ViperEkura
aa2ea4f3a6
- SFTProcessor 输出 per-sample position_ids（torch.arange），每个样本从 0 开始 - position_ids 与 sequence/loss_mask 一同打包，边界处自然重置 - PT 路径不生成 position_ids - 新增 TestPositionIds 测试及 SFTProcessor 相关测试
108 lines
3.9 KiB
Python
108 lines
3.9 KiB
Python
"""Supervised fine-tuning 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("sft")
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class SFTProcessor(BaseProcessor):
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"""Supervised fine-tuning data processor.
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Supports two input formats:
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1. messages (recommended):
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``{"messages": [{"role": "user", "content": "..."},
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{"role": "assistant", "content": "..."}]}``
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Multi-turn and system prompts are supported.
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The tokenizer's ``apply_chat_template`` is used for rendering.
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2. legacy query/response:
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``{"query": "...", "response": "..."}``
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Falls back to the configured PromptStrategy (ChatML by default).
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Output schema:
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- sequence: int32 tensor - Combined token IDs (prompt + response)
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- loss_mask: bool tensor - True for response tokens (compute loss)
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- position_ids: int32 tensor - Per-sample position IDs starting from 0
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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
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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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"messages": list,
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"query": str,
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"response": str,
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},
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output_fields={
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"sequence": torch.int32,
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"loss_mask": torch.bool,
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"position_ids": torch.int32,
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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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if "messages" in input_dict:
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return self._process_messages(input_dict["messages"])
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if "query" in input_dict and "response" in input_dict:
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return self._process_legacy(input_dict)
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raise KeyError(
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"Input must contain 'messages' or 'query'/'response' pair"
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)
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def _process_messages(self, messages: List[Dict[str, str]]) -> Dict[str, Tensor]:
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if not messages:
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raise ValueError("Messages list is empty")
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if messages[-1]["role"] != "assistant":
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raise ValueError("Last message must have role 'assistant'")
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last_asst_idx = max(
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i for i, m in enumerate(messages) if m["role"] == "assistant"
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)
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prompt_tokens = self.tokenizer.apply_chat_template(
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messages[:last_asst_idx],
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add_generation_prompt=True,
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tokenize=True,
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)
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resp_content = messages[last_asst_idx]["content"]
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im_end = getattr(self.tokenizer, "im_end", "<|im_end|>")
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resp_tokens = self.tokenizer.encode(
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f"{resp_content}{im_end}\n", add_special_tokens=False
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)
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tokens, loss_mask = encode_with_mask(prompt_tokens, resp_tokens)
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position_ids = torch.arange(len(tokens), dtype=torch.int32)
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return {"sequence": tokens, "loss_mask": loss_mask, "position_ids": position_ids}
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def _process_legacy(self, input_dict: Dict[str, Any]) -> Dict[str, Tensor]:
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strategy = self.strategy or ChatMLStrategy(self.tokenizer)
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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 = strategy.assemble_prompt(query_tokens)
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response = strategy.assemble_response(response_tokens)
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tokens, loss_mask = encode_with_mask(prompt, response)
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position_ids = torch.arange(len(tokens), dtype=torch.int32)
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return {"sequence": tokens, "loss_mask": loss_mask, "position_ids": position_ids}
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@property
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def output_keys(self) -> List[str]:
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return ["sequence", "loss_mask", "position_ids"]
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