60 lines
1.9 KiB
Python
60 lines
1.9 KiB
Python
"""Supervised fine-tuning data processor."""
|
|
|
|
from typing import Any, Dict, List, Optional
|
|
|
|
import torch
|
|
from torch import Tensor
|
|
|
|
from pipeline.tokenize import AutoTokenizer
|
|
from pipeline.strategies import PromptStrategy, ChatMLStrategy
|
|
from pipeline.processors.base import BaseProcessor, ProcessorSchema, encode_with_mask
|
|
from pipeline.processors.factory import ProcessorFactory
|
|
|
|
|
|
@ProcessorFactory.register("sft")
|
|
class SFTProcessor(BaseProcessor):
|
|
"""Supervised fine-tuning data processor.
|
|
|
|
Processes query-response pairs into tokenized sequences with loss masks.
|
|
|
|
Input schema:
|
|
- query: str - User query/prompt
|
|
- response: str - Assistant response
|
|
|
|
Output schema:
|
|
- sequence: int32 tensor - Combined token IDs (query + response)
|
|
- loss_mask: bool tensor - True for response tokens (compute loss)
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
tokenizer: AutoTokenizer,
|
|
strategy: Optional[PromptStrategy] = None,
|
|
):
|
|
self.tokenizer = tokenizer
|
|
self.strategy = strategy or ChatMLStrategy(tokenizer)
|
|
|
|
@property
|
|
def schema(self) -> ProcessorSchema:
|
|
return ProcessorSchema(
|
|
input_fields={"query": str, "response": str},
|
|
output_fields={
|
|
"sequence": torch.int32,
|
|
"loss_mask": torch.bool,
|
|
},
|
|
)
|
|
|
|
def process(self, input_dict: Dict[str, Any]) -> Dict[str, Tensor]:
|
|
query_tokens = self.tokenizer.encode(input_dict["query"])
|
|
response_tokens = self.tokenizer.encode(input_dict["response"])
|
|
|
|
prompt = self.strategy.assemble_prompt(query_tokens)
|
|
response = self.strategy.assemble_response(response_tokens)
|
|
|
|
tokens, loss_mask = encode_with_mask(prompt, response)
|
|
return {"sequence": tokens, "loss_mask": loss_mask}
|
|
|
|
@property
|
|
def output_keys(self) -> List[str]:
|
|
return ["sequence", "loss_mask"]
|