diff --git "a/modeling/bagel/qwen2_navit.py" "b/modeling/bagel/qwen2_navit.py"
--- "a/modeling/bagel/qwen2_navit.py"
+++ "b/modeling/bagel/qwen2_navit.py"
@@ -1,1157 +1,1157 @@
-# Copyright (c) 2024 The Qwen Team and The HuggingFace Inc. team.
-# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates.
-# SPDX-License-Identifier: Apache-2.0
-#
-# This file has been modified by ByteDance Ltd. and/or its affiliates. on 2025-05-20.
-#
-# Original file was released under Apache-2.0, with the full license text
-# available at https://github.com/huggingface/transformers/blob/main/LICENSE.
-#
-# This modified file is released under the same license.
-
-
-from dataclasses import dataclass
-from functools import partial
-from typing import List, Optional, Tuple
-
-import torch
-from torch import nn
-from torch.nn.attention import SDPBackend, sdpa_kernel
-from torch.nn.attention.flex_attention import flex_attention
-from torch.nn.functional import scaled_dot_product_attention
-from transformers.utils import ModelOutput
-
-from flash_attn import flash_attn_varlen_func
-from modeling.qwen2.modeling_qwen2 import (
-    Qwen2Attention, 
-    Qwen2MLP, 
-    Qwen2PreTrainedModel, 
-    Qwen2RMSNorm, 
-    Qwen2RotaryEmbedding,
-    apply_rotary_pos_emb,
-)
-
-from modeling.qwen2.configuration_qwen2 import Qwen2Config as _Qwen2Config
-
-
-torch._dynamo.config.cache_size_limit = 512
-torch._dynamo.config.accumulated_cache_size_limit = 4096
-# flex_attention = torch.compile(flex_attention) # , dynamic=True, mode='max-autotune'
-flex_attention = torch.compile(flex_attention)
-
-
-class Qwen2Config(_Qwen2Config):
-    r"""
-    This is the configuration class to store the configuration of a [`Qwen2Model`]. It is used to instantiate a
-    Qwen2 model according to the specified arguments, defining the model architecture. Instantiating a configuration
-    with the defaults will yield a similar configuration to that of
-    Qwen2-7B-beta [Qwen/Qwen2-7B-beta](https://huggingface.co/Qwen/Qwen2-7B-beta).
-
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-
-    Args:
-        vocab_size (`int`, *optional*, defaults to 151936):
-            Vocabulary size of the Qwen2 model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`Qwen2Model`]
-        hidden_size (`int`, *optional*, defaults to 4096):
-            Dimension of the hidden representations.
-        intermediate_size (`int`, *optional*, defaults to 22016):
-            Dimension of the MLP representations.
-        num_hidden_layers (`int`, *optional*, defaults to 32):
-            Number of hidden layers in the Transformer encoder.
-        num_attention_heads (`int`, *optional*, defaults to 32):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        num_key_value_heads (`int`, *optional*, defaults to 32):
-            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
-            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
-            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
-            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
-            by meanpooling all the original heads within that group. For more details checkout [this
-            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`.
-        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
-            The non-linear activation function (function or string) in the decoder.
-        max_position_embeddings (`int`, *optional*, defaults to 32768):
-            The maximum sequence length that this model might ever be used with.
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
-            The epsilon used by the rms normalization layers.
-        use_cache (`bool`, *optional*, defaults to `True`):
-            Whether or not the model should return the last key/values attentions (not used by all models). Only
-            relevant if `config.is_decoder=True`.
-        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
-            Whether the model's input and output word embeddings should be tied.
-        rope_theta (`float`, *optional*, defaults to 10000.0):
-            The base period of the RoPE embeddings.
-        rope_scaling (`Dict`, *optional*):
-            Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
-            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
-            accordingly.
-            Expected contents:
-                `rope_type` (`str`):
-                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
-                    'llama3'], with 'default' being the original RoPE implementation.
-                `factor` (`float`, *optional*):
-                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
-                    most scaling types, a `factor` of x will enable the model to handle sequences of length x *
-                    original maximum pre-trained length.
-                `original_max_position_embeddings` (`int`, *optional*):
-                    Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
-                    pretraining.
-                `attention_factor` (`float`, *optional*):
-                    Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
-                    computation. If unspecified, it defaults to value recommended by the implementation, using the
-                    `factor` field to infer the suggested value.
-                `beta_fast` (`float`, *optional*):
-                    Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
-                    ramp function. If unspecified, it defaults to 32.
-                `beta_slow` (`float`, *optional*):
-                    Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
-                    ramp function. If unspecified, it defaults to 1.
-                `short_factor` (`List[float]`, *optional*):
-                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
-                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
-                    size divided by the number of attention heads divided by 2
-                `long_factor` (`List[float]`, *optional*):
-                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
-                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
-                    size divided by the number of attention heads divided by 2
-                `low_freq_factor` (`float`, *optional*):
-                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
-                `high_freq_factor` (`float`, *optional*):
-                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
-        use_sliding_window (`bool`, *optional*, defaults to `False`):
-            Whether to use sliding window attention.
-        sliding_window (`int`, *optional*, defaults to 4096):
-            Sliding window attention (SWA) window size. If not specified, will default to `4096`.
-        max_window_layers (`int`, *optional*, defaults to 28):
-            The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention.
-        attention_dropout (`float`, *optional*, defaults to 0.0):
-            The dropout ratio for the attention probabilities.
-
-    ```python
-    >>> from transformers import Qwen2Model, Qwen2Config
-
-    >>> # Initializing a Qwen2 style configuration
-    >>> configuration = Qwen2Config()
-
-    >>> # Initializing a model from the Qwen2-7B style configuration
-    >>> model = Qwen2Model(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-
-    model_type = "qwen2"
-    keys_to_ignore_at_inference = ["past_key_values"]
-
-    def __init__(
-        self,
-        vocab_size=151936,
-        hidden_size=4096,
-        intermediate_size=22016,
-        num_hidden_layers=32,
-        num_attention_heads=32,
-        num_key_value_heads=32,
-        hidden_act="silu",
-        max_position_embeddings=32768,
-        initializer_range=0.02,
-        rms_norm_eps=1e-6,
-        use_cache=True,
-        tie_word_embeddings=False,
-        rope_theta=10000.0,
-        rope_scaling=None,
-        use_sliding_window=False,
-        sliding_window=4096,
-        max_window_layers=28,
-        attention_dropout=0.0,
-        is_causal=True,
-        _attn_implementation="flash_attention_2",
-        qk_norm=True,
-        layer_module="Qwen2DecoderLayer",
-        freeze_und=False,
-        **kwargs,
-    ):
-        super().__init__(
-            vocab_size=vocab_size,
-            hidden_size=hidden_size,
-            intermediate_size=intermediate_size,
-            num_hidden_layers=num_hidden_layers,
-            num_attention_heads=num_attention_heads,
-            num_key_value_heads=num_key_value_heads,
-            hidden_act=hidden_act,
-            max_position_embeddings=max_position_embeddings,
-            initializer_range=initializer_range,
-            rms_norm_eps=rms_norm_eps,
-            use_cache=use_cache,
-            tie_word_embeddings=tie_word_embeddings,
-            rope_theta=rope_theta,
-            rope_scaling=rope_scaling,
-            use_sliding_window=use_sliding_window,
-            sliding_window=sliding_window,
-            max_window_layers=max_window_layers,
-            attention_dropout=attention_dropout,
-            is_causal=is_causal,
-            _attn_implementation=_attn_implementation,
-            **kwargs,
-        )
-        self.qk_norm = qk_norm
-        self.layer_module = layer_module
-        self.freeze_und = freeze_und
-
-
-class NaiveCache:
-    def __init__(self, num_layers):
-        self.key_cache = {k: None for k in range(num_layers)}
-        self.value_cache = {k: None for k in range(num_layers)}
-
-    @property
-    def num_layers(self):
-        return len(self.key_cache)
-
-    @property
-    def seq_lens(self):
-        if self.key_cache[0] is not None:
-            return self.key_cache[0].shape[0]
-        else:
-            return 0
-
-
-@dataclass
-class BaseNavitOutputWithPast(ModelOutput):
-    packed_query_sequence: torch.FloatTensor = None
-    past_key_values: Optional[NaiveCache] = None
-
-
-def pad_sequence(tensor, pad_size):
-    H, L, D = tensor.shape
-    pad_tensor = tensor.new_zeros((H, pad_size, D))
-    return torch.cat([tensor, pad_tensor], dim=1)
-
-
-class PackedAttention(Qwen2Attention):
-    def __init__(self, config, layer_idx: Optional[int] = None):
-        super().__init__(config, layer_idx)
-        if self.config.qk_norm:
-            self.q_norm = Qwen2RMSNorm(self.head_dim, eps=config.rms_norm_eps)
-            self.k_norm = Qwen2RMSNorm(self.head_dim, eps=config.rms_norm_eps)
-        else:
-            self.q_norm = nn.Identity()
-            self.k_norm = nn.Identity()
-
-    def forward(self, *args, **kwargs):
-        if self.training:
-            return self.forward_train(*args, **kwargs)
-        else:
-            return self.forward_inference(*args, **kwargs)
-
-    def forward_train(
-        self,
-        packed_sequence: torch.Tensor,
-        sample_lens: List[int],
-        attention_mask: List[torch.Tensor],
-        packed_position_embeddings: Tuple[torch.Tensor, torch.Tensor],
-    ):
-        packed_query_states = self.q_proj(packed_sequence).view(-1, self.num_heads, self.head_dim)
-        packed_key_states = self.k_proj(packed_sequence).view(-1, self.num_key_value_heads, self.head_dim)
-        packed_value_states = self.v_proj(packed_sequence).view(-1, self.num_key_value_heads, self.head_dim)
-
-        packed_query_states = self.q_norm(packed_query_states)
-        packed_key_states = self.k_norm(packed_key_states)
-
-        packed_cos, packed_sin = packed_position_embeddings
-        packed_query_states, packed_key_states = apply_rotary_pos_emb(
-            packed_query_states, packed_key_states, packed_cos, packed_sin, unsqueeze_dim=1
-        )
-
-        if isinstance(attention_mask, List):
-            packed_key_states = packed_key_states[:, :, None, :].repeat(1, 1, self.num_key_value_groups, 1)
-            packed_key_states = packed_key_states.reshape(-1, self.num_heads, self.head_dim)
-            packed_value_states = packed_value_states[:, :, None, :].repeat(1, 1, self.num_key_value_groups, 1)
-            packed_value_states = packed_value_states.reshape(-1, self.num_heads, self.head_dim)
-
-            unpacked_query_states = packed_query_states.transpose(0, 1).split(sample_lens, dim=1)
-            unpacked_key_states = packed_key_states.transpose(0, 1).split(sample_lens, dim=1)
-            unpacked_value_states = packed_value_states.transpose(0, 1).split(sample_lens, dim=1)
-            upacked_attn_output = []
-            for query_states, key_states, value_states, attention_mask_per_sample in zip(
-                unpacked_query_states, unpacked_key_states, unpacked_value_states, attention_mask
-            ):
-                with sdpa_kernel(backends=[SDPBackend.EFFICIENT_ATTENTION]):
-                    attn_output = scaled_dot_product_attention(
-                        query_states.to(torch.bfloat16).unsqueeze(0), 
-                        key_states.to(torch.bfloat16).unsqueeze(0), 
-                        value_states.to(torch.bfloat16).unsqueeze(0),
-                        attention_mask_per_sample.to(torch.bfloat16).unsqueeze(0),
-                    )
-                upacked_attn_output.append(attn_output.squeeze(0))
-            packed_attn_output = torch.cat(upacked_attn_output, dim=1)
-        else:
-            pad_size = sum(sample_lens) - packed_query_states.shape[0]
-            packed_query_states = pad_sequence(packed_query_states.permute(1, 0, 2), pad_size)
-            packed_key_states = pad_sequence(packed_key_states.permute(1, 0, 2), pad_size)
-            packed_value_states = pad_sequence(packed_value_states.permute(1, 0, 2), pad_size)
-            packed_attn_output = flex_attention(
-                packed_query_states.unsqueeze(0), 
-                packed_key_states.unsqueeze(0), 
-                packed_value_states.unsqueeze(0), 
-                enable_gqa=True,
-                block_mask=attention_mask,
-            )
-            end_index = packed_attn_output.shape[2] - pad_size
-            packed_attn_output = packed_attn_output[0, :, :end_index, :]
-
-        packed_attn_output = packed_attn_output.transpose(0, 1).reshape(-1, self.hidden_size)
-        packed_attn_output = self.o_proj(packed_attn_output)
-
-        return packed_attn_output
-
-    def forward_inference(
-        self,
-        packed_query_sequence: torch.Tensor,
-        query_lens: torch.Tensor,
-        packed_query_position_embeddings: torch.Tensor,
-        packed_query_indexes: torch.Tensor,
-        past_key_values: Optional[NaiveCache] = None,
-        key_values_lens: Optional[torch.Tensor] = None,
-        packed_key_value_indexes: Optional[torch.Tensor] = None,
-        update_past_key_values=True,
-        is_causal=True,
-    ):
-        packed_query_states = self.q_proj(packed_query_sequence).view(-1, self.num_heads, self.head_dim)
-        packed_key_states = self.k_proj(packed_query_sequence).view(-1, self.num_key_value_heads, self.head_dim)
-        packed_value_states = self.v_proj(packed_query_sequence).view(-1, self.num_key_value_heads, self.head_dim)
-
-        packed_query_states = self.q_norm(packed_query_states)
-        packed_key_states = self.k_norm(packed_key_states)
-
-        packed_cos, packed_sin = packed_query_position_embeddings
-        packed_query_states, packed_key_states = apply_rotary_pos_emb(
-            packed_query_states, packed_key_states, packed_cos, packed_sin, unsqueeze_dim=1
-        )
-
-        packed_query_states = packed_query_states.to(torch.bfloat16)
-        packed_key_states = packed_key_states.to(torch.bfloat16)
-        packed_value_states = packed_value_states.to(torch.bfloat16)
-
-        if past_key_values is not None and past_key_values.key_cache[self.layer_idx] is not None:
-            past_key_states = past_key_values.key_cache[self.layer_idx]
-            past_value_states = past_key_values.value_cache[self.layer_idx]
-
-            seqlens = sum(query_lens) + sum(key_values_lens)
-            merged_key_states = past_key_states.new_zeros((seqlens, self.num_key_value_heads, self.head_dim))
-            merged_value_states = past_key_states.new_zeros((seqlens, self.num_key_value_heads, self.head_dim))
-            merged_key_states[packed_query_indexes] = packed_key_states
-            merged_key_states[packed_key_value_indexes] = past_key_states
-            merged_value_states[packed_query_indexes] = packed_value_states
-            merged_value_states[packed_key_value_indexes] = past_value_states
-            key_values_lens = key_values_lens + query_lens
-        else:
-            merged_key_states = packed_key_states
-            merged_value_states = packed_value_states
-            key_values_lens = query_lens
-
-        cu_seqlens_q = torch.nn.functional.pad(torch.cumsum(query_lens, dim=0), (1, 0))
-        cu_seqlens_k = torch.nn.functional.pad(torch.cumsum(key_values_lens, dim=0), (1, 0))
-
-        packed_attn_output = flash_attn_varlen_func(
-            q=packed_query_states,
-            k=merged_key_states,
-            v=merged_value_states,
-            cu_seqlens_q=cu_seqlens_q.to(torch.int32),
-            cu_seqlens_k=cu_seqlens_k.to(torch.int32),
-            max_seqlen_q=max(query_lens).item(),
-            max_seqlen_k=max(key_values_lens).item(),
-            causal=is_causal,
-        )
-        packed_attn_output = packed_attn_output.reshape(-1, self.hidden_size)
-        packed_attn_output = self.o_proj(packed_attn_output)
-
-        if update_past_key_values:
-            past_key_values.key_cache[self.layer_idx] = merged_key_states
-            past_key_values.value_cache[self.layer_idx] = merged_value_states
-
-        return packed_attn_output, past_key_values
-
-
-class PackedAttentionMoT(Qwen2Attention):
-    def __init__(self, config, layer_idx: Optional[int] = None):
-        super().__init__(config, layer_idx)
-        if self.config.qk_norm:
-            self.q_norm = Qwen2RMSNorm(self.head_dim, eps=config.rms_norm_eps)
-            self.k_norm = Qwen2RMSNorm(self.head_dim, eps=config.rms_norm_eps)
-            self.q_norm_moe_gen = Qwen2RMSNorm(self.head_dim, eps=config.rms_norm_eps)
-            self.k_norm_moe_gen = Qwen2RMSNorm(self.head_dim, eps=config.rms_norm_eps)
-        else:
-            self.q_norm = nn.Identity()
-            self.k_norm = nn.Identity()
-            self.q_norm_moe_gen = nn.Identity()
-            self.k_norm_moe_gen = nn.Identity()
-
-        self.q_proj_moe_gen = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=True)
-        self.k_proj_moe_gen = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
-        self.v_proj_moe_gen = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
-        self.o_proj_moe_gen = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
-
-    def forward(self, *args, **kwargs):
-        if self.training:
-            return self.forward_train(*args, **kwargs)
-        else:
-            return self.forward_inference(*args, **kwargs)
-
-    def forward_train(
-        self,
-        packed_sequence: torch.Tensor,
-        sample_lens: List[int],
-        attention_mask,
-        packed_position_embeddings: Tuple[torch.Tensor, torch.Tensor],
-        packed_und_token_indexes: torch.LongTensor,
-        packed_gen_token_indexes: torch.LongTensor,
-    ):
-        packed_query_states = packed_sequence.new_zeros((packed_sequence.shape[0], self.num_heads * self.head_dim))
-        packed_key_states = packed_sequence.new_zeros((packed_sequence.shape[0], self.num_key_value_heads * self.head_dim))
-        packed_value_states = packed_sequence.new_zeros((packed_sequence.shape[0], self.num_key_value_heads * self.head_dim))
-
-        packed_sequence_und = packed_sequence[packed_und_token_indexes]
-        packed_sequence_gen = packed_sequence[packed_gen_token_indexes]
-
-        packed_query_states[packed_und_token_indexes] = self.q_proj(packed_sequence_und)
-        packed_query_states[packed_gen_token_indexes] = self.q_proj_moe_gen(packed_sequence_gen)
-
-        packed_key_states[packed_und_token_indexes] = self.k_proj(packed_sequence_und)
-        packed_key_states[packed_gen_token_indexes] = self.k_proj_moe_gen(packed_sequence_gen)
-
-        packed_value_states[packed_und_token_indexes] = self.v_proj(packed_sequence_und)
-        packed_value_states[packed_gen_token_indexes] = self.v_proj_moe_gen(packed_sequence_gen)
-
-        packed_query_states = packed_query_states.view(-1, self.num_heads, self.head_dim)
-        packed_key_states = packed_key_states.view(-1, self.num_key_value_heads, self.head_dim)
-        packed_value_states = packed_value_states.view(-1, self.num_key_value_heads, self.head_dim)
-        if self.config.freeze_und:
-            packed_value_states[packed_und_token_indexes] = packed_value_states[packed_und_token_indexes].detach()
-
-        packed_query_states_ = packed_query_states.new_zeros(packed_query_states.shape)
-        packed_key_states_ = packed_key_states.new_zeros(packed_key_states.shape)
-
-        packed_query_states_[packed_und_token_indexes] = self.q_norm(packed_query_states[packed_und_token_indexes])
-        if self.config.freeze_und:
-            packed_query_states_[packed_und_token_indexes] = packed_query_states_[packed_und_token_indexes].detach()
-        packed_query_states_[packed_gen_token_indexes] = self.q_norm_moe_gen(packed_query_states[packed_gen_token_indexes])
-
-        packed_key_states_[packed_und_token_indexes] = self.k_norm(packed_key_states[packed_und_token_indexes])
-        if self.config.freeze_und:
-            packed_key_states_[packed_und_token_indexes] = packed_key_states_[packed_und_token_indexes].detach()
-        packed_key_states_[packed_gen_token_indexes] = self.k_norm_moe_gen(packed_key_states[packed_gen_token_indexes])
-
-        packed_cos, packed_sin = packed_position_embeddings
-        packed_query_states_, packed_key_states_ = apply_rotary_pos_emb(
-            packed_query_states_, packed_key_states_, packed_cos, packed_sin, unsqueeze_dim=1
-        )
-
-        if isinstance(attention_mask, List):
-            packed_key_states_ = packed_key_states_[:, :, None, :].repeat(1, 1, self.num_key_value_groups, 1)
-            packed_key_states_ = packed_key_states_.reshape(-1, self.num_heads, self.head_dim)
-            packed_value_states = packed_value_states[:, :, None, :].repeat(1, 1, self.num_key_value_groups, 1)
-            packed_value_states = packed_value_states.reshape(-1, self.num_heads, self.head_dim)
-
-            unpacked_query_states = packed_query_states_.transpose(0, 1).split(sample_lens, dim=1)
-            unpacked_key_states = packed_key_states_.transpose(0, 1).split(sample_lens, dim=1)
-            unpacked_value_states = packed_value_states.transpose(0, 1).split(sample_lens, dim=1)
-            upacked_attn_output = []
-            for query_states, key_states, value_states, attention_mask_per_sample in zip(
-                unpacked_query_states, unpacked_key_states, unpacked_value_states, attention_mask
-            ):
-                with sdpa_kernel(backends=[SDPBackend.EFFICIENT_ATTENTION]):
-                    attn_output = scaled_dot_product_attention(
-                        query_states.to(torch.bfloat16).unsqueeze(0), 
-                        key_states.to(torch.bfloat16).unsqueeze(0), 
-                        value_states.to(torch.bfloat16).unsqueeze(0),
-                        attention_mask_per_sample.to(torch.bfloat16).unsqueeze(0),
-                    )
-                upacked_attn_output.append(attn_output.squeeze(0))
-            packed_attn_output = torch.cat(upacked_attn_output, dim=1)
-        else:
-            pad_size = sum(sample_lens) - packed_query_states.shape[0]
-            packed_query_states_ = pad_sequence(packed_query_states_.permute(1, 0, 2), pad_size)
-            packed_key_states_ = pad_sequence(packed_key_states_.permute(1, 0, 2), pad_size)
-            packed_value_states = pad_sequence(packed_value_states.permute(1, 0, 2), pad_size)
-            packed_attn_output = flex_attention(
-                packed_query_states_.unsqueeze(0), # 1, num_head, L, head_dim
-                packed_key_states_.unsqueeze(0), 
-                packed_value_states.unsqueeze(0), 
-                enable_gqa=True,
-                block_mask=attention_mask,
-            )
-            end_index = packed_attn_output.shape[2] - pad_size
-            packed_attn_output = packed_attn_output[0, :, :end_index, :]
-
-        packed_attn_output = packed_attn_output.transpose(0, 1).reshape(-1, self.num_heads * self.head_dim)
-        packed_attn_output_ = packed_attn_output.new_zeros(packed_attn_output.shape)
-        packed_attn_output_[packed_und_token_indexes] = self.o_proj(packed_attn_output[packed_und_token_indexes])
-        packed_attn_output_[packed_gen_token_indexes] = self.o_proj_moe_gen(packed_attn_output[packed_gen_token_indexes])
-
-        return packed_attn_output_
-
-    def forward_inference(
-        self,
-        packed_query_sequence: torch.Tensor,
-        query_lens: torch.Tensor,
-        packed_query_position_embeddings: torch.Tensor,
-        packed_query_indexes: torch.Tensor,
-        past_key_values: Optional[NaiveCache] = None,
-        key_values_lens: Optional[torch.Tensor] = None,
-        packed_key_value_indexes: Optional[torch.Tensor] = None,
-        update_past_key_values=True,
-        is_causal=True,
-        mode="und",
-        packed_vae_token_indexes=None,
-        packed_text_indexes=None,
-    ):
-        if mode == 'und':
-            packed_query_states = self.q_proj(packed_query_sequence).view(-1, self.num_heads, self.head_dim)
-            packed_key_states = self.k_proj(packed_query_sequence).view(-1, self.num_key_value_heads, self.head_dim)
-            packed_value_states = self.v_proj(packed_query_sequence).view(-1, self.num_key_value_heads, self.head_dim)
-            packed_query_states = self.q_norm(packed_query_states)
-            packed_key_states = self.k_norm(packed_key_states)
-        elif mode == 'gen':
-            packed_query_sequence = packed_query_sequence.to(torch.bfloat16)
-            packed_query_states = packed_query_sequence.new_zeros((packed_query_sequence.shape[0], self.num_heads * self.head_dim))
-            packed_key_states = packed_query_sequence.new_zeros((packed_query_sequence.shape[0], self.num_key_value_heads * self.head_dim))
-            packed_value_states = packed_query_sequence.new_zeros((packed_query_sequence.shape[0], self.num_key_value_heads * self.head_dim))
-
-            packed_text_query_sequence = packed_query_sequence[packed_text_indexes]
-            packed_vae_query_sequence = packed_query_sequence[packed_vae_token_indexes]
-
-            packed_query_states[packed_text_indexes] = self.q_proj(packed_text_query_sequence)
-            packed_query_states[packed_vae_token_indexes] = self.q_proj_moe_gen(packed_vae_query_sequence)
-
-            packed_key_states[packed_text_indexes] = self.k_proj(packed_text_query_sequence)
-            packed_key_states[packed_vae_token_indexes] = self.k_proj_moe_gen(packed_vae_query_sequence)
-
-            packed_value_states[packed_text_indexes] = self.v_proj(packed_text_query_sequence)
-            packed_value_states[packed_vae_token_indexes] = self.v_proj_moe_gen(packed_vae_query_sequence)
-
-            packed_query_states = packed_query_states.view(-1, self.num_heads, self.head_dim)
-            packed_key_states = packed_key_states.view(-1, self.num_key_value_heads, self.head_dim)
-            packed_value_states = packed_value_states.view(-1, self.num_key_value_heads, self.head_dim)
-
-            packed_query_states = packed_query_states.to(torch.float32)
-            packed_query_states[packed_text_indexes] = self.q_norm(packed_query_states[packed_text_indexes])
-            packed_query_states[packed_vae_token_indexes] = self.q_norm_moe_gen(packed_query_states[packed_vae_token_indexes])
-
-            packed_key_states = packed_key_states.to(torch.float32)
-            packed_key_states[packed_text_indexes] = self.k_norm(packed_key_states[packed_text_indexes])
-            packed_key_states[packed_vae_token_indexes] = self.k_norm_moe_gen(packed_key_states[packed_vae_token_indexes])
-
-        packed_cos, packed_sin = packed_query_position_embeddings
-        packed_query_states, packed_key_states = apply_rotary_pos_emb(
-            packed_query_states, packed_key_states, packed_cos, packed_sin, unsqueeze_dim=1
-        )
-
-        packed_query_states = packed_query_states.to(torch.bfloat16)
-        packed_key_states = packed_key_states.to(torch.bfloat16)
-        packed_value_states = packed_value_states.to(torch.bfloat16)
-
-        if past_key_values is not None and past_key_values.key_cache[self.layer_idx] is not None:
-            past_key_states = past_key_values.key_cache[self.layer_idx]
-            past_value_states = past_key_values.value_cache[self.layer_idx]
-
-            seqlens = sum(query_lens) + sum(key_values_lens)
-            merged_key_states = past_key_states.new_zeros(size=[seqlens, self.num_key_value_heads, self.head_dim])
-            merged_value_states = past_key_states.new_zeros(size=[seqlens, self.num_key_value_heads, self.head_dim])
-            merged_key_states[packed_query_indexes] = packed_key_states
-            merged_key_states[packed_key_value_indexes] = past_key_states
-            merged_value_states[packed_query_indexes] = packed_value_states
-            merged_value_states[packed_key_value_indexes] = past_value_states
-            key_values_lens = key_values_lens + query_lens
-        else:
-            merged_key_states = packed_key_states
-            merged_value_states = packed_value_states
-            key_values_lens = query_lens
-
-        cu_seqlens_q = torch.nn.functional.pad(torch.cumsum(query_lens, dim=0), (1, 0))
-        cu_seqlens_k = torch.nn.functional.pad(torch.cumsum(key_values_lens, dim=0), (1, 0))
-
-        packed_attn_output = flash_attn_varlen_func(
-            q=packed_query_states,
-            k=merged_key_states,
-            v=merged_value_states,
-            cu_seqlens_q=cu_seqlens_q.to(torch.int32),
-            cu_seqlens_k=cu_seqlens_k.to(torch.int32),
-            max_seqlen_q=max(query_lens).item(),
-            max_seqlen_k=max(key_values_lens).item(),
-            causal=is_causal,
-        )
-        packed_attn_output = packed_attn_output.reshape(-1, self.hidden_size)
-        if mode == 'und':
-            packed_attn_output = self.o_proj(packed_attn_output)
-        elif mode == 'gen':
-            packed_attn_output[packed_text_indexes] = self.o_proj(packed_attn_output[packed_text_indexes])
-            packed_attn_output[packed_vae_token_indexes] = self.o_proj_moe_gen(packed_attn_output[packed_vae_token_indexes])
-
-        if update_past_key_values:
-            past_key_values.key_cache[self.layer_idx] = merged_key_states
-            past_key_values.value_cache[self.layer_idx] = merged_value_states
-
-        return packed_attn_output, past_key_values
-
-
-class Qwen2DecoderLayer(nn.Module):
-    def __init__(self, config, layer_idx: Optional[int] = None):
-        super().__init__()
-        self.hidden_size = config.hidden_size
-
-        self.self_attn = PackedAttention(config, layer_idx)
-
-        self.mlp = Qwen2MLP(config)
-        self.input_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.post_attention_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-
-    def forward(self, *args, **kwargs):
-        if self.training:
-            return self.forward_train(*args, **kwargs)
-        else:
-            return self.forward_inference(*args, **kwargs)
-
-    def forward_train(
-        self,
-        packed_sequence: torch.Tensor,
-        sample_lens: List[int],
-        attention_mask,
-        packed_position_embeddings: Tuple[torch.Tensor, torch.Tensor],
-    ) -> torch.Tensor:
-
-        residual = packed_sequence
-        packed_sequence = self.input_layernorm(packed_sequence)
-
-        # Self Attention
-        packed_sequence = self.self_attn(
-            packed_sequence=packed_sequence,
-            sample_lens=sample_lens,
-            attention_mask=attention_mask,
-            packed_position_embeddings=packed_position_embeddings,
-        )
-        packed_sequence = residual + packed_sequence
-
-        # Fully Connected
-        residual = packed_sequence
-        packed_sequence = self.post_attention_layernorm(packed_sequence)
-        packed_sequence = self.mlp(packed_sequence)
-        packed_sequence = residual + packed_sequence
-
-        return packed_sequence
-
-    def forward_inference(
-        self,
-        packed_query_sequence: torch.Tensor,
-        query_lens: torch.Tensor,
-        packed_query_position_embeddings: torch.Tensor,
-        packed_query_indexes: torch.Tensor,
-        past_key_values: Optional[NaiveCache] = None,
-        key_values_lens: Optional[torch.Tensor] = None,
-        packed_key_value_indexes: Optional[torch.Tensor] = None,
-        update_past_key_values=True,
-        is_causal=True,
-    ) -> BaseNavitOutputWithPast:
-
-        residual = packed_query_sequence
-        packed_query_sequence = self.input_layernorm(packed_query_sequence)
-
-        # Self Attention
-        packed_query_sequence, past_key_values = self.self_attn(
-            packed_query_sequence=packed_query_sequence,
-            query_lens=query_lens,
-            packed_query_position_embeddings=packed_query_position_embeddings,
-            packed_query_indexes=packed_query_indexes,
-            past_key_values=past_key_values,
-            key_values_lens=key_values_lens,
-            packed_key_value_indexes=packed_key_value_indexes,
-            update_past_key_values=update_past_key_values,
-            is_causal=is_causal,
-        )
-        packed_query_sequence = residual + packed_query_sequence
-
-        # Fully Connected
-        residual = packed_query_sequence
-        packed_query_sequence = self.post_attention_layernorm(packed_query_sequence)
-        packed_query_sequence = self.mlp(packed_query_sequence)
-        packed_query_sequence = residual + packed_query_sequence
-
-        return packed_query_sequence, past_key_values
-
-
-class Qwen2MoTDecoderLayer(nn.Module):
-    def __init__(
-        self, 
-        config, 
-        layer_idx: Optional[int] = None, 
-        attn_module: Optional[Qwen2Attention] = PackedAttentionMoT,
-    ):
-        super().__init__()
-        self.hidden_size = config.hidden_size
-        self.freeze_und = config.freeze_und
-
-        self.self_attn = attn_module(config, layer_idx)
-
-        self.mlp = Qwen2MLP(config)
-        self.mlp_moe_gen = Qwen2MLP(config)
-        self.input_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.input_layernorm_moe_gen = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.post_attention_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.post_attention_layernorm_moe_gen = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-
-    def forward(self, *args, **kwargs):
-        if self.training:
-            return self.forward_train(*args, **kwargs)
-        else:
-            return self.forward_inference(*args, **kwargs)
-
-    def forward_train(
-        self,
-        packed_sequence: torch.Tensor,
-        sample_lens: List[int],
-        attention_mask,
-        packed_position_embeddings: Tuple[torch.Tensor, torch.Tensor],
-        packed_und_token_indexes: torch.LongTensor,
-        packed_gen_token_indexes: torch.LongTensor,
-    ) -> torch.Tensor:
-
-        residual = packed_sequence
-        packed_sequence_ = packed_sequence.new_zeros(packed_sequence.shape)
-        packed_sequence_[packed_und_token_indexes] = self.input_layernorm(packed_sequence[packed_und_token_indexes])
-        packed_sequence_[packed_gen_token_indexes] = self.input_layernorm_moe_gen(packed_sequence[packed_gen_token_indexes])
-
-        # Self Attention
-        packed_sequence_ = self.self_attn(
-            packed_sequence=packed_sequence_,
-            sample_lens=sample_lens,
-            attention_mask=attention_mask,
-            packed_position_embeddings=packed_position_embeddings,
-            packed_und_token_indexes=packed_und_token_indexes,
-            packed_gen_token_indexes=packed_gen_token_indexes,
-        )
-        if self.freeze_und:
-            packed_sequence_[packed_und_token_indexes] = packed_sequence_[packed_und_token_indexes].detach()
-        packed_sequence = residual + packed_sequence_
-
-        # Fully Connected
-        residual = packed_sequence
-        packed_sequence_ = packed_sequence.new_zeros(packed_sequence.shape)
-        packed_sequence_[packed_und_token_indexes] = self.mlp(
-            self.post_attention_layernorm(packed_sequence[packed_und_token_indexes])
-        )
-        if self.freeze_und:
-            packed_sequence_[packed_und_token_indexes] = packed_sequence_[packed_und_token_indexes].detach()
-    
-        packed_sequence_[packed_gen_token_indexes] = self.mlp_moe_gen(
-            self.post_attention_layernorm_moe_gen(packed_sequence[packed_gen_token_indexes])
-        )
-        packed_sequence = residual + packed_sequence_
-
-        return packed_sequence
-
-    def forward_inference(
-        self,
-        packed_query_sequence: torch.Tensor,
-        query_lens: torch.Tensor,
-        packed_query_position_embeddings: torch.Tensor,
-        packed_query_indexes: torch.Tensor,
-        past_key_values: Optional[NaiveCache] = None,
-        key_values_lens: Optional[torch.Tensor] = None,
-        packed_key_value_indexes: Optional[torch.Tensor] = None,
-        update_past_key_values=True,
-        is_causal=True,
-        mode="und",
-        packed_vae_token_indexes=None,
-        packed_text_indexes=None,
-    ) -> BaseNavitOutputWithPast:
-
-        residual = packed_query_sequence
-        if mode == "und":
-            packed_query_sequence = self.input_layernorm(packed_query_sequence)
-        elif mode == "gen":
-            packed_query_sequence_ = torch.zeros_like(packed_query_sequence)
-            packed_query_sequence_[packed_text_indexes] = self.input_layernorm(packed_query_sequence[packed_text_indexes])
-            packed_query_sequence_[packed_vae_token_indexes] = self.input_layernorm_moe_gen(packed_query_sequence[packed_vae_token_indexes])
-            packed_query_sequence = packed_query_sequence_
-
-        # Self Attention
-        packed_query_sequence, past_key_values = self.self_attn(
-            packed_query_sequence=packed_query_sequence,
-            query_lens=query_lens,
-            packed_query_position_embeddings=packed_query_position_embeddings,
-            packed_query_indexes=packed_query_indexes,
-            past_key_values=past_key_values,
-            key_values_lens=key_values_lens,
-            packed_key_value_indexes=packed_key_value_indexes,
-            update_past_key_values=update_past_key_values,
-            is_causal=is_causal,
-            mode=mode,
-            packed_vae_token_indexes=packed_vae_token_indexes,
-            packed_text_indexes=packed_text_indexes,
-        )
-        packed_query_sequence = residual + packed_query_sequence
-
-        # Fully Connected
-        residual = packed_query_sequence
-        if mode == "und":
-            packed_query_sequence = self.post_attention_layernorm(packed_query_sequence)
-            packed_query_sequence = self.mlp(packed_query_sequence)
-        elif mode == "gen":
-            packed_text_query_sequence = packed_query_sequence[packed_text_indexes]
-            packed_vae_query_sequence = packed_query_sequence[packed_vae_token_indexes]
-            packed_text_query_sequence = self.post_attention_layernorm(packed_text_query_sequence).to(torch.bfloat16)
-            packed_vae_query_sequence = self.post_attention_layernorm_moe_gen(packed_vae_query_sequence).to(torch.bfloat16)
-
-            packed_query_sequence_ = torch.zeros_like(packed_query_sequence).to(torch.bfloat16)
-            packed_query_sequence_[packed_text_indexes] = self.mlp(packed_text_query_sequence)
-            packed_query_sequence_[packed_vae_token_indexes] = self.mlp_moe_gen(packed_vae_query_sequence)
-            packed_query_sequence = packed_query_sequence_
-
-        packed_query_sequence = residual + packed_query_sequence
-        return packed_query_sequence, past_key_values
-
-
-class Qwen2MoEDecoderLayer(nn.Module):
-    def __init__(self, config, layer_idx: Optional[int] = None):
-        super().__init__()
-        self.hidden_size = config.hidden_size
-
-        self.self_attn = PackedAttention(config, layer_idx)
-
-        self.mlp = Qwen2MLP(config)
-        self.mlp_moe_gen = Qwen2MLP(config)
-        self.input_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.post_attention_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-
-    def forward(self, *args, **kwargs):
-        if self.training:
-            return self.forward_train(*args, **kwargs)
-        else:
-            return self.forward_inference(*args, **kwargs)
-
-    def forward_train(
-        self,
-        packed_sequence: torch.Tensor,
-        sample_lens: List[int],
-        attention_mask,
-        packed_position_embeddings: Tuple[torch.Tensor, torch.Tensor],
-        packed_und_token_indexes: torch.LongTensor,
-        packed_gen_token_indexes: torch.LongTensor,
-    ) -> torch.Tensor:
-
-        residual = packed_sequence
-        packed_sequence = self.input_layernorm(packed_sequence)
-
-        # Self Attention
-        packed_sequence = self.self_attn(
-            packed_sequence=packed_sequence,
-            sample_lens=sample_lens,
-            attention_mask=attention_mask,
-            packed_position_embeddings=packed_position_embeddings,
-        )
-        packed_sequence = residual + packed_sequence
-
-        # Fully Connected
-        residual = packed_sequence
-        packed_sequence = self.post_attention_layernorm(packed_sequence)
-
-        packed_sequence_new = packed_sequence.new_zeros(packed_sequence.shape)
-        packed_sequence_und = self.mlp(packed_sequence[packed_und_token_indexes])
-        packed_sequence_gen = self.mlp_moe_gen(packed_sequence[packed_gen_token_indexes])
-        packed_sequence_new[packed_und_token_indexes] = packed_sequence_und
-        packed_sequence_new[packed_gen_token_indexes] = packed_sequence_gen
-
-        packed_sequence = residual + packed_sequence_new
-
-        return packed_sequence
-
-    def forward_inference(
-        self,
-        packed_query_sequence: torch.Tensor,
-        query_lens: torch.Tensor,
-        packed_query_position_embeddings: torch.Tensor,
-        packed_query_indexes: torch.Tensor,
-        past_key_values: Optional[NaiveCache] = None,
-        key_values_lens: Optional[torch.Tensor] = None,
-        packed_key_value_indexes: Optional[torch.Tensor] = None,
-        update_past_key_values=True,
-        is_causal=True,
-        mode="und",
-        packed_vae_token_indexes=None,
-        packed_text_indexes=None,
-    ) -> BaseNavitOutputWithPast:
-
-        residual = packed_query_sequence
-        packed_query_sequence = self.input_layernorm(packed_query_sequence)
-
-        # Self Attention
-        packed_query_sequence, past_key_values = self.self_attn(
-            packed_query_sequence=packed_query_sequence,
-            query_lens=query_lens,
-            packed_query_position_embeddings=packed_query_position_embeddings,
-            packed_query_indexes=packed_query_indexes,
-            past_key_values=past_key_values,
-            key_values_lens=key_values_lens,
-            packed_key_value_indexes=packed_key_value_indexes,
-            update_past_key_values=update_past_key_values,
-            is_causal=is_causal,
-        )
-        packed_query_sequence = residual + packed_query_sequence
-
-        # Fully Connected
-        residual = packed_query_sequence
-        packed_query_sequence = self.post_attention_layernorm(packed_query_sequence)
-        if mode == "und":
-            packed_query_sequence = self.mlp(packed_query_sequence)
-        elif mode == "gen":
-            packed_query_sequence_ = torch.zeros_like(packed_query_sequence).to(torch.bfloat16)
-            packed_query_sequence_[packed_text_indexes] = self.mlp(packed_query_sequence[packed_text_indexes])
-            packed_query_sequence_[packed_vae_token_indexes] = self.mlp_moe_gen(packed_query_sequence[packed_vae_token_indexes])
-            packed_query_sequence = packed_query_sequence_
-        packed_query_sequence = residual + packed_query_sequence
-
-        return packed_query_sequence, past_key_values
-
-
-Decoder_layer_dict = {
-    "Qwen2DecoderLayer": Qwen2DecoderLayer,
-    "Qwen2MoEDecoderLayer": Qwen2MoEDecoderLayer,
-    "Qwen2MoTDecoderLayer": partial(Qwen2MoTDecoderLayer, attn_module=PackedAttentionMoT),
-}
-
-
-class Qwen2Model(Qwen2PreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.padding_idx = config.pad_token_id
-        self.vocab_size = config.vocab_size
-        self.use_moe = 'Mo' in config.layer_module
-
-        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
-        layer_module = Decoder_layer_dict[config.layer_module]
-        self.layers = nn.ModuleList(
-            [layer_module(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
-        )
-
-        self.norm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        if self.use_moe:
-            self.norm_moe_gen = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.rotary_emb = Qwen2RotaryEmbedding(config=config)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def forward(self, *args, **kwargs):
-        if self.training:
-            return self.forward_train(*args, **kwargs)
-        else:
-            return self.forward_inference(*args, **kwargs)
-
-    def forward_train(
-        self,
-        packed_sequence: torch.Tensor,
-        sample_lens: List[int],
-        attention_mask,
-        packed_position_ids: torch.Tensor,
-        packed_und_token_indexes: Optional[torch.LongTensor] = None,
-        packed_gen_token_indexes: Optional[torch.LongTensor] = None,
-    ) -> torch.Tensor:
-
-        if self.config.freeze_und:
-            packed_sequence[packed_und_token_indexes] = packed_sequence[packed_und_token_indexes].detach()
-
-        # create position embeddings to be shared across the decoder layers
-        cos, sin = self.rotary_emb(packed_sequence, packed_position_ids.unsqueeze(0))
-        cos = cos.squeeze(0)
-        sin = sin.squeeze(0)
-        packed_position_embeddings = (cos, sin)
-
-        extra_inputs = {}
-        if self.use_moe:
-            assert packed_und_token_indexes is not None
-            if packed_gen_token_indexes is None:
-                packed_gen_token_indexes = packed_und_token_indexes.new_ones(size=[0])
-            extra_inputs.update(
-                packed_und_token_indexes=packed_und_token_indexes,
-                packed_gen_token_indexes=packed_gen_token_indexes,
-            )
-
-        for decoder_layer in self.layers:
-            packed_sequence = decoder_layer(
-                packed_sequence=packed_sequence,
-                sample_lens=sample_lens,
-                attention_mask=attention_mask,
-                packed_position_embeddings=packed_position_embeddings,
-                **extra_inputs
-            )
-
-        if self.use_moe:
-            packed_sequence_ = torch.zeros_like(packed_sequence)
-            packed_sequence_[packed_und_token_indexes] = self.norm(packed_sequence[packed_und_token_indexes])
-            if self.config.freeze_und:
-                packed_sequence_[packed_und_token_indexes] = packed_sequence_[packed_und_token_indexes].detach()
-            packed_sequence_[packed_gen_token_indexes] = self.norm_moe_gen(packed_sequence[packed_gen_token_indexes])
-            return packed_sequence_
-        else:
-            return self.norm(packed_sequence)
-
-    def forward_inference(
-        self,
-        packed_query_sequence: torch.Tensor,
-        query_lens: torch.Tensor,
-        packed_query_position_ids: torch.Tensor,
-        packed_query_indexes: torch.Tensor,
-        past_key_values: Optional[NaiveCache] = None,
-        key_values_lens: Optional[torch.Tensor] = None,
-        packed_key_value_indexes: Optional[torch.Tensor] = None,
-        update_past_key_values=True,
-        is_causal=True,
-        mode="und",
-        packed_vae_token_indexes=None,
-        packed_text_indexes=None,
-    ) -> BaseNavitOutputWithPast:
-
-        # create position embeddings to be shared across the decoder layers
-        cos, sin = self.rotary_emb(packed_query_sequence, packed_query_position_ids.unsqueeze(0))
-        cos = cos.squeeze(0)
-        sin = sin.squeeze(0)
-        packed_query_position_embeddings = (cos, sin)
-
-        extra_inputs = {}
-        if self.use_moe:
-            extra_inputs.update(mode=mode)
-            if mode == 'gen':
-                assert packed_vae_token_indexes is not None
-                assert packed_text_indexes is not None
-                extra_inputs.update(
-                    packed_vae_token_indexes=packed_vae_token_indexes,
-                    packed_text_indexes=packed_text_indexes,
-                )
-
-        for decoder_layer in self.layers:
-            packed_query_sequence, past_key_values = decoder_layer(
-                packed_query_sequence=packed_query_sequence,
-                query_lens=query_lens,
-                packed_query_position_embeddings=packed_query_position_embeddings,
-                packed_query_indexes=packed_query_indexes,
-                past_key_values=past_key_values,
-                key_values_lens=key_values_lens,
-                packed_key_value_indexes=packed_key_value_indexes,
-                update_past_key_values=update_past_key_values,
-                is_causal=is_causal,
-                **extra_inputs,
-            )
-
-        if self.use_moe:
-            if mode == "und":
-                packed_query_sequence = self.norm(packed_query_sequence)
-            elif mode == "gen":
-                packed_query_sequence_ = torch.zeros_like(packed_query_sequence)
-                packed_query_sequence_[packed_text_indexes] = self.norm(packed_query_sequence[packed_text_indexes])
-                packed_query_sequence_[packed_vae_token_indexes] = self.norm_moe_gen(packed_query_sequence[packed_vae_token_indexes])
-                packed_query_sequence = packed_query_sequence_
-        else:
-            packed_query_sequence = self.norm(packed_query_sequence)
-
-        return BaseNavitOutputWithPast(
-            packed_query_sequence=packed_query_sequence,
-            past_key_values=past_key_values,
-        )
-
-
-class Qwen2ForCausalLM(Qwen2PreTrainedModel):
-    _tied_weights_keys = ["lm_head.weight"]
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.model = Qwen2Model(config)
-        self.vocab_size = config.vocab_size
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def init_moe(self):
-        for name, param in self.named_parameters():
-            if "moe_gen" in name:
-                original_name = name.replace("_moe_gen", "")
-                param.data.copy_(self.state_dict()[original_name].data)
-
-    def get_input_embeddings(self):
-        return self.model.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.model.embed_tokens = value
-
-    def get_output_embeddings(self):
-        return self.lm_head
-
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
-
-    def set_decoder(self, decoder):
-        self.model = decoder
-
-    def get_decoder(self):
-        return self.model
-
-    def forward(self, *args, **kwargs):
-        if self.training:
-            return self.forward_train(*args, **kwargs)
-        else:
-            return self.forward_inference(*args, **kwargs)
-
-    def forward_train(
-        self,
-        packed_sequence: torch.Tensor,
-        sample_lens: List[int],
-        attention_mask,
-        packed_position_ids: torch.Tensor,
-        packed_und_token_indexes: Optional[torch.LongTensor] = None,
-        packed_gen_token_indexes: Optional[torch.LongTensor] = None,
-    ) -> torch.Tensor:
-
-        outputs = self.model(
-            packed_sequence=packed_sequence,
-            sample_lens=sample_lens,
-            packed_position_ids=packed_position_ids,
-            attention_mask=attention_mask,
-            packed_und_token_indexes=packed_und_token_indexes,
-            packed_gen_token_indexes=packed_gen_token_indexes,
-        )
-        return outputs
-
-    def forward_inference(
-        self,
-        packed_query_sequence: torch.Tensor,
-        query_lens: torch.Tensor,
-        packed_query_position_ids: torch.Tensor,
-        packed_query_indexes: torch.Tensor,
-        past_key_values: Optional[NaiveCache] = None,
-        key_values_lens: Optional[torch.Tensor] = None,
-        packed_key_value_indexes: Optional[torch.Tensor] = None,
-        update_past_key_values=True,
-        is_causal=True,
-        mode="und",
-        packed_vae_token_indexes=None,
-        packed_text_indexes=None,
-    ) -> BaseNavitOutputWithPast:
-
-        outputs = self.model(
-            packed_query_sequence=packed_query_sequence,
-            query_lens=query_lens,
-            packed_query_position_ids=packed_query_position_ids,
-            packed_query_indexes=packed_query_indexes,
-            past_key_values=past_key_values,
-            key_values_lens=key_values_lens,
-            packed_key_value_indexes=packed_key_value_indexes,
-            update_past_key_values=update_past_key_values,
-            is_causal=is_causal,
-            mode=mode,
-            packed_vae_token_indexes=packed_vae_token_indexes,
-            packed_text_indexes=packed_text_indexes,
-        )
-
-        return outputs
+# Copyright (c) 2024 The Qwen Team and The HuggingFace Inc. team.
+# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates.
+# SPDX-License-Identifier: Apache-2.0
+#
+# This file has been modified by ByteDance Ltd. and/or its affiliates. on 2025-05-20.
+#
+# Original file was released under Apache-2.0, with the full license text
+# available at https://github.com/huggingface/transformers/blob/main/LICENSE.
+#
+# This modified file is released under the same license.
+
+
+from dataclasses import dataclass
+from functools import partial
+from typing import List, Optional, Tuple
+
+import torch
+from torch import nn
+from torch.nn.attention import SDPBackend, sdpa_kernel
+from torch.nn.attention.flex_attention import flex_attention
+from torch.nn.functional import scaled_dot_product_attention
+from transformers.utils import ModelOutput
+
+from flash_attn import flash_attn_varlen_func
+from modeling.qwen2.modeling_qwen2 import (
+    Qwen2Attention, 
+    Qwen2MLP, 
+    Qwen2PreTrainedModel, 
+    Qwen2RMSNorm, 
+    Qwen2RotaryEmbedding,
+    apply_rotary_pos_emb,
+)
+
+from modeling.qwen2.configuration_qwen2 import Qwen2Config as _Qwen2Config
+
+
+torch._dynamo.config.cache_size_limit = 512
+torch._dynamo.config.accumulated_cache_size_limit = 4096
+# flex_attention = torch.compile(flex_attention) # , dynamic=True, mode='max-autotune'
+flex_attention = torch.compile(flex_attention)
+
+
+class Qwen2Config(_Qwen2Config):
+    r"""
+    This is the configuration class to store the configuration of a [`Qwen2Model`]. It is used to instantiate a
+    Qwen2 model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of
+    Qwen2-7B-beta [Qwen/Qwen2-7B-beta](https://huggingface.co/Qwen/Qwen2-7B-beta).
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 151936):
+            Vocabulary size of the Qwen2 model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`Qwen2Model`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 22016):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 32):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 32768):
+            The maximum sequence length that this model might ever be used with.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied.
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
+            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
+            accordingly.
+            Expected contents:
+                `rope_type` (`str`):
+                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
+                    'llama3'], with 'default' being the original RoPE implementation.
+                `factor` (`float`, *optional*):
+                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
+                    most scaling types, a `factor` of x will enable the model to handle sequences of length x *
+                    original maximum pre-trained length.
+                `original_max_position_embeddings` (`int`, *optional*):
+                    Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
+                    pretraining.
+                `attention_factor` (`float`, *optional*):
+                    Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
+                    computation. If unspecified, it defaults to value recommended by the implementation, using the
+                    `factor` field to infer the suggested value.
+                `beta_fast` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 32.
+                `beta_slow` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 1.
+                `short_factor` (`List[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `long_factor` (`List[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `low_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
+                `high_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
+        use_sliding_window (`bool`, *optional*, defaults to `False`):
+            Whether to use sliding window attention.
+        sliding_window (`int`, *optional*, defaults to 4096):
+            Sliding window attention (SWA) window size. If not specified, will default to `4096`.
+        max_window_layers (`int`, *optional*, defaults to 28):
+            The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+
+    ```python
+    >>> from transformers import Qwen2Model, Qwen2Config
+
+    >>> # Initializing a Qwen2 style configuration
+    >>> configuration = Qwen2Config()
+
+    >>> # Initializing a model from the Qwen2-7B style configuration
+    >>> model = Qwen2Model(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "qwen2"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=151936,
+        hidden_size=4096,
+        intermediate_size=22016,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=32,
+        hidden_act="silu",
+        max_position_embeddings=32768,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        use_sliding_window=False,
+        sliding_window=4096,
+        max_window_layers=28,
+        attention_dropout=0.0,
+        is_causal=True,
+        _attn_implementation="flash_attention_2",
+        qk_norm=True,
+        layer_module="Qwen2DecoderLayer",
+        freeze_und=False,
+        **kwargs,
+    ):
+        super().__init__(
+            vocab_size=vocab_size,
+            hidden_size=hidden_size,
+            intermediate_size=intermediate_size,
+            num_hidden_layers=num_hidden_layers,
+            num_attention_heads=num_attention_heads,
+            num_key_value_heads=num_key_value_heads,
+            hidden_act=hidden_act,
+            max_position_embeddings=max_position_embeddings,
+            initializer_range=initializer_range,
+            rms_norm_eps=rms_norm_eps,
+            use_cache=use_cache,
+            tie_word_embeddings=tie_word_embeddings,
+            rope_theta=rope_theta,
+            rope_scaling=rope_scaling,
+            use_sliding_window=use_sliding_window,
+            sliding_window=sliding_window,
+            max_window_layers=max_window_layers,
+            attention_dropout=attention_dropout,
+            is_causal=is_causal,
+            _attn_implementation=_attn_implementation,
+            **kwargs,
+        )
+        self.qk_norm = qk_norm
+        self.layer_module = layer_module
+        self.freeze_und = freeze_und
+
+
+class NaiveCache:
+    def __init__(self, num_layers):
+        self.key_cache = {k: None for k in range(num_layers)}
+        self.value_cache = {k: None for k in range(num_layers)}
+
+    @property
+    def num_layers(self):
+        return len(self.key_cache)
+
+    @property
+    def seq_lens(self):
+        if self.key_cache[0] is not None:
+            return self.key_cache[0].shape[0]
+        else:
+            return 0
+
+
+@dataclass
+class BaseNavitOutputWithPast(ModelOutput):
+    packed_query_sequence: torch.FloatTensor = None
+    past_key_values: Optional[NaiveCache] = None
+
+
+def pad_sequence(tensor, pad_size):
+    H, L, D = tensor.shape
+    pad_tensor = tensor.new_zeros((H, pad_size, D))
+    return torch.cat([tensor, pad_tensor], dim=1)
+
+
+class PackedAttention(Qwen2Attention):
+    def __init__(self, config, layer_idx: Optional[int] = None):
+        super().__init__(config, layer_idx)
+        if self.config.qk_norm:
+            self.q_norm = Qwen2RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+            self.k_norm = Qwen2RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+        else:
+            self.q_norm = nn.Identity()
+            self.k_norm = nn.Identity()
+
+    def forward(self, *args, **kwargs):
+        if self.training:
+            return self.forward_train(*args, **kwargs)
+        else:
+            return self.forward_inference(*args, **kwargs)
+
+    def forward_train(
+        self,
+        packed_sequence: torch.Tensor,
+        sample_lens: List[int],
+        attention_mask: List[torch.Tensor],
+        packed_position_embeddings: Tuple[torch.Tensor, torch.Tensor],
+    ):
+        packed_query_states = self.q_proj(packed_sequence).view(-1, self.num_heads, self.head_dim)
+        packed_key_states = self.k_proj(packed_sequence).view(-1, self.num_key_value_heads, self.head_dim)
+        packed_value_states = self.v_proj(packed_sequence).view(-1, self.num_key_value_heads, self.head_dim)
+
+        packed_query_states = self.q_norm(packed_query_states)
+        packed_key_states = self.k_norm(packed_key_states)
+
+        packed_cos, packed_sin = packed_position_embeddings
+        packed_query_states, packed_key_states = apply_rotary_pos_emb(
+            packed_query_states, packed_key_states, packed_cos, packed_sin, unsqueeze_dim=1
+        )
+
+        if isinstance(attention_mask, List):
+            packed_key_states = packed_key_states[:, :, None, :].repeat(1, 1, self.num_key_value_groups, 1)
+            packed_key_states = packed_key_states.reshape(-1, self.num_heads, self.head_dim)
+            packed_value_states = packed_value_states[:, :, None, :].repeat(1, 1, self.num_key_value_groups, 1)
+            packed_value_states = packed_value_states.reshape(-1, self.num_heads, self.head_dim)
+
+            unpacked_query_states = packed_query_states.transpose(0, 1).split(sample_lens, dim=1)
+            unpacked_key_states = packed_key_states.transpose(0, 1).split(sample_lens, dim=1)
+            unpacked_value_states = packed_value_states.transpose(0, 1).split(sample_lens, dim=1)
+            upacked_attn_output = []
+            for query_states, key_states, value_states, attention_mask_per_sample in zip(
+                unpacked_query_states, unpacked_key_states, unpacked_value_states, attention_mask
+            ):
+                with sdpa_kernel(backends=[SDPBackend.EFFICIENT_ATTENTION]):
+                    attn_output = scaled_dot_product_attention(
+                        query_states.to(torch.bfloat16).unsqueeze(0), 
+                        key_states.to(torch.bfloat16).unsqueeze(0), 
+                        value_states.to(torch.bfloat16).unsqueeze(0),
+                        attention_mask_per_sample.to(torch.bfloat16).unsqueeze(0),
+                    )
+                upacked_attn_output.append(attn_output.squeeze(0))
+            packed_attn_output = torch.cat(upacked_attn_output, dim=1)
+        else:
+            pad_size = sum(sample_lens) - packed_query_states.shape[0]
+            packed_query_states = pad_sequence(packed_query_states.permute(1, 0, 2), pad_size)
+            packed_key_states = pad_sequence(packed_key_states.permute(1, 0, 2), pad_size)
+            packed_value_states = pad_sequence(packed_value_states.permute(1, 0, 2), pad_size)
+            packed_attn_output = flex_attention(
+                packed_query_states.unsqueeze(0), 
+                packed_key_states.unsqueeze(0), 
+                packed_value_states.unsqueeze(0), 
+                enable_gqa=True,
+                block_mask=attention_mask,
+            )
+            end_index = packed_attn_output.shape[2] - pad_size
+            packed_attn_output = packed_attn_output[0, :, :end_index, :]
+
+        packed_attn_output = packed_attn_output.transpose(0, 1).reshape(-1, self.hidden_size)
+        packed_attn_output = self.o_proj(packed_attn_output)
+
+        return packed_attn_output
+
+    def forward_inference(
+        self,
+        packed_query_sequence: torch.Tensor,
+        query_lens: torch.Tensor,
+        packed_query_position_embeddings: torch.Tensor,
+        packed_query_indexes: torch.Tensor,
+        past_key_values: Optional[NaiveCache] = None,
+        key_values_lens: Optional[torch.Tensor] = None,
+        packed_key_value_indexes: Optional[torch.Tensor] = None,
+        update_past_key_values=True,
+        is_causal=True,
+    ):
+        packed_query_states = self.q_proj(packed_query_sequence).view(-1, self.num_heads, self.head_dim)
+        packed_key_states = self.k_proj(packed_query_sequence).view(-1, self.num_key_value_heads, self.head_dim)
+        packed_value_states = self.v_proj(packed_query_sequence).view(-1, self.num_key_value_heads, self.head_dim)
+
+        packed_query_states = self.q_norm(packed_query_states)
+        packed_key_states = self.k_norm(packed_key_states)
+
+        packed_cos, packed_sin = packed_query_position_embeddings
+        packed_query_states, packed_key_states = apply_rotary_pos_emb(
+            packed_query_states, packed_key_states, packed_cos, packed_sin, unsqueeze_dim=1
+        )
+
+        packed_query_states = packed_query_states.to(torch.bfloat16)
+        packed_key_states = packed_key_states.to(torch.bfloat16)
+        packed_value_states = packed_value_states.to(torch.bfloat16)
+
+        if past_key_values is not None and past_key_values.key_cache[self.layer_idx] is not None:
+            past_key_states = past_key_values.key_cache[self.layer_idx]
+            past_value_states = past_key_values.value_cache[self.layer_idx]
+
+            seqlens = sum(query_lens) + sum(key_values_lens)
+            merged_key_states = past_key_states.new_zeros((seqlens, self.num_key_value_heads, self.head_dim))
+            merged_value_states = past_key_states.new_zeros((seqlens, self.num_key_value_heads, self.head_dim))
+            merged_key_states[packed_query_indexes] = packed_key_states
+            merged_key_states[packed_key_value_indexes] = past_key_states
+            merged_value_states[packed_query_indexes] = packed_value_states
+            merged_value_states[packed_key_value_indexes] = past_value_states
+            key_values_lens = key_values_lens + query_lens
+        else:
+            merged_key_states = packed_key_states
+            merged_value_states = packed_value_states
+            key_values_lens = query_lens
+
+        cu_seqlens_q = torch.nn.functional.pad(torch.cumsum(query_lens, dim=0), (1, 0))
+        cu_seqlens_k = torch.nn.functional.pad(torch.cumsum(key_values_lens, dim=0), (1, 0))
+
+        packed_attn_output = flash_attn_varlen_func(
+            q=packed_query_states,
+            k=merged_key_states,
+            v=merged_value_states,
+            cu_seqlens_q=cu_seqlens_q.to(torch.int32),
+            cu_seqlens_k=cu_seqlens_k.to(torch.int32),
+            max_seqlen_q=max(query_lens).item(),
+            max_seqlen_k=max(key_values_lens).item(),
+            causal=is_causal,
+        )
+        packed_attn_output = packed_attn_output.reshape(-1, self.hidden_size)
+        packed_attn_output = self.o_proj(packed_attn_output)
+
+        if update_past_key_values:
+            past_key_values.key_cache[self.layer_idx] = merged_key_states
+            past_key_values.value_cache[self.layer_idx] = merged_value_states
+
+        return packed_attn_output, past_key_values
+
+
+class PackedAttentionMoT(Qwen2Attention):
+    def __init__(self, config, layer_idx: Optional[int] = None):
+        super().__init__(config, layer_idx)
+        if self.config.qk_norm:
+            self.q_norm = Qwen2RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+            self.k_norm = Qwen2RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+            self.q_norm_moe_gen = Qwen2RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+            self.k_norm_moe_gen = Qwen2RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+        else:
+            self.q_norm = nn.Identity()
+            self.k_norm = nn.Identity()
+            self.q_norm_moe_gen = nn.Identity()
+            self.k_norm_moe_gen = nn.Identity()
+
+        self.q_proj_moe_gen = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=True)
+        self.k_proj_moe_gen = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
+        self.v_proj_moe_gen = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
+        self.o_proj_moe_gen = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+
+    def forward(self, *args, **kwargs):
+        if self.training:
+            return self.forward_train(*args, **kwargs)
+        else:
+            return self.forward_inference(*args, **kwargs)
+
+    def forward_train(
+        self,
+        packed_sequence: torch.Tensor,
+        sample_lens: List[int],
+        attention_mask,
+        packed_position_embeddings: Tuple[torch.Tensor, torch.Tensor],
+        packed_und_token_indexes: torch.LongTensor,
+        packed_gen_token_indexes: torch.LongTensor,
+    ):
+        packed_query_states = packed_sequence.new_zeros((packed_sequence.shape[0], self.num_heads * self.head_dim))
+        packed_key_states = packed_sequence.new_zeros((packed_sequence.shape[0], self.num_key_value_heads * self.head_dim))
+        packed_value_states = packed_sequence.new_zeros((packed_sequence.shape[0], self.num_key_value_heads * self.head_dim))
+
+        packed_sequence_und = packed_sequence[packed_und_token_indexes]
+        packed_sequence_gen = packed_sequence[packed_gen_token_indexes]
+
+        packed_query_states[packed_und_token_indexes] = self.q_proj(packed_sequence_und)
+        packed_query_states[packed_gen_token_indexes] = self.q_proj_moe_gen(packed_sequence_gen)
+
+        packed_key_states[packed_und_token_indexes] = self.k_proj(packed_sequence_und)
+        packed_key_states[packed_gen_token_indexes] = self.k_proj_moe_gen(packed_sequence_gen)
+
+        packed_value_states[packed_und_token_indexes] = self.v_proj(packed_sequence_und)
+        packed_value_states[packed_gen_token_indexes] = self.v_proj_moe_gen(packed_sequence_gen)
+
+        packed_query_states = packed_query_states.view(-1, self.num_heads, self.head_dim)
+        packed_key_states = packed_key_states.view(-1, self.num_key_value_heads, self.head_dim)
+        packed_value_states = packed_value_states.view(-1, self.num_key_value_heads, self.head_dim)
+        if self.config.freeze_und:
+            packed_value_states[packed_und_token_indexes] = packed_value_states[packed_und_token_indexes].detach()
+
+        packed_query_states_ = packed_query_states.new_zeros(packed_query_states.shape)
+        packed_key_states_ = packed_key_states.new_zeros(packed_key_states.shape)
+
+        packed_query_states_[packed_und_token_indexes] = self.q_norm(packed_query_states[packed_und_token_indexes])
+        if self.config.freeze_und:
+            packed_query_states_[packed_und_token_indexes] = packed_query_states_[packed_und_token_indexes].detach()
+        packed_query_states_[packed_gen_token_indexes] = self.q_norm_moe_gen(packed_query_states[packed_gen_token_indexes])
+
+        packed_key_states_[packed_und_token_indexes] = self.k_norm(packed_key_states[packed_und_token_indexes])
+        if self.config.freeze_und:
+            packed_key_states_[packed_und_token_indexes] = packed_key_states_[packed_und_token_indexes].detach()
+        packed_key_states_[packed_gen_token_indexes] = self.k_norm_moe_gen(packed_key_states[packed_gen_token_indexes])
+
+        packed_cos, packed_sin = packed_position_embeddings
+        packed_query_states_, packed_key_states_ = apply_rotary_pos_emb(
+            packed_query_states_, packed_key_states_, packed_cos, packed_sin, unsqueeze_dim=1
+        )
+
+        if isinstance(attention_mask, List):
+            packed_key_states_ = packed_key_states_[:, :, None, :].repeat(1, 1, self.num_key_value_groups, 1)
+            packed_key_states_ = packed_key_states_.reshape(-1, self.num_heads, self.head_dim)
+            packed_value_states = packed_value_states[:, :, None, :].repeat(1, 1, self.num_key_value_groups, 1)
+            packed_value_states = packed_value_states.reshape(-1, self.num_heads, self.head_dim)
+
+            unpacked_query_states = packed_query_states_.transpose(0, 1).split(sample_lens, dim=1)
+            unpacked_key_states = packed_key_states_.transpose(0, 1).split(sample_lens, dim=1)
+            unpacked_value_states = packed_value_states.transpose(0, 1).split(sample_lens, dim=1)
+            upacked_attn_output = []
+            for query_states, key_states, value_states, attention_mask_per_sample in zip(
+                unpacked_query_states, unpacked_key_states, unpacked_value_states, attention_mask
+            ):
+                with sdpa_kernel(backends=[SDPBackend.EFFICIENT_ATTENTION]):
+                    attn_output = scaled_dot_product_attention(
+                        query_states.to(torch.bfloat16).unsqueeze(0), 
+                        key_states.to(torch.bfloat16).unsqueeze(0), 
+                        value_states.to(torch.bfloat16).unsqueeze(0),
+                        attention_mask_per_sample.to(torch.bfloat16).unsqueeze(0),
+                    )
+                upacked_attn_output.append(attn_output.squeeze(0))
+            packed_attn_output = torch.cat(upacked_attn_output, dim=1)
+        else:
+            pad_size = sum(sample_lens) - packed_query_states.shape[0]
+            packed_query_states_ = pad_sequence(packed_query_states_.permute(1, 0, 2), pad_size)
+            packed_key_states_ = pad_sequence(packed_key_states_.permute(1, 0, 2), pad_size)
+            packed_value_states = pad_sequence(packed_value_states.permute(1, 0, 2), pad_size)
+            packed_attn_output = flex_attention(
+                packed_query_states_.unsqueeze(0), # 1, num_head, L, head_dim
+                packed_key_states_.unsqueeze(0), 
+                packed_value_states.unsqueeze(0), 
+                enable_gqa=True,
+                block_mask=attention_mask,
+            )
+            end_index = packed_attn_output.shape[2] - pad_size
+            packed_attn_output = packed_attn_output[0, :, :end_index, :]
+
+        packed_attn_output = packed_attn_output.transpose(0, 1).reshape(-1, self.num_heads * self.head_dim)
+        packed_attn_output_ = packed_attn_output.new_zeros(packed_attn_output.shape)
+        packed_attn_output_[packed_und_token_indexes] = self.o_proj(packed_attn_output[packed_und_token_indexes])
+        packed_attn_output_[packed_gen_token_indexes] = self.o_proj_moe_gen(packed_attn_output[packed_gen_token_indexes])
+
+        return packed_attn_output_
+
+    def forward_inference(
+        self,
+        packed_query_sequence: torch.Tensor,
+        query_lens: torch.Tensor,
+        packed_query_position_embeddings: torch.Tensor,
+        packed_query_indexes: torch.Tensor,
+        past_key_values: Optional[NaiveCache] = None,
+        key_values_lens: Optional[torch.Tensor] = None,
+        packed_key_value_indexes: Optional[torch.Tensor] = None,
+        update_past_key_values=True,
+        is_causal=True,
+        mode="und",
+        packed_vae_token_indexes=None,
+        packed_text_indexes=None,
+    ):
+        if mode == 'und':
+            packed_query_states = self.q_proj(packed_query_sequence).view(-1, self.num_heads, self.head_dim)
+            packed_key_states = self.k_proj(packed_query_sequence).view(-1, self.num_key_value_heads, self.head_dim)
+            packed_value_states = self.v_proj(packed_query_sequence).view(-1, self.num_key_value_heads, self.head_dim)
+            packed_query_states = self.q_norm(packed_query_states)
+            packed_key_states = self.k_norm(packed_key_states)
+        elif mode == 'gen':
+            packed_query_sequence = packed_query_sequence.to(torch.bfloat16)
+            packed_query_states = packed_query_sequence.new_zeros((packed_query_sequence.shape[0], self.num_heads * self.head_dim))
+            packed_key_states = packed_query_sequence.new_zeros((packed_query_sequence.shape[0], self.num_key_value_heads * self.head_dim))
+            packed_value_states = packed_query_sequence.new_zeros((packed_query_sequence.shape[0], self.num_key_value_heads * self.head_dim))
+
+            packed_text_query_sequence = packed_query_sequence[packed_text_indexes]
+            packed_vae_query_sequence = packed_query_sequence[packed_vae_token_indexes]
+
+            packed_query_states[packed_text_indexes] = self.q_proj(packed_text_query_sequence)
+            packed_query_states[packed_vae_token_indexes] = self.q_proj_moe_gen(packed_vae_query_sequence)
+
+            packed_key_states[packed_text_indexes] = self.k_proj(packed_text_query_sequence)
+            packed_key_states[packed_vae_token_indexes] = self.k_proj_moe_gen(packed_vae_query_sequence)
+
+            packed_value_states[packed_text_indexes] = self.v_proj(packed_text_query_sequence)
+            packed_value_states[packed_vae_token_indexes] = self.v_proj_moe_gen(packed_vae_query_sequence)
+
+            packed_query_states = packed_query_states.view(-1, self.num_heads, self.head_dim)
+            packed_key_states = packed_key_states.view(-1, self.num_key_value_heads, self.head_dim)
+            packed_value_states = packed_value_states.view(-1, self.num_key_value_heads, self.head_dim)
+
+            packed_query_states = packed_query_states.to(torch.float32)
+            packed_query_states[packed_text_indexes] = self.q_norm(packed_query_states[packed_text_indexes])
+            packed_query_states[packed_vae_token_indexes] = self.q_norm_moe_gen(packed_query_states[packed_vae_token_indexes])
+
+            packed_key_states = packed_key_states.to(torch.float32)
+            packed_key_states[packed_text_indexes] = self.k_norm(packed_key_states[packed_text_indexes])
+            packed_key_states[packed_vae_token_indexes] = self.k_norm_moe_gen(packed_key_states[packed_vae_token_indexes])
+
+        packed_cos, packed_sin = packed_query_position_embeddings
+        packed_query_states, packed_key_states = apply_rotary_pos_emb(
+            packed_query_states, packed_key_states, packed_cos, packed_sin, unsqueeze_dim=1
+        )
+
+        packed_query_states = packed_query_states.to(torch.bfloat16)
+        packed_key_states = packed_key_states.to(torch.bfloat16)
+        packed_value_states = packed_value_states.to(torch.bfloat16)
+
+        if past_key_values is not None and past_key_values.key_cache[self.layer_idx] is not None:
+            past_key_states = past_key_values.key_cache[self.layer_idx]
+            past_value_states = past_key_values.value_cache[self.layer_idx]
+
+            seqlens = sum(query_lens) + sum(key_values_lens)
+            merged_key_states = past_key_states.new_zeros(size=[seqlens, self.num_key_value_heads, self.head_dim])
+            merged_value_states = past_key_states.new_zeros(size=[seqlens, self.num_key_value_heads, self.head_dim])
+            merged_key_states[packed_query_indexes] = packed_key_states
+            merged_key_states[packed_key_value_indexes] = past_key_states
+            merged_value_states[packed_query_indexes] = packed_value_states
+            merged_value_states[packed_key_value_indexes] = past_value_states
+            key_values_lens = key_values_lens + query_lens
+        else:
+            merged_key_states = packed_key_states
+            merged_value_states = packed_value_states
+            key_values_lens = query_lens
+
+        cu_seqlens_q = torch.nn.functional.pad(torch.cumsum(query_lens, dim=0), (1, 0))
+        cu_seqlens_k = torch.nn.functional.pad(torch.cumsum(key_values_lens, dim=0), (1, 0))
+
+        packed_attn_output = flash_attn_varlen_func(
+            q=packed_query_states,
+            k=merged_key_states,
+            v=merged_value_states,
+            cu_seqlens_q=cu_seqlens_q.to(torch.int32),
+            cu_seqlens_k=cu_seqlens_k.to(torch.int32),
+            max_seqlen_q=max(query_lens).item(),
+            max_seqlen_k=max(key_values_lens).item(),
+            causal=is_causal,
+        )
+        packed_attn_output = packed_attn_output.reshape(-1, self.hidden_size)
+        if mode == 'und':
+            packed_attn_output = self.o_proj(packed_attn_output)
+        elif mode == 'gen':
+            packed_attn_output[packed_text_indexes] = self.o_proj(packed_attn_output[packed_text_indexes])
+            packed_attn_output[packed_vae_token_indexes] = self.o_proj_moe_gen(packed_attn_output[packed_vae_token_indexes])
+
+        if update_past_key_values:
+            past_key_values.key_cache[self.layer_idx] = merged_key_states
+            past_key_values.value_cache[self.layer_idx] = merged_value_states
+
+        return packed_attn_output, past_key_values
+
+
+class Qwen2DecoderLayer(nn.Module):
+    def __init__(self, config, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = PackedAttention(config, layer_idx)
+
+        self.mlp = Qwen2MLP(config)
+        self.input_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(self, *args, **kwargs):
+        if self.training:
+            return self.forward_train(*args, **kwargs)
+        else:
+            return self.forward_inference(*args, **kwargs)
+
+    def forward_train(
+        self,
+        packed_sequence: torch.Tensor,
+        sample_lens: List[int],
+        attention_mask,
+        packed_position_embeddings: Tuple[torch.Tensor, torch.Tensor],
+    ) -> torch.Tensor:
+
+        residual = packed_sequence
+        packed_sequence = self.input_layernorm(packed_sequence)
+
+        # Self Attention
+        packed_sequence = self.self_attn(
+            packed_sequence=packed_sequence,
+            sample_lens=sample_lens,
+            attention_mask=attention_mask,
+            packed_position_embeddings=packed_position_embeddings,
+        )
+        packed_sequence = residual + packed_sequence
+
+        # Fully Connected
+        residual = packed_sequence
+        packed_sequence = self.post_attention_layernorm(packed_sequence)
+        packed_sequence = self.mlp(packed_sequence)
+        packed_sequence = residual + packed_sequence
+
+        return packed_sequence
+
+    def forward_inference(
+        self,
+        packed_query_sequence: torch.Tensor,
+        query_lens: torch.Tensor,
+        packed_query_position_embeddings: torch.Tensor,
+        packed_query_indexes: torch.Tensor,
+        past_key_values: Optional[NaiveCache] = None,
+        key_values_lens: Optional[torch.Tensor] = None,
+        packed_key_value_indexes: Optional[torch.Tensor] = None,
+        update_past_key_values=True,
+        is_causal=True,
+    ) -> BaseNavitOutputWithPast:
+
+        residual = packed_query_sequence
+        packed_query_sequence = self.input_layernorm(packed_query_sequence)
+
+        # Self Attention
+        packed_query_sequence, past_key_values = self.self_attn(
+            packed_query_sequence=packed_query_sequence,
+            query_lens=query_lens,
+            packed_query_position_embeddings=packed_query_position_embeddings,
+            packed_query_indexes=packed_query_indexes,
+            past_key_values=past_key_values,
+            key_values_lens=key_values_lens,
+            packed_key_value_indexes=packed_key_value_indexes,
+            update_past_key_values=update_past_key_values,
+            is_causal=is_causal,
+        )
+        packed_query_sequence = residual + packed_query_sequence
+
+        # Fully Connected
+        residual = packed_query_sequence
+        packed_query_sequence = self.post_attention_layernorm(packed_query_sequence)
+        packed_query_sequence = self.mlp(packed_query_sequence)
+        packed_query_sequence = residual + packed_query_sequence
+
+        return packed_query_sequence, past_key_values
+
+
+class Qwen2MoTDecoderLayer(nn.Module):
+    def __init__(
+        self, 
+        config, 
+        layer_idx: Optional[int] = None, 
+        attn_module: Optional[Qwen2Attention] = PackedAttentionMoT,
+    ):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.freeze_und = config.freeze_und
+
+        self.self_attn = attn_module(config, layer_idx)
+
+        self.mlp = Qwen2MLP(config)
+        self.mlp_moe_gen = Qwen2MLP(config)
+        self.input_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.input_layernorm_moe_gen = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm_moe_gen = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(self, *args, **kwargs):
+        if self.training:
+            return self.forward_train(*args, **kwargs)
+        else:
+            return self.forward_inference(*args, **kwargs)
+
+    def forward_train(
+        self,
+        packed_sequence: torch.Tensor,
+        sample_lens: List[int],
+        attention_mask,
+        packed_position_embeddings: Tuple[torch.Tensor, torch.Tensor],
+        packed_und_token_indexes: torch.LongTensor,
+        packed_gen_token_indexes: torch.LongTensor,
+    ) -> torch.Tensor:
+
+        residual = packed_sequence
+        packed_sequence_ = packed_sequence.new_zeros(packed_sequence.shape)
+        packed_sequence_[packed_und_token_indexes] = self.input_layernorm(packed_sequence[packed_und_token_indexes])
+        packed_sequence_[packed_gen_token_indexes] = self.input_layernorm_moe_gen(packed_sequence[packed_gen_token_indexes])
+
+        # Self Attention
+        packed_sequence_ = self.self_attn(
+            packed_sequence=packed_sequence_,
+            sample_lens=sample_lens,
+            attention_mask=attention_mask,
+            packed_position_embeddings=packed_position_embeddings,
+            packed_und_token_indexes=packed_und_token_indexes,
+            packed_gen_token_indexes=packed_gen_token_indexes,
+        )
+        if self.freeze_und:
+            packed_sequence_[packed_und_token_indexes] = packed_sequence_[packed_und_token_indexes].detach()
+        packed_sequence = residual + packed_sequence_
+
+        # Fully Connected
+        residual = packed_sequence
+        packed_sequence_ = packed_sequence.new_zeros(packed_sequence.shape)
+        packed_sequence_[packed_und_token_indexes] = self.mlp(
+            self.post_attention_layernorm(packed_sequence[packed_und_token_indexes])
+        )
+        if self.freeze_und:
+            packed_sequence_[packed_und_token_indexes] = packed_sequence_[packed_und_token_indexes].detach()
+    
+        packed_sequence_[packed_gen_token_indexes] = self.mlp_moe_gen(
+            self.post_attention_layernorm_moe_gen(packed_sequence[packed_gen_token_indexes])
+        )
+        packed_sequence = residual + packed_sequence_
+
+        return packed_sequence
+
+    def forward_inference(
+        self,
+        packed_query_sequence: torch.Tensor,
+        query_lens: torch.Tensor,
+        packed_query_position_embeddings: torch.Tensor,
+        packed_query_indexes: torch.Tensor,
+        past_key_values: Optional[NaiveCache] = None,
+        key_values_lens: Optional[torch.Tensor] = None,
+        packed_key_value_indexes: Optional[torch.Tensor] = None,
+        update_past_key_values=True,
+        is_causal=True,
+        mode="und",
+        packed_vae_token_indexes=None,
+        packed_text_indexes=None,
+    ) -> BaseNavitOutputWithPast:
+
+        residual = packed_query_sequence
+        if mode == "und":
+            packed_query_sequence = self.input_layernorm(packed_query_sequence)
+        elif mode == "gen":
+            packed_query_sequence_ = torch.zeros_like(packed_query_sequence)
+            packed_query_sequence_[packed_text_indexes] = self.input_layernorm(packed_query_sequence[packed_text_indexes])
+            packed_query_sequence_[packed_vae_token_indexes] = self.input_layernorm_moe_gen(packed_query_sequence[packed_vae_token_indexes])
+            packed_query_sequence = packed_query_sequence_
+
+        # Self Attention
+        packed_query_sequence, past_key_values = self.self_attn(
+            packed_query_sequence=packed_query_sequence,
+            query_lens=query_lens,
+            packed_query_position_embeddings=packed_query_position_embeddings,
+            packed_query_indexes=packed_query_indexes,
+            past_key_values=past_key_values,
+            key_values_lens=key_values_lens,
+            packed_key_value_indexes=packed_key_value_indexes,
+            update_past_key_values=update_past_key_values,
+            is_causal=is_causal,
+            mode=mode,
+            packed_vae_token_indexes=packed_vae_token_indexes,
+            packed_text_indexes=packed_text_indexes,
+        )
+        packed_query_sequence = residual + packed_query_sequence
+
+        # Fully Connected
+        residual = packed_query_sequence
+        if mode == "und":
+            packed_query_sequence = self.post_attention_layernorm(packed_query_sequence)
+            packed_query_sequence = self.mlp(packed_query_sequence)
+        elif mode == "gen":
+            packed_text_query_sequence = packed_query_sequence[packed_text_indexes]
+            packed_vae_query_sequence = packed_query_sequence[packed_vae_token_indexes]
+            packed_text_query_sequence = self.post_attention_layernorm(packed_text_query_sequence).to(torch.bfloat16)
+            packed_vae_query_sequence = self.post_attention_layernorm_moe_gen(packed_vae_query_sequence).to(torch.bfloat16)
+
+            packed_query_sequence_ = torch.zeros_like(packed_query_sequence).to(torch.bfloat16)
+            packed_query_sequence_[packed_text_indexes] = self.mlp(packed_text_query_sequence)
+            packed_query_sequence_[packed_vae_token_indexes] = self.mlp_moe_gen(packed_vae_query_sequence)
+            packed_query_sequence = packed_query_sequence_
+
+        packed_query_sequence = residual + packed_query_sequence
+        return packed_query_sequence, past_key_values
+
+
+class Qwen2MoEDecoderLayer(nn.Module):
+    def __init__(self, config, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = PackedAttention(config, layer_idx)
+
+        self.mlp = Qwen2MLP(config)
+        self.mlp_moe_gen = Qwen2MLP(config)
+        self.input_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(self, *args, **kwargs):
+        if self.training:
+            return self.forward_train(*args, **kwargs)
+        else:
+            return self.forward_inference(*args, **kwargs)
+
+    def forward_train(
+        self,
+        packed_sequence: torch.Tensor,
+        sample_lens: List[int],
+        attention_mask,
+        packed_position_embeddings: Tuple[torch.Tensor, torch.Tensor],
+        packed_und_token_indexes: torch.LongTensor,
+        packed_gen_token_indexes: torch.LongTensor,
+    ) -> torch.Tensor:
+
+        residual = packed_sequence
+        packed_sequence = self.input_layernorm(packed_sequence)
+
+        # Self Attention
+        packed_sequence = self.self_attn(
+            packed_sequence=packed_sequence,
+            sample_lens=sample_lens,
+            attention_mask=attention_mask,
+            packed_position_embeddings=packed_position_embeddings,
+        )
+        packed_sequence = residual + packed_sequence
+
+        # Fully Connected
+        residual = packed_sequence
+        packed_sequence = self.post_attention_layernorm(packed_sequence)
+
+        packed_sequence_new = packed_sequence.new_zeros(packed_sequence.shape)
+        packed_sequence_und = self.mlp(packed_sequence[packed_und_token_indexes])
+        packed_sequence_gen = self.mlp_moe_gen(packed_sequence[packed_gen_token_indexes])
+        packed_sequence_new[packed_und_token_indexes] = packed_sequence_und
+        packed_sequence_new[packed_gen_token_indexes] = packed_sequence_gen
+
+        packed_sequence = residual + packed_sequence_new
+
+        return packed_sequence
+
+    def forward_inference(
+        self,
+        packed_query_sequence: torch.Tensor,
+        query_lens: torch.Tensor,
+        packed_query_position_embeddings: torch.Tensor,
+        packed_query_indexes: torch.Tensor,
+        past_key_values: Optional[NaiveCache] = None,
+        key_values_lens: Optional[torch.Tensor] = None,
+        packed_key_value_indexes: Optional[torch.Tensor] = None,
+        update_past_key_values=True,
+        is_causal=True,
+        mode="und",
+        packed_vae_token_indexes=None,
+        packed_text_indexes=None,
+    ) -> BaseNavitOutputWithPast:
+
+        residual = packed_query_sequence
+        packed_query_sequence = self.input_layernorm(packed_query_sequence)
+
+        # Self Attention
+        packed_query_sequence, past_key_values = self.self_attn(
+            packed_query_sequence=packed_query_sequence,
+            query_lens=query_lens,
+            packed_query_position_embeddings=packed_query_position_embeddings,
+            packed_query_indexes=packed_query_indexes,
+            past_key_values=past_key_values,
+            key_values_lens=key_values_lens,
+            packed_key_value_indexes=packed_key_value_indexes,
+            update_past_key_values=update_past_key_values,
+            is_causal=is_causal,
+        )
+        packed_query_sequence = residual + packed_query_sequence
+
+        # Fully Connected
+        residual = packed_query_sequence
+        packed_query_sequence = self.post_attention_layernorm(packed_query_sequence)
+        if mode == "und":
+            packed_query_sequence = self.mlp(packed_query_sequence)
+        elif mode == "gen":
+            packed_query_sequence_ = torch.zeros_like(packed_query_sequence).to(torch.bfloat16)
+            packed_query_sequence_[packed_text_indexes] = self.mlp(packed_query_sequence[packed_text_indexes])
+            packed_query_sequence_[packed_vae_token_indexes] = self.mlp_moe_gen(packed_query_sequence[packed_vae_token_indexes])
+            packed_query_sequence = packed_query_sequence_
+        packed_query_sequence = residual + packed_query_sequence
+
+        return packed_query_sequence, past_key_values
+
+
+Decoder_layer_dict = {
+    "Qwen2DecoderLayer": Qwen2DecoderLayer,
+    "Qwen2MoEDecoderLayer": Qwen2MoEDecoderLayer,
+    "Qwen2MoTDecoderLayer": partial(Qwen2MoTDecoderLayer, attn_module=PackedAttentionMoT),
+}
+
+
+class Qwen2Model(Qwen2PreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+        self.use_moe = 'Mo' in config.layer_module
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        layer_module = Decoder_layer_dict[config.layer_module]
+        self.layers = nn.ModuleList(
+            [layer_module(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+
+        self.norm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        if self.use_moe:
+            self.norm_moe_gen = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = Qwen2RotaryEmbedding(config=config)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward(self, *args, **kwargs):
+        if self.training:
+            return self.forward_train(*args, **kwargs)
+        else:
+            return self.forward_inference(*args, **kwargs)
+
+    def forward_train(
+        self,
+        packed_sequence: torch.Tensor,
+        sample_lens: List[int],
+        attention_mask,
+        packed_position_ids: torch.Tensor,
+        packed_und_token_indexes: Optional[torch.LongTensor] = None,
+        packed_gen_token_indexes: Optional[torch.LongTensor] = None,
+    ) -> torch.Tensor:
+
+        if self.config.freeze_und:
+            packed_sequence[packed_und_token_indexes] = packed_sequence[packed_und_token_indexes].detach()
+
+        # create position embeddings to be shared across the decoder layers
+        cos, sin = self.rotary_emb(packed_sequence, packed_position_ids.unsqueeze(0))
+        cos = cos.squeeze(0)
+        sin = sin.squeeze(0)
+        packed_position_embeddings = (cos, sin)
+
+        extra_inputs = {}
+        if self.use_moe:
+            assert packed_und_token_indexes is not None
+            if packed_gen_token_indexes is None:
+                packed_gen_token_indexes = packed_und_token_indexes.new_ones(size=[0])
+            extra_inputs.update(
+                packed_und_token_indexes=packed_und_token_indexes,
+                packed_gen_token_indexes=packed_gen_token_indexes,
+            )
+
+        for decoder_layer in self.layers:
+            packed_sequence = decoder_layer(
+                packed_sequence=packed_sequence,
+                sample_lens=sample_lens,
+                attention_mask=attention_mask,
+                packed_position_embeddings=packed_position_embeddings,
+                **extra_inputs
+            )
+
+        if self.use_moe:
+            packed_sequence_ = torch.zeros_like(packed_sequence)
+            packed_sequence_[packed_und_token_indexes] = self.norm(packed_sequence[packed_und_token_indexes])
+            if self.config.freeze_und:
+                packed_sequence_[packed_und_token_indexes] = packed_sequence_[packed_und_token_indexes].detach()
+            packed_sequence_[packed_gen_token_indexes] = self.norm_moe_gen(packed_sequence[packed_gen_token_indexes])
+            return packed_sequence_
+        else:
+            return self.norm(packed_sequence)
+
+    def forward_inference(
+        self,
+        packed_query_sequence: torch.Tensor,
+        query_lens: torch.Tensor,
+        packed_query_position_ids: torch.Tensor,
+        packed_query_indexes: torch.Tensor,
+        past_key_values: Optional[NaiveCache] = None,
+        key_values_lens: Optional[torch.Tensor] = None,
+        packed_key_value_indexes: Optional[torch.Tensor] = None,
+        update_past_key_values=True,
+        is_causal=True,
+        mode="und",
+        packed_vae_token_indexes=None,
+        packed_text_indexes=None,
+    ) -> BaseNavitOutputWithPast:
+
+        # create position embeddings to be shared across the decoder layers
+        cos, sin = self.rotary_emb(packed_query_sequence, packed_query_position_ids.unsqueeze(0))
+        cos = cos.squeeze(0)
+        sin = sin.squeeze(0)
+        packed_query_position_embeddings = (cos, sin)
+
+        extra_inputs = {}
+        if self.use_moe:
+            extra_inputs.update(mode=mode)
+            if mode == 'gen':
+                assert packed_vae_token_indexes is not None
+                assert packed_text_indexes is not None
+                extra_inputs.update(
+                    packed_vae_token_indexes=packed_vae_token_indexes,
+                    packed_text_indexes=packed_text_indexes,
+                )
+
+        for decoder_layer in self.layers:
+            packed_query_sequence, past_key_values = decoder_layer(
+                packed_query_sequence=packed_query_sequence,
+                query_lens=query_lens,
+                packed_query_position_embeddings=packed_query_position_embeddings,
+                packed_query_indexes=packed_query_indexes,
+                past_key_values=past_key_values,
+                key_values_lens=key_values_lens,
+                packed_key_value_indexes=packed_key_value_indexes,
+                update_past_key_values=update_past_key_values,
+                is_causal=is_causal,
+                **extra_inputs,
+            )
+
+        if self.use_moe:
+            if mode == "und":
+                packed_query_sequence = self.norm(packed_query_sequence)
+            elif mode == "gen":
+                packed_query_sequence_ = torch.zeros_like(packed_query_sequence)
+                packed_query_sequence_[packed_text_indexes] = self.norm(packed_query_sequence[packed_text_indexes])
+                packed_query_sequence_[packed_vae_token_indexes] = self.norm_moe_gen(packed_query_sequence[packed_vae_token_indexes])
+                packed_query_sequence = packed_query_sequence_
+        else:
+            packed_query_sequence = self.norm(packed_query_sequence)
+
+        return BaseNavitOutputWithPast(
+            packed_query_sequence=packed_query_sequence,
+            past_key_values=past_key_values,
+        )
+
+
+class Qwen2ForCausalLM(Qwen2PreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = Qwen2Model(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def init_moe(self):
+        for name, param in self.named_parameters():
+            if "moe_gen" in name:
+                original_name = name.replace("_moe_gen", "")
+                param.data.copy_(self.state_dict()[original_name].data)
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    def forward(self, *args, **kwargs):
+        if self.training:
+            return self.forward_train(*args, **kwargs)
+        else:
+            return self.forward_inference(*args, **kwargs)
+
+    def forward_train(
+        self,
+        packed_sequence: torch.Tensor,
+        sample_lens: List[int],
+        attention_mask,
+        packed_position_ids: torch.Tensor,
+        packed_und_token_indexes: Optional[torch.LongTensor] = None,
+        packed_gen_token_indexes: Optional[torch.LongTensor] = None,
+    ) -> torch.Tensor:
+
+        outputs = self.model(
+            packed_sequence=packed_sequence,
+            sample_lens=sample_lens,
+            packed_position_ids=packed_position_ids,
+            attention_mask=attention_mask,
+            packed_und_token_indexes=packed_und_token_indexes,
+            packed_gen_token_indexes=packed_gen_token_indexes,
+        )
+        return outputs
+
+    def forward_inference(
+        self,
+        packed_query_sequence: torch.Tensor,
+        query_lens: torch.Tensor,
+        packed_query_position_ids: torch.Tensor,
+        packed_query_indexes: torch.Tensor,
+        past_key_values: Optional[NaiveCache] = None,
+        key_values_lens: Optional[torch.Tensor] = None,
+        packed_key_value_indexes: Optional[torch.Tensor] = None,
+        update_past_key_values=True,
+        is_causal=True,
+        mode="und",
+        packed_vae_token_indexes=None,
+        packed_text_indexes=None,
+    ) -> BaseNavitOutputWithPast:
+
+        outputs = self.model(
+            packed_query_sequence=packed_query_sequence,
+            query_lens=query_lens,
+            packed_query_position_ids=packed_query_position_ids,
+            packed_query_indexes=packed_query_indexes,
+            past_key_values=past_key_values,
+            key_values_lens=key_values_lens,
+            packed_key_value_indexes=packed_key_value_indexes,
+            update_past_key_values=update_past_key_values,
+            is_causal=is_causal,
+            mode=mode,
+            packed_vae_token_indexes=packed_vae_token_indexes,
+            packed_text_indexes=packed_text_indexes,
+        )
+
+        return outputs