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@ -37,7 +37,7 @@ from mlx.nn.layers.containers import Sequential
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from mlx.nn.layers.convolution import Conv1d, Conv2d
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from mlx.nn.layers.dropout import Dropout, Dropout2d
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from mlx.nn.layers.embedding import Embedding
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from mlx.nn.layers.linear import Identity, Linear, Bilinear
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from mlx.nn.layers.linear import Bilinear, Identity, Linear
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from mlx.nn.layers.normalization import BatchNorm, GroupNorm, LayerNorm, RMSNorm
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from mlx.nn.layers.positional_encoding import ALiBi, RoPE, SinusoidalPositionalEncoding
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from mlx.nn.layers.quantized import QuantizedLinear
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@ -50,7 +50,7 @@ class Linear(Module):
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self.reset_parameters()
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def reset_parameters(self):
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scale = math.sqrt(1. / self.input_dims)
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scale = math.sqrt(1.0 / self.input_dims)
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self.weight = mx.random.uniform(
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low=-scale,
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high=scale,
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@ -92,7 +92,9 @@ class Bilinear(Module):
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not use a bias. Default ``True``.
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"""
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def __init__(self, input1_dims: int, input2_dims: int, output_dims: int, bias: bool = True):
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def __init__(
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self, input1_dims: int, input2_dims: int, output_dims: int, bias: bool = True
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):
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super().__init__()
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self.input1_dims = input1_dims
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self.input2_dims = input2_dims
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@ -104,7 +106,7 @@ class Bilinear(Module):
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self.reset_parameters()
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def reset_parameters(self):
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scale = math.sqrt(1. / self.input1_dims)
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scale = math.sqrt(1.0 / self.input1_dims)
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self.weight = mx.random.uniform(
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low=-scale,
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high=scale,
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@ -118,8 +120,10 @@ class Bilinear(Module):
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)
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def _extra_repr(self):
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return (f"input1_dims={self.input1_dims}, input2_dims={self.input2_dims}, output_dims={self.output_dims}, "
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f"bias={'bias' in self}")
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return (
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f"input1_dims={self.input1_dims}, input2_dims={self.input2_dims}, output_dims={self.output_dims}, "
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f"bias={'bias' in self}"
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)
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def __call__(self, input1, input2):
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output = (input1 @ self.weight * input2.reshape(1, *input2.shape)).sum(-1).T
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