mirror of
https://github.com/ml-explore/mlx.git
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4ba0c24a8f
* export and import functions * refactor + works for few primitives * nit * allow primitives with state * nit * nit * simplify serialize / deserialize * fix for constants * python bindings * maybe fix serialize failure case * add example * more primitives, training kind of works * same result for python and c++ * some fixes * fix export * template it up * some simplificatoin * rebase * allow kwargs and multiple functions * exporter * more primitives for exporting * deal with endianness * handle invalid stream * add docstring
53 lines
1.3 KiB
Python
53 lines
1.3 KiB
Python
# Copyright © 2024 Apple Inc.
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import mlx.core as mx
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import mlx.nn as nn
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import mlx.utils
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class MLP(nn.Module):
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"""A simple MLP."""
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def __init__(
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self, num_layers: int, input_dim: int, hidden_dim: int, output_dim: int
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):
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super().__init__()
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layer_sizes = [input_dim] + [hidden_dim] * num_layers + [output_dim]
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self.layers = [
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nn.Linear(idim, odim)
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for idim, odim in zip(layer_sizes[:-1], layer_sizes[1:])
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]
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def __call__(self, x):
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for l in self.layers[:-1]:
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x = nn.relu(l(x))
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return self.layers[-1](x)
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if __name__ == "__main__":
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batch_size = 8
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input_dim = 32
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output_dim = 10
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# Load the model
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mx.random.seed(0) # Seed for params
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model = MLP(num_layers=5, input_dim=input_dim, hidden_dim=64, output_dim=output_dim)
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mx.eval(model)
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# Note, the model parameters are saved in the export function
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def forward(x):
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return model(x)
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mx.random.seed(42) # Seed for input
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example_x = mx.random.uniform(shape=(batch_size, input_dim))
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mx.export_function("eval_mlp.mlxfn", forward, example_x)
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# Import in Python
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imported_forward = mx.import_function("eval_mlp.mlxfn")
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expected = forward(example_x)
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(out,) = imported_forward(example_x)
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assert mx.allclose(expected, out)
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print(out)
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