modeling_nllb_moe.py

# coding=utf-8
# Copyright 2023 NllbMoe Authors and HuggingFace Inc. team.
#
# 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.
""" PyTorch NLLB-MoE model."""


import math
from typing import List, Optional, Tuple, Union

import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss

from ...activations import ACT2FN
from ...integrations.deepspeed import is_deepspeed_zero3_enabled
from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask
from ...modeling_outputs import (
    MoEModelOutput,
    MoEModelOutputWithPastAndCrossAttentions,
    Seq2SeqMoEModelOutput,
    Seq2SeqMoEOutput,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from .configuration_nllb_moe import NllbMoeConfig


logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "NllbMoeConfig"
_CHECKPOINT_FOR_DOC = "hf-internal-testing/dummy-nllb-moe-2-experts"
_REAL_CHECKPOINT_FOR_DOC = "facebook/nllb-moe-54b"


####################################################
# This dict contains ids and associated url
# for the pretrained weights provided with the models
####################################################
NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "facebook/nllb-moe-54b",
    # See all NLLB-MOE models at https://huggingface.co/models?filter=nllb-moe
]


# Copied from transformers.models.bart.modeling_bart.shift_tokens_right
def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
    """
    Shift input ids one token to the right.
    """
    shifted_input_ids = input_ids.new_zeros(input_ids.shape)
    shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
    shifted_input_ids[:, 0] = decoder_start_token_id

    if pad_token_id is None:
        raise ValueError("self.model.config.pad_token_id has to be defined.")
    # replace possible -100 values in labels by `pad_token_id`
    shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)

    return shifted_input_ids


# Copied from transformers.models.roberta.modeling_roberta.create_position_ids_from_input_ids
def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
    """
    Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
    are ignored. This is modified from fairseq's `utils.make_positions`.

    Args:
        x: torch.Tensor x:

    Returns: torch.Tensor
    """
    # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
    mask = input_ids.ne(padding_idx).int()
    incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask
    return incremental_indices.long() + padding_idx


def load_balancing_loss_func(router_probs: torch.Tensor, expert_indices: torch.Tensor) -> float:
    r"""
    Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.

    See Switch Transformer (https://arxiv.org/abs/2101.03961) for more details. This function implements the loss
    function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
    experts is too unbalanced.

    Args:
        router_probs (`torch.Tensor`):
            Probability assigned to each expert per token. Shape: [batch_size, seqeunce_length, num_experts].
        expert_indices (`torch.Tensor`):
            Indices tensor of shape [batch_size, seqeunce_length] identifying the selected expert for a given token.

    Returns:
        The auxiliary loss.
    """
    if router_probs is None:
        return 0

    num_experts = router_probs.shape[-1]

    # cast the expert indices to int64, otherwise one-hot encoding will fail
    if expert_indices.dtype != torch.int64:
        expert_indices = expert_indices.to(torch.int64)

    if len(expert_indices.shape) == 2:
        expert_indices = expert_indices.unsqueeze(2)

    expert_mask = torch.nn.functional.one_hot(expert_indices, num_experts)

    # For a given token, determine if it was routed to a given expert.
    expert_mask = torch.max(expert_mask, axis=-2).values

    # cast to float32 otherwise mean will fail
    expert_mask = expert_mask.to(torch.float32)
    tokens_per_group_and_expert = torch.mean(expert_mask, axis=-2)

    router_prob_per_group_and_expert = torch.mean(router_probs, axis=-2)
    return torch.mean(tokens_per_group_and_expert * router_prob_per_group_and_expert) * (num_experts**2)


# Copied from transformers.models.m2m_100.modeling_m2m_100.M2M100SinusoidalPositionalEmbedding
class NllbMoeSinusoidalPositionalEmbedding(nn.Module):
    """This module produces sinusoidal positional embeddings of any length."""

    def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None):
        super().__init__()
        self.offset = 2
        self.embedding_dim = embedding_dim
        self.padding_idx = padding_idx
        self.make_weights(num_positions + self.offset, embedding_dim, padding_idx)

    def make_weights(self, num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None):
        emb_weights = self.get_embedding(num_embeddings, embedding_dim, padding_idx)
        if hasattr(self, "weights"):
            # in forward put the weights on the correct dtype and device of the param
            emb_weights = emb_weights.to(dtype=self.weights.dtype, device=self.weights.device)

        self.register_buffer("weights", emb_weights, persistent=False)

    @staticmethod
    def get_embedding(num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None):
        """
        Build sinusoidal embeddings.

        This matches the implementation in tensor2tensor, but differs slightly from the description in Section 3.5 of
        "Attention Is All You Need".
        """
        half_dim = embedding_dim // 2
        emb = math.log(10000) / (half_dim - 1)
        emb = torch.exp(torch.arange(half_dim, dtype=torch.int64).float() * -emb)
        emb = torch.arange(num_embeddings, dtype=torch.int64).float().unsqueeze(1) * emb.unsqueeze(0)
        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)
        if embedding_dim % 2 == 1:
            # zero pad
            emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
        if padding_idx is not None:
            emb[padding_idx, :] = 0

        return emb.to(torch.get_default_dtype())

    @torch.no_grad()
    def forward(
        self, input_ids: torch.Tensor = None, inputs_embeds: torch.Tensor = None, past_key_values_length: int = 0
    ):
        if input_ids is not None:
            bsz, seq_len = input_ids.size()
            # Create the position ids from the input token ids. Any padded tokens remain padded.
            position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length).to(
                input_ids.device
            )
        else:
            bsz, seq_len = inputs_embeds.size()[:-1]
            position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds, past_key_values_length)

        # expand embeddings if needed
        max_pos = self.padding_idx + 1 + seq_len + past_key_values_length
        if max_pos > self.weights.size(0):
            self.make_weights(max_pos + self.offset, self.embedding_dim, self.padding_idx)

        return self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, self.weights.shape[-1]).detach()

    def create_position_ids_from_inputs_embeds(self, inputs_embeds, past_key_values_length):
        """
        We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.

        Args:
            inputs_embeds: torch.Tensor

        Returns: torch.Tensor
        """
        input_shape = inputs_embeds.size()[:-1]
        sequence_length = input_shape[1]

        position_ids = torch.arange(
            self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
        )
        return position_ids.unsqueeze(0).expand(input_shape).contiguous() + past_key_values_length


class NllbMoeTop2Router(nn.Module):
    """
    Router using tokens choose top-2 experts assignment.

    This router uses the same mechanism as in NLLB-MoE from the fairseq repository. Items are sorted by router_probs
    and then routed to their choice of expert until the expert's expert_capacity is reached. **There is no guarantee
    that each token is processed by an expert**, or that each expert receives at least one token.

    The router combining weights are also returned to make sure that the states that are not updated will be masked.

    """

    def __init__(self, config: NllbMoeConfig):
        super().__init__()
        self.num_experts = config.num_experts
        self.expert_capacity = config.expert_capacity
        self.classifier = nn.Linear(config.hidden_size, self.num_experts, bias=config.router_bias)
        self.router_ignore_padding_tokens = config.router_ignore_padding_tokens
        self.dtype = getattr(torch, config.router_dtype)

        self.second_expert_policy = config.second_expert_policy
        self.normalize_router_prob_before_dropping = config.normalize_router_prob_before_dropping
        self.batch_prioritized_routing = config.batch_prioritized_routing
        self.moe_eval_capacity_token_fraction = config.moe_eval_capacity_token_fraction

    def _cast_classifier(self):
        r"""
        `bitsandbytes` `Linear8bitLt` layers does not support manual casting Therefore we need to check if they are an
        instance of the `Linear8bitLt` class by checking special attributes.
        """
        if not (hasattr(self.classifier, "SCB") or hasattr(self.classifier, "CB")):
            self.classifier = self.classifier.to(self.dtype)

    def normalize_router_probabilities(self, router_probs, top_1_mask, top_2_mask):
        top_1_max_probs = (router_probs * top_1_mask).sum(dim=1)
        top_2_max_probs = (router_probs * top_2_mask).sum(dim=1)
        denom_s = torch.clamp(top_1_max_probs + top_2_max_probs, min=torch.finfo(router_probs.dtype).eps)
        top_1_max_probs = top_1_max_probs / denom_s
        top_2_max_probs = top_2_max_probs / denom_s
        return top_1_max_probs, top_2_max_probs

    def route_tokens(
        self,
        router_logits: torch.Tensor,
        input_dtype: torch.dtype = torch.float32,
        padding_mask: Optional[torch.LongTensor] = None,
    ) -> Tuple:
        """
        Computes the `dispatch_mask` and the `dispatch_weights` for each experts. The masks are adapted to the expert
        capacity.
        """
        nb_tokens = router_logits.shape[0]
        # Apply Softmax and cast back to the original `dtype`
        router_probs = nn.functional.softmax(router_logits, dim=-1, dtype=self.dtype).to(input_dtype)
        top_1_expert_index = torch.argmax(router_probs, dim=-1)
        top_1_mask = torch.nn.functional.one_hot(top_1_expert_index, num_classes=self.num_experts)

        if self.second_expert_policy == "sampling":
            gumbel = torch.distributions.gumbel.Gumbel(0, 1).rsample
            router_logits += gumbel(router_logits.shape).to(router_logits.device)

        # replace top_1_expert_index with min values
        logits_except_top_1 = router_logits.masked_fill(top_1_mask.bool(), float("-inf"))
        top_2_expert_index = torch.argmax(logits_except_top_1, dim=-1)
        top_2_mask = torch.nn.functional.one_hot(top_2_expert_index, num_classes=self.num_experts)

        if self.normalize_router_prob_before_dropping:
            top_1_max_probs, top_2_max_probs = self.normalize_router_probabilities(
                router_probs, top_1_mask, top_2_mask
            )

        if self.second_expert_policy == "random":
            top_2_max_probs = (router_probs * top_2_mask).sum(dim=1)
            sampled = (2 * top_2_max_probs) > torch.rand_like(top_2_max_probs.float())
            top_2_mask = top_2_mask * sampled.repeat(self.num_experts, 1).transpose(1, 0)

        if padding_mask is not None and not self.router_ignore_padding_tokens:
            if len(padding_mask.shape) == 4:
                # only get the last causal mask
                padding_mask = padding_mask[:, :, -1, :].reshape(-1)[-nb_tokens:]
            non_padding = ~padding_mask.bool()
            top_1_mask = top_1_mask * non_padding.unsqueeze(-1).to(top_1_mask.dtype)
            top_2_mask = top_2_mask * non_padding.unsqueeze(-1).to(top_1_mask.dtype)

        if self.batch_prioritized_routing:
            # sort tokens based on their routing probability
            # to make sure important tokens are routed, first
            importance_scores = -1 * router_probs.max(dim=1)[0]
            sorted_top_1_mask = top_1_mask[importance_scores.argsort(dim=0)]
            sorted_cumsum1 = (torch.cumsum(sorted_top_1_mask, dim=0) - 1) * sorted_top_1_mask
            locations1 = sorted_cumsum1[importance_scores.argsort(dim=0).argsort(dim=0)]

            sorted_top_2_mask = top_2_mask[importance_scores.argsort(dim=0)]
            sorted_cumsum2 = (torch.cumsum(sorted_top_2_mask, dim=0) - 1) * sorted_top_2_mask
            locations2 = sorted_cumsum2[importance_scores.argsort(dim=0).argsort(dim=0)]
            # Update 2nd's location by accounting for locations of 1st
            locations2 += torch.sum(top_1_mask, dim=0, keepdim=True)

        else:
            locations1 = torch.cumsum(top_1_mask, dim=0) - 1
            locations2 = torch.cumsum(top_2_mask, dim=0) - 1
            # Update 2nd's location by accounting for locations of 1st
            locations2 += torch.sum(top_1_mask, dim=0, keepdim=True)

        if not self.training and self.moe_eval_capacity_token_fraction > 0:
            self.expert_capacity = math.ceil(self.moe_eval_capacity_token_fraction * nb_tokens)
        else:
            capacity = 2 * math.ceil(nb_tokens / self.num_experts)
            self.expert_capacity = capacity if self.expert_capacity is None else self.expert_capacity

        # Remove locations outside capacity from ( cumsum < capacity = False will not be routed)
        top_1_mask = top_1_mask * torch.lt(locations1, self.expert_capacity)
        top_2_mask = top_2_mask * torch.lt(locations2, self.expert_capacity)

        if not self.normalize_router_prob_before_dropping:
            top_1_max_probs, top_2_max_probs = self.normalize_router_probabilities(
                router_probs, top_1_mask, top_2_mask
            )

        # Calculate combine_weights and dispatch_mask
        gates1 = top_1_max_probs[:, None] * top_1_mask
        gates2 = top_2_max_probs[:, None] * top_2_mask
        router_probs = gates1 + gates2

        return top_1_mask, router_probs

    def forward(self, hidden_states: torch.Tensor, padding_mask: Optional[torch.LongTensor] = None) -> Tuple:
        r"""
        The hidden states are reshaped to simplify the computation of the router probabilities (combining weights for
        each experts.)

        Args:
            hidden_states (`torch.Tensor`):
                (batch_size, sequence_length, hidden_dim) from which router probabilities are computed.
        Returns:
            top_1_mask (`torch.Tensor` of shape (batch_size, sequence_length)):
                Index tensor of shape [batch_size, sequence_length] corresponding to the expert selected for each token
                using the top1 probabilities of the router.
            router_probabilities (`torch.Tensor` of shape (batch_size, sequence_length, nump_experts)):
                Tensor of shape (batch_size, sequence_length, num_experts) corresponding to the probabilities for each
                token and expert. Used for routing tokens to experts.
            router_logits (`torch.Tensor` of shape (batch_size, sequence_length))):
                Logits tensor of shape (batch_size, sequence_length, num_experts) corresponding to raw router logits.
                This is used later for computing router z-loss.
        """
        self.input_dtype = hidden_states.dtype
        batch_size, sequence_length, hidden_dim = hidden_states.shape
        hidden_states = hidden_states.reshape((batch_size * sequence_length), hidden_dim)
        hidden_states = hidden_states.to(self.dtype)
        self._cast_classifier()
        router_logits = self.classifier(hidden_states)
        top_1_mask, router_probs = self.route_tokens(router_logits, self.input_dtype, padding_mask)
        return top_1_mask, router_probs


class NllbMoeDenseActDense(nn.Module):
    def __init__(self, config: NllbMoeConfig, ffn_dim: int):
        super().__init__()
        self.fc1 = nn.Linear(config.d_model, ffn_dim)
        self.fc2 = nn.Linear(ffn_dim, config.d_model)
        self.dropout = nn.Dropout(config.activation_dropout)
        self.act = ACT2FN[config.activation_function]

    def forward(self, hidden_states):
        hidden_states = self.fc1(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states = self.dropout(hidden_states)
        if (
            isinstance(self.fc2.weight, torch.Tensor)
            and hidden_states.dtype != self.fc2.weight.dtype
            and (self.fc2.weight.dtype != torch.int8 and self.fc2.weight.dtype != torch.uint8)
        ):
            hidden_states = hidden_states.to(self.fc2.weight.dtype)
        hidden_states = self.fc2(hidden_states)
        return hidden_states


class NllbMoeSparseMLP(nn.Module):
    r"""
    Implementation of the NLLB-MoE sparse MLP module.
    """

    def __init__(self, config: NllbMoeConfig, ffn_dim: int, expert_class: nn.Module = NllbMoeDenseActDense):
        super().__init__()
        self.router = NllbMoeTop2Router(config)
        self.moe_token_dropout = config.moe_token_dropout
        self.token_dropout = nn.Dropout(self.moe_token_dropout)
        self.num_experts = config.num_experts

        self.experts = nn.ModuleDict()
        for idx in range(self.num_experts):
            self.experts[f"expert_{idx}"] = expert_class(config, ffn_dim)

    def forward(self, hidden_states: torch.Tensor, padding_mask: Optional[torch.Tensor] = False):
        r"""
        The goal of this forward pass is to have the same number of operation as the equivalent `NllbMoeDenseActDense`
        (mlp) layer. This means that all of the hidden states should be processed at most twice ( since we are using a
        top_2 gating mecanism). This means that we keep the complexity to O(batch_size x sequence_length x hidden_dim)
        instead of O(num_experts x batch_size x sequence_length x hidden_dim).

        1- Get the `router_probs` from the `router`. The shape of the `router_mask` is `(batch_size X sequence_length,
        num_expert)` and corresponds to the boolean version of the `router_probs`. The inputs are masked using the
        `router_mask`.

        2- Dispatch the hidden_states to its associated experts. The router probabilities are used to weight the
        contribution of each experts when updating the masked hidden states.

        Args:
            hidden_states (`torch.Tensor` of shape `(batch_size, sequence_length, hidden_dim)`):
                The hidden states
            padding_mask (`torch.Tensor`, *optional*, defaults to `False`):
                Attention mask. Can be in the causal form or not.

        Returns:
            hidden_states (`torch.Tensor` of shape `(batch_size, sequence_length, hidden_dim)`):
                Updated hidden states
            router_logits (`torch.Tensor` of shape `(batch_size, sequence_length, num_experts)`):
                Needed for computing the loss

        """
        batch_size, sequence_length, hidden_dim = hidden_states.shape

        top_1_mask, router_probs = self.router(hidden_states, padding_mask)
        router_mask = router_probs.bool()
        hidden_states = hidden_states.reshape((batch_size * sequence_length), hidden_dim)
        masked_hidden_states = torch.einsum("bm,be->ebm", hidden_states, router_mask)
        for idx, expert in enumerate(self.experts.values()):
            token_indices = router_mask[:, idx]
            combining_weights = router_probs[token_indices, idx]
            expert_output = expert(masked_hidden_states[idx, token_indices])
            if self.moe_token_dropout > 0:
                if self.training:
                    expert_output = self.token_dropout(expert_output)
                else:
                    expert_output *= 1 - self.moe_token_dropout
            masked_hidden_states[idx, token_indices] = torch.einsum("b,be->be", combining_weights, expert_output)
        hidden_states = masked_hidden_states.sum(dim=0).reshape(batch_size, sequence_length, hidden_dim)

        top_1_expert_index = torch.argmax(top_1_mask, dim=-1)
        return hidden_states, (router_probs, top_1_expert_index)


# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->NllbMoe,key_value_states->encoder_hidden_states
class NllbMoeAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(
        self,
        embed_dim: int,
        num_heads: int,
        dropout: float = 0.0,
        is_decoder: bool = False,
        bias: bool = True,
        is_causal: bool = False,
        config: Optional[NllbMoeConfig] = None,
    ):
        super().__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.dropout = dropout
        self.head_dim = embed_dim // num_heads
        self.config = config

        if (self.head_dim * 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`: {num_heads})."
            )
        self.scaling = self.head_dim**-0.5
        self.is_decoder = is_decoder
        self.is_causal = is_causal

        self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)

    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()

    def forward(
        self,
        hidden_states: torch.Tensor,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        attention_mask: Optional[torch.Tensor] = None,
        layer_head_mask: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        """Input shape: Batch x Time x Channel"""

        # if encoder_hidden_states are provided this layer is used as a cross-attention layer
        # for the decoder
        is_cross_attention = encoder_hidden_states is not None

        bsz, tgt_len, _ = hidden_states.size()

        # get query proj
        query_states = self.q_proj(hidden_states) * self.scaling
        # get key, value proj
        # `past_key_value[0].shape[2] == encoder_hidden_states.shape[1]`
        # is checking that the `sequence_length` of the `past_key_value` is the same as
        # the provided `encoder_hidden_states` to support prefix tuning
        if (
            is_cross_attention
            and past_key_value is not None
            and past_key_value[0].shape[2] == encoder_hidden_states.shape[1]
        ):
            # reuse k,v, cross_attentions
            key_states = past_key_value[0]
            value_states = past_key_value[1]
        elif is_cross_attention:
            # cross_attentions
            key_states = self._shape(self.k_proj(encoder_hidden_states), -1, bsz)
            value_states = self._shape(self.v_proj(encoder_hidden_states), -1, bsz)
        elif past_key_value is not None:
            # reuse k, v, self_attention
            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
            key_states = torch.cat([past_key_value[0], key_states], dim=2)
            value_states = torch.cat([past_key_value[1], value_states], dim=2)
        else:
            # self_attention
            key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
            value_states = self._shape(self.v_proj(hidden_states), -1, bsz)

        if self.is_decoder:
            # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
            # Further calls to cross_attention layer can then reuse all cross-attention
            # key/value_states (first "if" case)
            # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
            # all previous decoder key/value_states. Further calls to uni-directional self-attention
            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
            # if encoder bi-directional self-attention `past_key_value` is always `None`
            past_key_value = (key_states, value_states)

        proj_shape = (bsz * self.num_heads, -1, self.head_dim)
        query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
        key_states = key_states.reshape(*proj_shape)
        value_states = value_states.reshape(*proj_shape)

        src_len = key_states.size(1)
        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))

        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
            raise ValueError(
                f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
                f" {attn_weights.size()}"
            )

        if attention_mask is not None:
            if attention_mask.size() != (bsz, 1, tgt_len, src_len):
                raise ValueError(
                    f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
                )
            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)

        attn_weights = nn.functional.softmax(attn_weights, dim=-1)

        if layer_head_mask is not None:
            if layer_head_mask.size() != (self.num_heads,):
                raise ValueError(
                    f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
                    f" {layer_head_mask.size()}"
                )
            attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)

        if output_attentions:
            # this operation is a bit awkward, but it's required to
            # make sure that attn_weights keeps its gradient.
            # In order to do so, attn_weights have to be reshaped
            # twice and have to be reused in the following
            attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
            attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
        else:
            attn_weights_reshaped = None

        attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)

        attn_output = torch.bmm(attn_probs, value_states)

        if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
            raise ValueError(
                f"`attn_output` should be of size {(bsz * self.num_heads, tgt_len, self.head_dim)}, but is"
                f" {attn_output.size()}"
            )

        attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
        attn_output = attn_output.transpose(1, 2)

        # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
        # partitioned across GPUs when using tensor-parallelism.
        attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)

        attn_output = self.out_proj(attn_output)

        return attn_output, attn_weights_reshaped, past_key_value


class NllbMoeEncoderLayer(nn.Module):
    def __init__(self, config: NllbMoeConfig, is_sparse: bool = False):
        super().__init__()
        self.embed_dim = config.d_model
        self.is_sparse = is_sparse
        self.self_attn = NllbMoeAttention(
            embed_dim=self.embed_dim,
            num_heads=config.encoder_attention_heads,
            dropout=config.attention_dropout,
        )
        self.attn_dropout = nn.Dropout(config.dropout)
        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
        if not self.is_sparse:
            self.ffn = NllbMoeDenseActDense(config, ffn_dim=config.encoder_ffn_dim)
        else:
            self.ffn = NllbMoeSparseMLP(config, ffn_dim=config.encoder_ffn_dim)
        self.ff_layer_norm = nn.LayerNorm(config.d_model)
        self.ff_dropout = nn.Dropout(config.activation_dropout)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor,
        layer_head_mask: torch.Tensor,
        output_attentions: bool = False,
        output_router_logits: bool = False,
    ) -> torch.Tensor:
        """
        Args:
            hidden_states (`torch.FloatTensor`):
                input to the layer of shape `(batch, seq_len, embed_dim)`
            attention_mask (`torch.FloatTensor`):
                attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very
                large negative values.
            layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
                `(encoder_attention_heads,)`.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
        """
        residual = hidden_states
        hidden_states = self.self_attn_layer_norm(hidden_states)
        hidden_states, attn_weights, _ = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            layer_head_mask=layer_head_mask,
            output_attentions=output_attentions,
        )
        hidden_states = self.attn_dropout(hidden_states)
        hidden_states = residual + hidden_states

        residual = hidden_states

        hidden_states = self.ff_layer_norm(hidden_states)
        if self.is_sparse:
            hidden_states, router_states = self.ffn(hidden_states, attention_mask)
        else:
            # router_states set to None to track which layers have None gradients.
            hidden_states, router_states = self.ffn(hidden_states), None

        hidden_states = self.ff_dropout(hidden_states)

        hidden_states = residual + hidden_states

        if hidden_states.dtype == torch.float16 and (
            torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
        ):
            clamp_value = torch.finfo(hidden_states.dtype).max - 1000
            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (attn_weights,)

        if output_router_logits:
            outputs += (router_states,)

        return outputs


class NllbMoeDecoderLayer(nn.Module):
    def __init__(self, config: NllbMoeConfig, is_sparse: bool = False):
        super().__init__()
        self.embed_dim = config.d_model
        self.is_sparse = is_sparse
        self.self_attn = NllbMoeAttention(
            embed_dim=self.embed_dim,
            num_heads=config.decoder_attention_heads,
            dropout=config.attention_dropout,
            is_decoder=True,
        )
        self.dropout = config.dropout
        self.activation_fn = ACT2FN[config.activation_function]
        self.attn_dropout = nn.Dropout(config.dropout)

        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
        self.cross_attention = NllbMoeAttention(
            self.embed_dim, config.decoder_attention_heads, config.attention_dropout, is_decoder=True
        )
        self.cross_attention_layer_norm = nn.LayerNorm(self.embed_dim)
        if not self.is_sparse:
            self.ffn = NllbMoeDenseActDense(config, ffn_dim=config.decoder_ffn_dim)
        else:
            self.ffn = NllbMoeSparseMLP(config, ffn_dim=config.decoder_ffn_dim)
        self.ff_layer_norm = nn.LayerNorm(config.d_model)
        self.ff_dropout = nn.Dropout(config.activation_dropout)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.Tensor] = None,
        layer_head_mask: Optional[torch.Tensor] = None,
        cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: Optional[bool] = False,
        output_router_logits: Optional[bool] = False,
        use_cache: Optional[bool] = True,
    ) -> torch.Tensor:
        """
        Args:
            hidden_states (`torch.FloatTensor`):
                input to the layer of shape `(batch, seq_len, embed_dim)`
            attention_mask (`torch.FloatTensor`):
                attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very
                large negative values.
            encoder_hidden_states (`torch.FloatTensor`):
                cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
            encoder_attention_mask (`torch.FloatTensor`):
                encoder attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by
                very large negative values.
            layer_head_mask (`torch.FloatTensor`):
                mask for attention heads in a given layer of size `(encoder_attention_heads,)`.
            cross_attn_layer_head_mask (`torch.FloatTensor`):
                mask for cross-attention heads in a given layer of size `(decoder_attention_heads,)`.
            past_key_value (`Tuple(torch.FloatTensor)`):
                cached past key and value projection states
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
        """
        residual = hidden_states
        hidden_states = self.self_attn_layer_norm(hidden_states)

        # Self Attention
        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
        # add present self-attn cache to positions 1,2 of present_key_value tuple
        hidden_states, self_attn_weights, present_key_value = self.self_attn(
            hidden_states=hidden_states,
            past_key_value=self_attn_past_key_value,
            attention_mask=attention_mask,
            layer_head_mask=layer_head_mask,
            output_attentions=output_attentions,
        )
        hidden_states = self.attn_dropout(hidden_states)
        hidden_states = residual + hidden_states

        # Cross-Attention Block
        cross_attn_present_key_value = None
        cross_attn_weights = None
        if encoder_hidden_states is not None:
            residual = hidden_states
            hidden_states = self.cross_attention_layer_norm(hidden_states)

            # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
            cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
            hidden_states, cross_attn_weights, cross_attn_present_key_value = self.cross_attention(
                hidden_states=hidden_states,
                encoder_hidden_states=encoder_hidden_states,
                past_key_value=cross_attn_past_key_value,
                attention_mask=encoder_attention_mask,
                layer_head_mask=cross_attn_layer_head_mask,
                output_attentions=output_attentions,
            )
            hidden_states = self.attn_dropout(hidden_states)
            hidden_states = residual + hidden_states

            # add cross-attn to positions 3,4 of present_key_value tuple
            present_key_value += cross_attn_present_key_value

        # Fully Connected
        residual = hidden_states

        hidden_states = self.ff_layer_norm(hidden_states)
        if self.is_sparse:
            hidden_states, router_states = self.ffn(hidden_states, attention_mask)
        else:
            hidden_states, router_states = self.ffn(hidden_states), None

        hidden_states = self.ff_dropout(hidden_states)

        hidden_states = residual + hidden_states

        # clamp inf values to enable fp16 training
        if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
            clamp_value = torch.finfo(hidden_states.dtype).max - 1000
            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)

        outputs = (hidden_states, present_key_value)

        if output_attentions:
            outputs += (self_attn_weights, cross_attn_weights)

        if output_router_logits:
            outputs += (router_states,)

        return outputs


class NllbMoePreTrainedModel(PreTrainedModel):
    config_class = NllbMoeConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["NllbMoeEncoderLayer", "NllbMoeDecoderLayer"]

    def _init_weights(self, module):
        """Initialize the weights"""
        std = self.config.init_std
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()


NLLB_MOE_START_DOCSTRING = r"""
    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`NllbMoeConfig`]):
            Model configuration class with all the parameters of the model. Initializing with a config file does not
            load the weights associated with the model, only the configuration. Check out the
            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

NLLB_MOE_GENERATION_EXAMPLE = r"""
    Translation example:

    ```python
    >>> from transformers import AutoTokenizer, NllbMoeForConditionalGeneration

    >>> model = NllbMoeForConditionalGeneration.from_pretrained("facebook/nllb-moe-54b")
    >>> tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-moe-54b")

    >>> text_to_translate = "Life is like a box of chocolates"
    >>> model_inputs = tokenizer(text_to_translate, return_tensors="pt")

    >>> # translate to French
    >>> gen_tokens = model.generate(**model_inputs, forced_bos_token_id=tokenizer.get_lang_id("eng_Latn"))
    >>> print(tokenizer.batch_decode(gen_tokens, skip_special_tokens=True))
    ```
"""

NLLB_MOE_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
            it.

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

            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.Tensor` 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)
        decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
            Indices of decoder input sequence tokens in the vocabulary.

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

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

            NllbMoe uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If
            `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
            `past_key_values`).
        decoder_attention_mask (`torch.LongTensor` of shape `(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.
        head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
            Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        decoder_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
            Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
            Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
            1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.
        encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
            Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
            `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
            hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
            This is useful if you want more control over how to convert `input_ids` indices into associated vectors
            than the model's internal embedding lookup matrix.
        decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
            representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
            input (see `past_key_values`). This is useful if you want more control over how to convert
            `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.

            If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
            of `inputs_embeds`.
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
        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.
        output_router_logits (`bool`, *optional*):
            Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
            should not be returned during inference.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""


class NllbMoeEncoder(NllbMoePreTrainedModel):
    """
    Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
    [`NllbMoeEncoderLayer`].

    Args:
        config:
            NllbMoeConfig
        embed_tokens (nn.Embedding):
            output embedding
    """

    def __init__(self, config: NllbMoeConfig, embed_tokens: Optional[nn.Embedding] = None):
        super().__init__(config)

        self.dropout = config.dropout
        self.layerdrop = config.encoder_layerdrop

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

        self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx)

        if embed_tokens is not None:
            self.embed_tokens.weight = embed_tokens.weight

        self.embed_positions = NllbMoeSinusoidalPositionalEmbedding(
            config.max_position_embeddings,
            embed_dim,
            self.padding_idx,
        )
        sparse_step = config.encoder_sparse_step
        self.layers = nn.ModuleList()
        for i in range(config.encoder_layers):
            is_sparse = (i + 1) % sparse_step == 0 if sparse_step > 0 else False
            self.layers.append(NllbMoeEncoderLayer(config, is_sparse))

        self.layer_norm = nn.LayerNorm(config.d_model)

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

    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        output_router_logits: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ):
        r"""
        Args:
            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
                Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
                provide it.

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

                [What are input IDs?](../glossary#input-ids)
            attention_mask (`torch.Tensor` 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)
            head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
                Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:

                - 1 indicates the head is **not masked**,
                - 0 indicates the head is **masked**.

            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
                Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
                This is useful if you want more control over how to convert `input_ids` indices into associated vectors
                than the model's internal embedding lookup matrix.
            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.
            output_router_logits (`bool`, *optional*):
                Whether or not to return the logits of all the routers. They are useful for computing the router loss,
                and should not be returned during inference.
            return_dict (`bool`, *optional*):
                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
        """
        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

        # retrieve input_ids and inputs_embeds
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
            input_shape = input_ids.size()
            input_ids = input_ids.view(-1, input_shape[-1])
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale

        embed_pos = self.embed_positions(input_ids, inputs_embeds)
        embed_pos = embed_pos.to(inputs_embeds.device)

        hidden_states = inputs_embeds + embed_pos
        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)

        # expand attention_mask
        if attention_mask is not None:
            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
            attention_mask = _prepare_4d_attention_mask(attention_mask, inputs_embeds.dtype)

        encoder_states = () if output_hidden_states else None
        all_router_probs = () if output_router_logits else None
        all_attentions = () if output_attentions else None

        # check if head_mask has a correct number of layers specified if desired
        if head_mask is not None:
            if head_mask.size()[0] != len(self.layers):
                raise ValueError(
                    f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
                    f" {head_mask.size()[0]}."
                )

        for idx, encoder_layer in enumerate(self.layers):
            if output_hidden_states:
                encoder_states = encoder_states + (hidden_states,)
            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
            dropout_probability = torch.rand([])
            if self.training and (dropout_probability < self.layerdrop):  # skip the layer
                layer_outputs = (None, None, None)
            else:
                if self.gradient_checkpointing and self.training:
                    layer_outputs = self._gradient_checkpointing_func(
                        encoder_layer.__call__,
                        hidden_states,
                        attention_mask,
                        (head_mask[idx] if head_mask is not None else None),
                        output_attentions,
                    )
                else:
                    layer_outputs = encoder_layer(
                        hidden_states,
                        attention_mask,
                        layer_head_mask=(head_mask[idx] if head_mask is not None else None),
                        output_attentions=output_attentions,
                        output_router_logits=output_router_logits,
                    )

                hidden_states = layer_outputs[0]

            if output_attentions:
                all_attentions += (layer_outputs[1],)

            if output_router_logits:
                all_router_probs += (layer_outputs[-1],)

        last_hidden_state = self.layer_norm(hidden_states)

        if output_hidden_states:
            encoder_states += (last_hidden_state,)

        if not return_dict:
            return tuple(
                v for v in [last_hidden_state, encoder_states, all_attentions, all_router_probs] if v is not None
            )

        return MoEModelOutput(
            last_hidden_state=last_hidden_state,
            hidden_states=encoder_states,
            attentions=all_attentions,
            router_probs=all_router_probs,
        )


class NllbMoeDecoder(NllbMoePreTrainedModel):
    """
    Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`NllbMoeDecoderLayer`]

    Args:
        config:
            NllbMoeConfig
        embed_tokens (nn.Embedding):
            output embedding
    """

    def __init__(self, config: NllbMoeConfig, embed_tokens: Optional[nn.Embedding] = None):
        super().__init__(config)
        self.dropout = config.dropout
        self.layerdrop = config.decoder_layerdrop
        self.padding_idx = config.pad_token_id
        self.max_target_positions = config.max_position_embeddings
        self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0

        self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx)

        if embed_tokens is not None:
            self.embed_tokens.weight = embed_tokens.weight

        self.embed_positions = NllbMoeSinusoidalPositionalEmbedding(
            config.max_position_embeddings,
            config.d_model,
            self.padding_idx,
        )

        sparse_step = config.decoder_sparse_step
        self.layers = nn.ModuleList()
        for i in range(config.decoder_layers):
            is_sparse = (i + 1) % sparse_step == 0 if sparse_step > 0 else False
            self.layers.append(NllbMoeDecoderLayer(config, is_sparse))

        self.layer_norm = nn.LayerNorm(config.d_model)

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

    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        cross_attn_head_mask: Optional[torch.Tensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        output_router_logits: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ):
        r"""
        Args:
            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
                Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
                provide it.

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

                [What are input IDs?](../glossary#input-ids)
            attention_mask (`torch.Tensor` 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)
            encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
                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 (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
                Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. 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)
            head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
                Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:

                - 1 indicates the head is **not masked**,
                - 0 indicates the head is **masked**.

            cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
                Mask to nullify selected heads of the cross-attention modules in the decoder to avoid performing
                cross-attention on hidden heads. Mask values selected in `[0, 1]`:

                - 1 indicates the head is **not masked**,
                - 0 indicates the head is **masked**.

            past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
                Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
                shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
                shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

                Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
                cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

                If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
                that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
                all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
                Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
                This is useful if you want more control over how to convert `input_ids` indices into associated vectors
                than the model's internal embedding lookup matrix.
            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.
            output_router_logits (`bool`, *optional*):
                Whether or not to return the logits of all the routers. They are useful for computing the router loss,
                and should not be returned during inference.
            return_dict (`bool`, *optional*):
                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
        """
        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
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        # retrieve input_ids and inputs_embeds
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
        elif input_ids is not None:
            input_shape = input_ids.size()
            input_ids = input_ids.view(-1, input_shape[-1])
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
        else:
            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")

        # past_key_values_length
        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale

        # create causal mask
        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
        combined_attention_mask = _prepare_4d_causal_attention_mask(
            attention_mask, input_shape, inputs_embeds, past_key_values_length
        )

        # expand encoder attention mask
        if encoder_hidden_states is not None and encoder_attention_mask is not None:
            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
            encoder_attention_mask = _prepare_4d_attention_mask(
                encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
            )

        # embed positions
        positions = self.embed_positions(input_ids, inputs_embeds, past_key_values_length)
        positions = positions.to(inputs_embeds.device)

        hidden_states = inputs_embeds + positions

        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)

        if self.gradient_checkpointing and self.training:
            if use_cache:
                logger.warning_once(
                    "`use_cache=True` is incompatible with gradient checkpointing. Setting" " `use_cache=False`..."
                )
                use_cache = False

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        all_router_probs = () if output_router_logits else None
        all_cross_attentions = () if output_attentions else None
        present_key_value_states = () if use_cache else None

        # check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
        for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
            if attn_mask is not None:
                if attn_mask.size()[0] != len(self.layers):
                    raise ValueError(
                        f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
                        f" {head_mask.size()[0]}."
                    )
        deepspeed_zero3_is_enabled = is_deepspeed_zero3_enabled()

        for idx, decoder_layer in enumerate(self.layers):
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
            dropout_probability = torch.rand([])

            skip_the_layer = True if self.training and (dropout_probability < self.layerdrop) else False
            if not skip_the_layer or deepspeed_zero3_is_enabled:
                layer_head_mask = head_mask[idx] if head_mask is not None else None
                cross_attn_layer_head_mask = cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None

                past_key_value = past_key_values[idx] if past_key_values is not None else None

                # under deepspeed zero3 all gpus must run in sync
                if self.gradient_checkpointing and self.training:
                    if use_cache:
                        logger.warning_once(
                            "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                        )
                        use_cache = False
                    layer_outputs = self._gradient_checkpointing_func(
                        decoder_layer.forward,
                        hidden_states,
                        combined_attention_mask,
                        encoder_hidden_states,
                        encoder_attention_mask,
                        layer_head_mask,
                        cross_attn_layer_head_mask,
                        None,  # past_key_value is always None with gradient checkpointing
                        use_cache,
                        output_attentions,
                    )
                else:
                    layer_outputs = decoder_layer(
                        hidden_states,
                        attention_mask=combined_attention_mask,
                        encoder_hidden_states=encoder_hidden_states,
                        encoder_attention_mask=encoder_attention_mask,
                        layer_head_mask=layer_head_mask,
                        cross_attn_layer_head_mask=cross_attn_layer_head_mask,
                        past_key_value=past_key_value,
                        use_cache=use_cache,
                        output_attentions=output_attentions,
                        output_router_logits=output_router_logits,
                    )

                hidden_states = layer_outputs[0]

            if skip_the_layer:
                continue

            if use_cache:
                present_key_value_states += (layer_outputs[1],)

            if output_attentions:
                all_self_attns += (layer_outputs[2],)
                all_cross_attentions += (layer_outputs[3],)

            if output_router_logits:
                all_router_probs += (layer_outputs[-1],)

        hidden_states = self.layer_norm(hidden_states)

        # Add last layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        if not return_dict:
            return tuple(
                v
                for v in [
                    hidden_states,
                    present_key_value_states,
                    all_hidden_states,
                    all_self_attns,
                    all_cross_attentions,
                    all_router_probs,
                ]
                if v is not None
            )
        return MoEModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
            past_key_values=present_key_value_states,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
            cross_attentions=all_cross_attentions,
            router_probs=all_router_probs,
        )


@add_start_docstrings(
    "The bare NllbMoe Model outputting raw hidden-states without any specific head on top.",
    NLLB_MOE_START_DOCSTRING,
)
class NllbMoeModel(NllbMoePreTrainedModel):
    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]

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

        padding_idx, vocab_size = config.pad_token_id, config.vocab_size
        self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx)

        self.encoder = NllbMoeEncoder(config, self.shared)
        self.decoder = NllbMoeDecoder(config, self.shared)

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

    def get_input_embeddings(self):
        return self.shared

    def set_input_embeddings(self, value):
        self.shared = value
        self.encoder.embed_tokens = self.shared
        self.decoder.embed_tokens = self.shared

    def _tie_weights(self):
        if self.config.tie_word_embeddings:
            self._tie_or_clone_weights(self.encoder.embed_tokens, self.shared)
            self._tie_or_clone_weights(self.decoder.embed_tokens, self.shared)

    def get_encoder(self):
        return self.encoder

    def get_decoder(self):
        return self.decoder

    @add_start_docstrings_to_model_forward(NLLB_MOE_INPUTS_DOCSTRING)
    @add_start_docstrings_to_model_forward(NLLB_MOE_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=Seq2SeqMoEModelOutput, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        decoder_input_ids: Optional[torch.LongTensor] = None,
        decoder_attention_mask: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        decoder_head_mask: Optional[torch.Tensor] = None,
        cross_attn_head_mask: Optional[torch.Tensor] = None,
        encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        output_router_logits: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.Tensor], Seq2SeqMoEModelOutput]:
        r"""
        Returns:

        Example:

        ```python
        >>> from transformers import AutoTokenizer, NllbMoeModel

        >>> tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/random-nllb-moe-2-experts")
        >>> model = SwitchTransformersModel.from_pretrained("hf-internal-testing/random-nllb-moe-2-experts")

        >>> input_ids = tokenizer(
        ...     "Studies have been shown that owning a dog is good for you", return_tensors="pt"
        ... ).input_ids  # Batch size 1
        >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids  # Batch size 1

        >>> # preprocess: Prepend decoder_input_ids with start token which is pad token for NllbMoeModel
        >>> decoder_input_ids = model._shift_right(decoder_input_ids)

        >>> # forward pass
        >>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
        >>> last_hidden_states = outputs.last_hidden_state
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.return_dict
        if encoder_outputs is None:
            encoder_outputs = self.encoder(
                input_ids=input_ids,
                attention_mask=attention_mask,
                head_mask=head_mask,
                inputs_embeds=inputs_embeds,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                output_router_logits=output_router_logits,
                return_dict=return_dict,
            )
        # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
        elif return_dict and not isinstance(encoder_outputs, MoEModelOutput):
            encoder_outputs = MoEModelOutput(
                last_hidden_state=encoder_outputs[0],
                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
                router_probs=encoder_outputs[3] if len(encoder_outputs) > 3 else None,
            )

        # decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
        decoder_outputs = self.decoder(
            input_ids=decoder_input_ids,
            attention_mask=decoder_attention_mask,
            encoder_hidden_states=encoder_outputs[0],
            encoder_attention_mask=attention_mask,
            head_mask=decoder_head_mask,
            cross_attn_head_mask=cross_attn_head_mask,
            past_key_values=past_key_values,
            inputs_embeds=decoder_inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            output_router_logits=output_router_logits,
            return_dict=return_dict,
        )

        if not return_dict:
            return decoder_outputs + encoder_outputs

        return Seq2SeqMoEModelOutput(
            past_key_values=decoder_outputs.past_key_values,
            cross_attentions=decoder_outputs.cross_attentions,
            last_hidden_state=decoder_outputs.last_hidden_state,
            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
            encoder_hidden_states=encoder_outputs.hidden_states,
            decoder_hidden_states=decoder_outputs.hidden_states,
            encoder_attentions=encoder_outputs.attentions,
            decoder_attentions=decoder_outputs.attentions,
            encoder_router_logits=encoder_outputs.router_probs,
            decoder_router_logits=decoder_outputs.router_probs,
        )


@add_start_docstrings(
    "The NllbMoe Model with a language modeling head. Can be used for summarization.", NLLB_MOE_START_DOCSTRING
)
class NllbMoeForConditionalGeneration(NllbMoePreTrainedModel):
    base_model_prefix = "model"
    _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]

    def __init__(self, config: NllbMoeConfig):
        super().__init__(config)
        self.model = NllbMoeModel(config)
        self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)

        self.router_z_loss_coef = config.router_z_loss_coef
        self.router_aux_loss_coef = config.router_aux_loss_coef
        # Initialize weights and apply final processing
        self.post_init()

    def get_encoder(self):
        return self.model.get_encoder()

    def get_decoder(self):
        return self.model.get_decoder()

    def get_output_embeddings(self):
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    @add_start_docstrings_to_model_forward(NLLB_MOE_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=Seq2SeqMoEOutput, config_class=_CONFIG_FOR_DOC)
    @add_end_docstrings(NLLB_MOE_GENERATION_EXAMPLE)
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        decoder_input_ids: Optional[torch.LongTensor] = None,
        decoder_attention_mask: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        decoder_head_mask: Optional[torch.Tensor] = None,
        cross_attn_head_mask: Optional[torch.Tensor] = None,
        encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        output_router_logits: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.Tensor], Seq2SeqMoEOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

        Returns:
        """
        return_dict = return_dict if return_dict is not None else self.config.return_dict
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_router_logits = (
            output_router_logits if output_router_logits is not None else self.config.output_router_logits
        )
        if labels is not None:
            if decoder_input_ids is None:
                decoder_input_ids = shift_tokens_right(
                    labels, self.config.pad_token_id, self.config.decoder_start_token_id
                )

        outputs = self.model(
            input_ids,
            attention_mask=attention_mask,
            decoder_input_ids=decoder_input_ids,
            encoder_outputs=encoder_outputs,
            decoder_attention_mask=decoder_attention_mask,
            head_mask=head_mask,
            decoder_head_mask=decoder_head_mask,
            cross_attn_head_mask=cross_attn_head_mask,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            decoder_inputs_embeds=decoder_inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            output_router_logits=output_router_logits,
            return_dict=return_dict,
        )
        lm_logits = self.lm_head(outputs[0])

        loss = None
        encoder_aux_loss = None
        decoder_aux_loss = None

        if labels is not None:
            loss_fct = CrossEntropyLoss(ignore_index=-100)
            # todo check in the config if router loss enables

            if output_router_logits:
                encoder_router_logits = outputs[-1]
                decoder_router_logits = outputs[3 if output_attentions else 4]

                # Compute the router loss (z_loss + auxiliary loss) for each router in the encoder and decoder
                encoder_router_logits, encoder_expert_indexes = self._unpack_router_logits(encoder_router_logits)
                encoder_aux_loss = load_balancing_loss_func(encoder_router_logits, encoder_expert_indexes)

                decoder_router_logits, decoder_expert_indexes = self._unpack_router_logits(decoder_router_logits)
                decoder_aux_loss = load_balancing_loss_func(decoder_router_logits, decoder_expert_indexes)

            loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))

            if output_router_logits and labels is not None:
                aux_loss = self.router_aux_loss_coef * (encoder_aux_loss + decoder_aux_loss)
                loss = loss + aux_loss

        output = (loss,) if loss is not None else ()
        if not return_dict:
            output += (lm_logits,)
            if output_router_logits:  # only return the loss if they are not None
                output += (
                    encoder_aux_loss,
                    decoder_aux_loss,
                    *outputs[1:],
                )
            else:
                output += outputs[1:]

            return output

        return Seq2SeqMoEOutput(
            loss=loss,
            logits=lm_logits,
            past_key_values=outputs.past_key_values,
            cross_attentions=outputs.cross_attentions,
            encoder_aux_loss=encoder_aux_loss,
            decoder_aux_loss=decoder_aux_loss,
            encoder_last_hidden_state=outputs.encoder_last_hidden_state,
            encoder_hidden_states=outputs.encoder_hidden_states,
            decoder_hidden_states=outputs.decoder_hidden_states,
            encoder_attentions=outputs.encoder_attentions,
            decoder_attentions=outputs.decoder_attentions,
            encoder_router_logits=outputs.encoder_router_logits,
            decoder_router_logits=outputs.decoder_router_logits,
        )

    def _unpack_router_logits(self, router_outputs):
        total_router_logits = []
        total_expert_indexes = []
        for router_output in router_outputs:
            if router_output is not None:
                router_logits, expert_indexes = router_output
                total_router_logits.append(router_logits)
                total_expert_indexes.append(expert_indexes)

        total_router_logits = torch.cat(total_router_logits, dim=1) if len(total_router_logits) > 0 else None
        total_expert_indexes = torch.stack(total_expert_indexes, dim=1) if len(total_expert_indexes) > 0 else None
        return total_router_logits, total_expert_indexes

    # Copied from transfomers.models.switch_transformers.SwitchTransformersForConditionalGeneration.prepare_inputs_for_generation
    def prepare_inputs_for_generation(
        self,
        decoder_input_ids,
        past_key_values=None,
        attention_mask=None,
        head_mask=None,
        decoder_head_mask=None,
        cross_attn_head_mask=None,
        use_cache=None,
        encoder_outputs=None,
        **kwargs,
    ):
        # cut decoder_input_ids if past is used
        if past_key_values is not None:
            past_length = past_key_values[0][0].shape[2]

            # Some generation methods already pass only the last input ID
            if decoder_input_ids.shape[1] > past_length:
                remove_prefix_length = past_length
            else:
                # Default to old behavior: keep only final ID
                remove_prefix_length = decoder_input_ids.shape[1] - 1

            decoder_input_ids = decoder_input_ids[:, remove_prefix_length:]

        return {
            "input_ids": None,  # encoder_outputs is defined. input_ids not needed
            "encoder_outputs": encoder_outputs,
            "past_key_values": past_key_values,
            "decoder_input_ids": decoder_input_ids,
            "attention_mask": attention_mask,
            "head_mask": head_mask,
            "decoder_head_mask": decoder_head_mask,
            "cross_attn_head_mask": cross_attn_head_mask,
            "use_cache": use_cache,  # change this to avoid caching (presumably for debugging)
        }

    @staticmethod
    def _reorder_cache(past_key_values, beam_idx):
        reordered_past = ()
        for layer_past in past_key_values:
            reordered_past += (
                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
            )
        return reordered_past