# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project # Copyright 2024 The Qwen team. # Copyright 2023 The vLLM team. # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # 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. """Inference-only Qwen3MoE model compatible with HuggingFace weights.""" import typing from collections.abc import Callable, Iterable from itertools import islice from typing import Any import torch import torch.nn.functional as F from torch import nn from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig, VllmConfig, get_current_vllm_config from vllm.distributed import ( get_ep_group, get_pp_group, get_tensor_model_parallel_world_size, tensor_model_parallel_all_gather, ) from vllm.logger import init_logger from vllm.model_executor.layers.activation import SiluAndMul from vllm.model_executor.layers.attention import Attention from vllm.model_executor.layers.fused_moe import SharedFusedMoE from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.linear import ( MergedColumnParallelLinear, QKVParallelLinear, ReplicatedLinear, RowParallelLinear, ) from vllm.model_executor.layers.logits_processor import LogitsProcessor from vllm.model_executor.layers.quantization 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.model_executor.models.utils import sequence_parallel_chunk from vllm.sequence import IntermediateTensors from .interfaces import ( EagleModelMixin, MixtureOfExperts, SupportsEagle, SupportsEagle3, SupportsLoRA, SupportsPP, ) from .utils import ( AutoWeightsLoader, PPMissingLayer, extract_layer_index, is_pp_missing_parameter, make_empty_intermediate_tensors_factory, make_layers, maybe_prefix, ) logger = init_logger(__name__) class Qwen3MoeMLP(nn.Module): def __init__( self, hidden_size: int, intermediate_size: int, hidden_act: str, quant_config: QuantizationConfig | None = None, reduce_results: bool = True, expert_gate: torch.nn.Linear | None = None, prefix: str = "", ) -> None: super().__init__() self.gate_up_proj = MergedColumnParallelLinear( hidden_size, [intermediate_size] * 2, bias=False, quant_config=quant_config, prefix=f"{prefix}.gate_up_proj", ) self.down_proj = RowParallelLinear( intermediate_size, hidden_size, bias=False, quant_config=quant_config, reduce_results=reduce_results, prefix=f"{prefix}.down_proj", ) if hidden_act != "silu": raise ValueError( f"Unsupported activation: {hidden_act}. Only silu is supported for now." ) self.act_fn = SiluAndMul() self.expert_gate = expert_gate def forward(self, x): gate_up, _ = self.gate_up_proj(x) out = self.act_fn(gate_up) out, _ = self.down_proj(out) if self.expert_gate is not None: out = F.sigmoid(self.expert_gate(x)[0]) * out return out class Qwen3MoeSparseMoeBlock(nn.Module): def __init__( self, vllm_config: VllmConfig, prefix: str = "", ): super().__init__() config = vllm_config.model_config.hf_text_config parallel_config = vllm_config.parallel_config quant_config = vllm_config.quant_config self.tp_size = get_tensor_model_parallel_world_size() self.ep_group = get_ep_group().device_group self.ep_rank = get_ep_group().rank_in_group self.ep_size = self.ep_group.size() self.n_routed_experts = config.num_experts self.is_sequence_parallel = parallel_config.use_sequence_parallel_moe if self.tp_size > config.num_experts: raise ValueError( f"Tensor parallel size {self.tp_size} is greater than " f"the number of experts {config.num_experts}." ) # Load balancing settings. vllm_config = get_current_vllm_config() eplb_config = vllm_config.parallel_config.eplb_config self.enable_eplb = parallel_config.enable_eplb self.n_logical_experts = self.n_routed_experts self.n_redundant_experts = eplb_config.num_redundant_experts self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts self.n_local_physical_experts = self.n_physical_experts // self.ep_size self.physical_expert_start = self.ep_rank * self.n_local_physical_experts self.physical_expert_end = ( self.physical_expert_start + self.n_local_physical_experts ) self.gate = ReplicatedLinear( config.hidden_size, config.num_experts, bias=False, quant_config=quant_config, prefix=f"{prefix}.gate", ) shared_expert_intermediate_size = getattr( config, "shared_expert_intermediate_size", 0 ) if shared_expert_intermediate_size > 0: self.shared_expert_gate = ReplicatedLinear( config.hidden_size, 1, bias=False, quant_config=None, prefix=f"{prefix}.shared_expert_gate", ) self.shared_expert = Qwen3MoeMLP( hidden_size=config.hidden_size, intermediate_size=shared_expert_intermediate_size, hidden_act=config.hidden_act, quant_config=quant_config, reduce_results=False, expert_gate=self.shared_expert_gate, prefix=f"{prefix}.shared_expert", ) else: self.shared_expert_gate = None self.shared_expert = None self.experts = SharedFusedMoE( shared_experts=self.shared_expert, gate=self.gate, num_experts=self.n_routed_experts, top_k=config.num_experts_per_tok, hidden_size=config.hidden_size, intermediate_size=config.moe_intermediate_size, reduce_results=False, renormalize=config.norm_topk_prob, quant_config=quant_config, prefix=f"{prefix}.experts", enable_eplb=self.enable_eplb, num_redundant_experts=self.n_redundant_experts, is_sequence_parallel=self.is_sequence_parallel, ) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: assert hidden_states.dim() <= 2, ( "Qwen3MoeSparseMoeBlock only supports 1D or 2D inputs" ) is_input_1d = hidden_states.dim() == 1 num_tokens, hidden_dim = hidden_states.shape hidden_states = hidden_states.view(-1, hidden_dim) if self.is_sequence_parallel: hidden_states = sequence_parallel_chunk(hidden_states) # router_logits: (num_tokens, n_experts) router_logits, _ = self.gate(hidden_states) shared_out, fused_out = self.experts( hidden_states=hidden_states, router_logits=router_logits ) final_hidden_states = ( shared_out + fused_out if shared_out is not None else fused_out ) if self.is_sequence_parallel: final_hidden_states = tensor_model_parallel_all_gather( final_hidden_states, 0 ) final_hidden_states = final_hidden_states[:num_tokens] elif self.tp_size > 1: final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel( # noqa E501 final_hidden_states ) # return to 1d if input is 1d return final_hidden_states.squeeze(0) if is_input_1d else final_hidden_states class Qwen3MoeAttention(nn.Module): def __init__( self, hidden_size: int, num_heads: int, num_kv_heads: int, rope_parameters: dict[str, Any], max_position_embeddings: int = 8192, head_dim: int | None = None, rms_norm_eps: float = 1e-06, qkv_bias: bool = False, cache_config: CacheConfig | None = None, quant_config: QuantizationConfig | None = None, prefix: str = "", dual_chunk_attention_config: dict[str, Any] | None = None, ) -> None: super().__init__() 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: # Number of KV heads is greater than TP size, so we partition # the KV heads across multiple tensor parallel GPUs. assert self.total_num_kv_heads % tp_size == 0 else: # Number of KV heads is less than TP size, so we replicate # the KV heads across multiple tensor parallel GPUs. 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.max_position_embeddings = max_position_embeddings self.dual_chunk_attention_config = dual_chunk_attention_config self.qkv_proj = QKVParallelLinear( hidden_size, self.head_dim, self.total_num_heads, self.total_num_kv_heads, bias=qkv_bias, quant_config=quant_config, prefix=f"{prefix}.qkv_proj", ) self.o_proj = RowParallelLinear( self.total_num_heads * self.head_dim, hidden_size, bias=False, quant_config=quant_config, prefix=f"{prefix}.o_proj", ) self.rotary_emb = get_rope( self.head_dim, max_position=max_position_embeddings, rope_parameters=rope_parameters, dual_chunk_attention_config=dual_chunk_attention_config, ) 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, prefix=f"{prefix}.attn", **{ "layer_idx": extract_layer_index(prefix), "dual_chunk_attention_config": dual_chunk_attention_config, } if dual_chunk_attention_config else {}, ) 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: qkv, _ = self.qkv_proj(hidden_states) q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1) # Add qk-norm q_by_head = q.view(*q.shape[:-1], q.shape[-1] // self.head_dim, self.head_dim) q_by_head = self.q_norm(q_by_head) q = q_by_head.view(q.shape) k_by_head = k.view(*k.shape[:-1], k.shape[-1] // self.head_dim, self.head_dim) k_by_head = self.k_norm(k_by_head) k = k_by_head.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 Qwen3MoeDecoderLayer(nn.Module): def __init__(self, vllm_config: VllmConfig, prefix: str = "") -> None: super().__init__() config = vllm_config.model_config.hf_text_config cache_config = vllm_config.cache_config quant_config = vllm_config.quant_config self.hidden_size = config.hidden_size max_position_embeddings = getattr(config, "max_position_embeddings", 8192) dual_chunk_attention_config = getattr( config, "dual_chunk_attention_config", None ) self.self_attn = Qwen3MoeAttention( hidden_size=self.hidden_size, num_heads=config.num_attention_heads, num_kv_heads=config.num_key_value_heads, rope_parameters=config.rope_parameters, max_position_embeddings=max_position_embeddings, rms_norm_eps=config.rms_norm_eps, qkv_bias=getattr(config, "attention_bias", False), head_dim=getattr(config, "head_dim", None), cache_config=cache_config, quant_config=quant_config, prefix=f"{prefix}.self_attn", dual_chunk_attention_config=dual_chunk_attention_config, ) # `mlp_only_layers` in the config. layer_idx = extract_layer_index(prefix) mlp_only_layers = ( [] if not hasattr(config, "mlp_only_layers") else config.mlp_only_layers ) if (layer_idx not in mlp_only_layers) and ( config.num_experts > 0 and (layer_idx + 1) % config.decoder_sparse_step == 0 ): self.mlp = Qwen3MoeSparseMoeBlock( vllm_config=vllm_config, prefix=f"{prefix}.mlp" ) else: self.mlp = Qwen3MoeMLP( hidden_size=config.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]: # Self Attention if residual is None: residual = hidden_states hidden_states = self.input_layernorm(hidden_states) else: hidden_states, residual = self.input_layernorm(hidden_states, residual) hidden_states = self.self_attn( positions=positions, hidden_states=hidden_states, ) # Fully Connected hidden_states, residual = self.post_attention_layernorm(hidden_states, residual) hidden_states = self.mlp(hidden_states) return hidden_states, residual @support_torch_compile class Qwen3MoeModel(nn.Module, EagleModelMixin): def __init__( self, *, vllm_config: VllmConfig, prefix: str = "", decoder_layer_type: type[torch.nn.Module] = Qwen3MoeDecoderLayer, ): super().__init__() config = vllm_config.model_config.hf_text_config quant_config = vllm_config.quant_config parallel_config = vllm_config.parallel_config eplb_config = parallel_config.eplb_config self.num_redundant_experts = eplb_config.num_redundant_experts self.vocab_size = config.vocab_size self.config = config self.quant_config = quant_config self.embed_tokens = VocabParallelEmbedding( config.vocab_size, config.hidden_size, quant_config=quant_config, prefix=f"{prefix}.embed_tokens", ) self.start_layer, self.end_layer, self.layers = make_layers( config.num_hidden_layers, lambda prefix: decoder_layer_type(vllm_config=vllm_config, prefix=prefix), prefix=f"{prefix}.layers", ) self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory( ["hidden_states", "residual"], config.hidden_size ) def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor: return self.embed_tokens(input_ids) def forward( self, input_ids: torch.Tensor | None, positions: torch.Tensor, intermediate_tensors: IntermediateTensors | None = None, inputs_embeds: torch.Tensor | None = None, ) -> torch.Tensor | IntermediateTensors | tuple[torch.Tensor, list[torch.Tensor]]: if get_pp_group().is_first_rank: if inputs_embeds is not None: hidden_states = inputs_embeds else: hidden_states = self.embed_input_ids(input_ids) residual = None else: assert intermediate_tensors is not None hidden_states = intermediate_tensors["hidden_states"] residual = intermediate_tensors["residual"] aux_hidden_states = self._maybe_add_hidden_state( [], self.start_layer, hidden_states, residual ) for layer_idx, layer in enumerate( islice(self.layers, self.start_layer, self.end_layer), start=self.start_layer, ): hidden_states, residual = layer(positions, hidden_states, residual) self._maybe_add_hidden_state( aux_hidden_states, layer_idx + 1, hidden_states, residual ) if not get_pp_group().is_last_rank: return IntermediateTensors( {"hidden_states": hidden_states, "residual": residual} ) hidden_states, _ = self.norm(hidden_states, residual) # Return auxiliary hidden states if collected if len(aux_hidden_states) > 0: return hidden_states, aux_hidden_states return hidden_states def get_expert_mapping(self) -> list[tuple[str, str, int, str]]: # Params for weights, fp8 weight scales, fp8 activation scales # (param_name, weight_name, expert_id, shard_id) return SharedFusedMoE.make_expert_params_mapping( self, ckpt_gate_proj_name="gate_proj", ckpt_down_proj_name="down_proj", ckpt_up_proj_name="up_proj", num_experts=self.config.num_experts, num_redundant_experts=self.num_redundant_experts, ) def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("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), ] # Skip loading extra parameters for GPTQ/modelopt models. ignore_suffixes = ( ".bias", "_bias", ".weight_scale", "_weight_scale", ".input_scale", "_input_scale", ) params_dict = dict(self.named_parameters()) loaded_params: set[str] = set() expert_params_mapping = self.get_expert_mapping() for name, loaded_weight in weights: if self.quant_config is not None and ( scale_name := self.quant_config.get_cache_scale(name) ): # Loading kv cache quantization scales param = params_dict[scale_name] weight_loader = getattr(param, "weight_loader", default_weight_loader) assert loaded_weight.numel() == 1, ( f"KV scale numel {loaded_weight.numel()} != 1" ) loaded_weight = loaded_weight.squeeze() weight_loader(param, loaded_weight) loaded_params.add(scale_name) continue if "scale" in name or "zero_point" in name: name = maybe_remap_kv_scale_name(name, params_dict) if name is None: continue for param_name, weight_name, shard_id in stacked_params_mapping: # Skip non-stacked layers and experts (experts handled below). if weight_name not in name: continue # We have mlp.experts[0].gate_proj in the checkpoint. # Since we handle the experts below in expert_params_mapping, # we need to skip here BEFORE we update the name, otherwise # name will be updated to mlp.experts[0].gate_up_proj, which # will then be updated below in expert_params_mapping # for mlp.experts[0].gate_gate_up_proj, which breaks load. if "mlp.experts" in name: continue name = name.replace(weight_name, param_name) # Skip loading extra parameters for GPTQ/modelopt models. if name.endswith(ignore_suffixes) and name not in params_dict: continue # Skip layers on other devices. if is_pp_missing_parameter(name, self): continue if name.endswith("scale"): # Remapping the name of FP8 kv-scale. name = maybe_remap_kv_scale_name(name, params_dict) if name is None: continue if name not in params_dict: continue param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) if weight_loader == default_weight_loader: weight_loader(param, loaded_weight) else: weight_loader(param, loaded_weight, shard_id) break else: is_expert_weight = False for mapping in expert_params_mapping: param_name, weight_name, expert_id, shard_id = mapping if weight_name not in name: continue # Anyway, this is an expert weight and should not be # attempted to load as other weights later is_expert_weight = True # Do not modify `name` since the loop may continue here # Instead, create a new variable name_mapped = name.replace(weight_name, param_name) if is_pp_missing_parameter(name_mapped, self): continue # Skip loading extra parameters for GPTQ/modelopt models. if ( name_mapped.endswith(ignore_suffixes) and name_mapped not in params_dict ): continue param = params_dict[name_mapped] # We should ask the weight loader to return success or not # here since otherwise we may skip experts with other # available replicas. weight_loader = typing.cast( Callable[..., bool], param.weight_loader ) success = weight_loader( param, loaded_weight, name_mapped, shard_id=shard_id, expert_id=expert_id, return_success=True, ) if success: name = name_mapped break else: if is_expert_weight: # We've checked that this is an expert weight # However it's not mapped locally to this rank # So we simply skip it continue # Skip loading extra parameters for GPTQ/modelopt models. if name.endswith(ignore_suffixes) and name not in params_dict: continue # Skip layers on other devices. if is_pp_missing_parameter(name, self): continue if name not in params_dict: continue 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 Qwen3MoeForCausalLM( nn.Module, SupportsPP, SupportsLoRA, SupportsEagle, SupportsEagle3, MixtureOfExperts ): packed_modules_mapping = { "qkv_proj": [ "q_proj", "k_proj", "v_proj", ] } embedding_modules = { "embed_tokens": "input_embeddings", "lm_head": "output_embeddings", } fall_back_to_pt_during_load = False def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() config = vllm_config.model_config.hf_text_config quant_config = vllm_config.quant_config self.config = config self.quant_config = quant_config # Only perform the following mapping when Qwen3MoeMLP exists if getattr(config, "mlp_only_layers", []): self.packed_modules_mapping["gate_up_proj"] = ["gate_proj", "up_proj"] self.model = Qwen3MoeModel( vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model") ) self.lm_head = ParallelLMHead( config.vocab_size, config.hidden_size, quant_config=quant_config, prefix=maybe_prefix(prefix, "lm_head"), ) if self.config.tie_word_embeddings: self.lm_head.weight = self.model.embed_tokens.weight self.logits_processor = LogitsProcessor(config.vocab_size) self.make_empty_intermediate_tensors = ( self.model.make_empty_intermediate_tensors ) # Set MoE hyperparameters self.expert_weights = [] self.moe_layers = [] example_layer = None for layer in self.model.layers: if isinstance(layer, PPMissingLayer): continue assert isinstance(layer, Qwen3MoeDecoderLayer) if isinstance(layer.mlp, Qwen3MoeSparseMoeBlock): example_layer = layer.mlp self.moe_layers.append(layer.mlp.experts) if example_layer is None: raise RuntimeError("No Qwen3MoE layer found in the model.layers.") self.num_moe_layers = len(self.moe_layers) self.num_expert_groups = 1 self.num_shared_experts = 0 self.num_logical_experts = example_layer.n_logical_experts self.num_physical_experts = example_layer.n_physical_experts self.num_local_physical_experts = example_layer.n_local_physical_experts self.num_routed_experts = example_layer.n_routed_experts self.num_redundant_experts = example_layer.n_redundant_experts def update_physical_experts_metadata( self, num_physical_experts: int, num_local_physical_experts: int, ) -> None: assert self.num_local_physical_experts == num_local_physical_experts self.num_physical_experts = num_physical_experts self.num_local_physical_experts = num_local_physical_experts self.num_redundant_experts = num_physical_experts - self.num_logical_experts for layer in self.model.layers: if isinstance(layer.mlp, Qwen3MoeSparseMoeBlock): moe = layer.mlp moe.n_local_physical_experts = num_local_physical_experts moe.n_physical_experts = num_physical_experts moe.n_redundant_experts = self.num_redundant_experts moe.experts.update_expert_map() def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor: return self.model.embed_input_ids(input_ids) def forward( self, input_ids: torch.Tensor | None, positions: torch.Tensor, intermediate_tensors: IntermediateTensors | None = None, inputs_embeds: torch.Tensor | None = None, ) -> torch.Tensor | IntermediateTensors: hidden_states = self.model( input_ids, positions, intermediate_tensors, inputs_embeds ) return hidden_states def compute_logits( self, hidden_states: torch.Tensor, ) -> torch.Tensor | None: logits = self.logits_processor(self.lm_head, hidden_states) return logits def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: loader = AutoWeightsLoader(self) return loader.load_weights(weights) def get_expert_mapping(self) -> list[tuple[str, str, int, str]]: return self.model.get_expert_mapping()