perf: 消除 PagedCache.write 中的 position_ids GPU 同步，解码提速 15%

- CacheView.write 用 total_len - k.size(1) 推导 start_pos，替代 position_ids[0,0].item()

- 移除 GQA/MLA/DecoderBlock 中不再使用的 position_ids 参数

- PagedCache.write 参数 position_ids:Tensor → start_pos:int

astrai/inference/cache.py

@@ -241,14 +241,13 @@ class PagedCache:
         self,
         layer_id: int,
         page_table: Tensor,
-        position_ids: Tensor,
+        start_pos: int,
         k: Tensor,
         v: Tensor,
     ) -> None:
         seq_len = k.size(1)
         if seq_len == 0:
             return
-        start_pos = position_ids[0, 0].item()
         page_size = self.page_size
         written = 0
         first_page = start_pos // page_size
@@ -289,8 +288,9 @@ class CacheView:
         self._page_table = page_table
         self._total_len = total_len
 
-    def write(self, layer_id: int, position_ids: Tensor, k: Tensor, v: Tensor) -> None:
-        self._cache.write(layer_id, self._page_table, position_ids, k, v)
+    def write(self, layer_id: int, k: Tensor, v: Tensor) -> None:
+        start_pos = self._total_len - k.size(1)
+        self._cache.write(layer_id, self._page_table, start_pos, k, v)
 
     def gather(self, layer_id: int) -> Tuple[Tensor, Tensor]:
         return self._cache.gather(layer_id, self._page_table, self._total_len)

astrai/model/module.py

@@ -165,7 +165,6 @@ class GQA(nn.Module):
         x: Tensor,
         rotary_emb: Tuple[Tensor, Tensor],
         attn_mask: Tensor = None,
-        position_ids: Optional[Tensor] = None,
         paged_cache: Optional[CacheView] = None,
     ) -> Tensor:
         is_causal = attn_mask is None
@@ -180,7 +179,7 @@ class GQA(nn.Module):
             q, k = self.q_norm(q), self.k_norm(k)
 
         if paged_cache is not None:
-            paged_cache.write(self.layer_id, position_ids, k, v)
+            paged_cache.write(self.layer_id, k, v)
             k, v = paged_cache.gather(self.layer_id)
 
         k, v = repeat_kv(k, self.n_rep), repeat_kv(v, self.n_rep)
@@ -246,7 +245,6 @@ class MLA(nn.Module):
         x: Tensor,
         rotary_emb: Tuple[Tensor, Tensor],
         attn_mask: Tensor = None,
-        position_ids: Optional[Tensor] = None,
         paged_cache: Optional[CacheView] = None,
     ) -> Tensor:
         bsz, seq_len, _ = x.size()
@@ -276,7 +274,7 @@ class MLA(nn.Module):
         k = torch.cat([k_nope, k_rope], dim=-1)
 
         if paged_cache is not None:
-            paged_cache.write(self.layer_id, position_ids, k, v)
+            paged_cache.write(self.layer_id, k, v)
             k, v = paged_cache.gather(self.layer_id)
 
         q = q.permute(0, 2, 1, 3)
@@ -326,7 +324,6 @@ class DecoderBlock(nn.Module):
         x: Tensor,
         rotary_emb: Tuple[Tensor, Tensor],
         attention_mask: Optional[Tensor] = None,
-        position_ids: Optional[Tensor] = None,
         paged_cache: Optional[CacheView] = None,
     ) -> Tensor:
         attn_output = self.attention(
@@ -334,11 +331,10 @@ class DecoderBlock(nn.Module):
             rotary_emb,
             attention_mask,
             paged_cache,
-            position_ids,
         )
         x = attn_output + x
-
         x = self.mlp(self.post_attention_norm(x)) + x
+
         return x
 
 

astrai/model/transformer.py

@@ -132,7 +132,7 @@ class Transformer(AutoModel):
         attn_mask = process_attention_mask(x, position_ids, input_mask, is_causal=True)
 
         for layer in self.layers:
-            x = layer(x, rotary_emb, attn_mask, paged_cache, position_ids)
+            x = layer(x, rotary_emb, attn_mask, paged_cache)
 
         hidden_states = self.norm(x)
         logits = self.lm_head(hidden_states)