from typing import Any, Dict, List, Optional, Tuple import torch from sglang.srt.layers.logits_processor import LogitsProcessor from sglang.srt.layers.radix_attention import RadixAttention from sglang.srt.managers.router.model_runner import InputMetadata from torch import nn from transformers import PretrainedConfig from vllm.model_executor.layers.activation import SiluAndMul from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.linear import ( LinearMethodBase, MergedColumnParallelLinear, QKVParallelLinear, RowParallelLinear, ) 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.parallel_utils.parallel_state import ( get_tensor_model_parallel_world_size, ) from vllm.model_executor.weight_utils import ( default_weight_loader, hf_model_weights_iterator, ) class QWenMLP(nn.Module): def __init__( self, hidden_size: int, intermediate_size: int, hidden_act: str = "silu", linear_method: Optional[LinearMethodBase] = None, ): super().__init__() self.gate_up_proj = MergedColumnParallelLinear( hidden_size, 2 * [intermediate_size], bias=False, gather_output=False, linear_method=linear_method, ) self.c_proj = RowParallelLinear( intermediate_size, hidden_size, bias=False, input_is_parallel=True, linear_method=linear_method, ) if hidden_act != "silu": raise ValueError( f"Unsupported activation: {hidden_act}. " "Only silu is supported for now." ) self.act_fn = SiluAndMul() def forward(self, x): gate_up, _ = self.gate_up_proj(x) x = self.act_fn(gate_up) x, _ = self.c_proj(x) return x class QWenAttention(nn.Module): def __init__( self, hidden_size: int, num_heads: int, max_position_embeddings: int, layer_id: int = 0, rope_theta: float = 10000, rope_scaling: Optional[Dict[str, Any]] = None, linear_method: Optional[LinearMethodBase] = None, ): super().__init__() self.hidden_size = hidden_size tensor_model_parallel_world_size = get_tensor_model_parallel_world_size() self.total_num_heads = num_heads assert self.total_num_heads % tensor_model_parallel_world_size == 0 self.num_heads = self.total_num_heads // tensor_model_parallel_world_size self.head_dim = hidden_size // self.total_num_heads # pylint: disable=invalid-name self.c_attn = QKVParallelLinear( hidden_size, self.head_dim, self.total_num_heads, bias=True, linear_method=linear_method, ) self.c_proj = RowParallelLinear( self.total_num_heads * self.head_dim, hidden_size, bias=False, input_is_parallel=True, linear_method=linear_method, ) 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.scaling = self.head_dim**-0.5 self.attn = RadixAttention( self.num_heads, self.head_dim, self.scaling, num_kv_heads=self.num_heads, layer_id=layer_id, ) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, input_metadata: InputMetadata, ) -> torch.Tensor: qkv, _ = self.c_attn(hidden_states) q, k, v = qkv.chunk(chunks=3, dim=-1) q, k = self.rotary_emb(positions, q, k) attn_output = self.attn(q, k, v, input_metadata) output, _ = self.c_proj(attn_output) return output class QWenBlock(nn.Module): def __init__(self, config: PretrainedConfig, layer_id, linear_method=None): super().__init__() self.ln_1 = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon) rope_theta = getattr(config, "rope_theta", 10000) rope_scaling = getattr(config, "rope_scaling", None) self.attn = QWenAttention( config.hidden_size, config.num_attention_heads, config.max_position_embeddings, rope_theta=rope_theta, rope_scaling=rope_scaling, layer_id=layer_id, linear_method=linear_method, ) self.ln_2 = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon) self.mlp = QWenMLP( config.hidden_size, config.intermediate_size // 2, linear_method=linear_method, ) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, input_metadata: InputMetadata, ) -> torch.Tensor: # Self Attention residual = hidden_states hidden_states = self.ln_1(hidden_states) hidden_states = self.attn( positions=positions, hidden_states=hidden_states, input_metadata=input_metadata, ) hidden_states = residual + hidden_states # Fully Connected residual = hidden_states hidden_states = self.ln_2(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states return hidden_states class QWenModel(nn.Module): def __init__(self, config: PretrainedConfig, linear_method=None): super().__init__() self.config = config self.vocab_size = config.vocab_size vocab_size = ((config.vocab_size + 63) // 64) * 64 self.wte = VocabParallelEmbedding( vocab_size, config.hidden_size, ) self.h = nn.ModuleList( [ QWenBlock(config, i, linear_method=linear_method) for i in range(config.num_hidden_layers) ] ) self.ln_f = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon) def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, input_metadata: InputMetadata, ) -> torch.Tensor: hidden_states = self.wte(input_ids) for i in range(len(self.h)): layer = self.h[i] hidden_states = layer( positions, hidden_states, input_metadata, ) hidden_states = self.ln_f(hidden_states) return hidden_states class QWenLMHeadModel(nn.Module): def __init__(self, config: PretrainedConfig, linear_method=None): super().__init__() self.config = config self.transformer = QWenModel(config, linear_method=linear_method) vocab_size = ((config.vocab_size + 63) // 64) * 64 self.lm_head = ParallelLMHead(vocab_size, config.hidden_size) self.logits_processor = LogitsProcessor(config) def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, input_metadata: InputMetadata, ): hidden_states = self.transformer(input_ids, positions, input_metadata) next_tokens = self.logits_processor( input_ids, hidden_states, self.lm_head.weight, input_metadata ) return next_tokens def load_weights( self, model_name_or_path: str, cache_dir: Optional[str] = None, load_format: str = "auto", revision: Optional[str] = None, ): stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("gate_up_proj", "w2", 0), ("gate_up_proj", "w1", 1), ] params_dict = dict(self.named_parameters()) for name, loaded_weight in hf_model_weights_iterator( model_name_or_path, cache_dir, load_format, revision ): if "rotary_emb.inv_freq" in name: continue for param_name, weight_name, shard_id in stacked_params_mapping: if weight_name not in name: continue name = name.replace(weight_name, param_name) # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, loaded_weight, shard_id) break else: # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) EntryClass = QWenLMHeadModel