# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project """Inference-only Qwen3Next model.""" from collections.abc import Iterable from itertools import islice import torch from torch import nn from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig, ModelConfig, VllmConfig from vllm.distributed import ( get_ep_group, get_pp_group, get_tensor_model_parallel_world_size, tensor_model_parallel_all_gather, tensor_model_parallel_reduce_scatter, ) from vllm.model_executor.layers.attention import Attention from vllm.model_executor.layers.fused_moe import FusedMoEFactory from vllm.model_executor.layers.fused_moe.utils import ( is_model_fused_shared_expert_compatible, resolve_layer_fused_shared_expert, ) from vllm.model_executor.layers.fused_qk_norm_rope import fused_qk_rmsnorm_rope_gate from vllm.model_executor.layers.layernorm import ( GemmaRMSNorm as Qwen3NextRMSNorm, ) from vllm.model_executor.layers.linear import ( QKVParallelLinear, ReplicatedLinear, RowParallelLinear, ) from vllm.model_executor.layers.logits_processor import LogitsProcessor from vllm.model_executor.layers.mamba.gdn.qwen_gdn_linear_attn import ( QwenGatedDeltaNetAttention, ) from vllm.model_executor.layers.mamba.mamba_utils import ( MambaStateCopyFunc, MambaStateCopyFuncCalculator, MambaStateDtypeCalculator, MambaStateShapeCalculator, ) from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.quantization.utils.config_utils import ( get_quark_ocp_mx_group_size, ) from vllm.model_executor.layers.rotary_embedding import MRotaryEmbedding, get_rope from vllm.model_executor.layers.vocab_parallel_embedding import ( ParallelLMHead, VocabParallelEmbedding, ) from vllm.model_executor.models.qwen2_moe import Qwen2MoeMLP as Qwen3NextMLP from vllm.model_executor.models.utils import sequence_parallel_chunk from vllm.platforms import current_platform from vllm.sequence import IntermediateTensors from vllm.transformers_utils.configs.qwen3_next import Qwen3NextConfig from vllm.v1.attention.backend import AttentionType from .interfaces import ( EagleModelMixin, HasInnerState, IsHybrid, MixtureOfExperts, SupportsEagle3, SupportsLoRA, SupportsPP, ) from .utils import ( AutoWeightsLoader, PPMissingLayer, WeightsMapper, extract_layer_index, make_empty_intermediate_tensors_factory, make_layers, maybe_fuse_shared_experts, maybe_prefix, ) KVCache = tuple[torch.Tensor, torch.Tensor] def _should_use_sequence_parallel(vllm_config: VllmConfig) -> bool: config = vllm_config.model_config.hf_text_config parallel_config = vllm_config.parallel_config return ( parallel_config.use_sequence_parallel_moe and parallel_config.pipeline_parallel_size == 1 and getattr(config, "num_experts", 0) > 0 and not getattr(config, "mlp_only_layers", []) and getattr(config, "decoder_sparse_step", 1) == 1 ) def _should_replicate_misaligned_shared_expert( intermediate_size: int, tp_size: int, group_size: int | None, enable_expert_parallel: bool, is_sequence_parallel: bool, ) -> bool: if intermediate_size <= 0 or group_size is None: return False partition_size, remainder = divmod(intermediate_size, tp_size) if remainder == 0 and partition_size % group_size == 0: return False if enable_expert_parallel or is_sequence_parallel: return True if remainder != 0: raise ValueError( f"Shared-expert intermediate size {intermediate_size} must be " f"divisible by tensor-parallel size {tp_size}." ) raise ValueError( "The Quark OCP MX shared expert cannot be tensor-parallelized: " f"intermediate size {intermediate_size} with TP size {tp_size} " f"produces a partition of {partition_size}, which is not divisible by " f"the OCP MX group size {group_size}. Choose a compatible " "tensor-parallel size or enable expert parallelism." ) class Qwen3NextSparseMoeBlock(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_size = self.ep_group.size() self.n_routed_experts = config.num_experts self.is_sequence_parallel = parallel_config.use_sequence_parallel_moe shared_expert_group_size = get_quark_ocp_mx_group_size( quant_config, f"{prefix}.shared_expert.down_proj", ) self.replicate_shared_expert = _should_replicate_misaligned_shared_expert( config.shared_expert_intermediate_size, self.tp_size, shared_expert_group_size, parallel_config.enable_expert_parallel, self.is_sequence_parallel, ) 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. 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.gate = ReplicatedLinear( config.hidden_size, config.num_experts, bias=False, quant_config=None, prefix=f"{prefix}.gate", ) self.shared_expert_gate = ReplicatedLinear( config.hidden_size, 1, bias=False, quant_config=None, prefix=f"{prefix}.shared_expert_gate", ) self.is_fused_shared_expert_enabled = False if ( config.shared_expert_intermediate_size > 0 and not self.replicate_shared_expert ): self.is_fused_shared_expert_enabled = resolve_layer_fused_shared_expert( quant_config, prefix, shared_expert_name="shared_expert", ) if ( self.is_fused_shared_expert_enabled or config.shared_expert_intermediate_size <= 0 ): self.shared_expert = None else: self.shared_expert = Qwen3NextMLP( hidden_size=config.hidden_size, intermediate_size=config.shared_expert_intermediate_size, hidden_act=config.hidden_act, quant_config=quant_config, reduce_results=False, expert_gate=self.shared_expert_gate, is_sequence_parallel=self.is_sequence_parallel, disable_tp=self.replicate_shared_expert, prefix=f"{prefix}.shared_expert", ) self.experts = FusedMoEFactory( shared_experts=( None if self.replicate_shared_expert else 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, renormalize=getattr(config, "norm_topk_prob", True), 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, n_shared_experts=1 if self.shared_expert is None else None, fuse_shared_experts=self.is_fused_shared_expert_enabled, shared_expert_gate=self.shared_expert_gate if self.shared_expert is None else None, ) def forward( self, hidden_states: torch.Tensor, already_sequence_parallel: bool = False, ) -> torch.Tensor: # NOTE: hidden_states can have either 1D or 2D shape. orig_shape = hidden_states.shape num_tokens, hidden_dim = hidden_states.shape hidden_states = hidden_states.view(-1, hidden_dim) if self.is_sequence_parallel and not already_sequence_parallel: hidden_states = sequence_parallel_chunk(hidden_states) replicated_shared_output = ( self.shared_expert(hidden_states) if self.replicate_shared_expert and self.shared_expert is not None else None ) final_hidden_states = self.experts( hidden_states=hidden_states, router_logits=hidden_states ) if replicated_shared_output is not None: final_hidden_states += replicated_shared_output if self.is_sequence_parallel and not already_sequence_parallel: final_hidden_states = tensor_model_parallel_all_gather( final_hidden_states, 0 ) final_hidden_states = final_hidden_states[:num_tokens] return final_hidden_states.view(orig_shape) class Qwen3NextAttention(nn.Module): def __init__( self, config: Qwen3NextConfig, model_config: ModelConfig | None = None, cache_config: CacheConfig | None = None, quant_config: QuantizationConfig | None = None, reduce_results: bool = True, prefix: str = "", ) -> None: super().__init__() self.config = config self.hidden_size = config.hidden_size tp_size = get_tensor_model_parallel_world_size() self.total_num_heads = config.num_attention_heads assert self.total_num_heads % tp_size == 0 self.num_heads = self.total_num_heads // tp_size self.total_num_kv_heads = config.num_key_value_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 = config.head_dim or (self.hidden_size // self.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.dual_chunk_attention_config = getattr( config, "dual_chunk_attention_config", None ) self.attn_output_gate = getattr(config, "attn_output_gate", True) self.qkv_proj = QKVParallelLinear( config.hidden_size, self.head_dim, self.total_num_heads * (1 + self.attn_output_gate), self.total_num_kv_heads, bias=getattr(config, "qkv_bias", False), quant_config=quant_config, prefix=f"{prefix}.qkv_proj", ) self.o_proj = RowParallelLinear( self.total_num_heads * self.head_dim, config.hidden_size, bias=False, reduce_results=reduce_results, quant_config=quant_config, prefix=f"{prefix}.o_proj", ) self.rotary_emb = get_rope( head_size=self.head_dim, max_position=config.max_position_embeddings, rope_parameters=config.rope_parameters, dual_chunk_attention_config=self.dual_chunk_attention_config, ) # Late-interaction retrieval models (e.g. ColQwen3.5) run BIDIRECTIONAL # attention on the full_attention layers; they set config.is_causal=False # via a VerifyAndUpdateConfig handler. Generation models leave is_causal # unset (-> causal/DECODER), so this is a no-op for them. Mirrors qwen3.py. attn_type = ( AttentionType.DECODER if getattr(config, "is_causal", True) else AttentionType.ENCODER_ONLY ) 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", attn_type=attn_type, **{ "layer_idx": extract_layer_index(prefix), "dual_chunk_attention_config": self.dual_chunk_attention_config, } if self.dual_chunk_attention_config else {}, ) self.q_norm = Qwen3NextRMSNorm(self.head_dim, eps=config.rms_norm_eps) self.k_norm = Qwen3NextRMSNorm(self.head_dim, eps=config.rms_norm_eps) # Fuse the gated split + QK-RMSNorm + (partial) NeoX RoPE + gate copy. mm_config = model_config.multimodal_config if model_config else None text_only = mm_config is None or mm_config.language_model_only mrope_section = getattr(self.rotary_emb, "mrope_section", None) supports_mrope = bool( type(self.rotary_emb) is MRotaryEmbedding and mrope_section and len(mrope_section) == 3 and sum(mrope_section) == self.rotary_emb.rotary_dim // 2 and getattr(self.rotary_emb, "mrope_interleaved", False) ) supports_dtype = getattr(self.rotary_emb, "dtype", None) in ( torch.float16, torch.bfloat16, ) self.use_fused_qk_norm_rope_gate = ( self.attn_output_gate and getattr(self.rotary_emb, "is_neox_style", False) and current_platform.is_cuda() and supports_dtype and (text_only or supports_mrope) ) def _project_qkv_gate( self, qkv: torch.Tensor, positions: torch.Tensor, ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor | None]: """Return post-norm, post-RoPE (q, k, v) and the pre-sigmoid gate. Dispatches between the fused Triton kernel and the eager split + QK-RMSNorm + RoPE path. ``gate`` is ``None`` when output gating is disabled. """ if self.use_fused_qk_norm_rope_gate: q_gate, k, v = qkv.split( [self.q_size * 2, self.kv_size, self.kv_size], dim=-1 ) if positions.ndim == 2 and not getattr( self.rotary_emb, "mrope_section", None ): positions = positions[0] q, k, gate = fused_qk_rmsnorm_rope_gate( q_gate, k, self.q_norm.weight, self.k_norm.weight, self.rotary_emb.cos_sin_cache, positions, self.q_norm.variance_epsilon, self.num_heads, self.num_kv_heads, self.head_dim, self.rotary_emb.rotary_dim, norm_beta=1.0, mrope_section=( getattr(self.rotary_emb, "mrope_section", None) if positions.ndim == 2 else None ), ) return q, k, v, gate if self.attn_output_gate: q_gate, k, v = qkv.split( [self.q_size * 2, self.kv_size, self.kv_size], dim=-1 ) orig_shape = q_gate.shape[:-1] q_gate = q_gate.view(*orig_shape, self.num_heads, -1) q, gate = torch.chunk(q_gate, 2, dim=-1) q = q.reshape(*orig_shape, -1) gate = gate.reshape(*orig_shape, -1) else: q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1) gate = None q = self.q_norm(q.view(-1, self.num_heads, self.head_dim)).view( -1, self.num_heads * self.head_dim ) k = self.k_norm(k.view(-1, self.num_kv_heads, self.head_dim)).view( -1, self.num_kv_heads * self.head_dim ) q, k = self.rotary_emb(positions, q, k) return q, k, v, gate def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, ) -> torch.Tensor: qkv, _ = self.qkv_proj(hidden_states) q, k, v, gate = self._project_qkv_gate(qkv, positions) attn_output = self.attn(q, k, v) if gate is not None: attn_output = attn_output * torch.sigmoid(gate) output, _ = self.o_proj(attn_output) return output class Qwen3NextDecoderLayer(nn.Module): def __init__( self, vllm_config: VllmConfig, layer_type: str, prefix: str = "", ) -> None: super().__init__() config = vllm_config.model_config.hf_config model_config = vllm_config.model_config cache_config = vllm_config.cache_config quant_config = vllm_config.quant_config self.layer_type = layer_type self.layer_idx = extract_layer_index(prefix) mlp_only_layers = ( [] if not hasattr(config, "mlp_only_layers") else config.mlp_only_layers ) is_moe_layer = (self.layer_idx not in mlp_only_layers) and ( config.num_experts > 0 and (self.layer_idx + 1) % config.decoder_sparse_step == 0 ) self.use_attn_reduce_scatter_for_moe = _should_use_sequence_parallel( vllm_config ) if self.layer_type == "linear_attention": self.linear_attn = QwenGatedDeltaNetAttention( config, vllm_config=vllm_config, prefix=f"{prefix}.linear_attn", gqa_interleaved_layout=True, reduce_results=not self.use_attn_reduce_scatter_for_moe, ) elif self.layer_type == "full_attention": self.self_attn = Qwen3NextAttention( config, model_config=model_config, cache_config=cache_config, quant_config=quant_config, reduce_results=not self.use_attn_reduce_scatter_for_moe, prefix=f"{prefix}.self_attn", ) else: raise ValueError(f"Invalid layer_type {self.layer_type}") if is_moe_layer: self.mlp = Qwen3NextSparseMoeBlock( vllm_config=vllm_config, prefix=f"{prefix}.mlp", ) else: self.mlp = Qwen3NextMLP( 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 = Qwen3NextRMSNorm( config.hidden_size, eps=config.rms_norm_eps ) self.post_attention_layernorm = Qwen3NextRMSNorm( config.hidden_size, eps=config.rms_norm_eps ) self.layer_scale = getattr(config, "layer_scale", False) if self.layer_scale: self.attn_layer_scale = torch.nn.Parameter( torch.zeros( 1, 1, config.hidden_size, ), ) self.ffn_layer_scale = torch.nn.Parameter( torch.zeros( 1, 1, config.hidden_size, ), ) def forward( self, hidden_states: torch.Tensor, residual: torch.Tensor | None, positions: torch.Tensor, **kwargs: object, ): full_num_tokens = positions.shape[-1] if residual is None: residual = hidden_states hidden_states = self.input_layernorm(hidden_states) else: hidden_states, residual = self.input_layernorm(hidden_states, residual) if self.use_attn_reduce_scatter_for_moe: hidden_states = tensor_model_parallel_all_gather(hidden_states, 0) hidden_states = hidden_states[:full_num_tokens] if self.layer_type == "linear_attention": hidden_states = self.linear_attn(hidden_states=hidden_states) elif self.layer_type == "full_attention": hidden_states = self.self_attn( hidden_states=hidden_states, positions=positions, ) else: raise ValueError("Invalid layer_type") if self.layer_scale: if len(hidden_states.shape) == 2: hidden_states = hidden_states * ( self.attn_layer_scale.to(hidden_states.dtype)[0] + 1 ) else: hidden_states = hidden_states * ( self.attn_layer_scale.to(hidden_states.dtype) + 1 ) if self.use_attn_reduce_scatter_for_moe: tp_world_size = get_tensor_model_parallel_world_size() # small trick using minus, eg. -17 % 8 = 7 sp_pad = (-hidden_states.shape[0]) % tp_world_size # pad if not divisible by world size hidden_states = torch.nn.functional.pad(hidden_states, (0, 0, 0, sp_pad)) hidden_states = tensor_model_parallel_reduce_scatter(hidden_states, 0) # Fully Connected hidden_states, residual = self.post_attention_layernorm(hidden_states, residual) if self.use_attn_reduce_scatter_for_moe: hidden_states = self.mlp( hidden_states, already_sequence_parallel=True, ) else: hidden_states = self.mlp(hidden_states) if self.layer_scale: if len(hidden_states.shape) == 2: hidden_states = hidden_states * ( self.ffn_layer_scale.to(hidden_states.dtype)[0] + 1 ) else: assert len(hidden_states.shape) == len(self.ffn_layer_scale.shape), ( f"shape must be the same {len(hidden_states.shape)}, " f"{len(self.ffn_layer_scale.shape)}" ) hidden_states = hidden_states * ( self.ffn_layer_scale.to(hidden_states.dtype) + 1 ) return hidden_states, residual @support_torch_compile class Qwen3NextModel(nn.Module, EagleModelMixin): hf_to_vllm_mapper = WeightsMapper( orig_to_new_stacked={ # weight_name: (param_name, shard_id) ".q_proj": (".qkv_proj", "q"), ".k_proj": (".qkv_proj", "k"), ".v_proj": (".qkv_proj", "v"), ".mlp.gate_proj": (".mlp.gate_up_proj", 0), ".mlp.up_proj": (".mlp.gate_up_proj", 1), ".shared_expert.gate_proj": (".shared_expert.gate_up_proj", 0), ".shared_expert.up_proj": (".shared_expert.gate_up_proj", 1), } ) def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() config: Qwen3NextConfig = vllm_config.model_config.hf_text_config parallel_config = vllm_config.parallel_config eplb_config = parallel_config.eplb_config self.num_redundant_experts = eplb_config.num_redundant_experts self.config = config self.vocab_size = config.vocab_size self.embed_tokens = VocabParallelEmbedding( self.vocab_size, config.hidden_size, ) def get_layer(prefix: str): return Qwen3NextDecoderLayer( vllm_config, layer_type=config.layer_types[extract_layer_index(prefix)], prefix=prefix, ) self.start_layer, self.end_layer, self.layers = make_layers( config.num_hidden_layers, get_layer, prefix=f"{prefix}.layers" ) self.is_fused_shared_expert_enabled = is_model_fused_shared_expert_compatible( self.layers, Qwen3NextSparseMoeBlock, "mlp", ) self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory( ["hidden_states", "residual"], config.hidden_size ) if get_pp_group().is_last_rank: self.norm = Qwen3NextRMSNorm(config.hidden_size, eps=config.rms_norm_eps) else: self.norm = PPMissingLayer() self.aux_hidden_state_layers: tuple[int, ...] = () def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor: return self.embed_tokens(input_ids) @property def use_sequence_parallel(self) -> bool: return self.layers[self.start_layer].use_attn_reduce_scatter_for_moe 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"] full_num_tokens = positions.shape[-1] if self.use_sequence_parallel: hidden_states = sequence_parallel_chunk(hidden_states) assert residual is None aux_hidden_states = self._maybe_add_hidden_state([], 0, 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=positions, hidden_states=hidden_states, residual=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) if self.use_sequence_parallel: if aux_hidden_states: hidden_size = hidden_states.shape[-1] hidden_states = torch.cat([hidden_states, *aux_hidden_states], dim=-1) hidden_states = tensor_model_parallel_all_gather(hidden_states, 0) hidden_states = hidden_states[:full_num_tokens] hidden_states, *aux_hidden_states = hidden_states.split( hidden_size, dim=-1 ) else: hidden_states = tensor_model_parallel_all_gather(hidden_states, 0) hidden_states = hidden_states[:full_num_tokens] if aux_hidden_states: return hidden_states, aux_hidden_states return hidden_states def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: weights = maybe_fuse_shared_experts( weights, enabled=self.is_fused_shared_expert_enabled, n_routed_experts=getattr(self.config, "num_experts", 0), n_shared_experts=1, ckpt_prefix="mlp.shared_expert", ) loader = AutoWeightsLoader(self) return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper) class QwenNextMixtureOfExperts(MixtureOfExperts): 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, Qwen3NextSparseMoeBlock): 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 set_moe_parameters(self): self.moe_layers = [] example_moe = None for layer in self.model.layers: if isinstance(layer, Qwen3NextDecoderLayer) and isinstance( layer.mlp, Qwen3NextSparseMoeBlock ): example_moe = layer.mlp self.moe_layers.append(layer.mlp.experts) if example_moe is None: raise RuntimeError("No Qwen3Next layer found in the model.layers.") # Set MoE hyperparameters self.num_moe_layers = len(self.moe_layers) self.num_expert_groups = 1 self.num_shared_experts = 0 self.num_logical_experts = example_moe.n_logical_experts self.num_physical_experts = example_moe.n_physical_experts self.num_local_physical_experts = example_moe.n_local_physical_experts self.num_routed_experts = example_moe.n_routed_experts self.num_redundant_experts = example_moe.n_redundant_experts class Qwen3NextForCausalLM( nn.Module, HasInnerState, SupportsLoRA, SupportsPP, QwenNextMixtureOfExperts, IsHybrid, SupportsEagle3, ): # MTP weights are loaded by the draft model, not this one. hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={"mtp.": None}) packed_modules_mapping = { "qkv_proj": [ "q_proj", "k_proj", "v_proj", ], "gate_up_proj": ["gate_proj", "up_proj"], "in_proj_qkvz": ["in_proj_qkvz"], "in_proj_ba": ["in_proj_ba"], } def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): config = vllm_config.model_config.hf_text_config self.vllm_config = vllm_config self.model_config = vllm_config.model_config cache_config = vllm_config.cache_config scheduler_config = vllm_config.scheduler_config if cache_config.mamba_cache_mode == "all": raise NotImplementedError( "Qwen3Next currently does not support 'all' prefix caching, " "please use '--mamba-cache-mode=align' instead" ) self.quant_config = vllm_config.quant_config super().__init__() self.config = config self.scheduler_config = scheduler_config self.model = Qwen3NextModel( vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model") ) self.lm_head = ParallelLMHead( config.vocab_size, config.hidden_size, prefix=maybe_prefix(prefix, "lm_head"), ) self.logits_processor = LogitsProcessor(config.vocab_size) self.make_empty_intermediate_tensors = ( self.model.make_empty_intermediate_tensors ) # Set MoE hyperparameters self.set_moe_parameters() 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, **kwargs: object, ): hidden_states = self.model( input_ids, positions, intermediate_tensors, inputs_embeds ) return hidden_states @classmethod def get_mamba_state_dtype_from_config( cls, vllm_config: "VllmConfig", ) -> tuple[torch.dtype, torch.dtype]: return MambaStateDtypeCalculator.gated_delta_net_state_dtype( vllm_config.model_config.dtype, vllm_config.cache_config.mamba_cache_dtype, vllm_config.cache_config.mamba_ssm_cache_dtype, ) @classmethod def get_mamba_state_shape_from_config( cls, vllm_config: "VllmConfig" ) -> tuple[tuple[int, int], tuple[int, int]]: parallel_config = vllm_config.parallel_config hf_config = vllm_config.model_config.hf_text_config tp_size = parallel_config.tensor_parallel_size num_spec = ( vllm_config.speculative_config.num_speculative_tokens if vllm_config.speculative_config else 0 ) return MambaStateShapeCalculator.gated_delta_net_state_shape( tp_size, hf_config.linear_num_key_heads, hf_config.linear_num_value_heads, hf_config.linear_key_head_dim, hf_config.linear_value_head_dim, hf_config.linear_conv_kernel_dim, num_spec, ) @classmethod def get_mamba_state_copy_func(cls) -> tuple[MambaStateCopyFunc, MambaStateCopyFunc]: return MambaStateCopyFuncCalculator.gated_delta_net_state_copy_func() def compute_logits( self, hidden_states: torch.Tensor, ) -> torch.Tensor | None: return self.logits_processor(self.lm_head, hidden_states) def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: loader = AutoWeightsLoader(self) return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)