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GeeeekExplorer
247
nanovllm/models/qwen3.py
Executable file
247
nanovllm/models/qwen3.py
Executable file
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import torch
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from torch import nn
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from transformers import Qwen3Config
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from nanovllm.layers.activation import SiluAndMul
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from nanovllm.layers.attention import Attention
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from nanovllm.layers.layernorm import RMSNorm
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from nanovllm.layers.linear import QKVParallelLinear, MergedColumnParallelLinear, RowParallelLinear
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from nanovllm.layers.rotary_embedding import get_rope
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from nanovllm.layers.embed_head import VocabParallelEmbedding, ParallelLMHead
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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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max_position: int = 4096 * 32,
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head_dim: int | None = None,
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rms_norm_eps: float = 1e-06,
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qkv_bias: bool = False,
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rope_theta: float = 10000,
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rope_scaling: tuple | None = 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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tp_size = 1 # get_tensor_model_parallel_world_size()
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self.total_num_heads = num_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = num_kv_heads
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assert self.total_num_kv_heads % tp_size == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // 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.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=qkv_bias,
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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=False,
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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,
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base=self.rope_theta,
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rope_scaling=rope_scaling,
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)
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self.attn = Attention(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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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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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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) -> 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_by_head = q.view(-1, self.num_heads, 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.view(-1, self.num_kv_heads, 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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q, k = self.rotary_emb(positions, q, k)
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o = self.attn(q, k, v)
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output = self.o_proj(o)
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return output
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class Qwen3MLP(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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hidden_act: str,
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) -> None:
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super().__init__()
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self.gate_up_proj = MergedColumnParallelLinear(
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hidden_size,
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[intermediate_size] * 2,
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bias=False,
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)
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self.down_proj = RowParallelLinear(
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intermediate_size,
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hidden_size,
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bias=False,
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)
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assert hidden_act == "silu"
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self.act_fn = SiluAndMul()
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def forward(self, x):
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gate_up = self.gate_up_proj(x)
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x = self.act_fn(gate_up)
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x = self.down_proj(x)
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return x
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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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) -> None:
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super().__init__()
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self.hidden_size = config.hidden_size
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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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max_position=config.max_position_embeddings,
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rms_norm_eps=config.rms_norm_eps,
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qkv_bias=getattr(config, 'attention_bias', False),
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head_dim=getattr(config, 'head_dim', None),
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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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)
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self.mlp = Qwen3MLP(
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hidden_size=config.hidden_size,
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intermediate_size=config.intermediate_size,
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hidden_act=config.hidden_act,
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)
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self.input_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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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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residual: torch.Tensor | None,
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) -> tuple[torch.Tensor, torch.Tensor]:
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if residual is None:
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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else:
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hidden_states, residual = self.input_layernorm(hidden_states, residual)
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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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)
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hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
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hidden_states = self.mlp(hidden_states)
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return hidden_states, residual
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class Qwen3Model(nn.Module):
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def __init__(
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self,
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config: Qwen3Config,
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):
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super().__init__()
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self.vocab_size = config.vocab_size
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self.embed_tokens = VocabParallelEmbedding(config.vocab_size, config.hidden_size)
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self.layers = nn.ModuleList([Qwen3DecoderLayer(config) for _ in range(config.num_hidden_layers)])
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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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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) -> torch.Tensor:
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hidden_states = self.embed_tokens(input_ids)
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residual = None
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for layer in self.layers:
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hidden_states, residual = layer(
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positions,
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hidden_states,
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residual,
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)
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hidden_states, _ = self.norm(hidden_states, residual)
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return hidden_states
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class Qwen3ForCausalLM(nn.Module):
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packed_modules_mapping = {
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"qkv_proj": [
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"q_proj",
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"k_proj",
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"v_proj",
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],
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"gate_up_proj": [
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"gate_proj",
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"up_proj",
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],
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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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):
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super().__init__()
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self.model = Qwen3Model(config)
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self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
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if config.tie_word_embeddings:
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self.lm_head.weight.data = self.model.embed_tokens.weight.data
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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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) -> torch.Tensor:
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hidden_states = self.model(input_ids, positions)
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return hidden_states
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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) -> torch.Tensor:
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logits = self.lm_head(hidden_states)
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return logits
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def load_weights(self, path: str):
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import os
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from safetensors import safe_open
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default_weight_loader = lambda param, loaded_weight: param.data.copy_(loaded_weight)
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with safe_open(os.path.join(path, "model.safetensors"), "pt", "cpu") as f:
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for n, p in self.named_parameters():
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for x in self.packed_modules_mapping:
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if x in n:
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weight_loader = getattr(p, "weight_loader", default_weight_loader)
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for i, y in enumerate(self.packed_modules_mapping[x]):
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weight_loader(p, f.get_tensor(n.replace(x, y)), y[0] if x == "qkv_proj" else i)
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break
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else:
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weight_loader = getattr(p, "weight_loader", default_weight_loader)
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weight_loader(p, f.get_tensor(n))
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