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| import torch | ||
| import numpy as np | ||
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| def test_pytorch_mlp(): |
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example of how to test a pytorch vs mlx implementation, cc: @bdeanhardt @ethanernst11 @levinkhho
Also the config classes, which define the model's structure, are the same so you don't need to copy in any @dataclass configurations, you can import them from the pytorch files as done in the file above.
The benefit there is that the exact same config class can be used for either implementation, which keeps it transparent to the user
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Because the test case for |
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a first pass at the MLX implementation for UNET