1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
|
/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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.
==============================================================================*/
// LSH Projection projects an input to a bit vector via locality sensitive
// hashing.
//
// Options:
// Sparse:
// Computed bit vector is considered to be sparse.
// Each output element is an int32 made up by multiple bits computed from
// hash functions.
//
// Dense:
// Computed bit vector is considered to be dense. Each output element is
// either 0 or 1 that represents a bit.
//
// Input:
// Tensor[0]: Hash functions. Dim.size == 2, DataType: Float.
// Tensor[0].Dim[0]: Num of hash functions.
// Tensor[0].Dim[1]: Num of projected output bits generated by
// each hash function.
// In sparse case, Tensor[0].Dim[1] + ceil( log2(Tensor[0].Dim[0] )) <= 32.
//
// Tensor[1]: Input. Dim.size >= 1, No restriction on DataType.
// Tensor[2]: Optional, Weight. Dim.size == 1, DataType: Float.
// If not set, each element of input is considered to have same
// weight of 1.0 Tensor[1].Dim[0] == Tensor[2].Dim[0]
//
// Output:
// Sparse:
// Output.Dim == { Tensor[0].Dim[0] }
// A tensor of int32 that represents hash signatures,
//
// NOTE: To avoid collisions across hash functions, an offset value of
// k * (1 << Tensor[0].Dim[1]) will be added to each signature,
// k is the index of the hash function.
// Dense:
// Output.Dim == { Tensor[0].Dim[0] * Tensor[0].Dim[1] }
// A flattened tensor represents projected bit vectors.
#include <cassert>
#include <cmath>
#include <cstdio>
#include <cstdlib>
#include <cstring>
#include <iostream>
#include <limits>
#include <memory>
#include "tensorflow/contrib/lite/c/builtin_op_data.h"
#include "tensorflow/contrib/lite/c/c_api_internal.h"
#include "tensorflow/contrib/lite/kernels/kernel_util.h"
#include "tensorflow/contrib/lite/kernels/op_macros.h"
#include <farmhash.h>
namespace tflite {
namespace ops {
namespace builtin {
namespace lsh_projection {
TfLiteStatus Resize(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteLSHProjectionParams*>(node->builtin_data);
TF_LITE_ENSURE(context, NumInputs(node) == 2 || NumInputs(node) == 3);
TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
const TfLiteTensor* hash = GetInput(context, node, 0);
TF_LITE_ENSURE_EQ(context, NumDimensions(hash), 2);
// Support up to 32 bits.
TF_LITE_ENSURE(context, SizeOfDimension(hash, 1) <= 32);
const TfLiteTensor* input = GetInput(context, node, 1);
TF_LITE_ENSURE(context, NumDimensions(input) >= 1);
if (NumInputs(node) == 3) {
const TfLiteTensor* weight = GetInput(context, node, 2);
TF_LITE_ENSURE_EQ(context, NumDimensions(weight), 1);
TF_LITE_ENSURE_EQ(context, SizeOfDimension(weight, 0),
SizeOfDimension(input, 0));
}
TfLiteTensor* output = GetOutput(context, node, 0);
TfLiteIntArray* outputSize = TfLiteIntArrayCreate(1);
switch (params->type) {
case kTfLiteLshProjectionSparse:
outputSize->data[0] = SizeOfDimension(hash, 0);
break;
case kTfLiteLshProjectionDense:
outputSize->data[0] = SizeOfDimension(hash, 0) * SizeOfDimension(hash, 1);
break;
default:
return kTfLiteError;
}
return context->ResizeTensor(context, output, outputSize);
}
// 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.
//
int RunningSignBit(const TfLiteTensor* input, const TfLiteTensor* weight,
float seed) {
double score = 0.0;
int input_item_bytes = input->bytes / SizeOfDimension(input, 0);
char* input_ptr = input->data.raw;
const size_t seed_size = sizeof(float);
const size_t key_bytes = sizeof(float) + input_item_bytes;
std::unique_ptr<char[]> key(new char[key_bytes]);
for (int 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 = ::util::Fingerprint64(key.get(), key_bytes);
double running_value = static_cast<double>(hash_signature);
input_ptr += input_item_bytes;
if (weight == nullptr) {
score += running_value;
} else {
score += weight->data.f[i] * running_value;
}
}
return (score > 0) ? 1 : 0;
}
void SparseLshProjection(const TfLiteTensor* hash, const TfLiteTensor* input,
const TfLiteTensor* 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++) {
float seed = hash->data.f[i * num_bits + j];
int bit = RunningSignBit(input, weight, seed);
hash_signature = (hash_signature << 1) | bit;
}
*out_buf++ = hash_signature + i * (1 << num_bits);
}
}
void DenseLshProjection(const TfLiteTensor* hash, const TfLiteTensor* input,
const TfLiteTensor* 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++) {
float seed = hash->data.f[i * num_bits + j];
int bit = RunningSignBit(input, weight, seed);
*out_buf++ = bit;
}
}
}
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
auto* params =
reinterpret_cast<TfLiteLSHProjectionParams*>(node->builtin_data);
int32_t* out_buf = GetOutput(context, node, 0)->data.i32;
const TfLiteTensor* hash = GetInput(context, node, 0);
const TfLiteTensor* input = GetInput(context, node, 1);
const TfLiteTensor* weight =
NumInputs(node) == 2 ? nullptr : GetInput(context, node, 2);
switch (params->type) {
case kTfLiteLshProjectionDense:
DenseLshProjection(hash, input, weight, out_buf);
break;
case kTfLiteLshProjectionSparse:
SparseLshProjection(hash, input, weight, out_buf);
break;
default:
return kTfLiteError;
}
return kTfLiteOk;
}
} // namespace lsh_projection
TfLiteRegistration* Register_LSH_PROJECTION() {
static TfLiteRegistration r = {nullptr, nullptr, lsh_projection::Resize,
lsh_projection::Eval};
return &r;
}
} // namespace builtin
} // namespace ops
} // namespace tflite
|
