435 lines
16 KiB
Python
435 lines
16 KiB
Python
# 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, 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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split_tensor_along_last_dim,
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tensor_model_parallel_all_gather,
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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.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
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Qwen3Config = None
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logger = logging.getLogger(__name__)
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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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attention_bias: bool = False,
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prefix: str = "",
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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.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.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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prefix=add_prefix("attn", prefix),
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)
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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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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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q = q_by_head.view(q.shape)
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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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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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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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) -> None:
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super().__init__()
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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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rope_scaling = getattr(config, "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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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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prefix=add_prefix("self_attn", prefix),
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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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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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)
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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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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.model.get_input_embeddings(input_ids)
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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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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, hidden_states, self.lm_head, forward_batch
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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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@property
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def start_layer(self):
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return self.model.start_layer
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@property
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def end_layer(self):
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return self.model.end_layer
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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("qkv_proj", "q_proj", "q"),
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("qkv_proj", "k_proj", "k"),
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("qkv_proj", "v_proj", "v"),
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("gate_up_proj", "gate_proj", 0),
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("gate_up_proj", "up_proj", 1),
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]
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params_dict = dict(self.named_parameters())
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for name, loaded_weight in weights:
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if "Embedding" in self.config.name_or_path:
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name = add_prefix(name, "model")
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layer_id = get_layer_id(name)
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if (
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layer_id is not None
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and hasattr(self.model, "start_layer")
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and (
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layer_id < self.model.start_layer
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or layer_id >= self.model.end_layer
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)
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):
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continue
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if "rotary_emb.inv_freq" in name or "projector" in name:
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continue
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if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
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# Models trained using ColossalAI may include these tensors in
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# the checkpoint. Skip them.
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continue
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if self.config.tie_word_embeddings and "lm_head.weight" in name:
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if self.pp_group.world_size > 1 and self.pp_group.is_last_rank:
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# Handle pp weight tying here
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# find the embed_tokens.weight in the weights
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embed_token_weights = next(
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filter(lambda x: x[0] == "model.embed_tokens.weight", weights)
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)[1]
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loaded_weight = embed_token_weights
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else:
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continue
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if name.startswith("model.vision_tower") and name not in params_dict:
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continue
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for param_name, weight_name, shard_id in stacked_params_mapping:
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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if name in params_dict.keys():
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param = params_dict[name]
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weight_loader = getattr(
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param, "weight_loader", default_weight_loader
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)
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weight_loader(param, loaded_weight)
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else:
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logger.warning(f"Parameter {name} not found in params_dict")
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def get_embed_and_head(self):
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return self.model.embed_tokens.weight, self.lm_head.weight
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def set_embed_and_head(self, embed, head):
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del self.model.embed_tokens.weight
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del self.lm_head.weight
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self.model.embed_tokens.weight = embed
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self.lm_head.weight = head
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torch.cuda.empty_cache()
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torch.cuda.synchronize()
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def load_kv_cache_scales(self, quantization_param_path: str) -> None:
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self.model.load_kv_cache_scales(quantization_param_path)
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EntryClass = Qwen3ForCausalLM
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