fix: 修复 CLI 参数缺失/重复、device_ids 越界、generate 参数名不一致、scheduler 时序、非流式截断等 bug

- train.py: 补上 --batch_size、--grpo_clip_eps，删除 3 处重复 --group_size
- generate.py: --model_dir 改为 --param_path 对齐 README
- automodel.py: from_pretrained 新增 strict 参数（默认 True）
- parallel/setup.py: 修复 device_ids 索引越界
- train_callback.py: scheduler.step() 移至 on_step_end
- test_train_strategy.py: 测试中补 optimizer.step()
- engine.py: 非流式改为循环等待所有任务完成，补 remove_task 清理
- scheduler.py: Task 添加 _pages_freed 标志，杜绝双重释放
- trainer.py: accumulation_steps=0 时 clamp 为 1
- tokenizer.py: save_pretrained 添加 _tokenizer is None 检查
- benchmark.py: 修复 ModelConfig 过时 import 路径
- inference/__init__.py: 修复 stale docstring

astrai/inference/__init__.py

@@ -3,7 +3,7 @@
 Layers:
   - engine.py:    Facade (InferenceEngine), Value Object (GenerationParams, GenerationRequest)
   - scheduler.py: Continuous-batching loop, Task state machine, TaskStatus enum
-  - cache.py:     Object Pool (SlotAllocator), PrefixCacheManager
+  - cache.py:     PagedCache (page-table-indirected KV cache with alloc/free)
   - sampling.py:  Strategy pattern (TemperatureStrategy, TopKStrategy, TopPStrategy)
   - server.py:    FastAPI HTTP server (OpenAI-compatible endpoints)
 """

astrai/inference/engine.py

@@ -408,13 +408,14 @@ class InferenceEngine:
             Single string for one prompt, list of strings for batch.
         """
         result = _Result(count=len(prompts))
+        task_ids = []
 
         for i, p in enumerate(prompts):
 
             def make_cb(idx):
                 return lambda tok: result.append(tok, idx)
 
-            self.scheduler.add_task(
+            task_id = self.scheduler.add_task(
                 prompt=p,
                 max_tokens=max_tokens,
                 temperature=temperature,
@@ -422,8 +423,14 @@ class InferenceEngine:
                 top_k=top_k,
                 stream_callback=make_cb(i),
             )
+            task_ids.append(task_id)
+
+        while result._completed < result._total:
+            result.wait(timeout=1.0)
+
+        for task_id in task_ids:
+            self.scheduler.remove_task(task_id)
 
-        result.wait()
         res = result.get_results()
         return res if is_batch else res[0]
 

astrai/inference/scheduler.py

@@ -56,6 +56,7 @@ class Task:
         self.arrival_time = time.time()
         self.finish_time: Optional[float] = None
         self.stream_callback = stream_callback
+        self._pages_freed: bool = False
 
     @property
     def next_pos(self) -> int:
@@ -167,9 +168,11 @@ class InferenceScheduler:
             self.active_tasks = [t for t in self.active_tasks if t.task_id != task_id]
 
         for task in removed_active:
-            self._free_pages(task.page_table)
+            if not task._pages_freed:
-            task.page_table.clear()
+                self._free_pages(task.page_table)
-            task.n_pages = 0
+                task.page_table.clear()
+                task.n_pages = 0
+                task._pages_freed = True
 
     def _free_pages(self, indices: List[int]) -> None:
         for idx in indices:
@@ -185,9 +188,11 @@ class InferenceScheduler:
                 self._total_tokens += task.output_tokens
 
         for task in finished:
-            self._free_pages(task.page_table)
+            if not task._pages_freed:
-            task.page_table.clear()
+                self._free_pages(task.page_table)
-            task.n_pages = 0
+                task.page_table.clear()
+                task.n_pages = 0
+                task._pages_freed = True
 
         self.active_tasks = [
             t for t in self.active_tasks if t.status != TaskStatus.FINISHED

astrai/model/automodel.py

@@ -84,6 +84,7 @@ class AutoModel(nn.Module):
         cls,
         path: Union[str, Path],
         disable_random_init: bool = True,
+        strict: bool = True,
     ) -> nn.Module:
 
         model_path = Path(path)
@@ -106,7 +107,7 @@ class AutoModel(nn.Module):
         weights_path = model_path / "model.safetensors"
         if weights_path.exists():
             state_dict = st.load_file(str(weights_path))
-            model.load_state_dict(state_dict, strict=False)
+            model.load_state_dict(state_dict, strict=strict)
 
         return model
 

astrai/parallel/setup.py

@@ -48,8 +48,9 @@ def setup_parallel(
     if device_ids is None:
         device_ids = [i for i in range(world_size)]
 
-    rank = device_ids[rank % len(device_ids)]
+    effective_rank = rank % len(device_ids)
-    device_id = torch.device(device_type, device_ids[rank])
+    device_id = torch.device(device_type, device_ids[effective_rank])
+    rank = device_ids[effective_rank]
 
     os.environ["MASTER_ADDR"] = master_addr
     os.environ["MASTER_PORT"] = master_port

astrai/tokenize/tokenizer.py

@@ -64,6 +64,11 @@ class AutoTokenizer:
             save_path: Path to save the tokenizer
         """
 
+        if self._tokenizer is None:
+            raise RuntimeError(
+                "Tokenizer not initialized. Load or create a tokenizer first."
+            )
+
         save_path = Path(save_path)
         save_path.mkdir(parents=True, exist_ok=True)
 

astrai/trainer/train_callback.py

@@ -104,7 +104,7 @@ class SchedulerCallback(TrainCallback):
             if "initial_lr" not in group:
                 group["initial_lr"] = group["lr"]
 
-    def on_batch_end(self, context: TrainContext):
+    def on_step_end(self, context: TrainContext):
         if context.scheduler:
             context.scheduler.step()
 

astrai/trainer/trainer.py

@@ -68,8 +68,9 @@ class Trainer:
                 context.epoch = epoch
                 self._call_callbacks("on_epoch_begin", context)
 
+                accumulation_steps = max(self.train_config.accumulation_steps, 1)
                 for batch in context.dataloader:
-                    if context.iteration % self.train_config.accumulation_steps == 0:
+                    if context.iteration % accumulation_steps == 0:
                         # 2. step
                         self._call_callbacks("on_step_begin", context)
                         context.optimizer.step()
@@ -83,7 +84,7 @@ class Trainer:
                     context.iteration += 1
 
                     # to make the loss normalized by accumulation steps
-                    stand_loss = loss / self.train_config.accumulation_steps
+                    stand_loss = loss / accumulation_steps
                     stand_loss.backward()
 
                     self._call_callbacks("on_batch_end", context)

scripts/tools/benchmark.py

@@ -6,8 +6,9 @@ from typing import Any, Dict
 import torch
 from torch import Tensor
 
+from astrai.config import ModelConfig
 from astrai.inference.cache import PagedCache
-from astrai.model.transformer import ModelConfig, Transformer
+from astrai.model.transformer import Transformer
 
 
 @dataclass

scripts/tools/generate.py

@@ -9,7 +9,7 @@ from astrai.tokenize import AutoTokenizer
 
 
 def processor(
-    model_dir: str,
+    param_path: str,
     input_json_file: str,
     output_json_file: str,
     temperature: float,
@@ -20,8 +20,8 @@ def processor(
     max_tokens: int,
 ):
     # Load model and tokenizer
-    model = AutoModel.from_pretrained(model_dir)
+    model = AutoModel.from_pretrained(param_path)
-    tokenizer = AutoTokenizer.from_pretrained(model_dir)
+    tokenizer = AutoTokenizer.from_pretrained(param_path)
     model.to(device="cuda", dtype=torch.bfloat16)
 
     # Create inference engine
@@ -72,7 +72,7 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Run generate with a Khaosz model.")
 
     parser.add_argument(
-        "--model_dir", type=str, required=True, help="Path to the model directory."
+        "--param_path", type=str, required=True, help="Path to the model directory."
     )
     parser.add_argument(
         "--input_json_file",

scripts/tools/train.py

@@ -42,7 +42,7 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument(
         "--n_epoch", type=int, default=1, help="Number of epochs to train."
     )
-    parser.add_argument("--group_size", type=int, default=4, help="GRPO group size.")
+    parser.add_argument("--batch_size", type=int, default=1, help="Batch size per GPU.")
     parser.add_argument(
         "--accumulation_steps",
         type=int,
@@ -53,7 +53,7 @@ def parse_args() -> argparse.Namespace:
         "--warmup_steps",
         type=int,
         default=1000,
-        help="Number of iters between warnings.",
+        help="Number of warmup steps for LR scheduler.",
     )
     parser.add_argument(
         "--max_lr", type=float, default=3e-4, help="Max learning rate for training."
@@ -98,23 +98,19 @@ def parse_args() -> argparse.Namespace:
         "--window_size",
         type=int,
         default=None,
-        help="the max length of the input sequence.",
+        help="Max length of the input sequence.",
     )
     parser.add_argument(
-        "--stride", type=int, default=None, help="the step size of the input sequence."
+        "--stride", type=int, default=None, help="Step size of the input sequence."
     )
     parser.add_argument("--dpo_beta", type=float, default=0.1, help="DPO beta value.")
     parser.add_argument("--group_size", type=int, default=4, help="GRPO group size.")
     parser.add_argument(
-        "--on_policy",
+        "--grpo_clip_eps", type=float, default=0.2, help="GRPO clipping epsilon."
-        action="store_true",
-        default=False,
-        help="Enable on-policy GRPO mode.",
     )
     parser.add_argument(
         "--grpo_kl_coef", type=float, default=0.01, help="GRPO KL penalty coefficient."
     )
-    parser.add_argument("--group_size", type=int, default=4, help="GRPO group size.")
     parser.add_argument(
         "--label_smoothing",
         type=float,
@@ -134,7 +130,6 @@ def parse_args() -> argparse.Namespace:
         default="checkpoint",
         help="Directory to save checkpoints.",
     )
-    parser.add_argument("--group_size", type=int, default=4, help="GRPO group size.")
     parser.add_argument(
         "--grpo_sync_interval",
         type=int,

tests/trainer/test_train_strategy.py

@@ -72,6 +72,7 @@ def test_schedule_factory_random_configs():
 
             # Test scheduler step functionality
             initial_lr = scheduler.get_last_lr()
+            optimizer.step()
             scheduler.step()
             new_lr = scheduler.get_last_lr()
 
@@ -112,6 +113,7 @@ def test_schedule_factory_edge_cases():
 
         # Test multiple steps
         for _ in range(10):
+            optimizer.step()
             scheduler.step()
 
 
@@ -136,6 +138,7 @@ def test_schedule_factory_state_persistence():
 
     # Take a few steps
     for _ in range(5):
+        optimizer.step()
         scheduler.step()
 
     # Save state