42 lines
1.3 KiB
Python
42 lines
1.3 KiB
Python
"""Supervised fine-tuning data processor."""
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from typing import Dict, List, Any, Optional
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import torch
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from torch import Tensor
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from pipeline.tokenize import AutoTokenizer
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from pipeline.strategies import PromptStrategy, ChatMLStrategy
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from pipeline.processors.base import BaseProcessor, _encode_with_mask
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from pipeline.processors.factory import ProcessorFactory
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@ProcessorFactory.register("sft")
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class SFTProcessor(BaseProcessor):
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"""Supervised fine-tuning data processor.
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Supports custom prompt strategy via constructor parameter.
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"""
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def __init__(
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self,
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tokenizer: AutoTokenizer,
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strategy: Optional[PromptStrategy] = None,
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):
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self.tokenizer = tokenizer
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self.strategy = strategy or ChatMLStrategy(tokenizer)
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def process(self, input_dict: Dict[str, Any]) -> Dict[str, Tensor]:
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query_tokens = self.tokenizer.encode(input_dict["query"])
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response_tokens = self.tokenizer.encode(input_dict["response"])
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prompt = self.strategy.assemble_prompt(query_tokens)
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response = self.strategy.assemble_response(response_tokens)
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tokens, loss_mask = _encode_with_mask(prompt, response)
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return {"sequence": tokens, "loss_mask": loss_mask}
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@property
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def output_keys(self) -> List[str]:
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return ["sequence", "loss_mask"]
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