modeling_afmoe.py

from typing import Callable, Optional, Tuple, Union

import torch
import torch.nn.functional as F
from torch import nn

from transformers.activations import ACT2FN
from transformers.generation import GenerationMixin
from transformers.modeling_outputs import (
    MoeCausalLMOutputWithPast,
    MoeModelOutputWithPast,
)
from transformers.modeling_utils import PreTrainedModel, ALL_ATTENTION_FUNCTIONS
from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
from transformers.masking_utils import (
    create_causal_mask,
    create_sliding_window_causal_mask,
)
from transformers.modeling_layers import GradientCheckpointingLayer
from transformers.processing_utils import Unpack
from transformers.utils import TransformersKwargs
from transformers.cache_utils import Cache, DynamicCache
from transformers.integrations import use_kernel_forward_from_hub


try:
    from .configuration_afmoe import AfmoeConfig
except:
    from configuration_afmoe import AfmoeConfig

class AfmoeRotaryEmbedding(nn.Module):

    def __init__(self, config: AfmoeConfig, device=None):
        super().__init__()
        # BC: "rope_type" was originally "type"
        if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
        else:
            self.rope_type = "default"
        self.max_seq_len_cached = config.max_position_embeddings
        self.original_max_seq_len = config.max_position_embeddings

        self.config = config
        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]

        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self.original_inv_freq = self.inv_freq

    def _dynamic_frequency_update(self, position_ids, device):
        """
        dynamic RoPE layers should recompute `inv_freq` in the following situations:
        1 - growing beyond the cached sequence length (allow scaling)
        2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
        """
        seq_len = torch.max(position_ids) + 1
        if seq_len > self.max_seq_len_cached:  # growth
            inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, seq_len=seq_len)
            self.register_buffer("inv_freq", inv_freq, persistent=False)  # TODO joao: may break with compilation
            self.max_seq_len_cached = seq_len

        if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len:  # reset
            # This .to() is needed if the model has been moved to a device after being initialized (because
            # the buffer is automatically moved, but not the original copy)
            self.original_inv_freq = self.original_inv_freq.to(device)
            self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
            self.max_seq_len_cached = self.original_max_seq_len

    @torch.no_grad()
    def forward(self, x, position_ids):
        if "dynamic" in self.rope_type:
            self._dynamic_frequency_update(position_ids, device=x.device)

        # Core RoPE block
        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
        position_ids_expanded = position_ids[:, None, :].float()
        # Force float32 (see https://github.com/huggingface/transformers/pull/29285)
        device_type = x.device.type
        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
        with torch.autocast(device_type=device_type, enabled=False):
            freqs = (inv_freq_expanded.float().to(x.device) @ position_ids_expanded.float()).transpose(1, 2)
            emb = torch.cat((freqs, freqs), dim=-1)
            cos = emb.cos()
            sin = emb.sin()

        # Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention
        cos = cos * self.attention_scaling
        sin = sin * self.attention_scaling

        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)


def rotate_half(x):
    """Rotates half the hidden dims of the input."""
    x1 = x[..., : x.shape[-1] // 2]
    x2 = x[..., x.shape[-1] // 2 :]
    return torch.cat((-x2, x1), dim=-1)


def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
    """Applies Rotary Position Embedding to the query and key tensors.

    Args:
        q (`torch.Tensor`): The query tensor.
        k (`torch.Tensor`): The key tensor.
        cos (`torch.Tensor`): The cosine part of the rotary embedding.
        sin (`torch.Tensor`): The sine part of the rotary embedding.
        position_ids (`torch.Tensor`, *optional*):
            Deprecated and unused.
        unsqueeze_dim (`int`, *optional*, defaults to 1):
            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
    Returns:
        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
    """
    cos = cos.unsqueeze(unsqueeze_dim)
    sin = sin.unsqueeze(unsqueeze_dim)
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed


def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
    """
    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
    """
    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
    if n_rep == 1:
        return hidden_states
    hidden_states = hidden_states[:, :, None, :, :].expand(
        batch, num_key_value_heads, n_rep, slen, head_dim
    )
    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)

@use_kernel_forward_from_hub("RMSNorm")
class AfmoeRMSNorm(nn.Module):
    def __init__(self, hidden_size: int, eps: float):
        """
        AfmoeRMSNorm is equivalent to T5LayerNorm
        """
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, hidden_states):
        input_dtype = hidden_states.dtype
        hidden_states = hidden_states.to(torch.float32)
        variance = hidden_states.pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
        return self.weight * hidden_states.to(input_dtype)

    def extra_repr(self):
        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"



def eager_attention_forward(
    module: nn.Module,
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    attention_mask: Optional[torch.Tensor],
    scaling: float,
    dropout: float = 0.0,
    **kwargs,
):
    key_states = repeat_kv(key, module.num_key_value_groups)
    value_states = repeat_kv(value, module.num_key_value_groups)

    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
    if attention_mask is not None:
        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
        attn_weights = attn_weights + causal_mask

    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(
        query.dtype
    )
    attn_weights = nn.functional.dropout(
        attn_weights, p=dropout, training=module.training
    )
    attn_output = torch.matmul(attn_weights, value_states)
    attn_output = attn_output.transpose(1, 2).contiguous()

    return attn_output, attn_weights


class AfmoeMLP(nn.Module):
    def __init__(self, config, intermediate_size=None):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.intermediate_size = intermediate_size or config.intermediate_size
        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
        self.act_fn = ACT2FN[config.hidden_act]

    def forward(self, x):
        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))


class AfmoeTokenChoiceRouter(nn.Module):
    """Token-choice top-K router for MoE routing."""

    def __init__(self, config):
        super().__init__()
        self.config = config
        self.top_k = config.num_experts_per_tok
        self.num_experts = config.num_experts
        self.score_func = config.score_func
        self.route_norm = config.route_norm
        self.route_scale = config.route_scale
        self.gate = nn.Linear(config.hidden_size, config.num_experts, bias=False)     

    def forward(self, hidden_states, expert_bias: torch.Tensor | None):
        _, _, hidden_dim = hidden_states.shape
        hidden_states = hidden_states.view(-1, hidden_dim)

        scores = self.gate(hidden_states)

        # Apply scoring function in float32 for stability
        if self.score_func == "sigmoid":
            scores = torch.sigmoid(scores.to(torch.float32))
        else:
            scores = F.softmax(scores.to(torch.float32), dim=-1)

        if expert_bias is not None:
            _, selected_experts = torch.topk(scores + expert_bias, k=self.top_k, dim=1)
            top_scores = scores.gather(dim=1, index=selected_experts)
        else:
            top_scores, selected_experts = torch.topk(scores, k=self.top_k, dim=1)

        # Normalize weights if using sigmoid
        if self.score_func == "sigmoid" and self.route_norm:
            denominator = top_scores.sum(dim=-1, keepdim=True) + 1e-20
            top_scores = top_scores / denominator

        top_scores = top_scores * self.route_scale
        return top_scores, selected_experts

class AfmoeMoE(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.router = AfmoeTokenChoiceRouter(config)

        self.shared_experts = None
        if config.num_shared_experts > 0:
            self.shared_experts = AfmoeMLP(
                config, config.moe_intermediate_size * config.num_shared_experts
            )
        self.experts = nn.ModuleList(
            [AfmoeMLP(
                config, intermediate_size=config.moe_intermediate_size
            ) for _ in range(config.num_experts)]
        )
        self.expert_bias = nn.Parameter(torch.zeros(config.num_experts, dtype=torch.float32), requires_grad=False)
        

    def forward(self, hidden_states):
        batch_size, seq_len, hidden_dim = hidden_states.shape
        hidden_states_flat = hidden_states.view(-1, hidden_dim)

        # Get routing decisions
        top_scores, selected_experts = self.router(hidden_states, self.expert_bias)

        # Process through shared experts
        if self.shared_experts is not None:
            shared_output = self.shared_experts(hidden_states_flat)
        else:
            shared_output = torch.zeros_like(hidden_states_flat)

        # Reorder tokens by expert for efficient processing
        token_indices_sorted = torch.argsort(selected_experts.view(-1), stable=True)
        top_scores_sorted = top_scores.view(-1)[token_indices_sorted]
        token_to_expert = selected_experts.view(-1)[token_indices_sorted]
        token_indices_sorted = token_indices_sorted // self.config.num_experts_per_tok

        # Gather input tokens
        token_indices_expanded = token_indices_sorted.unsqueeze(-1).expand(
            -1, hidden_dim
        )
        routed_input = torch.gather(
            hidden_states_flat, dim=0, index=token_indices_expanded
        )

        routed_output = torch.zeros_like(routed_input)
        for expert_id in range(self.config.num_experts):
            mask = token_to_expert == expert_id
            if mask.any():
                expert_input = routed_input[mask]
                expert_out = self.experts[expert_id](expert_input)
                routed_output[mask] = expert_out
          
        routed_output = (
            routed_output.to(torch.float32) * top_scores_sorted.unsqueeze(-1)
        ).to(hidden_states.dtype)

        # Scatter back to original positions
        output = shared_output.scatter_add(
            dim=0, index=token_indices_expanded, src=routed_output
        )

        return output.view(batch_size, seq_len, hidden_dim)


class AfmoeAttention(nn.Module):
    """Multi-headed attention with local/global pattern and gating."""

    def __init__(self, config: AfmoeConfig, layer_idx: int):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
        self.num_heads = config.num_attention_heads
        self.num_key_value_heads = config.num_key_value_heads
        self.num_key_value_groups = self.num_heads // self.num_key_value_heads

        self.scaling = self.head_dim**-0.5
        self.attention_dropout = config.attention_dropout
        self.is_local_attention = config.layer_types[layer_idx] == "sliding_attention"
        self.sliding_window = config.sliding_window if self.is_local_attention else None

        self.q_proj = nn.Linear(
            config.hidden_size, self.num_heads * self.head_dim, bias=False
        )
        self.k_proj = nn.Linear(
            config.hidden_size, self.num_key_value_heads * self.head_dim, bias=False
        )
        self.v_proj = nn.Linear(
            config.hidden_size, self.num_key_value_heads * self.head_dim, bias=False
        )
        self.o_proj = nn.Linear(
            self.num_heads * self.head_dim, config.hidden_size, bias=False
        )

        self.q_norm = AfmoeRMSNorm(self.head_dim, eps=config.rms_norm_eps)
        self.k_norm = AfmoeRMSNorm(self.head_dim, eps=config.rms_norm_eps)

        self.gate_proj = nn.Linear(
            config.hidden_size, self.num_heads * self.head_dim, bias=False
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: tuple[torch.Tensor, torch.Tensor],
        attention_mask: Optional[torch.Tensor],
        past_key_value: Optional[Cache] = None,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> torch.Tensor:   

        input_shape = hidden_states.shape[:-1]
        hidden_shape = (*input_shape, -1, self.head_dim)

        query_states = self.q_proj(hidden_states).view(hidden_shape)
        key_states = self.k_proj(hidden_states).view(hidden_shape)
        value_states = self.v_proj(hidden_states).view(hidden_shape)
        gate_states = self.gate_proj(hidden_states)

        query_states = self.q_norm(query_states)
        key_states = self.k_norm(key_states)
        
        query_states = query_states.transpose(1, 2)
        key_states = key_states.transpose(1, 2)
        value_states = value_states.transpose(1, 2)

        if self.is_local_attention:
            cos, sin = position_embeddings
            query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

        if past_key_value is not None:
            cache_kwargs = {"cache_position": cache_position}
            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

        attention_interface: Callable = eager_attention_forward
        if self.config._attn_implementation != "eager":
            attention_interface = ALL_ATTENTION_FUNCTIONS[
                self.config._attn_implementation
            ]

        output, _ = attention_interface(
            self,
            query_states,
            key_states,
            value_states,
            attention_mask=attention_mask,
            dropout=0.0 if not self.training else self.attention_dropout,
            scaling=self.scaling,
            sliding_window=self.sliding_window,
            **kwargs,
        )

        output = output.view(*input_shape, -1).contiguous()
        output = output * F.sigmoid(gate_states)
        return self.o_proj(output)


class AfmoeDecoderLayer(GradientCheckpointingLayer):
    def __init__(self, config: AfmoeConfig, layer_idx: int):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.layer_idx = layer_idx

        self.self_attn = AfmoeAttention(config=config, layer_idx=layer_idx)
        self.attention_type = config.layer_types[layer_idx]

        # Dual normalization for attention
        self.input_layernorm = AfmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = AfmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

        # Dual normalization for FFN
        self.pre_mlp_layernorm = AfmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_mlp_layernorm = AfmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

        # MoE or dense FFN
        self.moe_enabled = layer_idx >= config.num_dense_layers
        if self.moe_enabled:
            self.mlp = AfmoeMoE(config)
        else:
            self.mlp = AfmoeMLP(config)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        use_cache: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> torch.FloatTensor:
        residual = hidden_states

        # Self Attention with dual normalization
        hidden_states = self.input_layernorm(hidden_states)
        hidden_states = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_value=past_key_value,
            use_cache=use_cache,
            cache_position=cache_position,
            position_embeddings=position_embeddings,
            **kwargs,
        )
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = residual + hidden_states

        # FFN with dual normalization
        residual = hidden_states
        hidden_states = self.pre_mlp_layernorm(hidden_states)

        if self.moe_enabled:
            hidden_states = self.mlp(hidden_states)
        else:
            hidden_states = self.mlp(hidden_states)

        hidden_states = self.post_mlp_layernorm(hidden_states)
        hidden_states = residual + hidden_states
        return hidden_states


class AfmoePreTrainedModel(PreTrainedModel):
    config_class = AfmoeConfig
    base_model_prefix = "model"
    _no_split_modules = ["AfmoeDecoderLayer"]
    _skip_keys_device_placement = ["past_key_values"]
    _keep_in_fp32_modules = [
        "input_layernorm",
        "post_attention_layernorm",
        "pre_mlp_layernorm",
        "post_mlp_layernorm",
        "q_norm",
        "k_norm",
        "norm",
    ]
    _supports_sdpa = True
    _supports_attention_backend = True
    supports_gradient_checkpointing = True


class AfmoeModel(AfmoePreTrainedModel):
    _no_split_modules = ["AfmoeDecoderLayer"]

    def __init__(self, config: AfmoeConfig):
        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(
            [
                AfmoeDecoderLayer(config, layer_idx)
                for layer_idx in range(config.num_hidden_layers)
            ]
        )
        self.norm = AfmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.rotary_emb = AfmoeRotaryEmbedding(config=config)
        self.gradient_checkpointing = False

        self.post_init()

    def get_input_embeddings(self):
        return self.embed_tokens

    def set_input_embeddings(self, value):
        self.embed_tokens = value


    def forward(
        self,
        input_ids: torch.LongTensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[list[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> MoeModelOutputWithPast:
        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError(
                "You must specify exactly one of input_ids or inputs_embeds"
            )

        if use_cache and past_key_values is None:
            past_key_values = DynamicCache()

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

        if cache_position is None:
            past_seen_tokens = (
                past_key_values.get_seq_length() if past_key_values is not None else 0
            )
            cache_position = torch.arange(
                past_seen_tokens,
                past_seen_tokens + inputs_embeds.shape[1],
                device=inputs_embeds.device,
            )
        if position_ids is None:
            position_ids = cache_position.unsqueeze(0)

        # It may already have been prepared by e.g. `generate`
        if not isinstance(causal_mask_mapping := attention_mask, dict):
            mask_kwargs = {
                "config": self.config,
                "input_embeds": inputs_embeds,
                "attention_mask": attention_mask,
                "cache_position": cache_position,
                "past_key_values": past_key_values,
            }
            causal_mask_mapping = {
                "full_attention": create_causal_mask(**mask_kwargs),
                "sliding_attention": create_sliding_window_causal_mask(**mask_kwargs),
            }

        hidden_states = inputs_embeds

        # Apply muP input scaling if enabled
        if self.config.mup_enabled:
            hidden_states = hidden_states * (self.config.hidden_size**0.5)

        position_embeddings = self.rotary_emb(hidden_states, position_ids)

        for decoder_layer in self.layers:
            hidden_states = decoder_layer(
                hidden_states,
                attention_mask=causal_mask_mapping[decoder_layer.attention_type],
                position_ids=position_ids,
                past_key_value=past_key_values,
                use_cache=use_cache,
                cache_position=cache_position,
                position_embeddings=position_embeddings,
                **kwargs,
            )

        hidden_states = self.norm(hidden_states)
        return MoeModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=past_key_values,
        )


class AfmoeForCausalLM(AfmoePreTrainedModel, GenerationMixin):
    _tied_weights_keys = ["lm_head.weight"]
    _tp_plan = {"lm_head": "colwise_rep"}
    _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}

    def __init__(self, config):
        super().__init__(config)
        self.model = AfmoeModel(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 get_input_embeddings(self):
        return self.model.embed_tokens

    def set_input_embeddings(self, value):
        self.model.embed_tokens = value

    def get_output_embeddings(self):
        return self.lm_head

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

    def set_decoder(self, decoder):
        self.model = decoder

    def get_decoder(self):
        return self.model

    def forward(
        self,
        input_ids: torch.LongTensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Cache] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        logits_to_keep: Union[int, torch.Tensor] = 0,
        token_type_ids: Optional[torch.Tensor] = None,  # will be ignored
        **kwargs: Unpack[TransformersKwargs],
    ) -> Union[Tuple, MoeCausalLMOutputWithPast]:
        outputs: MoeModelOutputWithPast = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            cache_position=cache_position,
            **kwargs,
        )

        hidden_states = outputs.last_hidden_state
        # Only compute necessary logits
        slice_indices = (
            slice(-logits_to_keep, None)
            if isinstance(logits_to_keep, int)
            else logits_to_keep
        )
        logits = self.lm_head(hidden_states[:, slice_indices, :])

        loss = None
        if labels is not None:
            loss = self.loss_function(logits, labels, self.vocab_size, **kwargs)


        return MoeCausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            router_logits=outputs.router_logits,
        )


__all__ = [
    "AfmoeForCausalLM",
    "AfmoeModel",
    "AfmoePreTrainedModel",
]