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Fix multigpu training for train.py script #149

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Fix multigpu training for train.py script #149

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54 changes: 41 additions & 13 deletions train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -44,8 +44,8 @@ def parse_args():

# Data args
training_group.add_argument('--dataset', default='shakespeare_char', type=str)
training_group.add_argument('--gradient_accumulation_steps', default=1, type=int)
training_group.add_argument('--batch_size', default=64, type=int)
training_group.add_argument('--gradient_accumulation_steps', default=40, type=int)
training_group.add_argument('--batch_size', default=12, type=int)
training_group.add_argument("--seed", default=1337, type=int)

# Model args
Expand Down Expand Up @@ -207,32 +207,32 @@ def parse_args():


class Trainer:
def __init__(self, args, model_group):
def __init__(self, args, model_group, training_group):
self.args = args
self.model_group = model_group
self.training_group = training_group
self.setup()

def setup(self):
# Setup DDP
self.ddp = int(os.environ.get('RANK', -1)) != -1
if self.ddp:
init_process_group(backend=self.args.backend)
print(self.args)
self.ddp_rank = int(os.environ['RANK'])
self.ddp_local_rank = int(os.environ['LOCAL_RANK'])
self.ddp_world_size = int(os.environ['WORLD_SIZE'])
self.device = f'cuda:{self.ddp_local_rank}'
print("this is my device", self.device)
torch.cuda.set_device(self.device)
self.master_process = self.ddp_rank == 0
self.master_process = (self.ddp_rank == 0)
self.seed_offset = self.ddp_rank
self.gradient_accumulation_steps //= self.ddp_world_size
self.args.gradient_accumulation_steps //= self.ddp_world_size
else:
self.device = self.args.device
self.master_process = True
self.seed_offset = 0
self.ddp_world_size = 1


self.tokens_per_iter = self.args.gradient_accumulation_steps * self.ddp_world_size * self.args.batch_size * self.args.block_size

if self.master_process:
Expand Down Expand Up @@ -492,17 +492,44 @@ def train(self):
if self.iter_num == 0 and self.args.eval_only:
break

loss = None
for micro_step in range(self.args.gradient_accumulation_steps):
if self.ddp:
self.model.require_backward_grad_sync = (micro_step == self.args.gradient_accumulation_steps - 1)

with self.ctx:
logits, loss = self.model(self.X, self.Y)
loss = loss / self.args.gradient_accumulation_steps

self.X, self.Y = self.get_batch('train')


self.scaler.scale(loss).backward()

if micro_step == self.args.gradient_accumulation_steps - 1:
if self.args.grad_clip != 0.0:
self.scaler.unscale_(self.optimizer)
torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.args.grad_clip)

self.scaler.step(self.optimizer)
self.scaler.update()
self.optimizer.zero_grad(set_to_none=True)

self.X, self.Y = self.get_batch('train')

if loss is not None: # Check if loss has a valid value
lossf = loss.item() * self.args.gradient_accumulation_steps
lossf = loss.item() * self.args.gradient_accumulation_steps
t1 = time.time()
dt = t1 - t0
t0 = t1
if self.iter_num % self.args.log_interval == 0 and self.master_process:
if local_iter_num >= 5:
mfu = self.raw_model.estimate_mfu(self.args.batch_size * self.args.gradient_accumulation_steps, dt)
running_mfu = mfu if running_mfu == -1.0 else 0.9*running_mfu + 0.1*mfu
print(f"iter {self.iter_num}: loss {lossf:.4f}, time {dt*1000:.2f} ms, mfu {running_mfu*100:.2f}%")
if math.isnan(lossf):
sys.exit("Exiting training loss is NaN")
self.log_metrics_non_validation(lossf, running_mfu, self.iter_num)
else:
print(f"Warning: loss is None at iteration {self.iter_num}")

if self.args.grad_clip != 0.0:
self.scaler.unscale_(self.optimizer)
Expand Down Expand Up @@ -553,8 +580,9 @@ def train(self):
wandb.finish()

def main():
args, model_group, _, _ = parse_args()
trainer = Trainer(args, model_group)
args, model_group, training_group, _ = parse_args()
trainer = Trainer(args, model_group, training_group)

trainer.train()

if trainer.ddp:
Expand Down
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