forked from pytorch/pytorch
-
Notifications
You must be signed in to change notification settings - Fork 0
/
onnxifi_op.h
455 lines (396 loc) · 15.8 KB
/
onnxifi_op.h
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
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
#pragma once
#include <unordered_map>
#include "onnx/onnx_pb.h"
#include "c10/util/SmallVector.h"
#include "caffe2/core/context.h"
#include "caffe2/core/logging.h"
#include "caffe2/core/operator.h"
#include "caffe2/onnx/onnxifi_graph_info.h"
#include "caffe2/onnx/onnxifi_init.h"
#include "caffe2/opt/shape_info.h"
#include "caffe2/utils/proto_utils.h"
#include "caffe2/utils/string_utils.h"
namespace caffe2 {
namespace details {
/// Provides slicing info for the outputs. All the vector members should be of
/// the same size as number of outpus of the Onnxifi op.
struct OutputReshapeInfo {
std::vector<Tensor> begins;
std::vector<Tensor> ends;
std::vector<bool> fast_path;
};
struct TensorInfo {
std::vector<uint64_t> dims;
uint64_t onnxifi_type;
bool quantized;
uint32_t quantizationAxis;
uint64_t quantizationParams;
std::vector<float> scales;
std::vector<int32_t> biases;
explicit TensorInfo(const TensorProto& t);
explicit TensorInfo(const QTensorProto& t);
TensorInfo(TensorInfo&&) = default;
TensorInfo& operator=(TensorInfo&&) = default;
};
} // namespace details
template <typename Context>
class OnnxifiOp final : public Operator<Context> {
public:
USE_OPERATOR_CONTEXT_FUNCTIONS;
explicit OnnxifiOp(const OperatorDef& operator_def, Workspace* ws)
: Operator<Context>(operator_def, ws),
use_onnx_(this->template GetSingleArgument<int>("use_onnx", 0)),
use_glow_aot_(this->template GetSingleArgument<int>("use_glow_aot", 0)),
max_batch_size_(
this->template GetSingleArgument<int>("max_batch_size", 0)),
max_seq_size_(this->template GetSingleArgument<int>("max_seq_size", 0)),
timeout_(this->template GetSingleArgument<int>("timeout", 0)),
nominal_batch_idx_(
this->template GetSingleArgument<int>("nominal_batch_idx", 0)),
adjust_quantized_offset_(this->template GetSingleArgument<int>(
"adjust_quantized_offset",
128)) {
lib_ = onnx::initOnnxifiLibrary();
backend_graph_map_ptr_ = onnx::getOnnxBackendGraphMap();
CAFFE_ENFORCE(lib_, "Cannot initialize ONNXIFI library");
auto onnx_model_str =
this->template GetSingleArgument<std::string>("onnx_model", "");
CAFFE_ENFORCE(!onnx_model_str.empty(), "onnx_model cannot be empty");
if (use_glow_aot_) {
auto netdef_str =
this->template GetSingleArgument<std::string>("netdef_str", "");
CAFFE_ENFORCE(ParseProtoFromLargeString(netdef_str, &netdef_));
} else if (!use_onnx_) {
CAFFE_ENFORCE(ParseProtoFromLargeString(onnx_model_str, &netdef_));
}
// Setup input/output descriptor templates
input_names_ =
this->template GetRepeatedArgument<std::string>("input_names");
output_names_ =
this->template GetRepeatedArgument<std::string>("output_names");
CAFFE_ENFORCE_EQ(input_names_.size(), operator_def.input_size());
CAFFE_ENFORCE_EQ(output_names_.size(), operator_def.output_size());
for (const auto& input : input_names_) {
input_desc_.push_back(onnxTensorDescriptorV1());
input_desc_.back().name = input.c_str();
}
all_offsets_.reserve(ws->Blobs().size());
all_scales_.reserve(ws->Blobs().size());
input_shapes_.resize(input_names_.size());
output_shapes_.resize(output_names_.size());
quantized_outputs_.resize(output_names_.size(), false);
int output_idx = 0;
ArgumentHelper helper(operator_def);
auto output_shape_info =
helper.GetRepeatedArgument<TensorProto>("output_shape_info");
auto output_qshape_info =
helper.GetRepeatedArgument<QTensorProto>("output_qshape_info");
std::unordered_map<std::string, TensorProto> output_shape_map;
for (const auto& info : output_shape_info) {
output_shape_map.emplace(info.name(), info);
}
std::unordered_map<std::string, QTensorProto> output_qshape_map;
for (const auto& info : output_qshape_info) {
output_qshape_map.emplace(info.name(), info);
}
bool has_quantized_output = false;
for (const auto& output : output_names_) {
output_desc_.push_back(onnxTensorDescriptorV1());
output_desc_.back().name = output.c_str();
// For output, we try to get its output size hint
const auto it = output_shape_map.find(output);
if (it != output_shape_map.end()) {
output_shape_hints_.emplace(
output_idx, details::TensorInfo(it->second));
} else {
const auto qit = output_qshape_map.find(output);
if (qit != output_qshape_map.end()) {
output_shape_hints_.emplace(
output_idx, details::TensorInfo(qit->second));
quantized_outputs_[output_idx] = true;
has_quantized_output = true;
}
}
++output_idx;
}
if (!has_quantized_output) {
adjust_quantized_offset_ = 0;
}
// Get output resizing hints
adjust_output_batch_ =
this->template GetSingleArgument<int>("adjust_output_batch", 0);
// Encode arguments starting with "custom_" to backend
std::vector<uint64_t> property_pointers;
std::vector<int64_t> int_args;
std::vector<float> float_args;
buildPropertyList(operator_def, &property_pointers, &int_args, &float_args);
// Initialize the backend if it has not been already created. When we
// initialized the backend, we will get the weights (initializers) from the
// workspace and offload onto the backend. This should be done only once.
// Subsequent call of this function with the same model id should find a
// cached backend and therefore there is no need to repeat the above
// process.
buildBackendAndGraph(ws, property_pointers, onnx_model_str);
}
~OnnxifiOp() {
backend_graph_shared_ptr_.reset();
backend_graph_map_ptr_->remove(op_id_string_);
#ifdef ONNXIFI_ENABLE_EXT
traces_.reset();
#endif
}
bool RunOnDevice() override;
void setEnableTracing(bool b) {
enable_tracing_ = b;
}
#ifdef ONNXIFI_ENABLE_EXT
std::shared_ptr<onnxTraceEventList> traces() const {
return traces_;
}
#endif
private:
void setOutputShapeAndType(int output_idx);
void buildPropertyList(
const OperatorDef& /* unused */,
std::vector<uint64_t>* property_list,
std::vector<int64_t>* /* unused */,
std::vector<float>* /* unused */) {
property_list->push_back(ONNXIFI_BACKEND_PROPERTY_NONE);
}
void buildBackendAndGraph(
Workspace* ws,
const std::vector<uint64_t>& property_pointers,
const std::string& onnx_model_str) {
op_id_string_ =
this->template GetSingleArgument<std::string>("model_id", "") + ":" +
this->template GetSingleArgument<std::string>("net_pos", "");
auto initializers =
this->template GetRepeatedArgument<std::string>("initializers");
// Build the Onnxifi engine
auto backend_index =
this->template GetSingleArgument<int>("backend_id", use_onnx_ ? 1 : 0);
// If using Glow AOT, override the backend_id to 1, since it uses a custom
// ONNX format, and that's the id we use for the ONNX backend.
if (use_glow_aot_) {
backend_index = 1;
}
auto creator = [this,
ws,
property_pointers,
backend_index,
&onnx_model_str,
&initializers]() {
std::vector<onnxBackendID> backend_ids;
size_t num_backends{0};
CAFFE_ENFORCE_EQ(
lib_->onnxGetBackendIDs(nullptr, &num_backends),
ONNXIFI_STATUS_FALLBACK);
CAFFE_ENFORCE_GT(
num_backends, 0, "At least 1 onnxifi backend should be available");
CAFFE_ENFORCE_LT(
backend_index,
num_backends,
"Backend idx out of bound: ",
backend_index,
", #backends: ",
num_backends);
backend_ids.resize(num_backends);
CAFFE_ENFORCE_EQ(
lib_->onnxGetBackendIDs(backend_ids.data(), &num_backends),
ONNXIFI_STATUS_SUCCESS);
onnxBackendID backend_id = backend_ids[backend_index];
onnxBackend backend{nullptr};
CAFFE_ENFORCE_EQ(
lib_->onnxInitBackend(backend_id, property_pointers.data(), &backend),
ONNXIFI_STATUS_SUCCESS);
// Release unused backend ids.
for (size_t i = 0; i < num_backends; ++i) {
if (i == backend_index) {
continue;
}
lib_->onnxReleaseBackendID(backend_ids[i]);
}
// Get weights
std::vector<std::string> weight_names;
std::vector<std::vector<uint64_t>> weight_shapes;
auto weight_descs = buildInitializationList(
ws,
initializers,
&weight_names,
&weight_shapes,
&all_scales_,
&all_offsets_);
// Extra weight shapes
std::unordered_map<std::string, ShapeInfo> weight_shape_info;
for (size_t i = 0; i < weight_names.size(); ++i) {
TensorShape shape;
const auto& shape0 = weight_shapes[i];
for (const auto d : shape0) {
shape.add_dims(d);
}
weight_shape_info[weight_names[i]] = ShapeInfo(
std::vector<TensorBoundShape::DimType>(
shape0.size(), TensorBoundShape_DimType_CONSTANT),
std::move(shape));
}
Blob* defered_blob_reader = nullptr;
if (ws->HasBlob("__DEFERRED_BLOB_READER__")) {
defered_blob_reader = ws->GetBlob("__DEFERRED_BLOB_READER__");
}
onnxGraph graph{nullptr};
static const uint64_t auxPropertiesListAOT[] = {
ONNXIFI_OPTIMIZATION_AOT, ONNXIFI_GRAPH_PROPERTY_NONE};
CAFFE_ENFORCE_EQ(
lib_->onnxInitGraph(
backend,
use_glow_aot_ ? auxPropertiesListAOT : nullptr,
onnx_model_str.size(),
(const void*)(onnx_model_str.c_str()),
weight_descs.size(),
weight_descs.data(),
&graph,
static_cast<uint32_t>(max_seq_size_),
defered_blob_reader),
ONNXIFI_STATUS_SUCCESS);
return std::make_shared<onnx::BackendGraphInfo>(
backend_id, backend, graph, lib_, std::move(weight_shape_info));
};
backend_graph_shared_ptr_ =
backend_graph_map_ptr_->insert(op_id_string_, creator);
backend_id_ = backend_graph_shared_ptr_->backend_id;
backend_ = backend_graph_shared_ptr_->backend;
graph_ = backend_graph_shared_ptr_->graph;
input_shape_info_ = backend_graph_shared_ptr_->weight_shape_info;
getExtFunctionPointers();
}
/// Set up function pointer if onnxifi_ext is enabled
void getExtFunctionPointers() {
#ifdef ONNXIFI_ENABLE_EXT
union {
onnxExtensionFunctionPointer p;
decltype(onnxSetIOAndRunGraphPointer_) set;
decltype(onnxReleaseTraceEventsPointer_) release;
decltype(onnxWaitEventForPointer_) waitfor;
} u;
if (lib_->onnxGetExtensionFunctionAddress(
backend_id_, "onnxSetIOAndRunGraphFunction", &u.p) !=
ONNXIFI_STATUS_SUCCESS) {
onnxSetIOAndRunGraphPointer_ = nullptr;
} else {
onnxSetIOAndRunGraphPointer_ = u.set;
}
if (lib_->onnxGetExtensionFunctionAddress(
backend_id_, "onnxReleaseTraceEventsFunction", &u.p) !=
ONNXIFI_STATUS_SUCCESS) {
onnxReleaseTraceEventsPointer_ = nullptr;
} else {
onnxReleaseTraceEventsPointer_ = u.release;
}
if (lib_->onnxGetExtensionFunctionAddress(
backend_id_, "onnxWaitEventForFunction", &u.p) !=
ONNXIFI_STATUS_SUCCESS) {
onnxWaitEventForPointer_ = nullptr;
} else {
onnxWaitEventForPointer_ = u.waitfor;
}
#endif
}
/// Extract output batch size. If the output batch size is going to be at
/// max_batch_size_, return true indicating that no output shape adjustment is
/// needed. Otherwise, return false.
int extractOutputBatchSizes();
/// Adjust output tensor shape based on the current input batch size.
/// If the output shape is conditioned on first dim (batch size), we have a
/// fast path to shrink the tensor shape by just manipulating the meta data.
/// Otherwise, we have to slice it in the middle of the dimension with copy
/// invoked. This is a slow path and we don't expect it to happen very often.
/// We can already omit this step by setting "adjust_output_batch_" to false
void adjustOutputBatchSizes(int current_batch_size);
std::vector<onnxTensorDescriptorV1> buildInitializationList(
Workspace* ws,
const std::vector<std::string>& initializers,
std::vector<std::string>* weight_names,
std::vector<std::vector<uint64_t>>* weight_shapes,
std::vector<std::vector<float>>* all_scales,
std::vector<std::vector<int32_t>>* all_offsets) const;
/// initialize an OutputReshapeInfo object
details::OutputReshapeInfo initOutputReshapeInfo() const;
// pointer to loaded onnxifi library
onnxifi_library* lib_{nullptr};
onnx::OnnxBackendGraphMap* backend_graph_map_ptr_;
std::string op_id_string_;
onnxBackendID backend_id_{nullptr};
onnxBackend backend_{nullptr};
onnxGraph graph_{nullptr};
onnx::SharedPtrBackendGraphInfo backend_graph_shared_ptr_;
// input/output descriptors
std::vector<onnxTensorDescriptorV1> input_desc_;
std::vector<onnxTensorDescriptorV1> output_desc_;
// Output reshape info
// It is a map keyed on batch size and the value OutputReshapeInfo for the
// batch size.
std::unordered_map<int, details::OutputReshapeInfo> output_reshape_info_;
#ifdef ONNXIFI_ENABLE_EXT
// onnxifi extension mode function pointer
onnxStatus (*onnxSetIOAndRunGraphPointer_)(
onnxGraph,
uint32_t,
const onnxTensorDescriptorV1*,
uint32_t,
const onnxTensorDescriptorV1*,
onnxMemoryFenceV1*,
onnxTraceEventList*);
onnxStatus (*onnxReleaseTraceEventsPointer_)(onnxTraceEventList*);
onnxStatus (*onnxWaitEventForPointer_)(
onnxEvent event,
uint32_t timeoutMs,
onnxEventState* eventState,
onnxStatus* eventStatus,
char* message,
size_t* messageLength);
std::shared_ptr<onnxTraceEventList> traces_{nullptr};
#endif
// ONNX model or not
bool use_onnx_{false};
// Glow AOT model or not
bool use_glow_aot_{false};
// max batch size
int max_batch_size_;
// max sequence lookup size
int max_seq_size_;
// Inference timeout limits. Default 0 means no timeout.
int timeout_;
// index of the input whose first dimension represents the batch size
int nominal_batch_idx_{0};
// We bind the op input/output by position while ONNXIFI binds input/output by
// names. In addition, op input/output names can be writtten by, for example,
// memonger. We cache the original input/output name of ONNX object here and
// bind them by position.
std::vector<std::string> input_names_;
std::vector<std::string> output_names_;
// NetDef of the onnxifi subgraph for shape inference
NetDef netdef_;
std::vector<c10::SmallVector<uint64_t, 4>> input_shapes_;
std::vector<c10::SmallVector<uint64_t, 4>> output_shapes_;
// Indicate if i-th output is a quantized tensor
std::vector<bool> quantized_outputs_;
// A cache vector to avoid repeated reallocation. The existence of this is not
// ideal, which is purely due to the factor that we use int64_t for c2::tensor
// dim but uint64_t for onnxDesciptor dim. Maybe we should just use int64_t
c10::SmallVector<int64_t, 4> tensor_dims_int64_;
// This is for multi group quantization info
std::vector<std::vector<float>> all_scales_;
std::vector<std::vector<int32_t>> all_offsets_;
// output shape hints
std::unordered_map<int, details::TensorInfo> output_shape_hints_;
// input shape info. Used by shape inference when inputs are not at
// max_batch_size
std::unordered_map<std::string, ShapeInfo> input_shape_info_;
// Whether we need to resize outputs or not
bool adjust_output_batch_{false};
// Whether we enable tracing in one run of inference
bool enable_tracing_{false};
// Adjust the quantized offset to compensate mismatch of certain backend
uint8_t adjust_quantized_offset_{0};
};
} // namespace caffe2