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https://github.com/Wan-Video/Wan2.1.git
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96 lines
3.1 KiB
Python
96 lines
3.1 KiB
Python
import torch
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from torch import nn
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from torch.nn import functional as F
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class ChannelLastConv1d(nn.Conv1d):
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x = x.permute(0, 2, 1)
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x = super().forward(x)
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x = x.permute(0, 2, 1)
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return x
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# https://github.com/Stability-AI/sd3-ref
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class MLP(nn.Module):
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def __init__(
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self,
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dim: int,
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hidden_dim: int,
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multiple_of: int = 256,
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):
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"""
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Initialize the FeedForward module.
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Args:
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dim (int): Input dimension.
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hidden_dim (int): Hidden dimension of the feedforward layer.
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multiple_of (int): Value to ensure hidden dimension is a multiple of this value.
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Attributes:
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w1 (ColumnParallelLinear): Linear transformation for the first layer.
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w2 (RowParallelLinear): Linear transformation for the second layer.
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w3 (ColumnParallelLinear): Linear transformation for the third layer.
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"""
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super().__init__()
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hidden_dim = int(2 * hidden_dim / 3)
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hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
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self.w1 = nn.Linear(dim, hidden_dim, bias=False)
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self.w2 = nn.Linear(hidden_dim, dim, bias=False)
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self.w3 = nn.Linear(dim, hidden_dim, bias=False)
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def forward(self, x):
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return self.w2(F.silu(self.w1(x)) * self.w3(x))
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class ConvMLP(nn.Module):
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def __init__(
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self,
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dim: int,
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hidden_dim: int,
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multiple_of: int = 256,
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kernel_size: int = 3,
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padding: int = 1,
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):
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"""
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Initialize the FeedForward module.
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Args:
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dim (int): Input dimension.
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hidden_dim (int): Hidden dimension of the feedforward layer.
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multiple_of (int): Value to ensure hidden dimension is a multiple of this value.
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Attributes:
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w1 (ColumnParallelLinear): Linear transformation for the first layer.
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w2 (RowParallelLinear): Linear transformation for the second layer.
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w3 (ColumnParallelLinear): Linear transformation for the third layer.
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"""
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super().__init__()
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hidden_dim = int(2 * hidden_dim / 3)
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hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
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self.w1 = ChannelLastConv1d(dim,
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hidden_dim,
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bias=False,
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kernel_size=kernel_size,
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padding=padding)
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self.w2 = ChannelLastConv1d(hidden_dim,
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dim,
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bias=False,
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kernel_size=kernel_size,
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padding=padding)
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self.w3 = ChannelLastConv1d(dim,
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hidden_dim,
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bias=False,
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kernel_size=kernel_size,
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padding=padding)
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def forward(self, x):
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return self.w2(F.silu(self.w1(x)) * self.w3(x))
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