273 lines
9.4 KiB
Python
273 lines
9.4 KiB
Python
"""
|
|
SFT (Supervised Fine-Tuning) script for the 1B Transformer.
|
|
|
|
Takes the pretrained base model and fine-tunes it on instruction-response
|
|
conversations from UltraChat 200K.
|
|
|
|
Launch: torchrun --nproc_per_node=8 train_sft.py
|
|
"""
|
|
|
|
import os
|
|
import sys
|
|
import math
|
|
import time
|
|
import json
|
|
import datetime
|
|
|
|
import torch
|
|
import torch.distributed as dist
|
|
from torch.nn.parallel import DistributedDataParallel as DDP
|
|
from torch.utils.data.distributed import DistributedSampler
|
|
|
|
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
|
|
from model.config import ModelConfig
|
|
from model.transformer import Transformer
|
|
from model.data import get_tokenizer
|
|
from model.sft_data import SFTDataset, sft_collate_fn
|
|
|
|
|
|
# === Config ===
|
|
BASE_CHECKPOINT = "/jfs/deepak-kumar/checkpoints/step_19000.pt"
|
|
SFT_CHECKPOINT_DIR = "/jfs/deepak-kumar/checkpoints_sft"
|
|
LOG_DIR = "/home/jovyan/training/logs"
|
|
DATA_CACHE = "/jfs/deepak-kumar/data"
|
|
|
|
NUM_EPOCHS = 2
|
|
BATCH_SIZE_PER_GPU = 4
|
|
GRADIENT_ACCUMULATION = 4 # effective batch = 4 * 8 * 4 = 128
|
|
MAX_SEQ_LEN = 2048
|
|
LEARNING_RATE = 2e-5 # much lower than pretraining — we're fine-tuning
|
|
MIN_LR = 2e-6
|
|
WARMUP_STEPS = 200
|
|
WEIGHT_DECAY = 0.01
|
|
GRAD_CLIP = 1.0
|
|
LOG_INTERVAL = 10
|
|
SAVE_INTERVAL = 500
|
|
|
|
|
|
def get_cosine_lr(step, warmup_steps, total_steps, max_lr, min_lr):
|
|
if step < warmup_steps:
|
|
return max_lr * step / max(warmup_steps, 1)
|
|
progress = (step - warmup_steps) / max(total_steps - warmup_steps, 1)
|
|
return min_lr + 0.5 * (max_lr - min_lr) * (1 + math.cos(math.pi * progress))
|
|
|
|
|
|
def main():
|
|
dist.init_process_group("nccl", timeout=datetime.timedelta(minutes=30))
|
|
rank = int(os.environ.get("RANK", 0))
|
|
local_rank = int(os.environ.get("LOCAL_RANK", 0))
|
|
world_size = int(os.environ.get("WORLD_SIZE", 1))
|
|
torch.cuda.set_device(local_rank)
|
|
device = torch.device(f"cuda:{local_rank}")
|
|
|
|
if rank == 0:
|
|
os.makedirs(SFT_CHECKPOINT_DIR, exist_ok=True)
|
|
os.makedirs(LOG_DIR, exist_ok=True)
|
|
print("=" * 70)
|
|
print(" SFT: INSTRUCTION FINE-TUNING 1B TRANSFORMER")
|
|
print("=" * 70)
|
|
|
|
# Tokenizer
|
|
tokenizer = get_tokenizer()
|
|
|
|
# Load base model
|
|
model_config = ModelConfig()
|
|
torch.manual_seed(42)
|
|
model = Transformer(model_config)
|
|
|
|
if rank == 0:
|
|
print(f"[Init] Loading base model from {BASE_CHECKPOINT}")
|
|
ckpt = torch.load(BASE_CHECKPOINT, map_location="cpu", weights_only=False)
|
|
model.load_state_dict(ckpt["model"])
|
|
base_step = ckpt.get("step", 0)
|
|
base_loss = ckpt.get("loss", "?")
|
|
if rank == 0:
|
|
print(f"[Init] Base model: step={base_step}, pretrain_loss={base_loss}")
|
|
del ckpt
|
|
|
|
# Add chat tokens to embedding — expand vocab if needed
|
|
special_tokens = ["<|user|>", "<|assistant|>", "<|end|>"]
|
|
vocab = tokenizer.get_vocab()
|
|
new_tokens = [t for t in special_tokens if t not in vocab]
|
|
if new_tokens:
|
|
tokenizer.add_tokens(new_tokens, special_tokens=True)
|
|
|
|
new_vocab_size = len(tokenizer)
|
|
if new_vocab_size > model_config.vocab_size:
|
|
if rank == 0:
|
|
print(f"[Init] Expanding vocab: {model_config.vocab_size} -> {new_vocab_size}")
|
|
|
|
old_emb_weight = model.tok_embeddings.weight.data
|
|
model.tok_embeddings = torch.nn.Embedding(new_vocab_size, model_config.hidden_dim)
|
|
model.tok_embeddings.weight.data[:model_config.vocab_size] = old_emb_weight
|
|
# Init new token embeddings as mean of existing (better than random)
|
|
mean_emb = old_emb_weight.mean(dim=0)
|
|
for i in range(model_config.vocab_size, new_vocab_size):
|
|
model.tok_embeddings.weight.data[i] = mean_emb
|
|
|
|
old_output_weight = model.output.weight.data
|
|
model.output = torch.nn.Linear(model_config.hidden_dim, new_vocab_size, bias=False)
|
|
model.output.weight.data[:model_config.vocab_size] = old_output_weight
|
|
|
|
model.config.vocab_size = new_vocab_size
|
|
|
|
model = model.to(device)
|
|
model = DDP(model, device_ids=[local_rank])
|
|
|
|
if rank == 0:
|
|
n = sum(p.numel() for p in model.parameters())
|
|
print(f"[Init] Params: {n:,} | GPUs: {world_size}x H100")
|
|
|
|
# Dataset (only load on each process)
|
|
dataset = SFTDataset(
|
|
tokenizer=tokenizer,
|
|
max_seq_len=MAX_SEQ_LEN,
|
|
split="train_sft",
|
|
cache_dir=DATA_CACHE,
|
|
)
|
|
|
|
sampler = DistributedSampler(dataset, num_replicas=world_size, rank=rank, shuffle=True)
|
|
dataloader = torch.utils.data.DataLoader(
|
|
dataset,
|
|
batch_size=BATCH_SIZE_PER_GPU,
|
|
sampler=sampler,
|
|
num_workers=4,
|
|
pin_memory=True,
|
|
collate_fn=lambda b: sft_collate_fn(b, pad_id=tokenizer.pad_token_id),
|
|
)
|
|
|
|
steps_per_epoch = len(dataloader) // GRADIENT_ACCUMULATION
|
|
total_steps = steps_per_epoch * NUM_EPOCHS
|
|
|
|
if rank == 0:
|
|
eff_batch = BATCH_SIZE_PER_GPU * world_size * GRADIENT_ACCUMULATION
|
|
print(f"[Init] Dataset: {len(dataset):,} examples")
|
|
print(f"[Init] Effective batch: {eff_batch} | Steps/epoch: {steps_per_epoch}")
|
|
print(f"[Init] Total steps: {total_steps} | Epochs: {NUM_EPOCHS}")
|
|
print(f"[Init] LR: {LEARNING_RATE} → {MIN_LR} (cosine)")
|
|
print("-" * 70)
|
|
|
|
# Optimizer — lower LR for fine-tuning
|
|
decay_params = [p for n, p in model.named_parameters() if p.dim() >= 2 and p.requires_grad]
|
|
nodecay_params = [p for n, p in model.named_parameters() if p.dim() < 2 and p.requires_grad]
|
|
optimizer = torch.optim.AdamW([
|
|
{"params": decay_params, "weight_decay": WEIGHT_DECAY},
|
|
{"params": nodecay_params, "weight_decay": 0.0},
|
|
], lr=LEARNING_RATE, betas=(0.9, 0.95), fused=True)
|
|
|
|
# Training
|
|
model.train()
|
|
global_step = 0
|
|
running_loss = 0.0
|
|
t0 = time.time()
|
|
step_t0 = time.time()
|
|
|
|
log_file = open(os.path.join(LOG_DIR, "sft_log.jsonl"), "w") if rank == 0 else None
|
|
|
|
for epoch in range(NUM_EPOCHS):
|
|
sampler.set_epoch(epoch)
|
|
data_iter = iter(dataloader)
|
|
micro_step = 0
|
|
|
|
if rank == 0:
|
|
print(f"\n[Epoch {epoch + 1}/{NUM_EPOCHS}]")
|
|
|
|
while True:
|
|
optimizer.zero_grad(set_to_none=True)
|
|
batch_loss = 0.0
|
|
|
|
for _ in range(GRADIENT_ACCUMULATION):
|
|
try:
|
|
input_ids, labels = next(data_iter)
|
|
except StopIteration:
|
|
break
|
|
|
|
input_ids = input_ids.to(device, non_blocking=True)
|
|
labels = labels.to(device, non_blocking=True)
|
|
|
|
with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
|
|
_, loss = model(input_ids, labels)
|
|
loss = loss / GRADIENT_ACCUMULATION
|
|
|
|
loss.backward()
|
|
batch_loss += loss.item()
|
|
micro_step += 1
|
|
|
|
if batch_loss == 0:
|
|
break
|
|
|
|
torch.nn.utils.clip_grad_norm_(model.parameters(), GRAD_CLIP)
|
|
|
|
lr = get_cosine_lr(global_step, WARMUP_STEPS, total_steps, LEARNING_RATE, MIN_LR)
|
|
for pg in optimizer.param_groups:
|
|
pg["lr"] = lr
|
|
|
|
optimizer.step()
|
|
global_step += 1
|
|
running_loss += batch_loss
|
|
|
|
if global_step % LOG_INTERVAL == 0:
|
|
dt = time.time() - step_t0
|
|
avg = running_loss / LOG_INTERVAL
|
|
elapsed = time.time() - t0
|
|
pct = 100.0 * global_step / total_steps
|
|
|
|
if rank == 0:
|
|
gpu_mem = torch.cuda.max_memory_allocated(device) / 1e9
|
|
eta = (elapsed / max(global_step, 1)) * (total_steps - global_step)
|
|
print(
|
|
f" [Step {global_step:>5d}/{total_steps}] "
|
|
f"loss={avg:.4f} | lr={lr:.2e} | "
|
|
f"GPU={gpu_mem:.1f}GB | {pct:.1f}% | ETA={eta/60:.0f}m",
|
|
flush=True,
|
|
)
|
|
if log_file:
|
|
log_file.write(json.dumps({
|
|
"step": global_step, "epoch": epoch + 1,
|
|
"loss": round(avg, 4), "lr": lr,
|
|
"elapsed_s": round(elapsed, 1),
|
|
}) + "\n")
|
|
log_file.flush()
|
|
|
|
running_loss = 0.0
|
|
step_t0 = time.time()
|
|
|
|
if global_step % SAVE_INTERVAL == 0:
|
|
dist.barrier()
|
|
if rank == 0:
|
|
path = os.path.join(SFT_CHECKPOINT_DIR, f"sft_step_{global_step}.pt")
|
|
torch.save({
|
|
"step": global_step,
|
|
"model": model.module.state_dict(),
|
|
"config": model_config.__dict__,
|
|
"vocab_size": new_vocab_size,
|
|
}, path)
|
|
print(f" >> Checkpoint: {path}", flush=True)
|
|
dist.barrier()
|
|
|
|
# Final save
|
|
dist.barrier()
|
|
if rank == 0:
|
|
final_path = os.path.join(SFT_CHECKPOINT_DIR, "sft_final.pt")
|
|
torch.save({
|
|
"step": global_step,
|
|
"model": model.module.state_dict(),
|
|
"config": model_config.__dict__,
|
|
"vocab_size": new_vocab_size,
|
|
}, final_path)
|
|
total_time = time.time() - t0
|
|
print("=" * 70)
|
|
print(f" SFT COMPLETE")
|
|
print(f" Steps: {global_step:,} | Epochs: {NUM_EPOCHS}")
|
|
print(f" Time: {total_time/60:.1f} minutes")
|
|
print(f" Final model: {final_path}")
|
|
print("=" * 70)
|
|
if log_file:
|
|
log_file.close()
|
|
|
|
dist.destroy_process_group()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|