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
|
#ifndef CAFFE2_OPERATORS_LENGTHS_REDUCER_ROWWISE_8bits_OP_H_
#define CAFFE2_OPERATORS_LENGTHS_REDUCER_ROWWISE_8bits_OP_H_
// SparseLengthsSum8bits
#include "caffe2/core/context.h"
#include "caffe2/core/logging.h"
#include "caffe2/core/operator.h"
#include "caffe2/operators/reducer_functors.h"
#include "caffe2/perfkernels/embedding_lookup.h"
#include "caffe2/utils/eigen_utils.h"
#include "caffe2/utils/math.h"
namespace caffe2 {
namespace {
const float kEqualityThreshold = 1e-10f;
}
template <
class Context,
bool USE_WEIGHTS = 0,
bool USE_MEAN = 0,
class OutDataT = float>
class SparseLengths8BitsRowwiseOp : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
USE_SIMPLE_CTOR_DTOR(SparseLengths8BitsRowwiseOp);
bool RunOnDevice() override {
return DispatchHelper<TensorTypes<int32_t, int64_t>>::call(
this, Input(INDICES));
}
template <typename IndexType>
bool DoRunWithType() {
auto& dataInput = Input(DATA);
auto& lengthsInput = Input(LENGTHS);
auto* scale_bias = Input(SCALE_BIAS).template data<float>();
CAFFE_ENFORCE_EQ(1, lengthsInput.dim(), "LENGTHS must be a vector");
const int64_t outputSize = lengthsInput.size(0);
auto& indicesInput = Input(INDICES);
CAFFE_ENFORCE_EQ(2, Input(SCALE_BIAS).dim(), "scale_bias has to be matrix");
CAFFE_ENFORCE_EQ(
dataInput.size(0),
Input(SCALE_BIAS).size(0),
"scale_bias must have the same first dim as data");
CAFFE_ENFORCE_EQ(
2,
Input(SCALE_BIAS).size(1),
"the second dim of scale_bias has to be equal to 2");
CAFFE_ENFORCE_EQ(1, indicesInput.dim(), "INDICES must be a vector");
const IndexType* indices = indicesInput.template data<IndexType>();
const int* lengths = lengthsInput.template data<int>();
vector<int64_t> shape = dataInput.sizes().vec();
shape[0] = outputSize;
auto* output = Output(0, shape, at::dtype<OutDataT>());
const float* w = nullptr;
if (USE_WEIGHTS) {
w = Input(WEIGHTS).template data<float>();
}
int64_t in_block_size = dataInput.size_from_dim(1);
OutDataT* out = output->template mutable_data<OutDataT>();
const uint8_t* input_data = dataInput.template data<uint8_t>();
// delegate work to perfkernel that branches based on architecture
const int64_t indices_size = indicesInput.numel();
const int64_t N = dataInput.size(0);
EmbeddingLookup(
in_block_size,
outputSize,
indices_size,
N, // embedding table length
input_data,
indices,
lengths,
w,
scale_bias,
USE_MEAN,
out);
return true;
}
enum {
DATA = 0,
WEIGHTS = 1,
INDICES = 1 + USE_WEIGHTS,
LENGTHS = 2 + USE_WEIGHTS,
SCALE_BIAS = 3 + USE_WEIGHTS
};
};
template <class Context>
class FloatToRowwiseQuantized8BitsOp : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
USE_SIMPLE_CTOR_DTOR(FloatToRowwiseQuantized8BitsOp);
bool RunOnDevice() override {
auto& input = Input(DATA_FLOAT);
auto* input_data = input.template data<float>();
auto* output = Output(DATA_UINT8, input.sizes(), at::dtype<uint8_t>());
vector<int64_t> scale_bias_dims = {input.size(0), 2};
auto* scale_bias = Output(SCALE_BIAS, scale_bias_dims, at::dtype<float>());
auto* output_data = output->template mutable_data<uint8_t>();
float* scale_bias_data = scale_bias->template mutable_data<float>();
size_t n_blocks = input.size(0);
size_t block_size = input.size_from_dim(1);
for (const auto i : c10::irange(n_blocks)) {
ConstEigenVectorArrayMap<float> input_row(
input_data + i * block_size, block_size);
EigenVectorArrayMap<uint8_t> output_row(
output_data + i * block_size, block_size);
auto min_element = input_row.minCoeff();
auto max_element = input_row.maxCoeff();
if (max_element - min_element < kEqualityThreshold) {
scale_bias_data[2 * i] = 1.0f;
scale_bias_data[2 * i + 1] = min_element;
memset(output_data + i * block_size, 0, block_size);
} else {
scale_bias_data[2 * i] = (max_element - min_element) / 255.0f;
scale_bias_data[2 * i + 1] = min_element;
const float inv_scale = 1.0f / scale_bias_data[2 * i];
output_row = ((input_row - scale_bias_data[2 * i + 1]) * inv_scale)
.round()
.template cast<uint8_t>();
}
}
return true;
}
private:
INPUT_TAGS(DATA_FLOAT);
OUTPUT_TAGS(DATA_UINT8, SCALE_BIAS);
};
template <class Context>
class Rowwise8BitQuantizedToFloatOp : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
USE_SIMPLE_CTOR_DTOR(Rowwise8BitQuantizedToFloatOp);
bool RunOnDevice() override {
auto& input = Input(DATA_UINT8);
auto& scale_bias = Input(SCALE_BIAS);
CAFFE_ENFORCE_EQ(2, scale_bias.dim(), "scale_bias has to be matrix");
CAFFE_ENFORCE_EQ(
input.size(0),
scale_bias.size(0),
"scale_bias must have the same first dim as data");
CAFFE_ENFORCE_EQ(
2,
scale_bias.size(1),
"the second dim of scale_bias has to be equal to 2");
auto* output = Output(DATA_FLOAT, input.sizes(), at::dtype<float>());
auto* input_data = input.template data<uint8_t>();
auto* scale_bias_data = scale_bias.template data<float>();
auto* output_data = output->template mutable_data<float>();
size_t block_size = input.size_from_dim(1);
size_t n_blocks = input.size(0);
for (const auto i : c10::irange(n_blocks)) {
ConstEigenVectorArrayMap<uint8_t> input_row(
input_data + i * block_size, block_size);
EigenVectorArrayMap<float> output_row(
output_data + i * block_size, block_size);
output_row = input_row.template cast<float>() * scale_bias_data[2 * i] +
scale_bias_data[2 * i + 1];
}
return true;
}
private:
INPUT_TAGS(DATA_UINT8, SCALE_BIAS);
OUTPUT_TAGS(DATA_FLOAT);
};
} // namespace caffe2
#endif // CAFFE2_OPERATORS_LENGTHS_REDUCER_ROWWISE_8bits_H_
|
