mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-08-30 02:53:41 +08:00
69 lines
2.1 KiB
Python
69 lines
2.1 KiB
Python
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from typing import Tuple, Optional, Union
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import mlx.core as mx
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import mlx.nn as nn
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from bijectors import MaskedCoupling, AffineBijector
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from distributions import Normal
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class MLP(nn.Module):
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def __init__(
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self,
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n_layers: int,
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d_in: int,
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d_hidden: int,
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d_out: int,
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):
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super().__init__()
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layer_sizes = [d_in] + [d_hidden] * n_layers + [d_out]
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self.layers = [nn.Linear(idim, odim) for idim, odim in zip(layer_sizes[:-1], layer_sizes[1:])]
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def __call__(self, x):
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for l in self.layers[:-1]:
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x = nn.gelu(l(x))
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return self.layers[-1](x)
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class RealNVP(nn.Module):
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def __init__(
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self,
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n_transforms: int,
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d_params: int,
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d_hidden: int,
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n_layers: int,
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):
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super().__init__()
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# Alternating masks
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self.mask_list = [mx.arange(d_params) % 2 == i % 2 for i in range(n_transforms)]
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self.mask_list = [mask.astype(mx.bool_) for mask in self.mask_list]
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self.freeze(keys=["mask_list"])
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# Conditioning MLP
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self.conditioner_list = [MLP(n_layers, d_params, d_hidden, 2 * d_params) for _ in range(n_transforms)]
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self.bast_dist = Normal(mx.zeros(d_params), mx.ones(d_params))
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def log_prob(self, x: mx.array):
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log_prob = mx.zeros(x.shape[0])
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for mask, conditioner in zip(self.mask_list[::-1], self.conditioner_list[::-1]):
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x, ldj = MaskedCoupling(mask, conditioner, AffineBijector).inverse_and_log_det(x)
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log_prob += ldj
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return log_prob + self.bast_dist.log_prob(x).sum(-1)
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def sample(
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self,
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sample_shape: Union[int, Tuple[int, ...]],
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key: Optional[mx.array] = None,
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n_transforms: Optional[int] = None,
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):
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x = self.bast_dist.sample(sample_shape, key=key)
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for mask, conditioner in zip(self.mask_list[:n_transforms], self.conditioner_list[:n_transforms]):
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x, _ = MaskedCoupling(mask, conditioner, AffineBijector).forward_and_log_det(x)
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return x
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def __call__(self, x: mx.array):
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return self.log_prob(x)
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