161 lines
6.1 KiB
Python
161 lines
6.1 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
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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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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. Each assistant
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turn gets ``loss_mask = 1``; all other roles get 0.
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2. legacy query/response:
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``{"query": "...", "response": "..."}``
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Internally converted to messages.
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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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Only the final assistant message is trained (mask_history behavior).
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All earlier turns are context/prompt and masked from loss.
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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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max_seq_len: Optional[int] = None,
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):
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self.tokenizer = tokenizer
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self.strategy = strategy
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self.max_seq_len = max_seq_len
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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_messages([
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{"role": "user", "content": input_dict["query"]},
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{"role": "assistant", "content": input_dict["response"]},
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])
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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 _extract_messages(self, input_dict: Dict[str, Any]) -> Optional[List[Dict[str, str]]]:
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if "messages" in input_dict:
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return input_dict["messages"]
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if "query" in input_dict and "response" in input_dict:
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return [
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{"role": "user", "content": input_dict["query"]},
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{"role": "assistant", "content": input_dict["response"]},
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]
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return None
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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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strategy = self.strategy or ChatMLStrategy(self.tokenizer)
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prompt, resp = strategy.format_messages(messages)
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sequence = torch.tensor(prompt + resp, dtype=torch.int32)
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loss_mask = torch.zeros(len(sequence), dtype=torch.bool)
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loss_mask[len(prompt) :] = True
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if self.max_seq_len and len(sequence) > self.max_seq_len:
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sequence = sequence[: self.max_seq_len]
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loss_mask = loss_mask[: self.max_seq_len]
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position_ids = torch.arange(len(sequence), dtype=torch.int32)
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return {
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"sequence": sequence,
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"loss_mask": loss_mask,
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"position_ids": position_ids,
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}
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def process_batch(self, input_dicts: List[Dict[str, Any]]) -> List[Optional[Dict[str, Tensor]]]:
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strategy = self.strategy or ChatMLStrategy(self.tokenizer)
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prompts_text: List[str] = []
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fulls_text: List[str] = []
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indices: List[int] = []
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results: List[Optional[Dict[str, Tensor]]] = [None] * len(input_dicts)
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for i, d in enumerate(input_dicts):
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try:
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messages = self._extract_messages(d)
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if not messages or messages[-1]["role"] != "assistant":
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continue
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last_asst = max(j for j, m in enumerate(messages) if m["role"] == "assistant")
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prompt_text = self.tokenizer.apply_chat_template(
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messages[:last_asst], add_generation_prompt=True, tokenize=False
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)
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full_text = self.tokenizer.apply_chat_template(
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messages[: last_asst + 1], add_generation_prompt=False, tokenize=False
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)
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prompts_text.append(prompt_text)
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fulls_text.append(full_text)
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indices.append(i)
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except Exception:
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continue
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if not prompts_text:
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return results
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prompt_tokens_list = self.tokenizer.encode(prompts_text)
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full_tokens_list = self.tokenizer.encode(fulls_text)
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for j, idx in enumerate(indices):
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prompt_tokens = prompt_tokens_list[j]
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full_tokens = full_tokens_list[j]
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resp_tokens = full_tokens[len(prompt_tokens):]
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sequence = torch.tensor(prompt_tokens + resp_tokens, dtype=torch.int32)
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loss_mask = torch.zeros(len(sequence), dtype=torch.bool)
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loss_mask[len(prompt_tokens):] = True
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if self.max_seq_len and len(sequence) > self.max_seq_len:
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sequence = sequence[: self.max_seq_len]
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loss_mask = loss_mask[: self.max_seq_len]
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position_ids = torch.arange(len(sequence), dtype=torch.int32)
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results[idx] = {
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"sequence": sequence,
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"loss_mask": loss_mask,
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"position_ids": position_ids,
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}
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return results
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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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