InputProcessor

Mon, 01 Jan 0001 00:00:00 +0000

InputProcessor carries the data preparation process for different tasks.

class InputProcessor:

 def __init__(self, device_mesh: Optional[DeviceMesh] = None,
 padding_free: bool = False,
 framework: Literal['transformers', 'megatron'] = 'transformers',
 **kwargs):
 ...

 def __call__(self, inputs: Union[InputFeature, List[InputFeature]], **kwargs) -> Union[InputFeature, List[InputFeature]]:
 # Overall processing entry point
 ...

 def prepare_inputs(self, inputs: Union[List[InputFeature], InputFeature], **kwargs) -> List[InputFeature]:
 # Move to cuda device
 ...

 def pad_cp(self, inputs: List[InputFeature], **kwargs) ->List[InputFeature]:
 # Handle cp
 ...

 def split_cp(self, inputs: List[Dict[str, Any]], **kwargs) -> List[Dict[str, Any]]:
 # Handle cp
 ...

 def collate_fn(self, inputs: List[InputFeature], micro_batch_size: Optional[int] = None,
 variable_seq_lengths=False, **kwargs) -> List[InputFeature]:
 # data_collator
 ...

device_mesh: Used to split cp. If there is no cp, the device_mesh parameter can be omitted.
padding_free: Whether to concatenate multiple samples into one. This function is similar to PackingDataset, but PackingDataset makes the length of each batch basically consistent, while padding_free only considers concatenation within this batch.
- Using PackingDataset will automatically trigger padding_free in InputProcessor
framework: Supports transformers and megatron. Different model architectures return slightly different model inputs

Twinkle places collate_fn in InputProcessor because different tasks (sft/grpo, etc.) have different input requirements. Currently, InputProcessor is executed on the model side by default, because this decouples DataLoader and the model. Because collate_fn is related to the running task, Megatron’s micro_batch_size and other information, if run in DataLoader, it will cause DataLoader to be unable to become an independent component, and its logic will also become complex.

InputProcessor implements the call method, so you can use your own function to complete your own task data preparation process:

def my_processor(inputs: Union[InputFeature, List[InputFeature]]) -> Union[InputFeature, List[InputFeature]]:
 return ...

model.set_processor(my_processor)
# Or
dataloader.set_processor(my_processor)

Task Processor | Twinkle

InputProcessor