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ModelHub XC
558
sglang/qwen3.py
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558
sglang/qwen3.py
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# Adapted from qwen2.py
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import logging
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from functools import partial
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from typing import Any, Dict, Iterable, List, Optional, Tuple
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import torch
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from torch import nn
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from sglang.srt.distributed import (
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get_pp_group,
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get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size,
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)
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from sglang.srt.layers.communicator import LayerCommunicator, LayerScatterModes
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from sglang.srt.layers.dp_attention import get_attention_tp_rank, get_attention_tp_size
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from sglang.srt.layers.layernorm import RMSNorm
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from sglang.srt.layers.linear import QKVParallelLinear, RowParallelLinear
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from sglang.srt.layers.logits_processor import LogitsProcessor
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from sglang.srt.layers.pooler import Pooler, PoolingType
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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from sglang.srt.layers.radix_attention import RadixAttention
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from sglang.srt.layers.rotary_embedding import get_rope
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from sglang.srt.layers.utils import PPMissingLayer, get_layer_id
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from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
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from sglang.srt.model_executor.cuda_graph_runner import get_is_capture_mode
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
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from sglang.srt.model_loader.weight_utils import default_weight_loader
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from sglang.srt.models.qwen2 import Qwen2MLP as Qwen3MLP
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from sglang.srt.models.qwen2 import Qwen2Model
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from sglang.srt.utils import add_prefix, is_cuda
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Qwen3Config = None
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logger = logging.getLogger(__name__)
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_is_cuda = is_cuda()
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# Aligned with HF's implementation, using sliding window inclusive with the last token
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# SGLang assumes exclusive
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def get_attention_sliding_window_size(config):
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if getattr(config, "sliding_window", None) is not None:
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return config.sliding_window - 1
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else:
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return None
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class Qwen3Attention(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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num_heads: int,
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num_kv_heads: int,
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layer_id: int = 0,
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rope_theta: float = 1000000,
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rope_scaling: Optional[Dict[str, Any]] = None,
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head_dim: Optional[int] = None,
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max_position_embeddings: int = 32768,
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quant_config: Optional[QuantizationConfig] = None,
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rms_norm_eps: float = None,
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config=None,
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use_rope: bool = True,
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attention_bias: bool = False,
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prefix: str = "",
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alt_stream: Optional[torch.cuda.Stream] = None,
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) -> None:
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super().__init__()
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self.hidden_size = hidden_size
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self.tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = num_heads
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attn_tp_rank = get_attention_tp_rank()
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attn_tp_size = get_attention_tp_size()
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assert self.total_num_heads % attn_tp_size == 0
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self.num_heads = self.total_num_heads // attn_tp_size
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self.total_num_kv_heads = num_kv_heads
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if self.total_num_kv_heads >= attn_tp_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % attn_tp_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert attn_tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // attn_tp_size)
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self.head_dim = head_dim or hidden_size // self.total_num_heads
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = self.head_dim**-0.5
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self.rope_theta = rope_theta
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self.max_position_embeddings = max_position_embeddings
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self.tp_rank = get_tensor_model_parallel_rank()
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self.q_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)
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self.k_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)
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self.qkv_proj = QKVParallelLinear(
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hidden_size,
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self.head_dim,
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self.total_num_heads,
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self.total_num_kv_heads,
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bias=attention_bias,
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quant_config=quant_config,
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tp_rank=attn_tp_rank,
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tp_size=attn_tp_size,
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prefix=add_prefix("qkv_proj", prefix),
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)
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self.o_proj = RowParallelLinear(
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self.total_num_heads * self.head_dim,
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hidden_size,
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bias=attention_bias,
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quant_config=quant_config,
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tp_rank=attn_tp_rank,
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tp_size=attn_tp_size,
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reduce_results=False,
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prefix=add_prefix("o_proj", prefix),
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)
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self.use_rope = use_rope
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.head_dim,
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max_position=max_position_embeddings,
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base=rope_theta,
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rope_scaling=rope_scaling,
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)
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self.is_sliding = config.layer_types[layer_id] == "sliding_attention"
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self.attn = RadixAttention(
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self.num_heads,
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self.head_dim,
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self.scaling,
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num_kv_heads=self.num_kv_heads,
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layer_id=layer_id,
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sliding_window_size=(
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get_attention_sliding_window_size(config) if self.is_sliding else None
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),
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prefix=add_prefix("attn", prefix),
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)
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self.alt_stream = alt_stream
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def _apply_qk_norm(
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self, q: torch.Tensor, k: torch.Tensor
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) -> Tuple[torch.Tensor, torch.Tensor]:
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# overlap qk norm
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if self.alt_stream is not None and get_is_capture_mode():
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current_stream = torch.cuda.current_stream()
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self.alt_stream.wait_stream(current_stream)
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q_by_head = q.reshape(-1, self.head_dim)
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q_by_head = self.q_norm(q_by_head)
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with torch.cuda.stream(self.alt_stream):
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k_by_head = k.reshape(-1, self.head_dim)
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k_by_head = self.k_norm(k_by_head)
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current_stream.wait_stream(self.alt_stream)
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else:
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q_by_head = q.reshape(-1, self.head_dim)
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q_by_head = self.q_norm(q_by_head)
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k_by_head = k.reshape(-1, self.head_dim)
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k_by_head = self.k_norm(k_by_head)
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q = q_by_head.view(q.shape)
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k = k_by_head.view(k.shape)
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return q, k
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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forward_batch: ForwardBatch,
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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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q, k = self._apply_qk_norm(q, k)
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if self.use_rope:
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q, k = self.rotary_emb(positions, q, k)
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attn_output = self.attn(q, k, v, forward_batch)
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output, _ = self.o_proj(attn_output)
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return output
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class Qwen3DecoderLayer(nn.Module):
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def __init__(
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self,
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config: Qwen3Config,
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layer_id: int = 0,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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alt_stream: Optional[torch.cuda.Stream] = None,
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) -> None:
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super().__init__()
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self.layer_id = layer_id
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self.hidden_size = config.hidden_size
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rope_theta = getattr(config, "rope_theta", 1000000)
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if hasattr(config, "rope_parameters") and config.rope_parameters is not None:
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rope_scaling = config.rope_parameters
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else:
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rope_scaling = getattr(config, "rope_scaling", None)
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self.use_rope = True
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# nested → flat 변환
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if rope_scaling is not None:
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first_value = next(iter(rope_scaling.values()), None)
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if isinstance(first_value, dict):
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layer_type = config.layer_types[layer_id]
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if layer_type in rope_scaling:
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layer_rope = rope_scaling[layer_type]
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rope_theta = layer_rope.get("rope_theta", rope_theta)
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rope_scaling = layer_rope
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else:
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self.use_rope = False
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rope_scaling = None
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max_position_embeddings = getattr(config, "max_position_embeddings", 32768)
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head_dim = getattr(config, "head_dim", None)
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self.self_attn = Qwen3Attention(
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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num_kv_heads=config.num_key_value_heads,
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layer_id=layer_id,
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rope_theta=rope_theta,
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rope_scaling=rope_scaling,
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use_rope=self.use_rope,
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head_dim=head_dim,
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max_position_embeddings=max_position_embeddings,
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quant_config=quant_config,
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rms_norm_eps=config.rms_norm_eps,
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attention_bias=config.attention_bias,
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config=config,
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prefix=add_prefix("self_attn", prefix),
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alt_stream=alt_stream,
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)
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self.mlp = Qwen3MLP(
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hidden_size=self.hidden_size,
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intermediate_size=config.intermediate_size,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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prefix=add_prefix("mlp", prefix),
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)
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self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(
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config.hidden_size, eps=config.rms_norm_eps
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)
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self.layer_scatter_modes = LayerScatterModes.init_new(
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layer_id=layer_id,
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num_layers=config.num_hidden_layers,
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is_layer_sparse=False,
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is_previous_layer_sparse=False,
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)
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self.layer_communicator = LayerCommunicator(
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layer_scatter_modes=self.layer_scatter_modes,
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input_layernorm=self.input_layernorm,
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post_attention_layernorm=self.post_attention_layernorm,
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)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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forward_batch: ForwardBatch,
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residual: Optional[torch.Tensor],
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) -> Tuple[torch.Tensor, torch.Tensor]:
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# Self Attention
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hidden_states, residual = self.layer_communicator.prepare_attn(
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hidden_states, residual, forward_batch
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)
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if hidden_states.shape[0] != 0:
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hidden_states = self.self_attn(
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positions=positions,
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hidden_states=hidden_states,
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forward_batch=forward_batch,
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)
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# Fully Connected
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hidden_states, residual = self.layer_communicator.prepare_mlp(
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hidden_states, residual, forward_batch
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)
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hidden_states = self.mlp(hidden_states)
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hidden_states, residual = self.layer_communicator.postprocess_layer(
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hidden_states, residual, forward_batch
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)
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return hidden_states, residual
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class Qwen3Model(Qwen2Model):
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def __init__(
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self,
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config: Qwen3Config,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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alt_stream = torch.cuda.Stream() if _is_cuda else None
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super().__init__(
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config=config,
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quant_config=quant_config,
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prefix=prefix,
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decoder_layer_type=Qwen3DecoderLayer,
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alt_stream=alt_stream,
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)
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class Qwen3ForCausalLM(nn.Module):
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# BitandBytes specific attributes
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default_bitsandbytes_target_modules = [
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".gate_proj.",
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".down_proj.",
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".up_proj.",
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".q_proj.",
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".k_proj.",
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".v_proj.",
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".o_proj.",
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]
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bitsandbytes_stacked_params_mapping = {
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# shard_name, weight_name, index
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"q_proj": ("qkv_proj", 0),
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"k_proj": ("qkv_proj", 1),
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"v_proj": ("qkv_proj", 2),
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"gate_proj": ("gate_up_proj", 0),
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"up_proj": ("gate_up_proj", 1),
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}
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def __init__(
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self,
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config: Qwen3Config,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.pp_group = get_pp_group()
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self.config = config
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self.quant_config = quant_config
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self.model = Qwen3Model(
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config, quant_config=quant_config, prefix=add_prefix("model", prefix)
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)
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# handle the lm head on different pp ranks
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if self.pp_group.is_last_rank:
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if self.pp_group.world_size == 1 and config.tie_word_embeddings:
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self.lm_head = self.model.embed_tokens
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else:
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self.lm_head = ParallelLMHead(
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config.vocab_size,
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config.hidden_size,
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quant_config=quant_config,
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prefix=add_prefix("lm_head", prefix),
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)
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else:
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# ranks other than the last rank will have a placeholder layer
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self.lm_head = PPMissingLayer()
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# perform weight tying for PP
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if self.pp_group.world_size > 1 and config.tie_word_embeddings:
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if self.pp_group.is_first_rank:
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self.pp_group.send(
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self.model.embed_tokens.weight, dst=self.pp_group.last_rank
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)
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else:
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emb_token_weight = self.pp_group.recv(
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size=(config.vocab_size, config.hidden_size),
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dtype=next(self.model.parameters()).dtype,
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src=self.pp_group.first_rank,
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)
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self.lm_head.weight.copy_(emb_token_weight)
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self.logits_processor = LogitsProcessor(config)
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self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
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# For EAGLE3 support
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self.capture_aux_hidden_states = False
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def get_input_embeddings(self) -> nn.Embedding:
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return self.model.get_input_embeddings()
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def get_attention_sliding_window_size(self):
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return get_attention_sliding_window_size(self.config)
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@torch.no_grad()
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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forward_batch: ForwardBatch,
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input_embeds: torch.Tensor = None,
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get_embedding: bool = False,
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pp_proxy_tensors: Optional[PPProxyTensors] = None,
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) -> torch.Tensor:
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hidden_states = self.model(
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input_ids,
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positions,
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forward_batch,
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input_embeds,
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pp_proxy_tensors=pp_proxy_tensors,
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)
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aux_hidden_states = None
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if self.capture_aux_hidden_states:
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hidden_states, aux_hidden_states = hidden_states
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if self.pp_group.is_last_rank:
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if not get_embedding:
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return self.logits_processor(
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input_ids,
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hidden_states,
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self.lm_head,
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forward_batch,
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aux_hidden_states,
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)
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else:
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return self.pooler(hidden_states, forward_batch)
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else:
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return hidden_states
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@torch.no_grad()
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def forward_split_prefill(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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forward_batch: ForwardBatch,
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split_interval: Tuple[int, int], # [start, end) 0-based
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input_embeds: torch.Tensor = None,
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):
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start, end = split_interval
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# embed
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if start == 0:
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if input_embeds is None:
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forward_batch.hidden_states = self.model.embed_tokens(input_ids)
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else:
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forward_batch.hidden_states = input_embeds
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# decoder layer
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for i in range(start, end):
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layer = self.model.layers[i]
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forward_batch.hidden_states, forward_batch.residual = layer(
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positions,
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forward_batch.hidden_states,
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forward_batch,
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forward_batch.residual,
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)
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if end == self.model.config.num_hidden_layers:
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# norm
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||||
hidden_states, _ = self.model.norm(
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forward_batch.hidden_states, forward_batch.residual
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)
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forward_batch.hidden_states = hidden_states
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# logits process
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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 "Embedding" in self.config.name_or_path:
|
||||
name = add_prefix(name, "model")
|
||||
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 self.config.tie_word_embeddings and "lm_head.weight" in name:
|
||||
if self.pp_group.world_size > 1 and self.pp_group.is_last_rank:
|
||||
# Handle pp weight tying here
|
||||
# find the embed_tokens.weight in the weights
|
||||
embed_token_weights = next(
|
||||
filter(lambda x: x[0] == "model.embed_tokens.weight", weights)
|
||||
)[1]
|
||||
loaded_weight = embed_token_weights
|
||||
else:
|
||||
continue
|
||||
if name.startswith("model.vision_tower") and name not in params_dict:
|
||||
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
|
||||
Reference in New Issue
Block a user