# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import io from collections.abc import Iterable import torch import torch.nn.functional as F from torch import nn from transformers import Qwen3Config from vllm import _custom_ops as ops from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig, VllmConfig, get_current_vllm_config from vllm.distributed import ( get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size, ) from vllm.logger import init_logger from vllm.model_executor.layers.attention import Attention from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.linear import ( QKVParallelLinear, ReplicatedLinear, RowParallelLinear, ) from vllm.model_executor.layers.logits_processor import LogitsProcessor from vllm.model_executor.layers.quantization.base_config import QuantizationConfig from vllm.model_executor.layers.rotary_embedding import get_rope from vllm.model_executor.layers.vocab_parallel_embedding import ( ParallelLMHead, VocabParallelEmbedding, ) from vllm.model_executor.model_loader.weight_utils import ( default_weight_loader, maybe_remap_kv_scale_name, ) from vllm.multimodal.inputs import NestedTensors from vllm.transformers_utils.config import set_default_rope_theta from vllm.transformers_utils.repo_utils import get_hf_file_bytes from vllm.v1.attention.backend import AttentionType from .qwen2 import Qwen2MLP as Qwen3MLP from .qwen3 import Qwen3ForCausalLM from .utils import ( AutoWeightsLoader, get_draft_quant_config, maybe_prefix, process_eagle_weight, ) logger = init_logger(__name__) def _resolve_layer_attention( config: Qwen3Config, layer_idx: int ) -> tuple[int | None, bool]: """Resolve ``(sliding_window, causal)`` for one DFlash draft layer. +----------------------+-------------------------+--------------------------------+ | Config | ``layer_type`` | *``causal`` | +======================+=========================+================================+ | ``layer_types`` | SWA if ``use_swa`` | True if ``layer_types[i]=SWA`` | | | else ``layer_types[i]`` | else False | +----------------------+-------------------------+--------------------------------+ | ``layer_types=None`` | SWA | False | | + ``use_swa=True`` | | | +----------------------+-------------------------+--------------------------------+ | ``layer_types=None`` | Full | False | | + ``use_swa=False`` | | | +----------------------+-------------------------+--------------------------------+ * If ``dflash_config.causal`` is set, its value overrides ``causal`` for all layers. This is to support a varied ecosystem of checkpoints, including: - XiaomiMiMo/MiMo-V2.5-Pro-FP4-DFlash (sets "use_swa", assumes non-causal) - z-lab/gemma-4-31B-it-DFlash (has mixed layer types, assumes causal only for SWA) - z-lab/Qwen3.5-9B-DFlash ("standard" DFlash, all full attn, assumes non-causal) """ dflash_config = getattr(config, "dflash_config", None) or {} layer_types = getattr(config, "layer_types", None) use_swa = dflash_config.get("use_swa", False) config_causal = dflash_config.get("causal", None) SLIDING_ATTENTION = "sliding_attention" any_sliding = False if layer_types is not None: num_sliding = sum(lt == SLIDING_ATTENTION for lt in layer_types) any_sliding = num_sliding > 0 # Mixed sliding/full attention needs multiple KV groups (V2 runner only). if ( 0 < num_sliding < len(layer_types) and not get_current_vllm_config().use_v2_model_runner ): raise NotImplementedError( "DFlash drafters with mixed sliding/full attention require " "the V2 model runner; relaunch with " "VLLM_USE_V2_MODEL_RUNNER=1." ) default_causal = False if layer_types is None or (use_swa and not any_sliding): # An absent ``layer_types`` (or the all-"full_attention" one that may # be synthesized when the checkpoint omits it) must not override # ``dflash_config.use_swa``, which forces SWA on every layer. is_sliding = use_swa else: is_sliding = layer_types[layer_idx] == SLIDING_ATTENTION # Full-attention layers default non-causal; SWA layers default causal. default_causal = is_sliding sliding_window = None if is_sliding: sliding_window = dflash_config.get( "swa_window_size", getattr(config, "sliding_window", None) ) if sliding_window is None: raise ValueError( "DFlash sliding attention requires a window size configured in " "dflash_config.swa_window_size or the top-level sliding_window." ) causal = config_causal if config_causal is not None else default_causal return sliding_window, causal class DFlashQwen3Attention(nn.Module): """Attention for DFlash speculative decoding. Context KVs are pre-inserted into the KV cache before the forward pass. This layer handles only query tokens via standard attention. Adapted from Qwen3Attention.""" def __init__( self, hidden_size: int, num_heads: int, num_kv_heads: int, rope_parameters: dict, max_position: int = 4096 * 32, head_dim: int | None = None, rms_norm_eps: float = 1e-06, attention_bias: bool = False, add_swa_attention_sink_bias: bool = False, sliding_window: int | None = None, causal: bool = False, cache_config: CacheConfig | None = None, quant_config: QuantizationConfig | None = None, prefix: str = "", attn_type: str = AttentionType.DECODER, ) -> None: super().__init__() self.layer_name = prefix self.hidden_size = hidden_size tp_size = get_tensor_model_parallel_world_size() self.total_num_heads = num_heads assert self.total_num_heads % tp_size == 0 self.num_heads = self.total_num_heads // tp_size self.total_num_kv_heads = num_kv_heads if self.total_num_kv_heads >= tp_size: assert self.total_num_kv_heads % tp_size == 0 else: assert tp_size % self.total_num_kv_heads == 0 self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size) self.head_dim = head_dim or hidden_size // self.total_num_heads self.q_size = self.num_heads * self.head_dim self.kv_size = self.num_kv_heads * self.head_dim self.scaling = self.head_dim**-0.5 self.qkv_proj = QKVParallelLinear( hidden_size, self.head_dim, self.total_num_heads, self.total_num_kv_heads, bias=attention_bias, quant_config=quant_config, prefix=f"{prefix}.qkv_proj", ) self.o_proj = RowParallelLinear( self.total_num_heads * self.head_dim, hidden_size, bias=attention_bias, # DFlash has o_proj bias when using attention bias quant_config=quant_config, prefix=f"{prefix}.o_proj", ) self.rotary_emb = get_rope( self.head_dim, max_position=max_position, rope_parameters=rope_parameters, ) self.attention_sink_bias = ( torch.nn.Parameter(torch.empty(self.num_heads), requires_grad=False) if add_swa_attention_sink_bias else None ) self.sliding_window = sliding_window self.attn = Attention( self.num_heads, self.head_dim, self.scaling, num_kv_heads=self.num_kv_heads, cache_config=cache_config, quant_config=quant_config, per_layer_sliding_window=sliding_window, prefix=f"{prefix}.attn", attn_type=attn_type, sinks=self.attention_sink_bias, ) self.causal = causal self.q_norm = RMSNorm(self.head_dim, eps=rms_norm_eps) self.k_norm = RMSNorm(self.head_dim, eps=rms_norm_eps) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, ) -> torch.Tensor: """DFlash attention assumes that the KV cache is already populated with the context K/V from the target model's hidden states. This forward op computes attention for the query tokens only. See also: precompute_and_store_context_kv""" qkv, _ = self.qkv_proj(hidden_states) q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1) # Per-head RMSNorm q_shape, k_shape = q.shape, k.shape q = self.q_norm( q.view(*q_shape[:-1], q_shape[-1] // self.head_dim, self.head_dim) ).view(q_shape) k = self.k_norm( k.view(*k_shape[:-1], k_shape[-1] // self.head_dim, self.head_dim) ).view(k_shape) q, k = self.rotary_emb(positions, q, k) attn_output = self.attn(q, k, v) output, _ = self.o_proj(attn_output) return output class DFlashQwen3DecoderLayer(nn.Module): def __init__( self, vllm_config: VllmConfig, *, config: Qwen3Config, layer_idx: int, cache_config: CacheConfig | None = None, quant_config: QuantizationConfig | None = None, prefix: str = "", ) -> None: super().__init__() self.hidden_size = config.hidden_size set_default_rope_theta(config, default_theta=1000000) attn_type = AttentionType.DECODER # DFlash drafts store the sink-bias flag inside dflash_config; fall back # to the top-level attribute used by other (e.g. MiMo) configs. dflash_config = getattr(config, "dflash_config", None) or {} add_swa_attention_sink_bias = dflash_config.get( "attention_sink_bias", getattr(config, "add_swa_attention_sink_bias", False), ) # Resolve this layer's attention mode (full vs sliding window, causal vs # non-causal) from the draft config. sliding_window, causal = _resolve_layer_attention(config, layer_idx) self.self_attn = DFlashQwen3Attention( hidden_size=self.hidden_size, num_heads=config.num_attention_heads, max_position=config.max_position_embeddings, num_kv_heads=config.num_key_value_heads, rms_norm_eps=config.rms_norm_eps, attention_bias=getattr(config, "attention_bias", False), add_swa_attention_sink_bias=add_swa_attention_sink_bias, sliding_window=sliding_window, causal=causal, head_dim=getattr(config, "head_dim", None), cache_config=cache_config, quant_config=quant_config, rope_parameters=config.rope_parameters, prefix=f"{prefix}.self_attn", attn_type=attn_type, ) self.mlp = Qwen3MLP( hidden_size=self.hidden_size, intermediate_size=config.intermediate_size, hidden_act=config.hidden_act, quant_config=quant_config, prefix=f"{prefix}.mlp", ) self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm = RMSNorm( config.hidden_size, eps=config.rms_norm_eps ) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, residual: torch.Tensor | None, ) -> tuple[torch.Tensor, torch.Tensor]: if residual is not None: hidden_states, residual = self.input_layernorm(hidden_states, residual) else: residual = hidden_states hidden_states = self.input_layernorm(hidden_states) hidden_states = self.self_attn( positions=positions, hidden_states=hidden_states, ) hidden_states, residual = self.post_attention_layernorm(hidden_states, residual) hidden_states = self.mlp(hidden_states) return hidden_states, residual @support_torch_compile class DFlashQwen3Model(nn.Module): def __init__( self, *, vllm_config: VllmConfig, start_layer_id: int = 0, prefix: str = "", ) -> None: super().__init__() self.config = vllm_config.speculative_config.draft_model_config.hf_config self.vocab_size = self.config.vocab_size self.quant_config = get_draft_quant_config(vllm_config) drafter_config = getattr(self.config, "eagle_config", {}) drafter_config.update(getattr(self.config, "dflash_config", {})) if drafter_config is not None and "use_aux_hidden_state" in drafter_config: self.use_aux_hidden_state = drafter_config["use_aux_hidden_state"] else: self.use_aux_hidden_state = True current_vllm_config = get_current_vllm_config() self.embed_tokens = VocabParallelEmbedding( self.config.vocab_size, self.config.hidden_size, prefix=maybe_prefix(prefix, "embed_tokens"), ) # Masked query slots are fed to the draft as `mask_token_id`. Most DFlash # checkpoints will have the mask embedding in the vocabulary embedding table # at that slot id. Some checkpoints (XiaomiMiMo/MiMo-V2.5-Pro-FP4-DFlash) ship # with a separate mask embedding tensor to use instead. When present, we load it # and substitute it for embed_tokens[mask_token_id] when computing embeddings. self.mask_token_id = drafter_config.get("mask_token_id") self.mask_embedding = nn.Parameter( torch.zeros(self.config.hidden_size, dtype=vllm_config.model_config.dtype), requires_grad=False, ) self.has_separate_mask_embedding = False self.layers = nn.ModuleList( [ DFlashQwen3DecoderLayer( current_vllm_config, config=self.config, layer_idx=layer_idx, cache_config=current_vllm_config.cache_config, quant_config=self.quant_config, prefix=maybe_prefix(prefix, f"layers.{layer_idx + start_layer_id}"), ) for layer_idx in range(self.config.num_hidden_layers) ] ) if self.use_aux_hidden_state: num_features_to_use = self.config.num_hidden_layers if "target_layer_ids" in drafter_config: num_features_to_use = len(drafter_config["target_layer_ids"]) elif "layer_ids" in drafter_config: num_features_to_use = len(drafter_config["layer_ids"]) if hasattr(self.config, "target_hidden_size"): fc_input_size = self.config.target_hidden_size * num_features_to_use else: fc_input_size = self.config.hidden_size * num_features_to_use self.fc = ReplicatedLinear( input_size=fc_input_size, output_size=self.config.hidden_size, bias=False, params_dtype=vllm_config.model_config.dtype, quant_config=self.quant_config, prefix=maybe_prefix(prefix, "fc"), return_bias=False, ) self.hidden_norm = RMSNorm( self.config.hidden_size, eps=self.config.rms_norm_eps, ) self.norm = RMSNorm( self.config.hidden_size, eps=self.config.rms_norm_eps, ) def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor: embeds = self.embed_tokens(input_ids) if self.has_separate_mask_embedding and self.mask_token_id is not None: # Replace masked slots with the dedicated mask embedding. is_mask = (input_ids == self.mask_token_id).unsqueeze(-1) embeds = torch.where(is_mask, self.mask_embedding.to(embeds.dtype), embeds) return embeds def _build_context_kv_buffers( self, layers_attn: list[nn.Module], has_bias: bool, ) -> None: self._hidden_norm_weight = self.hidden_norm.weight.data # KV projection weights: [num_layers * 2 * kv_size, hidden_size] kv_weights = [a.qkv_proj.weight[a.q_size :] for a in layers_attn] self._fused_kv_weight = torch.cat(kv_weights, dim=0) if has_bias: kv_biases = [a.qkv_proj.bias[a.q_size :] for a in layers_attn] self._fused_kv_bias: torch.Tensor | None = torch.cat(kv_biases, dim=0) else: self._fused_kv_bias = None # K-norm weights stacked into one contiguous [num_layers, head_dim] # tensor so the per-layer K-norm runs as a single grouped kernel. self._k_norm_weights = torch.stack( [a.k_norm.weight.data for a in layers_attn], dim=0 ).contiguous() def _build_fused_kv_buffers(self) -> None: """Build fused weight buffers for precompute_and_store_context_kv. Must be called after weights are loaded. Stacks the KV-projection weights, K-norm weights, and RoPE parameters from every attention layer so that precompute_and_store_context_kv can run one fused GEMM for all layers at once. Also aliases the weight of the hidden_norm. """ layers_attn = [layer.self_attn for layer in self.layers] attn0 = layers_attn[0] has_bias = attn0.qkv_proj.bias is not None self._build_context_kv_buffers(layers_attn, has_bias) # RoPE parameters self._rope_head_size = attn0.rotary_emb.head_size self._rope_cos_sin_cache = attn0.rotary_emb.cos_sin_cache self._rope_is_neox = attn0.rotary_emb.is_neox_style # Validation that RoPE params are the same across all layers for attn in layers_attn[1:]: assert ( attn.rotary_emb.head_size == self._rope_head_size and attn.rotary_emb.is_neox_style == self._rope_is_neox ), "All layers must have the same RoPE parameters for DFlash precomputation" # Layer metadata self._num_attn_layers = len(layers_attn) self._kv_size = attn0.kv_size self._head_dim = attn0.head_dim self._num_kv_heads = attn0.num_kv_heads self._rms_norm_eps = attn0.q_norm.variance_epsilon # Validation that all layers have the same attention config for attn in layers_attn[1:]: assert ( attn.kv_size == self._kv_size and attn.head_dim == self._head_dim and attn.num_kv_heads == self._num_kv_heads and attn.q_norm.variance_epsilon == self._rms_norm_eps ), "All layers must have the same attn config for DFlash precomputation" # References to inner Attention layers for direct cache writes self._attn_layers = [layer.self_attn.attn for layer in self.layers] def _project_context_kv( self, context_states: torch.Tensor, num_ctx: int, num_layers: int, num_kv_heads: int, head_dim: int, ) -> tuple[torch.Tensor, torch.Tensor]: # --- Fused KV projection (one GEMM for all layers) --- normed_context_states = torch.empty_like(context_states) ops.rms_norm( normed_context_states, context_states, self._hidden_norm_weight, self._rms_norm_eps, ) all_kv_flat = F.linear( normed_context_states, self._fused_kv_weight, self._fused_kv_bias ) # Single contiguous copy that separates K/V and transposes to # layer-major layout. Result: [2, L, num_ctx, nkv, hd] contiguous. # Indexing dim-0 gives contiguous [L, num_ctx, nkv, hd] for K and V. all_kv = ( all_kv_flat.view(num_ctx, num_layers, 2, num_kv_heads, head_dim) .permute(2, 1, 0, 3, 4) .contiguous() ) all_k = all_kv[0] # [L, num_ctx, nkv, hd], contiguous all_v = all_kv[1] # [L, num_ctx, nkv, hd], contiguous return all_k, all_v def _normalize_context_k(self, all_k: torch.Tensor) -> torch.Tensor: # --- Grouped RMSNorm K across all layers ([L, num_ctx, nkv, hd]) --- # The weight is selected per layer by the outermost (layer) index. all_k_normed = torch.empty_like(all_k) ops.rms_norm( all_k_normed, all_k, self._k_norm_weights, self._rms_norm_eps, ) return all_k_normed def precompute_and_store_context_kv( self, context_states: torch.Tensor, context_positions: torch.Tensor, context_slot_mapping: torch.Tensor | list[torch.Tensor | None] | None = None, ) -> None: """Precompute K/V for context states write them into each layer's KV cache. Input context states are projected to K/V, normed, and have RoPE applied. Since the context shape is different than the query shape, we can't rely on the regular forward pass to apply torch.compile and CUDA graphs to this section. As such, this function is optimized to minimize the number of torch ops present: we use fused vLLM kernels for RMSNorm and RoPE, fuse the GEMM into one large projection, and avoid cloning buffers (with .contiguous()) where possible. When context_slot_mapping is None (e.g. during dummy_run) only the computation runs, and no K/V is written to cache. """ if not hasattr(self, "_num_attn_layers"): logger.warning_once( "DFlash buffer initialization was skipped. If dummy weights are not " "in use, this may indicate an error in weight loading." ) self._build_fused_kv_buffers() num_ctx = context_states.shape[0] L = self._num_attn_layers kv = self._kv_size hd = self._head_dim nkv = self._num_kv_heads all_k, all_v = self._project_context_kv(context_states, num_ctx, L, nkv, hd) all_k_normed = self._normalize_context_k(all_k) # --- Fused RoPE across all layers --- # View as [L * num_ctx, kv] so RoPE sees one big batch (no copy). # In-place RoPE: pass K as the "query" arg with key=None. all_k_flat = all_k_normed.view(L * num_ctx, kv) positions_repeated = context_positions.repeat(L) cos_sin_cache = self._rope_cos_sin_cache if cos_sin_cache.dtype != all_k_flat.dtype: cos_sin_cache = cos_sin_cache.to(dtype=all_k_flat.dtype) ops.rotary_embedding( positions_repeated, all_k_flat, None, self._rope_head_size, cos_sin_cache, self._rope_is_neox, ) if context_slot_mapping is None: return # --- Per-layer cache insert --- all_k_final = all_k_flat.view(L, num_ctx, nkv, hd) per_layer = isinstance(context_slot_mapping, (list, tuple)) for i in range(L): slot_mapping = ( context_slot_mapping[i] if per_layer else context_slot_mapping ) if slot_mapping is None: continue # dummy run: skip cache ops attn = self._attn_layers[i] kv_cache = attn.kv_cache attn.impl.do_kv_cache_update( attn, all_k_final[i], all_v[i], kv_cache, slot_mapping, ) def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, input_embeds: torch.Tensor | None = None, ) -> torch.Tensor: if input_embeds is None: input_embeds = self.embed_input_ids(input_ids) hidden_states = input_embeds residual = None for layer in self.layers: hidden_states, residual = layer( positions=positions, hidden_states=hidden_states, residual=residual, ) hidden_states, _ = self.norm(hidden_states, residual) return hidden_states def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: stacked_params_mapping = [ (".qkv_proj", ".q_proj", "q"), (".qkv_proj", ".k_proj", "k"), (".qkv_proj", ".v_proj", "v"), (".gate_up_proj", ".gate_proj", 0), (".gate_up_proj", ".up_proj", 1), ] params_dict = dict(self.named_parameters()) loaded_params: set[str] = set() tp_rank = get_tensor_model_parallel_rank() tp_size = get_tensor_model_parallel_world_size() for name, loaded_weight in weights: if "midlayer." in name: name = name.replace("midlayer.", "layers.0.") if "scale" in name: name = maybe_remap_kv_scale_name(name, params_dict) if name is None: continue if "attention_sink_bias" in name: if name not in params_dict: continue # Sink bias is per-head; shard it across TP ranks like the # attention heads themselves. param = params_dict[name] heads_per_rank = loaded_weight.shape[0] // tp_size head_start = tp_rank * heads_per_rank narrow_weight = loaded_weight.narrow(0, head_start, heads_per_rank) param.data.copy_(narrow_weight) loaded_params.add(name) continue for param_name, weight_name, shard_id in stacked_params_mapping: if weight_name not in name: continue name = name.replace(weight_name, param_name) param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, loaded_weight, shard_id) break else: param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) loaded_params.add(name) return loaded_params class DFlashQwen3ForCausalLM(Qwen3ForCausalLM): def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): nn.Module.__init__(self) self.draft_model_config = vllm_config.speculative_config.draft_model_config self.config = self.draft_model_config.hf_config if getattr(self.config, "draft_vocab_size", None) is None: self.config.draft_vocab_size = getattr(self.config, "vocab_size", None) target_layer_num = vllm_config.model_config.get_num_layers( vllm_config.parallel_config ) self.model = DFlashQwen3Model( vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model"), start_layer_id=target_layer_num, ) logit_scale = getattr(self.config, "logit_scale", 1.0) self.lm_head = ParallelLMHead( self.config.draft_vocab_size, self.config.hidden_size, prefix=maybe_prefix(prefix, "lm_head"), ) self.logits_processor = LogitsProcessor( self.config.draft_vocab_size, scale=logit_scale ) target_vocab_size = vllm_config.model_config.get_vocab_size() if self.config.draft_vocab_size != target_vocab_size: self.draft_id_to_target_id = nn.Parameter( torch.zeros(self.config.draft_vocab_size, dtype=torch.long), requires_grad=False, ) else: self.draft_id_to_target_id = None def embed_input_ids( self, input_ids: torch.Tensor, multimodal_embeddings: NestedTensors | None = None, is_multimodal: torch.Tensor | None = None, ) -> torch.Tensor: return self.model.embed_input_ids(input_ids) def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, inputs_embeds: torch.Tensor | None = None, ) -> torch.Tensor: return self.model(input_ids, positions, inputs_embeds) def get_draft_kv_cache_layer_names(self) -> list[str]: return [layer.self_attn.attn.layer_name for layer in self.model.layers] def get_draft_attn_causal(self) -> list[bool]: """Per-layer attention causality, aligned with get_draft_kv_cache_layer_names.""" return [layer.self_attn.causal for layer in self.model.layers] def compute_logits( self, hidden_states: torch.Tensor, ) -> torch.Tensor | None: logits = self.logits_processor(self.lm_head, hidden_states) if self.draft_id_to_target_id is None: return logits base = torch.arange(self.config.draft_vocab_size, device=logits.device) targets = base + self.draft_id_to_target_id logits_new = logits.new_full( (logits.shape[0], self.config.vocab_size), float("-inf"), ) logits_new[:, targets] = logits return logits_new def precompute_and_store_context_kv( self, context_states: torch.Tensor, context_positions: torch.Tensor, context_slot_mapping: torch.Tensor | list[torch.Tensor | None] | None = None, ) -> None: """Precompute projected + RoPE'd K/V and write to cache.""" self.model.precompute_and_store_context_kv( context_states, context_positions, context_slot_mapping ) def combine_hidden_states( self, hidden_states: torch.Tensor, ) -> torch.Tensor: if not self.model.use_aux_hidden_state: return hidden_states needs_squeeze = hidden_states.dim() == 1 if needs_squeeze: hidden_states = hidden_states.unsqueeze(0) expected = self.model.fc.input_size if hidden_states.shape[-1] != expected: raise ValueError( f"DFlash drafter expects {expected} concatenated aux hidden " f"features but received {hidden_states.shape[-1]}. This usually " "means the draft model's target_layer_ids reference layers that " "do not exist in the target model (incompatible draft/target pair)." ) result = self.model.fc(hidden_states) if needs_squeeze: result = result.squeeze(0) return result def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]): model_weights = {} includes_draft_id_mapping = False includes_embed_tokens = False for name, loaded_weight in weights: assert "mask_hidden" not in name, ( "DFlash embeds masked slots via mask_token_id (optionally " "overridden by a mask_embedding.pt file); it should not ship a " "mask_hidden weight." ) if "t2d" in name: continue if "d2t" in name: name = name.replace("d2t", "draft_id_to_target_id") includes_draft_id_mapping = True elif "lm_head" not in name: name = "model." + name if "embed_tokens" in name: includes_embed_tokens = True model_weights[name] = loaded_weight process_eagle_weight(self, name) # Route the separately-trained mask embedding (if shipped) through the # standard weight loader alongside the rest of the draft weights. mask_embedding = self._read_mask_embedding() if mask_embedding is not None: model_weights["model.mask_embedding"] = mask_embedding self.model.has_separate_mask_embedding = True skip_substrs = [] if not includes_draft_id_mapping: skip_substrs.append("draft_id_to_target_id") if not includes_embed_tokens: skip_substrs.append("embed_tokens") if not self.model.use_aux_hidden_state: skip_substrs.append("fc.") if not self.model.has_separate_mask_embedding: skip_substrs.append("mask_embedding") loader = AutoWeightsLoader( self, skip_prefixes=None, skip_substrs=skip_substrs, ) loader.load_weights(model_weights.items()) self.model._build_fused_kv_buffers() def _read_mask_embedding(self) -> torch.Tensor | None: """Checks for an override mask embedding in `mask_embedding.pt` and returns it. Some checkpoints ship a separately-trained mask embedding for the mask token, which we use to overwrite the embedding for `mask_token_id`. This helper checks for the file, loads the pytorch tensor, and returns the embedding to use. Returns None if the override file is not present. """ mask_token_id = self.model.mask_token_id if mask_token_id is None: return None MASK_EMBEDDING_FILENAME = "mask_embedding.pt" data = get_hf_file_bytes( MASK_EMBEDDING_FILENAME, self.draft_model_config.model, self.draft_model_config.revision, ) if data is None: return None state = torch.load(io.BytesIO(data), weights_only=True) if isinstance(state, dict): if state.get("mask_token_id", mask_token_id) != mask_token_id: raise ValueError( f"{MASK_EMBEDDING_FILENAME} mask_token_id does not match " f"dflash_config.mask_token_id ({mask_token_id}). " f"Got {state.get('mask_token_id')}." ) state = state["embedding"] logger.info( "Loaded DFlash mask embedding for mask_token_id %s from %s", mask_token_id, MASK_EMBEDDING_FILENAME, ) return state.reshape(-1)