hf_model.py

import math, inspect
from functools import partial
from typing import List, Optional, Tuple, Union

import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from transformers.activations import ACT2FN
from transformers.cache_utils import Cache, DynamicCache, StaticCache, SinkCache
from transformers.modeling_attn_mask_utils import (
    _prepare_4d_causal_attention_mask,
    _prepare_4d_causal_attention_mask_for_sdpa,
)
from transformers.modeling_outputs import (
    BaseModelOutputWithPast,
    CausalLMOutputWithPast,
    SequenceClassifierOutputWithPast,
    TokenClassifierOutput,
    MoeModelOutputWithPast,
    MoeCausalLMOutputWithPast,
)
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    is_flash_attn_2_available,
    is_flash_attn_greater_or_equal_2_10,
    logging,
    replace_return_docstrings,
)
from transformers.utils.import_utils import is_torch_fx_available
from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13
from .hf_config import HFConfig
from .ops import L2Norm, FMatMul, ElementwiseAdd, ElementwiseMul, FCat


if is_flash_attn_2_available():
    from flash_attn import flash_attn_func, flash_attn_varlen_func
    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa

    _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)


# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
# It means that the function will not be traced through and simply appear as a node in the graph.
if is_torch_fx_available():
    if not is_torch_greater_or_equal_than_1_13:
        import torch.fx

    _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)


logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "HFConfig"


def load_balancing_loss_func(
    gate_logits: torch.Tensor, num_experts: torch.Tensor = None, top_k=2, attention_mask: Optional[torch.Tensor] = None
) -> 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:
        gate_logits (Union[`torch.Tensor`, Tuple[torch.Tensor]):
            Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
            shape [batch_size X sequence_length, num_experts].
        attention_mask (`torch.Tensor`, None):
            The attention_mask used in forward function
            shape [batch_size X sequence_length] if not None.
        num_experts (`int`, *optional*):
            Number of experts

    Returns:
        The auxiliary loss.
    """
    if gate_logits is None or not isinstance(gate_logits, tuple):
        return 0

    if isinstance(gate_logits, tuple):
        compute_device = gate_logits[0].device
        concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)

    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)

    _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)

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

    if attention_mask is None:
        # Compute the percentage of tokens routed to each experts
        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)

        # Compute the average probability of routing to these experts
        router_prob_per_expert = torch.mean(routing_weights, dim=0)
    else:
        batch_size, sequence_length = attention_mask.shape
        num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)

        # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
        expert_attention_mask = (
            attention_mask[None, :, :, None, None]
            .expand((num_hidden_layers, batch_size, sequence_length, 2, num_experts))
            .reshape(-1, 2, num_experts)
            .to(compute_device)
        )

        # Compute the percentage of tokens routed to each experts
        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
            expert_attention_mask, dim=0
        )

        # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
        router_per_expert_attention_mask = (
            attention_mask[None, :, :, None]
            .expand((num_hidden_layers, batch_size, sequence_length, num_experts))
            .reshape(-1, num_experts)
            .to(compute_device)
        )

        # Compute the average probability of routing to these experts
        router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
            router_per_expert_attention_mask, dim=0
        )

    overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0))
    return overall_loss * num_experts


def _get_unpad_data(attention_mask):
    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
    max_seqlen_in_batch = seqlens_in_batch.max().item()
    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
    return (
        indices,
        cu_seqlens,
        max_seqlen_in_batch,
    )


# def rms_norm(x, weight, epsilon, bias = None):
#     # z = x.float()
#     # z = z * torch.rsqrt(z.pow(2).mean(-1, keepdim=True) + epsilon)
#     # z = z.type_as(x) * weight
#     alpha = math.sqrt(x.shape[-1])
#     z = alpha * torch.nn.functional.normalize(x, p=2, dim=-1, eps=1e-12)
#     z = z * weight
#     if bias is not None:
#         z += bias
#     return z


class HFRMSNorm(nn.Module):
    def __init__(self, dim: int, eps: float = 1e-6, device = None, dtype = None, bias = None, l2norm_as_rmsnorm = False):
        super().__init__()
        self.eps = eps
        self.alpha = math.sqrt(dim)
        self.weight = nn.Parameter(torch.ones(dim, device=device, dtype=dtype))
        self.bias = None if bias is None else nn.Parameter(torch.zeros(dim, device=device, dtype=dtype))
        self.l2norm_as_rmsnorm = l2norm_as_rmsnorm
        if l2norm_as_rmsnorm:
            self.l2norm = L2Norm()
        self.elementwisemul = ElementwiseMul()
        self.reset_parameters()

    @property
    def variance_epsilon(self):
        return self.eps
    
    def reset_parameters(self) -> None:
        torch.nn.init.normal_(self.weight)
        if self.bias is not None:
            torch.nn.init.constant_(self.bias, 0.0)

    def _norm(self, x):
        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)

    def forward_impl(self, x, weight, bias):
        if self.l2norm_as_rmsnorm:
            output = self.alpha * self.l2norm(x)
        else:
            output = self._norm(x.float()).type_as(x)
        output = self.elementwisemul(weight, output)
        if bias is not None:
            output += bias
        return output

    def forward(self, x):
        return self.forward_impl(x, self.weight, self.bias)
        


class HFSkipRMSNorm(HFRMSNorm):
    def forward(self, x):
        output = self._norm(x.float()).type_as(x)
        return output * (self.weight + 1)


# class HFRotaryEmbedding(nn.Module):
#     def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
#         super().__init__()

#         self.dim = dim
#         self.max_position_embeddings = max_position_embeddings
#         self.base = base
#         inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
#         self.register_buffer("inv_freq", inv_freq, persistent=False)

#         # Build here to make `torch.jit.trace` work.
#         self._set_cos_sin_cache(
#             seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
#         )

#     def _set_cos_sin_cache(self, seq_len, device, dtype):
#         self.max_seq_len_cached = seq_len
#         t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)

#         freqs = torch.outer(t, self.inv_freq)
#         # Different from paper, but it uses a different permutation in order to obtain the same calculation
#         emb = torch.cat((freqs, freqs), dim=-1)
#         self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
#         self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)

#     def forward(self, x, seq_len=None):
#         # x: [bs, num_attention_heads, seq_len, head_size]
#         if seq_len > self.max_seq_len_cached:
#             self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)

#         return (
#             self.cos_cached[:seq_len].to(dtype=x.dtype),
#             self.sin_cached[:seq_len].to(dtype=x.dtype),
#         )


# class HFLinearScalingRotaryEmbedding(HFRotaryEmbedding):
#     """RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""

#     def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
#         self.scaling_factor = scaling_factor
#         super().__init__(dim, max_position_embeddings, base, device)

#     def _set_cos_sin_cache(self, seq_len, device, dtype):
#         self.max_seq_len_cached = seq_len
#         t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
#         t = t / self.scaling_factor

#         freqs = torch.outer(t, self.inv_freq)
#         # Different from paper, but it uses a different permutation in order to obtain the same calculation
#         emb = torch.cat((freqs, freqs), dim=-1)
#         self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
#         self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)


# class HFDynamicNTKScalingRotaryEmbedding(HFRotaryEmbedding):
#     """RotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""

#     def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
#         self.scaling_factor = scaling_factor
#         super().__init__(dim, max_position_embeddings, base, device)

#     def _set_cos_sin_cache(self, seq_len, device, dtype):
#         self.max_seq_len_cached = seq_len

#         if seq_len > self.max_position_embeddings:
#             base = self.base * (
#                 (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
#             ) ** (self.dim / (self.dim - 2))
#             inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
#             self.register_buffer("inv_freq", inv_freq, persistent=False)

#         t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)

#         freqs = torch.outer(t, self.inv_freq)
#         # Different from paper, but it uses a different permutation in order to obtain the same calculation
#         emb = torch.cat((freqs, freqs), dim=-1)
#         self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
#         self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)


class HFRotaryEmbedding(nn.Module):
    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
        super().__init__()

        self.dim = dim
        self.max_position_embeddings = max_position_embeddings
        self.base = base
        self.register_buffer("inv_freq", None, persistent=False)

    def _set_cos_sin_cache(self, seq_len, device, dtype):
        self.max_seq_len_cached = seq_len
        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)

        freqs = torch.outer(t, self.inv_freq)
        # Different from paper, but it uses a different permutation in order to obtain the same calculation
        emb = torch.cat((freqs, freqs), dim=-1)
        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)

    def forward(self, position_ids, device, dtype, seq_len=None):
        # x: [bs, num_attention_heads, seq_len, head_size]
        if self.inv_freq is None:
            self.inv_freq = 1.0 / (
                self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64, device=device).float() / self.dim)
            )
        if position_ids is not None:
            inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
            position_ids_expanded = position_ids[:, None, :].float()
            freqs = (inv_freq_expanded @ position_ids_expanded).transpose(1, 2)
            emb = torch.cat((freqs, freqs), dim=-1)
            return emb.cos().to(dtype=dtype), emb.sin().to(dtype=dtype)
        elif seq_len is not None:
            if seq_len > getattr(self, "max_seq_len_cached", seq_len-1):
                self._set_cos_sin_cache(seq_len=seq_len, device=device, dtype=dtype)

            return (
                self.cos_cached[:seq_len].to(dtype=dtype),
                self.sin_cached[:seq_len].to(dtype=dtype),
            )
        raise NotImplementedError


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`):
            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
            used to pass offsetted position ids when working with a KV-cache.
        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.
    """
    if position_ids is not None:
        cos = cos[position_ids].unsqueeze(unsqueeze_dim)
        sin = sin[position_ids].unsqueeze(unsqueeze_dim)
    else:
        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)


class HFAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    # Ignore copy
    def __init__(self, config: HFConfig, layer_idx: Optional[int] = None):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        if layer_idx is None:
            logger.warning_once(
                f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
                "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
                "when creating this class."
            )

        self.attention_dropout = config.attention_dropout
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = config.head_dim if config.head_dim is not None else self.hidden_size // self.num_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.max_position_embeddings = config.max_position_embeddings
        self.rope_theta = config.rope_theta
        self.is_causal = True
        self.partial_rotary_factor = config.partial_rotary_factor

        if self.hidden_size % self.num_heads != 0:
            raise ValueError(
                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
                f" and `num_heads`: {self.num_heads})."
            )

        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias and (not config.use_qkv_bias_only))
        self.rotary_emb = HFRotaryEmbedding(
            int(self.partial_rotary_factor * self.head_dim),
            max_position_embeddings=self.max_position_embeddings,
            base=self.rope_theta,
        )
        self.qk_bmm = FMatMul()
        self.pv_bmm = FMatMul()

    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,
        output_attentions: bool = False,
        use_cache: bool = False,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:

        device, dtype = hidden_states.device, hidden_states.dtype

        bsz, q_len, _ = hidden_states.size()

        query_states = self.q_proj(hidden_states)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)

        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)

        past_key_value = getattr(self, "past_key_value", past_key_value)

        # ####################################################################################################
        # # Mistral style
        # kv_seq_len = key_states.shape[-2]
        # if past_key_value is not None:
        #     if self.layer_idx is None:
        #         raise ValueError(
        #             f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
        #             "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
        #             "with a layer index."
        #         )
        #     kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)

        # # Because the input can be padded, the absolute sequence length depends on the max position id.
        # rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1
        # cos, sin = self.rotary_emb(position_ids=None, device=device, dtype=dtype, seq_len=rotary_seq_len)
        # if self.rotary_emb.dim == self.head_dim:
        #     query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
        # else:
        #     # Partial rotary embedding
        #     query_rot, query_pass = (
        #         query_states[..., : self.rotary_emb.dim],
        #         query_states[..., self.rotary_emb.dim :],
        #     )
        #     key_rot, key_pass = (
        #         key_states[..., : self.rotary_emb.dim],
        #         key_states[..., self.rotary_emb.dim :],
        #     )
        #     query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)
        #     query_states = torch.cat((query_rot, query_pass), dim=-1)
        #     key_states = torch.cat((key_rot, key_pass), dim=-1)

        ####################################################################################################
        # Gemma style

        cos, sin = self.rotary_emb(position_ids, device=device, dtype=dtype, seq_len=None)
        if self.rotary_emb.dim == self.head_dim:
            query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, None)
        else:
            # Partial rotary embedding
            query_rot, query_pass = (
                query_states[..., : self.rotary_emb.dim],
                query_states[..., self.rotary_emb.dim :],
            )
            key_rot, key_pass = (
                key_states[..., : self.rotary_emb.dim],
                key_states[..., self.rotary_emb.dim :],
            )
            query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, None)
            query_states = torch.cat((query_rot, query_pass), dim=-1)
            key_states = torch.cat((key_rot, key_pass), dim=-1)

        

        if past_key_value is not None:
            # sin and cos are specific to RoPE models; position_ids needed for the static cache
            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position, "partial_rotation_size": self.rotary_emb.dim}
            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

        key_states = repeat_kv(key_states, self.num_key_value_groups)
        value_states = repeat_kv(value_states, self.num_key_value_groups)

        # TODO: Queries and keys upcast to fp32 is required by Phi-2 to avoid overflow
        qk_bmm = self.qk_bmm if self.config.use_matmul_as_module else torch.matmul
        attn_weights = qk_bmm(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)

        if attention_mask is not None:  # no matter the length, we just slice it
            if cache_position is not None:
                causal_mask = attention_mask[:, :, cache_position, : key_states.shape[-2]]
            else:
                causal_mask = attention_mask
            kv_seq_len = key_states.shape[-2]
            if causal_mask.size() != (bsz, 1, q_len, kv_seq_len):
                raise ValueError(
                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
                )
            attn_weights = attn_weights + causal_mask

        # upcast attention to fp32
        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)

        pv_bmm = self.pv_bmm if self.config.use_matmul_as_module else torch.matmul
        attn_output = pv_bmm(attn_weights, value_states)

        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
            raise ValueError(
                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
                f" {attn_output.size()}"
            )
        attn_output = attn_output.transpose(1, 2).contiguous()

        attn_output = attn_output.view(bsz, q_len, -1)
        attn_output = self.o_proj(attn_output)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights, past_key_value


class HFFlashAttention2(HFAttention):
    """
    HF flash attention module. This module inherits from `HFAttention` as the weights of the module stays
    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
    flash attention and deal with padding tokens in case the input contains any of them.
    """

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

        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()

    # Ignore copy
    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        if output_attentions:
            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
            logger.warning_once(
                "HFModel is using HFFlashAttention2, which does not support `output_attentions=True`. Falling back to the manual attention implementation, "
                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
            )
            return super().forward(
                hidden_states=hidden_states,
                attention_mask=attention_mask,
                position_ids=position_ids,
                past_key_value=past_key_value,
                output_attentions=output_attentions,
                use_cache=use_cache,
                cache_position=cache_position,
            )
        if "padding_mask" in kwargs:
            warnings.warn(
                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
            )

            # overwrite attention_mask with padding_mask
            attention_mask = kwargs.pop("padding_mask")

        device, dtype = hidden_states.device, hidden_states.dtype
        
        bsz, q_len, _ = hidden_states.size()

        query_states = self.q_proj(hidden_states)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)

        # Flash attention requires the input to have the shape
        # batch_size x seq_length x head_dim x hidden_dim
        # therefore we just need to keep the original shape
        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)

        past_key_value = getattr(self, "past_key_value", past_key_value)

        # ####################################################################################################
        # # Mistral style
        # kv_seq_len = key_states.shape[-2]
        # if past_key_value is not None:
        #     if self.layer_idx is None:
        #         raise ValueError(
        #             f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
        #             "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
        #             "with a layer index."
        #         )
        #     kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)

        # # Because the input can be padded, the absolute sequence length depends on the max position id.
        # rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1
        # cos, sin = self.rotary_emb(position_ids=None, device=device, dtype=dtype, seq_len=rotary_seq_len)
        # if self.rotary_emb.dim == self.head_dim:
        #     query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
        # else:
        #     # Partial rotary embedding
        #     query_rot, query_pass = (
        #         query_states[..., : self.rotary_emb.dim],
        #         query_states[..., self.rotary_emb.dim :],
        #     )
        #     key_rot, key_pass = (
        #         key_states[..., : self.rotary_emb.dim],
        #         key_states[..., self.rotary_emb.dim :],
        #     )
        #     query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)
        #     query_states = torch.cat((query_rot, query_pass), dim=-1)
        #     key_states = torch.cat((key_rot, key_pass), dim=-1)

        ####################################################################################################
        # Gemma style

        cos, sin = self.rotary_emb(position_ids, device=device, dtype=dtype, seq_len=None)
        if self.rotary_emb.dim == self.head_dim:
            query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, None)
        else:
            # Partial rotary embedding
            query_rot, query_pass = (
                query_states[..., : self.rotary_emb.dim],
                query_states[..., self.rotary_emb.dim :],
            )
            key_rot, key_pass = (
                key_states[..., : self.rotary_emb.dim],
                key_states[..., self.rotary_emb.dim :],
            )
            query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, None)
            query_states = torch.cat((query_rot, query_pass), dim=-1)
            key_states = torch.cat((key_rot, key_pass), dim=-1)

        if not _flash_supports_window_size:
            logger.warning_once(
                "The current flash attention version does not support sliding window attention, for a more memory efficient implementation"
                " make sure to upgrade flash-attn library."
            )

        kv_seq_len = key_states.shape[-2]
        if past_key_value is not None:
            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
            # Activate slicing cache only if the config has a value `sliding_windows` attribute
            cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0
            if (
                getattr(self.config, "sliding_window", None) is not None
                and kv_seq_len > self.config.sliding_window
                and cache_has_contents
            ):
                slicing_tokens = 1 - self.config.sliding_window

                past_key = past_key_value[self.layer_idx][0]
                past_value = past_key_value[self.layer_idx][1]

                past_key = past_key[:, :, slicing_tokens:, :].contiguous()
                past_value = past_value[:, :, slicing_tokens:, :].contiguous()

                if past_key.shape[-2] != self.config.sliding_window - 1:
                    raise ValueError(
                        f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got"
                        f" {past_key.shape}"
                    )

                if attention_mask is not None:
                    attention_mask = attention_mask[:, slicing_tokens:]
                    attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1)

            # sin and cos are specific to RoPE models; position_ids needed for the static cache
            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position, "partial_rotation_size": self.rotary_emb.dim}
            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

        use_sliding_windows = (
            _flash_supports_window_size
            and getattr(self.config, "sliding_window", None) is not None
            and kv_seq_len > self.config.sliding_window
        )

        # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
        # to be able to avoid many of these transpose/reshape/view.
        query_states = query_states.transpose(1, 2)
        key_states = key_states.transpose(1, 2)
        value_states = value_states.transpose(1, 2)

        dropout_rate = self.attention_dropout if self.training else 0.0

        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
        # therefore the input hidden states gets silently casted in float32. Hence, we need
        # cast them back in the correct dtype just to be sure everything works as expected.
        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
        # in fp32. (HFRMSNorm handles it correctly)

        input_dtype = query_states.dtype
        if input_dtype == torch.float32:
            if torch.is_autocast_enabled():
                target_dtype = torch.get_autocast_gpu_dtype()
            # Handle the case where the model is quantized
            elif hasattr(self.config, "_pre_quantization_dtype"):
                target_dtype = self.config._pre_quantization_dtype
            else:
                target_dtype = self.q_proj.weight.dtype

            logger.warning_once(
                f"The input hidden states seems to be silently casted in float32, this might be related to"
                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
                f" {target_dtype}."
            )

            query_states = query_states.to(target_dtype)
            key_states = key_states.to(target_dtype)
            value_states = value_states.to(target_dtype)

        attn_output = self._flash_attention_forward(
            query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate, use_sliding_windows=use_sliding_windows,
        )

        attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
        attn_output = self.o_proj(attn_output)

        return attn_output, None, past_key_value

    def _flash_attention_forward(
        self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None, use_sliding_windows=False,
    ):
        """
        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
        first unpad the input, then computes the attention scores and pad the final attention scores.

        Args:
            query_states (`torch.Tensor`):
                Input query states to be passed to Flash Attention API
            key_states (`torch.Tensor`):
                Input key states to be passed to Flash Attention API
            value_states (`torch.Tensor`):
                Input value states to be passed to Flash Attention API
            attention_mask (`torch.Tensor`):
                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
                position of padding tokens and 1 for the position of non-padding tokens.
            dropout (`int`, *optional*):
                Attention dropout
            softmax_scale (`float`, *optional*):
                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
            use_sliding_windows (`bool`, *optional*):
                Whether to activate sliding window attention.
        """
        if not self._flash_attn_uses_top_left_mask:
            causal = self.is_causal
        else:
            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in GemmaFlashAttention2 __init__.
            causal = self.is_causal and query_length != 1

        # Contains at least one padding token in the sequence
        if attention_mask is not None:
            batch_size = query_states.shape[0]
            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
                query_states, key_states, value_states, attention_mask, query_length
            )

            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens

            if not use_sliding_windows:
                attn_output_unpad = flash_attn_varlen_func(
                    query_states,
                    key_states,
                    value_states,
                    cu_seqlens_q=cu_seqlens_q,
                    cu_seqlens_k=cu_seqlens_k,
                    max_seqlen_q=max_seqlen_in_batch_q,
                    max_seqlen_k=max_seqlen_in_batch_k,
                    dropout_p=dropout,
                    softmax_scale=softmax_scale,
                    causal=causal,
                )
            else:
                attn_output_unpad = flash_attn_varlen_func(
                    query_states,
                    key_states,
                    value_states,
                    cu_seqlens_q=cu_seqlens_q,
                    cu_seqlens_k=cu_seqlens_k,
                    max_seqlen_q=max_seqlen_in_batch_q,
                    max_seqlen_k=max_seqlen_in_batch_k,
                    dropout_p=dropout,
                    softmax_scale=softmax_scale,
                    causal=causal,
                    window_size=(self.config.sliding_window, self.config.sliding_window),
                )

            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
        else:
            if not use_sliding_windows:
                attn_output = flash_attn_func(
                    query_states,
                    key_states,
                    value_states,
                    dropout,
                    softmax_scale=softmax_scale,
                    causal=causal,
                )
            else:
                attn_output = flash_attn_func(
                    query_states,
                    key_states,
                    value_states,
                    dropout,
                    softmax_scale=softmax_scale,
                    causal=causal,
                    window_size=(self.config.sliding_window, self.config.sliding_window),
                )

        return attn_output

    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape

        # On the first iteration we need to properly re-create the padding mask
        # by slicing it on the proper place
        if kv_seq_len != attention_mask.shape[-1]:
            attention_mask_num_tokens = attention_mask.shape[-1]
            attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :]

        key_layer = index_first_axis(
            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
        )
        value_layer = index_first_axis(
            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
        )
        if query_length == kv_seq_len:
            query_layer = index_first_axis(
                query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
            )
            cu_seqlens_q = cu_seqlens_k
            max_seqlen_in_batch_q = max_seqlen_in_batch_k
            indices_q = indices_k
        elif query_length == 1:
            max_seqlen_in_batch_q = 1
            cu_seqlens_q = torch.arange(
                batch_size + 1, dtype=torch.int32, device=query_layer.device
            )  # There is a memcpy here, that is very bad.
            indices_q = cu_seqlens_q[:-1]
            query_layer = query_layer.squeeze(1)
        else:
            # The -q_len: slice assumes left padding.
            attention_mask = attention_mask[:, -query_length:]
            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)

        return (
            query_layer,
            key_layer,
            value_layer,
            indices_q,
            (cu_seqlens_q, cu_seqlens_k),
            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
        )


class HFSdpaAttention(HFAttention):
    """
    HF attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
    `HFAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
    SDPA API.
    """

    # Ignore copy
    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,
        output_attentions: bool = False,
        use_cache: bool = False,
        cache_position: Optional[torch.LongTensor] = None,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        if output_attentions:
            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
            logger.warning_once(
                "HFModel is using HFSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
            )
            return super().forward(
                hidden_states=hidden_states,
                attention_mask=attention_mask,
                position_ids=position_ids,
                past_key_value=past_key_value,
                output_attentions=output_attentions,
                use_cache=use_cache,
                cache_position=cache_position,
            )

        device, dtype = hidden_states.device, hidden_states.dtype

        bsz, q_len, _ = hidden_states.size()

        query_states = self.q_proj(hidden_states)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)

        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)

        past_key_value = getattr(self, "past_key_value", past_key_value)

        # ####################################################################################################
        # # Mistral style
        # kv_seq_len = key_states.shape[-2]
        # if past_key_value is not None:
        #     if self.layer_idx is None:
        #         raise ValueError(
        #             f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
        #             "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
        #             "with a layer index."
        #         )
        #     kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)

        # # Because the input can be padded, the absolute sequence length depends on the max position id.
        # rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1
        # cos, sin = self.rotary_emb(position_ids=None, device=device, dtype=dtype, seq_len=rotary_seq_len)
        # if self.rotary_emb.dim == self.head_dim:
        #     query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
        # else:
        #     # Partial rotary embedding
        #     query_rot, query_pass = (
        #         query_states[..., : self.rotary_emb.dim],
        #         query_states[..., self.rotary_emb.dim :],
        #     )
        #     key_rot, key_pass = (
        #         key_states[..., : self.rotary_emb.dim],
        #         key_states[..., self.rotary_emb.dim :],
        #     )
        #     query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)
        #     query_states = torch.cat((query_rot, query_pass), dim=-1)
        #     key_states = torch.cat((key_rot, key_pass), dim=-1)

        ####################################################################################################
        # Gemma style
        cos, sin = self.rotary_emb(position_ids, device=device, dtype=dtype, seq_len=None)
        if self.rotary_emb.dim == self.head_dim:
            query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, None)
        else:
            # Partial rotary embedding
            query_rot, query_pass = (
                query_states[..., : self.rotary_emb.dim],
                query_states[..., self.rotary_emb.dim :],
            )
            key_rot, key_pass = (
                key_states[..., : self.rotary_emb.dim],
                key_states[..., self.rotary_emb.dim :],
            )
            query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, None)
            query_states = torch.cat((query_rot, query_pass), dim=-1)
            key_states = torch.cat((key_rot, key_pass), dim=-1)


        if past_key_value is not None:
            # sin and cos are specific to RoPE models; position_ids needed for the static cache
            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position, "partial_rotation_size": self.rotary_emb.dim}
            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

        key_states = repeat_kv(key_states, self.num_key_value_groups)
        value_states = repeat_kv(value_states, self.num_key_value_groups)

        causal_mask = attention_mask
        if attention_mask is not None and cache_position is not None:
            causal_mask = causal_mask[:, :, cache_position, : key_states.shape[-2]]

        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
        # Reference: https://github.com/pytorch/pytorch/issues/112577.
        if query_states.device.type == "cuda" and causal_mask is not None:
            query_states = query_states.contiguous()
            key_states = key_states.contiguous()
            value_states = value_states.contiguous()

        attn_output = torch.nn.functional.scaled_dot_product_attention(
            query_states,
            key_states,
            value_states,
            attn_mask=causal_mask,
            dropout_p=self.attention_dropout if self.training else 0.0,
            # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
            is_causal=self.is_causal and attention_mask is None and q_len > 1,
        )

        attn_output = attn_output.transpose(1, 2).contiguous()
        attn_output = attn_output.view(bsz, q_len, -1)

        attn_output = self.o_proj(attn_output)

        return attn_output, None, past_key_value


HF_ATTENTION_CLASSES = {
    "eager": HFAttention,
    "flash_attention_2": HFFlashAttention2,
    "sdpa": HFSdpaAttention,
}

HF_NORM_CLASSES = {
    "rmsnorm": HFRMSNorm,
    "layernorm": nn.LayerNorm,
    "skiprms": HFSkipRMSNorm,
}

class HFMLP(nn.Module):
    def __init__(self, config: HFConfig):
        super().__init__()
        self.ffn_dim = config.intermediate_size
        self.hidden_dim = config.hidden_size
        self.num_linears_per_mlp = config.num_linears_per_mlp

        self.w1 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=config.mlp_bias)
        self.w2 = nn.Linear(self.ffn_dim, self.hidden_dim, bias=config.mlp_bias)
        if self.num_linears_per_mlp == 3:
            self.w3 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=config.mlp_bias)
            self.elementwisemul = ElementwiseMul()

        self.act_fn = ACT2FN[config.hidden_act]

    def forward(self, hidden_states):
        current_hidden_states = self.act_fn(self.w1(hidden_states))
        if self.num_linears_per_mlp == 3:
            current_hidden_states = self.elementwisemul(current_hidden_states, self.w3(hidden_states))
        current_hidden_states = self.w2(current_hidden_states)
        return current_hidden_states


class HFMoEBlock(nn.Module):
    """
    This implementation is
    strictly equivalent to standard MoE with full capacity (no
    dropped tokens). It's faster since it formulates MoE operations
    in terms of block-sparse operations to accomodate imbalanced
    assignments of tokens to experts, whereas standard MoE either
    (1) drop tokens at the cost of reduced performance or (2) set
    capacity factor to number of experts and thus waste computation
    and memory on padding.
    """

    def __init__(self, config):
        super().__init__()
        self.config = config
        self.hidden_dim = config.hidden_size
        self.ffn_dim = config.intermediate_size
        self.num_experts = config.num_local_experts
        self.top_k = config.num_experts_per_tok

        # gating
        self.gate = nn.Linear(self.hidden_dim, self.num_experts, bias=False)

        self.experts = nn.ModuleList([HFMLP(config) for _ in range(self.num_experts)])

    @torch.inference_mode()
    def to_megablocks(self):
        from megablocks.layers.arguments import Arguments
        from megablocks.layers import dmoe
        args = Arguments(
            hidden_size=self.hidden_dim,
            ffn_hidden_size=self.ffn_dim,
            activation_fn=torch.nn.functional.silu,
            moe_num_experts=self.num_experts,
            moe_capacity_factor=1,
            moe_top_k=self.top_k,
            init_method=partial(torch.nn.init.normal_, mean=0.0, std=0.1),
            memory_optimized_mlp=False,
            mlp_type='glu',
            mlp_impl="sparse",
            fp16=self.config.torch_dtype == torch.float16,
            bf16=self.config.torch_dtype == torch.bfloat16,
            return_bias=False,
        )
        mlp = dmoe.dMoE(args).to(self.gate.weight.device)
    
        with torch.no_grad():
            mlp.router.layer.weight.copy_(self.gate.weight)
            mlp.experts.mlp.w1.copy_(torch.cat([e.w1.weight for e in self.experts]))
            mlp.experts.mlp.w2.copy_(torch.cat([e.w2.weight for e in self.experts], dim=-1).transpose(0,1))
            mlp.experts.mlp.v1.copy_(torch.cat([e.w3.weight for e in self.experts]))
        return mlp


    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        """ """
        batch_size, sequence_length, hidden_dim = hidden_states.shape
        hidden_states = hidden_states.view(-1, hidden_dim)
        # router_logits: (batch * sequence_length, n_experts)
        router_logits = self.gate(hidden_states)

        routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
        routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
        routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
        # we cast back to the input dtype
        routing_weights = routing_weights.to(hidden_states.dtype)

        final_hidden_states = torch.zeros(
            (batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
        )

        # One hot encode the selected experts to create an expert mask
        # this will be used to easily index which expert is going to be sollicitated
        expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0)

        # Loop over all available experts in the model and perform the computation on each expert
        for expert_idx in range(self.num_experts):
            expert_layer = self.experts[expert_idx]
            idx, top_x = torch.where(expert_mask[expert_idx])

            if top_x.shape[0] == 0:
                continue

            # in torch it is faster to index using lists than torch tensors
            top_x_list = top_x.tolist()
            idx_list = idx.tolist()

            # Index the correct hidden states and compute the expert hidden state for
            # the current expert. We need to make sure to multiply the output hidden
            # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
            current_state = hidden_states[None, top_x_list].reshape(-1, hidden_dim)
            current_hidden_states = expert_layer(current_state) * routing_weights[top_x_list, idx_list, None]

            # However `index_add_` only support torch tensors for indexing so we'll use
            # the `top_x` tensor here.
            final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
        final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
        return final_hidden_states, router_logits


class HFDecoderLayer(nn.Module):
    def __init__(self, config: HFConfig, layer_idx: int):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.self_attn = HF_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
        self.resid_dropout = nn.Dropout(config.resid_pdrop)
        self.shared_attention_norm = config.shared_attention_norm
        self.parallel_residual = config.parallel_residual
        if config.num_local_experts > 1:
            if config.use_megablocks:
                from megablocks.layers.arguments import Arguments
                from megablocks.layers import dmoe
                args = Arguments(
                    hidden_size=self.hidden_size,
                    ffn_hidden_size=config.intermediate_size,
                    activation_fn=torch.nn.functional.silu,
                    moe_num_experts=config.num_local_experts,
                    moe_capacity_factor=2,
                    moe_top_k=config.num_experts_per_tok,
                    init_method=partial(torch.nn.init.normal_, mean=0.0, std=0.1),
                    memory_optimized_mlp=False,
                    mlp_type='glu',
                    mlp_impl="sparse",
                    fp16=config.torch_dtype == torch.float16,
                    bf16=config.torch_dtype == torch.bfloat16,
                    return_bias=False,
                )
                self.mlp = dmoe.dMoE(args)
            else:
                self.mlp = HFMoEBlock(config)
        else:
            self.mlp = HFMLP(config)
        self.norm_class = HF_NORM_CLASSES[config.norm_class.lower()]
        kwargs = {}
        if "rms" in config.norm_class.lower():
            kwargs["l2norm_as_rmsnorm"] = config.l2norm_as_rmsnorm
        self.input_layernorm = self.norm_class(config.hidden_size, eps=config.layer_norm_eps, **kwargs)
        if not self.shared_attention_norm:
            self.post_attention_layernorm = self.norm_class(config.hidden_size, eps=config.layer_norm_eps, **kwargs)

        self.resid_add_1 = ElementwiseAdd()
        self.resid_add_2 = ElementwiseAdd()

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: Optional[bool] = False,
        output_router_logits: Optional[bool] = False,
        use_cache: Optional[bool] = False,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs,
    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
        if "padding_mask" in kwargs:
            warnings.warn(
                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
            )
        """
        Args:
            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
                `(batch, sequence_length)` where padding elements are indicated by 0.
            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): 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.
            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.
            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`).
        """

        residual = hidden_states

        hidden_states = self.input_layernorm(hidden_states)

        # Self Attention
        attn_outputs, self_attn_weights, present_key_value = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_value=past_key_value,
            output_attentions=output_attentions,
            use_cache=use_cache,
            cache_position=cache_position,
        )
        attn_outputs = self.resid_dropout(attn_outputs)
        # residual = residual + attn_outputs
        residual = self.resid_add_1(residual, attn_outputs)

        if not self.parallel_residual:
            hidden_states = residual

        if not self.shared_attention_norm:
            hidden_states = self.post_attention_layernorm(hidden_states)

        mlp_outputs = self.mlp(hidden_states)
        router_logits = None
        if isinstance(mlp_outputs, tuple):
            mlp_outputs, router_logits = mlp_outputs
        # hidden_states = residual + self.resid_dropout(mlp_outputs)
        hidden_states = self.resid_add_2(residual, self.resid_dropout(mlp_outputs))

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights,)

        if use_cache:
            outputs += (present_key_value,)

        if output_router_logits:
            outputs += (router_logits,)

        return outputs


HF_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 ([`HFConfig`]):
            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.
"""


@add_start_docstrings(
    "The bare HF Model outputting raw hidden-states without any specific head on top.",
    HF_START_DOCSTRING,
)
class HFPreTrainedModel(PreTrainedModel):
    config_class = HFConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    # _keep_in_fp32_modules = ["inv_freq", "rotary_emb", "cos_cached", "sin_cached"]
    _no_split_modules = ["HFDecoderLayer"]
    _skip_keys_device_placement = ["past_key_values", "causal_mask"]
    _supports_flash_attn_2 = True
    _supports_sdpa = True
    _supports_cache_class = True

    def _init_weights(self, module):
        std = self.config.initializer_range
        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_()

    def _setup_cache(self, cache_cls, max_batch_size, max_cache_len: Optional[int] = None):
        if self.config._attn_implementation == "flash_attention_2" and cache_cls == StaticCache:
            raise ValueError(
                "`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` "
                "make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers"
            )

        if max_cache_len > self.model.causal_mask.shape[-1] or self.device != self.model.causal_mask.device:
            causal_mask = torch.full((max_cache_len, max_cache_len), fill_value=1, device=self.device)
            self.register_buffer("causal_mask", torch.triu(causal_mask, diagonal=1), persistent=False)

        for layer in self.model.layers:
            weights = layer.self_attn.o_proj.weight
            layer.self_attn.past_key_value = cache_cls(
                self.config, max_batch_size, max_cache_len, device=weights.device, dtype=weights.dtype
            )

    def _reset_cache(self):
        for layer in self.model.layers:
            layer.self_attn.past_key_value = None


HF_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)

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

            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
            `past_key_values`).

            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
            information on the default strategy.

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.
        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.n_positions - 1]`.

            [What are position IDs?](../glossary#position-ids)
        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.
        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.
"""

@add_start_docstrings(
    "The bare HF Model outputting raw hidden-states without any specific head on top.",
    HF_START_DOCSTRING,
)
class HFModel(HFPreTrainedModel):
    """
    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`HFDecoderLayer`]

    Args:
        config: HFConfig
    """

    def __init__(self, config: HFConfig):
        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.embed_dropout = nn.Dropout(config.embd_pdrop)
        self.layers = nn.ModuleList(
            [HFDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self._attn_implementation = config._attn_implementation
        self.norm_class = HF_NORM_CLASSES[config.norm_class.lower()]
        self.norm = self.norm_class(config.hidden_size, eps=config.layer_norm_eps)

        self.gradient_checkpointing = False

        # Register a causal mask to separate causal and padding mask creation. Merging happens in the attention class.
        # NOTE: This is not friendly with TorchScript, ONNX, ExportedProgram serialization for very large `max_position_embeddings`.
        if config.static_causal_mask:
            causal_mask = torch.full(
                (config.max_position_embeddings, config.max_position_embeddings), fill_value=True, dtype=torch.bool
            )
            self.register_buffer("causal_mask", torch.triu(causal_mask, diagonal=1), persistent=False)

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

    def get_input_embeddings(self):
        return self.embed_tokens

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

    # Ignore copy
    @add_start_docstrings_to_model_forward(HF_INPUTS_DOCSTRING)
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        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,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        output_router_logits: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
    ) -> Union[Tuple, MoeModelOutputWithPast]:
        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
        )
        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.use_return_dict

        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError(
                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
            )

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

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

        past_seen_tokens = 0
        if use_cache:  # kept for BC (cache positions)
            if not isinstance(past_key_values, StaticCache):
                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
            past_seen_tokens = past_key_values.get_seq_length()

        if cache_position is None:
            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)

        if self.config.static_causal_mask:
            causal_mask = self._update_causal_mask(attention_mask, inputs_embeds)
        else:
            batch_size, seq_length = inputs_embeds.shape[:2]
            if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
                is_padding_right = attention_mask[:, -1].sum().item() != batch_size
                if is_padding_right:
                    raise ValueError(
                        "You are attempting to perform batched generation with padding_side='right'"
                        " this may lead to unexpected behaviour for Flash Attention version of Mixtral. Make sure to "
                        " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
                    )

            if self._attn_implementation == "flash_attention_2":
                # 2d mask is passed through the layers
                attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
            elif self._attn_implementation == "sdpa" and not output_attentions:
                # output_attentions=True can not be supported when using SDPA, and we fall back on
                # the manual implementation that requires a 4D causal mask in all cases.
                attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
                    attention_mask,
                    (batch_size, seq_length),
                    inputs_embeds,
                    past_seen_tokens,
                )
            else:
                # 4d mask is passed through the layers
                attention_mask = _prepare_4d_causal_attention_mask(
                    attention_mask,
                    (batch_size, seq_length),
                    inputs_embeds,
                    past_seen_tokens,
                    sliding_window=self.config.sliding_window,
                )
            causal_mask = attention_mask

        hidden_states = inputs_embeds

        if self.config.normalize_embed:
            hidden_states = hidden_states * (self.config.hidden_size**0.5)

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        all_router_logits = () if output_router_logits else None
        next_decoder_cache = None

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

            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(
                    decoder_layer.__call__,
                    hidden_states,
                    causal_mask,
                    position_ids,
                    past_key_values,
                    output_attentions,
                    output_router_logits,
                    use_cache,
                    cache_position,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=causal_mask,
                    position_ids=position_ids,
                    past_key_value=past_key_values,
                    output_attentions=output_attentions,
                    output_router_logits=output_router_logits,
                    use_cache=use_cache,
                    cache_position=cache_position,
                )

            hidden_states = layer_outputs[0]

            if use_cache:
                next_decoder_cache = layer_outputs[2 if output_attentions else 1]

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

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

        hidden_states = self.norm(hidden_states)

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

        next_cache = None
        if use_cache:
            next_cache = (
                next_decoder_cache.to_legacy_cache() if isinstance(next_decoder_cache, Cache) else next_decoder_cache
            )

        if not return_dict:
            return tuple(
                v
                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_router_logits]
                if v is not None
            )
        return MoeModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=next_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
            router_logits=all_router_logits,
        )

    # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
    # KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
    # (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
    # `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
    def _update_causal_mask(self, attention_mask, input_tensor):
        if self.config._attn_implementation == "flash_attention_2":
            if attention_mask is not None and 0.0 in attention_mask:
                return attention_mask
            return None

        batch_size, seq_length = input_tensor.shape[:2]
        dtype = input_tensor.dtype
        device = input_tensor.device

        # support going beyond cached `max_position_embedding`
        if seq_length > self.causal_mask.shape[-1]:
            causal_mask = torch.full((2 * self.causal_mask.shape[-1], 2 * self.causal_mask.shape[-1]), fill_value=1)
            self.register_buffer("causal_mask", torch.triu(causal_mask, diagonal=1), persistent=False)

        # We use the current dtype to avoid any overflows
        # min_dtype = torch.finfo(dtype).min
        min_dtype = -40000
        causal_mask = self.causal_mask[None, None, :, :].repeat(batch_size, 1, 1, 1).to(dtype) * min_dtype

        causal_mask = causal_mask.to(dtype=dtype, device=device)
        if attention_mask is not None and attention_mask.dim() == 2:
            mask_length = attention_mask.shape[-1]
            padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[:, None, None, :].eq(0.0)
            causal_mask[..., :mask_length] = causal_mask[..., :mask_length].masked_fill(padding_mask, min_dtype)

        if self.config._attn_implementation == "sdpa" and attention_mask is not None:
            # TODO: For dynamo, rather use a check on fullgraph=True once this is possible (https://github.com/pytorch/pytorch/pull/120400).
            is_tracing = (
                torch.jit.is_tracing()
                or isinstance(input_tensor, torch.fx.Proxy)
                or (hasattr(torch, "_dynamo") and torch._dynamo.is_compiling())
            )
            if not is_tracing and torch.any(attention_mask != 1):
                # Attend to all tokens in masked rows from the causal_mask, for example the relevant first rows when
                # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
                # Details: https://github.com/pytorch/pytorch/issues/110213
                causal_mask = causal_mask.mul(~torch.all(causal_mask == min_dtype, dim=-1, keepdim=True)).to(dtype)

        return causal_mask


class HFForCausalLM(HFPreTrainedModel):
    _tied_weights_keys = ["lm_head.weight"]

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

        # 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

    # Ignore copy
    @add_start_docstrings_to_model_forward(HF_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        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,
        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,
        cache_position: Optional[torch.LongTensor] = None,
    ) -> Union[Tuple, MoeCausalLMOutputWithPast]:
        r"""
        Args:
            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:

        Example:

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

        >>> model = GemmaForCausalLM.from_pretrained("google/gemma-7b")
        >>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")

        >>> prompt = "What is your favorite condiment?"
        >>> inputs = tokenizer(prompt, return_tensors="pt")

        >>> # Generate
        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
        "What is your favorite condiment?"
        ```"""
        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
        )

        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.use_return_dict

        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        outputs = 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,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            output_router_logits=output_router_logits,
            return_dict=return_dict,
            cache_position=cache_position,
        )

        hidden_states = outputs[0]
        logits = self.lm_head(hidden_states)
        logits = logits.float()

        loss = None
        if labels is not None:
            # Shift so that tokens < n predict n
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            # Flatten the tokens
            loss_fct = CrossEntropyLoss()
            shift_logits = shift_logits.view(-1, self.config.vocab_size)
            shift_labels = shift_labels.view(-1)
            # Enable model parallelism
            shift_labels = shift_labels.to(shift_logits.device)
            loss = loss_fct(shift_logits, shift_labels)

        aux_loss = None
        if output_router_logits:
            aux_loss = load_balancing_loss_func(
                outputs.router_logits if return_dict else outputs[-1],
                self.num_experts,
                self.num_experts_per_tok,
                attention_mask,
            )
            if labels is not None:
                loss += self.router_aux_loss_coef * aux_loss.to(loss.device)  # make sure to reside in the same device

        if not return_dict:
            output = (logits,) + outputs[1:]
            if output_router_logits:
                output = (aux_loss,) + output
            return (loss,) + output if loss is not None else output

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

    def prepare_inputs_for_generation(
        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
    ):
        past_length = 0
        if past_key_values is not None:
            if isinstance(past_key_values, Cache):
                cache_length = past_key_values.get_seq_length()
                past_length = past_key_values.seen_tokens
                max_cache_length = past_key_values.get_max_length()
            else:
                cache_length = past_length = past_key_values[0][0].shape[2]
                max_cache_length = None

            # Keep only the unprocessed tokens:
            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
            # input)
            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
            # input_ids based on the past_length.
            elif past_length < input_ids.shape[1]:
                input_ids = input_ids[:, past_length:]
            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.

            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
            if (
                max_cache_length is not None
                and attention_mask is not None
                and cache_length + input_ids.shape[1] > max_cache_length
            ):
                attention_mask = attention_mask[:, -max_cache_length:]

        position_ids = kwargs.get("position_ids", None)
        if attention_mask is not None and position_ids is None:
            # create position_ids on the fly for batch generation
            position_ids = attention_mask.long().cumsum(-1) - 1
            position_ids.masked_fill_(attention_mask == 0, 1)
            if past_key_values:
                position_ids = position_ids[:, -input_ids.shape[1] :]

        if getattr(self.model.layers[0].self_attn, "past_key_value", None) is not None:
            # generation with static cache
            cache_position = kwargs.get("cache_position", None)
            if cache_position is None:
                past_length = 0
            else:
                past_length = cache_position[-1] + 1
            input_ids = input_ids[:, past_length:]
            position_ids = position_ids[:, past_length:]

        # TODO @gante we should only keep a `cache_position` in generate, and do +=1.
        # same goes for position ids. Could also help with continued generation.
        cache_position = torch.arange(past_length, past_length + position_ids.shape[-1], device=position_ids.device)

        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
        if inputs_embeds is not None and past_key_values is None:
            model_inputs = {"inputs_embeds": inputs_embeds}
        else:
            # The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise
            # recompiles graphs as the stride of the inputs is a guard. Ref: https://github.com/huggingface/transformers/pull/29114
            # TODO: use `next_tokens` directly instead.
            model_inputs = {"input_ids": input_ids.contiguous()}

        model_inputs.update(
            {
                "position_ids": position_ids.contiguous(),
                "cache_position": cache_position,
                "past_key_values": past_key_values,
                "use_cache": kwargs.get("use_cache"),
                "attention_mask": attention_mask,
            }
        )
        return model_inputs

    @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