File size: 35,216 Bytes
982e2b6
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
from transformers import TrainerCallback, Trainer
from trl import SFTTrainer, DataCollatorForCompletionOnlyLM
from peft import PeftModel
from datasets import Dataset
from transformers.utils import is_sagemaker_mp_enabled, is_sagemaker_dp_enabled
from typing import Any, Dict, Union, Optional, Tuple
from torch.nn import MSELoss

import warnings
import torch
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
import numpy as np
import time
import os
import copy

from transformers.models.mistral.modeling_mistral import (
    MistralMLP,
    MistralAttention,
    MistralModel,
    MistralDecoderLayer,
    MistralConfig,
    MISTRAL_ATTENTION_CLASSES,
    MistralRMSNorm,
    MistralForCausalLM,
)
from experiments.models.sparse_mistral.svd_router import (
    low_rank_approximation,
    SparsePredictor,
)
from utils.utils import (
    print_size_of_model,
    is_running_deepspeed,
    is_mainprocess,
    get_datetime,
    ds_print,
)


class SparseSFTTTrainer(SFTTrainer):
    def __init__(self, *args, **kwargs):
        self.regularization_coefficient = kwargs.pop("regularization_coefficient", 10)
        self.use_sparse_regularization = kwargs.pop("use_sparse_regularization", False)
        self.use_spm_loss = False
        self.freeze_original_weights = False
        self.regularization_type = kwargs.pop(
            "regularization_type", "L1 positive activation"
        )
        assert self.regularization_type in [
            "L2 activation",
            "L1 positive activation",
        ], f"Invalid regularization type: {self.regularization_type}"
        self.sparse_layers = []
        self.sparse_decoder_layers = []
        super(SparseSFTTTrainer, self).__init__(*args, **kwargs)

    def initialize_sparse_silu_layers(self, model):
        self.sparse_layers = [
            m for m in model.modules() if isinstance(m, MistralSparseSiluMLP)
        ]

    def initialize_sparse_decoder_layers(self, model):
        self.sparse_decoder_layers = [
            m for m in model.modules() if isinstance(m, SparseMistralDecoderLayer)
        ]

    def training_step(
        self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]
    ) -> torch.Tensor:
        """
        Override the huggingface's training_step function to add a regularization term.
        A regularization term is computed with intermediate values, which are freed after "backward()."
        You need to set `retain_graph=True` inside `backward` function to keep the values.
        """
        model.train()
        inputs = self._prepare_inputs(inputs)

        with self.compute_loss_context_manager():
            loss = self.compute_loss(model, inputs)

        if self.args.n_gpu > 1:
            loss = loss.mean()  # mean() to average on multi-gpu parallel training
        if not self.freeze_original_weights:
            if loss is not None:
                self.accelerator.backward(loss, retain_graph=False)

        if self.use_sparse_regularization:
            regularization_loss = self.compute_regularization(model)
            if self.args.n_gpu > 1:
                regularization_loss = regularization_loss.mean()
            if regularization_loss is not None:
                self.accelerator.backward(regularization_loss, retain_graph=True)
            loss += regularization_loss

        if self.use_spm_loss:
            spm_loss = self.compute_spm_loss(model)
            if self.args.n_gpu > 1:
                spm_loss = spm_loss.mean()
            if spm_loss is not None:
                self.accelerator.backward(spm_loss, retain_graph=False)
            loss += spm_loss

        return loss.detach() / self.args.gradient_accumulation_steps

    def compute_regularization(self, model):
        """
        Compute a sparse regularization loss for SiLU
        """
        loss = 0
        if len(self.sparse_layers) == 0:
            self.initialize_sparse_silu_layers(model)
        num_layers = len(self.sparse_layers)

        for module in self.sparse_layers:
            if module.activation_norm is not None:
                loss += module.activation_norm

        loss /= num_layers
        loss *= self.regularization_coefficient

        if self.state.global_step % 20 == 0 and loss != 0:
            print("Negative relularizer loss: ", loss.item())
        return loss

    def compute_spm_loss(self, model):
        loss = 0
        if len(self.sparse_decoder_layers) == 0:
            self.initialize_sparse_decoder_layers(model)
        for module in self.sparse_decoder_layers:
            if module.distill_loss != None:
                loss += module.distill_loss
        if self.state.global_step % 20 == 0 and loss != 0:
            print("Sparse Predictor Distillation loss: ", loss.item())
        return loss

    # def compute_loss(self, model, inputs, return_outputs=False):
    #     loss = super().compute_loss(model, inputs, return_outputs)
    #
    #     if is_sagemaker_mp_enabled():
    #         import smdistributed.modelparallel.torch as smp
    #         @smp.step()
    #         def smp_forward_backward(model, inputs, gradient_accumulation_steps=1):
    #             outputs = model(**inputs)
    #             loss = outputs["loss"] if isinstance(outputs, dict) else outputs[0]
    #             loss /= gradient_accumulation_steps
    #             model.backward(loss)
    #             return loss
    #
    #         loss_mb = smp_forward_backward(
    #             model, inputs, self.args.gradient_accumulation_steps
    #         )
    #         if self.use_sparse_regularization:
    #             return loss_mb.reduce_mean().detach().to(
    #                 self.args.device
    #             ) + self.regularization_coefficient * self.compute_regularization(model)
    #         else:
    #             return loss_mb.reduce_mean().detach().to(self)
    #
    #     if return_outputs:
    #         classification_loss, outputs = loss
    #     else:
    #         classification_loss = loss
    #
    #     loss = classification_loss
    #     if self.use_sparse_regularization:
    #         regularization_loss = self.compute_regularization(model)
    #         loss += self.regularization_coefficient * regularization_loss
    #
    #     return (loss, outputs) if return_outputs else loss


class SparseTrainer(Trainer):
    def __init__(self, *args, **kwargs):
        self.regularization_coefficient = kwargs.pop("regularization_coefficient", 10)
        self.use_sparse_regularization = kwargs.pop("use_sparse_regularization", False)
        self.use_spm_loss = False
        self.freeze_original_weights = False
        self.regularization_type = kwargs.pop(
            "regularization_type", "L1 positive activation"
        )
        assert self.regularization_type in [
            "L2 activation",
            "L1 positive activation",
        ], f"Invalid regularization type: {self.regularization_type}"
        self.sparse_layers = []
        self.sparse_decoder_layers = []
        super(SparseTrainer, self).__init__(*args, **kwargs)

    def initialize_sparse_silu_layers(self, model):
        self.sparse_layers = [
            m for m in model.modules() if isinstance(m, MistralSparseSiluMLP)
        ]

    def initialize_sparse_decoder_layers(self, model):
        self.sparse_decoder_layers = [
            m for m in model.modules() if isinstance(m, SparseMistralDecoderLayer)
        ]

    def training_step(
        self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]
    ) -> torch.Tensor:
        """
        Override the huggingface's training_step function to add a regularization term.
        A regularization term is computed with intermediate values, which are freed after "backward()."
        You need to set `retain_graph=True` inside `backward` function to keep the values.
        """
        model.train()
        inputs = self._prepare_inputs(inputs)

        with self.compute_loss_context_manager():
            loss = self.compute_loss(model, inputs)

        if self.args.n_gpu > 1:
            loss = loss.mean()  # mean() to average on multi-gpu parallel training
        if not self.freeze_original_weights:
            if loss is not None:
                self.accelerator.backward(loss, retain_graph=False)

        if self.use_sparse_regularization:
            regularization_loss = self.compute_regularization(model)
            if self.args.n_gpu > 1:
                regularization_loss = regularization_loss.mean()
            if regularization_loss is not None:
                self.accelerator.backward(regularization_loss, retain_graph=True)
            loss += regularization_loss

        if self.use_spm_loss:
            spm_loss = self.compute_spm_loss(model)
            if self.args.n_gpu > 1:
                spm_loss = spm_loss.mean()
            if spm_loss is not None:
                self.accelerator.backward(spm_loss, retain_graph=False)
            loss += spm_loss

        return loss.detach() / self.args.gradient_accumulation_steps

    def compute_regularization(self, model):
        """
        Compute a sparse regularization loss for SiLU
        """
        loss = 0
        if len(self.sparse_layers) == 0:
            self.initialize_sparse_silu_layers(model)
        num_layers = len(self.sparse_layers)

        for module in self.sparse_layers:
            if module.activation_norm is not None:
                loss += module.activation_norm

        loss /= num_layers
        loss *= self.regularization_coefficient

        if self.state.global_step % 20 == 0 and loss != 0:
            print("Negative relularizer loss: ", loss.item())
        return loss

    def compute_spm_loss(self, model):
        loss = 0
        if len(self.sparse_decoder_layers) == 0:
            self.initialize_sparse_decoder_layers(model)
        for module in self.sparse_decoder_layers:
            if module.distill_loss != None:
                loss += module.distill_loss
        if self.state.global_step % 20 == 0 and loss != 0:
            print("Sparse Predictor Distillation loss: ", loss.item())
        return loss


class SparseSiLU(nn.SiLU):
    def __init__(self, threshold):
        super(SparseSiLU, self).__init__()
        self.threshold = threshold
        self.m = nn.Threshold(self.threshold, 0)

    def set_new_threshold(self, threshold):
        self.threshold = threshold
        self.m = nn.Threshold(threshold, 0)

    def forward(self, x):
        act = super(SparseSiLU, self).forward(x)
        return self.m(act) - self.m(-act)


class MistralSparseSiluMLP(MistralMLP):
    def __init__(self, config, *args, **kwargs):
        super().__init__(config)
        self.swish_outputs = None
        self.relu = nn.ReLU()

        self.kill_sparse_swish_outputs = False
        self.dead_percentage = 0
        self.is_stats = False
        self.visit_counts = 0

        # Hyperparameters to tune
        self.dead_threshold = kwargs.pop("dead_threshold", 0)
        self.use_sparse_regularization = kwargs.pop("use_sparse_regularization", True)
        self.regularization_type = kwargs.pop(
            "regularization_type", "L1 regularization"
        )
        self.regularization_threshold = kwargs.pop("regularization_threshold", 0.5)
        self.use_relu = kwargs.pop("use_relu", False)
        self.activation_norm = None

        # Activation Histograms
        self.is_collect_histogram = False
        num_bins = 1000
        self.histogram_bins = torch.linspace(-1, 1, num_bins - 2)
        self.histogram_bins = torch.cat(
            [torch.tensor([-torch.inf]), self.histogram_bins, torch.tensor([torch.inf])]
        )
        self.pre_act_hist_counts = torch.zeros(num_bins - 1)
        self.post_act_hist_counts = torch.zeros(num_bins - 1)
        self.t = 0
        self.agg_sparsity = 0

        # Sparse activation function
        self.sparse_act_fn = SparseSiLU(threshold=self.dead_threshold)

    def activate_stats(self, is_collect_histogram: bool = True):
        self.is_stats = True
        self.dead_percentage = 0
        self.visit_counts = 0
        self.is_collect_histogram = is_collect_histogram
        self.histogram_counts = torch.zeros(2000)  # .to(self.down_proj.weight.device)

    def deactivate_stats(self):
        self.is_stats = False

    def collect_stats(self, pre_activation, post_activation):
        start_time = time.time()
        pre_activation = pre_activation.float().cpu().detach()
        post_activation = post_activation.float().cpu().detach()
        # self.histogram_bins=self.histogram_bins.to(pre_activation.device).type(pre_activation.dtype)
        self.pre_act_hist_counts += torch.histogram(
            pre_activation, bins=self.histogram_bins
        )[0]
        self.post_act_hist_counts += torch.histogram(
            torch.abs(post_activation), bins=self.histogram_bins
        )[0]
        self.t += time.time() - start_time
        if self.visit_counts % 30 == 0:
            print(f"Time taken to collect stats: {self.t}s.")

    def forward(
        self,
        x,
        sp_mask: torch.tensor = None,
    ):
        """
        If kill_sparse_swish_outputs is set to False, this layer functions exactly like a normal MLP layer.
        """
        if sp_mask != None:  # When sparse mask is given
            return self.down_proj(
                self.sparse_act_fn(self.gate_proj(x) * sp_mask) * self.up_proj(x)
            )  # Todo: This doesn't accelerate runtime (instead slowing down)

        elif self.use_relu:
            post_act = self.relu(self.gate_proj(x))

            if self.is_stats:
                dead_neurons = post_act == 0
                dead_percentage = dead_neurons.float().mean()
                agg_sparsity = dead_neurons.all(dim=0).float().mean()

                self.dead_percentage = (
                    self.dead_percentage * self.visit_counts + dead_percentage
                ) / (self.visit_counts + 1)
                self.agg_sparsity = (
                    self.agg_sparsity * self.visit_counts + agg_sparsity
                ) / (self.visit_counts + 1)
                self.visit_counts += 1

            return self.down_proj(post_act * self.up_proj(x))

        else:
            pre_act = self.gate_proj(x)
            post_act = self.act_fn(pre_act)
            if self.kill_sparse_swish_outputs:
                dead_neurons = post_act.abs() <= self.dead_threshold

                dead_percentage = dead_neurons.float().mean()
                agg_sparsity = dead_neurons.all(dim=0).float().mean()

                if self.is_stats:
                    self.dead_percentage = (
                        self.dead_percentage * self.visit_counts + dead_percentage
                    ) / (self.visit_counts + 1)
                    self.agg_sparsity = (
                        self.agg_sparsity * self.visit_counts + agg_sparsity
                    ) / (self.visit_counts + 1)
                    self.visit_counts += 1

                    # print(self.agg_sparsity)

                    # Collect histogram stats
                    if (
                        self.is_collect_histogram
                        and pre_act.eq(0).float().mean() < 0.99
                    ):  # Padded dataset
                        self.collect_stats(pre_act, post_act)

                post_act[dead_neurons] = 0

            out = self.down_proj(post_act * self.up_proj(x))
            if self.use_sparse_regularization:
                if self.regularization_type == "L1 regularization":
                    self.activation_norm = torch.abs(post_act)[
                        post_act < self.regularization_threshold
                    ].mean()
                elif self.regularization_type == "L2 regularization":
                    self.activation_norm = torch.sqrt(
                        torch.square(post_act)[post_act < self.regularization_threshold]
                    ).mean()

            return out


class SparseMistralDecoderLayer(MistralDecoderLayer):
    def __init__(
        self,
        config: MistralConfig,
        layer_idx: int,
        decoder_layer: MistralDecoderLayer,
        init_svd: bool = True,
        *args,
        **kwargs,
    ):
        assert isinstance(
            decoder_layer.mlp, MistralSparseSiluMLP
        ), f"{type(decoder_layer.mlp)} should MistralSparseSiluMLP."

        super().__init__(config, layer_idx)
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size

        self.init_svd = init_svd
        self.self_attn = decoder_layer.self_attn

        self.mlp = decoder_layer.mlp
        self.input_layernorm = decoder_layer.input_layernorm
        self.post_attention_layernorm = decoder_layer.post_attention_layernorm

        # Sparse predictor for mlp (initialized with SVD decomposed matrix)
        self.low_rank = kwargs.pop("low_rank", 64)
        self.sparse_act_func = decoder_layer.mlp.sparse_act_fn

        print(
            f"Setting {layer_idx}th mlp layer's sparse predictor... svd init: {init_svd}"
        )
        self.sp_mlp = low_rank_approximation(
            decoder_layer.mlp.gate_proj,
            act_func=self.sparse_act_func,
            init_svd=init_svd,
        )
        self.use_async = kwargs.pop("use_async", False)
        self.use_sparse_predictor = False
        self.distill_loss = None

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[bool] = False,
        **kwargs,
    ) -> Tuple[
        torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
    ]:
        print("hidden_states shape: ", hidden_states.shape)
        if "padding_mask" in kwargs:
            warnings.warn(
                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
            )

        residual = hidden_states
        sp_mask = None

        if self.use_async:
            sp_mask = self.sp_mlp(hidden_states)

        hidden_states = self.input_layernorm(hidden_states)

        # Self Attention
        hidden_states, self_attn_weights, present_key_value = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_value=past_key_value,
            output_attentions=output_attentions,
            use_cache=use_cache,
        )
        hidden_states = residual + hidden_states

        # Fully Connected
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)

        if not self.use_async:
            sp_mask = self.sp_mlp(hidden_states)

        # Compute distillation loss
        gating_output = self.mlp.sparse_act_fn(self.mlp.gate_proj(hidden_states))
        loss_func = MSELoss()
        self.distill_loss = loss_func(sp_mask, gating_output)

        # Convert sp mask into binary form
        sp_mask = sp_mask > 0

        if self.training:
            sp_mask = None
        # if not self.use_sparse_predictor:
        #     sp_mask = None

        hidden_states = self.mlp(hidden_states, sp_mask)
        hidden_states = residual + hidden_states

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights,)

        if use_cache:
            outputs += (present_key_value,)

        return outputs


class SparseMistralConfig(MistralConfig):
    model_type = "sparse_mistral"

    def __init__(self, **kwargs):
        super().__init__(**kwargs)


class SparseMistralforCausalLM(MistralForCausalLM):
    config_class = SparseMistralConfig

    def __init__(self, config):
        super().__init__(config)
        self.config = config
        if config.use_sparse_model:
            self.apply_sparse_mlp()
            if config.thresholds is not None:
                for idx, m in enumerate(self.model.layers):
                    if isinstance(m.mlp, MistralSparseSiluMLP):
                        m.mlp.dead_threshold = config.thresholds[idx]
                        m.mlp.sparse_act_fn.set_new_threshold(m.mlp.dead_threshold)
                        m.mlp.kill_sparse_swish_outputs = True
                        m.mlp.use_relu = config.use_relu
        if config.use_sparse_predictor:
            self.apply_sparse_predictor(init_svd=config.init_svd)

    def apply_sparse_mlp(self):
        apply_mistral_sparse_silu_mlp(
            self,
            config=self.config,
            use_sparse_regularization=self.config.use_sparse_regularization,
        )

    def apply_sparse_predictor(self, init_svd: bool = True):
        apply_mistral_sparse_decoder_layer(self, config=self.config, init_svd=init_svd)


class GracefulRegularizationScheduler(TrainerCallback):
    def __init__(
        self,
        num_warmup_steps=40,
        is_enabled: bool = False,
        model_name: str = "mistral",
        test_dataset: Dataset = None,
        targeted_sparsity: float = 0.5,
        keep_regularization_with_kill: bool = False,
    ):
        """Scheduler for regularizing the model first before applying the dead threshold.

        :param num_warmup_steps: number of training steps required to reach the dead threshold, defaults to 40
        :param increment_ratio: by how much to increase the dead threshold.
            For example, 0.5 means "increase the threshold by 0.5 * desired threshold
        """
        self.num_warmup_steps = num_warmup_steps
        self.is_enabled = is_enabled
        self.model_name = model_name
        self.test_dataset = test_dataset
        self.targeted_sparsity = targeted_sparsity
        self.keep_regularization_with_kill = keep_regularization_with_kill
        self.act_hist_path = (
            f"/matx/u/vxbrando/histograms/warm_up_reg_{targeted_sparsity}/act_hist.pt"
        )
        if self.is_enabled:
            print("GracefulRegularizationScheduler is enabled.")
        self.trainer = None

    def set_trainer(self, trainer):
        self.trainer = trainer

    def on_step_end(self, args, state, control, **kwargs):
        if not self.is_enabled:
            return

        model = kwargs["model"]
        if isinstance(model, PeftModel):
            base_model = model.get_base_model()
        else:
            base_model = model

        if state.global_step == 1:
            ds_print("Setting an initial reg threshold to 0.1")
            set_regularization_threshold(base_model, 0.1)

        # if state.global_step >= self.num_warmup_steps and state.global_step % 50 == 0:
        if state.global_step == self.num_warmup_steps:
            activate_stats(base_model)
            enable_sparse_silu(base_model)
            self.trainer.evaluate()
            save_act_hist(base_model, self.act_hist_path)
            set_sparse_threshold(base_model, self.targeted_sparsity, True)
            deactivate_stats(base_model)
            self.trainer.use_sparse_regularization = self.keep_regularization_with_kill
            # set_layer_specific_regularization(model.get_base_model())
            print_dead_neuron_stats(model.get_base_model())

        if state.global_step % 2000 == 0:
            if is_mainprocess():
                ds_print(
                    f"Saving to /scr/lukeai/{self.model_name}_{state.global_step}.pt",
                )
                torch.save(
                    model.state_dict(),
                    f"/scr/lukeai/{self.model_name}_{state.global_step}.pt",
                )


class GradualSparsificationScheduler(TrainerCallback):
    def __init__(
        self,
        num_warmup_steps=40,
        increment_ratio=0.5,
        is_enabled: bool = False,
        model_name: str = "mistral",
    ):
        """Scheduler for gradually increasing a dead threshold until it reaches the desired threshold.

        :param num_warmup_steps: number of training steps required to reach the dead threshold, defaults to 40
        :param increment_ratio: by how much to increase the dead threshold.
            For example, 0.5 means "increase the threshold by 0.5 * desired threshold
        """
        self.num_warmup_steps = num_warmup_steps
        self.increment_ratio = increment_ratio
        self.step_size = int(num_warmup_steps * increment_ratio)
        self.is_enabled = is_enabled
        self.model_name = model_name

    def on_step_end(self, args, state, control, **kwargs):
        model = kwargs["model"]

        if not self.is_enabled:
            if state.global_step <= 10:
                for module in model.modules():
                    if isinstance(module, MistralSparseSiluMLP):
                        module.current_dead_threshold = module.dead_threshold
            return

        current_dead_threshold = 0
        desired_dead_threshold = 0

        if is_mainprocess():
            ds_print(state.global_step)

        if state.global_step % self.step_size == 2:
            for module in model.modules():
                if isinstance(module, MistralSparseSiluMLP):
                    desired_dead_threshold = copy.deepcopy(module.dead_threshold)
                    current_dead_threshold = module.current_dead_threshold
                    current_dead_threshold += (
                        self.increment_ratio * desired_dead_threshold
                    )
                    module.current_dead_threshold = min(
                        desired_dead_threshold, current_dead_threshold
                    )

            if is_running_deepspeed and is_mainprocess():
                ds_print(
                    state.global_step,
                    current_dead_threshold,
                    desired_dead_threshold,
                )

        if state.global_step % 2000 == 0:
            if is_running_deepspeed and is_mainprocess():
                ds_print(
                    f"Saving to /matx/u/lukeai/{self.model_name}_{state.global_step - 2}.pt",
                )
                torch.save(
                    model.state_dict(),
                    f"/matx/u/lukeai/{self.model_name}_{state.global_step - 2}.pt",
                )


def get_sparse_mistral_config(
    config: MistralConfig,
    use_sparse_model=False,
    use_sparse_predictor=False,
    use_sparse_regularization=False,
    thresholds=None,
):
    new_config = SparseMistralConfig()
    new_config.__dict__.update(config.__dict__)
    config = new_config
    config.use_sparse_model = use_sparse_model
    config.use_sparse_predictor = use_sparse_predictor
    config.use_sparse_regularization = use_sparse_regularization
    config.thresholds = thresholds

    return config


def apply_mistral_sparse_silu_mlp(
    model,
    config,
    use_sparse_regularization: bool = False,
):
    # counts = 0
    for layer in model.model.layers:
        # counts += 1
        # if counts < 4:
        #     continue
        original_mlp = layer.mlp
        new_mlp = MistralSparseSiluMLP(
            config, use_sparse_regularization=use_sparse_regularization
        )
        new_mlp.gate_proj = original_mlp.gate_proj
        new_mlp.up_proj = original_mlp.up_proj
        new_mlp.down_proj = original_mlp.down_proj
        layer.mlp = new_mlp


def apply_mistral_sparse_decoder_layer(
    model,
    config,
    init_svd: bool = True,
):
    assert isinstance(model.model, MistralModel), "model.model must be a MistralModel."
    new_layers = []
    for layer_idx, layer in enumerate(model.model.layers):
        if isinstance(layer.mlp, MistralSparseSiluMLP):
            new_layers.append(
                SparseMistralDecoderLayer(
                    config=config,
                    layer_idx=layer_idx,
                    decoder_layer=layer,
                    init_svd=init_svd,
                )
            )
            print(f"{layer_idx}th mlp layer activation: {layer.mlp.sparse_act_fn}")
        else:
            new_layers.append(layer)
    model.model.layers = nn.ModuleList(new_layers)


def enable_sparse_predictor(
    model,
):
    for layer_idx, layer in enumerate(model.model.layers):
        if isinstance(layer, MistralDecoderLayer):
            layer.use_sparse_predictor = True


def disable_sparse_predictor(
    model,
):
    for layer_idx, layer in enumerate(model.model.layers):
        if isinstance(layer, MistralDecoderLayer):
            layer.use_sparse_predictor = False


def activate_stats(model, is_collect_histogram: bool = True):
    for layer in model.model.layers:
        if isinstance(layer.mlp, MistralSparseSiluMLP):
            layer.mlp.activate_stats(is_collect_histogram=is_collect_histogram)


def deactivate_stats(model):
    for layer in model.model.layers:
        if isinstance(layer.mlp, MistralSparseSiluMLP):
            layer.mlp.deactivate_stats()


def enable_sparse_silu(model):
    print("Enabling SparseSilu")
    for i, layer in enumerate(model.model.layers):
        if isinstance(layer.mlp, MistralSparseSiluMLP):
            layer.mlp.kill_sparse_swish_outputs = True


def print_dead_neuron_stats(model):
    total_sparsity = 0
    counts = 0
    for i, layer in enumerate(model.model.layers):
        if isinstance(layer.mlp, MistralSparseSiluMLP):
            dead_percentage = layer.mlp.dead_percentage * 100
            agg_sparsity = layer.mlp.agg_sparsity * 100
            print(f"layer {i} sparsity: {dead_percentage:.3f}%")
            print(f"layer {i} agg sparsity: {agg_sparsity:.3f}%")
            total_sparsity += dead_percentage
            counts += 1

    print(f"Total sparsity: {total_sparsity/counts: .3f}%")
    return total_sparsity / counts


def get_sparse_layers(model: MistralModel):
    sparse_layers = [
        m.mlp for m in model.layers() if isinstance(m.mlp, MistralSparseSiluMLP)
    ]
    return sparse_layers


def get_threshold(
    bin_edges: torch.tensor, histogram_counts: torch.tensor, sparsity_level: float
):  # Only for L1 Regularization
    assert (
        len(bin_edges.shape) == len(histogram_counts.shape) == 1
    ), "bin_edges and histogram are expected to be 1-dimensional."
    histogram_counts /= histogram_counts.sum()
    threshold_idx = torch.searchsorted(
        histogram_counts.cumsum(0), sparsity_level, side="right"
    )

    return bin_edges[threshold_idx]


def set_regularization_threshold(model, threshold: float = 0.1):
    for i, layer in enumerate(model.model.layers):
        if (
            isinstance(layer.mlp, MistralSparseSiluMLP) and layer.mlp.is_stats
        ):  # Can set the threshold only the relevant statistics is collected.
            layer.mlp.regularization_threshold = threshold  # TODO: find better param


def set_sparse_threshold(model, sparsity_level: float, use_relu: bool = False):
    for i, layer in enumerate(model.model.layers):
        if (
            isinstance(layer.mlp, MistralSparseSiluMLP) and layer.mlp.is_stats
        ):  # Can set the threshold only the relevant statistics is collected.
            if use_relu:
                layer.mlp.sparse_act_fn = nn.ReLU()
                layer.mlp.use_relu = True
            else:
                layer.mlp.dead_threshold = get_threshold(
                    layer.mlp.histogram_bins,
                    layer.mlp.post_act_hist_counts,
                    sparsity_level,
                )
                layer.mlp.sparse_act_fn.set_new_threshold(layer.mlp.dead_threshold)
                layer.mlp.regularization_threshold = (
                    layer.mlp.dead_threshold * 1.2
                )  # TODO: find better param


def plot_histogram(
    bin_edges, histogram_counts: torch.tensor, title: str = "Activation Distribution", fig_dir: str = "figures"
):
    plt.bar(
        bin_edges[:-1], histogram_counts, width=np.diff(bin_edges), edgecolor="black"
    )
    plt.title(title)
    plt.xlabel("Activation Value")
    plt.ylabel("Frequency")
    os.makedirs(fig_dir, exist_ok=True)
    plt.savefig(f"{fig_dir}/{title}.png")
    # plt.show()
    plt.clf()


def plot_act(model, fig_dir: str = "figures"):
    for i, layer in enumerate(model.model.layers):
        if (
            isinstance(layer.mlp, MistralSparseSiluMLP) and layer.mlp.is_stats
        ):  # Can set the threshold only the relevant statistics is collected.
            plot_title = f"Layer: {i} Pre-Activation Distribution"
            plot_histogram(
                layer.mlp.histogram_bins, layer.mlp.pre_act_hist_counts, plot_title
            )

            plot_title = f"Layer: {i} Post-Activation Absolute Distribution"
            plot_histogram(
                layer.mlp.histogram_bins, layer.mlp.post_act_hist_counts, plot_title
            )


def save_act_hist(
    model, filename="/scr/jay/models/mistral/pre_finetune/cola_act_hist.pt"
):
    os.makedirs(os.path.dirname(filename), exist_ok=True)
    act_dict = {}
    for i, layer in enumerate(model.model.layers):
        if (
            isinstance(layer.mlp, MistralSparseSiluMLP) and layer.mlp.is_stats
        ):  # Can set the threshold only the relevant statistics is collected.
            act_dict[i] = (
                layer.mlp.histogram_bins,
                layer.mlp.pre_act_hist_counts,
                layer.mlp.post_act_hist_counts,
            )
    print("Saving activation histograms...\n\n\n")
    torch.save(act_dict, filename)


def load_act_hist(
    model, filename="/scr/jay/models/mistral/pre_finetune/cola_act_hist.pt"
):
    assert os.path.exists(
        filename
    ), f"{filename} does not exist when loading pre/post-activation histogram of SparseMistralSiluMLP."
    print("Loading activation histograms...\n\n\n")

    act_dict = torch.load(filename)
    for i, layer in enumerate(model.model.layers):
        if (
            isinstance(layer.mlp, MistralSparseSiluMLP) and layer.mlp.is_stats
        ):  # Can set the threshold only the relevant statistics is collected.
            (
                layer.mlp.histogram_bins,
                layer.mlp.pre_act_hist_counts,
                layer.mlp.post_act_hist_counts,
            ) = act_dict[i]


def enable_last_k_modules(model, start_module_idx: int):
    assert 32 > start_module_idx >= 0
    new_modules = []
    new_idx = 0
    for idx in range(start_module_idx, len(model.model.original_layers)):
        module = model.model.original_layers[idx]
        module.layer_idx = new_idx
        module.self_attn.layer_idx = new_idx
        new_modules.append(module)
        new_idx += 1
        print(module.layer_idx)

    model.model.layers = nn.ModuleList(new_modules)


def enable_first_k_modules(model, end_module_idx: int):
    assert 32 > end_module_idx >= 0
    new_modules = []
    new_idx = 0
    for idx in range(0, end_module_idx + 1):
        module = model.model.original_layers[idx]
        module.layer_idx = new_idx
        module.self_attn.layer_idx = new_idx
        new_modules.append(module)
        new_idx += 1
        print(module.layer_idx)

    model.model.layers = nn.ModuleList(new_modules)