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https://github.com/ml-explore/mlx.git
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37fc9db82c
* Added adafactor * Added Adafactor and ran pre-commit * modified operations * Added docstrings * Switched two ops to fix a bug * added underscore for internal functions and removed the plus sign in the last return statment * Removed parameter rms from the optimizer state because its not needed * Added simple MNIST test for Adafactor and temporary training log * remove test files * nits in docs * comment nit --------- Co-authored-by: Awni Hannun <awni@apple.com>
63 lines
1.8 KiB
Python
63 lines
1.8 KiB
Python
# Copyright © 2023 Apple Inc.
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import inspect
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import unittest
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import mlx.core as mx
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import mlx.optimizers as opt
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import mlx.utils
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import mlx_tests
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def get_all_optimizers():
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classes = dict()
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for name, obj in inspect.getmembers(opt):
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if inspect.isclass(obj):
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if obj.__name__ not in ["OptimizerState", "Optimizer"]:
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classes[name] = obj
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return classes
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optimizers_dict = get_all_optimizers()
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class TestOptimizers(mlx_tests.MLXTestCase):
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def test_optimizers(self):
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params = {
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"first": [mx.zeros((10,)), mx.zeros((1,))],
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"second": mx.zeros((1,)),
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}
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grads = mlx.utils.tree_map(lambda x: mx.ones_like(x), params)
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for optim_class in optimizers_dict.values():
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optim = optim_class(0.1)
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update = optim.apply_gradients(grads, params)
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mx.eval(update)
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equal_shape = mlx.utils.tree_map(
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lambda x, y: x.shape == y.shape, params, update
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)
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all_equal = all(v for _, v in mlx.utils.tree_flatten(equal_shape))
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self.assertTrue(all_equal)
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def test_adafactor(self):
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x = mx.zeros((5, 5))
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grad = mx.ones_like(x)
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optimizer = opt.Adafactor()
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for _ in range(2):
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xp = optimizer.apply_single(grad, x, optimizer.state)
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self.assertEqual(xp.dtype, x.dtype)
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self.assertEqual(xp.shape, x.shape)
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x = mx.zeros((5, 5), mx.float16)
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grad = mx.ones_like(x)
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optimizer = opt.Adafactor()
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for _ in range(2):
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xp = optimizer.apply_single(grad, x, optimizer.state)
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self.assertEqual(xp.dtype, x.dtype)
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self.assertEqual(xp.shape, x.shape)
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self.assertEqual(optimizer.state["step"], 2)
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if __name__ == "__main__":
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unittest.main()
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