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/*
* Copyright (C) 2017 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 "LSHProjection.h"
#include "CpuExecutor.h"
#include "HalInterfaces.h"
#include "Tracing.h"
#include "Utils.h"
#include <utils/hash/farmhash.h>
#include <memory>
namespace android {
namespace nn {
using namespace hal;
LSHProjection::LSHProjection(const Operation& operation, RunTimeOperandInfo* operands) {
input_ = GetInput(operation, operands, kInputTensor);
weight_ = GetInput(operation, operands, kWeightTensor);
hash_ = GetInput(operation, operands, kHashTensor);
type_ = static_cast<LSHProjectionType>(
getScalarData<int32_t>(*GetInput(operation, operands, kTypeParam)));
output_ = GetOutput(operation, operands, kOutputTensor);
}
bool LSHProjection::Prepare(const Operation& operation, RunTimeOperandInfo* operands,
Shape* outputShape) {
const int num_inputs = NumInputsWithValues(operation, operands);
NN_CHECK(num_inputs == 3 || num_inputs == 4);
NN_CHECK_EQ(NumOutputs(operation), 1);
const RunTimeOperandInfo* hash = GetInput(operation, operands, kHashTensor);
NN_CHECK_EQ(NumDimensions(hash), 2);
// Support up to 32 bits.
NN_CHECK(SizeOfDimension(hash, 1) <= 32);
const RunTimeOperandInfo* input = GetInput(operation, operands, kInputTensor);
NN_CHECK(NumDimensions(input) >= 1);
auto type = static_cast<LSHProjectionType>(
getScalarData<int32_t>(operands[operation.inputs[kTypeParam]]));
switch (type) {
case LSHProjectionType_SPARSE:
case LSHProjectionType_SPARSE_DEPRECATED:
NN_CHECK(NumInputsWithValues(operation, operands) == 3);
outputShape->dimensions = {SizeOfDimension(hash, 0)};
break;
case LSHProjectionType_DENSE: {
RunTimeOperandInfo* weight = GetInput(operation, operands, kWeightTensor);
NN_CHECK_EQ(NumInputsWithValues(operation, operands), 4);
NN_CHECK_EQ(NumDimensions(weight), 1);
NN_CHECK_EQ(SizeOfDimension(weight, 0), SizeOfDimension(input, 0));
outputShape->dimensions = {SizeOfDimension(hash, 0) * SizeOfDimension(hash, 1)};
break;
}
default:
return false;
}
outputShape->type = OperandType::TENSOR_INT32;
outputShape->offset = 0;
outputShape->scale = 0.f;
return true;
}
// Compute sign bit of dot product of hash(seed, input) and weight.
// NOTE: use float as seed, and convert it to double as a temporary solution
// to match the trained model. This is going to be changed once the new
// model is trained in an optimized method.
//
template <typename T>
int runningSignBit(const RunTimeOperandInfo* input, const RunTimeOperandInfo* weight, float seed) {
double score = 0.0;
int input_item_bytes = nonExtensionOperandSizeOfData(input->type, input->dimensions) /
SizeOfDimension(input, 0);
char* input_ptr = (char*)(input->buffer);
const size_t seed_size = sizeof(seed);
const size_t key_bytes = seed_size + input_item_bytes;
std::unique_ptr<char[]> key(new char[key_bytes]);
for (uint32_t i = 0; i < SizeOfDimension(input, 0); ++i) {
// Create running hash id and value for current dimension.
memcpy(key.get(), &seed, seed_size);
memcpy(key.get() + seed_size, input_ptr, input_item_bytes);
int64_t hash_signature = farmhash::Fingerprint64(key.get(), key_bytes);
double running_value = static_cast<double>(hash_signature);
input_ptr += input_item_bytes;
if (weight->lifetime == OperandLifeTime::NO_VALUE) {
score += running_value;
} else {
score += static_cast<double>(reinterpret_cast<T*>(weight->buffer)[i]) * running_value;
}
}
return (score > 0) ? 1 : 0;
}
template <typename T>
void SparseLshProjection(LSHProjectionType type, const RunTimeOperandInfo* hash,
const RunTimeOperandInfo* input, const RunTimeOperandInfo* weight,
int32_t* out_buf) {
int num_hash = SizeOfDimension(hash, 0);
int num_bits = SizeOfDimension(hash, 1);
for (int i = 0; i < num_hash; i++) {
int32_t hash_signature = 0;
for (int j = 0; j < num_bits; j++) {
T seed = reinterpret_cast<T*>(hash->buffer)[i * num_bits + j];
int bit = runningSignBit<T>(input, weight, static_cast<float>(seed));
hash_signature = (hash_signature << 1) | bit;
}
if (type == LSHProjectionType_SPARSE_DEPRECATED) {
*out_buf++ = hash_signature;
} else {
*out_buf++ = hash_signature + i * (1 << num_bits);
}
}
}
template <typename T>
void DenseLshProjection(const RunTimeOperandInfo* hash, const RunTimeOperandInfo* input,
const RunTimeOperandInfo* weight, int32_t* out_buf) {
int num_hash = SizeOfDimension(hash, 0);
int num_bits = SizeOfDimension(hash, 1);
for (int i = 0; i < num_hash; i++) {
for (int j = 0; j < num_bits; j++) {
T seed = reinterpret_cast<T*>(hash->buffer)[i * num_bits + j];
int bit = runningSignBit<T>(input, weight, static_cast<float>(seed));
*out_buf++ = bit;
}
}
}
template <typename T>
bool LSHProjection::Eval() {
NNTRACE_COMP("LSHProjection::Eval");
int32_t* out_buf = reinterpret_cast<int32_t*>(output_->buffer);
switch (type_) {
case LSHProjectionType_DENSE:
DenseLshProjection<T>(hash_, input_, weight_, out_buf);
break;
case LSHProjectionType_SPARSE:
case LSHProjectionType_SPARSE_DEPRECATED:
SparseLshProjection<T>(type_, hash_, input_, weight_, out_buf);
break;
default:
return false;
}
return true;
}
template bool LSHProjection::Eval<float>();
template bool LSHProjection::Eval<_Float16>();
template int runningSignBit<float>(const RunTimeOperandInfo* input,
const RunTimeOperandInfo* weight, float seed);
template int runningSignBit<_Float16>(const RunTimeOperandInfo* input,
const RunTimeOperandInfo* weight, float seed);
template void SparseLshProjection<float>(LSHProjectionType type, const RunTimeOperandInfo* hash,
const RunTimeOperandInfo* input,
const RunTimeOperandInfo* weight, int32_t* outBuffer);
template void SparseLshProjection<_Float16>(LSHProjectionType type, const RunTimeOperandInfo* hash,
const RunTimeOperandInfo* input,
const RunTimeOperandInfo* weight, int32_t* outBuffer);
template void DenseLshProjection<float>(const RunTimeOperandInfo* hash,
const RunTimeOperandInfo* input,
const RunTimeOperandInfo* weight, int32_t* outBuffer);
template void DenseLshProjection<_Float16>(const RunTimeOperandInfo* hash,
const RunTimeOperandInfo* input,
const RunTimeOperandInfo* weight, int32_t* outBuffer);
} // namespace nn
} // namespace android
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