feat: _generate_streaming 支持 batch 模式

- _Result.append 存储 (idx, token) 元组，pop_all 返回对应列表
- 单 prompt: Generator[str]（向后兼容）
- 多 prompt: Generator[Tuple[int, str]]，token 交错到达，调用方自行分流
- 不使用 dispatch 线程 / Queue，避免同步开销和内存积压

astrai/inference/engine.py

@@ -11,7 +11,7 @@ import asyncio
 import gc
 import threading
 from dataclasses import dataclass
-from typing import Any, AsyncGenerator, Dict, Generator, List, Optional, Union
+from typing import Any, AsyncGenerator, Dict, Generator, List, Optional, Tuple, Union
 
 import torch
 import torch.nn as nn
@@ -126,7 +126,7 @@ class _Result:
             idx: Index of the generation task this token belongs to.
         """
         with self._cond:
-            self.tokens.append(token)
+            self.tokens.append((idx, token))
             if token is not STOP:
                 self.results[idx] += token
             else:
@@ -136,11 +136,11 @@ class _Result:
                     self._cond.notify_all()
             self._event.set()
 
-    def pop_all(self) -> List[str]:
-        """Returns and clears all accumulated tokens.
+    def pop_all(self) -> List[Tuple[int, str]]:
+        """Returns and clears all accumulated (idx, token) pairs.
 
         Returns:
-            List of token strings since the last call.
+            List of (index, token_string) tuples since the last call.
         """
         with self._cond:
             out = self.tokens.copy()
@@ -238,20 +238,22 @@ class InferenceEngine:
         temperature: float = 1.0,
         top_p: float = 1.0,
         top_k: int = 50,
-    ) -> Union[Generator[str, None, None], str, List[str]]:
+    ) -> Union[Generator, str, List[str]]:
         """Generates text from a prompt.
 
         Args:
             prompt: Single string or list of strings for batch generation.
-            stream: If True, returns a generator yielding tokens one by one.
+            stream: If True, returns a generator yielding tokens.
             max_tokens: Maximum number of tokens to generate.
             temperature: Sampling temperature.
             top_p: Nucleus sampling probability threshold.
             top_k: Top-k sampling count (0 disables).
 
         Returns:
-            Generator (stream=True), single string (non-stream, single prompt),
-            or list of strings (non-stream, batch prompts).
+            stream=False, single prompt: str
+            stream=False, batch: List[str]
+            stream=True, single prompt: Generator[str, None, None]
+            stream=True, batch: Generator[Tuple[int, str], None, None]
         """
         is_batch = isinstance(prompt, list)
         prompts = prompt if is_batch else [prompt]
@@ -348,49 +350,60 @@ class InferenceEngine:
         temperature: float,
         top_p: float,
         top_k: int,
-    ) -> Generator[str, None, None]:
+    ) -> Generator:
         """Internal streaming generator.
 
         Polls the _Result accumulator in a loop, yielding tokens as they arrive.
-        Cleans up the scheduler task on GeneratorExit.
+        Single prompt yields raw token strings; batch yields (idx, token) tuples.
 
         Args:
-            prompts: List of prompts (only first is used; batch not yet supported).
-            is_batch: If True, raises NotImplementedError.
+            prompts: List of prompts.
+            is_batch: If True, yields (idx, token) tuples; else yields raw tokens.
             max_tokens: Maximum tokens to generate.
             temperature: Sampling temperature.
             top_p: Nucleus sampling threshold.
             top_k: Top-k sampling count.
 
         Yields:
-            Decoded token strings.
+            Single prompt: decoded token strings.
+            Batch: (sequence_index, token_string) tuples.
         """
-        if is_batch:
-            raise NotImplementedError("Batch streaming not yet supported")
+        n = len(prompts)
+        result = _Result(count=n)
+        task_ids = []
 
-        result = _Result()
+        for i, p in enumerate(prompts):
+            task_id = self.scheduler.add_task(
+                prompt=p,
+                max_tokens=max_tokens,
+                temperature=temperature,
+                top_p=top_p,
+                top_k=top_k,
+                stream_callback=lambda tok, idx=i: result.append(tok, idx),
+            )
+            task_ids.append(task_id)
 
-        task_id = self.scheduler.add_task(
-            prompt=prompts[0],
-            max_tokens=max_tokens,
-            temperature=temperature,
-            top_p=top_p,
-            top_k=top_k,
-            stream_callback=lambda tok: result.append(tok, 0),
-        )
+        remaining = n
+        finished = [False] * n
 
         def gen():
+            nonlocal remaining
             try:
-                while True:
-                    tokens = result.pop_all()
-                    for token in tokens:
+                while remaining > 0:
+                    items = result.pop_all()
+                    for idx, token in items:
                         if token is STOP:
-                            return
-                        yield token
-                    if not result.wait(timeout=0.05):
-                        pass
+                            if not finished[idx]:
+                                finished[idx] = True
+                                remaining -= 1
+                        else:
+                            yield (idx, token) if is_batch else token
+                    if remaining > 0:
+                        if not result.wait(timeout=0.05):
+                            pass
             finally:
-                self.scheduler.remove_task(task_id)
+                for tid in task_ids:
+                    self.scheduler.remove_task(tid)
 
         return gen()