[StreamingDataLoader, 5/N] Refactor StreamDataLoader implementation

README.md

@@ -247,7 +247,6 @@ batch_meta = client.get_meta(
     batch_size=8,
     partition_id="train_0",
     task_name="generate_sequences",
-    sampling_config={"n_samples_per_prompt": 4}  # Put the required sampling parameters here
 )
 ```
 

recipe/simple_use_case/async_demo.py

@@ -226,7 +226,6 @@ def _initialize_data_system(self):
         # self.data_system_controller = TransferQueueController.remote(sampler=grpo_sampler)
 
         # Then use sampling_config in get_meta calls:
-        # sampling_config={"n_samples_per_prompt": 4}
         self.data_system_controller = TransferQueueController.remote()
         logger.info("TransferQueueController has been created.")
 

recipe/simple_use_case/sync_demo.py

@@ -68,7 +68,6 @@ def initialize_data_system(config):
     # data_system_controller = TransferQueueController.remote(sampler=grpo_sampler)
 
     # Then use sampling_config in get_meta calls:
-    # sampling_config={"n_samples_per_prompt": 4}
     data_system_controller = TransferQueueController.remote()
     logger.info("TransferQueueController has been created.")
 

transfer_queue/client.py

@@ -179,7 +179,6 @@ async def async_get_meta(
                 - 'insert': Internal usage - should not be used by users
             task_name: Optional task name associated with the request
             sampling_config: Optional sampling configuration for custom samplers.
-                           For GRPOGroupNSampler, should include "n_samples_per_prompt": int
             socket: ZMQ async socket for message transmission (injected by decorator)
 
         Returns:
@@ -206,7 +205,6 @@ async def async_get_meta(
             ...     partition_id="train_0",
             ...     mode="fetch",
             ...     task_name="generate_sequences",
-            ...     sampling_config={"n_samples_per_prompt": 4}
             ... ))
             >>> print(batch_meta.is_ready)  # True if all samples ready
             >>>
@@ -698,7 +696,7 @@ async def async_check_consumption_status(
             partition_id=partition_id,
         )
 
-        if consumption_status is None:
+        if consumption_status is None or consumption_status.numel() == 0:
             return False
         return torch.all(consumption_status == 1).item()
 
@@ -883,7 +881,6 @@ def get_meta(
             partition_id: Target data partition id
             task_name: Optional task name associated with the request
             sampling_config: Optional sampling configuration for custom samplers.
-                           For GRPOGroupNSampler, should include "n_samples_per_prompt": int
 
         Returns:
             BatchMeta: Batch metadata containing data location information

transfer_queue/controller.py

@@ -781,7 +781,7 @@ def __init__(
                     - If a BaseSampler subclass is provided, it will be instantiated
                     - Defaults to SequentialSampler for simple sequential sampling
                     - Example: sampler=GRPOGroupNSampler() (instance)
-                    - Example: sampler=GRPOGroupNSampler (class)
+                    - Example: sampler=SequentialSampler (class)
             polling_mode: Whether to use polling mode for TransferQueue controller.
                     - If False, the controller will raise an error when no enough data is available.
                     - If True, the controller will return an empty BatchMeta when no enough data is available.
@@ -1015,12 +1015,12 @@ def get_metadata(
         Raises:
             TimeoutError: If waiting for sufficient data times out in fetch mode
         """
-        if partition_id not in self.partitions:
-            self.create_partition(partition_id)
 
         if mode == "insert":
-            partition = self._get_partition(partition_id)
+            if partition_id not in self.partitions:
+                self.create_partition(partition_id)
 
+            partition = self._get_partition(partition_id)
             if data_fields:
                 # This is called during put_data call without providing metadata.
                 # try to use pre-allocated global index first
@@ -1083,6 +1083,7 @@ def get_metadata(
                 ready_for_consume_indexes,
                 batch_size,
                 **(sampling_config or {}),
+                **kwargs,
             )
 
             # Check if we got valid results from the sampler
@@ -1240,6 +1241,7 @@ def clear_partition(self, partition_id: str, clear_consumption: bool = True):
         partition.clear_data(global_indexes_range, clear_consumption)
         self.index_manager.release_partition(partition_id)
         self.partitions.pop(partition_id)
+        self.sampler.clear_cache(partition_id)
 
     def clear_meta(
         self,

transfer_queue/dataloader/streaming_dataloader.py

@@ -111,6 +111,7 @@ def __init__(
             parameter in PyTorch DataLoader is set to None because batching is managed
             by the StreamingDataset in coordination with RankAwareSampler.
         """
+        self.dataset: StreamingDataset = dataset
 
         if collate_fn is None:
             # use identical collate function to directly return the self-defined
@@ -137,3 +138,32 @@ def __init__(
             persistent_workers=persistent_workers,
             pin_memory_device=pin_memory_device,
         )
+
+    def reset(self):
+        """Reset the dataset iterator to the beginning.
+
+        Clears the buffer and resets the batch index for a fresh iteration.
+        """
+        self.dataset.reset()
+
+    def step(self, partition_id):
+        """Switch to a new partition and reset the dataset state.
+
+        This method clears the buffer, resets the batch index, and updates the partition_id
+        to fetch data from a different partition (e.g., switching from "train" to "val").
+
+        Args:
+            partition_id: The new partition ID to switch to.
+        """
+        self.dataset.step(partition_id)
+
+    def get_buffer(self):
+        """Get the current buffer from the underlying dataset.
+
+        Returns the batch buffer maintained by StreamingDataset, which stores
+        pre-fetched batches for efficient data access.
+
+        Returns:
+            list: Buffer containing pre-fetched (TensorDict, BatchMeta) tuples.
+        """
+        return self.dataset.buffer