import torch from torch import nn from transformers import Qwen3Config from nanovllm.layers.activation import SiluAndMul from nanovllm.layers.attention import Attention from nanovllm.layers.layernorm import RMSNorm from nanovllm.layers.linear import QKVParallelLinear, MergedColumnParallelLinear, RowParallelLinear from nanovllm.layers.rotary_embedding import get_rope from nanovllm.layers.embed_head import VocabParallelEmbedding, ParallelLMHead class Qwen3Attention(nn.Module): def __init__( self, hidden_size: int, num_heads: int, num_kv_heads: int, max_position: int = 4096 * 32, head_dim: int | None = None, rms_norm_eps: float = 1e-06, qkv_bias: bool = False, rope_theta: float = 10000, rope_scaling: tuple | None = None, ) -> None: super().__init__() self.hidden_size = hidden_size tp_size = 1 # get_tensor_model_parallel_world_size() self.total_num_heads = num_heads assert self.total_num_heads % tp_size == 0 self.num_heads = self.total_num_heads // tp_size self.total_num_kv_heads = num_kv_heads assert self.total_num_kv_heads % tp_size == 0 self.num_kv_heads = max(1, self.total_num_kv_heads // 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.qkv_proj = QKVParallelLinear( hidden_size, self.head_dim, self.total_num_heads, self.total_num_kv_heads, bias=qkv_bias, ) self.o_proj = RowParallelLinear( self.total_num_heads * self.head_dim, hidden_size, bias=False, ) self.rotary_emb = get_rope( self.head_dim, rotary_dim=self.head_dim, max_position=max_position, base=self.rope_theta, rope_scaling=rope_scaling, ) self.attn = Attention(self.num_heads, self.head_dim, self.scaling, num_kv_heads=self.num_kv_heads) self.q_norm = RMSNorm(self.head_dim, eps=rms_norm_eps) self.k_norm = RMSNorm(self.head_dim, eps=rms_norm_eps) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, ) -> torch.Tensor: qkv = self.qkv_proj(hidden_states) q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1) q_by_head = q.view(-1, self.num_heads, self.head_dim) q_by_head = self.q_norm(q_by_head) q = q_by_head.view(q.shape) k_by_head = k.view(-1, self.num_kv_heads, self.head_dim) k_by_head = self.k_norm(k_by_head) k = k_by_head.view(k.shape) q, k = self.rotary_emb(positions, q, k) o = self.attn(q, k, v) output = self.o_proj(o) return output class Qwen3MLP(nn.Module): def __init__( self, hidden_size: int, intermediate_size: int, hidden_act: str, ) -> None: super().__init__() self.gate_up_proj = MergedColumnParallelLinear( hidden_size, [intermediate_size] * 2, bias=False, ) self.down_proj = RowParallelLinear( intermediate_size, hidden_size, bias=False, ) assert hidden_act == "silu" self.act_fn = SiluAndMul() def forward(self, x): gate_up = self.gate_up_proj(x) x = self.act_fn(gate_up) x = self.down_proj(x) return x class Qwen3DecoderLayer(nn.Module): def __init__( self, config: Qwen3Config, ) -> None: super().__init__() self.hidden_size = config.hidden_size self.self_attn = Qwen3Attention( hidden_size=self.hidden_size, num_heads=config.num_attention_heads, num_kv_heads=config.num_key_value_heads, max_position=config.max_position_embeddings, rms_norm_eps=config.rms_norm_eps, qkv_bias=getattr(config, 'attention_bias', False), head_dim=getattr(config, 'head_dim', None), rope_theta=getattr(config, "rope_theta", 1000000), rope_scaling=getattr(config, "rope_scaling", None), ) self.mlp = Qwen3MLP( hidden_size=config.hidden_size, intermediate_size=config.intermediate_size, hidden_act=config.hidden_act, ) self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, residual: torch.Tensor | None, ) -> tuple[torch.Tensor, torch.Tensor]: if residual is None: residual = hidden_states hidden_states = self.input_layernorm(hidden_states) else: hidden_states, residual = self.input_layernorm(hidden_states, residual) hidden_states = self.self_attn( positions=positions, hidden_states=hidden_states, ) hidden_states, residual = self.post_attention_layernorm(hidden_states, residual) hidden_states = self.mlp(hidden_states) return hidden_states, residual class Qwen3Model(nn.Module): def __init__( self, config: Qwen3Config, ): super().__init__() self.vocab_size = config.vocab_size self.embed_tokens = VocabParallelEmbedding(config.vocab_size, config.hidden_size) self.layers = nn.ModuleList([Qwen3DecoderLayer(config) for _ in range(config.num_hidden_layers)]) self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, ) -> torch.Tensor: hidden_states = self.embed_tokens(input_ids) residual = None for layer in self.layers: hidden_states, residual = layer( positions, hidden_states, residual, ) hidden_states, _ = self.norm(hidden_states, residual) return hidden_states class Qwen3ForCausalLM(nn.Module): packed_modules_mapping = { "qkv_proj": [ "q_proj", "k_proj", "v_proj", ], "gate_up_proj": [ "gate_proj", "up_proj", ], } def __init__( self, config: Qwen3Config ): super().__init__() self.model = Qwen3Model(config) self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size) if config.tie_word_embeddings: self.lm_head.weight.data = self.model.embed_tokens.weight.data def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, ) -> torch.Tensor: hidden_states = self.model(input_ids, positions) return hidden_states def compute_logits( self, hidden_states: torch.Tensor, ) -> torch.Tensor: logits = self.lm_head(hidden_states) return logits def load_weights(self, path: str): import os from safetensors import safe_open default_weight_loader = lambda param, loaded_weight: param.data.copy_(loaded_weight) with safe_open(os.path.join(path, "model.safetensors"), "pt", "cpu") as f: for n, p in self.named_parameters(): for x in self.packed_modules_mapping: if x in n: weight_loader = getattr(p, "weight_loader", default_weight_loader) for i, y in enumerate(self.packed_modules_mapping[x]): weight_loader(p, f.get_tensor(n.replace(x, y)), y[0] if x == "qkv_proj" else i) break else: weight_loader = getattr(p, "weight_loader", default_weight_loader) weight_loader(p, f.get_tensor(n))