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