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fix(train.py): mfu estimation to respect CPU-GPU sync point #527

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fix(train.py): mfu estimation to respect CPU-GPU sync point #527

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8 changes: 4 additions & 4 deletions train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -314,15 +314,15 @@ def get_lr(it):
optimizer.zero_grad(set_to_none=True)

# timing and logging
t1 = time.time()
dt = t1 - t0
t0 = t1
if iter_num % log_interval == 0 and master_process:
# 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
t1 = time.time()
dt = t1 - t0
t0 = t1
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 * log_interval, dt)
running_mfu = mfu if running_mfu == -1.0 else 0.9*running_mfu + 0.1*mfu
print(f"iter {iter_num}: loss {lossf:.4f}, time {dt*1000:.2f}ms, mfu {running_mfu*100:.2f}%")
iter_num += 1
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