fix: 修复推理引擎 batch decode 中多项正确性与并发问题

- scheduler: decode 分组由幂次分桶改为精确 next_pos，消除 KV cache 位置错乱
- task: activate() 加锁操作 active_tasks，消除数据竞争
- engine: wait_completion 加超时，防止分配失败时永久死锁
- sample: TopKStrategy 向量化为 per-sample threshold，尊重各 task 的 top_k
- cache: Storage.write/gather 中 -1 页改用 mask 处理，防数据污染
- executor: prefill 逐 task 循环改为单次 tensor 调用

astrai/inference/core/cache.py

@@ -235,7 +235,16 @@ class Storage:
             write_end = min(page_start + page_size, start_pos + seq_len)
             offset = write_start - page_start
             chunk = write_end - write_start
-            if (phys_pages < 0).any():
+            valid = phys_pages >= 0
+            if not valid.all():
+                if valid.any():
+                    valid_pages = phys_pages[valid]
+                    self.k_cache[layer_id, valid_pages, offset : offset + chunk] = k[
+                        valid, written : written + chunk
+                    ]
+                    self.v_cache[layer_id, valid_pages, offset : offset + chunk] = v[
+                        valid, written : written + chunk
+                    ]
                 written += chunk
                 continue
             self.k_cache[layer_id, phys_pages, offset : offset + chunk] = k[
@@ -254,6 +263,16 @@ class Storage:
         v = self.v_cache[layer_id, safe]
         k = k.flatten(1, 2)
         v = v.flatten(1, 2)
+        if (page_table < 0).any():
+            invalid = (
+                (page_table < 0)
+                .unsqueeze(-1)
+                .expand(-1, -1, self.page_size)
+                .flatten(1, 2)
+            )
+            invalid = invalid[:, :, None, None].expand_as(k)
+            k = k.masked_fill(invalid, 0.0)
+            v = v.masked_fill(invalid, 0.0)
         k = k[:, :total_len]
         v = v[:, :total_len]
         return k, v

astrai/inference/core/executor.py

@@ -38,13 +38,11 @@ class Executor:
         tasks = sorted(tasks, key=lambda t: t.task_id)
         batch_sz = len(tasks)
 
-        seq_len = prompt_len - start_pos
-        input_ids = torch.empty(batch_sz, seq_len, dtype=torch.long, device=self.device)
-
-        for i, t in enumerate(tasks):
-            input_ids[i] = torch.tensor(
-                t.prompt_ids[start_pos:prompt_len], device=self.device
-            )
+        input_ids = torch.tensor(
+            [t.prompt_ids[start_pos:prompt_len] for t in tasks],
+            dtype=torch.long,
+            device=self.device,
+        )
 
         task_ids = [t.task_id for t in tasks]
         page_tables = self.page_cache.make_table_tensor(task_ids, self.device)

astrai/inference/core/scheduler.py

@@ -126,9 +126,7 @@ class InferenceScheduler:
 
                 pos_groups: Dict[int, List[Task]] = {}
                 for t in self._task_mgr.get_active_tasks():
-                    chunk = t.next_pos // self._page_cache.page_size
-                    key = chunk if chunk <= 1 else 1 << (chunk.bit_length() - 1)
-                    pos_groups.setdefault(key, []).append(t)
+                    pos_groups.setdefault(t.next_pos, []).append(t)
 
                 if pos_groups:
                     best_key = max(pos_groups, key=lambda k: len(pos_groups[k]))

astrai/inference/core/task.py

@@ -171,7 +171,8 @@ class TaskManager:
 
     def activate(self, task: Task) -> None:
         task.status = TaskStatus.RUNNING
-        self.active_tasks.append(task)
+        with self._lock:
+            self.active_tasks.append(task)
 
     def return_to_waiting(self, tasks: List[Task]) -> None:
         with self._lock: