| 1 | # SPDX-License-Identifier: Apache-2.0 |
| 2 | # SPDX-FileCopyrightText: Copyright contributors to the vLLM project |
| 3 | import typing |
| 4 | from collections.abc import Callable, Iterable |
| 5 | |
| 6 | import torch |
| 7 | import torch.nn as nn |
| 8 | from transformers import PreTrainedConfig |
| 9 | |
| 10 | from vllm.compilation.decorators import support_torch_compile |
| 11 | from vllm.config import VllmConfig |
| 12 | from vllm.distributed import tensor_model_parallel_all_gather |
| 13 | from vllm.model_executor.layers.fused_moe import ( |
| 14 | fused_moe_make_expert_params_mapping, |
| 15 | ) |
| 16 | from vllm.model_executor.layers.fused_moe.utils import ( |
| 17 | is_model_fused_shared_expert_compatible, |
| 18 | ) |
| 19 | from vllm.model_executor.layers.layernorm import RMSNorm |
| 20 | from vllm.model_executor.layers.logits_processor import LogitsProcessor |
| 21 | from vllm.model_executor.layers.quantization import QuantizationConfig |
| 22 | from vllm.model_executor.layers.vocab_parallel_embedding import ( |
| 23 | ParallelLMHead, |
| 24 | VocabParallelEmbedding, |
| 25 | ) |
| 26 | from vllm.model_executor.model_loader.mtp_validation import ( |
| 27 | is_mtp_completeness_check_enabled, |
| 28 | ) |
| 29 | from vllm.model_executor.model_loader.weight_utils import ( |
| 30 | default_weight_loader, |
| 31 | maybe_remap_kv_scale_name, |
| 32 | ) |
| 33 | from vllm.platforms import current_platform |
| 34 | from vllm.sequence import IntermediateTensors |
| 35 | |
| 36 | from .deepseek_v2 import ( |
| 37 | DeepseekV2DecoderLayer, |
| 38 | DeepseekV2MixtureOfExperts, |
| 39 | DeepseekV2MoE, |
| 40 | _try_load_fp8_indexer_wk, |
| 41 | ) |
| 42 | from .interfaces import SupportsPP |
| 43 | from .utils import ( |
| 44 | get_pp_missing_layer_names, |
| 45 | get_spec_layer_idx_from_weight_name, |
| 46 | make_empty_intermediate_tensors_factory, |
| 47 | maybe_prefix, |
| 48 | ) |
| 49 | |
| 50 | |
| 51 | class SharedHead(nn.Module): |
| 52 | def __init__( |
| 53 | self, |
| 54 | config: PreTrainedConfig, |
| 55 | prefix: str, |
| 56 | quant_config: QuantizationConfig | None = None, |
| 57 | *, |
| 58 | defer_lm_head: bool = False, |
| 59 | ) -> None: |
| 60 | super().__init__() |
| 61 | self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) |
| 62 | if defer_lm_head: |
| 63 | self.head = None |
| 64 | else: |
| 65 | self.head = ParallelLMHead( |
| 66 | config.vocab_size, |
| 67 | config.hidden_size, |
| 68 | quant_config=quant_config, |
| 69 | prefix=maybe_prefix(prefix, "head"), |
| 70 | ) |
| 71 | |
| 72 | def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: |
| 73 | return self.norm(hidden_states) |
| 74 | |
| 75 | |
| 76 | class DeepSeekMultiTokenPredictorLayer(nn.Module): |
| 77 | def __init__(self, vllm_config: VllmConfig, prefix: str) -> None: |
| 78 | super().__init__() |
| 79 | |
| 80 | assert vllm_config.speculative_config is not None |
| 81 | config = vllm_config.speculative_config.draft_model_config.hf_config |
| 82 | self.config = config |
| 83 | quant_config = vllm_config.quant_config |
| 84 | |
| 85 | self.enorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) |
| 86 | self.hnorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) |
| 87 | self.eh_proj = nn.Linear(config.hidden_size * 2, config.hidden_size, bias=False) |
| 88 | |
| 89 | self.device = current_platform.device_type |
| 90 | |
| 91 | self.is_v32 = hasattr(config, "index_topk") |
| 92 | if self.is_v32: |
| 93 | topk_tokens = config.index_topk |
| 94 | topk_indices_buffer = torch.empty( |
| 95 | vllm_config.scheduler_config.max_num_batched_tokens, |
| 96 | topk_tokens, |
| 97 | dtype=torch.int32, |
| 98 | device=self.device, |
| 99 | ) |
| 100 | else: |
| 101 | topk_indices_buffer = None |
| 102 | |
| 103 | self.shared_head = SharedHead( |
| 104 | config=config, prefix=prefix, quant_config=quant_config |
| 105 | ) |
| 106 | self.mtp_block = DeepseekV2DecoderLayer( |
| 107 | vllm_config, |
| 108 | prefix, |
| 109 | config=self.config, |
| 110 | topk_indices_buffer=topk_indices_buffer, |
| 111 | ) |
| 112 | |
| 113 | def forward( |
| 114 | self, |
| 115 | input_ids: torch.Tensor, |
| 116 | positions: torch.Tensor, |
| 117 | previous_hidden_states: torch.Tensor, |
| 118 | inputs_embeds: torch.Tensor | None = None, |
| 119 | spec_step_index: int = 0, |
| 120 | ) -> torch.Tensor: |
| 121 | assert inputs_embeds is not None |
| 122 | # masking inputs at position 0, as not needed by MTP |
| 123 | inputs_embeds = torch.where(positions.unsqueeze(-1) == 0, 0, inputs_embeds) |
| 124 | inputs_embeds = self.enorm(inputs_embeds) |
| 125 | previous_hidden_states = self.hnorm(previous_hidden_states) |
| 126 | |
| 127 | hidden_states = self.eh_proj( |
| 128 | torch.cat([inputs_embeds, previous_hidden_states], dim=-1) |
| 129 | ) |
| 130 | |
| 131 | hidden_states, residual = self.mtp_block( |
| 132 | positions=positions, |
| 133 | hidden_states=hidden_states, |
| 134 | residual=None, |
| 135 | ) |
| 136 | hidden_states = residual + hidden_states # pre-final-norm (logits hidden) |
| 137 | if self.mtp_block.use_sequence_parallel_moe: |
| 138 | hidden_states = tensor_model_parallel_all_gather(hidden_states, 0) |
| 139 | hidden_states = hidden_states[: positions.shape[0]] |
| 140 | # Recycle the post-final-norm hidden into the next draft step. |
| 141 | # compute_logits applies shared_head (== final norm) to the pre-norm |
| 142 | # element, so logits and the recycle each get exactly one final-norm. |
| 143 | # Matches SGLang's deepseek_nextn. |
| 144 | return hidden_states, self.shared_head(hidden_states) |
| 145 | |
| 146 | |
| 147 | class DeepSeekMultiTokenPredictor(nn.Module): |
| 148 | def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): |
| 149 | super().__init__() |
| 150 | config = vllm_config.model_config.hf_config |
| 151 | self.mtp_start_layer_idx = config.num_hidden_layers |
| 152 | self.num_mtp_layers = config.num_nextn_predict_layers |
| 153 | # to map the exact layer index from weights |
| 154 | |
| 155 | self.layers = torch.nn.ModuleDict( |
| 156 | { |
| 157 | str(idx): DeepSeekMultiTokenPredictorLayer( |
| 158 | vllm_config, f"{prefix}.layers.{idx}" |
| 159 | ) |
| 160 | for idx in range( |
| 161 | self.mtp_start_layer_idx, |
| 162 | self.mtp_start_layer_idx + self.num_mtp_layers, |
| 163 | ) |
| 164 | } |
| 165 | ) |
| 166 | self.embed_tokens = VocabParallelEmbedding( |
| 167 | config.vocab_size, |
| 168 | config.hidden_size, |
| 169 | prefix=maybe_prefix(prefix, "embed_tokens"), |
| 170 | ) |
| 171 | self.logits_processor = LogitsProcessor(config.vocab_size) |
| 172 | |
| 173 | def set_skip_topk(self, skip: bool): |
| 174 | """Toggle skip_topk on all MTP layers with sparse attention. |
| 175 | |
| 176 | Called by the proposer to implement index_share_for_mtp_iteration: |
| 177 | step 0 sets skip=False (compute own indices), steps 1+ set skip=True |
| 178 | (reuse step 0's indices). |
| 179 | """ |
| 180 | for layer in self.layers.values(): |
| 181 | mtp_block = getattr(layer, "mtp_block", None) |
| 182 | if mtp_block is not None: |
| 183 | self_attn = getattr(mtp_block, "self_attn", None) |
| 184 | if self_attn is not None: |
| 185 | mla_attn = getattr(self_attn, "mla_attn", None) |
| 186 | if mla_attn is not None and hasattr(mla_attn, "skip_topk"): |
| 187 | mla_attn.skip_topk = skip |
| 188 | |
| 189 | def compact_topk_indices(self, slot_ids: torch.Tensor): |
| 190 | """Gather the top-k index rows at ``slot_ids`` to the front of the buffer.""" |
| 191 | num_slots = slot_ids.numel() |
| 192 | for layer in self.layers.values(): |
| 193 | mtp_block = getattr(layer, "mtp_block", None) |
| 194 | if mtp_block is not None: |
| 195 | self_attn = getattr(mtp_block, "self_attn", None) |
| 196 | if self_attn is not None: |
| 197 | mla_attn = getattr(self_attn, "mla_attn", None) |
| 198 | if mla_attn is not None and hasattr( |
| 199 | mla_attn, "topk_indices_buffer" |
| 200 | ): |
| 201 | topk_indices_buffer = mla_attn.topk_indices_buffer |
| 202 | topk_indices_buffer[:num_slots] = topk_indices_buffer[slot_ids] |
| 203 | |
| 204 | def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor: |
| 205 | return self.embed_tokens(input_ids) |
| 206 | |
| 207 | def forward( |
| 208 | self, |
| 209 | input_ids: torch.Tensor, |
| 210 | positions: torch.Tensor, |
| 211 | previous_hidden_states: torch.Tensor, |
| 212 | inputs_embeds: torch.Tensor | None = None, |
| 213 | spec_step_idx: int = 0, |
| 214 | ) -> torch.Tensor: |
| 215 | if inputs_embeds is None: |
| 216 | inputs_embeds = self.embed_tokens(input_ids) |
| 217 | current_step_idx = spec_step_idx % self.num_mtp_layers |
| 218 | return self.layers[str(self.mtp_start_layer_idx + current_step_idx)]( |
| 219 | input_ids, |
| 220 | positions, |
| 221 | previous_hidden_states, |
| 222 | inputs_embeds, |
| 223 | current_step_idx, |
| 224 | ) |
| 225 | |
| 226 | def compute_logits( |
| 227 | self, |
| 228 | hidden_states: torch.Tensor, |
| 229 | spec_step_idx: int = 0, |
| 230 | ) -> torch.Tensor: |
| 231 | current_step_idx = spec_step_idx % self.num_mtp_layers |
| 232 | mtp_layer = self.layers[str(self.mtp_start_layer_idx + current_step_idx)] |
| 233 | logits = self.logits_processor( |
| 234 | mtp_layer.shared_head.head, mtp_layer.shared_head(hidden_states) |
| 235 | ) |
| 236 | return logits |
| 237 | |
| 238 | |
| 239 | @support_torch_compile |
| 240 | class DeepSeekMTP(nn.Module, DeepseekV2MixtureOfExperts, SupportsPP): |
| 241 | def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): |
| 242 | super().__init__() |
| 243 | self.config = vllm_config.model_config.hf_config |
| 244 | self.quant_config = vllm_config.quant_config |
| 245 | self.model = DeepSeekMultiTokenPredictor( |
| 246 | vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model") |
| 247 | ) |
| 248 | # Set MoE hyperparameters |
| 249 | self.set_moe_parameters() |
| 250 | self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory( |
| 251 | ["hidden_states", "residual"], self.config.hidden_size |
| 252 | ) |
| 253 | |
| 254 | def set_moe_parameters(self): |
| 255 | self.num_moe_layers = self.config.num_nextn_predict_layers |
| 256 | self.num_expert_groups = self.config.n_group |
| 257 | |
| 258 | self.moe_layers = [] |
| 259 | self.moe_mlp_layers = [] |
| 260 | example_moe = None |
| 261 | for layer in self.model.layers.values(): |
| 262 | assert isinstance(layer, DeepSeekMultiTokenPredictorLayer) |
| 263 | layer = layer.mtp_block |
| 264 | assert isinstance(layer, DeepseekV2DecoderLayer) |
| 265 | if isinstance(layer.mlp, DeepseekV2MoE): |
| 266 | example_moe = layer.mlp |
| 267 | self.moe_mlp_layers.append(layer.mlp) |
| 268 | self.moe_layers.append(layer.mlp.experts) |
| 269 | self.extract_moe_parameters(example_moe) |
| 270 | self.is_fused_shared_expert_enabled = is_model_fused_shared_expert_compatible( |
| 271 | self.model.layers.values(), |
| 272 | DeepseekV2MoE, |
| 273 | "mtp_block.mlp", |
| 274 | ) |
| 275 | |
| 276 | def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor: |
| 277 | return self.model.embed_input_ids(input_ids) |
| 278 | |
| 279 | def forward( # type: ignore[override] |
| 280 | self, |
| 281 | input_ids: torch.Tensor | None, |
| 282 | positions: torch.Tensor, |
| 283 | hidden_states: torch.Tensor, |
| 284 | intermediate_tensors: IntermediateTensors | None = None, |
| 285 | inputs_embeds: torch.Tensor | None = None, |
| 286 | spec_step_idx: int = 0, |
| 287 | ) -> torch.Tensor: |
| 288 | hidden_states = self.model( |
| 289 | input_ids, |
| 290 | positions, |
| 291 | hidden_states, |
| 292 | inputs_embeds, |
| 293 | spec_step_idx, |
| 294 | ) |
| 295 | return hidden_states |
| 296 | |
| 297 | def compute_logits( |
| 298 | self, |
| 299 | hidden_states: torch.Tensor, |
| 300 | spec_step_idx: int = 0, |
| 301 | ) -> torch.Tensor | None: |
| 302 | return self.model.compute_logits(hidden_states, spec_step_idx) |
| 303 | |
| 304 | def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: |
| 305 | stacked_params_mapping = [ |
| 306 | ("gate_up_proj", "gate_proj", 0), |
| 307 | ("gate_up_proj", "up_proj", 1), |
| 308 | ("fused_qkv_a_proj", "q_a_proj", 0), |
| 309 | ("fused_qkv_a_proj", "kv_a_proj_with_mqa", 1), |
| 310 | ] |
| 311 | |
| 312 | # Fused indexer wk + weights_proj (shard 0 = wk, shard 1 = weights_proj) |
| 313 | indexer_fused_mapping = [ |
| 314 | ("wk_weights_proj", "wk", 0), |
| 315 | ("wk_weights_proj", "weights_proj", 1), |
| 316 | ] |
| 317 | stacked_params_mapping.extend(indexer_fused_mapping) |
| 318 | |
| 319 | expert_params_mapping = fused_moe_make_expert_params_mapping( |
| 320 | self, |
| 321 | ckpt_gate_proj_name="gate_proj", |
| 322 | ckpt_down_proj_name="down_proj", |
| 323 | ckpt_up_proj_name="up_proj", |
| 324 | num_experts=self.config.n_routed_experts |
| 325 | + ( |
| 326 | self.config.n_shared_experts |
| 327 | if self.is_fused_shared_expert_enabled |
| 328 | else 0 |
| 329 | ), |
| 330 | ) |
| 331 | |
| 332 | pp_missing_layer_names = get_pp_missing_layer_names(self) |
| 333 | params_dict = dict(self.named_parameters()) |
| 334 | loaded_params: set[str] = set() |
| 335 | _pending_wk_fp8: dict = {} # FP8 indexer wk dequant buffer |
| 336 | for name, loaded_weight in weights: |
| 337 | if "rotary_emb.inv_freq" in name: |
| 338 | continue |
| 339 | spec_layer = get_spec_layer_idx_from_weight_name(self.config, name) |
| 340 | if spec_layer is None: |
| 341 | # A tied top-level embed_tokens has no spec layer to rewrite |
| 342 | # from; the draft needs its own copy under PP. |
| 343 | param = params_dict.get(name) if "embed_tokens" in name else None |
| 344 | if param is not None: |
| 345 | weight_loader = getattr( |
| 346 | param, "weight_loader", default_weight_loader |
| 347 | ) |
| 348 | weight_loader(param, loaded_weight) |
| 349 | loaded_params.add(name) |
| 350 | continue |
| 351 | is_fusion_moe_shared_experts_layer = ( |
| 352 | self.is_fused_shared_expert_enabled and ("mlp.shared_experts" in name) |
| 353 | ) |
| 354 | name = self._rewrite_spec_layer_name(spec_layer, name) |
| 355 | |
| 356 | if _try_load_fp8_indexer_wk( |
| 357 | name, |
| 358 | loaded_weight, |
| 359 | _pending_wk_fp8, |
| 360 | params_dict, |
| 361 | loaded_params, |
| 362 | pp_missing_layer_names, |
| 363 | ): |
| 364 | continue |
| 365 | |
| 366 | for param_name, weight_name, shard_id in stacked_params_mapping: |
| 367 | # Skip non-stacked layers and experts (experts handled below). |
| 368 | if weight_name not in name: |
| 369 | continue |
| 370 | # We have mlp.experts[0].gate_proj in the checkpoint. |
| 371 | # Since we handle the experts below in expert_params_mapping, |
| 372 | # we need to skip here BEFORE we update the name, otherwise |
| 373 | # name will be updated to mlp.experts[0].gate_up_proj, which |
| 374 | # will then be updated below in expert_params_mapping |
| 375 | # for mlp.experts[0].gate_gate_up_proj, which breaks load. |
| 376 | if ("mlp.experts." in name) and name not in params_dict: |
| 377 | continue |
| 378 | if is_fusion_moe_shared_experts_layer: |
| 379 | continue |
| 380 | name_mapped = name.replace(weight_name, param_name) |
| 381 | |
| 382 | # QKV fusion is optional, fall back to normal |
| 383 | # weight loading if it's not enabled |
| 384 | if ( |
| 385 | param_name == "fused_qkv_a_proj" |
| 386 | ) and name_mapped not in params_dict: |
| 387 | continue |
| 388 | else: |
| 389 | name = name_mapped |
| 390 | |
| 391 | # Skip loading extra bias for GPTQ models. |
| 392 | if name.endswith(".bias") and name not in params_dict: |
| 393 | continue |
| 394 | |
| 395 | param = params_dict[name] |
| 396 | weight_loader = param.weight_loader |
| 397 | weight_loader(param, loaded_weight, shard_id) |
| 398 | break |
| 399 | else: |
| 400 | # Special handling: when AITER fusion_shared_experts is enabled, |
| 401 | # checkpoints may provide a single widened shared_experts tensor |
| 402 | # without explicit expert indices |
| 403 | # (e.g. ...mlp.shared_experts.gate_proj.weight). |
| 404 | # For models with multiple shared experts, split that tensor |
| 405 | # evenly into per-shared-expert slices and load them into |
| 406 | # appended expert slots mlp.experts.{n_routed_experts + j}.* |
| 407 | # accordingly. |
| 408 | num_chunks = 1 |
| 409 | if is_fusion_moe_shared_experts_layer: |
| 410 | num_chunks = getattr(self.config, "n_shared_experts", 1) or 1 |
| 411 | # Determine split axis based on op type |
| 412 | # gate/up: ColumnParallel → split along dim 0 |
| 413 | # down: RowParallel → split along dim 1 |
| 414 | split_dim = ( |
| 415 | 1 |
| 416 | if ("down_proj.weight" in name and loaded_weight.ndim > 1) |
| 417 | else 0 |
| 418 | ) |
| 419 | total = loaded_weight.shape[split_dim] |
| 420 | assert total % num_chunks == 0, ( |
| 421 | f"Shared expert weight dim {total} " |
| 422 | f"not divisible by num_chunks {num_chunks}" |
| 423 | ) |
| 424 | chunk_size = total // num_chunks |
| 425 | |
| 426 | for j in range(num_chunks): |
| 427 | chunk_name = name |
| 428 | weight_to_load = loaded_weight |
| 429 | |
| 430 | if is_fusion_moe_shared_experts_layer: |
| 431 | chunk_slice = slice(j * chunk_size, (j + 1) * chunk_size) |
| 432 | if loaded_weight.ndim == 1: |
| 433 | weight_to_load = loaded_weight[chunk_slice] |
| 434 | elif split_dim == 0: |
| 435 | weight_to_load = loaded_weight[chunk_slice, :] |
| 436 | else: |
| 437 | weight_to_load = loaded_weight[:, chunk_slice] |
| 438 | # Synthesize an expert-style name so expert mapping |
| 439 | # can route it |
| 440 | chunk_name = name.replace( |
| 441 | "mlp.shared_experts", |
| 442 | f"mlp.experts.{self.config.n_routed_experts + j}", |
| 443 | ) |
| 444 | |
| 445 | # Use expert_params_mapping to locate the destination |
| 446 | # param and delegate to its expert-aware weight_loader |
| 447 | # with expert_id. |
| 448 | is_expert_weight = False |
| 449 | for mapping in expert_params_mapping: |
| 450 | param_name, weight_name, expert_id, expert_shard_id = mapping |
| 451 | if weight_name not in chunk_name: |
| 452 | continue |
| 453 | |
| 454 | # Anyway, this is an expert weight and should not be |
| 455 | # attempted to load as other weights later |
| 456 | is_expert_weight = True |
| 457 | |
| 458 | # Do not modify `name` since the loop may continue here |
| 459 | # Instead, create a new variable |
| 460 | name_mapped = chunk_name.replace(weight_name, param_name) |
| 461 | |
| 462 | param = params_dict[name_mapped] |
| 463 | # We should ask the weight loader to return success or |
| 464 | # not here since otherwise we may skip experts with |
| 465 | # other available replicas. |
| 466 | weight_loader = typing.cast( |
| 467 | Callable[..., bool], param.weight_loader |
| 468 | ) |
| 469 | success = weight_loader( |
| 470 | param, |
| 471 | weight_to_load, |
| 472 | name_mapped, |
| 473 | shard_id=expert_shard_id, |
| 474 | expert_id=expert_id, |
| 475 | return_success=True, |
| 476 | ) |
| 477 | if success: |
| 478 | if not is_fusion_moe_shared_experts_layer: |
| 479 | name = name_mapped |
| 480 | else: |
| 481 | loaded_params.add(name_mapped) |
| 482 | break |
| 483 | else: |
| 484 | if is_expert_weight: |
| 485 | # We've checked that this is an expert weight |
| 486 | # However it's not mapped locally to this rank |
| 487 | # So we simply skip it |
| 488 | continue |
| 489 | |
| 490 | # Skip loading extra bias for GPTQ models. |
| 491 | if name.endswith(".bias") and name not in params_dict: |
| 492 | continue |
| 493 | |
| 494 | remapped_name = maybe_remap_kv_scale_name(name, params_dict) |
| 495 | if remapped_name is None: |
| 496 | continue |
| 497 | name = remapped_name |
| 498 | |
| 499 | # According to DeepSeek-V3 Technical Report, MTP modules |
| 500 | # shares embedding layer. We only load the first weights. |
| 501 | if ( |
| 502 | spec_layer != self.model.mtp_start_layer_idx |
| 503 | and ".layers" not in name |
| 504 | ): |
| 505 | continue |
| 506 | |
| 507 | param = params_dict[name] |
| 508 | weight_loader = getattr( |
| 509 | param, "weight_loader", default_weight_loader |
| 510 | ) |
| 511 | weight_loader(param, loaded_weight) |
| 512 | if not is_fusion_moe_shared_experts_layer: |
| 513 | loaded_params.add(name) |
| 514 | |
| 515 | # Validate that weights were loaded for each expected MTP layer. |
| 516 | loaded_layers: set[int] = set() |
| 517 | for param_name in loaded_params: |
| 518 | spec_layer = get_spec_layer_idx_from_weight_name(self.config, param_name) |
| 519 | if spec_layer is not None: |
| 520 | loaded_layers.add(spec_layer) |
| 521 | for layer_idx in range( |
| 522 | self.model.mtp_start_layer_idx, |
| 523 | self.model.mtp_start_layer_idx + self.model.num_mtp_layers, |
| 524 | ): |
| 525 | if layer_idx not in loaded_layers and is_mtp_completeness_check_enabled(): |
| 526 | raise ValueError( |
| 527 | f"MTP speculative decoding layer {layer_idx} weights " |
| 528 | f"missing from checkpoint. The checkpoint may have " |
| 529 | f"been quantized without including the MTP layers. " |
| 530 | f"Use a checkpoint that includes MTP layer weights, " |
| 531 | f"or disable speculative decoding." |
| 532 | ) |
| 533 | |
| 534 | return loaded_params |
| 535 | |
| 536 | def _rewrite_spec_layer_name(self, spec_layer: int, name: str) -> str: |
| 537 | """Rewrite the weight name to match the format of the original model. |
| 538 | Add .mtp_block for modules in transformer layer block for spec layer |
| 539 | and rename shared layer weights to be top level. |
| 540 | """ |
| 541 | spec_layer_weight_names = [ |
| 542 | "embed_tokens", |
| 543 | "enorm", |
| 544 | "hnorm", |
| 545 | "eh_proj", |
| 546 | "shared_head", |
| 547 | ] |
| 548 | shared_weight_names = ["embed_tokens"] |
| 549 | spec_layer_weight = False |
| 550 | shared_weight = False |
| 551 | for weight_name in spec_layer_weight_names: |
| 552 | if weight_name in name: |
| 553 | spec_layer_weight = True |
| 554 | if weight_name in shared_weight_names: |
| 555 | shared_weight = True |
| 556 | break |
| 557 | if not spec_layer_weight: |
| 558 | # treat rest weights as weights for transformer layer block |
| 559 | name = name.replace( |
| 560 | f"model.layers.{spec_layer}.", f"model.layers.{spec_layer}.mtp_block." |
| 561 | ) |
| 562 | elif shared_weight: |
| 563 | # treat shared weights as top level weights |
| 564 | name = name.replace(f"model.layers.{spec_layer}.", "model.") |
| 565 | return name |