feat : 数据流水拼接策略支持 position_ids 预计算

- OutputConfig.position_ids_mode 三种模式控制边界策略
- pipeline._flush() 按配置生成扁平 position_ids 数组
- SFTDataset 在 __getitem__ 中返回 position_ids
- SFTStrategy 将 position_ids 传入 model.forward()

astrai/config/preprocess_config.py

@@ -45,6 +45,13 @@ class OutputConfig(BaseConfig):
     storage_format: str = "bin"
     max_tokens_per_shard: int = 100_000_000
     dtype: Dict[str, str] = field(default_factory=dict)
+    position_ids_mode: Optional[str] = None
+    """How to compute position_ids in packed sequences.
+
+    - ``None`` / ``"none"``: do not generate (backward compatible).
+    - ``"doc_reset"``: reset to 0 at each document boundary.
+    - ``"continuous"``: sequential 0, 1, 2, ... (pretrain, single doc).
+    """
 
 
 @dataclass

astrai/dataset/dataset.py

@@ -223,7 +223,7 @@ class SFTDataset(BaseDataset):
 
     @property
     def required_keys(self) -> List[str]:
-        return ["sequence", "loss_mask"]
+        return ["sequence", "loss_mask", "position_ids"]
 
     def _fetch_data(self, begin_idx: int, end_idx: int, key: str) -> Tensor:
         return self.storage.fetch(begin_idx, end_idx, key)
@@ -231,15 +231,17 @@ class SFTDataset(BaseDataset):
     def __getitem__(self, index):
         begin_idx, end_idx = self.get_index(index)
 
-        x = self._fetch_data(begin_idx, end_idx, "sequence").to(dtype=torch.long)
+        x = self._fetch_data(begin_idx, end_idx, "sequence")
-        y = self._fetch_data(begin_idx + 1, end_idx + 1, "sequence").to(
+        y = self._fetch_data(begin_idx + 1, end_idx + 1, "sequence")
-            dtype=torch.long
+        position_ids = self._fetch_data(begin_idx, end_idx, "position_ids")
-        )
+        loss_mask = self._fetch_data(begin_idx + 1, end_idx + 1, "loss_mask")
-        loss_mask = self._fetch_data(begin_idx + 1, end_idx + 1, "loss_mask").to(
-            dtype=torch.bool
-        )
 
-        return {"input_ids": x, "target_ids": y, "loss_mask": loss_mask}
+        return {
+            "input_ids": x.to(dtype=torch.long),
+            "target_ids": y.to(dtype=torch.long),
+            "position_ids": position_ids.to(dtype=torch.long),
+            "loss_mask": loss_mask.to(dtype=torch.bool),
+        }
 
 
 @DatasetFactory.register("dpo")

astrai/preprocessing/pipeline.py

@@ -144,6 +144,7 @@ class Pipeline:
     def _flush(self, domains, shard_idx):
         for domain, keys in domains.items():
             idx = shard_idx[domain]
+            chunk_dir = os.path.join(self.output_dir, domain)
             tensors = {}
             for key, ids_list in keys.items():
                 dt = _STR_TO_DTYPE.get(
@@ -152,10 +153,22 @@ class Pipeline:
                 tensors[key] = [
                     torch.tensor(list(chain.from_iterable(ids_list)), dtype=dt)
                 ]
-            chunk_dir = os.path.join(self.output_dir, domain)
+
+            pid_mode = self.config.output.position_ids_mode
+            if pid_mode and pid_mode != "none" and "sequence" in tensors:
+                pos_ids = []
+                if pid_mode == "doc_reset":
+                    for item in keys["sequence"]:
+                        pos_ids.extend(range(len(item)))
+                else:
+                    total = sum(len(item) for item in keys["sequence"])
+                    pos_ids = list(range(total))
+                tensors["position_ids"] = [torch.tensor(pos_ids, dtype=torch.int32)]
+
+            shard_path = os.path.join(chunk_dir, f"shard_{idx:04d}")
             fmt = self.config.output.storage_format
             if fmt == "bin":
-                save_bin(os.path.join(chunk_dir, f"shard_{idx:04d}"), tensors)
+                save_bin(shard_path, tensors)
             else:
                 save_h5(chunk_dir, f"data_{idx:04d}", tensors)
             shard_idx[domain] = idx + 1

astrai/trainer/strategy.py

@@ -180,14 +180,15 @@ class SFTStrategy(BaseStrategy):
 
     def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
         batch = move_to_device(batch, self.device)
-        input_ids, target_ids, loss_mask = (
+        input_ids, target_ids, position_ids, loss_mask = (
             batch["input_ids"],
             batch["target_ids"],
+            batch["position_ids"],
             batch["loss_mask"],
         )
 
         ignore_index = -100
-        logits = self.model(input_ids=input_ids)["logits"]
+        logits = self.model(input_ids=input_ids, position_ids=position_ids)["logits"]
         target_ids = target_ids.masked_fill(loss_mask == 0, ignore_index)
 
         loss = F.cross_entropy(