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
|
#ifndef CAFFE2_OPERATORS_INT8_CONV_OP_H_
#define CAFFE2_OPERATORS_INT8_CONV_OP_H_
#include <qnnpack.h>
#include "caffe2/core/context.h"
#include "caffe2/core/operator.h"
#include "caffe2/core/tensor_int8.h"
#include "caffe2/operators/conv_op_shared.h"
#include "caffe2/operators/conv_pool_op_base.h"
#include "caffe2/operators/quantized/int8_utils.h"
namespace caffe2 {
namespace int8 {
template <Activation Ac>
class Int8ConvOp final : public ConvPoolOpBase<CPUContext> {
public:
USE_CONV_POOL_BASE_FUNCTIONS(CPUContext);
template <class... Args>
explicit Int8ConvOp(Args&&... args)
: ConvPoolOpBase(std::forward<Args>(args)...) {
OPERATOR_NEEDS_FEATURE(
this->order_ == StorageOrder::NHWC,
"Int8Conv only supports NHWC order");
createSharedBuffer<CPUContext>(ws_);
}
~Int8ConvOp() {
if (this->qnnpackObject_ != nullptr) {
qnnp_delete_operator(this->qnnpackObject_);
this->qnnpackObject_ = nullptr;
}
}
bool RunOnDeviceWithOrderNHWC() override {
CAFFE_ENFORCE_EQ(Inputs().size(), 3);
const auto& X = Inputs()[0]->template Get<Int8TensorCPU>();
const auto& W = Inputs()[1]->template Get<Int8TensorCPU>();
const auto& B = Inputs()[2]->template Get<Int8TensorCPU>();
auto* Y = Outputs()[0]->template GetMutable<Int8TensorCPU>();
const int32_t Y_offset =
this->template GetSingleArgument<int>("Y_zero_point", 0);
double Y_scale = this->template GetSingleArgument<float>("Y_scale", 1);
ConvPoolOpBase<CPUContext>::SetOutputSize(X.t, &(Y->t), W.t.dim32(0));
Y->scale = Y_scale;
Y->zero_point = Y_offset;
const auto M = W.t.size(0);
const auto KH = W.t.size(1);
const auto KW = W.t.size(2);
const auto KC = W.t.size(3);
const auto C = X.t.dim32(3);
const bool isDepthwise = this->group_ > 1 && this->group_ == M &&
this->group_ == C && KC == 1 && KH * KW == 9 && dilation_w() == 1;
TORCH_CHECK_EQ(Y->t.dim32(3), M);
runWithSharedBuffer<CPUContext>(ws_, [&](Tensor* buffer) {
initQNNPACK();
#if !defined(FBCODE_CAFFE2) && defined(USE_INTERNAL_PTHREADPOOL_IMPL)
pthreadpool_t threadpool =
reinterpret_cast<pthreadpool_t>(ws_->GetThreadPool());
#endif
if (this->qnnpackObject_ == nullptr) {
CAFFE_ENFORCE(
C % this->group_ == 0,
"number of input channels must be divisible by groups count");
CAFFE_ENFORCE(
M % this->group_ == 0,
"number of output channels must be divisible by groups count");
const qnnp_status createStatus = qnnp_create_convolution2d_nhwc_q8(
pad_t(),
pad_r(),
pad_b(),
pad_l(),
KH,
KW,
stride_h(),
stride_w(),
dilation_h(),
dilation_w(),
this->group_,
C / this->group_,
M / this->group_,
X.zero_point,
X.scale,
W.zero_point,
W.scale,
#if !defined(_MSC_VER) || defined(__clang__)
W.t.template data<uint8_t>(),
B.t.template data<int32_t>(),
#else
W.t.data<uint8_t>(),
B.t.data<int32_t>(),
#endif
Y->zero_point,
Y->scale,
activationLimits(Y->scale, Y->zero_point, Ac).first,
activationLimits(Y->scale, Y->zero_point, Ac).second,
0 /* flags */,
&this->qnnpackObject_);
CAFFE_ENFORCE(
createStatus == qnnp_status_success,
"failed to create QNNPACK convolution object");
CAFFE_ENFORCE(this->qnnpackObject_ != nullptr);
}
uint8_t* inputPtr = X.t.template mutable_data<uint8_t>();
if ((isDepthwise && this->group_ < 8) ||
(!isDepthwise && C / this->group_ < 8)) {
buffer->Resize(std::vector<int64_t>{X.t.numel() + 8});
inputPtr = buffer->template mutable_data<uint8_t>() + 8;
memcpy(inputPtr, X.t.template data<uint8_t>(), X.t.numel());
}
if (lastBatchSize_ != static_cast<size_t>(X.t.size(0)) ||
lastInputHeight_ != static_cast<size_t>(X.t.size(1)) ||
lastInputWidth_ != static_cast<size_t>(X.t.size(2)) ||
lastInputPointer_ != inputPtr ||
lastOutputPointer_ != Y->t.template mutable_data<uint8_t>()) {
const qnnp_status setupStatus = qnnp_setup_convolution2d_nhwc_q8(
this->qnnpackObject_,
X.t.size(0),
X.t.size(1),
X.t.size(2),
inputPtr,
X.t.size(3) /* input pixel stride */,
Y->t.template mutable_data<uint8_t>(),
Y->t.size(3) /* output pixel stride */,
nullptr /* threadpool */);
CAFFE_ENFORCE(
setupStatus == qnnp_status_success,
"failed to setup QNNPACK convolution object");
lastBatchSize_ = static_cast<size_t>(X.t.size(0));
lastInputHeight_ = static_cast<size_t>(X.t.size(1));
lastInputWidth_ = static_cast<size_t>(X.t.size(2));
lastInputPointer_ = inputPtr;
lastOutputPointer_ = Y->t.template mutable_data<uint8_t>();
}
#if defined(FBCODE_CAFFE2) || !defined(USE_INTERNAL_PTHREADPOOL_IMPL)
const qnnp_status runStatus =
qnnp_run_operator(this->qnnpackObject_, nullptr /* thread pool */);
#else
const qnnp_status runStatus =
qnnp_run_operator(this->qnnpackObject_, threadpool);
#endif
CAFFE_ENFORCE(
runStatus == qnnp_status_success,
"failed to run QNNPACK convolution");
});
return true;
}
private:
// QNNPACK convolution object
qnnp_operator_t qnnpackObject_{nullptr};
// batch size in the previous call to RunOnDeviceWithOrderNHWC
size_t lastBatchSize_{0};
// input height in the previous call to RunOnDeviceWithOrderNHWC
size_t lastInputHeight_{0};
// input width in the previous call to RunOnDeviceWithOrderNHWC
size_t lastInputWidth_{0};
// input pointer in the previous call to RunOnDeviceWithOrderNHWC
const void* lastInputPointer_{nullptr};
// output pointer in the previous call to RunOnDeviceWithOrderNHWC
void* lastOutputPointer_{nullptr};
};
} // namespace int8
} // namespace caffe2
#endif // CAFFE2_OPERATORS_INT8_CONV_OP_H_
|
