Replace Qwen3 configs/modeling with SDAR version

config.json

@@ -1,11 +1,11 @@
 {
   "architectures": [
-    "Qwen3ForCausalLM"
+    "SDARForCausalLM"
   ],
     "auto_map": {
-    "AutoConfig": "configuration_qwen3.Qwen3Config",
-    "AutoModel": "modeling_qwen3.Qwen3Model",
-    "AutoModelForCausalLM": "modeling_qwen3.Qwen3ForCausalLM"
+    "AutoConfig": "configuration_sdar.SDARConfig",
+    "AutoModel": "modeling_sdar.SDARModel",
+    "AutoModelForCausalLM": "modeling_sdar.SDARForCausalLM"
   },
   "attention_bias": false,
   "attention_dropout": 0.0,
@@ -19,7 +19,7 @@
   "intermediate_size": 6144,
   "max_position_embeddings": 32768,
   "max_window_layers": 28,
-  "model_type": "qwen3",
+  "model_type": "sdar",
   "num_attention_heads": 16,
   "num_hidden_layers": 28,
   "num_key_value_heads": 8,

configuration_qwen3.py → configuration_sdar.py

@@ -12,7 +12,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-"""Qwen3 model configuration"""
+"""SDAR model configuration"""
 
 from transformers.configuration_utils import PretrainedConfig
 from transformers.modeling_rope_utils import rope_config_validation
@@ -22,12 +22,12 @@ from transformers.utils import logging
 logger = logging.get_logger(__name__)
 
 
-class Qwen3Config(PretrainedConfig):
+class SDARConfig(PretrainedConfig):
     r"""
-    This is the configuration class to store the configuration of a [`Qwen3Model`]. It is used to instantiate a
-    Qwen3 model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    This is the configuration class to store the configuration of a [`SDARModel`]. It is used to instantiate a
+    SDAR model according to the specified arguments, defining the model architecture. Instantiating a configuration
     with the defaults will yield a similar configuration to that of
-    Qwen3-8B [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B).
+    SDAR-1.7B [DiffuOpen/SDAR-1.7B-Chat](https://huggingface.co/DiffuOpen/SDAR-1.7B-Chat/).
 
     Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
     documentation from [`PretrainedConfig`] for more information.
@@ -35,8 +35,8 @@ class Qwen3Config(PretrainedConfig):
 
     Args:
         vocab_size (`int`, *optional*, defaults to 151936):
-            Vocabulary size of the Qwen3 model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`Qwen3Model`]
+            Vocabulary size of the SDAR model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`SDARModel`]
         hidden_size (`int`, *optional*, defaults to 4096):
             Dimension of the hidden representations.
         intermediate_size (`int`, *optional*, defaults to 22016):
@@ -118,22 +118,22 @@ class Qwen3Config(PretrainedConfig):
             The dropout ratio for the attention probabilities.
 
     ```python
-    >>> from transformers import Qwen3Model, Qwen3Config
+    >>> from transformers import SDARModel, SDARConfig
 
-    >>> # Initializing a Qwen3 style configuration
-    >>> configuration = Qwen3Config()
+    >>> # Initializing a SDAR style configuration
+    >>> configuration = SDARConfig()
 
-    >>> # Initializing a model from the Qwen3-8B style configuration
-    >>> model = Qwen3Model(configuration)
+    >>> # Initializing a model from the SDAR-8B style configuration
+    >>> model = SDARModel(configuration)
 
     >>> # Accessing the model configuration
     >>> configuration = model.config
     ```"""
 
-    model_type = "qwen3"
+    model_type = "sdar"
     keys_to_ignore_at_inference = ["past_key_values"]
 
-    # Default tensor parallel plan for base model `Qwen3`
+    # Default tensor parallel plan for base model `SDAR`
     base_model_tp_plan = {
         "layers.*.self_attn.q_proj": "colwise",
         "layers.*.self_attn.k_proj": "colwise",
@@ -209,4 +209,4 @@ class Qwen3Config(PretrainedConfig):
         )
 
 
-__all__ = ["Qwen3Config"]
+__all__ = ["SDARConfig"]

modeling_qwen3_origin.py

@@ -1,1065 +0,0 @@
-#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
-#           This file was automatically generated from src/transformers/models/qwen3/modular_qwen3.py.
-#               Do NOT edit this file manually as any edits will be overwritten by the generation of
-#             the file from the modular. If any change should be done, please apply the change to the
-#                          modular_qwen3.py file directly. One of our CI enforces this.
-#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
-# coding=utf-8
-# Copyright 2025 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import Callable, Optional, Tuple, Union
-
-import torch
-from torch import nn
-from einops import rearrange
-
-from transformers.activations import ACT2FN
-from transformers.cache_utils import Cache, DynamicCache, SlidingWindowCache, StaticCache
-from transformers.generation import GenerationMixin
-from transformers.integrations import use_kernel_forward_from_hub
-from transformers.modeling_attn_mask_utils import AttentionMaskConverter
-from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
-from transformers.modeling_layers import GradientCheckpointingLayer
-from transformers.modeling_outputs import (
-    BaseModelOutputWithPast,
-    CausalLMOutputWithPast,
-    QuestionAnsweringModelOutput,
-    SequenceClassifierOutputWithPast,
-    TokenClassifierOutput,
-)
-from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
-from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
-from transformers.processing_utils import Unpack
-from transformers.utils import LossKwargs, auto_docstring, can_return_tuple, is_torch_flex_attn_available, logging
-from .configuration_qwen3 import Qwen3Config
-
-from torch.nn import CrossEntropyLoss
-from fla.modules.activations import swiglu_linear
-from fla.modules import FusedLinearDiffusionCrossEntropyLoss
-from flash_attn.ops.triton.layer_norm import rms_norm_fn as flash_rms_norm
-
-if is_torch_flex_attn_available():
-    from torch.nn.attention.flex_attention import BlockMask, flex_attention
-
-    from transformers.integrations.flex_attention import make_flex_block_causal_mask
-
-# flex attn needs torch.compile to accelerate
-@torch.compile(fullgraph=True, mode="max-autotune-no-cudagraphs")
-def fused_flex_attention(query, key, value, attention_mask, **kwargs):
-    return flex_attention(query, key, value, block_mask=attention_mask, **kwargs)
-
-logger = logging.get_logger(__name__)
-
-
-@use_kernel_forward_from_hub("RMSNorm")
-class Qwen3RMSNorm(nn.Module):
-    def __init__(self, hidden_size, eps=1e-6):
-        """
-        Qwen3RMSNorm is equivalent to T5LayerNorm
-        """
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(hidden_size))
-        self.variance_epsilon = eps
-
-    def forward(self, hidden_states):
-        input_dtype = hidden_states.dtype
-        # hidden_states = hidden_states.to(torch.float32)
-        # variance = hidden_states.pow(2).mean(-1, keepdim=True)
-        # hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
-        return flash_rms_norm(
-            x=hidden_states, weight=self.weight, bias=None, eps=self.variance_epsilon).to(input_dtype)
-
-    def extra_repr(self):
-        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
-
-
-class Qwen3MLP(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.hidden_size = config.hidden_size
-        self.intermediate_size = config.intermediate_size
-        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
-        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
-        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
-        self.act_fn = ACT2FN[config.hidden_act]
-
-    def forward(self, x):
-        # down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
-        down_proj = swiglu_linear(self.gate_proj(x), self.up_proj(x), 
-                                  self.down_proj.weight, self.down_proj.bias)
-        return down_proj
-
-
-def rotate_half(x):
-    """Rotates half the hidden dims of the input."""
-    x1 = x[..., : x.shape[-1] // 2]
-    x2 = x[..., x.shape[-1] // 2 :]
-    return torch.cat((-x2, x1), dim=-1)
-
-
-def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
-    """Applies Rotary Position Embedding to the query and key tensors.
-
-    Args:
-        q (`torch.Tensor`): The query tensor.
-        k (`torch.Tensor`): The key tensor.
-        cos (`torch.Tensor`): The cosine part of the rotary embedding.
-        sin (`torch.Tensor`): The sine part of the rotary embedding.
-        position_ids (`torch.Tensor`, *optional*):
-            Deprecated and unused.
-        unsqueeze_dim (`int`, *optional*, defaults to 1):
-            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
-            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
-            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
-            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
-            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
-            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
-    Returns:
-        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
-    """
-    cos = cos.unsqueeze(unsqueeze_dim)
-    sin = sin.unsqueeze(unsqueeze_dim)
-    q_embed = (q * cos) + (rotate_half(q) * sin)
-    k_embed = (k * cos) + (rotate_half(k) * sin)
-    return q_embed, k_embed
-
-
-def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
-    """
-    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
-    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
-    """
-    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
-    if n_rep == 1:
-        return hidden_states
-    hidden_states = hidden_states[:, :, None, :, :].expand(
-        batch, num_key_value_heads, n_rep, slen, head_dim)
-    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
-
-
-def eager_attention_forward(
-    module: nn.Module,
-    query: torch.Tensor,
-    key: torch.Tensor,
-    value: torch.Tensor,
-    attention_mask: Optional[torch.Tensor],
-    scaling: float,
-    dropout: float = 0.0,
-    **kwargs,
-):
-    key_states = repeat_kv(key, module.num_key_value_groups)
-    value_states = repeat_kv(value, module.num_key_value_groups)
-
-    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
-    if attention_mask is not None:
-        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
-        attn_weights = attn_weights + causal_mask
-
-    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
-    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
-    attn_output = torch.matmul(attn_weights, value_states)
-    attn_output = attn_output.transpose(1, 2).contiguous()
-
-    return attn_output, attn_weights
-
-
-class Qwen3Attention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-
-    def __init__(self, config: Qwen3Config, layer_idx: int):
-        super().__init__()
-        self.config = config
-        self.layer_idx = layer_idx
-        self.num_attention_heads = config.num_attention_heads
-        self.num_key_value_heads = config.num_key_value_heads
-        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
-        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
-        self.scaling = self.head_dim**-0.5
-        self.attention_dropout = config.attention_dropout
-        self.is_causal = False
-
-        self.q_proj = nn.Linear(
-            config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
-        )
-        self.k_proj = nn.Linear(
-            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
-        )
-        self.v_proj = nn.Linear(
-            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
-        )
-        self.o_proj = nn.Linear(
-            config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
-        )
-        self.q_norm = Qwen3RMSNorm(self.head_dim, eps=config.rms_norm_eps)  # unlike olmo, only on the head dim!
-        self.k_norm = Qwen3RMSNorm(self.head_dim, eps=config.rms_norm_eps)  # thus post q_norm does not need reshape
-        self.sliding_window = config.sliding_window
-        if not (
-            self.config.use_sliding_window
-            and getattr(self.config, "sliding_window", None) is not None
-            and self.layer_idx >= self.config.max_window_layers
-        ):
-            self.sliding_window = None
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        position_embeddings: Tuple[torch.Tensor, torch.Tensor],
-        attention_mask: Optional[torch.Tensor],
-        past_key_value: Optional[Cache] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        **kwargs: Unpack[FlashAttentionKwargs],
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        input_shape = hidden_states.shape[:-1]
-        bsz, q_len = input_shape
-        hidden_shape = (*input_shape, -1, self.head_dim)
-
-        query_states = self.q_norm(self.q_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
-        key_states = self.k_norm(self.k_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
-        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
-
-        cos, sin = position_embeddings
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
-
-        if past_key_value is not None:
-            # sin and cos are specific to RoPE models; cache_position needed for the static cache
-            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
-            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
-        attention_interface: Callable = eager_attention_forward
-        if self.config._attn_implementation != "eager":
-            if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False):
-                logger.warning_once(
-                    "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
-                    'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
-                )
-            else:
-                attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
-
-        if self.config._attn_implementation == 'flex_attention':
-            # Although there exists `flex_attention_forward` in `AttentionInterface`,
-            # we still use our customized `fused_flex_attention` for debugging.
-            pad_length = kwargs.get("pad_length", 0)
-            # Used for SFT (packing + varlen), seq_len changes at each step
-            # seq_len must be divisible by BLOCK_SIZE in flex attn
-            pad_q = torch.zeros(
-                bsz, self.num_attention_heads, pad_length, self.head_dim, device=query_states.device, dtype=query_states.dtype)
-            pad_kv = torch.zeros(
-                bsz, self.num_key_value_heads, pad_length, self.head_dim, device=query_states.device, dtype=query_states.dtype)
-            attn_output, attn_weights = fused_flex_attention(
-                query=torch.cat([query_states, pad_q], dim=2),
-                key=torch.cat([key_states, pad_kv], dim=2),
-                value=torch.cat([value_states, pad_kv], dim=2),
-                attention_mask=attention_mask,
-                enable_gqa=True,
-                scale=self.scaling,
-                return_lse=True
-            )
-        
-            attn_output = attn_output[..., :q_len, :].contiguous()
-            attn_weights = attn_weights.to(value_states.dtype)
-            attn_output = rearrange(attn_output, 'b h l d -> b l (h d)') 
-        else: 
-            attn_output, attn_weights = attention_interface(
-                self,
-                query_states,
-                key_states,
-                value_states,
-                attention_mask,
-                dropout=0.0 if not self.training else self.attention_dropout,
-                scaling=self.scaling,
-                sliding_window=self.sliding_window,  # diff with Llama
-                **kwargs,
-            )  # output: [b, l, h, d]
-            attn_output = attn_output.reshape(*input_shape, -1).contiguous()
-        attn_output = self.o_proj(attn_output)
-        return attn_output, attn_weights
-
-
-class Qwen3DecoderLayer(GradientCheckpointingLayer):
-    def __init__(self, config: Qwen3Config, layer_idx: int):
-        super().__init__()
-        self.hidden_size = config.hidden_size
-        self.self_attn = Qwen3Attention(config=config, layer_idx=layer_idx)
-        self.mlp = Qwen3MLP(config)
-        self.input_layernorm = Qwen3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.post_attention_layernorm = Qwen3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        if (
-            config.sliding_window and config._attn_implementation != "flash_attention_2"
-        ):  # diff with Llama is this warning
-            logger.warning_once(
-                f"Sliding Window Attention is enabled but not implemented for `{config._attn_implementation}`; "
-                "unexpected results may be encountered."
-            )
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
-        output_attentions: Optional[bool] = False,
-        use_cache: Optional[bool] = False,
-        cache_position: Optional[torch.LongTensor] = None,
-        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
-        **kwargs: Unpack[FlashAttentionKwargs],
-    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
-        residual = hidden_states
-        hidden_states = self.input_layernorm(hidden_states)
-
-        # Self Attention
-        hidden_states, self_attn_weights = self.self_attn(
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_value=past_key_value,
-            output_attentions=output_attentions,
-            use_cache=use_cache,
-            cache_position=cache_position,
-            position_embeddings=position_embeddings,
-            **kwargs,
-        )
-        hidden_states = residual + hidden_states
-
-        # Fully Connected
-        residual = hidden_states
-        hidden_states = self.post_attention_layernorm(hidden_states)
-        hidden_states = self.mlp(hidden_states)
-        hidden_states = residual + hidden_states
-
-        outputs = (hidden_states,)
-        if output_attentions:
-            outputs += (self_attn_weights,)
-
-        return outputs
-
-
-@auto_docstring
-class Qwen3PreTrainedModel(PreTrainedModel):
-    config_class = Qwen3Config
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["Qwen3DecoderLayer"]
-    _skip_keys_device_placement = ["past_key_values"]
-    _supports_flash_attn_2 = True
-    _supports_sdpa = True
-    _supports_flex_attn = True
-    _supports_cache_class = True
-    _supports_quantized_cache = True
-    _supports_static_cache = True
-    _supports_attention_backend = True
-
-    def _init_weights(self, module):
-        std = self.config.initializer_range
-        if isinstance(module, nn.Linear):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-        elif isinstance(module, Qwen3RMSNorm):
-            module.weight.data.fill_(1.0)
-
-
-class Qwen3RotaryEmbedding(nn.Module):
-    def __init__(self, config: Qwen3Config, device=None):
-        super().__init__()
-        # BC: "rope_type" was originally "type"
-        if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
-            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
-        else:
-            self.rope_type = "default"
-        self.max_seq_len_cached = config.max_position_embeddings
-        self.original_max_seq_len = config.max_position_embeddings
-
-        self.config = config
-        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
-
-        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.original_inv_freq = self.inv_freq
-
-    @torch.no_grad()
-    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
-    def forward(self, x, position_ids):
-        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
-        position_ids_expanded = position_ids[:, None, :].float()
-
-        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
-        with torch.autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
-            emb = torch.cat((freqs, freqs), dim=-1)
-            cos = emb.cos() * self.attention_scaling
-            sin = emb.sin() * self.attention_scaling
-
-        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
-
-
-@auto_docstring
-class Qwen3Model(Qwen3PreTrainedModel):
-    def __init__(self, config: Qwen3Config):
-        super().__init__(config)
-        self.padding_idx = config.pad_token_id
-        self.vocab_size = config.vocab_size
-
-        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
-        self.layers = nn.ModuleList(
-            [Qwen3DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
-        )
-        self.norm = Qwen3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.rotary_emb = Qwen3RotaryEmbedding(config=config)
-        self.gradient_checkpointing = False
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.embed_tokens = value
-
-    @can_return_tuple
-    @auto_docstring
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Cache] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        **flash_attn_kwargs: Unpack[FlashAttentionKwargs],
-    ) -> BaseModelOutputWithPast:
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-
-        if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-
-        if self.gradient_checkpointing and self.training and use_cache:
-            logger.warning_once(
-                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
-            )
-            use_cache = False
-
-        # TODO (joao): remove this exception in v4.56 -- it exists for users that try to pass a legacy cache
-        if not isinstance(past_key_values, (type(None), Cache)):
-            raise ValueError("The `past_key_values` should be either a `Cache` object or `None`.")
-
-        if inputs_embeds is None:
-            inputs_embeds = self.embed_tokens(input_ids)
-
-        if use_cache and past_key_values is None:
-            past_key_values = DynamicCache()
-
-        if cache_position is None:
-            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
-            cache_position = torch.arange(
-                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
-            )
-
-        if position_ids is None:
-            position_ids = cache_position.unsqueeze(0)
-
-        causal_mask = self._update_causal_mask(
-            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
-        )
-
-        hidden_states = inputs_embeds
-
-        # create position embeddings to be shared across the decoder layers
-        position_embeddings = self.rotary_emb(hidden_states, position_ids)
-
-        # decoder layers
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-
-        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
-            if output_hidden_states:
-                all_hidden_states += (hidden_states,)
-
-            layer_outputs = decoder_layer(
-                hidden_states,
-                attention_mask=causal_mask,
-                position_ids=position_ids,
-                past_key_value=past_key_values,
-                output_attentions=output_attentions,
-                use_cache=use_cache,
-                cache_position=cache_position,
-                position_embeddings=position_embeddings,
-                **flash_attn_kwargs,
-            )
-
-            hidden_states = layer_outputs[0]
-
-            if output_attentions:
-                all_self_attns += (layer_outputs[1],)
-
-        hidden_states = self.norm(hidden_states)
-
-        # add hidden states from the last decoder layer
-        if output_hidden_states:
-            all_hidden_states += (hidden_states,)
-
-        return BaseModelOutputWithPast(
-            last_hidden_state=hidden_states,
-            past_key_values=past_key_values if use_cache else None,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attns,
-        )
-
-    def _update_causal_mask(
-        self,
-        attention_mask: Union[torch.Tensor, "BlockMask"],
-        input_tensor: torch.Tensor,
-        cache_position: torch.Tensor,
-        past_key_values: Cache,
-        output_attentions: bool = False,
-    ):
-        if self.config._attn_implementation == "flash_attention_2":
-            if attention_mask is not None and past_key_values is not None:
-                is_padding_right = attention_mask[:, -1].sum().item() != input_tensor.size()[0]
-                if is_padding_right:
-                    raise ValueError(
-                        "You are attempting to perform batched generation with padding_side='right'"
-                        " this may lead to unexpected behaviour for Flash Attention version of Qwen3. Make sure to "
-                        " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
-                    )
-            if attention_mask is not None and 0.0 in attention_mask:
-                return attention_mask
-            return None
-        if self.config._attn_implementation == "flex_attention":
-            # Use flex block mask directly
-            assert isinstance(attention_mask, BlockMask)
-            return attention_mask
-
-        # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
-        # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
-        # to infer the attention mask.
-        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
-        using_static_cache = isinstance(past_key_values, StaticCache)
-        using_sliding_window_cache = isinstance(past_key_values, SlidingWindowCache)
-
-        # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
-        if (
-            self.config._attn_implementation == "sdpa"
-            and not (using_static_cache or using_sliding_window_cache)
-            and not output_attentions
-        ):
-            if AttentionMaskConverter._ignore_causal_mask_sdpa(
-                attention_mask,
-                inputs_embeds=input_tensor,
-                past_key_values_length=past_seen_tokens,
-                sliding_window=self.config.sliding_window,
-                is_training=self.training,
-            ):
-                return None
-
-        dtype = input_tensor.dtype
-        min_dtype = torch.finfo(dtype).min
-        sequence_length = input_tensor.shape[1]
-        # SlidingWindowCache or StaticCache
-        if using_sliding_window_cache or using_static_cache:
-            target_length = past_key_values.get_max_cache_shape()
-        # DynamicCache or no cache
-        else:
-            target_length = (
-                attention_mask.shape[-1]
-                if isinstance(attention_mask, torch.Tensor)
-                else past_seen_tokens + sequence_length + 1
-            )
-
-        # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
-        causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
-            attention_mask,
-            sequence_length=sequence_length,
-            target_length=target_length,
-            dtype=dtype,
-            cache_position=cache_position,
-            batch_size=input_tensor.shape[0],
-            config=self.config,
-            past_key_values=past_key_values,
-        )
-
-        if (
-            self.config._attn_implementation == "sdpa"
-            and attention_mask is not None
-            and attention_mask.device.type in ["cuda", "xpu", "npu"]
-            and not output_attentions
-        ):
-            # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
-            # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
-            # Details: https://github.com/pytorch/pytorch/issues/110213
-            causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
-
-        return causal_mask
-
-    @staticmethod
-    def _prepare_4d_causal_attention_mask_with_cache_position(
-        attention_mask: torch.Tensor,
-        sequence_length: int,
-        target_length: int,
-        dtype: torch.dtype,
-        cache_position: torch.Tensor,
-        batch_size: int,
-        config: Qwen3Config,
-        past_key_values: Cache,
-    ):
-        """
-        Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
-        `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
-
-        Args:
-            attention_mask (`torch.Tensor`):
-                A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`.
-            sequence_length (`int`):
-                The sequence length being processed.
-            target_length (`int`):
-                The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet.
-            dtype (`torch.dtype`):
-                The dtype to use for the 4D attention mask.
-            cache_position (`torch.Tensor`):
-                Indices depicting the position of the input sequence tokens in the sequence.
-            batch_size (`torch.Tensor`):
-                Batch size.
-            config (`Qwen3Config`):
-                The model's configuration class
-            past_key_values (`Cache`):
-                The cache class that is being used currently to generate
-        """
-        if attention_mask is not None and attention_mask.dim() == 4:
-            # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
-            causal_mask = attention_mask
-        else:
-            min_dtype = torch.finfo(dtype).min
-            causal_mask = torch.full(
-                (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
-            )
-            diagonal_attend_mask = torch.arange(target_length, device=cache_position.device) > cache_position.reshape(
-                -1, 1
-            )
-            text_config = config.get_text_config()
-            if getattr(text_config, "use_sliding_window", True) and text_config.sliding_window is not None:
-                # if we have sliding window, we should not attend to tokens beyond sliding window length, so we mask them out also
-                # the check is needed to verify is current checkpoint was trained with sliding window or not
-                if not isinstance(past_key_values, SlidingWindowCache) or sequence_length > target_length:
-                    sliding_attend_mask = torch.arange(target_length, device=cache_position.device) <= (
-                        cache_position.reshape(-1, 1) - text_config.sliding_window
-                    )
-                    diagonal_attend_mask.bitwise_or_(sliding_attend_mask)
-            causal_mask *= diagonal_attend_mask
-            causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
-            if attention_mask is not None:
-                causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
-                if attention_mask.shape[-1] > target_length:
-                    attention_mask = attention_mask[:, :target_length]
-                mask_length = attention_mask.shape[-1]
-                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
-                    causal_mask.device
-                )
-                padding_mask = padding_mask == 0
-                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
-                    padding_mask, min_dtype
-                )
-        return causal_mask
-
-
-class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ...
-
-
-@auto_docstring
-class Qwen3ForCausalLM(Qwen3PreTrainedModel, GenerationMixin):
-    _tied_weights_keys = ["lm_head.weight"]
-    _tp_plan = {"lm_head": "colwise_rep"}
-    _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.model = Qwen3Model(config)
-        self.vocab_size = config.vocab_size
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.model.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.model.embed_tokens = value
-
-    def get_output_embeddings(self):
-        return self.lm_head
-
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
-
-    def set_decoder(self, decoder):
-        self.model = decoder
-
-    def get_decoder(self):
-        return self.model
-
-    @can_return_tuple
-    @auto_docstring
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Cache] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-        logits_to_keep: Union[int, torch.Tensor] = 0,
-        **kwargs: Unpack[KwargsForCausalLM],
-    ) -> CausalLMOutputWithPast:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
-            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
-
-        Example:
-
-        ```python
-        >>> from transformers import AutoTokenizer, Qwen3ForCausalLM
-
-        >>> model = Qwen3ForCausalLM.from_pretrained("Qwen/Qwen3-8B")
-        >>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")
-
-        >>> prompt = "Hey, are you conscious? Can you talk to me?"
-        >>> inputs = tokenizer(prompt, return_tensors="pt")
-
-        >>> # Generate
-        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
-        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
-        ```"""
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-
-        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-        outputs: BaseModelOutputWithPast = self.model(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            cache_position=cache_position,
-            **kwargs,
-        )
-
-        hidden_states = outputs.last_hidden_state
-        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-        logits_to_keep = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
-        hidden_states = hidden_states[:, logits_to_keep, :].contiguous()
-        fuse_linear_and_cross_entropy = self.config.fuse_cross_entropy and self.training
-        if fuse_linear_and_cross_entropy:
-            logits = None
-        else:
-            logits = self.lm_head(hidden_states)
-
-        loss = None
-        if labels is not None:
-            if fuse_linear_and_cross_entropy:
-                loss_fct = FusedLinearDiffusionCrossEntropyLoss(
-                    reduction='sum')
-            else:
-                loss_fct = CrossEntropyLoss()  # nn.CE
-
-            # you don't have to shift labels
-            # labels = labels.to(hidden_states.device)
-            # labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], loss_fct.ignore_index)), 1)
-            if fuse_linear_and_cross_entropy:
-                loss = loss_fct(  # it will return (sum_loss, unreduced_loss)
-                    x=hidden_states,  # conduct `view(-1, V)` inside the function
-                    target=labels,
-                    weight=self.lm_head.weight,
-                    bias=self.lm_head.bias,
-                    p_mask=kwargs['p_mask'],
-                )
-            else:
-                loss = loss_fct(
-                    logits.view(-1, self.config.vocab_size), labels.view(-1))
-
-        return CausalLMOutputWithPast(
-            loss=loss,
-            logits=logits,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-@auto_docstring(
-    custom_intro="""
-    The Qwen3 Model transformer with a sequence classification head on top (linear layer).
-
-    [`Qwen3ForSequenceClassification`] uses the last token in order to do the classification, as other causal models
-    (e.g. GPT-2) do.
-
-    Since it does classification on the last token, it requires to know the position of the last token. If a
-    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
-    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
-    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
-    each row of the batch).
-    """
-)
-class Qwen3ForSequenceClassification(Qwen3PreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.model = Qwen3Model(config)
-        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.model.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.model.embed_tokens = value
-
-    @can_return_tuple
-    @auto_docstring
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Cache] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-    ) -> SequenceClassifierOutputWithPast:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-        """
-
-        transformer_outputs: BaseModelOutputWithPast = self.model(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-        )
-        hidden_states = transformer_outputs.last_hidden_state
-        logits = self.score(hidden_states)
-
-        if input_ids is not None:
-            batch_size = input_ids.shape[0]
-        else:
-            batch_size = inputs_embeds.shape[0]
-
-        if self.config.pad_token_id is None and batch_size != 1:
-            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
-        if self.config.pad_token_id is None:
-            last_non_pad_token = -1
-        elif input_ids is not None:
-            # To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
-            non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
-            token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
-            last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
-        else:
-            last_non_pad_token = -1
-            logger.warning_once(
-                f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
-                "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
-            )
-
-        pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
-
-        loss = None
-        if labels is not None:
-            loss = self.loss_function(logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config)
-
-        return SequenceClassifierOutputWithPast(
-            loss=loss,
-            logits=pooled_logits,
-            past_key_values=transformer_outputs.past_key_values,
-            hidden_states=transformer_outputs.hidden_states,
-            attentions=transformer_outputs.attentions,
-        )
-
-
-@auto_docstring
-class Qwen3ForTokenClassification(Qwen3PreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.model = Qwen3Model(config)
-        if getattr(config, "classifier_dropout", None) is not None:
-            classifier_dropout = config.classifier_dropout
-        elif getattr(config, "hidden_dropout", None) is not None:
-            classifier_dropout = config.hidden_dropout
-        else:
-            classifier_dropout = 0.1
-        self.dropout = nn.Dropout(classifier_dropout)
-        self.score = nn.Linear(config.hidden_size, config.num_labels)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.model.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.model.embed_tokens = value
-
-    @can_return_tuple
-    @auto_docstring
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Cache] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-    ) -> TokenClassifierOutput:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-        """
-
-        outputs: BaseModelOutputWithPast = self.model(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-        )
-        sequence_output = outputs.last_hidden_state
-        sequence_output = self.dropout(sequence_output)
-        logits = self.score(sequence_output)
-
-        loss = None
-        if labels is not None:
-            loss = self.loss_function(logits, labels, self.config)
-
-        return TokenClassifierOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-@auto_docstring
-class Qwen3ForQuestionAnswering(Qwen3PreTrainedModel):
-    base_model_prefix = "transformer"
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.transformer = Qwen3Model(config)
-        self.qa_outputs = nn.Linear(config.hidden_size, 2)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.transformer.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.transformer.embed_tokens = value
-
-    @can_return_tuple
-    @auto_docstring
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Cache] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        start_positions: Optional[torch.LongTensor] = None,
-        end_positions: Optional[torch.LongTensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        **kwargs,
-    ) -> QuestionAnsweringModelOutput:
-        outputs: BaseModelOutputWithPast = self.transformer(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-        )
-
-        sequence_output = outputs.last_hidden_state
-
-        logits = self.qa_outputs(sequence_output)
-        start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1).contiguous()
-        end_logits = end_logits.squeeze(-1).contiguous()
-
-        loss = None
-        if start_positions is not None and end_positions is not None:
-            loss = self.loss_function(start_logits, end_logits, start_positions, end_positions, **kwargs)
-
-        return QuestionAnsweringModelOutput(
-            loss=loss,
-            start_logits=start_logits,
-            end_logits=end_logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-__all__ = [
-    "Qwen3ForCausalLM",
-    "Qwen3ForQuestionAnswering",
-    "Qwen3Model",
-    "Qwen3PreTrainedModel",
-    "Qwen3ForSequenceClassification",
-    "Qwen3ForTokenClassification",
-]

modeling_qwen3.py → modeling_sdar.py

@@ -1,3 +1,5 @@
+# This file is modified based on https://github.com/huggingface/transformers/blob/v4.52.4/src/transformers/models/qwen3/modeling_qwen3.py.
+#
 #                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
 #           This file was automatically generated from src/transformers/models/qwen3/modular_qwen3.py.
 #               Do NOT edit this file manually as any edits will be overwritten by the generation of
@@ -42,7 +44,7 @@ from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_u
 from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
 from transformers.processing_utils import Unpack
 from transformers.utils import LossKwargs, auto_docstring, can_return_tuple, is_torch_flex_attn_available, logging
-from .configuration_qwen3 import Qwen3Config
+from .configuration_sdar import SDARConfig
 
 from fla.modules.activations import swiglu_linear
 from fla.modules import (
@@ -81,10 +83,10 @@ def fused_flex_attention(query, key, value, attention_mask=None, **kwargs):
 
 
 @use_kernel_forward_from_hub("RMSNorm")
-class Qwen3RMSNorm(nn.Module):
+class SDARRMSNorm(nn.Module):
     def __init__(self, hidden_size, eps=1e-6):
         """
-        Qwen3RMSNorm is equivalent to T5LayerNorm
+        SDARRMSNorm is equivalent to T5LayerNorm
         """
         super().__init__()
         self.weight = nn.Parameter(torch.ones(hidden_size))
@@ -107,7 +109,7 @@ class Qwen3RMSNorm(nn.Module):
         return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
 
 
-class Qwen3MLP(nn.Module):
+class SDARMLP(nn.Module):
     def __init__(self, config):
         super().__init__()
         self.config = config
@@ -205,10 +207,10 @@ def eager_attention_forward(
     return attn_output, attn_weights
 
 
-class Qwen3Attention(nn.Module):
+class SDARAttention(nn.Module):
     """Multi-headed attention from 'Attention Is All You Need' paper"""
 
-    def __init__(self, config: Qwen3Config, layer_idx: int):
+    def __init__(self, config: SDARConfig, layer_idx: int):
         super().__init__()
         self.config = config
         self.layer_idx = layer_idx
@@ -236,9 +238,9 @@ class Qwen3Attention(nn.Module):
             config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
         )
         # unlike olmo, only on the head dim!
-        self.q_norm = Qwen3RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+        self.q_norm = SDARRMSNorm(self.head_dim, eps=config.rms_norm_eps)
         # thus post q_norm does not need reshape
-        self.k_norm = Qwen3RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+        self.k_norm = SDARRMSNorm(self.head_dim, eps=config.rms_norm_eps)
         self.sliding_window = config.sliding_window
         if not (
             self.config.use_sliding_window
@@ -275,7 +277,8 @@ class Qwen3Attention(nn.Module):
             # sin and cos are specific to RoPE models; cache_position needed for the static cache
             key_states, value_states = past_key_value.update(
                 key_states, value_states, self.layer_idx)
-        elif past_key_value is not None and not kwargs.get("store_kv", False) and len(past_key_value) > self.layer_idx:# 只取不存
+        elif past_key_value is not None and not kwargs.get("store_kv", False) and len(past_key_value) > self.layer_idx:
+            # only retrive, do not store kv
             past_key_states, past_value_states = past_key_value[self.layer_idx]
             key_states = torch.cat(
                 [past_key_states, key_states], dim=-2
@@ -283,75 +286,9 @@ class Qwen3Attention(nn.Module):
             value_states = torch.cat(
                 [past_value_states, value_states], dim=-2
                 )
-        # if past_key_value is not None:
-        #     # sin and cos are specific to RoPE models; cache_position needed for the static cache
-        #     cache_kwargs = {"sin": sin, "cos": cos,
-        #                     "cache_position": cache_position}
-        #     key_states, value_states = past_key_value.update(
-        #         key_states, value_states, self.layer_idx, cache_kwargs)
-
-        # attention_interface: Callable = eager_attention_forward
-        # if self.config._attn_implementation != "eager":
-        #     if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False):
-        #         logger.warning_once(
-        #             "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
-        #             'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
-        #         )
-        #     else:
-        #         attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
-
-        # if self.config._attn_implementation == 'flex_attention':
-        #     # Although `AttentionInterface` has `flex_attention_forward` implementation,
-        #     # we still use our customized `fused_flex_attention`
-        #     pad_length = kwargs.get("pad_length", None)
-        #     if pad_length is not None:
-        #         # Used for SFT (packing + varlen), seq_len changes at each step
-        #         # seq_len must be divisible by BLOCK_SIZE in flex attn
-        #         pad_q = torch.zeros(
-        #             bsz, self.num_attention_heads, pad_length, self.head_dim, device=query_states.device, dtype=query_states.dtype)
-        #         pad_kv = torch.zeros(
-        #             bsz, self.num_key_value_heads, pad_length, self.head_dim, device=query_states.device, dtype=query_states.dtype)
-        #         attn_output, attn_weights = fused_flex_attention(
-        #             query=torch.cat([query_states, pad_q], dim=2),
-        #             key=torch.cat([key_states, pad_kv], dim=2),
-        #             value=torch.cat([value_states, pad_kv], dim=2),
-        #             attention_mask=attention_mask,
-        #             enable_gqa=True,
-        #             scale=self.scaling,
-        #             return_lse=True
-        #         )
-        #         attn_output = attn_output[..., :q_len,
-        #                                   :].transpose(1, 2).contiguous()
-        #         attn_weights = attn_weights.to(value_states.dtype)
-        #     else:
-        #         attn_output, attn_weights = fused_flex_attention(
-        #             query=query_states,
-        #             key=key_states,
-        #             value=value_states,
-        #             attention_mask=attention_mask,
-        #             enable_gqa=True,
-        #             scale=self.scaling,
-        #             return_lse=True
-        #         )
-        #         attn_output = attn_output.transpose(1, 2).contiguous()
-        #         attn_weights = attn_weights.to(value_states.dtype)
-        # else:
-        #     attn_output, attn_weights = attention_interface(
-        #         self,
-        #         query_states,
-        #         key_states,
-        #         value_states,
-        #         attention_mask,
-        #         dropout=0.0 if not self.training else self.attention_dropout,
-        #         scaling=self.scaling,
-        #         sliding_window=self.sliding_window,  # diff with Llama
-        #         **kwargs,
-        #    )
-        # q: (b, h, l, d); k,v: (b, h', l, d); attn_output: (b, l, h, d);
-        # key_states = repeat_kv(key_states, 2)
-        # value_states = repeat_kv(value_states, 2)
+        
         attention_mask = attention_mask.bool() if attention_mask is not None else None
-        if torch.all(attention_mask): # 属于 decoding 阶段
+        if torch.all(attention_mask):  # decoding
             query_states = query_states.transpose(1, 2)
             key_states = key_states.transpose(1, 2)
             value_states = value_states.transpose(1, 2)
@@ -362,7 +299,7 @@ class Qwen3Attention(nn.Module):
                 causal=False,
                 softmax_scale=self.scaling)
 
-        else:
+        else:  # prefilling
             attn_output = F.scaled_dot_product_attention(
                 query=query_states,
                 key=key_states,
@@ -378,15 +315,15 @@ class Qwen3Attention(nn.Module):
         return attn_output, None #, attn_weights
 
 
-class Qwen3DecoderLayer(GradientCheckpointingLayer):
-    def __init__(self, config: Qwen3Config, layer_idx: int):
+class SDARDecoderLayer(GradientCheckpointingLayer):
+    def __init__(self, config: SDARConfig, layer_idx: int):
         super().__init__()
         self.hidden_size = config.hidden_size
-        self.self_attn = Qwen3Attention(config=config, layer_idx=layer_idx)
-        self.mlp = Qwen3MLP(config)
-        self.input_layernorm = Qwen3RMSNorm(
+        self.self_attn = SDARAttention(config=config, layer_idx=layer_idx)
+        self.mlp = SDARMLP(config)
+        self.input_layernorm = SDARRMSNorm(
             config.hidden_size, eps=config.rms_norm_eps)
-        self.post_attention_layernorm = Qwen3RMSNorm(
+        self.post_attention_layernorm = SDARRMSNorm(
             config.hidden_size, eps=config.rms_norm_eps)
         if (
             config.sliding_window and config._attn_implementation != "flash_attention_2"
@@ -443,11 +380,11 @@ class Qwen3DecoderLayer(GradientCheckpointingLayer):
 
 
 @auto_docstring
-class Qwen3PreTrainedModel(PreTrainedModel):
-    config_class = Qwen3Config
+class SDARPreTrainedModel(PreTrainedModel):
+    config_class = SDARConfig
     base_model_prefix = "model"
     supports_gradient_checkpointing = True
-    _no_split_modules = ["Qwen3DecoderLayer"]
+    _no_split_modules = ["SDARDecoderLayer"]
     _skip_keys_device_placement = ["past_key_values"]
     _supports_flash_attn_2 = True
     _supports_sdpa = True
@@ -467,12 +404,12 @@ class Qwen3PreTrainedModel(PreTrainedModel):
             module.weight.data.normal_(mean=0.0, std=std)
             if module.padding_idx is not None:
                 module.weight.data[module.padding_idx].zero_()
-        elif isinstance(module, Qwen3RMSNorm):
+        elif isinstance(module, SDARRMSNorm):
             module.weight.data.fill_(1.0)
 
 
-class Qwen3RotaryEmbedding(nn.Module):
-    def __init__(self, config: Qwen3Config, device=None):
+class SDARRotaryEmbedding(nn.Module):
+    def __init__(self, config: SDARConfig, device=None):
         super().__init__()
         # BC: "rope_type" was originally "type"
         if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
@@ -512,8 +449,8 @@ class Qwen3RotaryEmbedding(nn.Module):
 
 
 @auto_docstring
-class Qwen3Model(Qwen3PreTrainedModel):
-    def __init__(self, config: Qwen3Config):
+class SDARModel(SDARPreTrainedModel):
+    def __init__(self, config: SDARConfig):
         super().__init__(config)
         self.padding_idx = config.pad_token_id
         self.vocab_size = config.vocab_size
@@ -521,11 +458,11 @@ class Qwen3Model(Qwen3PreTrainedModel):
         self.embed_tokens = nn.Embedding(
             config.vocab_size, config.hidden_size, self.padding_idx)
         self.layers = nn.ModuleList(
-            [Qwen3DecoderLayer(config, layer_idx)
+            [SDARDecoderLayer(config, layer_idx)
              for layer_idx in range(config.num_hidden_layers)]
         )
-        self.norm = Qwen3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.rotary_emb = Qwen3RotaryEmbedding(config=config)
+        self.norm = SDARRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = SDARRotaryEmbedding(config=config)
         self.gradient_checkpointing = False
 
         # Initialize weights and apply final processing
@@ -745,7 +682,7 @@ class Qwen3Model(Qwen3PreTrainedModel):
         dtype: torch.dtype,
         cache_position: torch.Tensor,
         batch_size: int,
-        config: Qwen3Config,
+        config: SDARConfig,
         past_key_values: Cache,
     ):
         """
@@ -765,7 +702,7 @@ class Qwen3Model(Qwen3PreTrainedModel):
                 Indices depicting the position of the input sequence tokens in the sequence.
             batch_size (`torch.Tensor`):
                 Batch size.
-            config (`Qwen3Config`):
+            config (`SDARConfig`):
                 The model's configuration class
             past_key_values (`Cache`):
                 The cache class that is being used currently to generate
@@ -814,14 +751,14 @@ class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs):
 
 
 @auto_docstring
-class Qwen3ForCausalLM(Qwen3PreTrainedModel, GenerationMixin):
+class SDARForCausalLM(SDARPreTrainedModel, GenerationMixin):
     _tied_weights_keys = ["lm_head.weight"]
     _tp_plan = {"lm_head": "colwise_rep"}
     _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
 
     def __init__(self, config):
         super().__init__(config)
-        self.model = Qwen3Model(config)
+        self.model = SDARModel(config)
         self.vocab_size = config.vocab_size
         self.lm_head = nn.Linear(
             config.hidden_size, config.vocab_size, bias=False)
@@ -873,10 +810,10 @@ class Qwen3ForCausalLM(Qwen3PreTrainedModel, GenerationMixin):
         Example:
 
         ```python
-        >>> from transformers import AutoTokenizer, Qwen3ForCausalLM
+        >>> from transformers import AutoTokenizer, SDARForCausalLM
 
-        >>> model = Qwen3ForCausalLM.from_pretrained("Qwen/Qwen3-8B")
-        >>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")
+        >>> model = SDARForCausalLM.from_pretrained("DiffuOpen/SDAR-1.7B-Chat")
+        >>> tokenizer = AutoTokenizer.from_pretrained("DiffuOpen/SDAR-1.7B-Chat")
 
         >>> prompt = "Hey, are you conscious? Can you talk to me?"
         >>> inputs = tokenizer(prompt, return_tensors="pt")
@@ -958,251 +895,8 @@ class Qwen3ForCausalLM(Qwen3PreTrainedModel, GenerationMixin):
         )
 
 
-@auto_docstring(
-    custom_intro="""
-    The Qwen3 Model transformer with a sequence classification head on top (linear layer).
-
-    [`Qwen3ForSequenceClassification`] uses the last token in order to do the classification, as other causal models
-    (e.g. GPT-2) do.
-
-    Since it does classification on the last token, it requires to know the position of the last token. If a
-    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
-    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
-    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
-    each row of the batch).
-    """
-)
-class Qwen3ForSequenceClassification(Qwen3PreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.model = Qwen3Model(config)
-        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.model.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.model.embed_tokens = value
-
-    @can_return_tuple
-    @auto_docstring
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Cache] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-    ) -> SequenceClassifierOutputWithPast:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-        """
-
-        transformer_outputs: BaseModelOutputWithPast = self.model(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-        )
-        hidden_states = transformer_outputs.last_hidden_state
-        logits = self.score(hidden_states)
-
-        if input_ids is not None:
-            batch_size = input_ids.shape[0]
-        else:
-            batch_size = inputs_embeds.shape[0]
-
-        if self.config.pad_token_id is None and batch_size != 1:
-            raise ValueError(
-                "Cannot handle batch sizes > 1 if no padding token is defined.")
-        if self.config.pad_token_id is None:
-            last_non_pad_token = -1
-        elif input_ids is not None:
-            # To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
-            non_pad_mask = (input_ids != self.config.pad_token_id).to(
-                logits.device, torch.int32)
-            token_indices = torch.arange(
-                input_ids.shape[-1], device=logits.device, dtype=torch.int32)
-            last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
-        else:
-            last_non_pad_token = -1
-            logger.warning_once(
-                f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
-                "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
-            )
-
-        pooled_logits = logits[torch.arange(
-            batch_size, device=logits.device), last_non_pad_token]
-
-        loss = None
-        if labels is not None:
-            loss = self.loss_function(
-                logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config)
-
-        return SequenceClassifierOutputWithPast(
-            loss=loss,
-            logits=pooled_logits,
-            past_key_values=transformer_outputs.past_key_values,
-            hidden_states=transformer_outputs.hidden_states,
-            attentions=transformer_outputs.attentions,
-        )
-
-
-@auto_docstring
-class Qwen3ForTokenClassification(Qwen3PreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.model = Qwen3Model(config)
-        if getattr(config, "classifier_dropout", None) is not None:
-            classifier_dropout = config.classifier_dropout
-        elif getattr(config, "hidden_dropout", None) is not None:
-            classifier_dropout = config.hidden_dropout
-        else:
-            classifier_dropout = 0.1
-        self.dropout = nn.Dropout(classifier_dropout)
-        self.score = nn.Linear(config.hidden_size, config.num_labels)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.model.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.model.embed_tokens = value
-
-    @can_return_tuple
-    @auto_docstring
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Cache] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-    ) -> TokenClassifierOutput:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-        """
-
-        outputs: BaseModelOutputWithPast = self.model(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-        )
-        sequence_output = outputs.last_hidden_state
-        sequence_output = self.dropout(sequence_output)
-        logits = self.score(sequence_output)
-
-        loss = None
-        if labels is not None:
-            loss = self.loss_function(logits, labels, self.config)
-
-        return TokenClassifierOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
-@auto_docstring
-class Qwen3ForQuestionAnswering(Qwen3PreTrainedModel):
-    base_model_prefix = "transformer"
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.transformer = Qwen3Model(config)
-        self.qa_outputs = nn.Linear(config.hidden_size, 2)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.transformer.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.transformer.embed_tokens = value
-
-    @can_return_tuple
-    @auto_docstring
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Cache] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        start_positions: Optional[torch.LongTensor] = None,
-        end_positions: Optional[torch.LongTensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        **kwargs,
-    ) -> QuestionAnsweringModelOutput:
-        outputs: BaseModelOutputWithPast = self.transformer(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-        )
-
-        sequence_output = outputs.last_hidden_state
-
-        logits = self.qa_outputs(sequence_output)
-        start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1).contiguous()
-        end_logits = end_logits.squeeze(-1).contiguous()
-
-        loss = None
-        if start_positions is not None and end_positions is not None:
-            loss = self.loss_function(
-                start_logits, end_logits, start_positions, end_positions, **kwargs)
-
-        return QuestionAnsweringModelOutput(
-            loss=loss,
-            start_logits=start_logits,
-            end_logits=end_logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-
 __all__ = [
-    "Qwen3ForCausalLM",
-    "Qwen3ForQuestionAnswering",
-    "Qwen3Model",
-    "Qwen3PreTrainedModel",
-    "Qwen3ForSequenceClassification",
-    "Qwen3ForTokenClassification",
+    "SDARForCausalLM",
+    "SDARModel",
+    "SDARPreTrainedModel",
 ]