File size: 5,118 Bytes
2abcc30 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 | from __future__ import annotations
import json
from pathlib import Path
from .constants import (
DEFAULT_ADAPTER_DIR,
DEFAULT_PACK_DIR,
DEFAULT_MAX_SEQ_LEN,
GENESIS_DIR,
LORA_ALPHA,
LORA_RANK,
SMOKE_MAX_SEQ_LEN,
)
def train_sft(
*,
pack_path: Path,
output_dir: Path = DEFAULT_ADAPTER_DIR,
model_dir: Path = GENESIS_DIR,
max_seq_len: int = DEFAULT_MAX_SEQ_LEN,
max_steps: int | None = None,
num_epochs: float = 1.0,
per_device_batch_size: int = 1,
grad_accum: int = 8,
lr: float = 1e-4,
lora_rank: int = LORA_RANK,
lora_alpha: int = LORA_ALPHA,
smoke: bool = False,
) -> Path:
"""LoRA SFT on genesis. Assistant/completion tokens only."""
from local_eval.cuda_env import apply as apply_cuda
apply_cuda()
pack_path = Path(pack_path)
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
if smoke:
max_seq_len = min(max_seq_len, SMOKE_MAX_SEQ_LEN)
max_steps = max_steps or 20
rows = _load_pack(pack_path)
if not rows:
raise ValueError(f"empty pack: {pack_path}")
import torch
from datasets import Dataset
from peft import LoraConfig, get_peft_model
from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import SFTConfig, SFTTrainer
tokenizer = AutoTokenizer.from_pretrained(str(model_dir), trust_remote_code=False)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
dataset = Dataset.from_list(
[{"prompt": row["prompt"], "completion": row["completion"]} for row in rows]
)
model = AutoModelForCausalLM.from_pretrained(
str(model_dir),
torch_dtype=torch.bfloat16,
trust_remote_code=False,
attn_implementation="sdpa",
)
model.config.use_cache = False
if hasattr(model, "enable_input_require_grads"):
model.enable_input_require_grads()
targets = lora_target_modules(model)
model = get_peft_model(
model,
LoraConfig(
r=lora_rank,
lora_alpha=lora_alpha,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
target_modules=targets,
),
)
model.print_trainable_parameters()
args_kwargs = dict(
output_dir=str(output_dir),
bf16=True,
learning_rate=lr,
per_device_train_batch_size=per_device_batch_size,
gradient_accumulation_steps=grad_accum,
gradient_checkpointing=True,
logging_steps=1,
save_steps=max(max_steps or 200, 50),
warmup_ratio=0.03,
lr_scheduler_type="cosine",
report_to=[],
max_length=max_seq_len,
packing=False,
completion_only_loss=True,
remove_unused_columns=False,
)
if max_steps:
args_kwargs["max_steps"] = max_steps
else:
args_kwargs["num_train_epochs"] = num_epochs
config = SFTConfig(**_filter_kwargs(SFTConfig, args_kwargs))
trainer = SFTTrainer(
model=model,
args=config,
train_dataset=dataset,
processing_class=tokenizer,
)
trainer.train()
trainer.save_model(str(output_dir))
tokenizer.save_pretrained(str(output_dir))
(output_dir / "sft-report.json").write_text(
json.dumps(
{
"pack": str(pack_path),
"n": len(rows),
"max_steps": max_steps,
"max_seq_len": max_seq_len,
"lora_rank": lora_rank,
"target_modules": targets,
"smoke": smoke,
},
indent=2,
)
+ "\n"
)
print(f"adapter: {output_dir}", flush=True)
return output_dir
def lora_target_modules(model) -> list[str]:
import torch
wanted = {
"q_proj",
"k_proj",
"v_proj",
"o_proj",
"gate_proj",
"up_proj",
"down_proj",
"in_proj_qkv",
"in_proj",
"out_proj",
"gate",
}
found: set[str] = set()
for name, module in model.named_modules():
if not isinstance(module, torch.nn.Linear):
continue
leaf = name.rsplit(".", 1)[-1]
if leaf in wanted:
found.add(leaf)
if not found:
found = {"q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"}
return sorted(found)
def default_pack(pack_dir: Path = DEFAULT_PACK_DIR) -> Path:
packs = sorted(Path(pack_dir).glob("sft-*.jsonl"), key=lambda p: p.stat().st_mtime)
if not packs:
raise FileNotFoundError(f"no sft-*.jsonl under {pack_dir}")
return packs[-1]
def _load_pack(path: Path) -> list[dict]:
rows = []
for line in Path(path).read_text().splitlines():
if line.strip():
rows.append(json.loads(line))
return rows
def _filter_kwargs(cls, kwargs: dict) -> dict:
try:
fields = set(cls.__dataclass_fields__)
except Exception:
return kwargs
return {key: value for key, value in kwargs.items() if key in fields}
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