80 lines
2.6 KiB
Python
80 lines
2.6 KiB
Python
"""DPO preference learning data processor."""
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from typing import Any, Dict, List, 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, ProcessorSchema, 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 (Direct Preference Optimization) data processor.
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Processes query, chosen, and rejected responses for preference learning.
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Input schema:
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- query: str - User query/prompt
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- chosen: str - Preferred assistant response
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- rejected: str - Dispreferred assistant response
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Output schema:
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- chosen: int32 tensor - Token IDs for preferred response
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- chosen_mask: bool tensor - True for response tokens
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- rejected: int32 tensor - Token IDs for dispreferred response
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- rejected_mask: bool tensor - True for response tokens
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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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@property
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def schema(self) -> ProcessorSchema:
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return ProcessorSchema(
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input_fields={
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"query": str,
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"chosen": str,
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"rejected": str,
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},
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output_fields={
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"chosen": torch.int32,
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"chosen_mask": torch.bool,
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"rejected": torch.int32,
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"rejected_mask": torch.bool,
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},
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)
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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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