bytes;
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std::unique_ptr<char[]> key(new char[key_bytes]);
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for (uint32_t i = 0; i < SizeOfDimension(input, 0); ++i) {
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// Create running hash id and value for current dimension.
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memcpy(key.get(), &seed, seed_size);
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memcpy(key.get() + seed_size, input_ptr, input_item_bytes);
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int64_t hash_signature = farmhash::Fingerprint64(key.get(), key_bytes);
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double running_value = static_cast<double>(hash_signature);
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input_ptr += input_item_bytes;
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if (weight->lifetime == Operand::LifeTime::NO_VALUE) {
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score += running_value;
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} else {
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score += static_cast<double>(reinterpret_cast<T*>(weight->buffer)[i]) * running_value;
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}
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}
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return (score > 0) ? 1 : 0;
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}
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template <typename T>
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void SparseLshProjection(LSHProjectionType type, const RunTimeOperandInfo* hash,
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const RunTimeOperandInfo* input, const RunTimeOperandInfo* weight,
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int32_t* out_buf) {
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int num_hash = SizeOfDimension(hash, 0);
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int num_bits = SizeOfDimension(hash, 1);
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for (int i = 0; i < num_hash; i++) {
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int32_t hash_signature = 0;
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for (int j = 0; j < num_bits; j++) {
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T seed = reinterpret_cast<T*>(hash->buffer)[i * num_bits + j];
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int bit = runningSignBit<T>(input, weight, static_cast<float>(seed));
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hash_signature = (hash_signature << 1) | bit;
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}
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if (type == LSHProjectionType_SPARSE_DEPRECATED) {
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*out_buf++ = hash_signature;
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} else {
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*out_buf++ = hash_signature + i * (1 << num_bits);
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}
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}
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}
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template <typename T>
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void DenseLshProjection(const RunTimeOperandInfo* hash, const RunTimeOperandInfo* input,
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const RunTimeOperandInfo* weight, int32_t* out_buf) {
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int num_hash = SizeOfDimension(hash, 0);
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int num_bits = SizeOfDimension(hash, 1);
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for (int i = 0; i < num_hash; i++) {
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for (int j = 0; j < num_bits; j++) {
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T seed = reinterpret_cast<T*>(hash->buffer)[i * num_bits + j];
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int bit = runningSignBit<T>(input, weight, static_cast<float>(seed));
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*out_buf++ = bit;
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}
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}
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}
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template <typename T>
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bool LSHProjection::Eval() {
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NNTRACE_COMP("LSHProjection::Eval");
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int32_t* out_buf = reinterpret_cast<int32_t*>(output_->buffer);
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switch (type_) {
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case LSHProjectionType_DENSE:
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DenseLshProjection<T>(hash_, input_, weight_, out_buf);
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break;
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case LSHProjectionType_SPARSE:
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case LSHProjectionType_SPARSE_DEPRECATED:
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SparseLshProjection<T>(type_, hash_, input_, weight_, out_buf);
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break;
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default:
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return false;
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}
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return true;
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}
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template bool LSHProjection::Eval<float>();
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template bool LSHProjection::Eval<_Float16>();
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template int runningSignBit<float>(const RunTimeOperandInfo* input,
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const RunTimeOperandInfo* weight, float seed);
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template int runningSignBit<_Float16>(const RunTimeOperandInfo* input,
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const RunTimeOperandInfo* weight, float seed);
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template void SparseLshProjection<float>(LSHProjectionType type, const RunTimeOperandInfo* hash,
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const RunTimeOperandInfo* input,
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const RunTimeOperandInfo* weight, int32_t* outBuffer);
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template void SparseLshProjection<_Float16>(LSHProjectionType type, const RunTimeOperandInfo* hash,
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const RunTimeOperandInfo* input,
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const RunTimeOperandInfo* weight, int32_t* outBuffer);
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template void DenseLshProjection<float>(const RunTimeOperandInfo* hash,
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const RunTimeOperandInfo* input,
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const RunTimeOperandInfo* weight, int32_t* outBuffer);
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template void DenseLshProjection<_Float16>(const RunTimeOperandInfo* hash,
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const RunTimeOperandInfo* input,
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const RunTimeOperandInfo* weight, int32_t* outBuffer);
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} // namespace nn
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} // namespace android
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