# Adapted from qwen2.py import logging from typing import Any, Dict, Iterable, List, Optional, Tuple import torch from torch import nn from sglang.srt.distributed import ( get_pp_group, get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size, ) from sglang.srt.layers.communicator import LayerCommunicator, LayerScatterModes from sglang.srt.layers.dp_attention import get_attention_tp_rank, get_attention_tp_size from sglang.srt.layers.layernorm import RMSNorm from sglang.srt.layers.linear import QKVParallelLinear, RowParallelLinear from sglang.srt.layers.logits_processor import LogitsProcessor from sglang.srt.layers.pooler import Pooler, PoolingType from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.radix_attention import RadixAttention from sglang.srt.layers.rotary_embedding import get_rope from sglang.srt.layers.utils import PPMissingLayer, get_layer_id from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors from sglang.srt.model_loader.weight_utils import ( default_weight_loader, maybe_remap_kv_scale_name, ) from sglang.srt.models.qwen2 import Qwen2MLP as Qwen3MLP from sglang.srt.models.qwen2 import Qwen2Model from sglang.srt.models.utils import apply_qk_norm from sglang.srt.server_args import get_global_server_args from sglang.srt.utils import add_prefix, is_cuda, is_npu Qwen3Config = None logger = logging.getLogger(__name__) _is_cuda = is_cuda() _is_npu = is_npu() if _is_npu: from sgl_kernel_npu.norm.split_qkv_rmsnorm_rope import split_qkv_rmsnorm_rope from sglang.srt.hardware_backend.npu.cmo import get_cmo_stream, wait_cmo_stream class Qwen3Attention(nn.Module): def __init__( self, hidden_size: int, num_heads: int, num_kv_heads: int, layer_id: int = 0, rope_theta: float = 1000000, rope_scaling: Optional[Dict[str, Any]] = None, head_dim: Optional[int] = None, max_position_embeddings: int = 32768, quant_config: Optional[QuantizationConfig] = None, rms_norm_eps: float = None, attention_bias: bool = False, prefix: str = "", alt_stream: Optional[torch.cuda.Stream] = None, ) -> None: super().__init__() self.hidden_size = hidden_size self.tp_size = get_tensor_model_parallel_world_size() self.total_num_heads = num_heads attn_tp_rank = get_attention_tp_rank() attn_tp_size = get_attention_tp_size() assert self.total_num_heads % attn_tp_size == 0 self.num_heads = self.total_num_heads // attn_tp_size self.total_num_kv_heads = num_kv_heads if self.total_num_kv_heads >= attn_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 % attn_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 attn_tp_size % self.total_num_kv_heads == 0 self.num_kv_heads = max(1, self.total_num_kv_heads // attn_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.tp_rank = get_tensor_model_parallel_rank() norm_kwargs = ( dict( weight_dtype=torch.float32, cast_x_before_out_mul=True, ) if get_global_server_args().rl_on_policy_target is not None else {} ) self.q_norm = RMSNorm(self.head_dim, eps=rms_norm_eps, **norm_kwargs) self.k_norm = RMSNorm(self.head_dim, eps=rms_norm_eps, **norm_kwargs) self.qkv_proj = QKVParallelLinear( hidden_size, self.head_dim, self.total_num_heads, self.total_num_kv_heads, bias=attention_bias, quant_config=quant_config, tp_rank=attn_tp_rank, tp_size=attn_tp_size, prefix=add_prefix("qkv_proj", prefix), ) self.o_proj = RowParallelLinear( self.total_num_heads * self.head_dim, hidden_size, bias=attention_bias, quant_config=quant_config, tp_rank=attn_tp_rank, tp_size=attn_tp_size, reduce_results=False, prefix=add_prefix("o_proj", prefix), ) 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 = RadixAttention( self.num_heads, self.head_dim, self.scaling, num_kv_heads=self.num_kv_heads, layer_id=layer_id, prefix=add_prefix("attn", prefix), ) self.alt_stream = alt_stream def forward_prepare_native(self, positions, hidden_states): qkv, _ = self.qkv_proj(hidden_states) q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1) q, k = apply_qk_norm( q=q, k=k, q_norm=self.q_norm, k_norm=self.k_norm, head_dim=self.head_dim, alt_stream=self.alt_stream, ) q, k = self.rotary_emb(positions, q, k) return q, k, v def forward_prepare_npu(self, positions, hidden_states, forward_batch): qkv, _ = self.qkv_proj(hidden_states) if self.attn.layer_id == forward_batch.token_to_kv_pool.start_layer: self.rotary_emb.get_cos_sin_with_position(positions) q, k, v = split_qkv_rmsnorm_rope( qkv, self.rotary_emb.position_sin, self.rotary_emb.position_cos, self.q_size, self.kv_size, self.head_dim, eps=self.q_norm.variance_epsilon, q_weight=self.q_norm.weight, k_weight=self.k_norm.weight, q_bias=getattr(self.q_norm, "bias", None), k_bias=getattr(self.k_norm, "bias", None), ) return q, k, v def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, forward_batch: ForwardBatch, ) -> torch.Tensor: if get_global_server_args().rl_on_policy_target is not None: hidden_states = hidden_states.bfloat16() if not _is_npu or forward_batch.forward_mode.is_extend(): q, k, v = self.forward_prepare_native( positions=positions, hidden_states=hidden_states, ) else: q, k, v = self.forward_prepare_npu( positions=positions, hidden_states=hidden_states, forward_batch=forward_batch, ) if get_global_server_args().rl_on_policy_target is not None: q = q.to(torch.bfloat16) k = k.to(torch.bfloat16) attn_output = self.attn(q, k, v, forward_batch) output, _ = self.o_proj(attn_output) return output class Qwen3DecoderLayer(nn.Module): def __init__( self, config: Qwen3Config, layer_id: int = 0, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", alt_stream: Optional[torch.cuda.Stream] = None, ) -> None: super().__init__() self.hidden_size = config.hidden_size rope_theta = getattr(config, "rope_theta", 1000000) rope_scaling = getattr(config, "rope_scaling", None) max_position_embeddings = getattr(config, "max_position_embeddings", 32768) head_dim = getattr(config, "head_dim", None) self.self_attn = Qwen3Attention( hidden_size=self.hidden_size, num_heads=config.num_attention_heads, num_kv_heads=config.num_key_value_heads, layer_id=layer_id, rope_theta=rope_theta, rope_scaling=rope_scaling, head_dim=head_dim, max_position_embeddings=max_position_embeddings, quant_config=quant_config, rms_norm_eps=config.rms_norm_eps, attention_bias=config.attention_bias, prefix=add_prefix("self_attn", prefix), alt_stream=alt_stream, ) self.mlp = Qwen3MLP( hidden_size=self.hidden_size, intermediate_size=config.intermediate_size, hidden_act=config.hidden_act, quant_config=quant_config, prefix=add_prefix("mlp", prefix), ) norm_kwargs = ( dict( weight_dtype=torch.float32, cast_x_before_out_mul=True, override_orig_dtype=torch.float32, fp32_residual=True, ) if get_global_server_args().rl_on_policy_target is not None else {} ) self.input_layernorm = RMSNorm( config.hidden_size, eps=config.rms_norm_eps, **norm_kwargs ) self.post_attention_layernorm = RMSNorm( config.hidden_size, eps=config.rms_norm_eps, **norm_kwargs ) self.layer_scatter_modes = LayerScatterModes.init_new( layer_id=layer_id, num_layers=config.num_hidden_layers, is_layer_sparse=False, is_previous_layer_sparse=False, is_next_layer_sparse=False, ) self.layer_communicator = LayerCommunicator( layer_scatter_modes=self.layer_scatter_modes, input_layernorm=self.input_layernorm, post_attention_layernorm=self.post_attention_layernorm, ) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, forward_batch: ForwardBatch, residual: Optional[torch.Tensor], post_residual_addition: Optional[torch.Tensor] = None, ) -> Tuple[torch.Tensor, torch.Tensor]: # Self Attention hidden_states, residual = self.layer_communicator.prepare_attn( hidden_states, residual, forward_batch, post_residual_addition=post_residual_addition, ) if hidden_states.shape[0] != 0: hidden_states = self.self_attn( positions=positions, hidden_states=hidden_states, forward_batch=forward_batch, ) # Fully Connected hidden_states, residual = self.layer_communicator.prepare_mlp( hidden_states, residual, forward_batch, cache=( [self.mlp.gate_up_proj.weight, self.mlp.down_proj.weight] if _is_npu and not get_global_server_args().enable_piecewise_cuda_graph and ( hasattr(self.mlp.gate_up_proj, "weight") and hasattr(self.mlp.down_proj, "weight") ) else None ), ) hidden_states = self.mlp(hidden_states) if _is_npu and get_cmo_stream(): wait_cmo_stream() hidden_states, residual = self.layer_communicator.postprocess_layer( hidden_states, residual, forward_batch ) return hidden_states, residual class Qwen3Model(Qwen2Model): def __init__( self, config: Qwen3Config, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ) -> None: alt_stream = torch.cuda.Stream() if _is_cuda else None super().__init__( config=config, quant_config=quant_config, prefix=prefix, decoder_layer_type=Qwen3DecoderLayer, alt_stream=alt_stream, ) class Qwen3ForCausalLM(nn.Module): # BitandBytes specific attributes default_bitsandbytes_target_modules = [ ".gate_proj.", ".down_proj.", ".up_proj.", ".q_proj.", ".k_proj.", ".v_proj.", ".o_proj.", ] bitsandbytes_stacked_params_mapping = { # shard_name, weight_name, index "q_proj": ("qkv_proj", 0), "k_proj": ("qkv_proj", 1), "v_proj": ("qkv_proj", 2), "gate_proj": ("gate_up_proj", 0), "up_proj": ("gate_up_proj", 1), } def __init__( self, config: Qwen3Config, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ) -> None: super().__init__() self.pp_group = get_pp_group() self.config = config self.quant_config = quant_config self.model = Qwen3Model( config, quant_config=quant_config, prefix=add_prefix("model", prefix) ) # handle the lm head on different pp ranks if self.pp_group.is_last_rank: if self.pp_group.world_size == 1 and 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, use_attn_tp_group=get_global_server_args().enable_dp_lm_head, prefix=add_prefix("lm_head", prefix), ) else: # ranks other than the last rank will have a placeholder layer self.lm_head = PPMissingLayer() self.logits_processor = LogitsProcessor(config) self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True) # For EAGLE3 support self.capture_aux_hidden_states = False def get_input_embeddings(self) -> nn.Embedding: return self.model.get_input_embeddings() @torch.no_grad() def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, forward_batch: ForwardBatch, input_embeds: torch.Tensor = None, get_embedding: bool = False, pp_proxy_tensors: Optional[PPProxyTensors] = None, ) -> torch.Tensor: hidden_states = self.model( input_ids, positions, forward_batch, input_embeds, pp_proxy_tensors=pp_proxy_tensors, ) aux_hidden_states = None if self.capture_aux_hidden_states: hidden_states, aux_hidden_states = hidden_states if self.pp_group.is_last_rank: if not get_embedding: return self.logits_processor( input_ids, hidden_states, self.lm_head, forward_batch, aux_hidden_states, ) else: return self.pooler(hidden_states, forward_batch) else: return hidden_states @torch.no_grad() def forward_split_prefill( self, input_ids: torch.Tensor, positions: torch.Tensor, forward_batch: ForwardBatch, split_interval: Tuple[int, int], # [start, end) 0-based input_embeds: torch.Tensor = None, ): start, end = split_interval # embed if start == 0: if input_embeds is None: forward_batch.hidden_states = self.model.embed_tokens(input_ids) else: forward_batch.hidden_states = input_embeds # decoder layer for i in range(start, end): layer = self.model.layers[i] forward_batch.hidden_states, forward_batch.residual = layer( positions, forward_batch.hidden_states, forward_batch, forward_batch.residual, ) if end == self.model.config.num_hidden_layers: # norm hidden_states, _ = self.model.norm( forward_batch.hidden_states, forward_batch.residual ) forward_batch.hidden_states = hidden_states # logits process result = self.logits_processor( input_ids, forward_batch.hidden_states, self.lm_head, forward_batch ) else: result = None return result @property def start_layer(self): return self.model.start_layer @property def end_layer(self): return self.model.end_layer def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): 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), ] params_dict = dict(self.named_parameters()) for name, loaded_weight in weights: if not name.startswith("model.") and ( name.startswith("layers.") or name.startswith("embed_tokens.") or name.startswith("norm.") ): name = add_prefix(name, "model") if name == "model.embed_tokens.weight": if self.pp_group.is_last_rank and self.config.tie_word_embeddings: if "lm_head.weight" in params_dict: param = params_dict["lm_head.weight"] weight_loader = getattr( param, "weight_loader", default_weight_loader ) weight_loader(param, loaded_weight) layer_id = get_layer_id(name) if ( layer_id is not None and hasattr(self.model, "start_layer") and ( layer_id < self.model.start_layer or layer_id >= self.model.end_layer ) ): continue if "rotary_emb.inv_freq" in name or "projector" in name: continue if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name: # Models trained using ColossalAI may include these tensors in # the checkpoint. Skip them. continue if name.startswith("model.vision_tower") and name not in params_dict: continue if "scale" 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: 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 if name in params_dict.keys(): param = params_dict[name] weight_loader = getattr( param, "weight_loader", default_weight_loader ) weight_loader(param, loaded_weight) else: logger.warning(f"Parameter {name} not found in params_dict") def get_embed_and_head(self): return self.model.embed_tokens.weight, self.lm_head.weight def set_embed_and_head(self, embed, head): del self.model.embed_tokens.weight del self.lm_head.weight self.model.embed_tokens.weight = embed self.lm_head.weight = head torch.cuda.empty_cache() torch.cuda.synchronize() def load_kv_cache_scales(self, quantization_param_path: str) -> None: self.model.load_kv_cache_scales(quantization_param_path) def set_eagle3_layers_to_capture(self, layer_ids: Optional[List[int]] = None): if not self.pp_group.is_last_rank: return self.capture_aux_hidden_states = True if layer_ids is None: num_layers = self.config.num_hidden_layers self.model.layers_to_capture = [ 2, num_layers // 2, num_layers - 3, ] # Specific layers for EAGLE3 support else: self.model.layers_to_capture = [val + 1 for val in layer_ids] EntryClass = Qwen3ForCausalLM