147 lines
5.0 KiB
Python
147 lines
5.0 KiB
Python
"""Causal 2D convolutions for audio VAE."""
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from typing import Tuple, Union
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import mlx.core as mx
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import mlx.nn as nn
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from .causality_axis import CausalityAxis
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def _pair(x: Union[int, Tuple[int, int]]) -> Tuple[int, int]:
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"""Convert int or tuple to tuple pair."""
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if isinstance(x, int):
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return (x, x)
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return x
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class CausalConv2d(nn.Module):
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"""
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A causal 2D convolution.
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This layer ensures that the output at time `t` only depends on inputs
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at time `t` and earlier. It achieves this by applying asymmetric padding
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to the time dimension before the convolution.
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Note: MLX Conv2d expects input shape (N, H, W, C) - channels last.
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"""
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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kernel_size: Union[int, Tuple[int, int]],
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stride: int = 1,
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dilation: Union[int, Tuple[int, int]] = 1,
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groups: int = 1,
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bias: bool = True,
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causality_axis: CausalityAxis = CausalityAxis.HEIGHT,
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) -> None:
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super().__init__()
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self.causality_axis = causality_axis
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# Ensure kernel_size and dilation are tuples
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kernel_size = _pair(kernel_size)
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dilation = _pair(dilation)
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# Calculate padding dimensions
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pad_h = (kernel_size[0] - 1) * dilation[0]
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pad_w = (kernel_size[1] - 1) * dilation[1]
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# Store padding for manual application
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# MLX pad order: [(before_axis0, after_axis0), (before_axis1, after_axis1), ...]
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# For (N, H, W, C) format: axis 1 is H (height), axis 2 is W (width)
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if self.causality_axis == CausalityAxis.NONE:
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# Non-causal: symmetric padding
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self.padding = (pad_h // 2, pad_h - pad_h // 2, pad_w // 2, pad_w - pad_w // 2)
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elif self.causality_axis in (CausalityAxis.WIDTH, CausalityAxis.WIDTH_COMPATIBILITY):
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# Causal on width: pad left (before width axis)
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self.padding = (pad_h // 2, pad_h - pad_h // 2, pad_w, 0)
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elif self.causality_axis == CausalityAxis.HEIGHT:
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# Causal on height: pad top (before height axis)
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self.padding = (pad_h, 0, pad_w // 2, pad_w - pad_w // 2)
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else:
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raise ValueError(f"Invalid causality_axis: {causality_axis}")
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# The internal convolution layer uses no padding, as we handle it manually
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self.conv = nn.Conv2d(
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in_channels,
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out_channels,
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kernel_size,
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stride=stride,
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padding=0,
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dilation=dilation,
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groups=groups,
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bias=bias,
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)
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def __call__(self, x: mx.array) -> mx.array:
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"""
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Forward pass with causal padding.
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Args:
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x: Input tensor of shape (N, H, W, C) in MLX channels-last format
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Returns:
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Output tensor after causal convolution
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"""
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# Apply causal padding before convolution
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# padding format: (pad_h_top, pad_h_bottom, pad_w_left, pad_w_right)
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pad_h_top, pad_h_bottom, pad_w_left, pad_w_right = self.padding
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if any(p > 0 for p in self.padding):
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# MLX pad expects: [(before_0, after_0), (before_1, after_1), ...]
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# For (N, H, W, C): axis 0=N, axis 1=H, axis 2=W, axis 3=C
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x = mx.pad(x, [(0, 0), (pad_h_top, pad_h_bottom), (pad_w_left, pad_w_right), (0, 0)])
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return self.conv(x)
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def make_conv2d(
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in_channels: int,
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out_channels: int,
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kernel_size: Union[int, Tuple[int, int]],
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stride: int = 1,
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padding: Union[int, Tuple[int, int], None] = None,
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dilation: int = 1,
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groups: int = 1,
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bias: bool = True,
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causality_axis: CausalityAxis | None = None,
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) -> nn.Module:
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"""
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Create a 2D convolution layer that can be either causal or non-causal.
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Args:
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in_channels: Number of input channels
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out_channels: Number of output channels
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kernel_size: Size of the convolution kernel
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stride: Convolution stride
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padding: Padding (if None, will be calculated based on causal flag)
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dilation: Dilation rate
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groups: Number of groups for grouped convolution
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bias: Whether to use bias
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causality_axis: Dimension along which to apply causality.
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Returns:
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Either a regular Conv2d or CausalConv2d layer
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"""
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if causality_axis is not None:
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# For causal convolution, padding is handled internally by CausalConv2d
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return CausalConv2d(
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in_channels, out_channels, kernel_size, stride, dilation, groups, bias, causality_axis
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)
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else:
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# For non-causal convolution, use symmetric padding if not specified
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if padding is None:
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if isinstance(kernel_size, int):
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padding = kernel_size // 2
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else:
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padding = tuple(k // 2 for k in kernel_size)
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return nn.Conv2d(
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in_channels,
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out_channels,
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kernel_size,
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stride,
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padding,
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dilation,
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groups,
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bias,
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)
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