simplify

nanovllm/models/qwen3.py

@@ -8,7 +8,7 @@ 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
+from nanovllm.layers.embed_head import VocabParallelEmbedding, ParallelLMHead
 
 
 class Qwen3Attention(nn.Module):
@@ -26,19 +26,17 @@ class Qwen3Attention(nn.Module):
         rope_scaling: tuple | None = None,
     ) -> None:
         super().__init__()
-        self.hidden_size = hidden_size
         tp_size = dist.get_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.num_kv_heads = 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,
@@ -57,13 +55,15 @@ class Qwen3Attention(nn.Module):
             self.head_dim,
             rotary_dim=self.head_dim,
             max_position=max_position,
-            base=self.rope_theta,
+            base=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.attn = Attention(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            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)
 
@@ -122,9 +122,8 @@ class Qwen3DecoderLayer(nn.Module):
         config: Qwen3Config,
     ) -> None:
         super().__init__()
-        self.hidden_size = config.hidden_size
         self.self_attn = Qwen3Attention(
-            hidden_size=self.hidden_size,
+            hidden_size=config.hidden_size,
             num_heads=config.num_attention_heads,
             num_kv_heads=config.num_key_value_heads,
             max_position=config.max_position_embeddings,
@@ -139,10 +138,8 @@ class Qwen3DecoderLayer(nn.Module):
             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)
+        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,
@@ -155,10 +152,7 @@ class Qwen3DecoderLayer(nn.Module):
             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 = self.self_attn(positions, hidden_states)
         hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
         hidden_states = self.mlp(hidden_states)
         return hidden_states, residual
@@ -169,9 +163,8 @@ class Qwen3Model(nn.Module):
     def __init__(
         self,
         config: Qwen3Config,
-    ):
+    ) -> None:
         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)
@@ -184,11 +177,7 @@ class Qwen3Model(nn.Module):
         hidden_states = self.embed_tokens(input_ids)
         residual = None
         for layer in self.layers:
-            hidden_states, residual = layer(
-                positions,
-                hidden_states,
-                residual,
-            )
+            hidden_states, residual = layer(positions, hidden_states, residual)
         hidden_states, _ = self.norm(hidden_states, residual)
         return hidden_states
 
@@ -205,12 +194,11 @@ class Qwen3ForCausalLM(nn.Module):
     def __init__(
         self,
         config: Qwen3Config
-    ):
+    ) -> None:
         super().__init__()
         self.model = Qwen3Model(config)
         self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
-        self.tie_word_embeddings = config.tie_word_embeddings
-        if self.tie_word_embeddings:
+        if config.tie_word_embeddings:
             self.lm_head.weight.data = self.model.embed_tokens.weight.data
 
     def forward(