# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved. # Copyright 2024 The Qwen team. # Copyright 2023 The vLLM team. # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. # # 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. # Adapted from vllm/model_executor/models/qwen3_moe.py # This file is a part of the vllm-ascend project. from typing import Any, List, Optional, Union import torch from torch import nn from transformers import PretrainedConfig from vllm.attention import Attention, AttentionMetadata from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig, CompilationMode, VllmConfig from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size from vllm.distributed.parallel_state import (get_dp_group, get_ep_group, get_tp_group) from vllm.forward_context import get_forward_context from vllm.model_executor.layers.fused_moe.layer import FusedMoE 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 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.models.interfaces import (MixtureOfExperts, SupportsLoRA, SupportsPP) from vllm.model_executor.models.qwen3_moe import (Qwen3MoeAttention, Qwen3MoeDecoderLayer, Qwen3MoeForCausalLM, Qwen3MoeMLP, Qwen3MoeModel, Qwen3MoeSparseMoeBlock) from vllm.model_executor.models.utils import ( PPMissingLayer, extract_layer_index, make_empty_intermediate_tensors_factory, make_layers, maybe_prefix) from vllm.sequence import IntermediateTensors from vllm_ascend.ascend_config import get_ascend_config from vllm_ascend.attention.attention_v1 import AscendAttentionState from vllm_ascend.torchair.ops.sequence_parallel import (MetadataForPadding, init_metadata_for_sp) from vllm_ascend.torchair.ops.torchair_fused_moe import TorchairAscendFusedMoE class CustomSparseMoeBlock(Qwen3MoeSparseMoeBlock): def __init__( self, config: PretrainedConfig, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ): nn.Module.__init__(self) self.tp_size = get_tensor_model_parallel_world_size() 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}.") self.gate = ReplicatedLinear( config.hidden_size, config.num_experts, bias=False, quant_config=None, prefix=f"{prefix}.gate", ) self.experts = TorchairAscendFusedMoE( num_experts=config.num_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", ) self.top_k = config.num_experts_per_tok self.dp_size = get_dp_group().world_size self.tp_group = get_tp_group().device_group self.tp_rank = get_tp_group().rank_in_group self.ep_group = get_ep_group() self.params_dtype = torch.get_default_dtype() def forward( self, hidden_states, attn_metadata=None, _metadata_for_padding: Optional[MetadataForPadding] = None, ): if attn_metadata is None: attn_metadata = get_forward_context().attn_metadata # when profile runs, force experts to load balanced tokens # to avoid high memory consumption on a single rank. enable_force_load_balance = get_forward_context().in_profile_run is_prefill = get_forward_context().with_prefill # router_logits: (num_tokens, n_experts) router_logits, _ = self.gate(hidden_states) hidden_states = self.experts( hidden_states=hidden_states, router_logits=router_logits, is_prefill=is_prefill, top_k=self.top_k, enable_force_load_balance=enable_force_load_balance, shared_experts=None, _metadata_for_padding=_metadata_for_padding, ) return hidden_states class CustomQwen3MoeAttention(Qwen3MoeAttention): def __init__( self, hidden_size: int, num_heads: int, num_kv_heads: int, rope_theta: float = 10000, rope_scaling: Optional[dict[str, Any]] = None, max_position_embeddings: int = 8192, head_dim: Optional[int] = None, rms_norm_eps: float = 1e-06, qkv_bias: bool = False, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ) -> None: nn.Module.__init__(self) 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.rope_theta = rope_theta self.max_position_embeddings = max_position_embeddings 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, rotary_dim=self.head_dim, max_position=max_position_embeddings, base=rope_theta, rope_scaling=rope_scaling, ) 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") self.q_norm = RMSNorm(self.head_dim, eps=rms_norm_eps) self.k_norm = RMSNorm(self.head_dim, eps=rms_norm_eps) ascend_config = get_ascend_config() self.torchair_graph_enabled = ascend_config.torchair_graph_config.enabled @staticmethod def normalize_qkv(qkv: torch.Tensor, q_size: int, kv_size: int, head_dim: int, q_norm, k_norm): q, k, v = qkv.split([q_size, kv_size, kv_size], dim=-1) q_by_head = q.view(*q.shape[:-1], q.shape[-1] // head_dim, head_dim) q_by_head = q_norm(q_by_head) q = q_by_head.view(q.shape) k_by_head = k.view(*k.shape[:-1], k.shape[-1] // head_dim, head_dim) k_by_head = k_norm(k_by_head) k = k_by_head.view(k.shape) return q, k, v def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, kv_cache: Optional[torch.Tensor] = None, attn_metadata: Optional[AttentionMetadata] = None) -> torch.Tensor: qkv, _ = self.qkv_proj(hidden_states) q, k, v = self.normalize_qkv(qkv, self.q_size, self.kv_size, self.head_dim, self.q_norm, self.k_norm) if (self.torchair_graph_enabled and attn_metadata is not None and attn_metadata.attn_state == AscendAttentionState.DecodeOnly): q, k = self.rotary_emb(positions, q, k, is_prefill=False, is_qwen_torchair=True) forward_kwargs = {} output_shape = q.shape output = torch.empty(output_shape, dtype=q.dtype, device=q.device) forward_kwargs['output'] = output attn_output = self.attn.impl.forward(self.attn, q, k, v, kv_cache=kv_cache, attn_metadata=attn_metadata, **forward_kwargs) output, _ = self.o_proj(attn_output) return output else: q, k = self.rotary_emb(positions, q, k, is_qwen_torchair=True) attn_output = self.attn(q, k, v) output, _ = self.o_proj(attn_output) return output class CustomQwen3MoeDecoderLayer(Qwen3MoeDecoderLayer): def __init__( self, config: PretrainedConfig, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, vllm_config: Optional[VllmConfig] = None, prefix: str = "", ) -> None: nn.Module.__init__(self) self.hidden_size = config.hidden_size rope_theta = getattr(config, "rope_theta", 10000) rope_scaling = getattr(config, "rope_scaling", None) max_position_embeddings = getattr(config, "max_position_embeddings", 8192) self.self_attn = CustomQwen3MoeAttention( hidden_size=self.hidden_size, num_heads=config.num_attention_heads, num_kv_heads=config.num_key_value_heads, rope_theta=rope_theta, rope_scaling=rope_scaling, 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", ) # `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) self.use_aclgraph = (vllm_config is not None and vllm_config.compilation_config.mode == CompilationMode.VLLM_COMPILE and not vllm_config.model_config.enforce_eager) if (layer_idx not in mlp_only_layers) and ( config.num_experts > 0 and (layer_idx + 1) % config.decoder_sparse_step == 0): if not self.use_aclgraph: # FIXME: custom sparse moe block doesn't work with aclgraph. self.mlp = CustomSparseMoeBlock(config=config, quant_config=quant_config, prefix=f"{prefix}.mlp") else: 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) self.enable_sequence_parallelism = ( vllm_config.compilation_config.pass_config. enable_sequence_parallelism if vllm_config is not None else False) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, residual: Optional[torch.Tensor], kv_cache: Optional[torch.Tensor] = None, attn_metadata: Optional[AttentionMetadata] = None, _metadata_for_padding: Optional[MetadataForPadding] = None, ) -> torch.Tensor: # To prevent precision issues during the decoder phase when only prefilling enables SP if not self.enable_sequence_parallelism: self.self_attn.o_proj.reduce_results = True else: self.self_attn.o_proj.reduce_results = not _metadata_for_padding.not_dummy_and_is_prefill if _metadata_for_padding is not None else True # Self Attention if residual is None: residual = hidden_states if _metadata_for_padding and _metadata_for_padding.not_dummy_and_is_prefill: residual = _metadata_for_padding.padding_slice(residual) hidden_states = self.input_layernorm(hidden_states) else: hidden_states, residual = self.input_layernorm( hidden_states, residual) if _metadata_for_padding and _metadata_for_padding.not_dummy_and_is_prefill: hidden_states = _metadata_for_padding.allgather_unpadding_aligned( hidden_states) hidden_states = self.self_attn( positions=positions, hidden_states=hidden_states, kv_cache=kv_cache, attn_metadata=attn_metadata, ) if _metadata_for_padding and _metadata_for_padding.not_dummy_and_is_prefill: hidden_states = _metadata_for_padding.padding_aligned_reduce_scatter( hidden_states) # Fully Connected hidden_states, residual = self.post_attention_layernorm( hidden_states, residual) if not self.use_aclgraph: hidden_states = self.mlp( hidden_states, _metadata_for_padding=_metadata_for_padding) else: hidden_states = self.mlp(hidden_states) return hidden_states, residual @support_torch_compile class CustomQwen3MoeModel(Qwen3MoeModel): def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): nn.Module.__init__(self) config = vllm_config.model_config.hf_config cache_config = vllm_config.cache_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.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size self.config = config self.embed_tokens = VocabParallelEmbedding( config.vocab_size, config.hidden_size, prefix=f"{prefix}.embed_tokens") self.start_layer, self.end_layer, self.layers = make_layers( config.num_hidden_layers, lambda prefix: CustomQwen3MoeDecoderLayer( config=config, cache_config=cache_config, quant_config=quant_config, 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 forward( self, input_ids: torch.Tensor, positions: torch.Tensor, kv_caches: Optional[List[torch.Tensor]] = None, attn_metadata: Optional[AttentionMetadata] = None, intermediate_tensors: Optional[IntermediateTensors] = None, inputs_embeds: Optional[torch.Tensor] = None, _metadata_for_padding: Optional[MetadataForPadding] = None, ) -> Union[torch.Tensor, IntermediateTensors]: if get_pp_group().is_first_rank: if inputs_embeds is not None: hidden_states = inputs_embeds else: hidden_states = self.get_input_embeddings(input_ids) residual = None else: assert intermediate_tensors is not None hidden_states = intermediate_tensors["hidden_states"] residual = intermediate_tensors["residual"] for i in range(self.start_layer, self.end_layer): layer = self.layers[i] hidden_states, residual = layer( positions, hidden_states, residual, kv_caches[i - self.start_layer] if kv_caches is not None else None, attn_metadata, _metadata_for_padding=_metadata_for_padding) if not get_pp_group().is_last_rank: return IntermediateTensors({ "hidden_states": hidden_states, "residual": residual }) hidden_states, _ = self.norm(hidden_states, residual) if _metadata_for_padding and _metadata_for_padding.not_dummy_and_is_prefill: hidden_states = _metadata_for_padding.allgather_unpadding_aligned( hidden_states) return hidden_states class CustomQwen3MoeForCausalLM(Qwen3MoeForCausalLM): packed_modules_mapping = { "qkv_proj": [ "q_proj", "k_proj", "v_proj", ], "gate_up_proj": [ "gate_proj", "up_proj", ], "experts": ["experts.0.gate_proj", "experts.0.up_proj", "experts.0.down_proj"], } def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): nn.Module.__init__(self) SupportsPP.__init__(self) SupportsLoRA.__init__(self) MixtureOfExperts.__init__(self) config = vllm_config.model_config.hf_config quant_config = vllm_config.quant_config self.config = config self.quant_config = quant_config self.model = CustomQwen3MoeModel(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) self.enable_sequence_parallelism = vllm_config.compilation_config.pass_config.enable_sequence_parallelism # Set MoE hyperparameters self.expert_weights: list[torch.Tensor] = [] self.moe_layers: list[FusedMoE] = [] 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 def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, kv_caches: Optional[List[torch.Tensor]] = None, attn_metadata: Optional[AttentionMetadata] = None, intermediate_tensors: Optional[IntermediateTensors] = None, inputs_embeds: Optional[torch.Tensor] = None, ) -> Union[torch.Tensor, IntermediateTensors]: _metadata_for_padding = init_metadata_for_sp( input_ids, self.enable_sequence_parallelism) hidden_states = self.model(input_ids, positions, kv_caches, attn_metadata, intermediate_tensors, inputs_embeds, _metadata_for_padding) return hidden_states