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import torch
import torch.nn as nn
import torch.nn.functional as F
try:
from rotary_embedding_torch import RotaryEmbedding
except:
print(
"If you want use mossformer, lease install rotary_embedding_torch by: \n pip install -U rotary_embedding_torch"
)
from funasr_detach.models.transformer.layer_norm import (
GlobalLayerNorm,
CumulativeLayerNorm,
ScaleNorm,
)
from funasr_detach.models.transformer.embedding import ScaledSinuEmbedding
from funasr_detach.models.transformer.mossformer import FLASH_ShareA_FFConvM
def select_norm(norm, dim, shape):
"""Just a wrapper to select the normalization type."""
if norm == "gln":
return GlobalLayerNorm(dim, shape, elementwise_affine=True)
if norm == "cln":
return CumulativeLayerNorm(dim, elementwise_affine=True)
if norm == "ln":
return nn.GroupNorm(1, dim, eps=1e-8)
else:
return nn.BatchNorm1d(dim)
class MossformerBlock(nn.Module):
def __init__(
self,
*,
dim,
depth,
group_size=256,
query_key_dim=128,
expansion_factor=4.0,
causal=False,
attn_dropout=0.1,
norm_type="scalenorm",
shift_tokens=True
):
super().__init__()
assert norm_type in (
"scalenorm",
"layernorm",
), "norm_type must be one of scalenorm or layernorm"
if norm_type == "scalenorm":
norm_klass = ScaleNorm
elif norm_type == "layernorm":
norm_klass = nn.LayerNorm
self.group_size = group_size
rotary_pos_emb = RotaryEmbedding(dim=min(32, query_key_dim))
# max rotary embedding dimensions of 32, partial Rotary embeddings, from Wang et al - GPT-J
self.layers = nn.ModuleList(
[
FLASH_ShareA_FFConvM(
dim=dim,
group_size=group_size,
query_key_dim=query_key_dim,
expansion_factor=expansion_factor,
causal=causal,
dropout=attn_dropout,
rotary_pos_emb=rotary_pos_emb,
norm_klass=norm_klass,
shift_tokens=shift_tokens,
)
for _ in range(depth)
]
)
def forward(self, x, *, mask=None):
ii = 0
for flash in self.layers:
x = flash(x, mask=mask)
ii = ii + 1
return x
class MossFormer_MaskNet(nn.Module):
"""The MossFormer module for computing output masks.
Arguments
---------
in_channels : int
Number of channels at the output of the encoder.
out_channels : int
Number of channels that would be inputted to the intra and inter blocks.
num_blocks : int
Number of layers of Dual Computation Block.
norm : str
Normalization type.
num_spks : int
Number of sources (speakers).
skip_around_intra : bool
Skip connection around intra.
use_global_pos_enc : bool
Global positional encodings.
max_length : int
Maximum sequence length.
Example
---------
>>> mossformer_block = MossFormerM(1, 64, 8)
>>> mossformer_masknet = MossFormer_MaskNet(64, 64, intra_block, num_spks=2)
>>> x = torch.randn(10, 64, 2000)
>>> x = mossformer_masknet(x)
>>> x.shape
torch.Size([2, 10, 64, 2000])
"""
def __init__(
self,
in_channels,
out_channels,
num_blocks=24,
norm="ln",
num_spks=2,
skip_around_intra=True,
use_global_pos_enc=True,
max_length=20000,
):
super(MossFormer_MaskNet, self).__init__()
self.num_spks = num_spks
self.num_blocks = num_blocks
self.norm = select_norm(norm, in_channels, 3)
self.conv1d_encoder = nn.Conv1d(in_channels, out_channels, 1, bias=False)
self.use_global_pos_enc = use_global_pos_enc
if self.use_global_pos_enc:
self.pos_enc = ScaledSinuEmbedding(out_channels)
self.mdl = Computation_Block(
num_blocks,
out_channels,
norm,
skip_around_intra=skip_around_intra,
)
self.conv1d_out = nn.Conv1d(
out_channels, out_channels * num_spks, kernel_size=1
)
self.conv1_decoder = nn.Conv1d(out_channels, in_channels, 1, bias=False)
self.prelu = nn.PReLU()
self.activation = nn.ReLU()
# gated output layer
self.output = nn.Sequential(nn.Conv1d(out_channels, out_channels, 1), nn.Tanh())
self.output_gate = nn.Sequential(
nn.Conv1d(out_channels, out_channels, 1), nn.Sigmoid()
)
def forward(self, x):
"""Returns the output tensor.
Arguments
---------
x : torch.Tensor
Input tensor of dimension [B, N, S].
Returns
-------
out : torch.Tensor
Output tensor of dimension [spks, B, N, S]
where, spks = Number of speakers
B = Batchsize,
N = number of filters
S = the number of time frames
"""
# before each line we indicate the shape after executing the line
# [B, N, L]
x = self.norm(x)
# [B, N, L]
x = self.conv1d_encoder(x)
if self.use_global_pos_enc:
# x = self.pos_enc(x.transpose(1, -1)).transpose(1, -1) + x * (
# x.size(1) ** 0.5)
base = x
x = x.transpose(1, -1)
emb = self.pos_enc(x)
emb = emb.transpose(0, -1)
# print('base: {}, emb: {}'.format(base.shape, emb.shape))
x = base + emb
# [B, N, S]
# for i in range(self.num_modules):
# x = self.dual_mdl[i](x)
x = self.mdl(x)
x = self.prelu(x)
# [B, N*spks, S]
x = self.conv1d_out(x)
B, _, S = x.shape
# [B*spks, N, S]
x = x.view(B * self.num_spks, -1, S)
# [B*spks, N, S]
x = self.output(x) * self.output_gate(x)
# [B*spks, N, S]
x = self.conv1_decoder(x)
# [B, spks, N, S]
_, N, L = x.shape
x = x.view(B, self.num_spks, N, L)
x = self.activation(x)
# [spks, B, N, S]
x = x.transpose(0, 1)
return x
class MossFormerEncoder(nn.Module):
"""Convolutional Encoder Layer.
Arguments
---------
kernel_size : int
Length of filters.
in_channels : int
Number of input channels.
out_channels : int
Number of output channels.
Example
-------
>>> x = torch.randn(2, 1000)
>>> encoder = Encoder(kernel_size=4, out_channels=64)
>>> h = encoder(x)
>>> h.shape
torch.Size([2, 64, 499])
"""
def __init__(self, kernel_size=2, out_channels=64, in_channels=1):
super(MossFormerEncoder, self).__init__()
self.conv1d = nn.Conv1d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=kernel_size // 2,
groups=1,
bias=False,
)
self.in_channels = in_channels
def forward(self, x):
"""Return the encoded output.
Arguments
---------
x : torch.Tensor
Input tensor with dimensionality [B, L].
Return
------
x : torch.Tensor
Encoded tensor with dimensionality [B, N, T_out].
where B = Batchsize
L = Number of timepoints
N = Number of filters
T_out = Number of timepoints at the output of the encoder
"""
# B x L -> B x 1 x L
if self.in_channels == 1:
x = torch.unsqueeze(x, dim=1)
# B x 1 x L -> B x N x T_out
x = self.conv1d(x)
x = F.relu(x)
return x
class MossFormerM(nn.Module):
"""This class implements the transformer encoder.
Arguments
---------
num_blocks : int
Number of mossformer blocks to include.
d_model : int
The dimension of the input embedding.
attn_dropout : float
Dropout for the self-attention (Optional).
group_size: int
the chunk size
query_key_dim: int
the attention vector dimension
expansion_factor: int
the expansion factor for the linear projection in conv module
causal: bool
true for causal / false for non causal
Example
-------
>>> import torch
>>> x = torch.rand((8, 60, 512))
>>> net = TransformerEncoder_MossFormerM(num_blocks=8, d_model=512)
>>> output, _ = net(x)
>>> output.shape
torch.Size([8, 60, 512])
"""
def __init__(
self,
num_blocks,
d_model=None,
causal=False,
group_size=256,
query_key_dim=128,
expansion_factor=4.0,
attn_dropout=0.1,
):
super().__init__()
self.mossformerM = MossformerBlock(
dim=d_model,
depth=num_blocks,
group_size=group_size,
query_key_dim=query_key_dim,
expansion_factor=expansion_factor,
causal=causal,
attn_dropout=attn_dropout,
)
self.norm = nn.LayerNorm(d_model, eps=1e-6)
def forward(
self,
src,
):
"""
Arguments
----------
src : torch.Tensor
Tensor shape [B, L, N],
where, B = Batchsize,
L = time points
N = number of filters
The sequence to the encoder layer (required).
src_mask : tensor
The mask for the src sequence (optional).
src_key_padding_mask : tensor
The mask for the src keys per batch (optional).
"""
output = self.mossformerM(src)
output = self.norm(output)
return output
class Computation_Block(nn.Module):
"""Computation block for dual-path processing.
Arguments
---------
out_channels : int
Dimensionality of inter/intra model.
norm : str
Normalization type.
skip_around_intra : bool
Skip connection around the intra layer.
Example
---------
>>> comp_block = Computation_Block(64)
>>> x = torch.randn(10, 64, 100)
>>> x = comp_block(x)
>>> x.shape
torch.Size([10, 64, 100])
"""
def __init__(
self,
num_blocks,
out_channels,
norm="ln",
skip_around_intra=True,
):
super(Computation_Block, self).__init__()
##MossFormer2M: MossFormer with recurrence
# self.intra_mdl = MossFormer2M(num_blocks=num_blocks, d_model=out_channels)
##MossFormerM: the orignal MossFormer
self.intra_mdl = MossFormerM(num_blocks=num_blocks, d_model=out_channels)
self.skip_around_intra = skip_around_intra
# Norm
self.norm = norm
if norm is not None:
self.intra_norm = select_norm(norm, out_channels, 3)
def forward(self, x):
"""Returns the output tensor.
Arguments
---------
x : torch.Tensor
Input tensor of dimension [B, N, S].
Return
---------
out: torch.Tensor
Output tensor of dimension [B, N, S].
where, B = Batchsize,
N = number of filters
S = sequence time index
"""
B, N, S = x.shape
# intra RNN
# [B, S, N]
intra = x.permute(0, 2, 1).contiguous() # .view(B, S, N)
intra = self.intra_mdl(intra)
# [B, N, S]
intra = intra.permute(0, 2, 1).contiguous()
if self.norm is not None:
intra = self.intra_norm(intra)
# [B, N, S]
if self.skip_around_intra:
intra = intra + x
out = intra
return out
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