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fix minor bug where we have to scale the loss to account for gradient accumulation, which sums before backprop. note that this is not a major bug because AdamW is scale invariant. however, this did affect gradient clipping

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This commit is contained in:
Andrej Karpathy 2023-04-13 04:59:11 +00:00
parent a82b33b525
commit 553f949f46
1 changed files with 5 additions and 1 deletions
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6
train.py
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@ -93,6 +93,7 @@ else:
master_process = True master_process = True
seed_offset = 0 seed_offset = 0
gradient_accumulation_steps *= 8 # simulate 8 gpus gradient_accumulation_steps *= 8 # simulate 8 gpus
print("total number of tokens per iteration:", batch_size * block_size * gradient_accumulation_steps)
if master_process: if master_process:
os.makedirs(out_dir, exist_ok=True) os.makedirs(out_dir, exist_ok=True)
@ -287,6 +288,7 @@ while True:
model.require_backward_grad_sync = (micro_step == gradient_accumulation_steps - 1) model.require_backward_grad_sync = (micro_step == gradient_accumulation_steps - 1)
with ctx: with ctx:
logits, loss = model(X, Y) logits, loss = model(X, Y)
loss = loss / gradient_accumulation_steps # scale the loss to account for gradient accumulation
# immediately async prefetch next batch while model is doing the forward pass on the GPU # immediately async prefetch next batch while model is doing the forward pass on the GPU
X, Y = get_batch('train') X, Y = get_batch('train')
# backward pass, with gradient scaling if training in fp16 # backward pass, with gradient scaling if training in fp16
@ -306,7 +308,9 @@ while True:
dt = t1 - t0 dt = t1 - t0
t0 = t1 t0 = t1
if iter_num % log_interval == 0 and master_process: if iter_num % log_interval == 0 and master_process:
lossf = loss.item() # loss as float. note: this is a CPU-GPU sync point # get loss as float. note: this is a CPU-GPU sync point
# scale up to undo the division above, approximating the true total loss (exact would have been a sum)
lossf = loss.item() * gradient_accumulation_steps
if local_iter_num >= 5: # let the training loop settle a bit if local_iter_num >= 5: # let the training loop settle a bit
mfu = raw_model.estimate_mfu(batch_size * gradient_accumulation_steps, dt) mfu = raw_model.estimate_mfu(batch_size * gradient_accumulation_steps, dt)
running_mfu = mfu if running_mfu == -1.0 else 0.9*running_mfu + 0.1*mfu running_mfu = mfu if running_mfu == -1.0 else 0.9*running_mfu + 0.1*mfu