refactor: 重构 inference 模块架构，引入设计模式并分组文件

- 新增 protocol.py 协议层，Template Method 模式消除流/非流分支 45% 重复 - SSEBuilder 统一 SSE 构造，StopChecker 独立 stop_sequence 检测 - AnthropicHandler 追踪已产出文本，修复 stop 时重复 delta - server.py 路由从约 100 行缩减至 3 行 - 拆分为 core/（cache/executor/scheduler/task）和 api/（protocol/server） - 外部保持二级导入路径（from astrai.inference import Name） - 删除所有分隔线注释，代码按语义自然分组
2026-05-14 17:42:37 +08:00
parent 466c2e1efd
commit 2196c34c52
21 changed files with 743 additions and 485 deletions
@@ -0,0 +1,189 @@
+import logging
+import threading
+from typing import Any, Dict, List, Optional, Tuple
+
+import torch
+
+from astrai.inference.core.cache import PagedCache
+from astrai.inference.core.executor import Executor
+from astrai.inference.core.task import STOP, Task, TaskManager, TaskStatus
+from astrai.model.automodel import AutoModel
+from astrai.tokenize.tokenizer import AutoTokenizer
+
+logger = logging.getLogger(__name__)
+
+
+class InferenceScheduler:
+    """Four-phase continuous batching loop: cleanup -> refill -> prefill -> decode."""
+
+    def __init__(
+        self,
+        model: AutoModel,
+        tokenizer: AutoTokenizer,
+        max_batch_size: int = 16,
+        max_seq_len: Optional[int] = None,
+        max_prompt_len: int = 512,
+        page_size: int = 64,
+        device: Optional[str] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        config = model.config
+
+        self.max_seq_len = max_seq_len or config.max_len
+        self.device = device or next(model.parameters()).device
+        self.dtype = dtype or next(model.parameters()).dtype
+
+        n_pages = (
+            max_batch_size * (self.max_seq_len + page_size) + page_size - 1
+        ) // page_size
+
+        self._page_cache = PagedCache(
+            config.n_layers,
+            n_pages,
+            page_size,
+            config.n_kv_heads,
+            config.dim // config.n_heads,
+            self.device,
+            self.dtype,
+        )
+
+        self._task_mgr = TaskManager(
+            tokenizer=tokenizer,
+            max_batch_size=max_batch_size,
+            max_seq_len=self.max_seq_len,
+            max_prompt_len=max_prompt_len,
+        )
+
+        self._executor = Executor(
+            model=model,
+            tokenizer=tokenizer,
+            page_cache=self._page_cache,
+            device=self.device,
+            dtype=self.dtype,
+        )
+
+        self._running = False
+
+    def add_task(self, prompt: str, **kwargs) -> str:
+        return self._task_mgr.add_task(prompt, **kwargs)
+
+    def remove_task(self, task_id: str) -> None:
+        for task in self._task_mgr.remove_task(task_id):
+            self._page_cache.task_free(task.task_id)
+
+    def get_stats(self) -> Dict[str, Any]:
+        return self._task_mgr.get_stats()
+
+    def _run_generation_loop(self) -> None:
+        stop_ids = self._task_mgr.tokenizer.stop_ids
+        try:
+            while self._running:
+                finished = self._task_mgr.remove_finished_tasks(stop_ids)
+                for task in finished:
+                    self._page_cache.task_free(task.task_id)
+
+                active = self._task_mgr.get_active_tasks()
+                available = self._task_mgr.max_batch_size - len(active)
+                if available > 0:
+                    candidates = self._task_mgr.pull_candidates(available)
+                    failed = []
+                    for task in candidates:
+                        if self._page_cache.task_alloc(task.task_id, task.prompt_ids):
+                            self._task_mgr.activate(task)
+                        else:
+                            failed.append(task)
+                    if failed:
+                        self._task_mgr.return_to_waiting(failed)
+
+                if not self._task_mgr.has_work():
+                    self._task_mgr.wait_for_tasks(timeout=1.0)
+                    continue
+
+                to_prefill = [
+                    t for t in self._task_mgr.get_active_tasks() if t.output_tokens == 0
+                ]
+                if to_prefill:
+                    for t in to_prefill:
+                        t.input_tokens = len(t.prompt_ids)
+
+                    groups: Dict[Tuple[int, int], List[Task]] = {}
+                    for t in to_prefill:
+                        key = (
+                            len(t.prompt_ids),
+                            self._page_cache.task_cached(t.task_id),
+                        )
+                        groups.setdefault(key, []).append(t)
+
+                    for (prompt_len, start_pos), group in groups.items():
+                        self._executor.execute_prefill(group, prompt_len, start_pos)
+                        start_logical_page = start_pos // self._page_cache.page_size
+                        for t in group:
+                            self._page_cache.task_record_hashes(
+                                t.task_id,
+                                t.prompt_ids,
+                                start_logical_page=start_logical_page,
+                            )
+
+                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)
+
+                if pos_groups:
+                    best_key = max(pos_groups, key=lambda k: len(pos_groups[k]))
+                    group = sorted(pos_groups[best_key], key=lambda t: t.task_id)
+
+                    valid: List[Task] = []
+                    for t in group:
+                        if self._page_cache.task_extend(t.task_id, t.next_pos):
+                            valid.append(t)
+                        else:
+                            t.status = TaskStatus.ABORTED
+                            if t.stream_callback:
+                                t.stream_callback(STOP)
+
+                    if valid:
+                        next_tokens = self._executor.execute_decode(valid)
+
+                        for t, ntok in zip(valid, next_tokens):
+                            t.output_ids.append(ntok)
+                            t.output_tokens += 1
+                            pos = t.input_tokens + t.output_tokens
+                            self._page_cache.task_extend(t.task_id, pos)
+                            if t.stream_callback:
+                                t.stream_callback(
+                                    self._task_mgr.tokenizer.decode([ntok])
+                                )
+
+                        for t in valid:
+                            if t.is_finished(stop_ids):
+                                if t.stream_callback:
+                                    t.stream_callback(STOP)
+
+        except Exception as e:
+            logger.error(f"Scheduler loop crashed: {e}", exc_info=True)
+            for task in self._task_mgr.get_active_tasks():
+                if task.stream_callback:
+                    task.stream_callback(STOP)
+                self._page_cache.task_free(task.task_id)
+            self._task_mgr.clear_queues()
+            raise
+
+    def start(self) -> None:
+        if not self._running:
+            self._running = True
+            t = threading.Thread(target=self._run_generation_loop, daemon=True)
+            t.start()
+            self._loop_thread = t
+
+    def stop(self) -> None:
+        self._running = False
+        self._task_mgr.wake()
+        if hasattr(self, "_loop_thread"):
+            self._loop_thread.join(timeout=2.0)
+        for task in self._task_mgr.get_active_tasks():
+            self._page_cache.task_free(task.task_id)
+        self._task_mgr.clear_queues()
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()