# 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 Qwen3 model compatible with HuggingFace weights.""" from collections.abc import Iterable from typing import Any import torch from torch import nn from transformers import Qwen3Config from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig, VllmConfig from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size from vllm.logger import init_logger from vllm.model_executor.layers.attention.encoder_only_attention import ( Attention, EncoderOnlyAttention, ) from vllm.model_executor.layers.layernorm import RMSNorm, RMSNormQK from vllm.model_executor.layers.linear import QKVParallelLinear, 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 from vllm.sequence import IntermediateTensors from vllm.transformers_utils.config import set_default_rope_theta from vllm.v1.attention.backend import AttentionType from .interfaces import SupportsEagle3, SupportsLoRA, SupportsPP from .qwen2 import Qwen2MLP as Qwen3MLP from .qwen2 import Qwen2Model from .utils import AutoWeightsLoader, PPMissingLayer, extract_layer_index, maybe_prefix logger = init_logger(__name__) class Qwen3Attention(nn.Module): 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, qkv_bias: bool = False, cache_config: CacheConfig | None = None, quant_config: QuantizationConfig | None = None, prefix: str = "", attn_type: str = AttentionType.DECODER, 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.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, rope_parameters=rope_parameters, dual_chunk_attention_config=dual_chunk_attention_config, ) attn_cls = ( EncoderOnlyAttention if attn_type == AttentionType.ENCODER_ONLY else Attention ) self.attn = attn_cls( 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": 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) self.qk_norm = RMSNormQK(self.head_dim, 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.forward_native(q_by_head) # TODO(gyf) check why # 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.forward_native(k_by_head) # k = k_by_head.view(k.shape) q_by_head = q.view(*q.shape[:-1], q.shape[-1] // self.head_dim, self.head_dim) k_by_head = k.view(*k.shape[:-1], k.shape[-1] // self.head_dim, self.head_dim) out_q, out_k = self.qk_norm( q_by_head, k_by_head, self.q_norm.weight.data, self.k_norm.weight.data ) q = out_q.view(q.shape) k = out_k.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 Qwen3DecoderLayer(nn.Module): def __init__( self, config: Qwen3Config, 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) dual_chunk_attention_config = getattr( config, "dual_chunk_attention_config", None ) # By default, Qwen3 uses causal attention as it is a decoder-only model. # You can override the HF config with `is_causal=False` to enable # bidirectional attention, which is used in some embedding models # (e.g. Alibaba-NLP/gte-Qwen3-7B-instruct) if getattr(config, "is_causal", True): attn_type = AttentionType.DECODER else: attn_type = AttentionType.ENCODER_ONLY self.self_attn = Qwen3Attention( 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, qkv_bias=getattr(config, "attention_bias", False), 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, dual_chunk_attention_config=dual_chunk_attention_config, ) 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]: # Self Attention # 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 ALL_DECODER_LAYER_TYPES = { "attention": Qwen3DecoderLayer, } @support_torch_compile( dynamic_arg_dims={ "input_ids": 0, # positions is of shape (3, seq_len) if mrope is enabled for qwen2-vl, # otherwise (seq_len, ). "positions": -1, "intermediate_tensors": 0, "inputs_embeds": 0, } ) class Qwen3Model(Qwen2Model): def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__( vllm_config=vllm_config, prefix=prefix, decoder_layer_type=Qwen3DecoderLayer ) class Qwen3ForCausalLM(nn.Module, SupportsLoRA, SupportsPP, SupportsEagle3): packed_modules_mapping = { "qkv_proj": [ "q_proj", "k_proj", "v_proj", ], "gate_up_proj": [ "gate_proj", "up_proj", ], } embedding_modules = { "embed_tokens": "input_embeddings", "lm_head": "output_embeddings", } def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() config = vllm_config.model_config.hf_config quant_config = vllm_config.quant_config self.config = config self.quant_config = quant_config self.model = Qwen3Model( vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model") ) if get_pp_group().is_last_rank: if config.tie_word_embeddings: self.lm_head = self.model.embed_tokens else: self.lm_head = ParallelLMHead( config.vocab_size, config.hidden_size, quant_config=quant_config, prefix=maybe_prefix(prefix, "lm_head"), ) else: self.lm_head = PPMissingLayer() self.logits_processor = LogitsProcessor(config.vocab_size) self.make_empty_intermediate_tensors = ( self.model.make_empty_intermediate_tensors ) def set_aux_hidden_state_layers(self, layers: tuple[int, ...]) -> None: self.model.aux_hidden_state_layers = layers def get_eagle3_aux_hidden_state_layers(self) -> tuple[int, ...]: num_layers = len(self.model.layers) return (2, num_layers // 2, num_layers - 3) 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, skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None), ) return loader.load_weights(weights)