forked from PaddlePaddle/Paddle
/
ternary.cc
1298 lines (1226 loc) · 47.5 KB
/
ternary.cc
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
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
/* Copyright (c) 2022 PaddlePaddle 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. */
#include "paddle/phi/infermeta/ternary.h"
#include "glog/logging.h"
#include "paddle/phi/common/layout.h"
#include "paddle/phi/core/ddim.h"
#include "paddle/phi/kernels/funcs/common_shape.h"
#include "paddle/phi/kernels/impl/box_coder.h"
namespace phi {
void AccuracyInferMeta(const MetaTensor& out,
const MetaTensor& indice,
const MetaTensor& label,
MetaTensor* accuracy,
MetaTensor* correct,
MetaTensor* total,
MetaConfig config) {
auto inference_dim = out.dims();
auto label_dim = label.dims();
// Assume indices has same shape as inference, because
// it's the output of topk.
PADDLE_ENFORCE_EQ(
label_dim.size(),
2,
phi::errors::InvalidArgument(
"ShapeError: label's dimensions of AccuracyOp must be 2. "
"But received label's dimensions = %d, label's shape = [%s]",
label_dim.size(),
label_dim));
if (config.is_runtime) {
PADDLE_ENFORCE_EQ(label_dim[1],
1,
phi::errors::InvalidArgument(
"ShapeError: label's second dimension of "
"AccuracyOp must be 1. But received label's "
"second dimension is = %d, label's shape = [%s]",
label_dim[1],
label_dim));
PADDLE_ENFORCE_EQ(
inference_dim[0],
label_dim[0],
phi::errors::InvalidArgument(
"ShapeError: the output's num_rows of AccuracyOp must be"
" the same as label's num_rows. But received output's "
"shape = [%s], label's shape = [%s], output's num_rows = %d, "
"label's "
"num_rows = %d",
inference_dim,
label_dim,
inference_dim[0],
label_dim[0]));
}
accuracy->set_dims({1});
accuracy->set_dtype(out.dtype());
correct->set_dims({1});
correct->set_dtype(out.dtype());
total->set_dims({1});
total->set_dtype(out.dtype());
accuracy->share_lod(out);
}
void AddmmInferMeta(const MetaTensor& input,
const MetaTensor& x,
const MetaTensor& y,
float alpha,
float beta,
MetaTensor* out) {
auto input_dims = input.dims();
auto x_dims = x.dims();
auto y_dims = y.dims();
auto ndim_input = input_dims.size();
auto ndim_x = x_dims.size();
auto ndim_y = y_dims.size();
VLOG(3) << "addmm operator input.shape=" << input_dims
<< " x.shape=" << x_dims << " y.shape=" << y_dims << " beta=" << beta
<< " alpha=" << alpha << " ndim_input=" << ndim_input
<< " ndim_x=" << ndim_x << " ndim_y=" << ndim_y;
PADDLE_ENFORCE_NE(
product(input_dims),
0,
errors::PreconditionNotMet("The Input variable 'input' has not "
"been initialized. You may need to confirm "
"if you put exe.run(startup_program) "
"after optimizer.minimize function."));
PADDLE_ENFORCE_NE(
product(x_dims),
0,
errors::PreconditionNotMet("The Input variable 'x' has not "
"been initialized. You may need to confirm "
"if you put exe.run(startup_program) "
"after optimizer.minimize function."));
PADDLE_ENFORCE_NE(
product(y_dims),
0,
errors::PreconditionNotMet("The Input variable 'y' has not "
"been initialized. You may need to confirm "
"if you put exe.run(startup_program) "
"after optimizer.minimize function."));
// dim check
PADDLE_ENFORCE_EQ(ndim_input == 2 || ndim_input == 1,
true,
errors::InvalidArgument(
"The input tensor input's dimension must be 2 or 1. "
"But received input's dimension = [%d].",
ndim_input));
PADDLE_ENFORCE_EQ(
ndim_x,
2,
errors::InvalidArgument("The input tensor x's dimension must be 2. "
"But received x's dimension = [%d].",
ndim_x));
PADDLE_ENFORCE_EQ(
ndim_y,
2,
errors::InvalidArgument("The input tensor y's dimension must be 2. "
"But received y's dimension = [%d].",
ndim_y));
std::vector<int64_t> output_dims;
output_dims.push_back(x_dims[0]);
output_dims.push_back(y_dims[1]);
out->set_dims(make_ddim(output_dims));
out->share_lod(input);
out->set_dtype(input.dtype());
}
void BoxCoderInferMeta(const MetaTensor& prior_box,
const MetaTensor& prior_box_var,
const MetaTensor& target_box,
const std::string& code_type,
bool box_normalized,
int axis,
const std::vector<float>& variance,
MetaTensor* output_box,
MetaConfig config) {
auto prior_box_dims = prior_box.dims();
auto target_box_dims = target_box.dims();
if (config.is_runtime) {
PADDLE_ENFORCE_EQ(prior_box_dims.size(),
2,
phi::errors::InvalidArgument(
"The rank of Input PriorBox in BoxCoder operator "
"must be 2. But received rank = %d",
prior_box_dims.size()));
PADDLE_ENFORCE_EQ(prior_box_dims[1],
4,
phi::errors::InvalidArgument(
"The second dimension of PriorBox in BoxCoder "
"operator must be 4. But received dimension = %d",
prior_box_dims[1]));
if (prior_box_var) {
auto prior_box_var_dims = prior_box_var.dims();
PADDLE_ENFORCE_EQ(
prior_box_var_dims.size(),
2,
phi::errors::InvalidArgument(
"The rank of Input(PriorBoxVar) in BoxCoder operator"
" should be 2. But received rank = %d",
prior_box_var_dims.size()));
PADDLE_ENFORCE_EQ(
prior_box_dims,
prior_box_var_dims,
phi::errors::InvalidArgument(
"The dimension of Input(PriorBoxVar) should be equal to"
"the dimension of Input(PriorBox) in BoxCoder operator "
"when the rank is 2."));
}
}
auto box_code_type = phi::funcs::GetBoxCodeType(code_type);
if (box_code_type == phi::funcs::BoxCodeType::kEncodeCenterSize) {
PADDLE_ENFORCE_EQ(target_box_dims.size(),
2,
phi::errors::InvalidArgument(
"The rank of Input TargetBox in BoxCoder operator "
"must be 2. But received rank is %d",
target_box_dims.size()));
PADDLE_ENFORCE_EQ(target_box_dims[1],
4,
phi::errors::InvalidArgument(
"The second dimension of TargetBox in BoxCoder "
"operator is 4. But received dimension is %d",
target_box_dims[1]));
output_box->set_dims({target_box_dims[0], prior_box_dims[0], 4});
} else if (box_code_type == phi::funcs::BoxCodeType::kDecodeCenterSize) {
PADDLE_ENFORCE_EQ(target_box_dims.size(),
3,
phi::errors::InvalidArgument(
"The rank of Input TargetBox in BoxCoder "
"operator must be 3. But received rank is %d",
target_box_dims.size()));
PADDLE_ENFORCE_EQ(
axis == 0 || axis == 1,
true,
phi::errors::InvalidArgument("axis in BoxCoder operator must be 0 or 1."
"But received axis = %d",
axis));
if (config.is_runtime) {
if (axis == 0) {
PADDLE_ENFORCE_EQ(
target_box_dims[1],
prior_box_dims[0],
phi::errors::InvalidArgument(
"When axis is 0, The second "
"dimension of TargetBox in BoxCoder "
"should be equal to the first dimension of PriorBox."));
} else if (axis == 1) {
PADDLE_ENFORCE_EQ(
target_box_dims[0],
prior_box_dims[0],
phi::errors::InvalidArgument(
"When axis is 1, The first "
"dimension of TargetBox in BoxCoder "
"should be equal to the first dimension of PriorBox."));
}
PADDLE_ENFORCE_EQ(
target_box_dims[2],
prior_box_dims[1],
phi::errors::InvalidArgument("The third dimension of TargetBox"
" in BoxCoder should be equal to the "
"second dimension of PriorBox."));
}
output_box->share_dims(target_box);
}
if (box_code_type == phi::funcs::BoxCodeType::kDecodeCenterSize &&
axis == 1) {
output_box->share_lod(prior_box);
} else {
output_box->share_lod(target_box);
}
output_box->set_dtype(target_box.dtype());
}
void ArangeInferMeta(const MetaTensor& start,
const MetaTensor& end,
const MetaTensor& step,
MetaTensor* out) {
auto start_dims = start.dims();
auto end_dims = end.dims();
auto step_dims = step.dims();
PADDLE_ENFORCE_EQ(
start_dims.size(),
1,
phi::errors::InvalidArgument(
"The dim of the shape of Input(Start) should be 1, but got %d",
start_dims.size()));
PADDLE_ENFORCE_EQ(start_dims[0],
1,
phi::errors::InvalidArgument(
"The first dim of the shape of Input(Start) should "
"be 1, but got %d",
start_dims[0]));
PADDLE_ENFORCE_EQ(
end_dims.size(),
1,
phi::errors::InvalidArgument(
"The dim of the shape of Input(End) should be 1, but got %d",
end_dims.size()));
PADDLE_ENFORCE_EQ(
end_dims[0],
1,
phi::errors::InvalidArgument("The first dim of the shape of "
"Input(End) should be 1, but got %d",
end_dims[0]));
PADDLE_ENFORCE_EQ(
step_dims.size(),
1,
phi::errors::InvalidArgument(
"The dim of the shape of Input(Step) should be 1, but got %d",
step_dims.size()));
PADDLE_ENFORCE_EQ(step_dims[0],
1,
phi::errors::InvalidArgument(
"The first dim of the shape of Input(Step) should "
"be 1, but got %d",
step_dims[0]));
out->set_dims({-1});
out->set_dtype(start.dtype());
}
void InstanceNormInferMeta(const MetaTensor& x,
const MetaTensor& scale,
const MetaTensor& bias,
float epsilon,
MetaTensor* y,
MetaTensor* saved_mean,
MetaTensor* saved_variance,
MetaConfig config) {
PADDLE_ENFORCE_NE(y,
nullptr,
phi::errors::InvalidArgument(
"The y in InstanceNormInferMeta can't be nullptr."));
PADDLE_ENFORCE_NE(
saved_mean,
nullptr,
phi::errors::InvalidArgument(
"The saved_mean in InstanceNormInferMeta can't be nullptr."));
PADDLE_ENFORCE_NE(
saved_variance,
nullptr,
phi::errors::InvalidArgument(
"The saved_variance in InstanceNormInferMeta can't be nullptr."));
const auto x_dims = x.dims();
PADDLE_ENFORCE_NE(phi::product(x_dims),
0,
phi::errors::PreconditionNotMet(
"The Input variable X has not "
"been initialized. You may need to confirm "
"if you put exe.run(startup_program) "
"after optimizer.minimize function."));
PADDLE_ENFORCE_GE(
x_dims.size(),
2,
phi::errors::InvalidArgument(
"ShapeError: the dimension of input X must "
"greater than or equal to 2. But received: the shape of input "
"X = [%s], the dimension of input X =[%d]",
x_dims,
x_dims.size()));
PADDLE_ENFORCE_LE(
x_dims.size(),
5,
phi::errors::InvalidArgument(
"ShapeError: the dimension of input X must "
"smaller than or equal to 5, But received: the shape of input "
"X = [%s], the dimension of input X = [%d]",
x_dims,
x_dims.size()));
auto N = x_dims[0];
auto C = x_dims[1];
auto NxC = N * C;
if (scale) {
auto scale_dim = scale.dims();
PADDLE_ENFORCE_EQ(
scale_dim.size(),
1UL,
phi::errors::InvalidArgument(
"ShapeError: the dimension of scale must equal to 1."
"But received: the shape of scale is [%s], the dimension "
"of scale is [%d]",
scale_dim,
scale_dim.size()));
bool check = !((!config.is_runtime) && (phi::product(scale_dim) <= 0));
if (check) {
PADDLE_ENFORCE_EQ(scale_dim[0],
C,
phi::errors::InvalidArgument(
"ShapeError: the shape of scale must equal to [%d]"
"But received: the shape of scale is [%d]",
C,
scale_dim[0]));
}
}
if (bias) {
auto bias_dim = bias.dims();
PADDLE_ENFORCE_EQ(
bias_dim.size(),
1UL,
phi::errors::InvalidArgument(
"ShapeError: the dimension of bias must equal to 1."
"But received: the shape of bias is [%s],the dimension "
"of bias is [%d]",
bias_dim,
bias_dim.size()));
bool check = !((!config.is_runtime) && (phi::product(bias_dim) <= 0));
if (check) {
PADDLE_ENFORCE_EQ(bias_dim[0],
C,
phi::errors::InvalidArgument(
"ShapeError: the shape of bias must equal to [%d]"
"But received: the shape of bias is [%d]",
C,
bias_dim[0]));
}
}
y->set_dims(x_dims);
saved_mean->set_dims({NxC});
saved_variance->set_dims({NxC});
y->share_lod(x);
y->set_dtype(x.dtype());
y->set_layout(x.layout());
}
void GraphSendRecvInferMeta(const MetaTensor& x,
const MetaTensor& src_index,
const MetaTensor& dst_index,
const std::string& pool_type,
const IntArray& out_size,
MetaTensor* out,
MetaTensor* dst_count) {
auto src_index_dims = src_index.dims();
if (src_index_dims.size() == 2) {
PADDLE_ENFORCE_EQ(src_index_dims[1],
1,
phi::errors::InvalidArgument(
"The last dim of Src_index should be 1 when it "
"is 2D, but we get %d",
src_index_dims[1]));
} else {
PADDLE_ENFORCE_EQ(
src_index_dims.size(),
1,
phi::errors::InvalidArgument(
"The Src_index should be 1D, when it is not 2D, but we get %d",
src_index_dims.size()));
}
auto dst_index_dims = dst_index.dims();
if (dst_index_dims.size() == 2) {
PADDLE_ENFORCE_EQ(dst_index_dims[1],
1,
phi::errors::InvalidArgument(
"The last dim of Dst_index should be 1 when it "
"is 2D, but we get %d",
dst_index_dims[1]));
} else {
PADDLE_ENFORCE_EQ(
dst_index_dims.size(),
1,
phi::errors::InvalidArgument("The Dst_index should be 1D, "
"when it is not 2D, but we get %d",
dst_index_dims.size()));
}
PADDLE_ENFORCE_EQ(src_index_dims[0],
dst_index_dims[0],
phi::errors::InvalidArgument(
"Src_index and Dst_index should have the same shape."));
auto dims = x.dims();
std::vector<int64_t> dims_ = phi::vectorize(dims);
dims_[0] = -1;
out->set_dims(phi::make_ddim(dims_));
out->set_dtype(x.dtype());
if (pool_type == "MEAN") {
dst_count->set_dims({-1});
dst_count->set_dtype(DataType::INT32);
}
}
void GroupNormInferMeta(const MetaTensor& x,
const MetaTensor& scale,
const MetaTensor& bias,
float epsilon,
int groups,
const std::string& data_layout_str,
MetaTensor* y,
MetaTensor* mean,
MetaTensor* variance) {
PADDLE_ENFORCE_NE(y,
nullptr,
phi::errors::InvalidArgument(
"The y in GroupNormInferMeta can't be nullptr."));
PADDLE_ENFORCE_NE(mean,
nullptr,
phi::errors::InvalidArgument(
"The mean in GroupNormInferMeta can't be nullptr."));
PADDLE_ENFORCE_NE(
variance,
nullptr,
phi::errors::InvalidArgument(
"The variance in GroupNormInferMeta can't be nullptr."));
auto x_dim = x.dims();
PADDLE_ENFORCE_GE(
x_dim.size(),
2,
phi::errors::InvalidArgument(
"The Input(X)'s dimension of Op(group_norm) must be "
"greater than 1. But received: %u-D Tensor, which shape is [%s].",
x_dim.size(),
x_dim));
const DataLayout data_layout =
paddle::framework::StringToDataLayout(data_layout_str);
const int64_t channel_num =
(data_layout == DataLayout::kNCHW ? x_dim[1] : x_dim[x_dim.size() - 1]);
auto batch_size = x_dim[0];
PADDLE_ENFORCE_LE(
groups,
channel_num,
phi::errors::InvalidArgument(
"The Attr(groups) of Op(group_norm) must be less than or "
"equal to the number of channels. But received: groups "
"is [%s], channels is [%s], the Attr(data_layout) "
"is [%s]. The error may come from wrong data_layout setting.",
groups,
channel_num,
data_layout_str));
PADDLE_ENFORCE_GE(
groups,
1,
phi::errors::InvalidArgument(
"The Attr(groups) of Op(group_norm) must be "
"greater than or equal to 1. But received: groups is [%s].",
groups));
PADDLE_ENFORCE_EQ(
channel_num % groups,
0,
phi::errors::InvalidArgument(
"Expected number of channels in input to be divisible by "
"num_groups, but got input channel is %d and num_groups is %d",
channel_num,
groups));
if (scale) {
PADDLE_ENFORCE_EQ(
scale.dims().size(),
1UL,
phi::errors::InvalidArgument(
"The Input(Scale) of Op(group_norm) should be 1-D Tensor. "
"But received: %u-D Tensor, the shape of Input(Scale) is [%s].",
scale.dims().size(),
scale.dims()));
PADDLE_ENFORCE_EQ(
scale.dims()[0],
channel_num,
phi::errors::InvalidArgument(
"The Input(Scale)'s first dimension size of Op(group_norm) must "
"be equal to the number of channels. But received: the "
"Input(Scale)'s first dimension size is [%s], the channels is "
"[%s], the Attr(data_layout) is [%s]. The error may come "
"from wrong data_layout setting.",
scale.dims()[0],
channel_num,
data_layout_str));
}
if (bias) {
PADDLE_ENFORCE_EQ(
bias.dims().size(),
1UL,
phi::errors::InvalidArgument(
"The Input(Bias) of Op(group_norm) should be 1-D Tensor. "
"But received: %u-D Tensor, the shape of Input(Bias) is [%s].",
bias.dims().size(),
bias.dims()));
PADDLE_ENFORCE_EQ(
bias.dims()[0],
channel_num,
phi::errors::InvalidArgument(
"The Input(Bias)'s first dimension size of "
"Op(group_norm) must be equal to the number of channels. "
"But received: the Input(Bias)'s first dimension size is [%s], "
"the channels is [%s], the Attr(data_layout) is [%s]. The "
"error may come from wrong data_layout setting.",
bias.dims()[0],
channel_num,
data_layout_str));
}
y->set_dims(x_dim);
y->set_dtype(x.dtype());
y->share_lod(x);
mean->set_dims({batch_size, groups});
variance->set_dims({batch_size, groups});
}
void LayerNormInferMeta(const MetaTensor& x,
const MetaTensor& scale,
const MetaTensor& bias,
float epsilon,
int begin_norm_axis,
bool is_test,
MetaTensor* out,
MetaTensor* mean,
MetaTensor* variance,
MetaConfig config) {
auto x_dim = x.dims();
PADDLE_ENFORCE_LT(
begin_norm_axis,
x_dim.size(),
phi::errors::InvalidArgument(
"'begin_norm_axis' must be less than the dimensions of X,"
"But received 'begin_norm_axis' is [%d],"
"received the dimensions of X is [%d].",
begin_norm_axis,
x_dim.size()));
auto matrix_dim = phi::flatten_to_2d(x_dim, begin_norm_axis);
int left = static_cast<int>(matrix_dim[0]);
int right = static_cast<int>(matrix_dim[1]);
if (scale) {
PADDLE_ENFORCE_EQ(scale.dims().size(),
1,
phi::errors::InvalidArgument(
"The dimensions of Input(Scale) must be 1, but "
"received dimensions of"
"Input(Scale) is [%d]",
scale.dims().size()));
}
if (config.is_runtime && scale) {
PADDLE_ENFORCE_EQ(
scale.dims()[0],
right,
phi::errors::InvalidArgument(
"The first dimension value of Input(Scale) must equal to be the"
"second dimension value of the flattened 2D matrix of Input(X),"
"But received the first dimension value of Input(Scale) is"
"[%d], the second dimension value of the flattened 2D matrix of"
" Input(Scale) is [%d].",
scale.dims()[0],
right));
}
if (bias) {
PADDLE_ENFORCE_EQ(bias.dims().size(),
1,
phi::errors::InvalidArgument(
"The dimensions of Input(Bias) must be 1, but "
"received dimensions of"
"Input(Bias) is [%d]",
bias.dims().size()));
}
if (config.is_runtime && bias) {
PADDLE_ENFORCE_EQ(
bias.dims()[0],
right,
phi::errors::InvalidArgument(
"The first dimension value of Input(Bias) must equal to be the"
"second dimension value of the flattened 2D matrix of Input(X),"
"But received the first dimension value of Input(Bias) is"
"[%d], the second dimension value of the flattened 2D matrix of"
" Input(Bias) is [%d].",
bias.dims()[0],
right));
}
out->set_dims(x_dim);
if (mean) {
mean->set_dims({left});
}
if (variance) {
variance->set_dims({left});
}
out->share_lod(x);
}
void LayerNormGradInferMeta(const MetaTensor& x,
const MetaTensor& y,
const MetaTensor& z,
MetaTensor* dx,
MetaTensor* dy,
MetaTensor* dz) {
if (dx) {
dx->share_meta(x);
}
if (dy && y) {
dy->share_meta(y);
}
if (dz && z) {
dz->share_meta(z);
}
}
void LerpInferMeta(const MetaTensor& x,
const MetaTensor& y,
const MetaTensor& weight,
MetaTensor* out) {
auto x_dims = x.dims();
auto y_dims = y.dims();
auto w_dims = weight.dims();
DDim out_dims;
out_dims = funcs::GetOutputDims(x_dims, y_dims);
if (w_dims.size() > 1 || w_dims[0] != 1) {
out_dims = funcs::GetOutputDims(out_dims, w_dims);
}
out->set_dims(out_dims);
out->set_dtype(x.dtype());
out->share_lod(x);
}
void LinspaceRawInferMeta(const MetaTensor& start,
const MetaTensor& stop,
const MetaTensor& number,
MetaTensor* out) {
auto s_dims = start.dims();
PADDLE_ENFORCE_EQ(
(s_dims.size() == 1) && (s_dims[0] == 1),
true,
phi::errors::InvalidArgument("The shape of Input(Start) must be [1],"
"but received input shape is [%s].",
s_dims));
auto e_dims = stop.dims();
PADDLE_ENFORCE_EQ(
(e_dims.size() == 1) && (e_dims[0] == 1),
true,
phi::errors::InvalidArgument("The shape of Input(Stop) must be [1],"
"but received input shape is [%s].",
e_dims));
auto step_dims = number.dims();
PADDLE_ENFORCE_EQ(
(step_dims.size() == 1) && (step_dims[0] == 1),
true,
phi::errors::InvalidArgument("The shape of Input(Num) must be [1],"
"but received input shape is [%s].",
step_dims));
out->set_dims(phi::make_ddim({-1}));
out->set_dtype(start.dtype());
}
void LinspaceInferMeta(const MetaTensor& start,
const MetaTensor& stop,
const MetaTensor& number,
DataType dtype,
MetaTensor* out) {
LinspaceRawInferMeta(start, stop, number, out);
}
void MultiClassNMSInferMeta(const MetaTensor& bboxes,
const MetaTensor& scores,
const MetaTensor& rois_num,
float score_threshold,
int nms_top_k,
int keep_top_k,
float nms_threshold,
bool normalized,
float nms_eta,
int background_label,
MetaTensor* out,
MetaTensor* index,
MetaTensor* nms_rois_num,
MetaConfig config) {
auto box_dims = bboxes.dims();
auto score_dims = scores.dims();
auto score_size = score_dims.size();
if (config.is_runtime) {
PADDLE_ENFORCE_EQ(
score_size == 2 || score_size == 3,
true,
errors::InvalidArgument("The rank of Input(Scores) must be 2 or 3"
". But received rank = %d",
score_size));
PADDLE_ENFORCE_EQ(
box_dims.size(),
3,
errors::InvalidArgument("The rank of Input(BBoxes) must be 3"
". But received rank = %d",
box_dims.size()));
if (score_size == 3) {
PADDLE_ENFORCE_EQ(box_dims[2] == 4 || box_dims[2] == 8 ||
box_dims[2] == 16 || box_dims[2] == 24 ||
box_dims[2] == 32,
true,
errors::InvalidArgument(
"The last dimension of Input"
"(BBoxes) must be 4 or 8, "
"represents the layout of coordinate "
"[xmin, ymin, xmax, ymax] or "
"4 points: [x1, y1, x2, y2, x3, y3, x4, y4] or "
"8 points: [xi, yi] i= 1,2,...,8 or "
"12 points: [xi, yi] i= 1,2,...,12 or "
"16 points: [xi, yi] i= 1,2,...,16"));
PADDLE_ENFORCE_EQ(
box_dims[1],
score_dims[2],
errors::InvalidArgument(
"The 2nd dimension of Input(BBoxes) must be equal to "
"last dimension of Input(Scores), which represents the "
"predicted bboxes."
"But received box_dims[1](%s) != socre_dims[2](%s)",
box_dims[1],
score_dims[2]));
} else {
PADDLE_ENFORCE_EQ(box_dims[2],
4,
errors::InvalidArgument(
"The last dimension of Input"
"(BBoxes) must be 4. But received dimension = %d",
box_dims[2]));
PADDLE_ENFORCE_EQ(
box_dims[1],
score_dims[1],
errors::InvalidArgument(
"The 2nd dimension of Input"
"(BBoxes) must be equal to the 2nd dimension of Input(Scores). "
"But received box dimension = %d, score dimension = %d",
box_dims[1],
score_dims[1]));
}
}
PADDLE_ENFORCE_NE(out,
nullptr,
errors::InvalidArgument(
"The out in MultiClassNMSInferMeta can't be nullptr."));
PADDLE_ENFORCE_NE(
index,
nullptr,
errors::InvalidArgument(
"The index in MultiClassNMSInferMeta can't be nullptr."));
// Here the box_dims[0] is not the real dimension of output.
// It will be rewritten in the computing kernel.
out->set_dims(phi::make_ddim({-1, box_dims[2] + 2}));
out->set_dtype(bboxes.dtype());
index->set_dims(phi::make_ddim({-1, box_dims[2] + 2}));
index->set_dtype(DataType::INT32);
nms_rois_num->set_dims(phi::make_ddim({-1}));
nms_rois_num->set_dtype(DataType::INT32);
}
void NllLossRawInferMeta(const MetaTensor& input,
const MetaTensor& label,
const MetaTensor& weight,
int64_t ignore_index,
const std::string& reduction,
MetaTensor* out,
MetaTensor* total_weight,
MetaConfig config) {
auto x_dims = input.dims();
auto label_dims = label.dims();
PADDLE_ENFORCE_EQ(x_dims.size() == 2 || x_dims.size() == 4,
true,
phi::errors::InvalidArgument(
"The tensor rank of Input(X) must be 2 or 4."));
bool contain_unknown_dim =
phi::contain_unknown_dim(x_dims) || phi::contain_unknown_dim(label_dims);
bool check = config.is_runtime || !contain_unknown_dim;
if (check) {
PADDLE_ENFORCE_EQ(
x_dims[0],
label_dims[0],
phi::errors::InvalidArgument(
"ShapeError: Expected input batch_size to match label batch_size,"
"But received: the Input(x) batch_size is [%s], the Input(label) "
" batch_size is [%s].",
x_dims[0],
label_dims[0]));
if (weight) {
auto w_dims = weight.dims();
PADDLE_ENFORCE_EQ(
w_dims.size(),
1,
phi::errors::InvalidArgument("Input(Weight) should be a 1D tensor."));
PADDLE_ENFORCE_EQ(
x_dims[1],
w_dims[0],
phi::errors::InvalidArgument(
"Expected input tensor Weight's size should equal "
"to the first dimension of the input tensor X. But received "
"Weight's "
"size is %d, the first dimension of input X is %d",
w_dims[0],
x_dims[1]));
}
}
if (x_dims.size() == 2) {
if (reduction == "none") {
out->set_dims({x_dims[0]});
} else {
out->set_dims({1});
}
} else if (x_dims.size() == 4) {
PADDLE_ENFORCE_EQ(label_dims.size(),
3,
phi::errors::InvalidArgument(
"Expected Input(Lable) dimensions=3, received %d.",
label_dims.size()));
auto input0 = x_dims[0];
auto input2 = x_dims[2];
auto input3 = x_dims[3];
auto label0 = label_dims[0];
auto label1 = label_dims[1];
auto label2 = label_dims[2];
PADDLE_ENFORCE_EQ(
input0 == label0 && input2 == label1 && input3 == label2,
true,
phi::errors::InvalidArgument("Input(X) tensor shape should "
"match to Input(Label) tensor "
"shape."));
if (reduction == "none") {
out->set_dims({x_dims[0], x_dims[2], x_dims[3]});
} else {
out->set_dims({1});
}
}
total_weight->set_dims({1});
out->set_dtype(input.dtype());
total_weight->set_dtype(input.dtype());
}
void PutAlongAxisInferMeta(const MetaTensor& x,
const MetaTensor& index,
const MetaTensor& value,
int axis,
const std::string& reduce,
MetaTensor* out) {
out->set_dims(x.dims());
out->set_dtype(x.dtype());
}
void RoiAlignInferMeta(const MetaTensor& x,
const MetaTensor& boxes,
const MetaTensor& boxes_num,
int pooled_height,
int pooled_width,
float spatial_scale,
int sampling_ratio,
bool aligned,
MetaTensor* out,
MetaConfig config) {
auto input_dims = x.dims();
auto boxes_dims = boxes.dims();
if (boxes_num) {
auto boxes_num_dims = boxes_num.dims();
PADDLE_ENFORCE_EQ(
boxes_num_dims.size(),
1,
phi::errors::InvalidArgument("The size of boxes_num should be 1"
", but received size = %d",
boxes_num_dims.size()));
}
PADDLE_ENFORCE_EQ(input_dims.size(),
4,
phi::errors::InvalidArgument(
"The format of Input(x) in"
"RoiAlignOp is NCHW. And the rank of input must be 4. "
"But received rank = %d",
input_dims.size()));
PADDLE_ENFORCE_EQ(boxes_dims.size(),
2,
phi::errors::InvalidArgument("The rank of Input(boxes) "
"in RoiAlignOp should be 2. "
"But the rank of boxes is %d",
boxes_dims.size()));
if (config.is_runtime) {
PADDLE_ENFORCE_EQ(boxes_dims[1],
4,
phi::errors::InvalidArgument(
"The second dimension "
"of Input(boxes) should be 4. But received the "
"dimension = %d",
boxes_dims[1]));
}
PADDLE_ENFORCE_GT(pooled_height,
0,
phi::errors::InvalidArgument(
"The 'pooled_height' attribute in RoiAlignOp is "
"invalid. The height must be greater than 0. But "
"received 'pooled_height' = %d",
pooled_height));
PADDLE_ENFORCE_GT(pooled_width,
0,
phi::errors::InvalidArgument(
"The 'pooled_width' attribute in RoiAlignOp is "
"invalid. The width must be greater than 0. But "
"received 'pooled_width' = %d",
pooled_width));
PADDLE_ENFORCE_GT(spatial_scale,
0.0f,
phi::errors::InvalidArgument(
"The 'spatial_scale' attribute in RoiAlignOp is "
"invalid. The scale must be greater than 0. But "
"received 'spatial_scale' = %f",
spatial_scale));
auto out_dims = input_dims;
out_dims[0] = boxes_dims[0];
out_dims[1] = input_dims[1];
out_dims[2] = pooled_height;
out_dims[3] = pooled_width;
out->set_dims(out_dims);
out->set_dtype(x.dtype());
}
void RoiPoolInferMeta(const MetaTensor& x,
const MetaTensor& boxes,
const MetaTensor& boxes_num,
int pooled_height,
int pooled_width,