File size: 34,011 Bytes
628c132
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
# coding=utf-8
# Copyright 2021 The Fairseq Authors and The Google Flax Team Authors And The HuggingFace Inc. team. 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.
""" Flax Bart model."""

import math
import random
from functools import partial
from typing import Optional, Tuple

import numpy as np

import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict, unfreeze
from flax.linen import combine_masks, make_causal_mask
from flax.linen import partitioning as nn_partitioning
from flax.linen.attention import dot_product_attention_weights
from jax import lax
from jax.random import PRNGKey

from transformers.modeling_flax_outputs import (
    FlaxBaseModelOutputWithPastAndCrossAttentions,
    FlaxCausalLMOutputWithCrossAttentions,
)
from transformers.modeling_flax_utils import ACT2FN, FlaxPreTrainedModel

from models import BartConfig


scan_with_axes = nn_partitioning.scan_with_axes
remat = nn_partitioning.remat


def shift_tokens_right(input_ids: np.array, pad_token_id: int, decoder_start_token_id: int) -> np.ndarray:
    """
    Shift input ids one token to the right.
    """
    shifted_input_ids = np.zeros_like(input_ids)
    shifted_input_ids[:, 1:] = input_ids[:, :-1]
    shifted_input_ids[:, 0] = decoder_start_token_id

    shifted_input_ids = np.where(shifted_input_ids == -100, pad_token_id, shifted_input_ids)
    return shifted_input_ids


class FlaxBartAttention(nn.Module):
    config: BartConfig
    embed_dim: int
    num_heads: int
    dropout: float = 0.0
    causal: bool = False
    bias: bool = True
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self) -> None:
        self.head_dim = self.embed_dim // self.num_heads
        if self.head_dim * self.num_heads != self.embed_dim:
            raise ValueError(
                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
                f" and `num_heads`: {self.num_heads})."
            )

        dense = partial(
            nn.Dense,
            self.embed_dim,
            use_bias=self.bias,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.init_std),
        )

        self.q_proj, self.k_proj, self.v_proj = dense(), dense(), dense()

        self.fused_proj = nn.Dense(
            self.embed_dim * 3,
            use_bias=self.bias,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.init_std),
        )

        self.fused_key_value = nn.Dense(
            self.embed_dim * 2,
            use_bias=self.bias,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.init_std),
        )

        self.out_proj = dense()

        self.dropout_layer = nn.Dropout(rate=self.dropout)

        if self.causal:
            self.causal_mask = make_causal_mask(
                jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool"
            )

    def _split_heads(self, hidden_states):
        return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim))

    def _merge_heads(self, hidden_states):
        return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,))

    @nn.compact
    def _concatenate_to_cache(self, key, value, query, attention_mask):
        """
        This function takes projected key, value states from a single input token and concatenates the states to cached
        states from previous steps. This function is slighly adapted from the official Flax repository:
        https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252
        """
        # detect if we're initializing by absence of existing cache data.
        is_initialized = self.has_variable("cache", "cached_key")
        cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype)
        cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype)
        cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32))

        if is_initialized:
            *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape
            # update key, value caches with our new 1d spatial slices
            cur_index = cache_index.value
            indices = (0,) * len(batch_dims) + (cur_index, 0, 0)
            key = lax.dynamic_update_slice(cached_key.value, key, indices)
            value = lax.dynamic_update_slice(cached_value.value, value, indices)
            cached_key.value = key
            cached_value.value = value
            num_updated_cache_vectors = query.shape[1]
            cache_index.value = cache_index.value + num_updated_cache_vectors
            # causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements.
            pad_mask = jnp.broadcast_to(
                jnp.arange(max_length) < cur_index + num_updated_cache_vectors,
                tuple(batch_dims) + (1, num_updated_cache_vectors, max_length),
            )
            attention_mask = combine_masks(pad_mask, attention_mask)
        return key, value, attention_mask

    def __call__(
        self,
        hidden_states: jnp.ndarray,
        key_value_states: Optional[jnp.ndarray] = None,
        attention_mask: Optional[jnp.ndarray] = None,
        init_cache: bool = False,
        deterministic: bool = True,
    ) -> Tuple[jnp.ndarray]:
        """Input shape: Batch x Time x Channel"""

        # if key_value_states are provided this layer is used as a cross-attention layer
        # for the decoder
        is_cross_attention = key_value_states is not None
        batch_size = hidden_states.shape[0]

        if self.config.fuse_matmuls:
            # get key, value proj
            if is_cross_attention:
                # get query proj
                query_states = self.q_proj(hidden_states)
                # cross_attentions
                attention_states = self.fused_key_value(key_value_states)
                key_states, value_states = jnp.split(attention_states, 2, axis=-1)
            else:
                attention_states = self.fused_proj(hidden_states)
                query_states, key_states, value_states = jnp.split(attention_states, 3, axis=-1)

        else:
            # get query proj
            query_states = self.q_proj(hidden_states)
            # get key, value proj
            if is_cross_attention:
                # cross_attentions
                key_states = self.k_proj(key_value_states)
                value_states = self.v_proj(key_value_states)
            else:
                # self_attention
                key_states = self.k_proj(hidden_states)
                value_states = self.v_proj(hidden_states)

        query_states = self._split_heads(query_states)
        key_states = self._split_heads(key_states)
        value_states = self._split_heads(value_states)

        # handle cache prepare causal attention mask
        if self.causal:
            query_length, key_length = query_states.shape[1], key_states.shape[1]
            if self.has_variable("cache", "cached_key"):
                mask_shift = self.variables["cache"]["cache_index"]
                max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
                causal_mask = lax.dynamic_slice(
                    self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length)
                )
            else:
                causal_mask = self.causal_mask[:, :, :query_length, :key_length]
            causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:])

        # combine masks if needed
        if attention_mask is not None and self.causal:
            attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape)
            attention_mask = combine_masks(attention_mask, causal_mask)
        elif self.causal:
            attention_mask = causal_mask
        elif attention_mask is not None:
            attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))

        # During fast autoregressive decoding, we feed one position at a time,
        # and cache the keys and values step by step.
        if self.causal and (self.has_variable("cache", "cached_key") or init_cache):
            key_states, value_states, attention_mask = self._concatenate_to_cache(
                key_states, value_states, query_states, attention_mask
            )

        # Convert the boolean attention mask to an attention bias.
        if attention_mask is not None:
            # attention mask in the form of attention bias
            attention_bias = lax.select(
                attention_mask > 0,
                jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
                jnp.full(attention_mask.shape, float("-inf")).astype(self.dtype),
            )
        else:
            attention_bias = None

        dropout_rng = None
        if not deterministic and self.dropout > 0.0:
            dropout_rng = self.make_rng("dropout")

        attn_weights = dot_product_attention_weights(
            query_states,
            key_states,
            bias=attention_bias,
            dropout_rng=dropout_rng,
            dropout_rate=self.dropout,
            broadcast_dropout=True,
            deterministic=deterministic,
            dtype=self.dtype,
            precision=None,
        )

        attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
        attn_output = self._merge_heads(attn_output)
        attn_output = self.out_proj(attn_output)

        return attn_output, attn_weights


class FlaxBartDecoderLayer(nn.Module):
    config: BartConfig
    dtype: jnp.dtype = jnp.float32

    def setup(self) -> None:
        self.embed_dim = self.config.d_model
        self.self_attn = FlaxBartAttention(
            config=self.config,
            embed_dim=self.embed_dim,
            num_heads=self.config.decoder_attention_heads,
            dropout=self.config.attention_dropout,
            causal=True,
            dtype=self.dtype,
        )
        self.dropout_layer = nn.Dropout(rate=self.config.dropout)
        self.activation_fn = ACT2FN[self.config.activation_function]
        self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout)

        self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
        self.encoder_attn = FlaxBartAttention(
            config=self.config,
            embed_dim=self.embed_dim,
            num_heads=self.config.decoder_attention_heads,
            dropout=self.config.attention_dropout,
            dtype=self.dtype,
        )
        self.encoder_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
        self.fc1 = nn.Dense(
            self.config.encoder_ffn_dim,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.init_std),
        )
        self.fc2 = nn.Dense(
            self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std)
        )
        self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)

    def __call__(
        self,
        hidden_states: jnp.ndarray,
        attention_mask: jnp.ndarray,
        encoder_hidden_states: Optional[jnp.ndarray] = None,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        init_cache: bool = False,
        output_attentions: bool = True,
        deterministic: bool = True,
    ) -> Tuple[jnp.ndarray]:

        if self.config.use_scan:
            hidden_states = hidden_states[0]

        residual = hidden_states

        # Self Attention
        hidden_states, self_attn_weights = self.self_attn(
            hidden_states=hidden_states, attention_mask=attention_mask, init_cache=init_cache
        )
        hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
        hidden_states = residual + hidden_states
        hidden_states = self.self_attn_layer_norm(hidden_states)

        # Cross-Attention Block
        cross_attn_weights = None
        if encoder_hidden_states is not None:
            residual = hidden_states

            hidden_states, cross_attn_weights = self.encoder_attn(
                hidden_states=hidden_states,
                key_value_states=encoder_hidden_states,
                attention_mask=encoder_attention_mask,
            )
            hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
            hidden_states = residual + hidden_states
            hidden_states = self.encoder_attn_layer_norm(hidden_states)

        # Fully Connected
        residual = hidden_states
        hidden_states = self.activation_fn(self.fc1(hidden_states))
        hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic)
        hidden_states = self.fc2(hidden_states)
        hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
        hidden_states = residual + hidden_states
        hidden_states = self.final_layer_norm(hidden_states)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights, cross_attn_weights)

        if self.config.use_scan:
            outputs = (outputs, None)

        return outputs


class FlaxBartDecoderLayerCollection(nn.Module):
    config: BartConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    @nn.compact
    def __call__(
        self,
        hidden_states,
        attention_mask,
        encoder_hidden_states: Optional[jnp.ndarray] = None,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        deterministic: bool = True,
        init_cache: bool = False,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None

        num_decoder_layers = self.config.decoder_layers
        BlockDecoderLayer = (
            remat(
                FlaxBartDecoderLayer,
                static_argnums=(4, 5, 6),
                prevent_cse=not self.config.use_scan,
            )
            if self.config.gradient_checkpointing
            else FlaxBartDecoderLayer
        )

        if self.config.use_scan:
            # since all decoder layers are the same, we use nn.scan directly
            assert not output_attentions, "cannot use `scan` with `output_attentions` set to `True`"
            assert not output_hidden_states, "cannot use `scan` with `output_hidden_states` set to `True`"
            hidden_states = (hidden_states,)

            # TODO: add layerdrop in checkpointed scan (note: default value for layerdrop in config is zero)
            hidden_states, _ = scan_with_axes(
                BlockDecoderLayer,
                variable_axes={"params": 0, "cache": 0},
                split_rngs={"params": True, "dropout": True},
                in_axes=(nn.broadcast, nn.broadcast, nn.broadcast, nn.broadcast, nn.broadcast, nn.broadcast),
                length=num_decoder_layers,
            )(self.config, dtype=self.dtype, name="FlaxBartDecoderLayers")(
                hidden_states,
                attention_mask,
                encoder_hidden_states,
                encoder_attention_mask,
                init_cache,
                output_attentions,
                deterministic,
            )
            hidden_states = hidden_states[0]

        else:
            for layer in range(num_decoder_layers):
                if output_hidden_states:
                    all_hidden_states += (hidden_states,)
                # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
                dropout_probability = random.uniform(0, 1)
                if not deterministic and (dropout_probability < self.config.decoder_layerdrop):
                    layer_outputs = (None, None, None)
                else:
                    layer_outputs = BlockDecoderLayer(self.config, dtype=self.dtype, name=str(layer),)(
                        hidden_states,
                        attention_mask,
                        encoder_hidden_states,
                        encoder_attention_mask,
                        init_cache,
                        output_attentions,
                        deterministic,
                    )

                hidden_states = layer_outputs[0]
                if output_attentions:
                    all_self_attns += (layer_outputs[1],)

                    if encoder_hidden_states is not None:
                        all_cross_attentions += (layer_outputs[2],)

            # add hidden states from the last decoder layer
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

        outputs = [hidden_states, all_hidden_states, all_self_attns, all_cross_attentions]

        if not return_dict:
            return tuple(v for v in outputs if v is not None)

        return FlaxBaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
            cross_attentions=all_cross_attentions,
        )


class FlaxBartDecoder(nn.Module):
    config: BartConfig
    embed_tokens: nn.Embed
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.dropout_layer = nn.Dropout(rate=self.config.dropout)

        embed_dim = self.config.d_model
        self.padding_idx = self.config.pad_token_id
        self.max_target_positions = self.config.max_position_embeddings
        self.embed_scale = math.sqrt(self.config.d_model) if self.config.scale_embedding else 1.0

        # Bart is set up so that if padding_idx is specified then offset the embedding ids by 2
        # and adjust num_embeddings appropriately. Other models don't have this hack
        self.offset = 2
        self.embed_positions = nn.Embed(
            self.config.max_position_embeddings + self.offset,
            embed_dim,
            embedding_init=jax.nn.initializers.normal(self.config.init_std),
        )

        self.layers = FlaxBartDecoderLayerCollection(self.config, self.dtype)
        self.layernorm_embedding = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)

    def __call__(
        self,
        input_ids,
        attention_mask,
        position_ids,
        encoder_hidden_states: Optional[jnp.ndarray] = None,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        init_cache: bool = False,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
        deterministic: bool = True,
    ):
        input_shape = input_ids.shape
        input_ids = input_ids.reshape(-1, input_shape[-1])

        inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale

        # embed positions
        positions = self.embed_positions(position_ids + self.offset)

        hidden_states = inputs_embeds + positions
        hidden_states = self.layernorm_embedding(hidden_states)

        hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)

        outputs = self.layers(
            hidden_states,
            attention_mask,
            encoder_hidden_states,
            encoder_attention_mask,
            deterministic=deterministic,
            init_cache=init_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        if not return_dict:
            return outputs

        return FlaxBaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=outputs.last_hidden_state,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            cross_attentions=outputs.cross_attentions,
        )


class FlaxBartDecoderPreTrainedModel(FlaxPreTrainedModel):
    config_class = BartConfig
    base_model_prefix: str = "model"
    module_class: nn.Module = None

    def __init__(
        self,
        config: BartConfig,
        input_shape: Tuple[int] = (1, 1),
        seed: int = 0,
        dtype: jnp.dtype = jnp.float32,
        _do_init: bool = True,
        **kwargs
    ):
        config.is_decoder = True
        config.is_encoder_decoder = False
        module = self.module_class(config=config, dtype=dtype, **kwargs)
        super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)

    def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple) -> FrozenDict:
        # init input tensors
        input_ids = jnp.zeros(input_shape, dtype="i4")
        attention_mask = jnp.ones_like(input_ids)

        batch_size, sequence_length = input_ids.shape
        position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))

        params_rng, dropout_rng = jax.random.split(rng)
        rngs = {"params": params_rng, "dropout": dropout_rng}
        encoder_hidden_states = jnp.zeros(input_shape + (self.config.d_model,))
        encoder_attention_mask = attention_mask
        module_init_outputs = self.module.init(
            rngs,
            input_ids,
            attention_mask,
            position_ids,
            encoder_hidden_states,
            encoder_attention_mask,
            return_dict=False,
        )
        return module_init_outputs["params"]

    def init_cache(self, batch_size, max_length):
        r"""
        Args:
            batch_size (`int`):
                batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
            max_length (`int`):
                maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized
                cache.
        """
        # init input variables to retrieve cache
        input_ids = jnp.ones((batch_size, max_length), dtype="i4")
        attention_mask = jnp.ones_like(input_ids, dtype="i4")
        position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)

        init_variables = self.module.init(
            jax.random.PRNGKey(0), input_ids, attention_mask, position_ids, return_dict=False, init_cache=True
        )
        return unfreeze(init_variables["cache"])

    def __call__(
        self,
        input_ids: jnp.ndarray,
        attention_mask: Optional[jnp.ndarray] = None,
        position_ids: Optional[jnp.ndarray] = None,
        encoder_hidden_states: Optional[jnp.ndarray] = None,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        train: bool = False,
        params: dict = None,
        past_key_values: dict = None,
        dropout_rng: PRNGKey = None,
    ):
        """
        Args:
        input_ids (`jnp.ndarray` of shape `(target_batch_size, target_sequence_length)`):
            Indices of decoder input sequence tokens in the vocabulary.

            Indices can be obtained using [`BartTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are decoder input IDs?](../glossary#decoder-input-ids)

            For translation and summarization training, `decoder_input_ids` should be provided. If no
            `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right
            for denoising pre-training following the paper.
        attention_mask (`jnp.ndarray` of shape `(target_batch_size, target_sequence_length)`, *optional*):
            Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
            be used by default.

            If you want to change padding behavior, you should modify to your needs. See diagram 1 in [the
            paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy.
        position_ids (`numpy.ndarray` of shape `(target_batch_size, sequence_length)`, *optional*):
            Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
            range `[0, config.max_position_embeddings - 1]`.
        encoder_hidden_states (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`):
            A sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
        encoder_attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
        past_key_values (`Dict[str, np.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`):
            Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast
            auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*.
        """
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        if encoder_hidden_states is not None and encoder_attention_mask is None:
            batch_size, sequence_length = encoder_hidden_states.shape[:2]
            encoder_attention_mask = jnp.ones((batch_size, sequence_length))

        # prepare decoder inputs
        if attention_mask is None:
            attention_mask = jnp.ones_like(input_ids)
        if position_ids is None:
            batch_size, sequence_length = input_ids.shape
            position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))

        # Handle any PRNG if needed
        rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}

        inputs = {"params": params or self.params}

        # if past_key_values are passed then cache is already initialized a private flag init_cache has to be passed
        # down to ensure cache is used. It has to be made sure that cache is marked as mutable so that it can be
        # changed by FlaxBartAttention module
        if past_key_values:
            inputs["cache"] = past_key_values
            mutable = ["cache"]
        else:
            mutable = False

        outputs = self.module.apply(
            inputs,
            input_ids=jnp.array(input_ids, dtype="i4"),
            attention_mask=jnp.array(attention_mask, dtype="i4"),
            position_ids=jnp.array(position_ids, dtype="i4"),
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            deterministic=not train,
            rngs=rngs,
            mutable=mutable,
        )

        # add updated cache to model output
        if past_key_values is not None and return_dict:
            outputs, past_key_values = outputs
            outputs["past_key_values"] = unfreeze(past_key_values["cache"])
            return outputs
        elif past_key_values is not None and not return_dict:
            outputs, past_key_values = outputs
            outputs = outputs[:1] + (unfreeze(past_key_values["cache"]),) + outputs[1:]

        return outputs


class FlaxBartDecoderWrapper(nn.Module):
    """
    This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
    used in combination with the [`EncoderDecoderModel`] framework.
    """

    config: BartConfig
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        embed_dim = self.config.d_model
        embed_tokens = nn.Embed(
            self.config.vocab_size,
            embed_dim,
            embedding_init=jax.nn.initializers.normal(self.config.init_std),
        )
        self.decoder = FlaxBartDecoder(config=self.config, embed_tokens=embed_tokens, dtype=self.dtype)

    def __call__(self, *args, **kwargs):
        return self.decoder(*args, **kwargs)


class FlaxBartForCausalLMModule(nn.Module):
    """Bart Decoder Module with a language modeling head on top (linear layer with weights tied to the input embeddings)
    e.g. for autoregressive tasks.
    """

    config: BartConfig
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.model = FlaxBartDecoderWrapper(config=self.config, dtype=self.dtype)
        self.lm_head = nn.Dense(
            self.config.vocab_size,
            use_bias=False,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.init_std),
        )

    def __call__(
        self,
        input_ids,
        attention_mask,
        position_ids,
        encoder_hidden_states: Optional[jnp.ndarray] = None,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        init_cache: bool = False,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
        deterministic: bool = True,
    ):

        outputs = self.model(
            input_ids,
            attention_mask,
            position_ids,
            encoder_hidden_states,
            encoder_attention_mask,
            deterministic=deterministic,
            init_cache=init_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]

        if self.config.tie_word_embeddings:
            shared_embedding = self.model.variables["params"]["decoder"]["embed_tokens"]["embedding"]
            lm_logits = self.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
        else:
            lm_logits = self.lm_head(hidden_states)

        if not return_dict:
            return (lm_logits,) + outputs[1:]

        return FlaxCausalLMOutputWithCrossAttentions(
            logits=lm_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            cross_attentions=outputs.cross_attentions,
        )


class FlaxBartForCausalLM(FlaxBartDecoderPreTrainedModel):
    """Bart Decoder Model with a language modeling head on top (linear layer with weights tied to the input embeddings)
    e.g. for autoregressive tasks.
    """

    module_class = FlaxBartForCausalLMModule

    def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jnp.DeviceArray] = None):
        # initializing the cache
        batch_size, seq_length = input_ids.shape

        past_key_values = self.init_cache(batch_size, max_length)
        # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
        # But since the decoder uses a causal mask, those positions are masked anyway.
        # Thus, we can create a single static attention_mask here, which is more efficient for compilation
        extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
        if attention_mask is not None:
            position_ids = attention_mask.cumsum(axis=-1) - 1
            extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, attention_mask, (0, 0))
        else:
            position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length))

        return {
            "past_key_values": past_key_values,
            "attention_mask": extended_attention_mask,
            "position_ids": position_ids,
        }

    def update_inputs_for_generation(self, model_outputs, model_kwargs):
        model_kwargs["past_key_values"] = model_outputs.past_key_values
        model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1
        return model_kwargs