File size: 23,533 Bytes
9c1d2b0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
# Copyright 2024 Rhymes AI. All rights reserved.
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you 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.

import logging
import os
from typing import Tuple

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import nn
from transformers import LlamaConfig
from transformers.models.llama.modeling_llama import (
    ACT2FN,
    LLAMA_ATTENTION_CLASSES,
    LlamaDecoderLayer,
    LlamaForCausalLM,
    LlamaMLP,
    LlamaModel,
    LlamaRMSNorm,
    LlamaRotaryEmbedding,
)

logger = logging.getLogger(__name__)


class AriaMoELMConfig(LlamaConfig):
    """
    Configuration class for AriaMoE language model.

    This class extends the LlamaConfig to include additional parameters specific to the Mixture of Experts (MoE) architecture.
    """

    model_type = "aria_moe_lm"

    def __init__(
        self,
        moe_intermediate_size: int = 4096,
        moe_num_experts: int = 8,
        moe_topk: int = 2,
        moe_z_loss_coeff: float = 1e-5,
        moe_aux_loss_coeff: float = 1e-3,
        moe_num_shared_experts: int = 2,
        **kwargs,
    ):
        """
        Initialize the AriaMoELMConfig.

        Args:
            moe_intermediate_size (int): The intermediate size for MoE layers. Default is 4096.
            moe_num_experts (int): The number of experts in the MoE layer. Default is 8.
            moe_topk (int): The number of top experts to route to for each token. Default is 2.
            moe_z_loss_coeff (float): The coefficient for the auxiliary z-loss. Default is 1e-5.
            moe_aux_loss_coeff (float): The coefficient for the auxiliary load balancing loss. Default is 1e-3.
            moe_num_shared_experts (int): The number of shared experts. Default is 2.
            **kwargs: Additional keyword arguments to be passed to the parent LlamaConfig.
        """
        super().__init__(**kwargs)
        self.moe_intermediate_size = moe_intermediate_size
        self.moe_num_experts = moe_num_experts
        self.moe_topk = moe_topk
        self.moe_z_loss_coeff = moe_z_loss_coeff
        self.moe_aux_loss_coeff = moe_aux_loss_coeff
        self.moe_num_shared_experts = moe_num_shared_experts


# copied from https://github.com/NVIDIA/Megatron-LM/blob/54f1f78529cbc2b9cddad313e7f9d96ac0420a27/megatron/core/transformer/moe/moe_utils.py#L101-L142
class MoEAuxLossAutoScaler(torch.autograd.Function):
    """An AutoScaler that compute and scales the grad for auxiliary loss."""

    main_loss_backward_scale: torch.Tensor = torch.tensor(1.0)

    @staticmethod
    def forward(ctx, output: torch.Tensor, aux_loss: torch.Tensor):
        """Preserve the aux_loss by storing it in the context to avoid garbage collection.

        Args:
            output (torch.Tensor): The output tensor.
            aux_loss (torch.Tensor): The auxiliary loss tensor.

        Returns:
            torch.Tensor: The output tensor.
        """
        ctx.save_for_backward(aux_loss)
        return output

    @staticmethod
    def backward(ctx, grad_output: torch.Tensor):
        """Compute and scale the gradient for auxiliary loss..

        Args:
            grad_output (torch.Tensor): The gradient of the output.

        Returns:
            Tuple[torch.Tensor, torch.Tensor]: The gradient of the output, scaled auxiliary loss gradient.
        """
        (aux_loss,) = ctx.saved_tensors
        aux_loss_backward_scale = MoEAuxLossAutoScaler.main_loss_backward_scale
        scaled_aux_loss_grad = torch.ones_like(aux_loss) * aux_loss_backward_scale
        return grad_output, scaled_aux_loss_grad

    @staticmethod
    def set_loss_scale(scale: torch.Tensor):
        """set the scale of the aux loss.

        Args:
            scale (torch.Tensor): The scale value to set. Please ensure that the scale passed in matches the scale of the main_loss.
        """
        MoEAuxLossAutoScaler.main_loss_backward_scale = scale


def z_loss_func(logits, z_loss_coeff):
    """Encourages the router's logits to remain small to enhance stability.
    Please refer to the ST-MoE paper (https://arxiv.org/pdf/2202.08906.pdf) for details.

    Args:
        logits (torch.Tensor): The logits of the router.

    Returns:
        torch.Tensor: The logits after applying the z-loss.
    """

    z_loss = torch.mean(torch.square(torch.logsumexp(logits, dim=-1))) * z_loss_coeff
    return z_loss


def switch_load_balancing_loss_func(
    probs: torch.Tensor,
    tokens_per_expert: torch.Tensor,
    topk: int,
    moe_aux_loss_coeff: float,
):
    """Calculate the auxiliary loss for better load balacing.
    Please refer to the Switch Transformer paper (https://arxiv.org/abs/2101.03961) for details.

    Args:
        probs (torch.Tensor): The softmax probs output by the router for each token. [num_tokens, num_experts]
        tokens_per_expert (torch.Tensor): The number of assigned tokens for each expert. [num_experts]

    Returns:
        torch.Tensor: The auxiliary loss for load balancing.
    """
    num_tokens = probs.shape[0] * topk
    num_experts = probs.shape[1]

    probs_mean_per_expert = probs.mean(dim=0)
    aux_loss = torch.sum(probs_mean_per_expert * tokens_per_expert) * (
        num_experts / num_tokens * moe_aux_loss_coeff
    )
    return aux_loss


# adapted from https://github.com/NVIDIA/Megatron-LM/blob/54f1f78529cbc2b9cddad313e7f9d96ac0420a27/megatron/core/transformer/moe/router.py#L96-L304
class TopKRouter(nn.Module):
    """
    Top-K Router for Mixture of Experts (MoE) models.

    This router determines which experts should process each token based on the top-k scoring experts.
    It also applies auxiliary losses to encourage load balancing among experts.

    Args:
        config (AriaMoELMConfig): Configuration object containing MoE-related parameters.
    """

    def __init__(self, config: AriaMoELMConfig):
        super().__init__()
        self.config = config

        self.weight = nn.Parameter(
            torch.empty((self.config.moe_num_experts, self.config.hidden_size))
        )
        # FIXME: initialize the weight

    def gating(self, input: torch.Tensor) -> torch.Tensor:
        """
        Compute the gating logits for each token-expert pair.

        Args:
            input (torch.Tensor): Input tensor of shape [batch_size * seq_len, hidden_size].

        Returns:
            torch.Tensor: Logits tensor of shape [batch_size * seq_len, num_experts].
        """
        logits = torch.nn.functional.linear(input, self.weight)
        return logits

    def apply_z_loss(self, logits: torch.Tensor) -> torch.Tensor:
        """
        Apply z-loss to encourage router logits to remain small for enhanced stability.

        Args:
            logits (torch.Tensor): Router logits.

        Returns:
            torch.Tensor: Logits with z-loss applied.
        """
        z_loss = z_loss_func(logits, self.config.moe_z_loss_coeff)
        logits = MoEAuxLossAutoScaler.apply(logits, z_loss)
        return logits

    def apply_aux_loss(
        self,
        logits: torch.Tensor,
        tokens_per_expert: torch.Tensor,
        activation: torch.Tensor,
    ) -> torch.Tensor:
        """
        Apply auxiliary loss for load balancing among experts.

        Args:
            logits (torch.Tensor): Router logits.
            tokens_per_expert (torch.Tensor): Number of tokens assigned to each expert.
            activation (torch.Tensor): Activation values.

        Returns:
            torch.Tensor: Activation with auxiliary loss applied.
        """
        probs = torch.softmax(logits, dim=-1, dtype=torch.float32)
        aux_loss = switch_load_balancing_loss_func(
            probs,
            tokens_per_expert,
            self.config.moe_topk,
            self.config.moe_aux_loss_coeff,
        )
        return MoEAuxLossAutoScaler.apply(activation, aux_loss)

    def routing(
        self, logits: torch.Tensor
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        """
        Perform the routing operation to determine expert assignments.

        Args:
            logits (torch.Tensor): Router logits.

        Returns:
            Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
                - scores: Softmax probabilities for top-k experts.
                - top_indices: Indices of top-k experts for each token.
                - tokens_per_expert: Number of tokens assigned to each expert.
        """
        logits = self.apply_z_loss(logits)

        top_logits, top_indices = torch.topk(logits, k=self.config.moe_topk, dim=1)
        scores = torch.softmax(top_logits, dim=-1, dtype=torch.float32).type_as(logits)

        tokens_per_expert = torch.histc(
            top_indices.flatten(),
            bins=self.config.moe_num_experts,
            min=0,
            max=self.config.moe_num_experts - 1,
        )

        scores = self.apply_aux_loss(logits, tokens_per_expert, scores)
        return scores, top_indices, tokens_per_expert

    def forward(
        self, input: torch.Tensor
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        """
        Forward pass of the TopKRouter.

        Args:
            input (torch.Tensor): Input tensor of shape [batch_size * seq_len, hidden_size].

        Returns:
            Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
                - scores: Softmax probabilities for top-k experts.
                - top_indices: Indices of top-k experts for each token.
                - tokens_per_expert: Number of tokens assigned to each expert.
        """
        logits = self.gating(input)
        logits = logits.view(-1, self.config.moe_num_experts)
        scores, top_indices, tokens_per_expert = self.routing(logits)
        return scores, top_indices, tokens_per_expert


# adapted from https://github.com/NVIDIA/Megatron-LM/blob/54f1f78529cbc2b9cddad313e7f9d96ac0420a27/megatron/core/transformer/moe/token_dispatcher.py#L291-L587
class TokenDispatcher:
    """
    Handles the dispatching and gathering of tokens to and from experts.

    This class is responsible for permuting tokens based on expert assignments and
    unpermuting them after expert processing.

    Args:
        config (AriaMoELMConfig): Configuration object containing MoE-related parameters.
    """

    def __init__(self, config: AriaMoELMConfig):
        self.config = config
        self.hidden_states_shape = None
        self.reversed_input_permutation_mapping = None

    def token_permutation(
        self, hidden_states: torch.Tensor, indices: torch.Tensor
    ) -> torch.Tensor:
        """
        Permute tokens based on expert assignments.

        Args:
            hidden_states (torch.Tensor): Input hidden states.
            indices (torch.Tensor): Expert assignment indices.

        Returns:
            torch.Tensor: Permuted tokens.
        """
        self.hidden_states_shape = hidden_states.shape
        hidden_states = hidden_states.view(-1, hidden_states.size(-1))
        flatten_indices = indices.flatten()
        sorted_indices = torch.argsort(flatten_indices, stable=True)
        permuted_tokens = hidden_states.index_select(
            0, sorted_indices // self.config.moe_topk
        )
        self.reversed_input_permutation_mapping = sorted_indices
        return permuted_tokens

    def token_unpermutation(
        self, permuted_tokens: torch.Tensor, scores: torch.Tensor
    ) -> torch.Tensor:
        """
        Unpermute tokens and combine expert outputs.

        Args:
            permuted_tokens (torch.Tensor): Tokens after expert processing.
            scores (torch.Tensor): Expert assignment scores.

        Returns:
            torch.Tensor: Unpermuted and combined output.
        """
        num_unpermuted_tokens = scores.numel()
        unpermuted_tokens = torch.zeros(
            (num_unpermuted_tokens, permuted_tokens.size(1)),
            dtype=permuted_tokens.dtype,
            device=permuted_tokens.device,
        )
        unpermuted_tokens.index_copy_(
            0, self.reversed_input_permutation_mapping, permuted_tokens
        )
        unpermuted_tokens = unpermuted_tokens.reshape(
            -1, self.config.moe_topk, permuted_tokens.size(1)
        )

        unpermuted_tokens = unpermuted_tokens * scores.unsqueeze(-1)
        unpermuted_tokens = unpermuted_tokens.sum(dim=1).type_as(permuted_tokens)
        output = unpermuted_tokens.view(self.hidden_states_shape)
        return output


class SharedExpertMLP(LlamaMLP):
    """
    Shared Expert MLP for shared experts.

    Unlike routed experts, shared experts process all tokens without routing.
    This class reconfigures the intermediate size in comparison to the LlamaMLP.

    Args:
        config (AriaMoELMConfig): Configuration object for the AriaMoE language model.
    """

    def __init__(self, config: AriaMoELMConfig):
        nn.Module.__init__(self)
        self.config = config
        self.hidden_size = config.hidden_size
        self.intermediate_size = (
            config.moe_intermediate_size * config.moe_num_shared_experts
        )
        self.gate_proj = nn.Linear(
            self.hidden_size, self.intermediate_size, bias=config.mlp_bias
        )
        self.up_proj = nn.Linear(
            self.hidden_size, self.intermediate_size, bias=config.mlp_bias
        )
        self.down_proj = nn.Linear(
            self.intermediate_size, self.hidden_size, bias=config.mlp_bias
        )
        self.act_fn = ACT2FN[config.hidden_act]


def sequential_gemm(input, weight, tokens_per_expert):
    """
    Compute the matrix multiplication (GEMM) for each expert sequentially. This approach is computationally inefficient, especially when dealing with a large number of experts.

    Args:
        input (torch.Tensor): Input tensor of shape (num_tokens, in_features).
        weight (torch.Tensor): Weight tensor of shape (num_experts, in_features, out_features).
        tokens_per_expert (torch.Tensor): Number of tokens assigned to each expert.

    Returns:
        torch.Tensor: Output tensor of shape (num_tokens, out_features).
    """
    num_tokens = input.shape[0]
    out_features = weight.shape[-1]
    output = torch.zeros(
        num_tokens, out_features, dtype=input.dtype, device=input.device
    )

    cumsum_num_tokens = torch.cumsum(tokens_per_expert, dim=0)
    # Insert zero at the begining for offset index's convenience
    zero_tensor = torch.zeros(1, dtype=torch.long, device=cumsum_num_tokens.device)
    cumsum_num_tokens = torch.cat((zero_tensor, cumsum_num_tokens))

    for expert_num in range(weight.shape[0]):
        start = cumsum_num_tokens[expert_num]
        end = cumsum_num_tokens[expert_num + 1]
        tokens = input[start:end]

        out = torch.matmul(tokens, weight[expert_num])
        output[start:end] = out
    return output


class ExpertMLP(LlamaMLP):
    """
    Expert MLP for the Mixture of Experts (MoE) layer.

    This class represents an individual expert in the MoE architecture. It's a modified
    version of LlamaMLP with a configurable intermediate size specific to MoE.

    Args:
        config (AriaMoELMConfig): Configuration object for the AriaMoE language model.
    """

    def __init__(self, config: AriaMoELMConfig):
        nn.Module.__init__(self)
        self.config = config
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.moe_intermediate_size
        self.gate_proj = nn.Linear(
            self.hidden_size, self.intermediate_size, bias=config.mlp_bias
        )
        self.up_proj = nn.Linear(
            self.hidden_size, self.intermediate_size, bias=config.mlp_bias
        )
        self.down_proj = nn.Linear(
            self.intermediate_size, self.hidden_size, bias=config.mlp_bias
        )
        self.act_fn = ACT2FN[config.hidden_act]


class SequentialMLP(nn.Module):
    """
    Sequential MLP for handling multiple experts in the Mixture of Experts (MoE) layer.

    This class manages a collection of ExpertMLPs and processes tokens through them sequentially.

    Args:
        config (AriaMoELMConfig): Configuration object for the AriaMoE language model.
    """

    def __init__(self, config: AriaMoELMConfig):
        super().__init__()
        self.config = config
        self.experts = nn.ModuleList(
            [ExpertMLP(config) for _ in range(config.moe_num_experts)]
        )

    def forward(self, permuted_tokens: torch.Tensor, tokens_per_expert: torch.Tensor) -> torch.Tensor:
        """
        Forward pass of the SequentialMLP.

        This method processes the permuted tokens through each expert sequentially,
        based on the number of tokens assigned to each expert.

        Args:
            permuted_tokens (torch.Tensor): Permuted input tokens.
            tokens_per_expert (torch.Tensor): Number of tokens assigned to each expert.

        Returns:
            torch.Tensor: Processed output from all experts.
        """
        output = torch.zeros_like(permuted_tokens)

        cumsum_num_tokens = torch.cumsum(tokens_per_expert, dim=0)
        # Insert zero at the beginning for offset index's convenience
        zero_tensor = torch.zeros(1, dtype=torch.long, device=cumsum_num_tokens.device)
        cumsum_num_tokens = torch.cat((zero_tensor, cumsum_num_tokens))

        for expert_num, expert in enumerate(self.experts):
            start = cumsum_num_tokens[expert_num]
            end = cumsum_num_tokens[expert_num + 1]
            tokens = permuted_tokens[start:end]

            out = expert(tokens)
            output[start:end] = out
        return output


class MoELayer(nn.Module):
    """
    Mixture of Experts (MoE) Layer for the AriaMoE model.

    This layer implements the MoE mechanism, which routes input tokens to different experts
    based on a routing algorithm, processes them through the experts, and then combines
    the outputs.

    Args:
        config (AriaMoELMConfig): Configuration object for the MoE layer.
    """

    def __init__(self, config: AriaMoELMConfig):
        super().__init__()

        self.router = TopKRouter(config)
        self.token_dispatcher = TokenDispatcher(config)
        self.experts = SequentialMLP(config)
        self.shared_experts = SharedExpertMLP(config)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        """
        Forward pass of the MoE Layer.

        Args:
            hidden_states (torch.Tensor): Input tensor of shape (batch_size, sequence_length, hidden_size).

        Returns:
            torch.Tensor: Output tensor after passing through the MoE layer.

        Process:
        1. Route tokens to experts using the router.
        2. Permute tokens based on routing decisions.
        3. Process tokens through experts.
        4. Unpermute and combine expert outputs.
        5. Add shared expert output to the final result.
        """
        scores, indices, tokens_per_expert = self.router(hidden_states)

        permuted_tokens = self.token_dispatcher.token_permutation(
            hidden_states, indices
        )

        expert_output = self.experts(permuted_tokens, tokens_per_expert)

        output = self.token_dispatcher.token_unpermutation(expert_output, scores)

        shared_expert_output = self.shared_experts(hidden_states)
        output += shared_expert_output
        return output


class MoEDecoderLayer(LlamaDecoderLayer):
    """
    Custom Decoder Layer for the AriaMoE model which modifies the standard `LlamaDecoderLayer` by
    replacing the traditional MLP with a Mixture of Experts (MoE) Layer.

    Args:
        config (LlamaConfig): Configuration object for the layer.
        layer_idx (int): Index of the current layer in the model.
    """

    def __init__(self, config: LlamaConfig, layer_idx: int):
        nn.Module.__init__(self)
        self.hidden_size = config.hidden_size

        self.self_attn = LLAMA_ATTENTION_CLASSES[config._attn_implementation](
            config=config, layer_idx=layer_idx
        )

        self.mlp = MoELayer(config)
        self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = LlamaRMSNorm(
            config.hidden_size, eps=config.rms_norm_eps
        )


class AriaMoELMModel(LlamaModel):
    """
    Custom LlamaModel for the AriaMoE model which modifies the standard LlamaModel by
    replacing the `LlamaDecoderLayer` with `MoEDecoderLayer`.

    This model implements a Mixture of Experts (MoE) approach, where each layer contains
    multiple expert networks that specialize in different aspects of the input.

    Args:
        config (LlamaConfig): Configuration object for the model.
    """

    def __init__(self, config: LlamaConfig):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size

        self.embed_tokens = nn.Embedding(
            config.vocab_size, config.hidden_size, self.padding_idx
        )
        self.layers = nn.ModuleList(
            [
                MoEDecoderLayer(config, layer_idx)
                for layer_idx in range(config.num_hidden_layers)
            ]
        )
        self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.rotary_emb = LlamaRotaryEmbedding(config=config)
        self.gradient_checkpointing = False

        # Initialize weights and apply final processing
        self.post_init()


class AriaMoELMForCausalLM(LlamaForCausalLM):
    """
    AriaMoE model for causal language modeling tasks.

    This class extends LlamaForCausalLM to incorporate the Mixture of Experts (MoE) approach,
    allowing for more efficient and scalable language modeling.

    Args:
        config (AriaMoELMConfig): Configuration object for the model.
    """

    _tied_weights_keys = ["lm_head.weight"]
    config_class = AriaMoELMConfig
    _no_split_modules = ["MoEDecoderLayer"]

    def __init__(self, config):
        super().__init__(config)
        self.model = AriaMoELMModel(config)
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

    def set_z_loss_coeff(self, z_loss_coeff: float):
        """
        Set the coefficient for the z-loss in the MoE routing.

        Args:
            z_loss_coeff (float): The coefficient for the z-loss.
        """
        self.config.moe_z_loss_coeff = z_loss_coeff

    def set_aux_loss_coeff(self, aux_loss_coeff: float):
        """
        Set the coefficient for the auxiliary loss in the MoE routing.

        Args:
            aux_loss_coeff (float): The coefficient for the auxiliary loss.
        """
        self.config.moe_aux_loss_coeff = aux_loss_coeff