mirror of
https://github.com/karpathy/nanochat.git
synced 2026-04-02 13:45:21 +00:00
402 lines
19 KiB
Python
402 lines
19 KiB
Python
"""
|
|
Supervised fine-tuning (SFT) the model.
|
|
Run as:
|
|
|
|
python -m scripts.chat_sft
|
|
|
|
Or torchrun for training:
|
|
|
|
torchrun --standalone --nproc_per_node=8 -m scripts.chat_sft -- --device-batch-size=16
|
|
"""
|
|
|
|
import argparse
|
|
import os
|
|
os.environ["PYTORCH_ALLOC_CONF"] = "expandable_segments:True"
|
|
import time
|
|
import wandb
|
|
import torch
|
|
from contextlib import nullcontext
|
|
from nanochat.common import compute_init, compute_cleanup, print0, DummyWandb, get_base_dir, autodetect_device_type
|
|
from nanochat.tokenizer import get_token_bytes
|
|
from nanochat.checkpoint_manager import save_checkpoint
|
|
from nanochat.loss_eval import evaluate_bpb
|
|
from nanochat.checkpoint_manager import load_model
|
|
import torch.distributed as dist
|
|
|
|
from tasks.common import TaskMixture
|
|
from tasks.gsm8k import GSM8K
|
|
from tasks.mmlu import MMLU
|
|
from tasks.smoltalk import SmolTalk
|
|
from tasks.customjson import CustomJSON
|
|
from tasks.spellingbee import SimpleSpelling, SpellingBee
|
|
|
|
# -----------------------------------------------------------------------------
|
|
# CLI arguments
|
|
parser = argparse.ArgumentParser(description="Supervised fine-tuning (SFT) the model")
|
|
# Logging
|
|
parser.add_argument("--run", type=str, default="dummy", help="wandb run name ('dummy' disables wandb logging)")
|
|
# Runtime
|
|
parser.add_argument("--device-type", type=str, default="", help="cuda|cpu|mps (empty = autodetect)")
|
|
parser.add_argument("--dtype", type=str, default="bfloat16", help="float32|bfloat16")
|
|
# Model loading
|
|
parser.add_argument("--model-tag", type=str, default=None, help="model tag to load from")
|
|
parser.add_argument("--model-step", type=int, default=None, help="model step to load from")
|
|
# Training horizon
|
|
parser.add_argument("--num-iterations", type=int, default=-1, help="number of optimization steps (-1 = full epoch)")
|
|
# Batch sizes
|
|
parser.add_argument("--max-seq-len", type=int, default=2048, help="max context length")
|
|
parser.add_argument("--device-batch-size", type=int, default=32, help="per-device batch size")
|
|
parser.add_argument("--total-batch-size", type=int, default=524288, help="total batch size in tokens")
|
|
# Optimization
|
|
parser.add_argument("--embedding-lr", type=float, default=0.2, help="learning rate for embedding parameters (Adam)")
|
|
parser.add_argument("--unembedding-lr", type=float, default=0.004, help="learning rate for unembedding parameters (Adam)")
|
|
parser.add_argument("--matrix-lr", type=float, default=0.02, help="learning rate for matrix parameters (Muon)")
|
|
parser.add_argument("--weight-decay", type=float, default=0.0, help="weight decay for embedding/unembedding parameters (Adam)")
|
|
parser.add_argument("--init-lr-frac", type=float, default=1.0, help="initial LR as fraction of base LR")
|
|
# Evaluation
|
|
parser.add_argument("--eval-every", type=int, default=150, help="evaluate val bpb every N steps (-1 = disable)")
|
|
parser.add_argument("--eval-tokens", type=int, default=20*524288, help="number of tokens to evaluate val loss on")
|
|
# Output
|
|
parser.add_argument("--dry-run", action="store_true", help="log to wandb but skip checkpoints/report")
|
|
args = parser.parse_args()
|
|
user_config = vars(args).copy()
|
|
# -----------------------------------------------------------------------------
|
|
|
|
# Compute init
|
|
device_type = autodetect_device_type() if args.device_type == "" else args.device_type
|
|
ddp, ddp_rank, ddp_local_rank, ddp_world_size, device = compute_init(device_type)
|
|
master_process = ddp_rank == 0
|
|
ptdtype = torch.float32 if args.dtype == 'float32' else torch.bfloat16
|
|
autocast_ctx = torch.amp.autocast(device_type=device_type, dtype=ptdtype) if device_type == "cuda" else nullcontext()
|
|
synchronize = torch.cuda.synchronize if device_type == "cuda" else lambda: None
|
|
get_max_memory = torch.cuda.max_memory_allocated if device_type == "cuda" else lambda: 0
|
|
|
|
# wandb logging init
|
|
use_dummy_wandb = args.run == "dummy" or not master_process
|
|
wandb_run = DummyWandb() if use_dummy_wandb else wandb.init(project="nanochat-sft", name=args.run, config=user_config)
|
|
|
|
# Load the model and tokenizer
|
|
model, tokenizer, meta = load_model("base", device, phase="train", model_tag=args.model_tag, step=args.model_step)
|
|
pretrain_batch_size = meta.get("device_batch_size", None)
|
|
if pretrain_batch_size is not None and args.device_batch_size > pretrain_batch_size:
|
|
print0(f"FOOTGUN WARNING: base model training used device_batch_size {pretrain_batch_size}, did you pass in a good --device-batch-size to this script?")
|
|
orig_model = model
|
|
model = torch.compile(model, dynamic=False)
|
|
depth = model.config.n_layer
|
|
num_flops_per_token = model.estimate_flops()
|
|
tokens_per_fwdbwd = args.device_batch_size * args.max_seq_len # tokens per iteration for a single rank
|
|
world_tokens_per_fwdbwd = tokens_per_fwdbwd * ddp_world_size # total tokens per iteration for all ranks
|
|
assert args.total_batch_size % world_tokens_per_fwdbwd == 0
|
|
grad_accum_steps = args.total_batch_size // world_tokens_per_fwdbwd
|
|
print0(f"Tokens / micro-batch / rank: {args.device_batch_size} x {args.max_seq_len} = {tokens_per_fwdbwd:,}")
|
|
print0(f"Tokens / micro-batch: {world_tokens_per_fwdbwd:,}")
|
|
print0(f"Total batch size {args.total_batch_size:,} => gradient accumulation steps: {grad_accum_steps}")
|
|
token_bytes = get_token_bytes(device=device)
|
|
|
|
# Initialize the Optimizer (combined MuonAdamW: Muon for matrix params, AdamW for rest)
|
|
optimizer = model.setup_optimizer(unembedding_lr=args.unembedding_lr, embedding_lr=args.embedding_lr, matrix_lr=args.matrix_lr, weight_decay=args.weight_decay)
|
|
# Override the initial learning rate as a fraction of the base learning rate
|
|
for group in optimizer.param_groups:
|
|
group["lr"] = group["lr"] * args.init_lr_frac
|
|
group["initial_lr"] = group["lr"]
|
|
|
|
# SFT data mixture and DataLoader
|
|
base_dir = get_base_dir()
|
|
identity_conversations_filepath = os.path.join(base_dir, "identity_conversations.jsonl")
|
|
train_dataset = TaskMixture([
|
|
SmolTalk(split="train"), # 460K rows of general conversations
|
|
MMLU(subset="auxiliary_train", split="train"), # 100K rows of multiple choice problems drawn from ARC, MC_TEST, OBQA, RACE
|
|
GSM8K(subset="main", split="train"), # 8K rows teaching simple math and (calculator) tool use
|
|
GSM8K(subset="main", split="train"), # 2 epochs of GSM8K
|
|
CustomJSON(filepath=identity_conversations_filepath), # 1000 rows of synthetic identity conversations
|
|
CustomJSON(filepath=identity_conversations_filepath), # let's do 2 epochs of these
|
|
SimpleSpelling(size=200000, split="train"), # 200K rows of Simple Spelling (e.g. spell the word 'apple')
|
|
SpellingBee(size=80000, split="train"), # 80K rows of Spelling Bee (e.g. how many 'r' are in 'strawberry'?)
|
|
]) # total: 460K + 100K + 16K + 200K + 80K = 856K rows
|
|
val_dataset = TaskMixture([
|
|
SmolTalk(split="test"), # 24K rows in test set
|
|
MMLU(subset="all", split="test", stop=5200), # 14K rows in test set, use only 5.2K to match the train ratios
|
|
GSM8K(subset="main", split="test", stop=420), # 1.32K rows in test set, use only 420 to match the train ratios
|
|
]) # total: 24K + 14K + 1.32K ~= 39K rows
|
|
# DataLoader is defined here, it emits inputs, targets : 2D tensors of shape (device_batch_size, max_seq_len)
|
|
# A big problem is that we don't know the final num_iterations in advance. So we create
|
|
# these two global variables and update them from within the data generator.
|
|
last_step = False # we will toggle this to True when we reach the end of the training dataset
|
|
approx_progress = 0.0 # will go from 0 to 1 over the course of the epoch
|
|
current_epoch = 1 # track epoch for logging
|
|
def sft_data_generator_bos_bestfit(split, buffer_size=100):
|
|
"""
|
|
BOS-aligned dataloader for SFT with bestfit-pad packing.
|
|
|
|
Each row in the batch starts with BOS (beginning of a conversation).
|
|
Conversations are packed using best-fit algorithm. When no conversation fits,
|
|
the row is padded. If the row is still empty, we fall back to truncating one
|
|
conversation to avoid fully padded rows.
|
|
Padding positions have targets masked with -1 (ignore_index for cross-entropy).
|
|
"""
|
|
global last_step, approx_progress, current_epoch
|
|
assert split in {"train", "val"}, "split must be 'train' or 'val'"
|
|
dataset = train_dataset if split == "train" else val_dataset
|
|
dataset_size = len(dataset)
|
|
assert dataset_size > 0
|
|
row_capacity = args.max_seq_len + 1 # +1 for target at last position
|
|
bos_token = tokenizer.get_bos_token_id()
|
|
|
|
# Conversation buffer: list of token lists
|
|
conv_buffer = []
|
|
cursor = ddp_rank # Each rank processes different conversations (for fetching)
|
|
consumed = ddp_rank # Track actual consumption separately from buffering
|
|
epoch = 1
|
|
it = 0 # iteration counter
|
|
|
|
def refill_buffer():
|
|
nonlocal cursor, epoch
|
|
while len(conv_buffer) < buffer_size:
|
|
conversation = dataset[cursor]
|
|
ids, _ = tokenizer.render_conversation(conversation)
|
|
conv_buffer.append(ids)
|
|
cursor += ddp_world_size
|
|
if cursor >= dataset_size:
|
|
cursor = cursor % dataset_size
|
|
epoch += 1
|
|
# Note: last_step is now triggered based on consumption, not fetching
|
|
|
|
while True:
|
|
rows = []
|
|
row_lengths = [] # Track actual content length (excluding padding) for each row
|
|
for _ in range(args.device_batch_size):
|
|
row = []
|
|
padded = False
|
|
while len(row) < row_capacity:
|
|
# Ensure buffer has conversations
|
|
while len(conv_buffer) < buffer_size:
|
|
refill_buffer()
|
|
|
|
remaining = row_capacity - len(row)
|
|
|
|
# Find largest conversation that fits entirely
|
|
best_idx = -1
|
|
best_len = 0
|
|
for i, conv in enumerate(conv_buffer):
|
|
conv_len = len(conv)
|
|
if conv_len <= remaining and conv_len > best_len:
|
|
best_idx = i
|
|
best_len = conv_len
|
|
|
|
if best_idx >= 0:
|
|
# Found a conversation that fits - use it entirely
|
|
conv = conv_buffer.pop(best_idx)
|
|
row.extend(conv)
|
|
consumed += ddp_world_size # Track actual consumption
|
|
else:
|
|
if len(row) == 0:
|
|
# No conversation fits even on an empty row.
|
|
# Truncate the shortest one to avoid fully padded rows.
|
|
trunc_idx = -1
|
|
trunc_len = None
|
|
for i, conv in enumerate(conv_buffer):
|
|
conv_len = len(conv)
|
|
if conv_len > remaining and (trunc_len is None or conv_len < trunc_len):
|
|
trunc_idx = i
|
|
trunc_len = conv_len
|
|
conv = conv_buffer.pop(trunc_idx)
|
|
row.extend(conv[:row_capacity])
|
|
consumed += ddp_world_size
|
|
else:
|
|
# No conversation fits - pad the remainder
|
|
content_len = len(row)
|
|
row.extend([bos_token] * remaining) # Pad with BOS tokens
|
|
padded = True
|
|
break # Row is now full (with padding or truncation)
|
|
|
|
# Track content length: full row if no padding, otherwise the length before padding
|
|
if padded:
|
|
row_lengths.append(content_len)
|
|
else:
|
|
row_lengths.append(row_capacity)
|
|
rows.append(row[:row_capacity])
|
|
|
|
# Stopping condition to respect num_iterations, if given
|
|
it += 1
|
|
if 0 < args.num_iterations <= it and split == "train":
|
|
last_step = True
|
|
|
|
# Update progress tracking (based on consumed, not cursor, to account for buffering)
|
|
if split == "train":
|
|
current_epoch = epoch
|
|
if args.num_iterations > 0:
|
|
approx_progress = it / args.num_iterations
|
|
else:
|
|
approx_progress = consumed / dataset_size
|
|
# Trigger last_step when we've consumed enough (instead of when cursor wraps)
|
|
if consumed >= dataset_size:
|
|
last_step = True
|
|
|
|
# Build tensors
|
|
use_cuda = device_type == "cuda"
|
|
batch_tensor = torch.tensor(rows, dtype=torch.long, pin_memory=use_cuda)
|
|
inputs = batch_tensor[:, :-1].to(device=device, dtype=torch.int32, non_blocking=use_cuda)
|
|
targets = batch_tensor[:, 1:].to(device=device, dtype=torch.int64, non_blocking=use_cuda)
|
|
|
|
# Mask out padding positions in targets (set to -1 = ignore_index)
|
|
# For each row, positions >= (content_length - 1) in targets should be masked
|
|
for i, content_len in enumerate(row_lengths):
|
|
if content_len < row_capacity:
|
|
targets[i, content_len-1:] = -1
|
|
|
|
yield inputs, targets
|
|
|
|
train_loader = sft_data_generator_bos_bestfit("train")
|
|
build_val_loader = lambda: sft_data_generator_bos_bestfit("val")
|
|
progress = 0 # will go from 0 to 1 over the course of the epoch
|
|
|
|
# Learning rate scheduler
|
|
def get_lr_multiplier(progress):
|
|
# first 80% of training: no decay, then linearly ramp down to 0.
|
|
return 1 if progress < 0.8 else 1 - (progress - 0.8) / 0.2
|
|
|
|
# Momentum scheduler for Muon optimizer
|
|
def get_muon_momentum(it):
|
|
frac = min(it / 300, 1)
|
|
momentum = (1 - frac) * 0.85 + frac * 0.95
|
|
return momentum
|
|
|
|
# -----------------------------------------------------------------------------
|
|
# Training loop
|
|
x, y = next(train_loader) # prefetch the very first batch of data
|
|
min_val_bpb = float("inf")
|
|
smooth_train_loss = 0 # EMA of training loss
|
|
ema_beta = 0.9 # EMA decay factor
|
|
total_training_time = 0 # total wall-clock time of training
|
|
step = 0
|
|
while True:
|
|
flops_so_far = num_flops_per_token * args.total_batch_size * step
|
|
|
|
# Synchronize last_step across all ranks to avoid hangs in the distributed setting
|
|
if ddp:
|
|
last_step_tensor = torch.tensor(last_step, dtype=torch.int32, device=device)
|
|
dist.all_reduce(last_step_tensor, op=dist.ReduceOp.MAX)
|
|
last_step = bool(last_step_tensor.item())
|
|
|
|
# once in a while: evaluate the val bpb (all ranks participate)
|
|
if last_step or (args.eval_every > 0 and step % args.eval_every == 0):
|
|
model.eval()
|
|
val_loader = build_val_loader()
|
|
eval_steps = args.eval_tokens // (args.device_batch_size * args.max_seq_len * ddp_world_size)
|
|
with autocast_ctx:
|
|
val_bpb = evaluate_bpb(model, val_loader, eval_steps, token_bytes)
|
|
print0(f"Step {step:05d} | Validation bpb: {val_bpb:.4f}")
|
|
if val_bpb < min_val_bpb:
|
|
min_val_bpb = val_bpb
|
|
wandb_run.log({
|
|
"step": step,
|
|
"total_training_flops": flops_so_far,
|
|
"total_training_time": total_training_time,
|
|
"val/bpb": val_bpb,
|
|
})
|
|
model.train()
|
|
|
|
# save checkpoint at the end of the run (only on master process)
|
|
if master_process and last_step and not args.dry_run:
|
|
output_dirname = args.model_tag if args.model_tag else f"d{depth}" # e.g. d12
|
|
checkpoint_dir = os.path.join(base_dir, "sft_checkpoints", output_dirname)
|
|
save_checkpoint(
|
|
checkpoint_dir,
|
|
step,
|
|
orig_model.state_dict(),
|
|
optimizer.state_dict(),
|
|
{
|
|
"step": step,
|
|
"val_bpb": val_bpb, # loss at last step
|
|
"model_config": {
|
|
"sequence_len": args.max_seq_len,
|
|
"vocab_size": tokenizer.get_vocab_size(),
|
|
"n_layer": depth,
|
|
"n_head": model.config.n_head,
|
|
"n_kv_head": model.config.n_kv_head,
|
|
"n_embd": model.config.n_embd,
|
|
},
|
|
"user_config": user_config, # inputs to the training script
|
|
}
|
|
)
|
|
|
|
if last_step:
|
|
break
|
|
|
|
# -------------------------------------------------------------------------
|
|
# single training step
|
|
# evaluate the gradient
|
|
synchronize()
|
|
t0 = time.time()
|
|
for micro_step in range(grad_accum_steps):
|
|
with autocast_ctx:
|
|
loss = model(x, y)
|
|
train_loss = loss.detach() # for logging
|
|
loss = loss / grad_accum_steps # each .backward() is a grad sum => normalize loss here
|
|
loss.backward()
|
|
x, y = next(train_loader) # prefetch the next batch while the GPU is busy with forward/backward
|
|
progress = max(progress, approx_progress) # only increase progress monotonically
|
|
# step the optimizer
|
|
lrm = get_lr_multiplier(progress)
|
|
muon_momentum = get_muon_momentum(step)
|
|
for group in optimizer.param_groups:
|
|
group["lr"] = group["initial_lr"] * lrm
|
|
if group['kind'] == 'muon':
|
|
group["momentum"] = muon_momentum
|
|
optimizer.step()
|
|
model.zero_grad(set_to_none=True)
|
|
synchronize()
|
|
t1 = time.time()
|
|
dt = t1 - t0
|
|
# -------------------------------------------------------------------------
|
|
|
|
# State
|
|
step += 1
|
|
|
|
# logging
|
|
smooth_train_loss = ema_beta * smooth_train_loss + (1 - ema_beta) * train_loss.item() # EMA the training loss
|
|
debiased_smooth_loss = smooth_train_loss / (1 - ema_beta**(step + 1)) # debias the EMA
|
|
pct_done = 100 * progress
|
|
tok_per_sec = int(args.total_batch_size / dt)
|
|
flops_per_sec = num_flops_per_token * args.total_batch_size / dt
|
|
promised_flops_per_sec_h100 = 989e12 * ddp_world_size # bfloat16 H100 SXM and without 2:4 sparsity
|
|
mfu = 100 * flops_per_sec / promised_flops_per_sec_h100 # in %
|
|
if step > 10:
|
|
total_training_time += dt # only count the time after the first 10 steps
|
|
print0(f"step {step:05d} ({pct_done:.2f}%) | loss: {debiased_smooth_loss:.6f} | lrm: {lrm:.2f} | dt: {dt * 1000:.2f}ms | tok/sec: {tok_per_sec:,} | mfu: {mfu:.2f} | epoch: {current_epoch} | total time: {total_training_time/60:.2f}m")
|
|
if step % 10 == 0:
|
|
wandb_run.log({
|
|
"step": step,
|
|
"total_training_flops": flops_so_far,
|
|
"total_training_time": total_training_time,
|
|
"train/loss": debiased_smooth_loss,
|
|
"train/lrm": lrm,
|
|
"train/dt": dt,
|
|
"train/tok_per_sec": tok_per_sec,
|
|
"train/mfu": mfu,
|
|
"train/epoch": current_epoch,
|
|
})
|
|
|
|
# print a few more stats
|
|
print0(f"Peak memory usage: {get_max_memory() / 1024 / 1024:.2f}MiB")
|
|
print0(f"Total training time: {total_training_time/60:.2f}m")
|
|
print0(f"Minimum validation bpb: {min_val_bpb:.4f}")
|
|
|
|
# Log to report
|
|
if not args.dry_run:
|
|
from nanochat.report import get_report
|
|
get_report().log(section="SFT", data=[
|
|
user_config, # CLI args
|
|
{ # stats about the training setup
|
|
"Number of iterations": step,
|
|
"DDP world size": ddp_world_size,
|
|
},
|
|
{ # stats about training outcomes
|
|
"Minimum validation bpb": min_val_bpb,
|
|
}
|
|
])
|
|
|
|
# cleanup
|
|
wandb_run.finish() # wandb run finish
|
|
compute_cleanup()
|