diff --git a/train_mm_zero3_lora.sh b/train_mm_zero3_lora.sh
new file mode 100644
index 0000000..6d5ab5f
--- /dev/null
+++ b/train_mm_zero3_lora.sh
@@ -0,0 +1,16 @@
+deepspeed --hostfile hostfile \
+ --num_nodes 6 --num_gpus 4 \
+ train_sft_lora.py \
+ --model_name_or_path /home/test/Qwen3-32B \
+ --data_glob "/home/test/datasets/my_corpus/train*.jsonl" \
+ --output_dir /home/test/checkpoints/q3-32b-lora \
+ --seq_len 4096 \
+ --bf16 \
+ --gradient_accumulation_steps 64 \
+ --per_device_train_batch_size 1 \
+ --learning_rate 1e-4 \
+ --warmup_ratio 0.03 \
+ --lora_r 16 --lora_alpha 32 --lora_dropout 0.05 \
+ --lora_target auto \
+ --deepspeed /home/test/jd_train/ds_config_zero3.json \
+ --report_to wandb --wandb_project ds-qwen3-lora
diff --git a/train_sft_ds.py b/train_sft_ds.py
index 91b3c9b..43a7f4d 100644
--- a/train_sft_ds.py
+++ b/train_sft_ds.py
@@ -194,10 +194,174 @@ class CsvLossLogger(TrainerCallback):
f"{cur},{logs.get('loss','')},{logs.get('learning_rate','')},{logs.get('total_flos','')}\n"
)
-# ----------------- 仅监督 assistant 的数据集 -----------------
+# # ----------------- 仅监督 assistant 的数据集 -----------------
+# def _assistant_char_spans(rendered: str) -> List[Tuple[int, int]]:
+# """
+# 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)。
+# """
+# spans: List[Tuple[int, int]] = []
+# open_tag = "<|im_start|>assistant\n"
+# close_tag = "<|im_end|>\n"
+# pos = 0
+# while True:
+# a = rendered.find(open_tag, pos)
+# if a == -1:
+# break
+# start = a + len(open_tag)
+# b = rendered.find(close_tag, start)
+# if b == -1:
+# break
+# spans.append((start, b))
+# pos = b + len(close_tag)
+# return spans
+
+# class QwenChatSFTDataset(IterableDataset):
+# """
+# 期望 jsonl 每行形如：
+# {"messages":[{"role":"system","content":"..."},{"role":"user","content":"..."},{"role":"assistant","content":"..."}]}
+# 可选包含工具：
+# {"messages":[...], "tools":[{...}]}
+
+# 工作流：
+# - 使用 tokenizer.apply_chat_template 渲染
+# - 仅对 assistant 片段计损失（其他 token 的 label = -100）
+# - 超长序列保留尾部（通常包含回答）
+# """
+# def __init__(self,
+# ex_iter: Iterable[dict],
+# tokenizer: AutoTokenizer,
+# seq_len: int = 4096):
+# self.ex_iter = ex_iter
+# self.tok = tokenizer
+# self.seq_len = seq_len
+
+# def __iter__(self) -> Iterator[Dict[str, torch.Tensor]]:
+
+# # >>> DEBUG BEGIN
+# dbg_on = os.environ.get("DBG_SAMPLES", "0") == "1"
+# if not hasattr(self, "_dbg_seen"): self._dbg_seen = 0
+# dbg_limit = int(os.environ.get("DBG_SAMPLE_LIMIT", "3"))
+# rank = int(os.environ.get("RANK", "0"))
+# lrank = int(os.environ.get("LOCAL_RANK", "-1"))
+# host = socket.gethostname()
+# # >>> DEBUG END
+
+# for ex in self.ex_iter:
+# msgs = ex.get("messages", None)
+# if not msgs or not isinstance(msgs, list):
+# continue
+
+# # 可选过滤 think
+# bad = False
+# for m in msgs:
+# if m.get("role") == "assistant" and isinstance(m.get("content"), str):
+# c = m["content"]
+# if "" in c and "" in c:
+# inner = c.split("")[-1].split("")[0].strip()
+# if inner:
+# bad = True; break
+# # 注销这里就可以确保参与计算监督微调，打开就表示跳过
+# if bad:
+# continue
+
+# tools = ex.get("tools", None)
+
+# # 兼容老版本 tokenizer.apply_chat_template 不支持 tools 参数的情况
+# try:
+# rendered: str = self.tok.apply_chat_template(
+# msgs, tools=tools, add_generation_prompt=False, tokenize=False
+# )
+# except TypeError:
+# rendered: str = self.tok.apply_chat_template(
+# msgs, add_generation_prompt=False, tokenize=False
+# )
+
+
+# if not isinstance(rendered, str) or not rendered.strip():
+# continue
+
+# spans = _assistant_char_spans(rendered)
+# if not spans:
+# continue
+
+# enc = self.tok(
+# rendered,
+# add_special_tokens=False,
+# return_offsets_mapping=True
+# )
+# input_ids: List[int] = enc["input_ids"]
+# offsets: List[Tuple[int, int]] = enc["offset_mapping"]
+
+# if not input_ids:
+# continue
+
+# labels = [-100] * len(input_ids)
+
+# def in_any_span(lo: int, hi: int) -> bool:
+# for s, e in spans:
+# if not (hi <= s or lo >= e):
+# return True
+# return False
+
+# for i, (lo, hi) in enumerate(offsets):
+# if in_any_span(lo, hi):
+# labels[i] = input_ids[i]
+
+# # —— 固定长度策略：先截尾，再在 Dataset 层补到固定 seq_len ——
+# # 1) 截断到 seq_len（保留尾部）
+# if len(input_ids) > self.seq_len:
+# input_ids = input_ids[-self.seq_len:]
+# labels = labels[-self.seq_len:]
+
+# # 2) 左侧补齐到 seq_len（保证所有样本长度一致）
+# pad_id = self.tok.pad_token_id if self.tok.pad_token_id is not None else self.tok.eos_token_id
+# L = len(input_ids)
+# if L < self.seq_len:
+# pad = self.seq_len - L
+# input_ids = ([pad_id] * pad) + input_ids
+# labels = ([-100] * pad) + labels
+# attn_mask = [0] * pad + [1] * L
+# else:
+# # 恰好等于 seq_len
+# attn_mask = [1] * self.seq_len
+
+# # 若没有任何可训练 token（labels 全 -100），跳过
+# if all(v == -100 for v in labels):
+# continue
+
+# assert len(input_ids) == self.seq_len
+# assert len(labels) == self.seq_len
+# assert len(attn_mask) == self.seq_len
+
+# # >>> DEBUG PRINT（此时变量已定义）
+# if dbg_on and self._dbg_seen < dbg_limit:
+# sup_tok = sum(1 for v in labels if v != -100)
+# print(
+# f"[sample][host={host} RANK={rank} LRank={lrank}] "
+# f"rendered_len={len(rendered)} toks={len(input_ids)} sup_toks={sup_tok} "
+# f"seq_len={self.seq_len} pad_id={pad_id}",
+# flush=True
+# )
+# if sup_tok == 0:
+# print(
+# f"[WARN][host={host} RANK={rank}] sample has 0 supervised tokens -> would be skipped",
+# flush=True
+# )
+# self._dbg_seen += 1
+# # <<< DEBUG PRINT
+
+# yield {
+# "input_ids": torch.tensor(input_ids, dtype=torch.long),
+# "attention_mask": torch.tensor(attn_mask, dtype=torch.long),
+# "labels": torch.tensor(labels, dtype=torch.long),
+# }
+
+
+# ----------------- 工具：提取 assistant 字符区间 -----------------
def _assistant_char_spans(rendered: str) -> List[Tuple[int, int]]:
"""
- 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)。
+ 在 apply_chat_template 渲染后的纯文本中，返回所有 assistant 段的字符区间 [start, end)
+ 这些区间覆盖了 assistant 的全部内容（包括 ... 标签与正文）。
"""
spans: List[Tuple[int, int]] = []
open_tag = "<|im_start|>assistant\n"
@@ -207,14 +371,16 @@ def _assistant_char_spans(rendered: str) -> List[Tuple[int, int]]:
a = rendered.find(open_tag, pos)
if a == -1:
break
- start = a + len(open_tag)
- b = rendered.find(close_tag, start)
+ s = a + len(open_tag)
+ b = rendered.find(close_tag, s)
if b == -1:
break
- spans.append((start, b))
+ spans.append((s, b))
pos = b + len(close_tag)
return spans
+
+# ----------------- 数据集：SFT（监督 assistant 全段，含 标签与内容） -----------------
class QwenChatSFTDataset(IterableDataset):
"""
期望 jsonl 每行形如：
@@ -225,7 +391,7 @@ class QwenChatSFTDataset(IterableDataset):
工作流：
- 使用 tokenizer.apply_chat_template 渲染
- 仅对 assistant 片段计损失（其他 token 的 label = -100）
- - 超长序列保留尾部（通常包含回答）
+ - 截断时“优先确保最后一个 assistant 不被截断”；若其长度 > seq_len，则保留其“结尾”以避免切尾
"""
def __init__(self,
ex_iter: Iterable[dict],
@@ -251,18 +417,7 @@ class QwenChatSFTDataset(IterableDataset):
if not msgs or not isinstance(msgs, list):
continue
- # 可选过滤 think
- bad = False
- for m in msgs:
- if m.get("role") == "assistant" and isinstance(m.get("content"), str):
- c = m["content"]
- if "" in c and "" in c:
- inner = c.split("")[-1].split("")[0].strip()
- if inner:
- bad = True; break
- if bad:
- continue
-
+ # —— 不再过滤 ：显式允许其参与监督（包括标签与正文）
tools = ex.get("tools", None)
# 兼容老版本 tokenizer.apply_chat_template 不支持 tools 参数的情况
@@ -275,7 +430,6 @@ class QwenChatSFTDataset(IterableDataset):
msgs, add_generation_prompt=False, tokenize=False
)
-
if not isinstance(rendered, str) or not rendered.strip():
continue
@@ -283,6 +437,7 @@ class QwenChatSFTDataset(IterableDataset):
if not spans:
continue
+ # 编码并拿到字符偏移，确保与 rendered 对齐
enc = self.tok(
rendered,
add_special_tokens=False,
@@ -294,10 +449,12 @@ class QwenChatSFTDataset(IterableDataset):
if not input_ids:
continue
+ # 先对“所有 assistant 片段”打标签；包含 标签与内容、以及回答正文
labels = [-100] * len(input_ids)
def in_any_span(lo: int, hi: int) -> bool:
for s, e in spans:
+ # 与任一 [s, e) 有交集即监督
if not (hi <= s or lo >= e):
return True
return False
@@ -306,13 +463,68 @@ class QwenChatSFTDataset(IterableDataset):
if in_any_span(lo, hi):
labels[i] = input_ids[i]
- # —— 固定长度策略：先截尾，再在 Dataset 层补到固定 seq_len ——
- # 1) 截断到 seq_len（保留尾部）
- if len(input_ids) > self.seq_len:
- input_ids = input_ids[-self.seq_len:]
- labels = labels[-self.seq_len:]
+ # 若没有任何可训练 token（labels 全 -100），跳过
+ if all(v == -100 for v in labels):
+ continue
- # 2) 左侧补齐到 seq_len（保证所有样本长度一致）
+ # ======== Assistant 感知的截断策略（保证“最后一个 assistant 不被截掉”）========
+ if len(input_ids) > self.seq_len:
+ # 取“最后一个 assistant”的字符区间
+ s_last, e_last = spans[-1]
+
+ # 将字符区间映射到 token 索引区间 [j, k_excl)
+ # j: 第一个 token，其右端 hi > s_last
+ j = 0
+ while j < len(offsets) and offsets[j][1] <= s_last:
+ j += 1
+ # k_excl: 第一个 token，其左端 lo >= e_last（即不再与 [s_last, e_last) 相交）
+ k_excl = j
+ while k_excl < len(offsets) and offsets[k_excl][0] < e_last:
+ k_excl += 1
+
+ A = max(0, k_excl - j) # 最后一个 assistant 覆盖的 token 数
+
+ if A >= self.seq_len:
+ # 单个 assistant 本身超过窗口 —— 保“结尾”，避免被切尾
+ start = max(0, k_excl - self.seq_len)
+ end = start + self.seq_len
+ else:
+ # 有空间容纳整个 assistant：尽量把窗口对齐到包括完整 assistant
+ # 先试图把窗口从 j 开始，但要保证 k_excl 也在窗口内
+ start = max(0, min(j, len(input_ids) - self.seq_len))
+ end = start + self.seq_len
+ if end < k_excl:
+ # 还没覆盖到 assistant 末尾，则右移窗口到恰好覆盖末尾
+ end = k_excl
+ start = end - self.seq_len
+ if start < 0:
+ start = 0
+ end = self.seq_len
+
+ # 可选：尝试“居中”一点（留部分历史上下文），但仍需包含完整 [j, k_excl)
+ leftover = self.seq_len - A
+ # 把剩余的一半尽量分配给左侧上下文（不越界）
+ left_wish = leftover // 2
+ start = max(0, min(j - left_wish, start))
+ end = start + self.seq_len
+ if end < k_excl:
+ # 若居中导致末尾又被排除，再纠正一次
+ end = k_excl
+ start = end - self.seq_len
+ if start < 0:
+ start = 0
+ end = self.seq_len
+
+ # 真正切片
+ input_ids = input_ids[start:end]
+ labels = labels[start:end]
+ # 注意：offsets 后续不再使用（只为确定切片窗口），无需同步裁剪
+
+ # 训练注意：这里的策略保证：
+ # - 若最后一个 assistant <= seq_len：完整保留；
+ # - 若 > seq_len：至少保证 assistant 的“结尾”在窗口内，不会“切尾”。
+
+ # ======== 统一长度：左侧补齐到 seq_len ========
pad_id = self.tok.pad_token_id if self.tok.pad_token_id is not None else self.tok.eos_token_id
L = len(input_ids)
if L < self.seq_len:
@@ -321,24 +533,19 @@ class QwenChatSFTDataset(IterableDataset):
labels = ([-100] * pad) + labels
attn_mask = [0] * pad + [1] * L
else:
- # 恰好等于 seq_len
attn_mask = [1] * self.seq_len
- # 若没有任何可训练 token（labels 全 -100），跳过
- if all(v == -100 for v in labels):
- continue
-
+ # Sanity
assert len(input_ids) == self.seq_len
assert len(labels) == self.seq_len
assert len(attn_mask) == self.seq_len
- # >>> DEBUG PRINT（此时变量已定义）
+ # >>> DEBUG PRINT
if dbg_on and self._dbg_seen < dbg_limit:
sup_tok = sum(1 for v in labels if v != -100)
print(
f"[sample][host={host} RANK={rank} LRank={lrank}] "
- f"rendered_len={len(rendered)} toks={len(input_ids)} sup_toks={sup_tok} "
- f"seq_len={self.seq_len} pad_id={pad_id}",
+ f"toks={len(input_ids)} sup_toks={sup_tok} seq_len={self.seq_len} pad_id={pad_id}",
flush=True
)
if sup_tok == 0:
@@ -355,6 +562,8 @@ class QwenChatSFTDataset(IterableDataset):
"labels": torch.tensor(labels, dtype=torch.long),
}
+
+
# ----------------- 专用 Collator：pad inputs, pad labels=-100 -----------------
class SFTDataCollator:
def __init__(self, tokenizer: AutoTokenizer, pad_to_length: Optional[int] = None):
diff --git a/train_sft_lora.py b/train_sft_lora.py
new file mode 100644
index 0000000..c6fa521
--- /dev/null
+++ b/train_sft_lora.py
@@ -0,0 +1,730 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import os
+os.environ.pop("PYTHONNOUSERSITE", None)
+os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
+os.environ.setdefault("WANDB_START_METHOD", "thread")
+os.environ.setdefault("WANDB_DIR", f"/tmp/{os.environ.get('USER','user')}/wandb")
+
+import glob
+import socket
+import argparse
+import inspect
+import sys
+from typing import Dict, List, Iterable, Iterator, Tuple, Optional
+
+import torch
+import torch.distributed as dist
+from torch.utils.data import IterableDataset, Dataset
+from datasets import load_dataset
+from transformers import (
+ AutoTokenizer,
+ AutoModelForCausalLM,
+ TrainingArguments,
+ Trainer,
+ set_seed
+)
+from transformers.trainer_callback import TrainerCallback
+from transformers.trainer_utils import get_last_checkpoint
+
+# ---------- PATH / CUDA utils ----------
+import site, shutil
+home = os.path.expanduser("~")
+want = [f"{home}/.local/bin", "/usr/local/cuda-11.8/bin"]
+cur = os.environ.get("PATH", "").split(":")
+new = [d for d in want if d and d not in cur] + cur
+os.environ["PATH"] = ":".join(new)
+print(f"[env] PATH={os.environ['PATH']}", flush=True)
+print(f"[env] which ninja={shutil.which('ninja')} which nvcc={shutil.which('nvcc')}", flush=True)
+
+os.environ.setdefault("CUDA_HOME", "/usr/local/cuda-11.8")
+ld = os.environ.get("LD_LIBRARY_PATH", "")
+cuda_lib = "/usr/local/cuda-11.8/lib64"
+if cuda_lib not in ld.split(":"):
+ os.environ["LD_LIBRARY_PATH"] = f"{cuda_lib}:{ld}" if ld else cuda_lib
+
+print(f"[env] torch.version.cuda={torch.version.cuda} CUDA_HOME={os.environ['CUDA_HOME']}", flush=True)
+
+os.environ.pop("DS_BUILD_OPS", None)
+os.environ.pop("DS_SKIP_CUDA_BUILD", None)
+try:
+ user_site = site.getusersitepackages()
+ if user_site and user_site not in sys.path:
+ sys.path.insert(0, user_site)
+except Exception:
+ pass
+os.environ.setdefault("TORCH_EXTENSIONS_DIR", f"/tmp/{os.environ.get('USER','user')}/torch_ext")
+os.environ.setdefault("MAX_JOBS", "12")
+if shutil.which("ninja") is None:
+ os.environ["USE_NINJA"] = "0"
+ print("[env] no CLI ninja on PATH -> USE_NINJA=0 fallback", flush=True)
+try:
+ from deepspeed.ops.op_builder import CPUAdamBuilder
+ CPUAdamBuilder().load()
+ print("[env] CPUAdamBuilder JIT OK", flush=True)
+except Exception as e:
+ if "Ninja is required to load C++ extensions" in str(e):
+ os.environ["USE_NINJA"] = "0"
+ print("[env] no CLI ninja, retry with USE_NINJA=0 (fallback build)", flush=True)
+ from deepspeed.ops.op_builder import CPUAdamBuilder
+ CPUAdamBuilder().load()
+ print("[env] CPUAdamBuilder JIT OK (fallback)", flush=True)
+ else:
+ print(f"[env][host={socket.gethostname()} RANK={os.environ.get('RANK','?')}] PRE-JIT FAILED: {e}", flush=True)
+ # 不致命：LoRA 不依赖这个算子，继续运行
+ pass
+
+# ---------- helpers ----------
+def is_main_process():
+ return int(os.environ.get("RANK", "0")) == 0
+
+def print_once(*args, **kwargs):
+ if is_main_process():
+ print(*args, **kwargs, flush=True)
+
+class DebugTrainer(Trainer):
+ def training_step(self, model, inputs, num_items_in_batch=None):
+ if not hasattr(self, "_dbg_printed"):
+ rank = int(os.environ.get("RANK", "0"))
+ host = socket.gethostname()
+ ids = inputs["input_ids"]; msk = inputs["attention_mask"]; labs = inputs["labels"]
+ print(f"[step0] ids={ids.device} mask={msk.device} labs={labs.device} "
+ f"supervised={(labs!=-100).sum().item()}", flush=True)
+ print(f"[step0][host={host} RANK={rank}] "
+ f"input_ids.shape={tuple(ids.shape)} "
+ f"attention_mask.shape={tuple(msk.shape)} "
+ f"labels.shape={tuple(labs.shape)} "
+ f"num_items_in_batch={num_items_in_batch}", flush=True)
+ self._dbg_printed = True
+ return super().training_step(model, inputs, num_items_in_batch)
+
+class CsvLossLogger(TrainerCallback):
+ def __init__(self, csv_path: str):
+ self.csv_path = csv_path
+ if is_main_process():
+ os.makedirs(os.path.dirname(csv_path), exist_ok=True)
+ with open(self.csv_path, "w", encoding="utf-8") as f:
+ f.write("step,loss,lr,total_flos\n")
+
+ def on_train_begin(self, args, state, control, **kwargs):
+ tmp = (getattr(state, "max_steps", 0) or getattr(args, "max_steps", 0) or 0)
+ tot = tmp if isinstance(tmp, int) and tmp > 0 else 0
+ print(f"[{socket.gethostname()} rank={os.environ.get('RANK','?')}] total_steps={tot}", flush=True)
+
+ def on_log(self, args, state, control, logs=None, **kwargs):
+ if logs is None: return
+ cur = int(getattr(state, "global_step", 0) or 0)
+ tmp = (getattr(state, "max_steps", 0) or getattr(args, "max_steps", 0) or 0)
+ tot = tmp if isinstance(tmp, int) and tmp > 0 else 0
+ pct = (f"{(cur / tot * 100):.1f}%" if tot else "n/a")
+ if tot and not hasattr(self, "_tot_announced"):
+ print(f"[{socket.gethostname()} rank={os.environ.get('RANK','?')}] total_steps={tot}", flush=True)
+ self._tot_announced = True
+ print(f"[{socket.gethostname()} rank={os.environ.get('RANK','?')}] step {cur}/{tot} ({pct}) "
+ f"loss={logs.get('loss')} lr={logs.get('learning_rate')}", flush=True)
+ if not is_main_process(): return
+ with open(self.csv_path, "a", encoding="utf-8") as f:
+ f.write(f"{cur},{logs.get('loss','')},{logs.get('learning_rate','')},{logs.get('total_flos','')}\n")
+
+# ---------- assistant span detection ----------
+def _assistant_char_spans(rendered: str) -> List[Tuple[int, int]]:
+ spans: List[Tuple[int, int]] = []
+ open_tag = "<|im_start|>assistant\n"
+ close_tag = "<|im_end|>\n"
+ pos = 0
+ while True:
+ a = rendered.find(open_tag, pos)
+ if a == -1: break
+ s = a + len(open_tag)
+ b = rendered.find(close_tag, s)
+ if b == -1: break
+ spans.append((s, b))
+ pos = b + len(close_tag)
+ return spans
+
+# ---------- Dataset (supervise assistant incl. tags) ----------
+class QwenChatSFTDataset(IterableDataset):
+ def __init__(self, ex_iter: Iterable[dict], tokenizer: AutoTokenizer, seq_len: int = 4096):
+ self.ex_iter = ex_iter
+ self.tok = tokenizer
+ self.seq_len = seq_len
+
+ def __iter__(self) -> Iterator[Dict[str, torch.Tensor]]:
+ dbg_on = os.environ.get("DBG_SAMPLES", "0") == "1"
+ if not hasattr(self, "_dbg_seen"): self._dbg_seen = 0
+ dbg_limit = int(os.environ.get("DBG_SAMPLE_LIMIT", "3"))
+ rank = int(os.environ.get("RANK", "0"))
+ lrank = int(os.environ.get("LOCAL_RANK", "-1"))
+ host = socket.gethostname()
+
+ for ex in self.ex_iter:
+ msgs = ex.get("messages", None)
+ if not msgs or not isinstance(msgs, list): continue
+
+ tools = ex.get("tools", None)
+ try:
+ rendered: str = self.tok.apply_chat_template(
+ msgs, tools=tools, add_generation_prompt=False, tokenize=False
+ )
+ except TypeError:
+ rendered: str = self.tok.apply_chat_template(
+ msgs, add_generation_prompt=False, tokenize=False
+ )
+ if not isinstance(rendered, str) or not rendered.strip(): continue
+
+ spans = _assistant_char_spans(rendered)
+ if not spans: continue
+
+ enc = self.tok(rendered, add_special_tokens=False, return_offsets_mapping=True)
+ input_ids: List[int] = enc["input_ids"]
+ offsets: List[Tuple[int, int]] = enc["offset_mapping"]
+ if not input_ids: continue
+
+ labels = [-100] * len(input_ids)
+
+ def in_any_span(lo: int, hi: int) -> bool:
+ for s, e in spans:
+ if not (hi <= s or lo >= e):
+ return True
+ return False
+
+ for i, (lo, hi) in enumerate(offsets):
+ if in_any_span(lo, hi):
+ labels[i] = input_ids[i]
+
+ if all(v == -100 for v in labels): # 无监督 token
+ continue
+
+ # ---- assistant-aware truncation: keep last assistant not cut off
+ if len(input_ids) > self.seq_len:
+ s_last, e_last = spans[-1]
+ j = 0
+ while j < len(offsets) and offsets[j][1] <= s_last: j += 1
+ k_excl = j
+ while k_excl < len(offsets) and offsets[k_excl][0] < e_last: k_excl += 1
+ A = max(0, k_excl - j)
+ if A >= self.seq_len:
+ start = max(0, k_excl - self.seq_len); end = start + self.seq_len
+ else:
+ start = max(0, min(j, len(input_ids) - self.seq_len))
+ end = start + self.seq_len
+ if end < k_excl:
+ end = k_excl; start = end - self.seq_len
+ if start < 0: start = 0; end = self.seq_len
+ leftover = self.seq_len - A
+ left_wish = leftover // 2
+ start = max(0, min(j - left_wish, start))
+ end = start + self.seq_len
+ if end < k_excl:
+ end = k_excl; start = end - self.seq_len
+ if start < 0: start = 0; end = self.seq_len
+ input_ids = input_ids[start:end]
+ labels = labels[start:end]
+
+ pad_id = self.tok.pad_token_id if self.tok.pad_token_id is not None else self.tok.eos_token_id
+ L = len(input_ids)
+ if L < self.seq_len:
+ pad = self.seq_len - L
+ input_ids = ([pad_id]*pad) + input_ids
+ labels = ([-100]*pad) + labels
+ attn_mask = [0]*pad + [1]*L
+ else:
+ attn_mask = [1]*self.seq_len
+
+ assert len(input_ids) == self.seq_len
+ assert len(labels) == self.seq_len
+ assert len(attn_mask) == self.seq_len
+
+ if dbg_on and self._dbg_seen < dbg_limit:
+ sup_tok = sum(1 for v in labels if v != -100)
+ print(f"[sample][host={host} RANK={rank} LRank={lrank}] "
+ f"toks={len(input_ids)} sup_toks={sup_tok} seq_len={self.seq_len} pad_id={pad_id}", flush=True)
+ if sup_tok == 0:
+ print(f"[WARN][host={host} RANK={rank}] sample has 0 supervised tokens -> skipped", flush=True)
+ self._dbg_seen += 1
+
+ yield {
+ "input_ids": torch.tensor(input_ids, dtype=torch.long),
+ "attention_mask": torch.tensor(attn_mask, dtype=torch.long),
+ "labels": torch.tensor(labels, dtype=torch.long),
+ }
+
+# ---------- Collator ----------
+class SFTDataCollator:
+ def __init__(self, tokenizer: AutoTokenizer, pad_to_length: Optional[int] = None):
+ self.tok = tokenizer
+ self.pad_to_length = pad_to_length
+ assert self.tok.pad_token_id is not None
+
+ def __call__(self, features):
+ if not features:
+ raise RuntimeError(f"[FATAL][RANK={os.environ.get('RANK','?')}] Empty batch reached collator.")
+ def _to_list(x): return x.tolist() if isinstance(x, torch.Tensor) else list(x)
+ input_ids = [_to_list(f["input_ids"]) for f in features]
+ attn_masks = [_to_list(f["attention_mask"]) for f in features]
+ labels_list = [_to_list(f["labels"]) for f in features]
+ max_len_in_batch = max(len(x) for x in input_ids)
+ target_len = self.pad_to_length if self.pad_to_length is not None else max_len_in_batch
+ pad_id = self.tok.pad_token_id
+ batch_inp, batch_attn, batch_lab = [], [], []
+ for inp, msk, lab in zip(input_ids, attn_masks, labels_list):
+ pad_len = target_len - len(inp)
+ if pad_len < 0:
+ inp, msk, lab = inp[:target_len], msk[:target_len], lab[:target_len]
+ pad_len = 0
+ batch_inp.append(torch.tensor(inp + [pad_id]*pad_len, dtype=torch.long))
+ batch_attn.append(torch.tensor(msk + [0]*pad_len, dtype=torch.long))
+ batch_lab.append(torch.tensor(lab + [-100]*pad_len, dtype=torch.long))
+ if os.environ.get("DBG_COLLATE","0") == "1":
+ print(f"[collate][host={socket.gethostname()} RANK={os.environ.get('RANK','?')}] "
+ f"features={len(features)} target_len={target_len}", flush=True)
+ return {
+ "input_ids": torch.stack(batch_inp, dim=0),
+ "attention_mask": torch.stack(batch_attn, dim=0),
+ "labels": torch.stack(batch_lab, dim=0),
+ }
+
+# ---------- Args ----------
+def parse_args():
+ ap = argparse.ArgumentParser()
+ ap.add_argument("--model_name_or_path", type=str, required=True)
+ ap.add_argument("--data_glob", type=str, required=True)
+ ap.add_argument("--output_dir", type=str, required=True)
+ ap.add_argument("--seq_len", type=int, default=4096)
+ ap.add_argument("--learning_rate", type=float, default=1e-4) # LoRA 通常可更大学习率
+ ap.add_argument("--weight_decay", type=float, default=0.0) # LoRA 常设 0 或很小
+ ap.add_argument("--warmup_ratio", type=float, default=0.03)
+ ap.add_argument("--num_train_epochs", type=float, default=1.0)
+ ap.add_argument("--max_steps", type=int, default=-1)
+ ap.add_argument("--log_interval", type=int, default=10)
+ ap.add_argument("--save_steps", type=int, default=500)
+ ap.add_argument("--eval_ratio", type=float, default=0.0)
+ ap.add_argument("--seed", type=int, default=1337)
+ ap.add_argument("--gradient_checkpointing", action="store_true")
+ ap.add_argument("--bf16", action="store_true")
+ ap.add_argument("--per_device_train_batch_size", type=int, default=1)
+ ap.add_argument("--gradient_accumulation_steps", type=int, default=64)
+ ap.add_argument("--report_to", type=str, default="tensorboard", choices=["none","tensorboard","wandb"])
+ ap.add_argument("--wandb_project", type=str, default="ds-qwen3-lora")
+ ap.add_argument("--eval_data_glob", type=str, default=None)
+ ap.add_argument("--local_rank", type=int, default=-1)
+ ap.add_argument("--per_device_eval_batch_size", type=int, default=1)
+ ap.add_argument("--deepspeed", type=str, default=None)
+
+ # ---- LoRA specific ----
+ ap.add_argument("--lora_r", type=int, default=16)
+ ap.add_argument("--lora_alpha", type=float, default=32)
+ ap.add_argument("--lora_dropout", type=float, default=0.05)
+ ap.add_argument("--lora_target", type=str, default="auto",
+ help='逗号分隔，如 "q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj"；或 "auto"')
+
+ ap.add_argument("--qlora", action="store_true", help="使用 4bit (NF4) QLoRA（多机 DS 不建议）")
+ ap.add_argument("--merge_lora_and_save", action="store_true",
+ help="训练后在 rank0 合并 LoRA 到基座并另存（注意显存/内存占用）")
+ return ap.parse_args()
+
+# ---------- LoRA helpers ----------
+def _auto_lora_targets(model) -> List[str]:
+ """
+ 针对 Qwen/Llama 族，自动挑选常见的线性层名字；仅匹配存在的模块。
+ """
+ cand = ["q_proj","k_proj","v_proj","o_proj","gate_proj","up_proj","down_proj",
+ "w1","w2","w3", "W_pack", "o_attn", "o_proj"] # 覆盖不同实现命名
+ present = set()
+ for name, module in model.named_modules():
+ if any(name.endswith(f".{c}") or name == c for c in cand):
+ present.add(name.split(".")[-1])
+ # 回落：若一个都没匹配到，使用“所有 nn.Linear”
+ if not present:
+ return ["all-linear"]
+ # 去重且保序
+ order = []
+ for c in cand:
+ if c in present: order.append(c)
+ return order
+
+# ---------- main ----------
+def main():
+ args = parse_args()
+
+ if os.environ.get("RANK","0") != "0" and args.report_to == "wandb":
+ print(f"[rank {os.environ.get('RANK')}] force report_to=none", flush=True)
+ args.report_to = "none"
+
+ set_seed(args.seed)
+
+ # DeepSpeed enable?
+ use_ds = bool(args.deepspeed and os.path.isfile(args.deepspeed))
+ dschf = None
+ if use_ds:
+ try:
+ from transformers.integrations.deepspeed import HfDeepSpeedConfig
+ src = "transformers.integrations.deepspeed"
+ except Exception:
+ from transformers import HfDeepSpeedConfig
+ src = "transformers"
+ dschf = HfDeepSpeedConfig(args.deepspeed)
+ print(f"[dbg] HfDeepSpeedConfig loaded from {src}", flush=True)
+
+ if args.report_to == "wandb":
+ os.environ.setdefault("WANDB_PROJECT", args.wandb_project)
+
+ import transformers as hf
+ try:
+ import deepspeed as ds
+ ds_ver = ds.__version__
+ except Exception:
+ ds_ver = "n/a"
+
+ def dbg(msg):
+ print(f"[dbg][host={socket.gethostname()} RANK={os.environ.get('RANK','0')} "
+ f"LOCAL_RANK={os.environ.get('LOCAL_RANK', str(args.local_rank))}] {msg}", flush=True)
+
+ dbg(f"torch={torch.__version__}, transformers={hf.__version__}, deepspeed={ds_ver}")
+ dbg(f"args={args}")
+ dbg("ENV: WORLD_SIZE=%s RANK=%s LOCAL_RANK=%s MASTER_ADDR=%s MASTER_PORT=%s CUDA_VISIBLE_DEVICES=%s" % (
+ os.environ.get("WORLD_SIZE"), os.environ.get("RANK"),
+ os.environ.get("LOCAL_RANK", str(args.local_rank)),
+ os.environ.get("MASTER_ADDR"), os.environ.get("MASTER_PORT"),
+ os.environ.get("CUDA_VISIBLE_DEVICES"),
+ ))
+ dbg(f"cuda_available={torch.cuda.is_available()} device_count={torch.cuda.device_count()}")
+
+ # init dist
+ world_size = int(os.environ.get("WORLD_SIZE", "1"))
+ rank = int(os.environ.get("RANK", "0"))
+ local_rank = int(os.environ.get("LOCAL_RANK", str(args.local_rank)))
+ if torch.cuda.is_available() and local_rank >= 0:
+ torch.cuda.set_device(local_rank)
+ dbg(f"set_device({local_rank}); current_device={torch.cuda.current_device()} "
+ f"name={torch.cuda.get_device_name(torch.cuda.current_device())}")
+ if world_size > 1 and dist.is_available() and not dist.is_initialized():
+ backend = "nccl" if torch.cuda.is_available() else "gloo"
+ dbg(f"init_process_group backend={backend} via env://")
+ dist.init_process_group(backend=backend, init_method="env://")
+
+ # tokenizer
+ tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=True, trust_remote_code=True)
+ if tokenizer.pad_token is None:
+ tokenizer.pad_token = tokenizer.eos_token
+ try:
+ if getattr(tokenizer, "padding_side", None) != "left":
+ tokenizer.padding_side = "left"
+ except Exception:
+ pass
+
+ from transformers import PreTrainedTokenizerFast
+ if not isinstance(tokenizer, PreTrainedTokenizerFast) or not getattr(tokenizer, "is_fast", False):
+ raise RuntimeError("需要 Fast tokenizer 以获取 offset_mapping；请安装 tokenizers>=0.14。")
+ tokenizer.model_max_length = args.seq_len
+ dbg(f"tokenizer.pad_token_id={tokenizer.pad_token_id} model_max_length={tokenizer.model_max_length}")
+
+ # dtype
+ def _bf16_supported():
+ if not torch.cuda.is_available(): return False
+ if hasattr(torch.cuda, "is_bf16_supported"):
+ return torch.cuda.is_bf16_supported()
+ major, minor = torch.cuda.get_device_capability()
+ return (major, minor) >= (8, 0)
+ use_bf16 = bool(args.bf16 and _bf16_supported())
+ compute_dtype = torch.bfloat16 if use_bf16 else (torch.float16 if torch.cuda.is_available() else torch.float32)
+
+ # -------- load base model (with/without 4bit) --------
+ quantization_config = None
+ if args.qlora:
+ try:
+ from transformers import BitsAndBytesConfig
+ from peft import prepare_model_for_kbit_training
+ except Exception as e:
+ raise RuntimeError("使用 --qlora 需要安装 bitsandbytes>=0.41 与 peft。") from e
+ quantization_config = BitsAndBytesConfig(
+ load_in_4bit=True,
+ bnb_4bit_quant_type="nf4",
+ bnb_4bit_use_double_quant=True,
+ bnb_4bit_compute_dtype=compute_dtype
+ )
+ # 4bit 下不要传 attn_implementation="sdpa" 给部分旧版 torch
+ model = AutoModelForCausalLM.from_pretrained(
+ args.model_name_or_path,
+ torch_dtype=compute_dtype,
+ trust_remote_code=True,
+ low_cpu_mem_usage=True,
+ quantization_config=quantization_config,
+ device_map=None # 用 DeepSpeed/Trainer 接管
+ )
+ if args.gradient_checkpointing:
+ model.gradient_checkpointing_enable(gradient_checkpointing_kwargs={"use_reentrant": False})
+ model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=args.gradient_checkpointing)
+ else:
+ model = AutoModelForCausalLM.from_pretrained(
+ args.model_name_or_path,
+ torch_dtype=compute_dtype,
+ low_cpu_mem_usage=True,
+ trust_remote_code=True,
+ attn_implementation="sdpa",
+ )
+ if args.gradient_checkpointing:
+ model.gradient_checkpointing_enable(gradient_checkpointing_kwargs={"use_reentrant": False})
+
+ model.config.pad_token_id = tokenizer.pad_token_id
+ model.config.use_cache = False
+
+ # -------- wrap with LoRA --------
+ from peft import LoraConfig, get_peft_model, TaskType, PeftModel
+ if args.lora_target.strip().lower() == "auto":
+ targets = _auto_lora_targets(model)
+ else:
+ targets = [x.strip() for x in args.lora_target.split(",") if x.strip()]
+ if not targets:
+ targets = _auto_lora_targets(model)
+
+ lora_cfg = LoraConfig(
+ task_type=TaskType.CAUSAL_LM,
+ r=args.lora_r,
+ lora_alpha=args.lora_alpha,
+ lora_dropout=args.lora_dropout,
+ target_modules=targets,
+ bias="none",
+ inference_mode=False
+ )
+ model = get_peft_model(model, lora_cfg)
+
+ # 冻结确认
+ if is_main_process():
+ try:
+ model.print_trainable_parameters()
+ except Exception:
+ trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)
+ total = sum(p.numel() for p in model.parameters())
+ print(f"[LoRA] trainable={trainable:,} / total={total:,} ({trainable/total:.2%})", flush=True)
+
+ # -------- data streams --------
+ files = sorted(glob.glob(args.data_glob))
+ if len(files) == 0:
+ raise FileNotFoundError(f"No files matched DATA_GLOB={args.data_glob}")
+ if is_main_process():
+ print(f"[data] matched {len(files)} files, example[0]={files[0]}", flush=True)
+
+ 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("[data] 样本结构不合法或全部被裁切。")
+
+ ds_stream2 = load_dataset("json", data_files={"train": files}, split="train", streaming=True)
+ train_stream = QwenChatSFTDataset((ex for ex in ds_stream2), tokenizer, seq_len=args.seq_len)
+
+ ds_stream_probe2 = load_dataset("json", data_files={"train": files}, split="train", streaming=True)
+ probe_stream = QwenChatSFTDataset((ex for ex in ds_stream_probe2), 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。", 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。", flush=True)
+ sys.exit(2)
+
+ # eval
+ 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"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]] = [s for s in eval_iterable]
+ if len(eval_items) == 0:
+ raise RuntimeError("[eval] 读到了 0 条有效样本。")
+ eval_dataset = ListDataset(eval_items)
+ # pad to global batch size
+ ws = max(int(os.environ.get("WORLD_SIZE","1")), 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)}", flush=True)
+
+ # collator
+ data_collator = SFTDataCollator(tokenizer, pad_to_length=args.seq_len)
+
+ # training args
+ os.makedirs(args.output_dir, exist_ok=True)
+ logging_dir = os.path.join(args.output_dir, "logs"); os.makedirs(logging_dir, exist_ok=True)
+
+ 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"
+
+ ta_kwargs2 = dict(
+ 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 use_ds else None),
+ dataloader_drop_last=False,
+ dataloader_num_workers=0,
+ per_device_eval_batch_size=args.per_device_eval_batch_size,
+ report_to=([] if args.report_to == "none" else [args.report_to]),
+ gradient_checkpointing=args.gradient_checkpointing,
+ remove_unused_columns=False,
+ save_on_each_node=True,
+ logging_first_step=True,
+ **ta_kwargs,
+ )
+ # 精度：QLoRA/LoRA 均按 compute_dtype 设置
+ if "dataloader_pin_memory" in sig: ta_kwargs2["dataloader_pin_memory"] = False
+ if "torch_compile" in sig: ta_kwargs2["torch_compile"] = False
+ ta_kwargs2.update({
+ "bf16": (compute_dtype==torch.bfloat16),
+ "fp16": (compute_dtype==torch.float16),
+ })
+ training_args = TrainingArguments(**ta_kwargs2)
+
+ # pass tokenizer / processing_class
+ trainer_kwargs = {}
+ if "processing_class" in inspect.signature(Trainer.__init__).parameters:
+ trainer_kwargs["processing_class"] = tokenizer
+ else:
+ trainer_kwargs["tokenizer"] = tokenizer
+
+ trainer = DebugTrainer(
+ model=model,
+ args=training_args,
+ train_dataset=train_stream,
+ eval_dataset=eval_dataset,
+ data_collator=data_collator,
+ **trainer_kwargs
+ )
+ trainer.add_callback(CsvLossLogger(csv_path=os.path.join(args.output_dir, "loss.csv")))
+
+ # resume (per-node local checkpoint agreement)
+ def last_step(path: str) -> int:
+ ck = get_last_checkpoint(path)
+ if ck is None: return -1
+ base = os.path.basename(ck)
+ try: return int(base.split("-")[-1])
+ except Exception: return -1
+
+ local_last = last_step(args.output_dir)
+ device = torch.device(f"cuda:{local_rank}" if (torch.cuda.is_available() and local_rank>=0) else "cpu")
+ resume_flag = None
+ if dist.is_available() and dist.is_initialized():
+ has_local = torch.tensor(1 if local_last >= 0 else 0, device=device)
+ dist.all_reduce(has_local, op=dist.ReduceOp.MIN)
+ if has_local.item() == 1:
+ ts = torch.tensor(local_last, device=device)
+ world = dist.get_world_size()
+ buf = [torch.zeros_like(ts) for _ in range(world)]
+ dist.all_gather(buf, ts)
+ steps = [b.item() for b in buf]
+ k = min(steps)
+ if k >= 0:
+ resume_flag = os.path.join(args.output_dir, f"checkpoint-{k}")
+ if is_main_process():
+ print(f"[resume] steps={steps}, resume={resume_flag}", flush=True)
+ else:
+ if local_last >= 0:
+ resume_flag = os.path.join(args.output_dir, f"checkpoint-{local_last}")
+
+ print_once(f"[host={socket.gethostname()}] Resume = {resume_flag is not None}")
+ if dist.is_available() and dist.is_initialized():
+ present = torch.tensor(1 if (resume_flag is not None and os.path.isdir(resume_flag)) else 0, device=device)
+ dist.all_reduce(present, op=dist.ReduceOp.MIN)
+ if present.item() == 0:
+ if is_main_process():
+ print(f"[resume] {resume_flag} missing on some ranks -> disable resume.", flush=True)
+ resume_flag = None
+ dist.barrier()
+ else:
+ if resume_flag is not None and not os.path.isdir(resume_flag):
+ print(f"[resume] {resume_flag} not found locally -> disable resume.", flush=True)
+ resume_flag = None
+
+ print_once(f"[resume] final = {resume_flag if resume_flag else 'None (fresh start)'}")
+ print_once("***** Starting LoRA training *****")
+ print(f"[dbg] allocated={torch.cuda.memory_allocated()/1024**2:.1f} MB, "
+ f"reserved={torch.cuda.memory_reserved()/1024**2:.1f} MB", flush=True)
+
+ train_result = trainer.train(resume_from_checkpoint=resume_flag)
+
+ # save adapter (not the full base)
+ trainer.save_model() # 对 PeftModel：只保存 adapter 权重到 output_dir
+ metrics = train_result.metrics
+ trainer.log_metrics("train", metrics)
+ trainer.save_metrics("train", metrics)
+ trainer.save_state()
+
+ # eval
+ 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)
+
+ # optional merge
+ if args.merge_lora_and_save and is_main_process():
+ print("[merge] Merging LoRA into base model ...", flush=True)
+ try:
+ if isinstance(trainer.model, PeftModel):
+ merged = trainer.model.merge_and_unload()
+ else:
+ merged = trainer.model
+ merge_dir = os.path.join(args.output_dir, "merged-full-model")
+ os.makedirs(merge_dir, exist_ok=True)
+ merged.save_pretrained(merge_dir, safe_serialization=True)
+ tokenizer.save_pretrained(merge_dir)
+ print(f"[merge] Saved merged model to: {merge_dir}", flush=True)
+ except Exception as e:
+ print(f"[merge] FAILED: {e}", flush=True)
+
+ print_once("Done.")
+
+if __name__ == "__main__":
+ main()