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import torch
import torch.nn as nn
import torch.nn.functional as Func
class RMSNorm(nn.Module):
def __init__(self, dim):
super().__init__()
self.scale = dim ** 0.5
self.gamma = nn.Parameter(torch.ones(dim))
def forward(self, x):
return Func.normalize(x, dim=-1) * self.scale * self.gamma
class MambaModule(nn.Module):
def __init__(self, d_model, d_state, d_conv, d_expand):
super().__init__()
self.norm = RMSNorm(dim=d_model)
self.mamba = Mamba(
d_model=d_model,
d_state=d_state,
d_conv=d_conv,
d_expand=d_expand
)
def forward(self, x):
x = x + self.mamba(self.norm(x))
return x
class RNNModule(nn.Module):
"""
RNNModule class implements a recurrent neural network module with LSTM cells.
Args:
- input_dim (int): Dimensionality of the input features.
- hidden_dim (int): Dimensionality of the hidden state of the LSTM.
- bidirectional (bool, optional): If True, uses bidirectional LSTM. Defaults to True.
Shapes:
- Input: (B, T, D) where
B is batch size,
T is sequence length,
D is input dimensionality.
- Output: (B, T, D) where
B is batch size,
T is sequence length,
D is input dimensionality.
"""
def __init__(self, input_dim: int, hidden_dim: int, bidirectional: bool = True):
"""
Initializes RNNModule with input dimension, hidden dimension, and bidirectional flag.
"""
super().__init__()
self.groupnorm = nn.GroupNorm(num_groups=1, num_channels=input_dim)
self.rnn = nn.LSTM(
input_dim, hidden_dim, batch_first=True, bidirectional=bidirectional
)
self.fc = nn.Linear(hidden_dim * 2 if bidirectional else hidden_dim, input_dim)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Performs forward pass through the RNNModule.
Args:
- x (torch.Tensor): Input tensor of shape (B, T, D).
Returns:
- torch.Tensor: Output tensor of shape (B, T, D).
"""
x = x.transpose(1, 2)
x = self.groupnorm(x)
x = x.transpose(1, 2)
x, (hidden, _) = self.rnn(x)
x = self.fc(x)
return x
class RFFTModule(nn.Module):
"""
RFFTModule class implements a module for performing real-valued Fast Fourier Transform (FFT)
or its inverse on input tensors.
Args:
- inverse (bool, optional): If False, performs forward FFT. If True, performs inverse FFT. Defaults to False.
Shapes:
- Input: (B, F, T, D) where
B is batch size,
F is the number of features,
T is sequence length,
D is input dimensionality.
- Output: (B, F, T // 2 + 1, D * 2) if performing forward FFT.
(B, F, T, D // 2, 2) if performing inverse FFT.
"""
def __init__(self, inverse: bool = False):
"""
Initializes RFFTModule with inverse flag.
"""
super().__init__()
self.inverse = inverse
def forward(self, x: torch.Tensor, time_dim: int) -> torch.Tensor:
"""
Performs forward or inverse FFT on the input tensor x.
Args:
- x (torch.Tensor): Input tensor of shape (B, F, T, D).
- time_dim (int): Input size of time dimension.
Returns:
- torch.Tensor: Output tensor after FFT or its inverse operation.
"""
dtype = x.dtype
B, F, T, D = x.shape
# RuntimeError: cuFFT only supports dimensions whose sizes are powers of two when computing in half precision
x = x.float()
if not self.inverse:
x = torch.fft.rfft(x, dim=2)
x = torch.view_as_real(x)
x = x.reshape(B, F, T // 2 + 1, D * 2)
else:
x = x.reshape(B, F, T, D // 2, 2)
x = torch.view_as_complex(x)
x = torch.fft.irfft(x, n=time_dim, dim=2)
x = x.to(dtype)
return x
def extra_repr(self) -> str:
"""
Returns extra representation string with module's configuration.
"""
return f"inverse={self.inverse}"
class DualPathRNN(nn.Module):
"""
DualPathRNN class implements a neural network with alternating layers of RNNModule and RFFTModule.
Args:
- n_layers (int): Number of layers in the network.
- input_dim (int): Dimensionality of the input features.
- hidden_dim (int): Dimensionality of the hidden state of the RNNModule.
Shapes:
- Input: (B, F, T, D) where
B is batch size,
F is the number of features (frequency dimension),
T is sequence length (time dimension),
D is input dimensionality (channel dimension).
- Output: (B, F, T, D) where
B is batch size,
F is the number of features (frequency dimension),
T is sequence length (time dimension),
D is input dimensionality (channel dimension).
"""
def __init__(
self,
n_layers: int,
input_dim: int,
hidden_dim: int,
use_mamba: bool = False,
d_state: int = 16,
d_conv: int = 4,
d_expand: int = 2
):
"""
Initializes DualPathRNN with the specified number of layers, input dimension, and hidden dimension.
"""
super().__init__()
if use_mamba:
from mamba_ssm.modules.mamba_simple import Mamba
net = MambaModule
dkwargs = {"d_model": input_dim, "d_state": d_state, "d_conv": d_conv, "d_expand": d_expand}
ukwargs = {"d_model": input_dim * 2, "d_state": d_state, "d_conv": d_conv, "d_expand": d_expand * 2}
else:
net = RNNModule
dkwargs = {"input_dim": input_dim, "hidden_dim": hidden_dim}
ukwargs = {"input_dim": input_dim * 2, "hidden_dim": hidden_dim * 2}
self.layers = nn.ModuleList()
for i in range(1, n_layers + 1):
kwargs = dkwargs if i % 2 == 1 else ukwargs
layer = nn.ModuleList([
net(**kwargs),
net(**kwargs),
RFFTModule(inverse=(i % 2 == 0)),
])
self.layers.append(layer)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Performs forward pass through the DualPathRNN.
Args:
- x (torch.Tensor): Input tensor of shape (B, F, T, D).
Returns:
- torch.Tensor: Output tensor of shape (B, F, T, D).
"""
time_dim = x.shape[2]
for time_layer, freq_layer, rfft_layer in self.layers:
B, F, T, D = x.shape
x = x.reshape((B * F), T, D)
x = time_layer(x)
x = x.reshape(B, F, T, D)
x = x.permute(0, 2, 1, 3)
x = x.reshape((B * T), F, D)
x = freq_layer(x)
x = x.reshape(B, T, F, D)
x = x.permute(0, 2, 1, 3)
x = rfft_layer(x, time_dim)
return x
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