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refactor: SFT 统一 messages 格式 + ChatML 纯 jinja 渲染

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ViperEkura
2026-07-04 14:32:35 +08:00
parent 06735b9cb3
commit 816c02dab0
11 changed files with 177 additions and 89 deletions
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pipeline/processors/sft.py
+22 -38
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@@ -7,7 +7,7 @@ from torch import Tensor
from pipeline.tokenize import AutoTokenizer
from pipeline.strategies import PromptStrategy, ChatMLStrategy
from pipeline.processors.base import BaseProcessor, ProcessorSchema, encode_with_mask
from pipeline.processors.base import BaseProcessor, ProcessorSchema
from pipeline.processors.factory import ProcessorFactory
@@ -15,20 +15,20 @@ from pipeline.processors.factory import ProcessorFactory
class SFTProcessor(BaseProcessor):
"""Supervised fine-tuning data processor.
Supports two input formats:
Input formats:
1. messages (recommended):
``{"messages": [{"role": "user", "content": "..."},
{"role": "assistant", "content": "..."}]}``
Multi-turn and system prompts are supported.
The tokenizer's ``apply_chat_template`` is used for rendering.
Multi-turn and system prompts are supported. Each assistant
turn gets ``loss_mask = 1``; all other roles get 0.
2. legacy query/response:
``{"query": "...", "response": "..."}``
Falls back to the configured PromptStrategy (ChatML by default).
Internally converted to messages.
Output schema:
- sequence: int32 tensor - Combined token IDs (prompt + response)
- loss_mask: bool tensor - True for response tokens (compute loss)
- position_ids: int32 tensor - Per-sample position IDs starting from 0
- sequence: int32 tensor - Combined token IDs
- loss_mask: bool tensor - True for assistant response tokens
- position_ids: int32 tensor - Per-sample position IDs, start from 0
"""
def __init__(
@@ -58,7 +58,10 @@ class SFTProcessor(BaseProcessor):
if "messages" in input_dict:
return self._process_messages(input_dict["messages"])
if "query" in input_dict and "response" in input_dict:
return self._process_legacy(input_dict)
return self._process_messages([
{"role": "user", "content": input_dict["query"]},
{"role": "assistant", "content": input_dict["response"]},
])
raise KeyError(
"Input must contain 'messages' or 'query'/'response' pair"
)
@@ -69,38 +72,19 @@ class SFTProcessor(BaseProcessor):
if messages[-1]["role"] != "assistant":
raise ValueError("Last message must have role 'assistant'")
last_asst_idx = max(
i for i, m in enumerate(messages) if m["role"] == "assistant"
)
prompt_tokens = self.tokenizer.apply_chat_template(
messages[:last_asst_idx],
add_generation_prompt=True,
tokenize=True,
)
resp_content = messages[last_asst_idx]["content"]
im_end = getattr(self.tokenizer, "im_end", "<|im_end|>")
resp_tokens = self.tokenizer.encode(
f"{resp_content}{im_end}\n", add_special_tokens=False
)
tokens, loss_mask = encode_with_mask(prompt_tokens, resp_tokens)
position_ids = torch.arange(len(tokens), dtype=torch.int32)
return {"sequence": tokens, "loss_mask": loss_mask, "position_ids": position_ids}
def _process_legacy(self, input_dict: Dict[str, Any]) -> Dict[str, Tensor]:
strategy = self.strategy or ChatMLStrategy(self.tokenizer)
query_tokens = self.tokenizer.encode(input_dict["query"])
response_tokens = self.tokenizer.encode(input_dict["response"])
prompt, resp = strategy.format_messages(messages)
prompt = strategy.assemble_prompt(query_tokens)
response = strategy.assemble_response(response_tokens)
tokens, loss_mask = encode_with_mask(prompt, response)
position_ids = torch.arange(len(tokens), dtype=torch.int32)
return {"sequence": tokens, "loss_mask": loss_mask, "position_ids": position_ids}
sequence = torch.tensor(prompt + resp, dtype=torch.int32)
loss_mask = torch.zeros(len(sequence), dtype=torch.bool)
loss_mask[len(prompt) :] = True
position_ids = torch.arange(len(sequence), dtype=torch.int32)
return {
"sequence": sequence,
"loss_mask": loss_mask,
"position_ids": position_ids,
}
@property
def output_keys(self) -> List[str]: