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moving all distributed ops to cpu

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ivanfioravanti 2025-01-13 23:06:58 +00:00
parent ff1719afc3
commit 2e08e8b96c
1 changed files with 4 additions and 5 deletions
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9
llms/mlx_lm/tuner/trainer.py
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@ -159,9 +159,8 @@ def evaluate(
ntokens += toks ntokens += toks
mx.eval(all_losses, ntokens) mx.eval(all_losses, ntokens)
all_losses = mx.distributed.all_sum(all_losses) all_losses = mx.distributed.all_sum(all_losses, stream=mx.cpu)
stream = mx.cpu if mx.distributed.init().size() > 1 else None ntokens = mx.distributed.all_sum(ntokens, stream=mx.cpu)
ntokens = mx.distributed.all_sum(ntokens, stream=stream)
return (all_losses / ntokens).item() return (all_losses / ntokens).item()
@ -273,9 +272,9 @@ def train(
if it % args.steps_per_report == 0 or it == args.iters: if it % args.steps_per_report == 0 or it == args.iters:
stop = time.perf_counter() stop = time.perf_counter()
train_loss = mx.distributed.all_sum(losses).item() train_loss = mx.distributed.all_sum(losses, stream=mx.cpu).item()
train_loss /= steps * mx.distributed.init().size() train_loss /= steps * mx.distributed.init().size()
n_tokens = mx.distributed.all_sum(n_tokens).item() n_tokens = mx.distributed.all_sum(n_tokens, stream=mx.cpu).item()
learning_rate = optimizer.learning_rate.item() learning_rate = optimizer.learning_rate.item()
it_sec = args.steps_per_report / (stop - start) it_sec = args.steps_per_report / (stop - start)
tokens_sec = float(n_tokens) / (stop - start) tokens_sec = float(n_tokens) / (stop - start)