700 lines
28 KiB
Python
700 lines
28 KiB
Python
#!/usr/bin/env python3
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import os
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os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
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import glob
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import socket
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import argparse
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import inspect
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import sys
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from typing import Dict, List, Iterable, Iterator, Tuple, Optional
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import torch
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import torch.distributed as dist
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from torch.utils.data import IterableDataset, Dataset
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from datasets import load_dataset
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from transformers import (
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AutoTokenizer,
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AutoModelForCausalLM,
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TrainingArguments,
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Trainer,
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set_seed
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)
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from transformers.trainer_callback import TrainerCallback
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# ----------------- 进程工具 -----------------
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def is_main_process():
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return int(os.environ.get("RANK", "0")) == 0
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def print_once(*args, **kwargs):
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if is_main_process():
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print(*args, **kwargs, flush=True)
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class DebugTrainer(Trainer):
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def training_step(self, model, inputs, num_items_in_batch=None):
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if not hasattr(self, "_dbg_printed"):
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rank = int(os.environ.get("RANK", "0"))
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host = socket.gethostname()
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ids = inputs["input_ids"]
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msk = inputs["attention_mask"]
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labs = inputs["labels"]
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print(f"[step0] ids={ids.device} mask={msk.device} labs={labs.device} "
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f"supervised={(labs!=-100).sum().item()}",
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flush=True)
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print(
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f"[step0][host={host} RANK={rank}] "
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f"input_ids.shape={tuple(ids.shape)} "
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f"attention_mask.shape={tuple(msk.shape)} "
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f"labels.shape={tuple(labs.shape)} "
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f"num_items_in_batch={num_items_in_batch}",
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flush=True
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)
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self._dbg_printed = True
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return super().training_step(model, inputs, num_items_in_batch)
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# ----------------- 日志回调 -----------------
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class CsvLossLogger(TrainerCallback):
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def __init__(self, csv_path: str):
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self.csv_path = csv_path
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if is_main_process():
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os.makedirs(os.path.dirname(csv_path), exist_ok=True)
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with open(self.csv_path, "w", encoding="utf-8") as f:
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f.write("step,loss,lr,total_flos\n")
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def on_log(self, args, state, control, logs=None, **kwargs):
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if not is_main_process() or logs is None:
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return
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with open(self.csv_path, "a", encoding="utf-8") as f:
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f.write(f"{state.global_step},{logs.get('loss','')},{logs.get('learning_rate','')},{logs.get('total_flos','')}\n")
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# ----------------- 仅监督 assistant 的数据集 -----------------
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def _assistant_char_spans(rendered: str) -> List[Tuple[int, int]]:
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"""
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在 apply_chat_template 渲染后的文本中，返回所有 assistant 内容的字符区间 [start, end)。
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"""
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spans: List[Tuple[int, int]] = []
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open_tag = "<|im_start|>assistant\n"
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close_tag = "<|im_end|>\n"
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pos = 0
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while True:
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a = rendered.find(open_tag, pos)
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if a == -1:
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break
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start = a + len(open_tag)
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b = rendered.find(close_tag, start)
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if b == -1:
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break
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spans.append((start, b))
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pos = b + len(close_tag)
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return spans
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class QwenChatSFTDataset(IterableDataset):
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"""
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期望 jsonl 每行形如：
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{"messages":[{"role":"system","content":"..."},{"role":"user","content":"..."},{"role":"assistant","content":"..."}]}
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可选包含工具：
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{"messages":[...], "tools":[{...}]}
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工作流：
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- 使用 tokenizer.apply_chat_template 渲染
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- 仅对 assistant 片段计损失（其他 token 的 label = -100）
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- 超长序列保留尾部（通常包含回答）
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"""
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def __init__(self,
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ex_iter: Iterable[dict],
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tokenizer: AutoTokenizer,
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seq_len: int = 4096):
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self.ex_iter = ex_iter
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self.tok = tokenizer
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self.seq_len = seq_len
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def __iter__(self) -> Iterator[Dict[str, torch.Tensor]]:
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# >>> DEBUG BEGIN
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dbg_on = os.environ.get("DBG_SAMPLES", "0") == "1"
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if not hasattr(self, "_dbg_seen"): self._dbg_seen = 0
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dbg_limit = int(os.environ.get("DBG_SAMPLE_LIMIT", "3"))
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rank = int(os.environ.get("RANK", "0"))
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lrank = int(os.environ.get("LOCAL_RANK", "-1"))
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host = socket.gethostname()
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# >>> DEBUG END
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for ex in self.ex_iter:
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msgs = ex.get("messages", None)
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if not msgs or not isinstance(msgs, list):
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continue
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# 可选过滤 think
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bad = False
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for m in msgs:
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if m.get("role") == "assistant" and isinstance(m.get("content"), str):
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c = m["content"]
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if "<think>" in c and "</think>" in c:
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inner = c.split("<think>")[-1].split("</think>")[0].strip()
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if inner:
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bad = True; break
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if bad:
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continue
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tools = ex.get("tools", None)
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rendered: str = self.tok.apply_chat_template(
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msgs, tools=tools, add_generation_prompt=False, tokenize=False
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)
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if not isinstance(rendered, str) or not rendered.strip():
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continue
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spans = _assistant_char_spans(rendered)
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if not spans:
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continue
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enc = self.tok(
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rendered,
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add_special_tokens=False,
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return_offsets_mapping=True
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)
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input_ids: List[int] = enc["input_ids"]
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offsets: List[Tuple[int, int]] = enc["offset_mapping"]
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if not input_ids:
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continue
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labels = [-100] * len(input_ids)
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def in_any_span(lo: int, hi: int) -> bool:
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for s, e in spans:
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if not (hi <= s or lo >= e):
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return True
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return False
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for i, (lo, hi) in enumerate(offsets):
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if in_any_span(lo, hi):
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labels[i] = input_ids[i]
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# —— 固定长度策略：先截尾，再在 Dataset 层补到固定 seq_len ——
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# 1) 截断到 seq_len（保留尾部）
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if len(input_ids) > self.seq_len:
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input_ids = input_ids[-self.seq_len:]
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labels = labels[-self.seq_len:]
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# 2) 左侧补齐到 seq_len（保证所有样本长度一致）
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pad_id = self.tok.pad_token_id if self.tok.pad_token_id is not None else self.tok.eos_token_id
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L = len(input_ids)
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if L < self.seq_len:
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pad = self.seq_len - L
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input_ids = ([pad_id] * pad) + input_ids
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labels = ([-100] * pad) + labels
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attn_mask = [0] * pad + [1] * L
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else:
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# 恰好等于 seq_len
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attn_mask = [1] * self.seq_len
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# 若没有任何可训练 token（labels 全 -100），跳过
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if all(v == -100 for v in labels):
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continue
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assert len(input_ids) == self.seq_len
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assert len(labels) == self.seq_len
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assert len(attn_mask) == self.seq_len
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# >>> DEBUG PRINT（此时变量已定义）
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if dbg_on and self._dbg_seen < dbg_limit:
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sup_tok = sum(1 for v in labels if v != -100)
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print(
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f"[sample][host={host} RANK={rank} LRank={lrank}] "
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f"rendered_len={len(rendered)} toks={len(input_ids)} sup_toks={sup_tok} "
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f"seq_len={self.seq_len} pad_id={pad_id}",
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flush=True
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)
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if sup_tok == 0:
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print(
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f"[WARN][host={host} RANK={rank}] sample has 0 supervised tokens -> would be skipped",
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flush=True
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)
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self._dbg_seen += 1
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# <<< DEBUG PRINT
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yield {
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"input_ids": torch.tensor(input_ids, dtype=torch.long),
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"attention_mask": torch.tensor(attn_mask, dtype=torch.long),
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"labels": torch.tensor(labels, dtype=torch.long),
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}
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# ----------------- 专用 Collator：pad inputs, pad labels=-100 -----------------
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class SFTDataCollator:
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def __init__(self, tokenizer: AutoTokenizer, pad_to_length: Optional[int] = None):
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self.tok = tokenizer
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self.pad_to_length = pad_to_length
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assert self.tok.pad_token_id is not None
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def __call__(self, features):
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# if not features:
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# raise RuntimeError(f"EMPTY BATCH in collator on rank={os.environ.get('RANK','0')}. "
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# f"Check sampler/sharding & make eval size >= world_size * per_device_eval_batch_size.")
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if not features:
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raise RuntimeError(
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f"[FATAL][RANK={os.environ.get('RANK','?')}] Empty batch reached collator. "
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f"Check dataset sharding/streaming."
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)
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def _to_list(x): return x.tolist() if isinstance(x, torch.Tensor) else list(x)
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input_ids = [_to_list(f["input_ids"]) for f in features]
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attn_masks = [_to_list(f["attention_mask"]) for f in features]
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labels_list = [_to_list(f["labels"]) for f in features]
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max_len_in_batch = max(len(x) for x in input_ids)
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target_len = self.pad_to_length if self.pad_to_length is not None else max_len_in_batch
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pad_id = self.tok.pad_token_id
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batch_inp, batch_attn, batch_lab = [], [], []
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for inp, msk, lab in zip(input_ids, attn_masks, labels_list):
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pad_len = target_len - len(inp)
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if pad_len < 0:
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inp, msk, lab = inp[:target_len], msk[:target_len], lab[:target_len]
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pad_len = 0
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batch_inp.append(torch.tensor(inp + [pad_id]*pad_len, dtype=torch.long))
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batch_attn.append(torch.tensor(msk + [0]*pad_len, dtype=torch.long))
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batch_lab.append(torch.tensor(lab + [-100]*pad_len, dtype=torch.long))
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# >>> DEBUG BEGIN
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dbg_on = os.environ.get("DBG_COLLATE", "0") == "1"
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if dbg_on:
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rank = int(os.environ.get("RANK", "0"))
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host = socket.gethostname()
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bs = len(features)
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first_len = len(input_ids[0]) if bs > 0 else None
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print(
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f"[collate][host={host} RANK={rank}] features={bs} "
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f"target_len={target_len} first_len={first_len}",
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flush=True
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)
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# 额外严苛校验：防止空 batch 继续往下走
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# if not features:
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# raise RuntimeError(
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# f"[FATAL][RANK={os.environ.get('RANK','?')}] Empty batch reached collator. "
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# f"Check dataset sharding/streaming."
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# )
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# >>> DEBUG END
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return {
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"input_ids": torch.stack(batch_inp, dim=0),
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"attention_mask": torch.stack(batch_attn, dim=0),
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"labels": torch.stack(batch_lab, dim=0),
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}
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# ----------------- 参数 -----------------
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def parse_args():
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ap = argparse.ArgumentParser()
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ap.add_argument("--model_name_or_path", type=str, required=True,
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help="本地权重目录或 HF 名称（如 /home/test/Qwen3-8B）")
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ap.add_argument("--data_glob", type=str, required=True,
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help="本地 jsonl 通配符（每台机器都需有同路径数据；每行应含 messages/可选 tools）")
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ap.add_argument("--output_dir", type=str, required=True,
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help="本地输出目录（各节点各自本地写）")
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ap.add_argument("--seq_len", type=int, default=4096)
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ap.add_argument("--learning_rate", type=float, default=2e-5)
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ap.add_argument("--weight_decay", type=float, default=0.1)
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ap.add_argument("--warmup_ratio", type=float, default=0.02)
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ap.add_argument("--num_train_epochs", type=float, default=1.0)
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ap.add_argument("--max_steps", type=int, default=-1)
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ap.add_argument("--log_interval", type=int, default=10)
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ap.add_argument("--save_steps", type=int, default=500)
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ap.add_argument("--eval_ratio", type=float, default=0.0)
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ap.add_argument("--seed", type=int, default=1337)
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#ap.add_argument("--deepspeed", type=str, default="ds_config_zero3.json")
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ap.add_argument("--gradient_checkpointing", action="store_true")
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ap.add_argument("--bf16", action="store_true",
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help="3090/A100/H100 等可开 bf16；同时在 DS 配置里也要开")
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ap.add_argument("--per_device_train_batch_size", type=int, default=1)
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ap.add_argument("--gradient_accumulation_steps", type=int, default=64)
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ap.add_argument("--report_to", type=str, default="tensorboard",
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choices=["none","tensorboard","wandb"])
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ap.add_argument("--wandb_project", type=str, default="ds-qwen3")
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ap.add_argument("--eval_data_glob", type=str, default=None,
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help="(可选) 测试/验证集 jsonl 通配符；如提供则优先使用")
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ap.add_argument("--local_rank", type=int, default=-1,
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help="for deepspeed/torchrun launcher; ignored by user code")
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ap.add_argument("--per_device_eval_batch_size", type=int, default=1)
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ap.add_argument("--deepspeed", type=str, default=None)
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return ap.parse_args()
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# ----------------- 主函数 -----------------
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def main():
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args = parse_args()
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set_seed(args.seed)
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# -------- 调试打印工具（每个 rank 都打）--------
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host = socket.gethostname()
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def dbg(msg):
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print(
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f"[dbg][host={host} RANK={os.environ.get('RANK','0')} "
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f"LOCAL_RANK={os.environ.get('LOCAL_RANK', str(args.local_rank))}] {msg}",
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flush=True
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)
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# 版本 & 启动参数 & 关键环境变量
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import transformers as hf
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try:
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import deepspeed as ds
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ds_ver = ds.__version__
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except Exception:
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ds_ver = "n/a"
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dbg(f"torch={torch.__version__}, transformers={hf.__version__}, deepspeed={ds_ver}")
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dbg(f"args={args}")
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dbg("ENV: WORLD_SIZE=%s RANK=%s LOCAL_RANK=%s MASTER_ADDR=%s MASTER_PORT=%s CUDA_VISIBLE_DEVICES=%s" % (
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os.environ.get("WORLD_SIZE"),
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os.environ.get("RANK"),
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os.environ.get("LOCAL_RANK", str(args.local_rank)),
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os.environ.get("MASTER_ADDR"),
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os.environ.get("MASTER_PORT"),
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os.environ.get("CUDA_VISIBLE_DEVICES"),
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))
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dbg(f"cuda_available={torch.cuda.is_available()} device_count={torch.cuda.device_count()}")
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# ---- 初始化分布式（供一致性探针使用）----
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world_size = int(os.environ.get("WORLD_SIZE", "1"))
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rank = int(os.environ.get("RANK", "0"))
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local_rank = int(os.environ.get("LOCAL_RANK", str(args.local_rank)))
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dbg(f"pre-init: world_size={world_size}, rank={rank}, local_rank={local_rank}")
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if torch.cuda.is_available() and local_rank >= 0:
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torch.cuda.set_device(local_rank)
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dbg(f"set_device({local_rank}); current_device={torch.cuda.current_device()} "
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f"name={torch.cuda.get_device_name(torch.cuda.current_device())}")
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else:
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dbg("no cuda or invalid local_rank; not calling set_device")
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if world_size > 1 and dist.is_available() and not dist.is_initialized():
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backend = "nccl" if torch.cuda.is_available() else "gloo"
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dbg(f"init_process_group backend={backend} via env://")
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dist.init_process_group(backend=backend, init_method="env://")
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else:
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dbg(f"skip init_process_group: world_size>1? {world_size>1}, dist_available={dist.is_available()}, already_init={dist.is_initialized()}")
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if dist.is_available() and dist.is_initialized():
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try:
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dbg(f"dist.get_backend()={dist.get_backend()} "
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f"dist.get_world_size()={dist.get_world_size()} dist.get_rank()={dist.get_rank()}")
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except Exception as e:
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dbg(f"dist query error: {e}")
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# 1) 先补 tokenizer 的 pad
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tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=True, trust_remote_code=True)
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if tokenizer.pad_token is None:
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tokenizer.pad_token = tokenizer.eos_token
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tokenizer.model_max_length = args.seq_len
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dbg(f"tokenizer.pad_token_id={tokenizer.pad_token_id} "
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f"pad_token={repr(tokenizer.pad_token)} model_max_length={tokenizer.model_max_length}")
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# 2) 再加载模型
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model = AutoModelForCausalLM.from_pretrained(
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args.model_name_or_path,
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torch_dtype=(torch.bfloat16 if args.bf16 else torch.float16),
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low_cpu_mem_usage=True,
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trust_remote_code=True
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)
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dbg(f"model loaded: dtype={next(model.parameters()).dtype} "
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f"use_cache={getattr(model.config,'use_cache',None)} "
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f"pad_token_id={getattr(model.config,'pad_token_id',None)}")
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# 3) pad/alibi 等配置
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model.config.pad_token_id = tokenizer.pad_token_id
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model.config.use_cache = False
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if args.gradient_checkpointing:
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model.gradient_checkpointing_enable(gradient_checkpointing_kwargs={"use_reentrant": False})
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try:
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torch.backends.cuda.enable_flash_sdp(False)
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torch.backends.cuda.enable_mem_efficient_sdp(False)
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torch.backends.cuda.enable_math_sdp(True)
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except Exception:
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pass
|
||
|
||
# ===== 数据鲁棒性检查（多机各自执行）=====
|
||
host = socket.gethostname()
|
||
|
||
files = sorted(glob.glob(args.data_glob))
|
||
if len(files) == 0:
|
||
raise FileNotFoundError(
|
||
f"[host={host} rank={rank}] No files matched DATA_GLOB={args.data_glob}\n"
|
||
"每台机器都必须在相同本地路径下放置数据；"
|
||
"可通过 DATA_GLOB=<your_glob> ./run_ds.sh 覆写。"
|
||
)
|
||
if is_main_process():
|
||
print(f"[data] matched {len(files)} files on host={host}, example[0]={files[0]}", flush=True)
|
||
|
||
# ====== 小探针：样本结构 ======
|
||
ds_stream_probe = load_dataset("json", data_files={"train": files}, split="train", streaming=True)
|
||
def ex_iter_probe():
|
||
for ex in ds_stream_probe:
|
||
yield ex
|
||
train_stream_probe = QwenChatSFTDataset(ex_iter_probe(), tokenizer, seq_len=args.seq_len)
|
||
try:
|
||
_ = next(iter(train_stream_probe))
|
||
except StopIteration:
|
||
raise RuntimeError(
|
||
f"[host={host} rank={rank}] 数据文件匹配到了，但没有产生任何可训练样本。\n"
|
||
"请确认每行 JSON 至少包含 'messages'（列表，含 user/assistant）字段；"
|
||
"若含 <think>…</think> 请确保不包含真实思维文本，或移除。\n"
|
||
"另外检查 seq_len 是否过小导致全部被裁。"
|
||
)
|
||
|
||
# ====== 正式训练流 ======
|
||
ds_stream2 = load_dataset("json", data_files={"train": files}, split="train", streaming=True)
|
||
if world_size > 1 and len(files) >= world_size:
|
||
# 多文件，按文件连续分片
|
||
ds_stream2 = ds_stream2.shard(num_shards=world_size, index=rank, contiguous=True)
|
||
train_stream = QwenChatSFTDataset((ex for ex in ds_stream2), tokenizer, seq_len=args.seq_len)
|
||
else:
|
||
# 单文件或文件数不足，按样本取模轮转
|
||
def ex_iter2():
|
||
for i, ex in enumerate(ds_stream2):
|
||
if i % max(world_size, 1) == rank:
|
||
yield ex
|
||
train_stream = QwenChatSFTDataset(ex_iter2(), tokenizer, seq_len=args.seq_len)
|
||
|
||
# ====== 一致性探针（与上面保持同逻辑）=====
|
||
ds_stream_probe2 = load_dataset("json", data_files={"train": files}, split="train", streaming=True)
|
||
if world_size > 1 and len(files) >= world_size:
|
||
ds_stream_probe2 = ds_stream_probe2.shard(num_shards=world_size, index=rank, contiguous=True)
|
||
probe_stream = QwenChatSFTDataset((ex for ex in ds_stream_probe2), tokenizer, seq_len=args.seq_len)
|
||
else:
|
||
def ex_iter2_probe():
|
||
for i, ex in enumerate(ds_stream_probe2):
|
||
if i % max(world_size, 1) == rank:
|
||
yield ex
|
||
probe_stream = QwenChatSFTDataset(ex_iter2_probe(), tokenizer, seq_len=args.seq_len)
|
||
|
||
def has_at_least(stream, n: int):
|
||
it = iter(stream)
|
||
for _ in range(n):
|
||
try:
|
||
next(it)
|
||
except StopIteration:
|
||
return 0
|
||
return 1
|
||
|
||
need = max(1, args.gradient_accumulation_steps)
|
||
|
||
local_ok = has_at_least(probe_stream, need)
|
||
|
||
if dist.is_available() and dist.is_initialized():
|
||
t = torch.tensor(local_ok, device=(f"cuda:{local_rank}" if torch.cuda.is_available() and local_rank >= 0 else "cpu"))
|
||
dist.all_reduce(t, op=dist.ReduceOp.MIN)
|
||
if t.item() == 0:
|
||
if is_main_process():
|
||
print(
|
||
f"[FATAL] 至少有一个 rank 在一个优化 step 内供不上 {need} 个微批 (GA={need})。 "
|
||
f"请减少 GA 或扩大/清洗数据；本次训练不会启动。",
|
||
flush=True
|
||
)
|
||
dist.barrier()
|
||
sys.exit(2)
|
||
else:
|
||
if local_ok == 0:
|
||
print(
|
||
f"[FATAL] 本机在一个优化 step 内供不上 {need} 个微批 (GA={need})。 "
|
||
f"请减少 GA 或扩大/清洗数据；本次训练不会启动。",
|
||
flush=True
|
||
)
|
||
sys.exit(2)
|
||
|
||
# if dist.is_available() and dist.is_initialized():
|
||
# # t = torch.tensor(local_ok, device=("cuda" if torch.cuda.is_available() else "cpu"))
|
||
# t = torch.tensor(
|
||
# local_ok,
|
||
# device=(f"cuda:{local_rank}" if torch.cuda.is_available() and local_rank >= 0 else "cpu")
|
||
# )
|
||
# dist.all_reduce(t, op=dist.ReduceOp.MIN)
|
||
# if t.item() == 0:
|
||
# if is_main_process():
|
||
# print("[FATAL] 至少有一个 rank 没有任何样本。请减少 WORLD_SIZE 或修正分片；本次训练不会启动。", flush=True)
|
||
# dist.barrier()
|
||
# sys.exit(2)
|
||
# else:
|
||
# if local_ok == 0:
|
||
# print("[FATAL] 本机无样本，退出。", flush=True); sys.exit(2)
|
||
|
||
# ---- Eval 构造：优先使用 --eval_data_glob；否则才用 eval_ratio 抽样 ----
|
||
eval_dataset: Optional[Dataset] = None
|
||
|
||
class ListDataset(Dataset):
|
||
def __init__(self, items): self.items = items
|
||
def __len__(self): return len(self.items)
|
||
def __getitem__(self, idx): return self.items[idx]
|
||
|
||
if args.eval_data_glob:
|
||
eval_files = sorted(glob.glob(args.eval_data_glob))
|
||
if len(eval_files) == 0:
|
||
raise FileNotFoundError(f"[host={host} rank={rank}] No eval files matched EVAL_DATA_GLOB={args.eval_data_glob}")
|
||
if is_main_process():
|
||
print(f"[eval] matched {len(eval_files)} files, example[0]={eval_files[0]}", flush=True)
|
||
|
||
ds_eval_stream = load_dataset("json", data_files={"eval": eval_files}, split="eval", streaming=True)
|
||
def ex_iter_eval():
|
||
for ex in ds_eval_stream:
|
||
yield ex
|
||
|
||
eval_iterable = QwenChatSFTDataset(ex_iter_eval(), tokenizer, seq_len=args.seq_len)
|
||
eval_items: List[Dict[str, torch.Tensor]] = []
|
||
for sample in eval_iterable:
|
||
eval_items.append(sample)
|
||
|
||
if len(eval_items) == 0:
|
||
raise RuntimeError("[eval] eval_data_glob 读到了 0 条有效样本，请检查 messages 结构。")
|
||
|
||
eval_dataset = ListDataset(eval_items)
|
||
|
||
elif args.eval_ratio and args.eval_ratio > 0:
|
||
desired_eval_batches = 200
|
||
tmp_stream = load_dataset("json", data_files={"train": files}, split="train", streaming=True)
|
||
def ex_iter_eval2():
|
||
for ex in tmp_stream:
|
||
yield ex
|
||
eval_stream = QwenChatSFTDataset(ex_iter_eval2(), tokenizer, seq_len=args.seq_len)
|
||
eval_samples = []
|
||
it = iter(eval_stream)
|
||
for _ in range(desired_eval_batches):
|
||
try:
|
||
eval_samples.append(next(it))
|
||
except StopIteration:
|
||
break
|
||
if len(eval_samples) > 0:
|
||
eval_dataset = ListDataset(eval_samples)
|
||
|
||
# ---- 统一补齐 eval 集（确保不会出现空 batch）----
|
||
if eval_dataset is not None:
|
||
ws = max(world_size, 1)
|
||
be = max(1, args.per_device_eval_batch_size)
|
||
global_bs = ws * be
|
||
|
||
r = len(eval_dataset) % global_bs
|
||
if r != 0:
|
||
pad_need = global_bs - r
|
||
eval_dataset.items += eval_dataset.items[:pad_need]
|
||
|
||
if is_main_process():
|
||
print(f"[eval] padded eval set to {len(eval_dataset)} "
|
||
f"(world_size={ws}, per_device_eval_batch_size={be}, global_bs={global_bs})",
|
||
flush=True)
|
||
|
||
# 补齐后再做 sanity check
|
||
assert len(eval_dataset) % global_bs == 0, \
|
||
f"eval size {len(eval_dataset)} still not divisible by global_bs {global_bs}"
|
||
|
||
# 更稳：联调阶段不强行 pad 到 4096
|
||
# data_collator = SFTDataCollator(tokenizer, pad_to_length=None)
|
||
data_collator = SFTDataCollator(tokenizer, pad_to_length=args.seq_len)
|
||
|
||
os.makedirs(args.output_dir, exist_ok=True)
|
||
logging_dir = os.path.join(args.output_dir, "logs")
|
||
os.makedirs(logging_dir, exist_ok=True)
|
||
|
||
# ---- 兼容 4.51（eval_strategy）与旧版（evaluation_strategy） ----
|
||
ta_kwargs = {}
|
||
sig = inspect.signature(TrainingArguments.__init__).parameters
|
||
if eval_dataset is not None:
|
||
if "eval_strategy" in sig:
|
||
ta_kwargs["eval_strategy"] = "steps"
|
||
elif "evaluation_strategy" in sig:
|
||
ta_kwargs["evaluation_strategy"] = "steps"
|
||
else:
|
||
if "eval_strategy" in sig:
|
||
ta_kwargs["eval_strategy"] = "no"
|
||
elif "evaluation_strategy" in sig:
|
||
ta_kwargs["evaluation_strategy"] = "no"
|
||
|
||
training_args = TrainingArguments(
|
||
output_dir=args.output_dir,
|
||
logging_dir=logging_dir,
|
||
do_train=True,
|
||
do_eval=(eval_dataset is not None),
|
||
eval_steps=max(50, args.save_steps // 5) if eval_dataset is not None else None,
|
||
per_device_train_batch_size=args.per_device_train_batch_size,
|
||
gradient_accumulation_steps=args.gradient_accumulation_steps,
|
||
learning_rate=args.learning_rate,
|
||
weight_decay=args.weight_decay,
|
||
warmup_ratio=args.warmup_ratio,
|
||
num_train_epochs=args.num_train_epochs if args.max_steps < 0 else 1.0,
|
||
max_steps=args.max_steps if args.max_steps > 0 else -1,
|
||
lr_scheduler_type="cosine",
|
||
logging_steps=args.log_interval,
|
||
save_steps=args.save_steps,
|
||
save_total_limit=2,
|
||
deepspeed=(args.deepspeed if args.deepspeed and os.path.isfile(args.deepspeed) else None),
|
||
dataloader_drop_last=False, # 关键：别丢尾，避免空 batch
|
||
dataloader_num_workers=0,
|
||
dataloader_prefetch_factor=None,
|
||
dataloader_pin_memory=False,
|
||
per_device_eval_batch_size=args.per_device_eval_batch_size,
|
||
report_to=([] if args.report_to == "none" else [args.report_to]),
|
||
bf16=args.bf16,
|
||
fp16=(not args.bf16),
|
||
gradient_checkpointing=args.gradient_checkpointing,
|
||
remove_unused_columns=False,
|
||
torch_compile=False,
|
||
save_on_each_node=False,
|
||
logging_first_step=True,
|
||
**ta_kwargs,
|
||
)
|
||
|
||
|
||
trainer = DebugTrainer(
|
||
model=model,
|
||
args=training_args,
|
||
train_dataset=train_stream,
|
||
eval_dataset=eval_dataset,
|
||
#tokenizer=tokenizer,
|
||
processing_class=tokenizer,
|
||
data_collator=data_collator
|
||
)
|
||
|
||
# trainer = Trainer(
|
||
# model=model,
|
||
# args=training_args,
|
||
# train_dataset=train_stream,
|
||
# eval_dataset=eval_dataset,
|
||
# processing_class=tokenizer,
|
||
# data_collator=data_collator
|
||
# )
|
||
trainer.add_callback(CsvLossLogger(csv_path=os.path.join(args.output_dir, "loss.csv")))
|
||
|
||
# 无共享盘：各节点本地 output_dir 下是否已有 checkpoint-*
|
||
ckpt_exists = (os.path.isdir(args.output_dir)
|
||
and any(n.startswith("checkpoint-") for n in os.listdir(args.output_dir)))
|
||
resume_flag = True if ckpt_exists else None
|
||
|
||
print_once(f"[host={socket.gethostname()}] Resume = {resume_flag is True}")
|
||
print_once("***** Starting training *****")
|
||
train_result = trainer.train(resume_from_checkpoint=resume_flag)
|
||
trainer.save_model() # DeepSpeed stage3_gather_16bit_weights_on_model_save=true 时，在 rank0 聚合整模型
|
||
|
||
metrics = train_result.metrics
|
||
trainer.log_metrics("train", metrics)
|
||
trainer.save_metrics("train", metrics)
|
||
trainer.save_state()
|
||
|
||
if eval_dataset is not None:
|
||
print_once("***** Running eval *****")
|
||
eval_metrics = trainer.evaluate()
|
||
trainer.log_metrics("eval", eval_metrics)
|
||
trainer.save_metrics("eval", eval_metrics)
|
||
|
||
print_once("Done.")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|