from typing import Optional, Callable from typing_extensions import Unpack, Tuple import torch from torch import nn from transformers.models.qwen3.modeling_qwen3 import ( Qwen3RMSNorm, Qwen3RotaryEmbedding, Qwen3Config, Qwen3PreTrainedModel, Qwen3MLP, GradientCheckpointingLayer, FlashAttentionKwargs, rotate_half, eager_attention_forward, ALL_ATTENTION_FUNCTIONS, ) from transformers.modeling_outputs import CausalLMOutputWithPast from transformers.cache_utils import Cache def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): cos = cos.unsqueeze(unsqueeze_dim) sin = sin.unsqueeze(unsqueeze_dim) q_len = q.size(-2) q_embed = (q * cos[..., -q_len:, :]) + (rotate_half(q) * sin[..., -q_len:, :]) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed class Qwen3DFlashAttention(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.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) self.k_norm = Qwen3RMSNorm(self.head_dim, eps=config.rms_norm_eps) self.sliding_window = config.sliding_window if config.layer_types[layer_idx] == "sliding_attention" else None def forward( self, hidden_states: torch.Tensor, target_hidden: torch.Tensor, position_embeddings: tuple[torch.Tensor, torch.Tensor], attention_mask: Optional[torch.Tensor], past_key_values: Optional[Cache] = None, cache_position: Optional[torch.LongTensor] = None, **kwargs: Unpack[FlashAttentionKwargs], ) -> tuple[torch.Tensor, Optional[torch.Tensor]]: bsz, q_len = hidden_states.shape[:-1] ctx_len = target_hidden.shape[1] q = self.q_proj(hidden_states) q = q.view(bsz, q_len, -1, self.head_dim) q = self.q_norm(q).transpose(1, 2) k_ctx = self.k_proj(target_hidden) k_noise = self.k_proj(hidden_states) v_ctx = self.v_proj(target_hidden) v_noise = self.v_proj(hidden_states) k = torch.cat([k_ctx, k_noise], dim=1).view(bsz, ctx_len + q_len, -1, self.head_dim) v = torch.cat([v_ctx, v_noise], dim=1).view(bsz, ctx_len + q_len, -1, self.head_dim) k = self.k_norm(k).transpose(1, 2) v = v.transpose(1, 2) cos, sin = position_embeddings q, k = apply_rotary_pos_emb(q, k, cos, sin) if past_key_values is not None: cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} k, v = past_key_values.update(k, v, self.layer_idx, cache_kwargs) attn_fn: Callable = eager_attention_forward if self.config._attn_implementation != "eager": attn_fn = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation] attn_output, attn_weights = attn_fn( self, q, k, v, attention_mask, dropout=0.0 if not self.training else self.attention_dropout, scaling=self.scaling, sliding_window=self.sliding_window, **kwargs, ) attn_output = attn_output.reshape(bsz, q_len, -1) attn_output = self.o_proj(attn_output) return attn_output, attn_weights class Qwen3DFlashDecoderLayer(GradientCheckpointingLayer): def __init__(self, config: Qwen3Config, layer_idx: int): super().__init__() self.hidden_size = config.hidden_size self.self_attn = Qwen3DFlashAttention(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) def forward( self, target_hidden: Optional[torch.Tensor] = None, hidden_states: Optional[torch.Tensor] = None, 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) hidden_states = self.self_attn( hidden_states=hidden_states, target_hidden=target_hidden, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_value, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position, position_embeddings=position_embeddings, **kwargs, )[0] hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states return hidden_states class DFlashDraftModel(Qwen3PreTrainedModel): config_class = Qwen3Config _no_split_modules = ["Qwen3DFlashDecoderLayer"] def __init__(self, config) -> None: super().__init__(config) self.config = config self.layers = nn.ModuleList( [Qwen3DFlashDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] ) self.target_layer_ids = self.config.dflash_config.get("target_layer_ids", None) self.norm = Qwen3RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.rotary_emb = Qwen3RotaryEmbedding(config) self.fc = nn.Linear(len(self.target_layer_ids) * config.hidden_size, config.hidden_size, bias=False) self.hidden_norm = Qwen3RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.block_size = config.block_size self.mask_token_id = self.config.dflash_config.get("mask_token_id", None) self.post_init() def forward( self, position_ids: torch.LongTensor, attention_mask: Optional[torch.Tensor] = None, noise_embedding: Optional[torch.Tensor] = None, target_hidden: Optional[torch.Tensor] = None, past_key_values: Optional[Cache] = None, use_cache: bool = False, **kwargs, ) -> CausalLMOutputWithPast: hidden_states = noise_embedding target_hidden = self.hidden_norm(self.fc(target_hidden)) position_embeddings = self.rotary_emb(hidden_states, position_ids) for layer in self.layers: hidden_states = layer( hidden_states=hidden_states, target_hidden=target_hidden, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_values, use_cache=use_cache, position_embeddings=position_embeddings, **kwargs, ) return self.norm(hidden_states)