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Also added a speedup for gradient clipping: Pre: 3.96 Benchmark: import time
import mlx.core as mx
import mlx.optimizers as opt
grads = [mx.random.uniform(shape=(2048, 2048)) for _ in range(10)]
def fn(grads):
for _ in range(10):
grads, norm = opt.clip_grad_norm(grads, 1.0)
return grads
for _ in range(100):
mx.eval(fn(grads))
tic = time.time()
for _ in range(100):
mx.eval(fn(grads))
toc = time.time()
print(toc - tic) |
angeloskath
approved these changes
Oct 1, 2025
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| clipped_grads = tree_map(clipper, grads) | ||
| normalizer = mx.minimum(max_norm / (total_norm + 1e-6), 1.0) | ||
| clipped_grads = tree_map(lambda g: g * normalizer, grads) |
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Why do a multiplication when you can do a multiplication and a useless conditional... 🙈
faisalmemon
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to faisalmemon/mlx
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Oct 30, 2025
* fix cross entropy axis param * faster grad clipping
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Minor fix to use axis parameter correctly in
losses.cross_entropy_loss.