/
onnxruntime_lite_custom_op.h
742 lines (679 loc) · 43.9 KB
/
onnxruntime_lite_custom_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
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
// Summary
// The header has APIs to save custom op authors the trouble of defining schemas,
// which will be inferred by functions' signature, as long as their argument list has types supported here.
// Input could be:
// 1. Tensor of onnx data types.
// 2. Span of onnx data types.
// 3. Scalar of onnx data types.
// A input could be optional if indicated as std::optional<...>.
// For an output, it must be a tensor of onnx data types.
// Further, the header also has utility for a simple custom struct, where resources could be kept, to be registered as a custom op.
// For concrete examples, please search keyword "LiteCustomOpTest" under "<cloned_src_dir>/onnxruntime/test/".
// Note - all APIs in this header are ABI.
#pragma once
#include "onnxruntime_cxx_api.h"
#include <optional>
#include <numeric>
#include <unordered_set>
namespace Ort {
namespace Custom {
class TensorBase {
public:
TensorBase(OrtKernelContext* ctx) : ctx_(ctx) {}
virtual ~TensorBase() {}
operator bool() const {
return shape_.has_value();
}
protected:
struct KernelContext ctx_;
std::optional<std::vector<int64_t>> shape_;
};
template <typename T>
struct Span {
const T* data_ = {};
size_t size_ = {};
void Assign(const T* data, size_t size) {
data_ = data;
size_ = size;
}
size_t size() const { return size_; }
T operator[](size_t indice) const {
return data_[indice];
}
};
template <typename T>
class Tensor : public TensorBase {
public:
using TT = typename std::remove_reference<T>::type;
Tensor(OrtKernelContext* ctx, size_t indice, bool is_input) : TensorBase(ctx), indice_(indice), is_input_(is_input) {
if (is_input_) {
if (indice >= ctx_.GetInputCount()) {
ORT_CXX_API_THROW("invalid indice for Ort::Custom::Tensor", OrtErrorCode::ORT_INVALID_ARGUMENT);
}
const_value_ = ctx_.GetInput(indice);
auto type_shape_info = const_value_.GetTensorTypeAndShapeInfo();
shape_ = type_shape_info.GetShape();
}
}
const std::vector<int64_t>& Shape() const {
if (!shape_.has_value()) {
ORT_CXX_API_THROW("tensor shape is not yet initialized", OrtErrorCode::ORT_RUNTIME_EXCEPTION);
}
return shape_.value();
}
int64_t NumberOfElement() const {
if (shape_.has_value()) {
return std::accumulate(shape_->begin(), shape_->end(), 1LL, std::multiplies<int64_t>());
} else {
return 0;
}
}
const TT* Data() const {
return reinterpret_cast<const TT*>(const_value_.GetTensorRawData());
}
TT* Allocate(const std::vector<int64_t>& shape) {
shape_ = shape;
if (!data_) {
shape_ = shape;
data_ = ctx_.GetOutput(indice_, shape).template GetTensorMutableData<TT>();
}
return data_;
}
static TT GetT() { return (TT)0; }
const Span<T>& AsSpan() {
if (!shape_.has_value() || shape_->size() != 1) {
ORT_CXX_API_THROW("invalid shape while trying to get a span out of Ort::Custom::Tensor",
OrtErrorCode::ORT_RUNTIME_EXCEPTION);
}
span_.Assign(Data(), static_cast<size_t>((*shape_)[0]));
return span_;
}
const T& AsScalar() {
if (!shape_.has_value() || shape_->size() != 1 || (*shape_)[0] != 1) {
ORT_CXX_API_THROW("invalid shape while trying to get a scalar from Ort::Custom::Tensor",
OrtErrorCode::ORT_RUNTIME_EXCEPTION);
}
return *Data();
}
private:
size_t indice_;
bool is_input_;
ConstValue const_value_; // for input
TT* data_{}; // for output
Span<T> span_;
};
template <>
class Tensor<std::string> : public TensorBase {
public:
using strings = std::vector<std::string>;
Tensor(OrtKernelContext* ctx, size_t indice, bool is_input) : TensorBase(ctx), indice_(indice), is_input_(is_input) {
if (is_input_) {
if (indice >= ctx_.GetInputCount()) {
ORT_CXX_API_THROW("invalid indice for Ort::Custom::Tensor", OrtErrorCode::ORT_INVALID_ARGUMENT);
}
auto const_value = ctx_.GetInput(indice);
auto type_shape_info = const_value.GetTensorTypeAndShapeInfo();
shape_ = type_shape_info.GetShape();
auto num_chars = const_value.GetStringTensorDataLength();
// note - there will be copy ...
auto num_strings = static_cast<size_t>(NumberOfElement());
if (num_strings) {
std::vector<char> chars(num_chars + 1, '\0');
std::vector<size_t> offsets(num_strings);
const_value.GetStringTensorContent(static_cast<void*>(chars.data()), num_chars, offsets.data(), offsets.size());
auto upper_bound = num_strings - 1;
input_strings_.resize(num_strings);
for (size_t i = upper_bound;; --i) {
if (i < upper_bound) {
chars[offsets[i + 1]] = '\0';
}
input_strings_[i] = chars.data() + offsets[i];
if (0 == i) {
break;
}
}
}
}
}
int64_t NumberOfElement() const {
if (shape_.has_value()) {
return std::accumulate(shape_->begin(), shape_->end(), 1ULL, std::multiplies<int64_t>());
} else {
return 0;
}
}
const strings& Data() const {
return input_strings_;
}
void SetStringOutput(const strings& ss, const std::vector<int64_t>& dims) {
shape_ = dims;
std::vector<const char*> raw;
for (const auto& s : ss) {
raw.push_back(s.data());
}
auto output = ctx_.GetOutput(indice_, dims.data(), dims.size());
// note - there will be copy ...
output.FillStringTensor(raw.data(), raw.size());
}
const Span<std::string>& AsSpan() {
ORT_CXX_API_THROW("span for TensorT of string not implemented", OrtErrorCode::ORT_RUNTIME_EXCEPTION);
}
const std::string& AsScalar() {
if (input_strings_.size() != 1) {
ORT_CXX_API_THROW("invalid shape while trying to get a scalar string from Ort::Custom::Tensor",
OrtErrorCode::ORT_RUNTIME_EXCEPTION);
}
return input_strings_[0];
}
private:
size_t indice_;
bool is_input_;
std::vector<std::string> input_strings_; // for input
};
template <>
class Tensor<std::string_view> : public TensorBase {
public:
using strings = std::vector<std::string>;
using string_views = std::vector<std::string_view>;
Tensor(OrtKernelContext* ctx, size_t indice, bool is_input) : TensorBase(ctx), indice_(indice), is_input_(is_input) {
if (is_input_) {
if (indice >= ctx_.GetInputCount()) {
ORT_CXX_API_THROW("invalid indice for Ort::Custom::Tensor", OrtErrorCode::ORT_INVALID_ARGUMENT);
}
auto const_value = ctx_.GetInput(indice);
auto type_shape_info = const_value.GetTensorTypeAndShapeInfo();
shape_ = type_shape_info.GetShape();
auto num_chars = const_value.GetStringTensorDataLength();
chars_.resize(num_chars + 1, '\0');
auto num_strings = static_cast<size_t>(NumberOfElement());
if (num_strings) {
std::vector<size_t> offsets(num_strings);
const_value.GetStringTensorContent(static_cast<void*>(chars_.data()), num_chars, offsets.data(), offsets.size());
offsets.push_back(num_chars);
for (size_t i = 0; i < num_strings; ++i) {
input_string_views_.emplace_back(chars_.data() + offsets[i], offsets[i + 1] - offsets[i]);
}
}
}
}
int64_t NumberOfElement() const {
if (shape_.has_value()) {
return std::accumulate(shape_->begin(), shape_->end(), 1ULL, std::multiplies<int64_t>());
} else {
return 0;
}
}
const string_views& Data() const {
return input_string_views_;
}
void SetStringOutput(const strings& ss, const std::vector<int64_t>& dims) {
shape_ = dims;
std::vector<const char*> raw;
for (const auto& s : ss) {
raw.push_back(s.data());
}
auto output = ctx_.GetOutput(indice_, dims.data(), dims.size());
// note - there will be copy ...
output.FillStringTensor(raw.data(), raw.size());
}
const Span<std::string_view>& AsSpan() {
ORT_CXX_API_THROW("span for TensorT of string view not implemented", OrtErrorCode::ORT_RUNTIME_EXCEPTION);
}
std::string_view AsScalar() {
if (input_string_views_.size() != 1) {
ORT_CXX_API_THROW("invalid shape while trying to get a scalar string view from Ort::Custom::Tensor",
OrtErrorCode::ORT_RUNTIME_EXCEPTION);
}
return input_string_views_[0];
}
private:
size_t indice_;
bool is_input_;
std::vector<char> chars_; // for input
std::vector<std::string_view> input_string_views_; // for input
};
using TensorPtr = std::unique_ptr<Custom::TensorBase>;
//////////////////////////// OrtLiteCustomOp ////////////////////////////////
struct OrtLiteCustomOp : public OrtCustomOp {
using ConstOptionalFloatTensor = std::optional<const Custom::Tensor<float>&>;
using OptionalFloatTensor = std::optional<Custom::Tensor<float>>;
// CreateTuple
template <size_t ith_input, size_t ith_output, typename... Ts>
static typename std::enable_if<sizeof...(Ts) == 0, std::tuple<>>::type
CreateTuple(OrtKernelContext*, std::vector<TensorPtr>&, size_t, size_t, const std::string&) {
return std::make_tuple();
}
template <size_t ith_input, size_t ith_output, typename T, typename... Ts>
static typename std::enable_if<std::is_same<T, OrtKernelContext*>::value, std::tuple<T, Ts...>>::type
CreateTuple(OrtKernelContext* context, std::vector<TensorPtr>& tensors, size_t num_input, size_t num_output, const std::string& ep) {
std::tuple<T> current = std::tuple<OrtKernelContext*>{context};
auto next = CreateTuple<ith_input, ith_output, Ts...>(context, tensors, num_input, num_output, ep);
return std::tuple_cat(current, next);
}
template <size_t ith_input, size_t ith_output, typename T, typename... Ts>
static typename std::enable_if<std::is_same<T, OrtKernelContext&>::value, std::tuple<T, Ts...>>::type
CreateTuple(OrtKernelContext* context, std::vector<TensorPtr>& tensors, size_t num_input, size_t num_output, const std::string& ep) {
std::tuple<T> current = std::tuple<OrtKernelContext&>{*context};
auto next = CreateTuple<ith_input, ith_output, Ts...>(context, tensors, num_input, num_output, ep);
return std::tuple_cat(current, next);
}
#ifdef ORT_CUDA_CTX
template <size_t ith_input, size_t ith_output, typename T, typename... Ts>
static typename std::enable_if<std::is_same<T, const CudaContext&>::value, std::tuple<T, Ts...>>::type
CreateTuple(OrtKernelContext* context, std::vector<TensorPtr>& tensors, size_t num_input, size_t num_output, const std::string& ep) {
thread_local CudaContext cuda_context;
cuda_context.Init(*context);
std::tuple<T> current = std::tuple<const CudaContext&>{cuda_context};
auto next = CreateTuple<ith_input, ith_output, Ts...>(context, tensors, num_input, num_output, ep);
return std::tuple_cat(current, next);
}
#endif
#ifdef ORT_ROCM_CTX
template <size_t ith_input, size_t ith_output, typename T, typename... Ts>
static typename std::enable_if<std::is_same<T, const RocmContext&>::value, std::tuple<T, Ts...>>::type
CreateTuple(OrtKernelContext* context, std::vector<TensorPtr>& tensors, size_t num_input, size_t num_output, const std::string& ep) {
thread_local RocmContext rocm_context;
rocm_context.Init(*context);
std::tuple<T> current = std::tuple<const RocmContext&>{rocm_context};
auto next = CreateTuple<ith_input, ith_output, Ts...>(context, tensors, num_input, num_output, ep);
return std::tuple_cat(current, next);
}
#endif
#define CREATE_TUPLE_INPUT(data_type) \
template <size_t ith_input, size_t ith_output, typename T, typename... Ts> \
static typename std::enable_if<std::is_same<T, const Custom::Tensor<data_type>*>::value, std::tuple<T, Ts...>>::type \
CreateTuple(OrtKernelContext* context, std::vector<TensorPtr>& tensors, size_t num_input, size_t num_output, const std::string& ep) { \
tensors.push_back(std::make_unique<Custom::Tensor<data_type>>(context, ith_input, true)); \
std::tuple<T> current = std::tuple<T>{reinterpret_cast<T>(tensors.back().get())}; \
auto next = CreateTuple<ith_input + 1, ith_output, Ts...>(context, tensors, num_input, num_output, ep); \
return std::tuple_cat(current, next); \
} \
template <size_t ith_input, size_t ith_output, typename T, typename... Ts> \
static typename std::enable_if<std::is_same<T, const Custom::Tensor<data_type>&>::value, std::tuple<T, Ts...>>::type \
CreateTuple(OrtKernelContext* context, std::vector<TensorPtr>& tensors, size_t num_input, size_t num_output, const std::string& ep) { \
tensors.push_back(std::make_unique<Custom::Tensor<data_type>>(context, ith_input, true)); \
std::tuple<T> current = std::tuple<T>{reinterpret_cast<T>(*tensors.back().get())}; \
auto next = CreateTuple<ith_input + 1, ith_output, Ts...>(context, tensors, num_input, num_output, ep); \
return std::tuple_cat(current, next); \
} \
template <size_t ith_input, size_t ith_output, typename T, typename... Ts> \
static typename std::enable_if<std::is_same<T, std::optional<const Custom::Tensor<data_type>*>>::value, std::tuple<T, Ts...>>::type \
CreateTuple(OrtKernelContext* context, std::vector<TensorPtr>& tensors, size_t num_input, size_t num_output, const std::string& ep) { \
if (ith_input < num_input) { \
tensors.push_back(std::make_unique<Custom::Tensor<data_type>>(context, ith_input, true)); \
std::tuple<T> current = std::tuple<T>{reinterpret_cast<Custom::Tensor<data_type>*>(tensors.back().get())}; \
auto next = CreateTuple<ith_input + 1, ith_output, Ts...>(context, tensors, num_input, num_output, ep); \
return std::tuple_cat(current, next); \
} else { \
std::tuple<T> current = std::tuple<T>{}; \
auto next = CreateTuple<ith_input + 1, ith_output, Ts...>(context, tensors, num_input, num_output, ep); \
return std::tuple_cat(current, next); \
} \
} \
template <size_t ith_input, size_t ith_output, typename T, typename... Ts> \
static typename std::enable_if<std::is_same<T, const Custom::Span<data_type>*>::value, std::tuple<T, Ts...>>::type \
CreateTuple(OrtKernelContext* context, std::vector<TensorPtr>& tensors, size_t num_input, size_t num_output, const std::string& ep) { \
if ("CPUExecutionProvider" != ep) { \
ORT_CXX_API_THROW("span input could only be applied to CPU EP", OrtErrorCode::ORT_RUNTIME_EXCEPTION); \
} \
tensors.push_back(std::make_unique<Custom::Tensor<data_type>>(context, ith_input, true)); \
std::tuple<T> current = std::tuple<T>{&reinterpret_cast<Custom::Tensor<data_type>*>(tensors.back().get())->AsSpan()}; \
auto next = CreateTuple<ith_input + 1, ith_output, Ts...>(context, tensors, num_input, num_output, ep); \
return std::tuple_cat(current, next); \
} \
template <size_t ith_input, size_t ith_output, typename T, typename... Ts> \
static typename std::enable_if<std::is_same<T, const Custom::Span<data_type>&>::value, std::tuple<T, Ts...>>::type \
CreateTuple(OrtKernelContext* context, std::vector<TensorPtr>& tensors, size_t num_input, size_t num_output, const std::string& ep) { \
if ("CPUExecutionProvider" != ep) { \
ORT_CXX_API_THROW("span input could only be applied to CPU EP", OrtErrorCode::ORT_RUNTIME_EXCEPTION); \
} \
tensors.push_back(std::make_unique<Custom::Tensor<data_type>>(context, ith_input, true)); \
std::tuple<T> current = std::tuple<T>{reinterpret_cast<Custom::Tensor<data_type>*>(tensors.back().get())->AsSpan()}; \
auto next = CreateTuple<ith_input + 1, ith_output, Ts...>(context, tensors, num_input, num_output, ep); \
return std::tuple_cat(current, next); \
} \
template <size_t ith_input, size_t ith_output, typename T, typename... Ts> \
static typename std::enable_if<std::is_same<T, std::optional<const Custom::Span<data_type>*>>::value, std::tuple<T, Ts...>>::type \
CreateTuple(OrtKernelContext* context, std::vector<TensorPtr>& tensors, size_t num_input, size_t num_output, const std::string& ep) { \
if (ith_input < num_input) { \
if ("CPUExecutionProvider" != ep) { \
ORT_CXX_API_THROW("span input could only be applied to CPU EP", OrtErrorCode::ORT_RUNTIME_EXCEPTION); \
} \
tensors.push_back(std::make_unique<Custom::Tensor<data_type>>(context, ith_input, true)); \
std::tuple<T> current = std::tuple<T>{&reinterpret_cast<Custom::Tensor<data_type>*>(tensors.back().get())->AsSpan()}; \
auto next = CreateTuple<ith_input + 1, ith_output, Ts...>(context, tensors, num_input, num_output, ep); \
return std::tuple_cat(current, next); \
} else { \
std::tuple<T> current = std::tuple<T>{}; \
auto next = CreateTuple<ith_input + 1, ith_output, Ts...>(context, tensors, num_input, num_output, ep); \
return std::tuple_cat(current, next); \
} \
} \
template <size_t ith_input, size_t ith_output, typename T, typename... Ts> \
static typename std::enable_if<std::is_same<T, data_type>::value, std::tuple<T, Ts...>>::type \
CreateTuple(OrtKernelContext* context, std::vector<TensorPtr>& tensors, size_t num_input, size_t num_output, const std::string& ep) { \
if ("CPUExecutionProvider" != ep) { \
ORT_CXX_API_THROW("scalar input could only be applied to CPU EP", OrtErrorCode::ORT_RUNTIME_EXCEPTION); \
} \
tensors.push_back(std::make_unique<Custom::Tensor<data_type>>(context, ith_input, true)); \
std::tuple<T> current = std::tuple<T>{reinterpret_cast<Custom::Tensor<data_type>*>(tensors.back().get())->AsScalar()}; \
auto next = CreateTuple<ith_input + 1, ith_output, Ts...>(context, tensors, num_input, num_output, ep); \
return std::tuple_cat(current, next); \
} \
template <size_t ith_input, size_t ith_output, typename T, typename... Ts> \
static typename std::enable_if<std::is_same<T, std::optional<data_type>>::value, std::tuple<T, Ts...>>::type \
CreateTuple(OrtKernelContext* context, std::vector<TensorPtr>& tensors, size_t num_input, size_t num_output, const std::string& ep) { \
if (ith_input < num_input) { \
if ("CPUExecutionProvider" != ep) { \
ORT_CXX_API_THROW("scalar input could only be applied to CPU EP", OrtErrorCode::ORT_RUNTIME_EXCEPTION); \
} \
tensors.push_back(std::make_unique<Custom::Tensor<data_type>>(context, ith_input, true)); \
std::tuple<T> current = std::tuple<T>{reinterpret_cast<Custom::Tensor<data_type>*>(tensors.back().get())->AsScalar()}; \
auto next = CreateTuple<ith_input + 1, ith_output, Ts...>(context, tensors, num_input, num_output, ep); \
return std::tuple_cat(current, next); \
} else { \
std::tuple<T> current = std::tuple<T>{}; \
auto next = CreateTuple<ith_input + 1, ith_output, Ts...>(context, tensors, num_input, num_output, ep); \
return std::tuple_cat(current, next); \
} \
}
#define CREATE_TUPLE_OUTPUT(data_type) \
template <size_t ith_input, size_t ith_output, typename T, typename... Ts> \
static typename std::enable_if<std::is_same<T, Custom::Tensor<data_type>*>::value, std::tuple<T, Ts...>>::type \
CreateTuple(OrtKernelContext* context, std::vector<TensorPtr>& tensors, size_t num_input, size_t num_output, const std::string& ep) { \
tensors.push_back(std::make_unique<Custom::Tensor<data_type>>(context, ith_output, false)); \
std::tuple<T> current = std::tuple<T>{reinterpret_cast<T>(tensors.back().get())}; \
auto next = CreateTuple<ith_input, ith_output + 1, Ts...>(context, tensors, num_input, num_output, ep); \
return std::tuple_cat(current, next); \
} \
template <size_t ith_input, size_t ith_output, typename T, typename... Ts> \
static typename std::enable_if<std::is_same<T, Custom::Tensor<data_type>&>::value, std::tuple<T, Ts...>>::type \
CreateTuple(OrtKernelContext* context, std::vector<TensorPtr>& tensors, size_t num_input, size_t num_output, const std::string& ep) { \
tensors.push_back(std::make_unique<Custom::Tensor<data_type>>(context, ith_output, false)); \
std::tuple<T> current = std::tuple<T>{reinterpret_cast<T>(*tensors.back().get())}; \
auto next = CreateTuple<ith_input, ith_output + 1, Ts...>(context, tensors, num_input, num_output, ep); \
return std::tuple_cat(current, next); \
} \
template <size_t ith_input, size_t ith_output, typename T, typename... Ts> \
static typename std::enable_if<std::is_same<T, std::optional<Custom::Tensor<data_type>*>>::value, std::tuple<T, Ts...>>::type \
CreateTuple(OrtKernelContext* context, std::vector<TensorPtr>& tensors, size_t num_input, size_t num_output, const std::string& ep) { \
if (ith_output < num_output) { \
tensors.push_back(std::make_unique<Custom::Tensor<data_type>>(context, ith_output, false)); \
std::tuple<T> current = std::tuple<T>{reinterpret_cast<Custom::Tensor<data_type>*>(tensors.back().get())}; \
auto next = CreateTuple<ith_input, ith_output + 1, Ts...>(context, tensors, num_input, num_output, ep); \
return std::tuple_cat(current, next); \
} else { \
std::tuple<T> current = std::tuple<T>{}; \
auto next = CreateTuple<ith_input, ith_output + 1, Ts...>(context, tensors, num_input, num_output, ep); \
return std::tuple_cat(current, next); \
} \
}
#define CREATE_TUPLE(data_type) \
CREATE_TUPLE_INPUT(data_type) \
CREATE_TUPLE_OUTPUT(data_type)
CREATE_TUPLE(bool)
CREATE_TUPLE(float)
CREATE_TUPLE(Ort::Float16_t)
CREATE_TUPLE(Ort::BFloat16_t)
CREATE_TUPLE(double)
CREATE_TUPLE(int8_t)
CREATE_TUPLE(int16_t)
CREATE_TUPLE(int32_t)
CREATE_TUPLE(int64_t)
CREATE_TUPLE(uint8_t)
CREATE_TUPLE(uint16_t)
CREATE_TUPLE(uint32_t)
CREATE_TUPLE(uint64_t)
CREATE_TUPLE(std::string)
CREATE_TUPLE_INPUT(std::string_view)
CREATE_TUPLE(Ort::Float8E4M3FN_t)
CREATE_TUPLE(Ort::Float8E4M3FNUZ_t)
CREATE_TUPLE(Ort::Float8E5M2_t)
CREATE_TUPLE(Ort::Float8E5M2FNUZ_t)
// ParseArgs ...
template <typename... Ts>
static typename std::enable_if<0 == sizeof...(Ts)>::type
ParseArgs(std::vector<ONNXTensorElementDataType>&, std::vector<ONNXTensorElementDataType>&) {
}
template <typename T, typename... Ts>
static typename std::enable_if<0 <= sizeof...(Ts) && std::is_same<T, OrtKernelContext*>::value>::type
ParseArgs(std::vector<ONNXTensorElementDataType>& input_types, std::vector<ONNXTensorElementDataType>& output_types) {
ParseArgs<Ts...>(input_types, output_types);
}
template <typename T, typename... Ts>
static typename std::enable_if<0 <= sizeof...(Ts) && std::is_same<T, OrtKernelContext&>::value>::type
ParseArgs(std::vector<ONNXTensorElementDataType>& input_types, std::vector<ONNXTensorElementDataType>& output_types) {
ParseArgs<Ts...>(input_types, output_types);
}
#ifdef ORT_CUDA_CTX
template <typename T, typename... Ts>
static typename std::enable_if<0 <= sizeof...(Ts) && std::is_same<T, const CudaContext&>::value>::type
ParseArgs(std::vector<ONNXTensorElementDataType>& input_types, std::vector<ONNXTensorElementDataType>& output_types) {
ParseArgs<Ts...>(input_types, output_types);
}
#endif
#ifdef ORT_ROCM_CTX
template <typename T, typename... Ts>
static typename std::enable_if<0 <= sizeof...(Ts) && std::is_same<T, const RocmContext&>::value>::type
ParseArgs(std::vector<ONNXTensorElementDataType>& input_types, std::vector<ONNXTensorElementDataType>& output_types) {
ParseArgs<Ts...>(input_types, output_types);
}
#endif
#define PARSE_INPUT_BASE(pack_type, onnx_type) \
template <typename T, typename... Ts> \
static typename std::enable_if<0 <= sizeof...(Ts) && std::is_same<T, pack_type>::value>::type \
ParseArgs(std::vector<ONNXTensorElementDataType>& input_types, std::vector<ONNXTensorElementDataType>& output_types) { \
input_types.push_back(onnx_type); \
ParseArgs<Ts...>(input_types, output_types); \
} \
template <typename T, typename... Ts> \
static typename std::enable_if<0 <= sizeof...(Ts) && std::is_same<T, std::optional<pack_type>>::value>::type \
ParseArgs(std::vector<ONNXTensorElementDataType>& input_types, std::vector<ONNXTensorElementDataType>& output_types) { \
input_types.push_back(onnx_type); \
ParseArgs<Ts...>(input_types, output_types); \
}
#define PARSE_INPUT(data_type, onnx_type) \
PARSE_INPUT_BASE(const Custom::Tensor<data_type>*, onnx_type) \
PARSE_INPUT_BASE(const Custom::Tensor<data_type>&, onnx_type) \
PARSE_INPUT_BASE(const Custom::Span<data_type>*, onnx_type) \
PARSE_INPUT_BASE(const Custom::Span<data_type>&, onnx_type) \
PARSE_INPUT_BASE(data_type, onnx_type)
#define PARSE_OUTPUT(data_type, onnx_type) \
template <typename T, typename... Ts> \
static typename std::enable_if<0 <= sizeof...(Ts) && std::is_same<T, Custom::Tensor<data_type>*>::value>::type \
ParseArgs(std::vector<ONNXTensorElementDataType>& input_types, std::vector<ONNXTensorElementDataType>& output_types) { \
output_types.push_back(onnx_type); \
ParseArgs<Ts...>(input_types, output_types); \
} \
template <typename T, typename... Ts> \
static typename std::enable_if<0 <= sizeof...(Ts) && std::is_same<T, Custom::Tensor<data_type>&>::value>::type \
ParseArgs(std::vector<ONNXTensorElementDataType>& input_types, std::vector<ONNXTensorElementDataType>& output_types) { \
output_types.push_back(onnx_type); \
ParseArgs<Ts...>(input_types, output_types); \
} \
template <typename T, typename... Ts> \
static typename std::enable_if<0 <= sizeof...(Ts) && std::is_same<T, std::optional<Custom::Tensor<data_type>*>>::value>::type \
ParseArgs(std::vector<ONNXTensorElementDataType>& input_types, std::vector<ONNXTensorElementDataType>& output_types) { \
output_types.push_back(onnx_type); \
ParseArgs<Ts...>(input_types, output_types); \
}
#define PARSE_ARGS(data_type, onnx_type) \
PARSE_INPUT(data_type, onnx_type) \
PARSE_OUTPUT(data_type, onnx_type)
PARSE_ARGS(bool, ONNX_TENSOR_ELEMENT_DATA_TYPE_BOOL)
PARSE_ARGS(float, ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT)
PARSE_ARGS(Ort::Float16_t, ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT16)
PARSE_ARGS(Ort::BFloat16_t, ONNX_TENSOR_ELEMENT_DATA_TYPE_BFLOAT16)
PARSE_ARGS(double, ONNX_TENSOR_ELEMENT_DATA_TYPE_DOUBLE)
PARSE_ARGS(int8_t, ONNX_TENSOR_ELEMENT_DATA_TYPE_INT8)
PARSE_ARGS(int16_t, ONNX_TENSOR_ELEMENT_DATA_TYPE_INT16)
PARSE_ARGS(int32_t, ONNX_TENSOR_ELEMENT_DATA_TYPE_INT32)
PARSE_ARGS(int64_t, ONNX_TENSOR_ELEMENT_DATA_TYPE_INT64)
PARSE_ARGS(uint8_t, ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT8)
PARSE_ARGS(uint16_t, ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT16)
PARSE_ARGS(uint32_t, ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT32)
PARSE_ARGS(uint64_t, ONNX_TENSOR_ELEMENT_DATA_TYPE_UINT64)
PARSE_ARGS(std::string, ONNX_TENSOR_ELEMENT_DATA_TYPE_STRING)
PARSE_ARGS(std::string_view, ONNX_TENSOR_ELEMENT_DATA_TYPE_STRING) // todo - remove string_view output
PARSE_ARGS(Ort::Float8E4M3FN_t, ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT8E4M3FN)
PARSE_ARGS(Ort::Float8E4M3FNUZ_t, ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT8E4M3FNUZ)
PARSE_ARGS(Ort::Float8E5M2_t, ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT8E5M2)
PARSE_ARGS(Ort::Float8E5M2FNUZ_t, ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT8E5M2FNUZ)
OrtLiteCustomOp(const char* op_name,
const char* execution_provider) : op_name_(op_name),
execution_provider_(execution_provider) {
OrtCustomOp::version = ORT_API_VERSION;
OrtCustomOp::GetName = [](const OrtCustomOp* op) { return static_cast<const OrtLiteCustomOp*>(op)->op_name_.c_str(); };
OrtCustomOp::GetExecutionProviderType = [](const OrtCustomOp* op) { return ((OrtLiteCustomOp*)op)->execution_provider_.c_str(); };
OrtCustomOp::GetInputMemoryType = [](const OrtCustomOp*, size_t) { return OrtMemTypeDefault; };
OrtCustomOp::GetInputTypeCount = [](const OrtCustomOp* op) {
auto self = reinterpret_cast<const OrtLiteCustomOp*>(op);
return self->input_types_.size();
};
OrtCustomOp::GetInputType = [](const OrtCustomOp* op, size_t indice) {
auto self = reinterpret_cast<const OrtLiteCustomOp*>(op);
return self->input_types_[indice];
};
OrtCustomOp::GetOutputTypeCount = [](const OrtCustomOp* op) {
auto self = reinterpret_cast<const OrtLiteCustomOp*>(op);
return self->output_types_.size();
};
OrtCustomOp::GetOutputType = [](const OrtCustomOp* op, size_t indice) {
auto self = reinterpret_cast<const OrtLiteCustomOp*>(op);
return self->output_types_[indice];
};
OrtCustomOp::GetInputCharacteristic = [](const OrtCustomOp*, size_t) {
return INPUT_OUTPUT_OPTIONAL;
};
OrtCustomOp::GetOutputCharacteristic = [](const OrtCustomOp*, size_t) {
return INPUT_OUTPUT_OPTIONAL;
};
OrtCustomOp::GetVariadicInputMinArity = [](const OrtCustomOp*) { return 0; };
OrtCustomOp::GetVariadicInputHomogeneity = [](const OrtCustomOp*) { return 0; };
OrtCustomOp::GetVariadicOutputMinArity = [](const OrtCustomOp*) { return 0; };
OrtCustomOp::GetVariadicOutputHomogeneity = [](const OrtCustomOp*) { return 0; };
}
const std::string op_name_;
const std::string execution_provider_;
std::vector<ONNXTensorElementDataType> input_types_;
std::vector<ONNXTensorElementDataType> output_types_;
};
//////////////////////////// OrtLiteCustomFunc ////////////////////////////////
// The struct is to implement function-as-op.
// E.g. a function might be defined as:
// void Filter(const Ort::Custom::Tensor<float>& floats_in, Ort::Custom::Tensor<float>& floats_out) { ... }
// It could be registered this way:
// Ort::CustomOpDomain v2_domain{"v2"};
// std::unique_ptr<OrtLiteCustomOp> fil_op_ptr{Ort::Custom::CreateLiteCustomOp("Filter", "CPUExecutionProvider", Filter)};
// v2_domain.Add(fil_op_ptr.get());
// session_options.Add(v2_domain);
// For the complete example, please search keyword "LiteCustomOpTest" under "<cloned_src_dir>/onnxruntime/test/".
template <typename... Args>
struct OrtLiteCustomFunc : public OrtLiteCustomOp {
using ComputeFn = void (*)(Args...);
using MyType = OrtLiteCustomFunc<Args...>;
struct Kernel {
size_t num_input_{};
size_t num_output_{};
ComputeFn compute_fn_{};
std::string ep_{};
};
OrtLiteCustomFunc(const char* op_name,
const char* execution_provider,
ComputeFn compute_fn) : OrtLiteCustomOp(op_name, execution_provider),
compute_fn_(compute_fn) {
ParseArgs<Args...>(input_types_, output_types_);
OrtCustomOp::KernelCompute = [](void* op_kernel, OrtKernelContext* context) {
auto kernel = reinterpret_cast<Kernel*>(op_kernel);
std::vector<TensorPtr> tensors;
auto t = CreateTuple<0, 0, Args...>(context, tensors, kernel->num_input_, kernel->num_output_, kernel->ep_);
std::apply([kernel](Args const&... t_args) { kernel->compute_fn_(t_args...); }, t);
};
OrtCustomOp::CreateKernel = [](const OrtCustomOp* this_, const OrtApi* ort_api, const OrtKernelInfo* info) {
auto kernel = std::make_unique<Kernel>();
kernel->compute_fn_ = static_cast<const MyType*>(this_)->compute_fn_;
Ort::ThrowOnError(ort_api->KernelInfo_GetInputCount(info, &kernel->num_input_));
Ort::ThrowOnError(ort_api->KernelInfo_GetOutputCount(info, &kernel->num_output_));
auto self = static_cast<const OrtLiteCustomFunc*>(this_);
kernel->ep_ = self->execution_provider_;
return reinterpret_cast<void*>(kernel.release());
};
OrtCustomOp::KernelDestroy = [](void* op_kernel) {
delete reinterpret_cast<Kernel*>(op_kernel);
};
}
ComputeFn compute_fn_;
}; // struct OrtLiteCustomFunc
/////////////////////////// OrtLiteCustomStruct ///////////////////////////
// The struct is to implement struct-as-op.
// E.g. a struct might be defined as:
// struct Merge {
// Merge(const OrtApi* ort_api, const OrtKernelInfo* info) {...}
// void Compute(const Ort::Custom::Tensor<std::string_view>& strings_in,
// std::string_view string_in,
// Ort::Custom::Tensor<std::string>* strings_out) {...}
// bool reverse_ = false;
// };
// It could be registered this way:
// Ort::CustomOpDomain v2_domain{"v2"};
// std::unique_ptr<OrtLiteCustomOp> mrg_op_ptr{Ort::Custom::CreateLiteCustomOp<Merge>("Merge", "CPUExecutionProvider")};
// v2_domain.Add(mrg_op_ptr.get());
// session_options.Add(v2_domain);
// For the complete example, please search keyword "LiteCustomOpTest" under "<cloned_src_dir>/onnxruntime/test/".
template <typename CustomOp>
struct OrtLiteCustomStruct : public OrtLiteCustomOp {
template <typename... Args>
using CustomComputeFn = void (CustomOp::*)(Args...);
using MyType = OrtLiteCustomStruct<CustomOp>;
struct Kernel {
size_t num_input_{};
size_t num_output_{};
std::unique_ptr<CustomOp> custom_op_;
std::string ep_{};
};
OrtLiteCustomStruct(const char* op_name,
const char* execution_provider) : OrtLiteCustomOp(op_name,
execution_provider) {
init(&CustomOp::Compute);
}
template <typename... Args>
void init(CustomComputeFn<Args...>) {
ParseArgs<Args...>(input_types_, output_types_);
OrtCustomOp::KernelCompute = [](void* op_kernel, OrtKernelContext* context) {
auto kernel = reinterpret_cast<Kernel*>(op_kernel);
std::vector<TensorPtr> tensors;
auto t = CreateTuple<0, 0, Args...>(context, tensors, kernel->num_input_, kernel->num_output_, kernel->ep_);
std::apply([kernel](Args const&... t_args) { kernel->custom_op_->Compute(t_args...); }, t);
};
OrtCustomOp::CreateKernel = [](const OrtCustomOp* this_, const OrtApi* ort_api, const OrtKernelInfo* info) {
auto kernel = std::make_unique<Kernel>();
Ort::ThrowOnError(ort_api->KernelInfo_GetInputCount(info, &kernel->num_input_));
Ort::ThrowOnError(ort_api->KernelInfo_GetOutputCount(info, &kernel->num_output_));
kernel->custom_op_ = std::make_unique<CustomOp>(ort_api, info);
auto self = static_cast<const OrtLiteCustomStruct*>(this_);
kernel->ep_ = self->execution_provider_;
return reinterpret_cast<void*>(kernel.release());
};
OrtCustomOp::KernelDestroy = [](void* op_kernel) {
delete reinterpret_cast<Kernel*>(op_kernel);
};
}
}; // struct OrtLiteCustomStruct
/////////////////////////// CreateLiteCustomOp ////////////////////////////
template <typename... Args>
OrtLiteCustomOp* CreateLiteCustomOp(const char* op_name,
const char* execution_provider,
void (*custom_compute_fn)(Args...)) {
using LiteOp = OrtLiteCustomFunc<Args...>;
return std::make_unique<LiteOp>(op_name, execution_provider, custom_compute_fn).release();
}
template <typename CustomOp>
OrtLiteCustomOp* CreateLiteCustomOp(const char* op_name,
const char* execution_provider) {
using LiteOp = OrtLiteCustomStruct<CustomOp>;
return std::make_unique<LiteOp>(op_name, execution_provider).release();
}
} // namespace Custom
} // namespace Ort