exdysa
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TraDo-4B-Instruct

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-# SPDX-License-Identifier: MIT
-# Adapted from https://huggingface.co/Gen-Verse/TraDo-8B-Instruct/blob/main/modeling_sdar.py
-# This file is modified based on https://github.com/huggingface/transformers/blob/v4.52.4/src/transformers/models/qwen3/modeling_qwen3.py.
-#
-# 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
-from nnll.init_gpu import device
-import torch
-from torch import nn
-import torch.nn.functional as F
-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_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 (
-    auto_docstring,
-    can_return_tuple,
-    is_torch_flex_attn_available,
-    logging,
-)
-from divisor.trado.configuration_sdar import SDARConfig
-logger = logging.get_logger(__name__)
-# Make FlashAttentionKwargs available for all devices (used in type hints)
-try:
-    from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
-except ImportError:
-    # Fallback if not available
-    from typing import TypedDict
-    FlashAttentionKwargs = TypedDict("FlashAttentionKwargs", {})
-# Conditionally import flash attention components (CUDA only)
-flash_rms_norm = None
-flash_attn_func = None
-flash_attn_varlen_func = None
-index_first_axis = None
-pad_input = None
-unpad_input = None
-if device.type == "cuda":
-    try:
-        from flash_attn.ops.triton.layer_norm import rms_norm_fn as flash_rms_norm
-    except (ImportError, ModuleNotFoundError):
-        logger.warning("Flash attention RMS norm not available. Falling back to standard implementation.")
-        flash_rms_norm = None
-    try:
-        from flash_attn import flash_attn_func, flash_attn_varlen_func
-        from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input
-    except (ImportError, ModuleNotFoundError):
-        logger.warning("Flash attention not available. Falling back to standard attention.")
-        flash_attn_func = None
-        flash_attn_varlen_func = None
-if is_torch_flex_attn_available():
-    try:
-        from torch.nn.attention.flex_attention import BlockMask, create_block_mask, flex_attention
-        from transformers.integrations.flex_attention import make_flex_block_causal_mask
-    except ImportError:
-        pass
-try:
-    from liger_kernel.ops.swiglu import LigerSiLUMulFunction  # noqa: F401
-    liger_kernel_is_available = True
-except ImportError:
-    liger_kernel_is_available = False
-@use_kernel_forward_from_hub("RMSNorm")
-class SDARRMSNorm(nn.Module):
-    def __init__(self, hidden_size, eps=1e-6):
-        """
-        SDARRMSNorm is equivalent to T5LayerNorm
-        """
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(hidden_size))
-        self.variance_epsilon = eps
-    def forward(self, hidden_states):
-        # Use flash RMS norm if available (CUDA only), otherwise fall back to standard implementation
-        if flash_rms_norm is not None and hidden_states.device.type == "cuda":
-            try:
-                return flash_rms_norm(hidden_states, weight=self.weight, bias=None, eps=self.variance_epsilon)
-            except Exception as e:
-                logger.warning(f"Flash RMS norm failed ({e}). Falling back to standard implementation.")
-                # Fall through to standard implementation
-        # Standard RMS norm implementation (fallback for MPS, CPU, or when flash_rms_norm fails)
-        input_dtype = hidden_states.dtype
-        hidden_states = hidden_states.to(torch.float32)
-        variance = hidden_states.pow(2).mean(-1, keepdim=True)
-        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
-        return self.weight * hidden_states.to(input_dtype)
-    def extra_repr(self):
-        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
-class SDARMLP(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):
-        if liger_kernel_is_available:
-            return self.down_proj(LigerSiLUMulFunction.apply(self.gate_proj(x), self.up_proj(x)))
-        else:
-            down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
-            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 SDARAttention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-    def __init__(self, config: SDARConfig, layer_idx: int):
-        super().__init__()
-        self.config = config
-        self.layer_idx = layer_idx
-        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
-        self.num_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 = True
-        self.hidden_size = config.hidden_size
-        self.num_attention_heads = config.num_attention_heads
-        self.num_key_value_heads = config.num_key_value_heads
-        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)
-        # unlike olmo, only on the head dim!
-        self.q_norm = SDARRMSNorm(self.head_dim, eps=config.rms_norm_eps)
-        # thus post q_norm does not need reshape
-        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 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 and kwargs.get("store_kv", False):
-            # 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:
-            # 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)
-            value_states = torch.cat([past_value_states, value_states], dim=-2)
-        """
-        attention_mask = attention_mask.bool() if attention_mask is not None else None
-        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)
-            attn_output = flash_attn_func(
-                query_states,
-                key_states,
-                value_states,
-                causal=False,
-                softmax_scale=self.scaling
-            )
-        else:  # prefilling
-            attn_output = F.scaled_dot_product_attention(
-                query=query_states,
-                key=key_states,
-                value=value_states,
-                attn_mask=attention_mask,
-                is_causal=False,
-                scale=self.scaling,
-                enable_gqa=True
-            )
-            attn_output = attn_output.transpose(1, 2).contiguous()
-        """
-        # print(query_states.shape, key_states.shape, value_states.shape)
-        # --- After RoPE and KV-cache handling, expand KV to all heads ---
-        key_states = repeat_kv(key_states, self.num_key_value_groups)  # [B, H, K, D]
-        value_states = repeat_kv(value_states, self.num_key_value_groups)  # [B, H, K, D]
-        # --- Convert a 0/1 or bool 4D mask into an *additive* mask, and align to [B, H, Q, K] ---
-        attn_mask = None
-        if attention_mask is not None:
-            k_len = key_states.shape[-2]
-            am = attention_mask
-            # Support either 2D [B, K] or 4D [B, 1/H, Q, K]
-            if am.dim() == 2:
-                am = am[:, None, None, :k_len]  # -> [B,1,1,K]
-            else:
-                am = am[:, :, :, :k_len]  # -> [B,1/H,Q,K]
-            finfo_min = torch.finfo(query_states.dtype).min
-            # 0/1 or bool -> float additive mask: 1->0, 0->-inf
-            if am.dtype == torch.bool:
-                zero = torch.zeros((), dtype=query_states.dtype, device=am.device)
-                neginf = torch.full((), finfo_min, dtype=query_states.dtype, device=am.device)
-                am = torch.where(am, zero, neginf)
-            else:
-                # For 0/1 float masks: values > 0 are treated as visible
-                am = am.to(query_states.dtype)
-                am = torch.where(am > 0, torch.zeros_like(am), torch.full_like(am, finfo_min))
-            # Expand to all heads
-            # if am.shape[1] == 1 and self.num_attention_heads > 1:
-            #    am = am.expand(am.shape[0], self.num_attention_heads, am.shape[2], am.shape[3])
-            # attn_mask = am.contiguous()
-            attn_mask = am
-        bsz, q_len = input_shape
-        if q_len == 1 and past_key_value is not None:
-            # --- Decoding: try flash-attn if available (CUDA only), otherwise fall back to SDPA ---
-            if flash_attn_func is not None and query_states.device.type == "cuda":
-                try:
-                    q = query_states.transpose(1, 2)  # [B,Q,H,D]
-                    k = key_states.transpose(1, 2)
-                    v = value_states.transpose(1, 2)
-                    attn_output = flash_attn_func(
-                        q,
-                        k,
-                        v,
-                        causal=True,  # For decoding, explicitly set causal=True
-                        softmax_scale=self.scaling,
-                    )
-                    attn_output = attn_output.transpose(1, 2).contiguous()
-                except Exception as e:
-                    logger.warning(f"Flash attention failed during decoding ({e}). Falling back to SDPA.")
-                    # Fall through to SDPA implementation below
-                    attn_output = F.scaled_dot_product_attention(
-                        query=query_states,  # [B,H,Q,D]
-                        key=key_states,  # [B,H,K,D]
-                        value=value_states,  # [B,H,K,D]
-                        attn_mask=attn_mask,  # float additive mask
-                        is_causal=False,  # All constraints are already encoded in the mask
-                        scale=self.scaling,
-                    )
-                    attn_output = attn_output.transpose(1, 2).contiguous()  # -> [B,Q,H,D]
-            else:
-                # Fallback to SDPA for MPS, CPU, or when flash_attn_func is not available
-                attn_output = F.scaled_dot_product_attention(
-                    query=query_states,  # [B,H,Q,D]
-                    key=key_states,  # [B,H,K,D]
-                    value=value_states,  # [B,H,K,D]
-                    attn_mask=attn_mask,  # float additive mask
-                    is_causal=False,  # All constraints are already encoded in the mask
-                    scale=self.scaling,
-                )
-                attn_output = attn_output.transpose(1, 2).contiguous()  # -> [B,Q,H,D]
-        else:
-            attn_output = F.scaled_dot_product_attention(
-                query=query_states,  # [B,H,Q,D]
-                key=key_states,  # [B,H,K,D]
-                value=value_states,  # [B,H,K,D]
-                attn_mask=attn_mask,  # float additive mask
-                is_causal=False,  # All constraints are already encoded in the mask
-                scale=self.scaling,
-            )
-            attn_output = attn_output.transpose(1, 2).contiguous()  # -> [B,Q,H,D]
-        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
-        attn_output = self.o_proj(attn_output)
-        return attn_output, None  # , attn_weights
-class SDARDecoderLayer(GradientCheckpointingLayer):
-    def __init__(self, config: SDARConfig, layer_idx: int):
-        super().__init__()
-        self.hidden_size = config.hidden_size
-        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 = SDARRMSNorm(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,
-        store_kv: Optional[bool] = False,
-        cache_position: Optional[torch.LongTensor] = None,
-        # necessary, but kept here for BC
-        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-        **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,
-            store_kv=store_kv,
-            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 SDARPreTrainedModel(PreTrainedModel):
-    config_class = SDARConfig
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["SDARDecoderLayer"]
-    _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, SDARRMSNorm):
-            module.weight.data.fill_(1.0)
-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:
-            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()
-    # power user: used with advanced RoPE types (e.g. dynamic rope)
-    @dynamic_rope_update
-    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 SDARModel(SDARPreTrainedModel):
-    def __init__(self, config: SDARConfig):
-        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([SDARDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)])
-        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
-        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,
-        store_kv: 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=attention_mask,
-                position_ids=position_ids,
-                past_key_value=past_key_values,
-                output_attentions=output_attentions,
-                use_cache=use_cache,
-                store_kv=store_kv,
-                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":
-            if isinstance(attention_mask, torch.Tensor):
-                seq_len_q, seq_len_kv = attention_mask.shape
-                assert seq_len_q == seq_len_kv, f"got {attention_mask.shape=}"
-                attention_mask = create_block_mask(
-                    # 2d bool tensor, shape: [2*seqlen, 2*seqlen]
-                    lambda b, h, q_idx, kv_idx: attention_mask[q_idx, kv_idx],
-                    B=None,
-                    H=None,
-                    Q_LEN=seq_len_q,
-                    KV_LEN=seq_len_kv,
-                )
-            else:
-                # Here we pass in flex mask computed externally
-                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", "mps"] 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: SDARConfig,
-        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 (`SDARConfig`):
-                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
-@auto_docstring
-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 = SDARModel(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: dict,
-    ) -> 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, SDARForCausalLM
-        >>> 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")
-        >>> # 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
-        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
-        hidden_states = hidden_states[:, slice_indices, :].contiguous()
-        fuse_linear_and_cross_entropy = self.config.fuse_cross_entropy and self.training
-        if fuse_linear_and_cross_entropy:
-            # When using fused_linear_ce_loss, we do not compute the whole logits on HBM
-            logits = None
-        else:
-            logits = self.lm_head(hidden_states)
-        loss = None
-        if labels is not None:
-            # FusedLinearCrossEntropyLoss will be implemented by monkey patch when training
-            # We don't use it when inferencing
-            loss_fct = nn.CrossEntropyLoss()  # nn.CE
-            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,
-        )
-__all__ = [
-    "SDARForCausalLM",
-    "SDARModel",
-    "SDARPreTrainedModel",
-]