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e6bc942 | 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 | #!/usr/bin/env python
"""LLM2Vec-style bidirectional adaptation of ProGen2 — training entrypoint.
Launch:
srun torchrun --standalone --nproc_per_node=4 pretrain.py [args]
Single-GPU / smoke also works without torchrun (falls back to rank 0).
"""
from __future__ import annotations
import argparse
import os
import sys
import time
import torch
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data import DataLoader, DistributedSampler
from transformers import AutoTokenizer, get_cosine_schedule_with_warmup
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from src.bidir_progen import load_bidir_progen # noqa: E402
from src.data import ProteinSeqDataset, MNTPCollator, CleanCollator, load_sequences # noqa: E402
def parse_args():
p = argparse.ArgumentParser()
p.add_argument("--model-name", default="hugohrban/progen2-base")
p.add_argument("--objective", default="joint", choices=["mntp", "simcse", "joint"])
p.add_argument("--output-dir", required=True)
# two-stage (LLM2Vec): SimCSE stage resumes the MNTP adapter and enables dropout
p.add_argument("--init-adapter", default=None,
help="LoRA adapter dir to resume (SimCSE stage starts from MNTP)")
p.add_argument("--simcse-dropout", type=float, default=None,
help="force all dropout to this prob (SimCSE positive-pair augmentation)")
# data
p.add_argument("--hf-dataset", default=None, help="HF dataset id of protein seqs")
p.add_argument("--hf-config", default=None)
p.add_argument("--text-column", default="sequence")
p.add_argument("--num-sequences", type=int, default=2000)
p.add_argument("--max-length", type=int, default=256)
p.add_argument("--mlm-probability", type=float, default=0.15)
# lora
p.add_argument("--lora-r", type=int, default=16)
p.add_argument("--lora-alpha", type=int, default=32)
p.add_argument("--lora-dropout", type=float, default=0.05)
# contrastive
p.add_argument("--simcse-weight", type=float, default=0.1)
p.add_argument("--temperature", type=float, default=0.05)
# optim
p.add_argument("--per-device-batch-size", type=int, default=8)
p.add_argument("--gradient-accumulation-steps", type=int, default=1)
p.add_argument("--lr", type=float, default=1e-4)
p.add_argument("--weight-decay", type=float, default=0.01)
p.add_argument("--warmup-steps", type=int, default=10)
p.add_argument("--max-steps", type=int, default=100)
p.add_argument("--logging-steps", type=int, default=1)
p.add_argument("--save-steps", type=int, default=100000)
p.add_argument("--seed", type=int, default=0)
return p.parse_args()
def setup_dist():
if "RANK" in os.environ and int(os.environ.get("WORLD_SIZE", "1")) > 1:
dist.init_process_group("nccl")
rank = dist.get_rank()
local_rank = int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(local_rank)
return rank, local_rank, dist.get_world_size(), True
local_rank = 0
if torch.cuda.is_available():
torch.cuda.set_device(0)
return 0, local_rank, 1, False
def is_main(rank):
return rank == 0
def log(rank, msg):
if is_main(rank):
print(f"[{time.strftime('%H:%M:%S')}] {msg}", flush=True)
def main():
args = parse_args()
torch.manual_seed(args.seed)
rank, local_rank, world_size, distributed = setup_dist()
device = torch.device("cuda", local_rank) if torch.cuda.is_available() else torch.device("cpu")
dtype = torch.bfloat16 if torch.cuda.is_available() else torch.float32
log(rank, f"world_size={world_size} device={device} dtype={dtype} objective={args.objective}")
tokenizer = AutoTokenizer.from_pretrained(args.model_name, trust_remote_code=True)
if tokenizer.pad_token_id is None:
tokenizer.pad_token = tokenizer.eos_token or tokenizer.convert_ids_to_tokens(0)
model, info = load_bidir_progen(
args.model_name, args.objective, args.lora_r, args.lora_alpha,
args.lora_dropout, args.simcse_weight, args.temperature, dtype=dtype,
init_adapter=args.init_adapter, attn_dropout=args.simcse_dropout,
)
log(rank, f"bidirectional patch: {info['patched_layers']} layers; "
f"lora targets: {info['lora_targets']}; "
f"resumed_adapter={info['resumed_adapter']}; dropout_set={info['dropout_set']}")
model.to(device)
if is_main(rank):
model.model.print_trainable_parameters()
seqs = load_sequences(args.num_sequences, args.hf_dataset, args.hf_config,
args.text_column, seed=args.seed)
log(rank, f"loaded {len(seqs)} sequences (e.g. len={len(seqs[0])})")
dataset = ProteinSeqDataset(seqs, tokenizer, max_length=args.max_length)
# SimCSE stage = clean (unmasked) input; MNTP/joint = BERT-style masking.
collator = (CleanCollator(tokenizer) if args.objective == "simcse"
else MNTPCollator(tokenizer, mlm_probability=args.mlm_probability))
sampler = DistributedSampler(dataset, num_replicas=world_size, rank=rank,
shuffle=True, seed=args.seed) if distributed else None
loader = DataLoader(dataset, batch_size=args.per_device_batch_size,
sampler=sampler, shuffle=sampler is None,
collate_fn=collator, drop_last=True)
if distributed:
model = DDP(model, device_ids=[local_rank], find_unused_parameters=True)
core = model.module if distributed else model
optim = torch.optim.AdamW(
[p for p in model.parameters() if p.requires_grad],
lr=args.lr, weight_decay=args.weight_decay,
)
sched = get_cosine_schedule_with_warmup(optim, args.warmup_steps, args.max_steps)
model.train()
step = 0
t0 = time.time()
data_iter = iter(loader)
epoch = 0
while step < args.max_steps:
optim.zero_grad(set_to_none=True)
accum_logs = {}
for micro in range(args.gradient_accumulation_steps):
try:
batch = next(data_iter)
except StopIteration:
epoch += 1
if sampler is not None:
sampler.set_epoch(epoch)
data_iter = iter(loader)
batch = next(data_iter)
batch = {k: v.to(device) for k, v in batch.items()}
out = model(**batch)
loss = out["loss"]
(loss / args.gradient_accumulation_steps).backward()
for k, v in out["logs"].items():
accum_logs[k] = accum_logs.get(k, 0.0) + v.item()
torch.nn.utils.clip_grad_norm_(
[p for p in model.parameters() if p.requires_grad], 1.0)
optim.step()
sched.step()
step += 1
if step % args.logging_steps == 0:
parts = " ".join(f"{k}={v/args.gradient_accumulation_steps:.4f}"
for k, v in accum_logs.items())
sps = step / (time.time() - t0)
log(rank, f"step {step}/{args.max_steps} loss={loss.item():.4f} {parts} "
f"lr={sched.get_last_lr()[0]:.2e} {sps:.2f} step/s")
if is_main(rank):
os.makedirs(args.output_dir, exist_ok=True)
core.model.save_pretrained(args.output_dir) # saves LoRA adapter
tokenizer.save_pretrained(args.output_dir)
log(rank, f"saved adapter + tokenizer to {args.output_dir}")
if distributed:
dist.barrier()
dist.destroy_process_group()
if __name__ == "__main__":
main()
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