52 lines
1.6 KiB
Python
52 lines
1.6 KiB
Python
"""DPO preference learning 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.tokenizer import BpeTokenizer
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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("dpo")
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class DPOProcessor(BaseProcessor):
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"""DPO preference learning 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: BpeTokenizer,
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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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chosen_tokens = self.tokenizer.encode(input_dict["chosen"])
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rejected_tokens = self.tokenizer.encode(input_dict["rejected"])
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prompt = self.strategy.assemble_prompt(query_tokens)
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chosen_t, chosen_m = _encode_with_mask(
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prompt, self.strategy.assemble_response(chosen_tokens)
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)
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rejected_t, rejected_m = _encode_with_mask(
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prompt, self.strategy.assemble_response(rejected_tokens)
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)
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return {
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"chosen": chosen_t,
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"chosen_mask": chosen_m,
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"rejected": rejected_t,
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"rejected_mask": rejected_m,
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}
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@property
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def output_keys(self) -> List[str]:
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return ["chosen", "chosen_mask", "rejected", "rejected_mask"]
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