rm

modeling_bailing_moe_linear_v2.py

@@ -1,1758 +0,0 @@
-# coding=utf-8
-# Copyright 2025 Antgroup and The HuggingFace Inc. team. All rights reserved.
-#
-# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
-# and OPT implementations in this library. It has been modified from its
-# original forms to accommodate minor architectural differences compared
-# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""PyTorch BailingMoE model."""
-
-import math
-import warnings
-from typing import List, Optional, Tuple, Union
-
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from transformers.activations import ACT2FN
-from transformers.cache_utils import Cache, DynamicCache
-from transformers.modeling_attn_mask_utils import (
-    AttentionMaskConverter,
-    _prepare_4d_attention_mask,
-    _prepare_4d_causal_attention_mask,
-    _prepare_4d_causal_attention_mask_for_sdpa,
-)
-from transformers.modeling_outputs import MoeModelOutputWithPast
-from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
-from transformers.modeling_utils import PreTrainedModel
-from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13
-from transformers.utils import (
-    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 .configuration_bailing_moe_linear_v2 import BailingMoeLinearV2Config
-from transformers.generation.utils import GenerationMixin
-from dataclasses import dataclass
-from transformers.utils import ModelOutput
-
-
-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
-
-from fla.ops.simple_gla.fused_recurrent import fused_recurrent_simple_gla
-from fla.ops.simple_gla.chunk import chunk_simple_gla
-
-
-# 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 = "BailingMoeLinearV2Config"
-
-
-def roll_tensor(tensor, shifts=-1, dims=-1, fill_value=0):
-    """Roll the tensor input along the given dimension(s).
-    Inserted elements are set to be 0.0.
-    """
-    rolled_tensor = torch.roll(tensor, shifts=shifts, dims=dims)
-    rolled_tensor.select(dims, shifts).fill_(fill_value)
-    return rolled_tensor, rolled_tensor.sum()
-
-
-@dataclass
-class MoEV2CausalLMOutputWithPast(ModelOutput):
-    """
-    Base class for causal language model (or autoregressive) outputs as well as Mixture of Expert's router hidden
-    states terms, to train a MoE model.
-    Args:
-        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
-            Language modeling loss (for next-token prediction).
-        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
-            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
-        past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
-            It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
-            Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
-            `past_key_values` input) to speed up sequential decoding.
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
-            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
-            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
-        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`.
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
-            heads.
-        z_loss (`torch.FloatTensor`, *optional*, returned when `labels` is provided):
-            z_loss for the sparse modules.
-        aux_loss (`torch.FloatTensor`, *optional*, returned when `labels` is provided):
-            aux_loss for the sparse modules.
-        router_logits (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_logits=True` is passed or when `config.add_router_probs=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`.
-            Router logits of the encoder model, useful to compute the auxiliary loss and the z_loss for the sparse
-            modules.
-    """
-
-    loss: Optional[torch.FloatTensor] = None
-    logits: Optional[torch.FloatTensor] = None
-    past_key_values: Optional[Cache] = None
-    hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
-    attentions: Optional[tuple[torch.FloatTensor, ...]] = None
-    z_loss: Optional[torch.FloatTensor] = None
-    aux_loss: Optional[torch.FloatTensor] = None
-    router_logits: Optional[tuple[torch.FloatTensor]] = None
-    mtp_loss: Optional[torch.FloatTensor] = None
-    mtp_logits: Optional[tuple[torch.FloatTensor, ...]] = None
-
-
-class MoeV2ModelOutputWithPast(MoeModelOutputWithPast):
-
-    def __init__(self, mtp_hidden_states=None, **kwargs):
-        super().__init__(**kwargs)
-        self.mtp_hidden_states = mtp_hidden_states
-
-
-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.torch.int32), (1, 0))
-    return (
-        indices,
-        cu_seqlens,
-        max_seqlen_in_batch,
-    )
-
-
-def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
-    warnings.warn(
-        "Calling `transformers.models.BailingMoeV2.modeling_BailingMoeV2._prepare_4d_attention_mask` is deprecated and will be removed in v4.37. Use `transformers.modeling_attn_mask_utils._prepare_4d_attention_mask"
-    )
-    return _prepare_4d_attention_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)
-
-
-def _make_causal_mask(
-    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
-):
-    warnings.warn(
-        "Calling `transformers.models.BailingMoeV2.modeling_BailingMoeV2._make_causal_mask` is deprecated and will be removed in v4.37. Use `transformers.models.BailingMoeV2.modeling_BailingMoeV2.AttentionMaskConverter._make_causal_mask"
-    )
-    return AttentionMaskConverter._make_causal_mask(
-        input_ids_shape=input_ids_shape, dtype=dtype, device=device, past_key_values_length=past_key_values_length
-    )
-
-
-class BailingMoeV2RMSNorm(nn.Module):
-    def __init__(self, hidden_size, eps=1e-6):
-        """
-        BailingMoeV2RMSNorm 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)
-
-
-class BailingMoeV2GroupRMSNorm(nn.Module):
-    def __init__(self, hidden_size, group_norm_size, eps=1e-6):
-        """
-        BailingMoeV2RMSNorm is equivalent to T5LayerNorm
-        """
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(hidden_size))
-        self.group_norm_size = group_norm_size
-        assert hidden_size % group_norm_size == 0, "hidden_size must be divisible by group_norm_size"
-        self.variance_epsilon = eps
-
-    def forward(self, hidden_states):
-        input_dtype = hidden_states.dtype
-        input_shape = hidden_states.size()
-        group_input_shape = input_shape[:-1] + (self.group_norm_size, input_shape[-1] // self.group_norm_size)
-        hidden_states = hidden_states.view(group_input_shape)
-        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).view(input_shape)
-
-
-ALL_LAYERNORM_LAYERS.append(BailingMoeV2RMSNorm)
-
-
-class BailingMoeV2RotaryEmbedding(nn.Module):
-    def __init__(self, config: BailingMoeLinearV2Config, 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
-
-    @torch.no_grad()
-    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
-    def forward(self, x, position_ids):
-        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
-        position_ids_expanded = position_ids[:, None, :].float()
-
-        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
-        with torch.autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
-            emb = torch.cat((freqs, freqs), dim=-1)
-            cos = emb.cos() * self.attention_scaling
-            sin = emb.sin() * self.attention_scaling
-
-        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
-
-
-# Copied from transformers.models.llama.modeling_llama.rotate_half
-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)
-
-
-# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
-def apply_rotary_pos_emb(q, k, cos, sin, 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.
-        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 the query and key tensors rotated using the Rotary Position Embedding.
-    """
-    cos = cos.unsqueeze(unsqueeze_dim)
-    sin = sin.unsqueeze(unsqueeze_dim)
-
-    # Keep half or full tensor for later concatenation
-    rotary_dim = cos.shape[-1]
-    q_rot, q_pass = q[..., :rotary_dim], q[..., rotary_dim:]
-    k_rot, k_pass = k[..., :rotary_dim], k[..., rotary_dim:]
-
-    # Apply rotary embeddings on the first half or full tensor
-    q_embed = (q_rot * cos) + (rotate_half(q_rot) * sin)
-    k_embed = (k_rot * cos) + (rotate_half(k_rot) * sin)
-
-    # Concatenate back to full shape
-    q_embed = torch.cat([q_embed, q_pass], dim=-1)
-    k_embed = torch.cat([k_embed, k_pass], dim=-1)
-    return q_embed, k_embed
-
-
-class BailingMoeV2MLP(nn.Module):
-    def __init__(self, config: BailingMoeLinearV2Config, intermediate_size: int):
-        super().__init__()
-        self.config = config
-        self.hidden_size = config.hidden_size
-        self.intermediate_size = 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 BailingMoeV2Gate(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.top_k = config.num_experts_per_tok
-        self.num_experts = config.num_experts
-
-        self.n_group = config.n_group
-        self.topk_group = config.topk_group
-
-        # topk selection algorithm
-        self.gating_dim = config.hidden_size
-        self.weight = nn.Parameter(torch.empty((self.num_experts, self.gating_dim)))
-        self.routed_scaling_factor = config.routed_scaling_factor
-
-        self.register_buffer("expert_bias", torch.zeros((self.num_experts)))
-        self.reset_parameters()
-
-    def reset_parameters(self) -> None:
-        import torch.nn.init as init
-
-        init.kaiming_uniform_(self.weight, a=math.sqrt(5))
-
-    def group_limited_topk(
-        self,
-        scores: torch.Tensor,
-    ):
-        num_tokens, _ = scores.size()
-        # Organize the experts into groups
-        group_scores = scores.view(num_tokens, self.n_group, -1).topk(2, dim=-1)[0].sum(dim=-1)
-        group_idx = torch.topk(group_scores, k=self.topk_group, dim=-1, sorted=False)[1]
-        group_mask = torch.zeros_like(group_scores)
-        group_mask.scatter_(1, group_idx, 1)
-
-        # Mask the experts based on selection groups
-        score_mask = (
-            group_mask.unsqueeze(-1)
-            .expand(num_tokens, self.n_group, self.num_experts // self.n_group)
-            .reshape(num_tokens, -1)
-        )
-
-        masked_scores = scores.masked_fill(~score_mask.bool(), float('-inf'))
-        probs, top_indices = torch.topk(masked_scores, k=self.top_k, dim=-1)
-
-        return probs, top_indices
-
-    def forward(self, hidden_states):
-        # compute gating score
-        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
-        logits = F.linear(hidden_states.type(torch.float32), self.weight.type(torch.float32))
-
-        scores = torch.sigmoid(logits.float()).type_as(logits)
-
-        scores_for_routing = scores + self.expert_bias
-        _, topk_idx = self.group_limited_topk(scores_for_routing)
-
-        scores = torch.gather(scores, dim=1, index=topk_idx).type_as(logits)
-
-        topk_weight = scores / (scores.sum(dim=-1, keepdim=True) + 1e-20) if self.top_k > 1 else scores
-        topk_weight = topk_weight * self.routed_scaling_factor
-
-        return topk_idx, topk_weight, logits
-
-
-class BailingMoeV2SparseMoeBlock(nn.Module):
-    """
-    A mixed expert module containing shared experts.
-    """
-
-    def __init__(self, config: BailingMoeLinearV2Config):
-        super().__init__()
-        self.config = config
-        self.num_experts_per_tok = config.num_experts_per_tok
-        self._setup_experts()
-        self.gate = BailingMoeV2Gate(config)
-        if config.num_shared_experts is not None:
-            self.shared_experts = BailingMoeV2MLP(
-                config=config, intermediate_size=config.moe_intermediate_size * config.num_shared_experts
-            )
-
-    def _setup_experts(self):
-        self.experts = nn.ModuleList(
-            [
-                BailingMoeV2MLP(config=self.config, intermediate_size=self.config.moe_intermediate_size)
-                for _ in range(self.config.num_experts)
-            ]
-        )
-
-    def forward(self, hidden_states):
-        identity = hidden_states
-        bsz, seq_len, h = hidden_states.shape
-        topk_idx, topk_weight, router_logits = self.gate(hidden_states)
-        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
-        flat_topk_idx = topk_idx.view(-1)
-        if self.training:
-            hidden_states = hidden_states.repeat_interleave(self.num_experts_per_tok, dim=0)
-            y = torch.empty_like(hidden_states)
-            for i, expert in enumerate(self.experts):
-                y[flat_topk_idx == i] = expert(hidden_states[flat_topk_idx == i])
-            y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
-            y = y.to(hidden_states.dtype).view(bsz, seq_len, h)
-        else:
-            y = self.moe_infer(hidden_states, topk_idx, topk_weight).view(bsz, seq_len, h)
-        if self.config.num_shared_experts is not None:
-            y = y + self.shared_experts(identity)
-        return y, (router_logits.view(bsz, seq_len, -1), topk_idx.view(bsz, seq_len, -1))
-
-    @torch.no_grad()
-    def moe_infer(self, x, topk_ids, topk_weight):
-        cnts = topk_ids.new_zeros((topk_ids.shape[0], len(self.experts)))
-        cnts.scatter_(1, topk_ids, 1)
-        tokens_per_expert = cnts.sum(dim=0)
-        idxs = topk_ids.view(-1).argsort()
-        sorted_tokens = x[idxs // topk_ids.shape[1]]
-        tokens_per_expert = tokens_per_expert.cpu().numpy()
-        outputs = []
-        start_idx = 0
-        for i, num_tokens in enumerate(tokens_per_expert):
-            end_idx = start_idx + num_tokens
-            if num_tokens == 0:
-                continue
-            expert = self.experts[i]
-            tokens_for_this_expert = sorted_tokens[start_idx:end_idx]
-            expert_out = expert(tokens_for_this_expert)
-            outputs.append(expert_out.to(x.device))
-            start_idx = end_idx
-
-        outs = torch.cat(outputs, dim=0) if len(outputs) else sorted_tokens.new_empty(0)
-        new_x = torch.empty_like(outs)
-        new_x[idxs] = outs
-        final_out = (
-            new_x.view(*topk_ids.shape, -1)
-            .type(topk_weight.dtype)
-            .mul_(topk_weight.unsqueeze(dim=-1))
-            .sum(dim=1)
-            .type(new_x.dtype)
-        )
-        return final_out
-
-
-# Copied from transformers.models.llama.modeling_llama.repeat_kv
-def repeat_kv(hidden_states: torch.Tensor, n_rep: int, head_first: bool = True) -> torch.Tensor:  
-    """
-    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). If head_first is True, the hidden states go from (batch,
-    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
-    """
-    if n_rep == 1:
-        return hidden_states
-    if head_first:
-        batch, num_key_value_heads, slen, head_dim = hidden_states.shape
-        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)
-    else:
-        batch, slen, num_key_value_heads, head_dim = hidden_states.shape
-        hidden_states = hidden_states[:, :, :, None, :].expand(batch, slen, num_key_value_heads, n_rep, head_dim)
-        return hidden_states.reshape(batch, slen, num_key_value_heads * n_rep, head_dim)
-
-
-# Copied from transformers.models.llama.modeling_llama.LlamaAttention with Llama->BailingMoeV2
-class BailingMoeV2Attention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-
-    def __init__(self, config: BailingMoeLinearV2Config, 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 `layer_idx` is not recommended and will "
-                "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 or self.hidden_size // self.num_heads
-        partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
-        self.rope_dim = int(self.head_dim * partial_rotary_factor)
-        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.query_key_value = nn.Linear(
-            self.hidden_size,
-            (self.num_heads + 2 * self.num_key_value_heads) * self.head_dim,
-            bias=config.use_qkv_bias,
-        )
-
-        if self.config.use_qk_norm:
-            self.query_layernorm = BailingMoeV2RMSNorm(self.head_dim, eps=config.rms_norm_eps)
-            self.key_layernorm = BailingMoeV2RMSNorm(self.head_dim, eps=config.rms_norm_eps)
-        self.dense = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.use_bias)
-
-    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
-        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        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,
-        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
-        **kwargs,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-
-        bsz, q_len, _ = hidden_states.size()
-
-        qkv = self.query_key_value(hidden_states)
-        qkv = qkv.view(bsz, q_len, self.num_heads + 2 * self.num_key_value_heads, self.head_dim)
-
-        query_states, key_states, value_states = qkv.split(
-            [self.num_heads, self.num_key_value_heads, self.num_key_value_heads], dim=-2
-        )
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.transpose(1, 2)
-
-        if self.config.use_qk_norm:
-            query_states = self.query_layernorm(query_states)
-            key_states = self.key_layernorm(key_states)
-
-        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:
-            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."
-                )
-            cache_kwargs = {"sin": sin, "cos": cos}
-            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)
-
-        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
-
-        kv_seq_len = key_states.shape[-2]
-        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
-            raise ValueError(
-                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
-                f" {attn_weights.size()}"
-            )
-
-        if attention_mask is not None:
-            if attention_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 + attention_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)
-        attn_output = torch.matmul(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.reshape(bsz, q_len, -1)
-
-        attn_output = self.dense(attn_output)
-
-        if not output_attentions:
-            attn_weights = None
-
-        return attn_output, attn_weights, past_key_value
-
-
-# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2 with Llama->BailingMoeV2
-class BailingMoeV2FlashAttention2(BailingMoeV2Attention):
-    """
-    BailingMoeV2 flash attention module. This module inherits from `BailingMoeV2Attention` 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()
-
-    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,
-        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
-        **kwargs,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        # BailingMoeV2FlashAttention2 attention does not support output_attentions
-        output_attentions = False
-
-        bsz, q_len, _ = hidden_states.size()
-
-        # 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
-
-        qkv = self.query_key_value(hidden_states)
-        qkv = qkv.view(bsz, q_len, self.num_heads + 2 * self.num_key_value_heads, self.head_dim)
-
-        query_states, key_states, value_states = qkv.split(
-            [self.num_heads, self.num_key_value_heads, self.num_key_value_heads], dim=-2
-        )
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.transpose(1, 2)
-
-        if self.config.use_qk_norm:
-            query_states = self.query_layernorm(query_states)
-            key_states = self.key_layernorm(key_states)
-
-        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 = {"sin": sin, "cos": cos}
-            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
-        # 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 cast in float32. Hence, we need
-        # cast them back in the correct dtype just to be sure everything works as expected.
-        # This might slow down training & inference so it is recommended to not cast the LayerNorms
-        # in fp32. (BailingMoeV2RMSNorm handles it correctly)
-
-        input_dtype = query_states.dtype
-        if input_dtype == torch.float32:
-            # Handle the case where the model is quantized
-            if hasattr(self.config, "_pre_quantization_dtype"):
-                target_dtype = self.config._pre_quantization_dtype
-            elif torch.is_autocast_enabled():
-                target_dtype = torch.get_autocast_gpu_dtype()
-            else:
-                target_dtype = self.query_key_value.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
-        )
-
-        attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
-        attn_output = self.dense(attn_output)
-
-        if not output_attentions:
-            attn_weights = None
-
-        return attn_output, attn_weights, past_key_value
-
-    def _flash_attention_forward(
-        self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
-    ):
-        """
-        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)
-            query_length (`int`):
-                The length of the query sequence in terms of tokens. This represents the number of tokens in the
-                `query_states` tensor along the sequence dimension. It is used to determine the effective sequence
-                length for attention computations.
-        """
-        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 BailingMoeV2FlashAttention2 __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
-
-            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,
-            )
-
-            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
-        else:
-            attn_output = flash_attn_func(
-                query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
-            )
-
-        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
-
-        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),
-        )
-
-
-# Copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->BailingMoeV2
-class BailingMoeV2SdpaAttention(BailingMoeV2Attention):
-    """
-    BailingMoeV2 attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
-    `BailingMoeV2Attention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
-    SDPA API.
-    """
-
-    # Adapted from BailingMoeV2Attention.forward
-    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,
-        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
-        **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(
-                "BailingMoeV2Model is using BailingMoeV2SdpaAttention, 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,
-            )
-
-        bsz, q_len, _ = hidden_states.size()
-
-        qkv = self.query_key_value(hidden_states)
-        qkv = qkv.view(bsz, q_len, self.num_heads + 2 * self.num_key_value_heads, self.head_dim)
-
-        query_states, key_states, value_states = qkv.split(
-            [self.num_heads, self.num_key_value_heads, self.num_key_value_heads], dim=-2
-        )
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.transpose(1, 2)
-
-        if self.config.use_qk_norm:
-            query_states = self.query_layernorm(query_states)
-            key_states = self.key_layernorm(key_states)
-
-        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 = {"sin": sin, "cos": cos}
-            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)
-
-        if attention_mask is not None:
-            kv_seq_len = key_states.shape[-2]
-            if attention_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()}"
-                )
-
-        # 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 attention_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=attention_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.reshape(bsz, q_len, -1)
-
-        attn_output = self.dense(attn_output)
-
-        return attn_output, None, past_key_value
-
-
-ATTENTION_CLASSES = {
-    "eager": BailingMoeV2Attention,
-    "flash_attention_2": BailingMoeV2FlashAttention2,
-    "sdpa": BailingMoeV2SdpaAttention,
-}
-
-
-class BailingMoeV2LinearAttention(nn.Module):
-    """
-    BailingMoeAttention implements a linear attention mechanism based on Lightning Attention-2
-    (https://arxiv.org/abs/2401.04658) with efficient computation using flash-linear-attention operators.
-    
-    The implementation leverages optimized kernels from the flash-linear-attention library
-    (https://github.com/fla-org/flash-linear-attention) for maximum performance.
-    """
-    def __init__(self, config: BailingMoeLinearV2Config, 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 `layer_idx` is not recommended and will "
-                "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
-                "when creating this class."
-            )
-        self.hidden_size = config.hidden_size
-        self.num_heads = config.num_attention_heads
-        self.head_dim = config.head_dim or self.hidden_size // self.num_heads
-        self.num_key_value_heads = config.num_attention_heads
-        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
-        partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
-        self.rope_dim = int(self.head_dim * partial_rotary_factor)
-
-        self.use_qk_norm = getattr(config, "use_qk_norm", False)
-        self.rms_norm_eps = getattr(config, "rms_norm_eps", 1e-5)
-        self.mode = 'chunk'
-
-        self.query_key_value = nn.Linear(
-            self.hidden_size,
-            (self.num_heads + 2 * self.num_key_value_heads) * self.head_dim,
-            bias=config.use_qkv_bias,
-        )
-
-        if self.config.use_qk_norm:
-            self.query_layernorm = BailingMoeV2RMSNorm(self.head_dim, eps=config.rms_norm_eps)
-            self.key_layernorm = BailingMoeV2RMSNorm(self.head_dim, eps=config.rms_norm_eps)
-
-        self.rotary_emb = BailingMoeV2RotaryEmbedding(config=config)
-
-        self.dense = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.use_bias)
-
-        self.g_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
-        self.g_norm = BailingMoeV2GroupRMSNorm(self.num_heads * self.head_dim, group_norm_size=config.group_norm_size, eps=self.rms_norm_eps)
-        slope = - BailingMoeV2LinearAttention.build_slope_tensor(self.num_heads) * (1 - (self.layer_idx - 1) / (self.config.num_hidden_layers - 1) + 1e-5)
-        self.register_buffer('slope', slope, persistent=False)
-
-        self.lightning_attn_ops = {
-            'chunk': chunk_simple_gla,
-            'fused_recurrent': fused_recurrent_simple_gla
-        }
-
-    @staticmethod
-    def build_slope_tensor(n_attention_heads: int):
-        """
-        Build a tensor of slopes for Lightning Attention-2 as described in the paper:
-        "Lightning Attention-2: A Free Lunch for Handling Unlimited Sequence Lengths in Large Language Models"
-        (https://arxiv.org/abs/2401.04658)
-        
-        This function computes the slope values that control the decay rate of attention scores
-        based on the number of attention heads. The slopes are designed to have specific
-        mathematical properties that work optimally when the number of heads is a power of 2.
-        
-        For non-power-of-2 head counts, a workaround is implemented to maintain similar properties.
-        
-        Args:
-            n_attention_heads (int): Number of attention heads in the model
-            
-        Returns:
-            torch.Tensor: A tensor of shape [n_attention_heads] containing the computed slopes
-            
-        Note:
-            Code copied from: https://github.com/OpenNLPLab/lightning-attention/blob/d15c38529bbd5c2c82b44ddda3cac885825aa873/lightning_attn/utils/utils.py#L6
-        """    
-        def get_slopes(n):
-            def get_slopes_power_of_2(n):
-                start = 2 ** (-(2 ** -(math.log2(n) - 3)))
-                ratio = start
-                return [start * ratio ** i for i in range(n)]
-
-            if math.log2(n).is_integer():
-                return get_slopes_power_of_2(
-                    n)  # In the paper, we only train models that have 2^a heads for some a. This function has
-            else:  # some good properties that only occur when the input is a power of 2. To maintain that even
-                closest_power_of_2 = 2 ** math.floor(
-                    math.log2(n))  # when the number of heads is not a power of 2, we use this workaround.
-                return (get_slopes_power_of_2(closest_power_of_2)
-                        + get_slopes(2 * closest_power_of_2)[0::2][:n - closest_power_of_2])
-
-        slopes = torch.tensor(get_slopes(n_attention_heads), dtype=torch.float)
-        return slopes
-
-
-    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,
-        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-        **kwargs,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        if attention_mask is not None:
-            assert len(attention_mask.shape) == 2, (
-                "Expected attention_mask as a 0-1 matrix with shape [batch_size, seq_len] "
-                "for padding purposes (0 indicating padding). "
-                "Arbitrary attention masks of shape [batch_size, seq_len, seq_len] are not allowed."
-            )
-
-        # launching the triton kernel for just one token will actually be slower
-        mode = 'fused_recurrent' if hidden_states.shape[1] <= 64 else self.mode
-
-        # Currently output_attentions can only be False, returning attention weights is not supported
-        assert not output_attentions, "output_attentions can only be False, returning attention weights is not supported"
-
-        bsz, q_len, _ = hidden_states.size()
-        device = hidden_states.device
-
-        qkv = self.query_key_value(hidden_states)
-        qkv = qkv.view(bsz, q_len, self.num_heads + 2 * self.num_key_value_heads, self.head_dim)
-        query_states, key_states, value_states = qkv.split(
-            [self.num_heads, self.num_key_value_heads, self.num_key_value_heads], dim=-2
-        )
-        if self.config.use_qk_norm:
-            query_states = self.query_layernorm(query_states)
-            key_states = self.key_layernorm(key_states)
-
-        cos, sin = position_embeddings
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, unsqueeze_dim=2)
-
-        if self.num_key_value_groups > 1:
-            # [bsz, q_len, n_kv_heads, head_dim] -> [bsz, q_len, n_heads, head_dim]
-            key_states = repeat_kv(key_states, self.num_key_value_groups, head_first=False)
-            value_states = repeat_kv(value_states, self.num_key_value_groups, head_first=False)
-
-        recurrent_state = None
-        if past_key_value is not None and isinstance(past_key_value, Cache):
-            # ensure the cache list is long enough
-            while len(past_key_value.layers) <= self.layer_idx:
-                past_key_value.layers.append(DynamicLayer())
-
-            if past_key_value.layers[self.layer_idx].keys is not None:
-                recurrent_state = past_key_value.layers[self.layer_idx].keys
-                # ensure recurrent_state is on the same device as hidden_states
-                if recurrent_state.device != hidden_states.device:
-                    recurrent_state = recurrent_state.to(device).contiguous()
-
-        if recurrent_state is None:
-            # dealing with left-padding
-            if attention_mask is not None and use_cache:
-                value_states = value_states.mul_(attention_mask[:, -q_len:, None, None])
-
-        o, recurrent_state = self.lightning_attn_ops[mode](
-            q=query_states,
-            k=key_states,
-            v=value_states,
-            g=self.slope[None, None, :].expand(bsz, q_len, self.num_heads),
-            initial_state=recurrent_state,
-            output_final_state=use_cache,
-        )
-
-        o = o.reshape(bsz, q_len, -1)
-        o = self.g_norm(o)
-        g_proj = self.g_proj(hidden_states)
-        o = o * torch.sigmoid_(g_proj)
-        o = self.dense(o)
-
-        if use_cache and past_key_value is not None and isinstance(past_key_value, Cache):
-            target_device = None
-            for cache in past_key_value.layers:
-                if cache.keys is not None:
-                    target_device = cache.keys.device
-                    break
-            if target_device is None:
-                target_device = recurrent_state.device
-
-            # move to target device
-            if recurrent_state.device != target_device:
-                recurrent_state = recurrent_state.to(target_device)
-
-            past_key_value.layers[self.layer_idx].keys = recurrent_state
-
-        return o, None, past_key_value
-
-
-class BailingMoeV2MTPLayer(nn.Module):
-    def __init__(self, config: BailingMoeLinearV2Config, layer_idx: int):
-        super().__init__()
-        self.layer_idx = layer_idx
-        self.input_layernorm = BailingMoeV2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.enorm = BailingMoeV2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-
-        self.eh_proj = nn.Linear(config.hidden_size * 2, config.hidden_size, bias=False)
-        self.post_attention_layernorm = BailingMoeV2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.attention = ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
-        self.mlp = BailingMoeV2SparseMoeBlock(config)
-
-        self.hnorm = BailingMoeV2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.final_layernorm = BailingMoeV2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-
-    def forward(
-        self,
-        input_embeds,
-        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,
-        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
-        **kwargs,
-    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
-        input_embeds = self.enorm(input_embeds)
-        hidden_states = self.hnorm(hidden_states)
-        hidden_states = self.eh_proj(torch.cat([input_embeds, hidden_states], dim=-1))
-        residual = hidden_states
-
-        hidden_states = self.input_layernorm(hidden_states)
-
-        # Self Attention
-        hidden_states, self_attn_weights, present_key_value = self.attention(
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_value=past_key_value,
-            output_attentions=output_attentions,
-            position_embeddings=position_embeddings,
-            use_cache=use_cache,
-        )
-        hidden_states = residual + hidden_states
-
-        # Fully Connected
-        residual = hidden_states
-        hidden_states = self.post_attention_layernorm(hidden_states)
-        hidden_states = self.mlp(hidden_states)
-        if isinstance(hidden_states, tuple):
-            hidden_states, router_logits = hidden_states
-        else:
-            router_logits = None
-        hidden_states = residual + hidden_states.to(residual.device)
-        hidden_states = self.final_layernorm(hidden_states)
-
-        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
-
-
-class BailingMoeLinearV2DecoderLayer(nn.Module):
-    def __init__(self, config: BailingMoeLinearV2Config, layer_idx: int):
-        super().__init__()
-        self.hidden_size = config.hidden_size
-        self.attention_layer_type = "attention" if (layer_idx + 1) % config.layer_group_size == 0 or \
-            layer_idx >= config.num_hidden_layers // config.layer_group_size * config.layer_group_size else "linear_attention"
-
-        if self.attention_layer_type == "attention":
-            self.attention = ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
-        else:
-            self.attention = BailingMoeV2LinearAttention(
-                config=config,
-                layer_idx=layer_idx
-            )
-
-        self.mlp = (
-            BailingMoeV2SparseMoeBlock(config)
-            if (config.num_experts is not None and layer_idx >= config.first_k_dense_replace)
-            else BailingMoeV2MLP(config=config, intermediate_size=config.intermediate_size)
-        )
-        self.input_layernorm = BailingMoeV2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.post_attention_layernorm = BailingMoeV2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-
-    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,
-        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
-        **kwargs,
-    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
-        """
-        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_size, sequence_length)` if flash attention is used or `(batch_size, 1,
-                query_sequence_length, key_sequence_length)` if default attention is used.
-            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]`.
-            past_key_value (`Tuple(torch.FloatTensor)`, *optional*):
-                cached past key and value projection states
-            output_attentions (`bool`, *optional*):
-                Whether 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
-        if self.attention_layer_type == "attention":
-            hidden_states, self_attn_weights, present_key_value = self.attention(
-                hidden_states=hidden_states,
-                attention_mask=attention_mask,
-                position_ids=position_ids,
-                past_key_value=past_key_value,
-                output_attentions=output_attentions,
-                position_embeddings=position_embeddings,
-                use_cache=use_cache,
-            )
-        else:
-            batch_size, seq_len = hidden_states.shape[0], hidden_states.shape[1]
-            device = hidden_states.device
-
-            if attention_mask is None:
-                # if attention_mask is None, create a full mask
-                attention_mask = torch.ones((batch_size, seq_len), dtype=torch.int32, device=device)
-            elif attention_mask.dim() == 4 and attention_mask.shape[1] == 1:
-                attention_mask = attention_mask[:, 0, -1, :].to(torch.int32)
-                attention_mask = (attention_mask > -1e4).to(torch.int32)
-            elif attention_mask.dim() == 2:
-                attention_mask = attention_mask.to(torch.int32)
-            else:
-                raise ValueError(f"Unsupported mask dimension: {attention_mask.shape}")
-
-            hidden_states, self_attn_weights, present_key_value = self.attention(
-                hidden_states=hidden_states,
-                attention_mask=attention_mask,
-                past_key_value=past_key_value,
-                position_ids=position_ids,
-                use_cache=use_cache,
-                output_attentions=output_attentions,
-                position_embeddings=position_embeddings,
-            )
-
-        hidden_states = residual + hidden_states
-
-        # Fully Connected
-        residual = hidden_states
-        hidden_states = self.post_attention_layernorm(hidden_states)
-        hidden_states = self.mlp(hidden_states)
-        if isinstance(hidden_states, tuple):
-            hidden_states, router_logits = hidden_states
-        else:
-            router_logits = None
-        hidden_states = residual + hidden_states.to(residual.device)
-
-        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
-
-
-BAILINGMOEV2_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 ([`BailingMoeLinearV2Config`]):
-            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 BailingMoeV2 Model outputting raw hidden-states without any specific head on top.",
-    BAILINGMOEV2_START_DOCSTRING,
-)
-class BailingMoeV2PreTrainedModel(PreTrainedModel):
-    config_class = BailingMoeLinearV2Config
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["BailingMoeLinearV2DecoderLayer"]
-    _skip_keys_device_placement = "past_key_values"
-    _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_()
-
-
-BAILINGMOEV2_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 `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 (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
-            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
-            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
-            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
-            Two formats are allowed:
-            - a [`~cache_utils.Cache`] instance;
-            - 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)`). This is also known as the legacy
-            cache format.
-            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
-            legacy cache format will be returned.
-            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
-            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `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.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-
-@add_start_docstrings(
-    "The bare BailingMoeV2 Model outputting raw hidden-states without any specific head on top.",
-    BAILINGMOEV2_START_DOCSTRING,
-)
-class BailingMoeLinearV2Model(BailingMoeV2PreTrainedModel):
-    """
-    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`BailingMoeLinearV2DecoderLayer`]
-    Args:
-        config: BailingMoeLinearV2Config
-    """
-
-    def __init__(self, config: BailingMoeLinearV2Config):
-        super().__init__(config)
-        self.padding_idx = config.pad_token_id
-        self.vocab_size = config.vocab_size
-        self.num_nextn_predict_layers = config.num_nextn_predict_layers
-
-        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
-        self.layers = []
-        for layer_idx in range(config.num_hidden_layers + config.num_nextn_predict_layers):
-            layer_cls = BailingMoeLinearV2DecoderLayer if layer_idx < config.num_hidden_layers else BailingMoeV2MTPLayer
-            self.layers.append(layer_cls(config, layer_idx))
-
-        self.layers = nn.ModuleList(self.layers)
-
-        self._use_sdpa = config._attn_implementation == "sdpa"
-        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
-        self.norm = BailingMoeV2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.rotary_emb = BailingMoeV2RotaryEmbedding(config=config)
-        self.gradient_checkpointing = False
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.word_embeddings
-
-    def set_input_embeddings(self, value):
-        self.word_embeddings = value
-
-    @add_start_docstrings_to_model_forward(BAILINGMOEV2_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,
-        **kwargs,
-    ) -> Union[Tuple, MoeV2ModelOutputWithPast]:
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        output_router_logits = (
-            output_router_logits if output_router_logits is not None else self.config.output_router_logits
-        )
-        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
-
-        # retrieve input_ids and inputs_embeds
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
-        elif input_ids is not None:
-            batch_size, seq_length = input_ids.shape[:2]
-        elif inputs_embeds is not None:
-            batch_size, seq_length = inputs_embeds.shape[:2]
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-        if self.gradient_checkpointing and self.training:
-            if use_cache:
-                logger.warning_once(
-                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`transformers."
-                )
-                use_cache = False
-
-        if use_cache and past_key_values is None:
-            past_key_values = DynamicCache()
-
-        if inputs_embeds is None:
-            inputs_embeds = self.word_embeddings(input_ids)
-
-        softmax_attention_layer_id = self.config.layer_group_size - 1
-        past_seen_tokens = past_key_values.get_seq_length(layer_idx=softmax_attention_layer_id) if past_key_values is not None else 0
-
-        if position_ids is None:
-            position_ids = torch.arange(
-                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
-            )
-            position_ids = position_ids.unsqueeze(0)
-
-        if self._use_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._use_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
-            )
-
-        # embed positions
-        hidden_states = inputs_embeds
-
-        # create position embeddings to be shared across the decoder layers
-        position_embeddings = self.rotary_emb(hidden_states, position_ids)
-
-        # 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
-        layers = self.layers[: -self.num_nextn_predict_layers] if self.num_nextn_predict_layers > 0 else self.layers
-        mtp_layers = self.layers[-self.num_nextn_predict_layers :] if self.num_nextn_predict_layers > 0 else None
-
-        for decoder_layer in 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,
-                    attention_mask,
-                    position_ids,
-                    past_key_values,
-                    output_attentions,
-                    output_router_logits,
-                    use_cache,
-                    position_embeddings,
-                )
-            else:
-                layer_outputs = decoder_layer(
-                    hidden_states,
-                    attention_mask=attention_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,
-                    position_embeddings=position_embeddings,
-                )
-            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 and layer_outputs[-1] is not None:
-                all_router_logits += (layer_outputs[-1],)
-
-        hidden_states = self.norm(hidden_states)
-        main_hidden_states = hidden_states
-
-        # add hidden states from the last decoder layer
-        if output_hidden_states:
-            all_hidden_states += (main_hidden_states,)
-
-        mtp_hidden_states = None
-
-        if mtp_layers:
-            for decoder_layer in mtp_layers:
-                input_ids, _ = roll_tensor(input_ids, shifts=-1, dims=-1)
-                inputs_embeds = self.word_embeddings(input_ids)
-
-                if self.gradient_checkpointing and self.training:
-                    layer_outputs = self._gradient_checkpointing_func(
-                        decoder_layer.__call__,
-                        inputs_embeds,
-                        hidden_states,
-                        attention_mask,
-                        position_ids,
-                        past_key_values,
-                        output_attentions,
-                        output_router_logits,
-                        use_cache,
-                        position_embeddings,
-                    )
-                else:
-                    layer_outputs = decoder_layer(
-                        inputs_embeds,
-                        hidden_states,
-                        attention_mask=attention_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,
-                        position_embeddings=position_embeddings,
-                    )
-                if mtp_hidden_states is None:
-                    mtp_hidden_states = []
-                hidden_states = layer_outputs[0]
-                mtp_hidden_states.append(hidden_states)
-
-                if output_hidden_states:
-                    all_hidden_states += (hidden_states,)
-
-                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 and layer_outputs[-1] is not None:
-                    all_router_logits += (layer_outputs[-1],)
-
-        next_cache = None
-        if use_cache:
-            next_cache = next_decoder_cache
-        if not return_dict:
-            return tuple(
-                v
-                for v in [main_hidden_states, next_cache, all_hidden_states, all_self_attns, all_router_logits]
-                if v is not None
-            )
-        return MoeV2ModelOutputWithPast(
-            last_hidden_state=main_hidden_states,
-            past_key_values=next_cache,
-            hidden_states=all_hidden_states,
-            mtp_hidden_states=mtp_hidden_states,
-            attentions=all_self_attns,
-            router_logits=all_router_logits,
-        )
-
-
-class BailingMoeLinearV2ForCausalLM(BailingMoeV2PreTrainedModel, GenerationMixin):
-    _tied_weights_keys = ["lm_head.weight"]
-
-    def __init__(self, config: BailingMoeLinearV2Config):
-        super().__init__(config)
-        self.model = BailingMoeLinearV2Model(config)
-        self.vocab_size = config.vocab_size
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-        self.num_nextn_predict_layers = config.num_nextn_predict_layers
-        self.mtp_loss_scaling_factor = config.mtp_loss_scaling_factor
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.model.word_embeddings
-
-    def set_input_embeddings(self, value):
-        self.model.word_embeddings = 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
-
-    @add_start_docstrings_to_model_forward(BAILINGMOEV2_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=MoEV2CausalLMOutputWithPast, 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,
-        **kwargs,
-    ) -> Union[Tuple, MoEV2CausalLMOutputWithPast]:
-        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
-        >>> model = BailingMoeLinearV2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
-        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
-        >>> prompt = "Hey, are you conscious? Can you talk to me?"
-        >>> 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]
-        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
-        ```"""
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        output_router_logits = (
-            output_router_logits if output_router_logits is not None else self.config.output_router_logits
-        )
-        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,
-            **kwargs,
-        )
-
-        loss = None
-        all_mtp_loss = None
-        aux_loss = None
-        hidden_states = outputs[0]
-        logits = self.lm_head(hidden_states)
-        logits = logits.float()
-
-        if labels is not None:
-            loss = self.loss_function(logits, labels, self.config.vocab_size, **kwargs)
-
-        all_mtp_logits = None
-        if self.num_nextn_predict_layers > 0:
-            mtp_hidden_states = outputs.mtp_hidden_states
-            shift_labels_mtp = None
-            for i in range(self.num_nextn_predict_layers):
-                mtp_hidden_states = mtp_hidden_states[i]
-                mtp_logits = self.lm_head(mtp_hidden_states).float()
-                if all_mtp_logits is None:
-                    all_mtp_logits = []
-                all_mtp_logits.append(mtp_logits)
-                if labels is not None:
-                    if shift_labels_mtp is None:
-                        shift_labels_mtp = labels.clone()
-                    shift_labels_mtp, _ = roll_tensor(shift_labels_mtp, shifts=-1, dims=-1, fill_value=-100)
-                    mtp_logits_ = mtp_logits.view(-1, self.config.vocab_size)
-                    mtp_loss = self.loss_function(mtp_logits_, shift_labels_mtp.to(mtp_logits_.device).view(-1), self.config.vocab_size, **kwargs)
-                    if loss is not None:
-                        loss += self.mtp_loss_scaling_factor * mtp_loss
-                    else:
-                        loss = self.mtp_loss_scaling_factor * mtp_loss
-
-                    if all_mtp_loss is None:
-                        all_mtp_loss = []
-                    all_mtp_loss.append(mtp_loss)
-
-        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 MoEV2CausalLMOutputWithPast(
-            loss=loss,
-            mtp_loss=all_mtp_loss,
-            aux_loss=aux_loss,
-            logits=logits,
-            mtp_logits=all_mtp_logits,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-            router_logits=outputs.router_logits,
-        )