mirror of
https://github.com/comfyanonymous/ComfyUI.git
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1068 lines
42 KiB
Python
1068 lines
42 KiB
Python
# Original from: https://github.com/ace-step/ACE-Step/blob/main/models/lyrics_utils/lyric_encoder.py
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from typing import Optional, Tuple, Union
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import math
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import torch
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from torch import nn
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import comfy.model_management
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class ConvolutionModule(nn.Module):
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"""ConvolutionModule in Conformer model."""
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def __init__(self,
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channels: int,
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kernel_size: int = 15,
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activation: nn.Module = nn.ReLU(),
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norm: str = "batch_norm",
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causal: bool = False,
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bias: bool = True,
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dtype=None, device=None, operations=None):
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"""Construct an ConvolutionModule object.
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Args:
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channels (int): The number of channels of conv layers.
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kernel_size (int): Kernel size of conv layers.
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causal (int): Whether use causal convolution or not
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"""
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super().__init__()
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self.pointwise_conv1 = operations.Conv1d(
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channels,
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2 * channels,
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kernel_size=1,
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stride=1,
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padding=0,
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bias=bias,
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dtype=dtype, device=device
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)
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# self.lorder is used to distinguish if it's a causal convolution,
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# if self.lorder > 0: it's a causal convolution, the input will be
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# padded with self.lorder frames on the left in forward.
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# else: it's a symmetrical convolution
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if causal:
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padding = 0
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self.lorder = kernel_size - 1
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else:
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# kernel_size should be an odd number for none causal convolution
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assert (kernel_size - 1) % 2 == 0
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padding = (kernel_size - 1) // 2
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self.lorder = 0
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self.depthwise_conv = operations.Conv1d(
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channels,
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channels,
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kernel_size,
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stride=1,
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padding=padding,
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groups=channels,
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bias=bias,
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dtype=dtype, device=device
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)
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assert norm in ['batch_norm', 'layer_norm']
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if norm == "batch_norm":
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self.use_layer_norm = False
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self.norm = nn.BatchNorm1d(channels)
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else:
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self.use_layer_norm = True
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self.norm = operations.LayerNorm(channels, dtype=dtype, device=device)
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self.pointwise_conv2 = operations.Conv1d(
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channels,
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channels,
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kernel_size=1,
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stride=1,
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padding=0,
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bias=bias,
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dtype=dtype, device=device
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)
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self.activation = activation
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def forward(
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self,
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x: torch.Tensor,
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mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
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cache: torch.Tensor = torch.zeros((0, 0, 0)),
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Compute convolution module.
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Args:
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x (torch.Tensor): Input tensor (#batch, time, channels).
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mask_pad (torch.Tensor): used for batch padding (#batch, 1, time),
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(0, 0, 0) means fake mask.
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cache (torch.Tensor): left context cache, it is only
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used in causal convolution (#batch, channels, cache_t),
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(0, 0, 0) meas fake cache.
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Returns:
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torch.Tensor: Output tensor (#batch, time, channels).
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"""
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# exchange the temporal dimension and the feature dimension
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x = x.transpose(1, 2) # (#batch, channels, time)
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# mask batch padding
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if mask_pad.size(2) > 0: # time > 0
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x.masked_fill_(~mask_pad, 0.0)
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if self.lorder > 0:
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if cache.size(2) == 0: # cache_t == 0
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x = nn.functional.pad(x, (self.lorder, 0), 'constant', 0.0)
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else:
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assert cache.size(0) == x.size(0) # equal batch
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assert cache.size(1) == x.size(1) # equal channel
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x = torch.cat((cache, x), dim=2)
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assert (x.size(2) > self.lorder)
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new_cache = x[:, :, -self.lorder:]
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else:
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# It's better we just return None if no cache is required,
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# However, for JIT export, here we just fake one tensor instead of
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# None.
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new_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device)
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# GLU mechanism
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x = self.pointwise_conv1(x) # (batch, 2*channel, dim)
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x = nn.functional.glu(x, dim=1) # (batch, channel, dim)
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# 1D Depthwise Conv
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x = self.depthwise_conv(x)
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if self.use_layer_norm:
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x = x.transpose(1, 2)
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x = self.activation(self.norm(x))
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if self.use_layer_norm:
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x = x.transpose(1, 2)
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x = self.pointwise_conv2(x)
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# mask batch padding
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if mask_pad.size(2) > 0: # time > 0
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x.masked_fill_(~mask_pad, 0.0)
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return x.transpose(1, 2), new_cache
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class PositionwiseFeedForward(torch.nn.Module):
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"""Positionwise feed forward layer.
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FeedForward are appied on each position of the sequence.
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The output dim is same with the input dim.
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Args:
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idim (int): Input dimenstion.
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hidden_units (int): The number of hidden units.
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dropout_rate (float): Dropout rate.
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activation (torch.nn.Module): Activation function
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"""
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def __init__(
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self,
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idim: int,
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hidden_units: int,
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dropout_rate: float,
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activation: torch.nn.Module = torch.nn.ReLU(),
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dtype=None, device=None, operations=None
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):
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"""Construct a PositionwiseFeedForward object."""
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super(PositionwiseFeedForward, self).__init__()
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self.w_1 = operations.Linear(idim, hidden_units, dtype=dtype, device=device)
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self.activation = activation
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self.dropout = torch.nn.Dropout(dropout_rate)
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self.w_2 = operations.Linear(hidden_units, idim, dtype=dtype, device=device)
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def forward(self, xs: torch.Tensor) -> torch.Tensor:
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"""Forward function.
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Args:
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xs: input tensor (B, L, D)
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Returns:
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output tensor, (B, L, D)
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"""
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return self.w_2(self.dropout(self.activation(self.w_1(xs))))
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class Swish(torch.nn.Module):
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"""Construct an Swish object."""
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""Return Swish activation function."""
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return x * torch.sigmoid(x)
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class MultiHeadedAttention(nn.Module):
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"""Multi-Head Attention layer.
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Args:
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n_head (int): The number of heads.
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n_feat (int): The number of features.
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dropout_rate (float): Dropout rate.
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"""
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def __init__(self,
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n_head: int,
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n_feat: int,
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dropout_rate: float,
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key_bias: bool = True,
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dtype=None, device=None, operations=None):
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"""Construct an MultiHeadedAttention object."""
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super().__init__()
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assert n_feat % n_head == 0
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# We assume d_v always equals d_k
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self.d_k = n_feat // n_head
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self.h = n_head
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self.linear_q = operations.Linear(n_feat, n_feat, dtype=dtype, device=device)
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self.linear_k = operations.Linear(n_feat, n_feat, bias=key_bias, dtype=dtype, device=device)
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self.linear_v = operations.Linear(n_feat, n_feat, dtype=dtype, device=device)
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self.linear_out = operations.Linear(n_feat, n_feat, dtype=dtype, device=device)
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self.dropout = nn.Dropout(p=dropout_rate)
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def forward_qkv(
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self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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"""Transform query, key and value.
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Args:
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query (torch.Tensor): Query tensor (#batch, time1, size).
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key (torch.Tensor): Key tensor (#batch, time2, size).
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value (torch.Tensor): Value tensor (#batch, time2, size).
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Returns:
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torch.Tensor: Transformed query tensor, size
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(#batch, n_head, time1, d_k).
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torch.Tensor: Transformed key tensor, size
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(#batch, n_head, time2, d_k).
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torch.Tensor: Transformed value tensor, size
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(#batch, n_head, time2, d_k).
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"""
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n_batch = query.size(0)
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q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k)
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k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k)
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v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k)
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q = q.transpose(1, 2) # (batch, head, time1, d_k)
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k = k.transpose(1, 2) # (batch, head, time2, d_k)
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v = v.transpose(1, 2) # (batch, head, time2, d_k)
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return q, k, v
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def forward_attention(
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self,
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value: torch.Tensor,
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scores: torch.Tensor,
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mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool)
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) -> torch.Tensor:
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"""Compute attention context vector.
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Args:
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value (torch.Tensor): Transformed value, size
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(#batch, n_head, time2, d_k).
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scores (torch.Tensor): Attention score, size
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(#batch, n_head, time1, time2).
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mask (torch.Tensor): Mask, size (#batch, 1, time2) or
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(#batch, time1, time2), (0, 0, 0) means fake mask.
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Returns:
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torch.Tensor: Transformed value (#batch, time1, d_model)
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weighted by the attention score (#batch, time1, time2).
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"""
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n_batch = value.size(0)
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if mask is not None and mask.size(2) > 0: # time2 > 0
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mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2)
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# For last chunk, time2 might be larger than scores.size(-1)
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mask = mask[:, :, :, :scores.size(-1)] # (batch, 1, *, time2)
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scores = scores.masked_fill(mask, -float('inf'))
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attn = torch.softmax(scores, dim=-1).masked_fill(
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mask, 0.0) # (batch, head, time1, time2)
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else:
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attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2)
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p_attn = self.dropout(attn)
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x = torch.matmul(p_attn, value) # (batch, head, time1, d_k)
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x = (x.transpose(1, 2).contiguous().view(n_batch, -1,
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self.h * self.d_k)
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) # (batch, time1, d_model)
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return self.linear_out(x) # (batch, time1, d_model)
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def forward(
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self,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
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pos_emb: torch.Tensor = torch.empty(0),
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cache: torch.Tensor = torch.zeros((0, 0, 0, 0))
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Compute scaled dot product attention.
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Args:
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query (torch.Tensor): Query tensor (#batch, time1, size).
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key (torch.Tensor): Key tensor (#batch, time2, size).
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value (torch.Tensor): Value tensor (#batch, time2, size).
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mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
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(#batch, time1, time2).
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1.When applying cross attention between decoder and encoder,
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the batch padding mask for input is in (#batch, 1, T) shape.
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2.When applying self attention of encoder,
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the mask is in (#batch, T, T) shape.
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3.When applying self attention of decoder,
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the mask is in (#batch, L, L) shape.
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4.If the different position in decoder see different block
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of the encoder, such as Mocha, the passed in mask could be
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in (#batch, L, T) shape. But there is no such case in current
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CosyVoice.
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cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2),
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where `cache_t == chunk_size * num_decoding_left_chunks`
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and `head * d_k == size`
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Returns:
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torch.Tensor: Output tensor (#batch, time1, d_model).
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torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2)
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where `cache_t == chunk_size * num_decoding_left_chunks`
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and `head * d_k == size`
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"""
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q, k, v = self.forward_qkv(query, key, value)
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if cache.size(0) > 0:
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key_cache, value_cache = torch.split(cache,
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cache.size(-1) // 2,
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dim=-1)
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k = torch.cat([key_cache, k], dim=2)
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v = torch.cat([value_cache, v], dim=2)
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new_cache = torch.cat((k, v), dim=-1)
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scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k)
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return self.forward_attention(v, scores, mask), new_cache
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class RelPositionMultiHeadedAttention(MultiHeadedAttention):
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"""Multi-Head Attention layer with relative position encoding.
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Paper: https://arxiv.org/abs/1901.02860
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Args:
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n_head (int): The number of heads.
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n_feat (int): The number of features.
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dropout_rate (float): Dropout rate.
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"""
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def __init__(self,
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n_head: int,
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n_feat: int,
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dropout_rate: float,
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key_bias: bool = True,
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dtype=None, device=None, operations=None):
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"""Construct an RelPositionMultiHeadedAttention object."""
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super().__init__(n_head, n_feat, dropout_rate, key_bias, dtype=dtype, device=device, operations=operations)
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# linear transformation for positional encoding
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self.linear_pos = operations.Linear(n_feat, n_feat, bias=False, dtype=dtype, device=device)
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# these two learnable bias are used in matrix c and matrix d
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# as described in https://arxiv.org/abs/1901.02860 Section 3.3
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self.pos_bias_u = nn.Parameter(torch.empty(self.h, self.d_k, dtype=dtype, device=device))
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self.pos_bias_v = nn.Parameter(torch.empty(self.h, self.d_k, dtype=dtype, device=device))
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# torch.nn.init.xavier_uniform_(self.pos_bias_u)
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# torch.nn.init.xavier_uniform_(self.pos_bias_v)
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def rel_shift(self, x: torch.Tensor) -> torch.Tensor:
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"""Compute relative positional encoding.
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Args:
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x (torch.Tensor): Input tensor (batch, head, time1, 2*time1-1).
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time1 means the length of query vector.
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Returns:
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torch.Tensor: Output tensor.
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"""
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zero_pad = torch.zeros((x.size()[0], x.size()[1], x.size()[2], 1),
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device=x.device,
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dtype=x.dtype)
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x_padded = torch.cat([zero_pad, x], dim=-1)
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x_padded = x_padded.view(x.size()[0],
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x.size()[1],
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x.size(3) + 1, x.size(2))
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x = x_padded[:, :, 1:].view_as(x)[
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:, :, :, : x.size(-1) // 2 + 1
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] # only keep the positions from 0 to time2
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return x
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def forward(
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self,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
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pos_emb: torch.Tensor = torch.empty(0),
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cache: torch.Tensor = torch.zeros((0, 0, 0, 0))
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Compute 'Scaled Dot Product Attention' with rel. positional encoding.
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Args:
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query (torch.Tensor): Query tensor (#batch, time1, size).
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key (torch.Tensor): Key tensor (#batch, time2, size).
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value (torch.Tensor): Value tensor (#batch, time2, size).
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mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
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(#batch, time1, time2), (0, 0, 0) means fake mask.
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pos_emb (torch.Tensor): Positional embedding tensor
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(#batch, time2, size).
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cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2),
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where `cache_t == chunk_size * num_decoding_left_chunks`
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and `head * d_k == size`
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Returns:
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torch.Tensor: Output tensor (#batch, time1, d_model).
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torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2)
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where `cache_t == chunk_size * num_decoding_left_chunks`
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and `head * d_k == size`
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"""
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q, k, v = self.forward_qkv(query, key, value)
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q = q.transpose(1, 2) # (batch, time1, head, d_k)
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if cache.size(0) > 0:
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key_cache, value_cache = torch.split(cache,
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cache.size(-1) // 2,
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dim=-1)
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k = torch.cat([key_cache, k], dim=2)
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v = torch.cat([value_cache, v], dim=2)
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# NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's
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# non-trivial to calculate `next_cache_start` here.
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new_cache = torch.cat((k, v), dim=-1)
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n_batch_pos = pos_emb.size(0)
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p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k)
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p = p.transpose(1, 2) # (batch, head, time1, d_k)
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# (batch, head, time1, d_k)
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q_with_bias_u = (q + comfy.model_management.cast_to(self.pos_bias_u, dtype=q.dtype, device=q.device)).transpose(1, 2)
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# (batch, head, time1, d_k)
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q_with_bias_v = (q + comfy.model_management.cast_to(self.pos_bias_v, dtype=q.dtype, device=q.device)).transpose(1, 2)
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# compute attention score
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# first compute matrix a and matrix c
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# as described in https://arxiv.org/abs/1901.02860 Section 3.3
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# (batch, head, time1, time2)
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matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1))
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# compute matrix b and matrix d
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# (batch, head, time1, time2)
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matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1))
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# NOTE(Xiang Lyu): Keep rel_shift since espnet rel_pos_emb is used
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if matrix_ac.shape != matrix_bd.shape:
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matrix_bd = self.rel_shift(matrix_bd)
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scores = (matrix_ac + matrix_bd) / math.sqrt(
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self.d_k) # (batch, head, time1, time2)
|
||
|
||
return self.forward_attention(v, scores, mask), new_cache
|
||
|
||
|
||
|
||
def subsequent_mask(
|
||
size: int,
|
||
device: torch.device = torch.device("cpu"),
|
||
) -> torch.Tensor:
|
||
"""Create mask for subsequent steps (size, size).
|
||
|
||
This mask is used only in decoder which works in an auto-regressive mode.
|
||
This means the current step could only do attention with its left steps.
|
||
|
||
In encoder, fully attention is used when streaming is not necessary and
|
||
the sequence is not long. In this case, no attention mask is needed.
|
||
|
||
When streaming is need, chunk-based attention is used in encoder. See
|
||
subsequent_chunk_mask for the chunk-based attention mask.
|
||
|
||
Args:
|
||
size (int): size of mask
|
||
str device (str): "cpu" or "cuda" or torch.Tensor.device
|
||
dtype (torch.device): result dtype
|
||
|
||
Returns:
|
||
torch.Tensor: mask
|
||
|
||
Examples:
|
||
>>> subsequent_mask(3)
|
||
[[1, 0, 0],
|
||
[1, 1, 0],
|
||
[1, 1, 1]]
|
||
"""
|
||
arange = torch.arange(size, device=device)
|
||
mask = arange.expand(size, size)
|
||
arange = arange.unsqueeze(-1)
|
||
mask = mask <= arange
|
||
return mask
|
||
|
||
|
||
def subsequent_chunk_mask(
|
||
size: int,
|
||
chunk_size: int,
|
||
num_left_chunks: int = -1,
|
||
device: torch.device = torch.device("cpu"),
|
||
) -> torch.Tensor:
|
||
"""Create mask for subsequent steps (size, size) with chunk size,
|
||
this is for streaming encoder
|
||
|
||
Args:
|
||
size (int): size of mask
|
||
chunk_size (int): size of chunk
|
||
num_left_chunks (int): number of left chunks
|
||
<0: use full chunk
|
||
>=0: use num_left_chunks
|
||
device (torch.device): "cpu" or "cuda" or torch.Tensor.device
|
||
|
||
Returns:
|
||
torch.Tensor: mask
|
||
|
||
Examples:
|
||
>>> subsequent_chunk_mask(4, 2)
|
||
[[1, 1, 0, 0],
|
||
[1, 1, 0, 0],
|
||
[1, 1, 1, 1],
|
||
[1, 1, 1, 1]]
|
||
"""
|
||
ret = torch.zeros(size, size, device=device, dtype=torch.bool)
|
||
for i in range(size):
|
||
if num_left_chunks < 0:
|
||
start = 0
|
||
else:
|
||
start = max((i // chunk_size - num_left_chunks) * chunk_size, 0)
|
||
ending = min((i // chunk_size + 1) * chunk_size, size)
|
||
ret[i, start:ending] = True
|
||
return ret
|
||
|
||
def add_optional_chunk_mask(xs: torch.Tensor,
|
||
masks: torch.Tensor,
|
||
use_dynamic_chunk: bool,
|
||
use_dynamic_left_chunk: bool,
|
||
decoding_chunk_size: int,
|
||
static_chunk_size: int,
|
||
num_decoding_left_chunks: int,
|
||
enable_full_context: bool = True):
|
||
""" Apply optional mask for encoder.
|
||
|
||
Args:
|
||
xs (torch.Tensor): padded input, (B, L, D), L for max length
|
||
mask (torch.Tensor): mask for xs, (B, 1, L)
|
||
use_dynamic_chunk (bool): whether to use dynamic chunk or not
|
||
use_dynamic_left_chunk (bool): whether to use dynamic left chunk for
|
||
training.
|
||
decoding_chunk_size (int): decoding chunk size for dynamic chunk, it's
|
||
0: default for training, use random dynamic chunk.
|
||
<0: for decoding, use full chunk.
|
||
>0: for decoding, use fixed chunk size as set.
|
||
static_chunk_size (int): chunk size for static chunk training/decoding
|
||
if it's greater than 0, if use_dynamic_chunk is true,
|
||
this parameter will be ignored
|
||
num_decoding_left_chunks: number of left chunks, this is for decoding,
|
||
the chunk size is decoding_chunk_size.
|
||
>=0: use num_decoding_left_chunks
|
||
<0: use all left chunks
|
||
enable_full_context (bool):
|
||
True: chunk size is either [1, 25] or full context(max_len)
|
||
False: chunk size ~ U[1, 25]
|
||
|
||
Returns:
|
||
torch.Tensor: chunk mask of the input xs.
|
||
"""
|
||
# Whether to use chunk mask or not
|
||
if use_dynamic_chunk:
|
||
max_len = xs.size(1)
|
||
if decoding_chunk_size < 0:
|
||
chunk_size = max_len
|
||
num_left_chunks = -1
|
||
elif decoding_chunk_size > 0:
|
||
chunk_size = decoding_chunk_size
|
||
num_left_chunks = num_decoding_left_chunks
|
||
else:
|
||
# chunk size is either [1, 25] or full context(max_len).
|
||
# Since we use 4 times subsampling and allow up to 1s(100 frames)
|
||
# delay, the maximum frame is 100 / 4 = 25.
|
||
chunk_size = torch.randint(1, max_len, (1, )).item()
|
||
num_left_chunks = -1
|
||
if chunk_size > max_len // 2 and enable_full_context:
|
||
chunk_size = max_len
|
||
else:
|
||
chunk_size = chunk_size % 25 + 1
|
||
if use_dynamic_left_chunk:
|
||
max_left_chunks = (max_len - 1) // chunk_size
|
||
num_left_chunks = torch.randint(0, max_left_chunks,
|
||
(1, )).item()
|
||
chunk_masks = subsequent_chunk_mask(xs.size(1), chunk_size,
|
||
num_left_chunks,
|
||
xs.device) # (L, L)
|
||
chunk_masks = chunk_masks.unsqueeze(0) # (1, L, L)
|
||
chunk_masks = masks & chunk_masks # (B, L, L)
|
||
elif static_chunk_size > 0:
|
||
num_left_chunks = num_decoding_left_chunks
|
||
chunk_masks = subsequent_chunk_mask(xs.size(1), static_chunk_size,
|
||
num_left_chunks,
|
||
xs.device) # (L, L)
|
||
chunk_masks = chunk_masks.unsqueeze(0) # (1, L, L)
|
||
chunk_masks = masks & chunk_masks # (B, L, L)
|
||
else:
|
||
chunk_masks = masks
|
||
return chunk_masks
|
||
|
||
|
||
class ConformerEncoderLayer(nn.Module):
|
||
"""Encoder layer module.
|
||
Args:
|
||
size (int): Input dimension.
|
||
self_attn (torch.nn.Module): Self-attention module instance.
|
||
`MultiHeadedAttention` or `RelPositionMultiHeadedAttention`
|
||
instance can be used as the argument.
|
||
feed_forward (torch.nn.Module): Feed-forward module instance.
|
||
`PositionwiseFeedForward` instance can be used as the argument.
|
||
feed_forward_macaron (torch.nn.Module): Additional feed-forward module
|
||
instance.
|
||
`PositionwiseFeedForward` instance can be used as the argument.
|
||
conv_module (torch.nn.Module): Convolution module instance.
|
||
`ConvlutionModule` instance can be used as the argument.
|
||
dropout_rate (float): Dropout rate.
|
||
normalize_before (bool):
|
||
True: use layer_norm before each sub-block.
|
||
False: use layer_norm after each sub-block.
|
||
"""
|
||
|
||
def __init__(
|
||
self,
|
||
size: int,
|
||
self_attn: torch.nn.Module,
|
||
feed_forward: Optional[nn.Module] = None,
|
||
feed_forward_macaron: Optional[nn.Module] = None,
|
||
conv_module: Optional[nn.Module] = None,
|
||
dropout_rate: float = 0.1,
|
||
normalize_before: bool = True,
|
||
dtype=None, device=None, operations=None
|
||
):
|
||
"""Construct an EncoderLayer object."""
|
||
super().__init__()
|
||
self.self_attn = self_attn
|
||
self.feed_forward = feed_forward
|
||
self.feed_forward_macaron = feed_forward_macaron
|
||
self.conv_module = conv_module
|
||
self.norm_ff = operations.LayerNorm(size, eps=1e-5, dtype=dtype, device=device) # for the FNN module
|
||
self.norm_mha = operations.LayerNorm(size, eps=1e-5, dtype=dtype, device=device) # for the MHA module
|
||
if feed_forward_macaron is not None:
|
||
self.norm_ff_macaron = operations.LayerNorm(size, eps=1e-5, dtype=dtype, device=device)
|
||
self.ff_scale = 0.5
|
||
else:
|
||
self.ff_scale = 1.0
|
||
if self.conv_module is not None:
|
||
self.norm_conv = operations.LayerNorm(size, eps=1e-5, dtype=dtype, device=device) # for the CNN module
|
||
self.norm_final = operations.LayerNorm(
|
||
size, eps=1e-5, dtype=dtype, device=device) # for the final output of the block
|
||
self.dropout = nn.Dropout(dropout_rate)
|
||
self.size = size
|
||
self.normalize_before = normalize_before
|
||
|
||
def forward(
|
||
self,
|
||
x: torch.Tensor,
|
||
mask: torch.Tensor,
|
||
pos_emb: torch.Tensor,
|
||
mask_pad: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
|
||
att_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
|
||
cnn_cache: torch.Tensor = torch.zeros((0, 0, 0, 0)),
|
||
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
|
||
"""Compute encoded features.
|
||
|
||
Args:
|
||
x (torch.Tensor): (#batch, time, size)
|
||
mask (torch.Tensor): Mask tensor for the input (#batch, time，time),
|
||
(0, 0, 0) means fake mask.
|
||
pos_emb (torch.Tensor): positional encoding, must not be None
|
||
for ConformerEncoderLayer.
|
||
mask_pad (torch.Tensor): batch padding mask used for conv module.
|
||
(#batch, 1，time), (0, 0, 0) means fake mask.
|
||
att_cache (torch.Tensor): Cache tensor of the KEY & VALUE
|
||
(#batch=1, head, cache_t1, d_k * 2), head * d_k == size.
|
||
cnn_cache (torch.Tensor): Convolution cache in conformer layer
|
||
(#batch=1, size, cache_t2)
|
||
Returns:
|
||
torch.Tensor: Output tensor (#batch, time, size).
|
||
torch.Tensor: Mask tensor (#batch, time, time).
|
||
torch.Tensor: att_cache tensor,
|
||
(#batch=1, head, cache_t1 + time, d_k * 2).
|
||
torch.Tensor: cnn_cahce tensor (#batch, size, cache_t2).
|
||
"""
|
||
|
||
# whether to use macaron style
|
||
if self.feed_forward_macaron is not None:
|
||
residual = x
|
||
if self.normalize_before:
|
||
x = self.norm_ff_macaron(x)
|
||
x = residual + self.ff_scale * self.dropout(
|
||
self.feed_forward_macaron(x))
|
||
if not self.normalize_before:
|
||
x = self.norm_ff_macaron(x)
|
||
|
||
# multi-headed self-attention module
|
||
residual = x
|
||
if self.normalize_before:
|
||
x = self.norm_mha(x)
|
||
x_att, new_att_cache = self.self_attn(x, x, x, mask, pos_emb,
|
||
att_cache)
|
||
x = residual + self.dropout(x_att)
|
||
if not self.normalize_before:
|
||
x = self.norm_mha(x)
|
||
|
||
# convolution module
|
||
# Fake new cnn cache here, and then change it in conv_module
|
||
new_cnn_cache = torch.zeros((0, 0, 0), dtype=x.dtype, device=x.device)
|
||
if self.conv_module is not None:
|
||
residual = x
|
||
if self.normalize_before:
|
||
x = self.norm_conv(x)
|
||
x, new_cnn_cache = self.conv_module(x, mask_pad, cnn_cache)
|
||
x = residual + self.dropout(x)
|
||
|
||
if not self.normalize_before:
|
||
x = self.norm_conv(x)
|
||
|
||
# feed forward module
|
||
residual = x
|
||
if self.normalize_before:
|
||
x = self.norm_ff(x)
|
||
|
||
x = residual + self.ff_scale * self.dropout(self.feed_forward(x))
|
||
if not self.normalize_before:
|
||
x = self.norm_ff(x)
|
||
|
||
if self.conv_module is not None:
|
||
x = self.norm_final(x)
|
||
|
||
return x, mask, new_att_cache, new_cnn_cache
|
||
|
||
|
||
|
||
class EspnetRelPositionalEncoding(torch.nn.Module):
|
||
"""Relative positional encoding module (new implementation).
|
||
|
||
Details can be found in https://github.com/espnet/espnet/pull/2816.
|
||
|
||
See : Appendix B in https://arxiv.org/abs/1901.02860
|
||
|
||
Args:
|
||
d_model (int): Embedding dimension.
|
||
dropout_rate (float): Dropout rate.
|
||
max_len (int): Maximum input length.
|
||
|
||
"""
|
||
|
||
def __init__(self, d_model: int, dropout_rate: float, max_len: int = 5000):
|
||
"""Construct an PositionalEncoding object."""
|
||
super(EspnetRelPositionalEncoding, self).__init__()
|
||
self.d_model = d_model
|
||
self.xscale = math.sqrt(self.d_model)
|
||
self.dropout = torch.nn.Dropout(p=dropout_rate)
|
||
self.pe = None
|
||
self.extend_pe(torch.tensor(0.0).expand(1, max_len))
|
||
|
||
def extend_pe(self, x: torch.Tensor):
|
||
"""Reset the positional encodings."""
|
||
if self.pe is not None:
|
||
# self.pe contains both positive and negative parts
|
||
# the length of self.pe is 2 * input_len - 1
|
||
if self.pe.size(1) >= x.size(1) * 2 - 1:
|
||
if self.pe.dtype != x.dtype or self.pe.device != x.device:
|
||
self.pe = self.pe.to(dtype=x.dtype, device=x.device)
|
||
return
|
||
# Suppose `i` means to the position of query vecotr and `j` means the
|
||
# position of key vector. We use position relative positions when keys
|
||
# are to the left (i>j) and negative relative positions otherwise (i<j).
|
||
pe_positive = torch.zeros(x.size(1), self.d_model)
|
||
pe_negative = torch.zeros(x.size(1), self.d_model)
|
||
position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
|
||
div_term = torch.exp(
|
||
torch.arange(0, self.d_model, 2, dtype=torch.float32)
|
||
* -(math.log(10000.0) / self.d_model)
|
||
)
|
||
pe_positive[:, 0::2] = torch.sin(position * div_term)
|
||
pe_positive[:, 1::2] = torch.cos(position * div_term)
|
||
pe_negative[:, 0::2] = torch.sin(-1 * position * div_term)
|
||
pe_negative[:, 1::2] = torch.cos(-1 * position * div_term)
|
||
|
||
# Reserve the order of positive indices and concat both positive and
|
||
# negative indices. This is used to support the shifting trick
|
||
# as in https://arxiv.org/abs/1901.02860
|
||
pe_positive = torch.flip(pe_positive, [0]).unsqueeze(0)
|
||
pe_negative = pe_negative[1:].unsqueeze(0)
|
||
pe = torch.cat([pe_positive, pe_negative], dim=1)
|
||
self.pe = pe.to(device=x.device, dtype=x.dtype)
|
||
|
||
def forward(self, x: torch.Tensor, offset: Union[int, torch.Tensor] = 0) \
|
||
-> Tuple[torch.Tensor, torch.Tensor]:
|
||
"""Add positional encoding.
|
||
|
||
Args:
|
||
x (torch.Tensor): Input tensor (batch, time, `*`).
|
||
|
||
Returns:
|
||
torch.Tensor: Encoded tensor (batch, time, `*`).
|
||
|
||
"""
|
||
self.extend_pe(x)
|
||
x = x * self.xscale
|
||
pos_emb = self.position_encoding(size=x.size(1), offset=offset)
|
||
return self.dropout(x), self.dropout(pos_emb)
|
||
|
||
def position_encoding(self,
|
||
offset: Union[int, torch.Tensor],
|
||
size: int) -> torch.Tensor:
|
||
""" For getting encoding in a streaming fashion
|
||
|
||
Attention!!!!!
|
||
we apply dropout only once at the whole utterance level in a none
|
||
streaming way, but will call this function several times with
|
||
increasing input size in a streaming scenario, so the dropout will
|
||
be applied several times.
|
||
|
||
Args:
|
||
offset (int or torch.tensor): start offset
|
||
size (int): required size of position encoding
|
||
|
||
Returns:
|
||
torch.Tensor: Corresponding encoding
|
||
"""
|
||
pos_emb = self.pe[
|
||
:,
|
||
self.pe.size(1) // 2 - size + 1: self.pe.size(1) // 2 + size,
|
||
]
|
||
return pos_emb
|
||
|
||
|
||
|
||
class LinearEmbed(torch.nn.Module):
|
||
"""Linear transform the input without subsampling
|
||
|
||
Args:
|
||
idim (int): Input dimension.
|
||
odim (int): Output dimension.
|
||
dropout_rate (float): Dropout rate.
|
||
|
||
"""
|
||
|
||
def __init__(self, idim: int, odim: int, dropout_rate: float,
|
||
pos_enc_class: torch.nn.Module, dtype=None, device=None, operations=None):
|
||
"""Construct an linear object."""
|
||
super().__init__()
|
||
self.out = torch.nn.Sequential(
|
||
operations.Linear(idim, odim, dtype=dtype, device=device),
|
||
operations.LayerNorm(odim, eps=1e-5, dtype=dtype, device=device),
|
||
torch.nn.Dropout(dropout_rate),
|
||
)
|
||
self.pos_enc = pos_enc_class #rel_pos_espnet
|
||
|
||
def position_encoding(self, offset: Union[int, torch.Tensor],
|
||
size: int) -> torch.Tensor:
|
||
return self.pos_enc.position_encoding(offset, size)
|
||
|
||
def forward(
|
||
self,
|
||
x: torch.Tensor,
|
||
offset: Union[int, torch.Tensor] = 0
|
||
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
|
||
"""Input x.
|
||
|
||
Args:
|
||
x (torch.Tensor): Input tensor (#batch, time, idim).
|
||
x_mask (torch.Tensor): Input mask (#batch, 1, time).
|
||
|
||
Returns:
|
||
torch.Tensor: linear input tensor (#batch, time', odim),
|
||
where time' = time .
|
||
torch.Tensor: linear input mask (#batch, 1, time'),
|
||
where time' = time .
|
||
|
||
"""
|
||
x = self.out(x)
|
||
x, pos_emb = self.pos_enc(x, offset)
|
||
return x, pos_emb
|
||
|
||
|
||
ATTENTION_CLASSES = {
|
||
"selfattn": MultiHeadedAttention,
|
||
"rel_selfattn": RelPositionMultiHeadedAttention,
|
||
}
|
||
|
||
ACTIVATION_CLASSES = {
|
||
"hardtanh": torch.nn.Hardtanh,
|
||
"tanh": torch.nn.Tanh,
|
||
"relu": torch.nn.ReLU,
|
||
"selu": torch.nn.SELU,
|
||
"swish": getattr(torch.nn, "SiLU", Swish),
|
||
"gelu": torch.nn.GELU,
|
||
}
|
||
|
||
|
||
def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor:
|
||
"""Make mask tensor containing indices of padded part.
|
||
|
||
See description of make_non_pad_mask.
|
||
|
||
Args:
|
||
lengths (torch.Tensor): Batch of lengths (B,).
|
||
Returns:
|
||
torch.Tensor: Mask tensor containing indices of padded part.
|
||
|
||
Examples:
|
||
>>> lengths = [5, 3, 2]
|
||
>>> make_pad_mask(lengths)
|
||
masks = [[0, 0, 0, 0 ,0],
|
||
[0, 0, 0, 1, 1],
|
||
[0, 0, 1, 1, 1]]
|
||
"""
|
||
batch_size = lengths.size(0)
|
||
max_len = max_len if max_len > 0 else lengths.max().item()
|
||
seq_range = torch.arange(0,
|
||
max_len,
|
||
dtype=torch.int64,
|
||
device=lengths.device)
|
||
seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len)
|
||
seq_length_expand = lengths.unsqueeze(-1)
|
||
mask = seq_range_expand >= seq_length_expand
|
||
return mask
|
||
|
||
#https://github.com/FunAudioLLM/CosyVoice/blob/main/examples/magicdata-read/cosyvoice/conf/cosyvoice.yaml
|
||
class ConformerEncoder(torch.nn.Module):
|
||
"""Conformer encoder module."""
|
||
|
||
def __init__(
|
||
self,
|
||
input_size: int,
|
||
output_size: int = 1024,
|
||
attention_heads: int = 16,
|
||
linear_units: int = 4096,
|
||
num_blocks: int = 6,
|
||
dropout_rate: float = 0.1,
|
||
positional_dropout_rate: float = 0.1,
|
||
attention_dropout_rate: float = 0.0,
|
||
input_layer: str = 'linear',
|
||
pos_enc_layer_type: str = 'rel_pos_espnet',
|
||
normalize_before: bool = True,
|
||
static_chunk_size: int = 1, # 1: causal_mask; 0: full_mask
|
||
use_dynamic_chunk: bool = False,
|
||
use_dynamic_left_chunk: bool = False,
|
||
positionwise_conv_kernel_size: int = 1,
|
||
macaron_style: bool =False,
|
||
selfattention_layer_type: str = "rel_selfattn",
|
||
activation_type: str = "swish",
|
||
use_cnn_module: bool = False,
|
||
cnn_module_kernel: int = 15,
|
||
causal: bool = False,
|
||
cnn_module_norm: str = "batch_norm",
|
||
key_bias: bool = True,
|
||
dtype=None, device=None, operations=None
|
||
):
|
||
"""Construct ConformerEncoder
|
||
|
||
Args:
|
||
input_size to use_dynamic_chunk, see in BaseEncoder
|
||
positionwise_conv_kernel_size (int): Kernel size of positionwise
|
||
conv1d layer.
|
||
macaron_style (bool): Whether to use macaron style for
|
||
positionwise layer.
|
||
selfattention_layer_type (str): Encoder attention layer type,
|
||
the parameter has no effect now, it's just for configure
|
||
compatibility. #'rel_selfattn'
|
||
activation_type (str): Encoder activation function type.
|
||
use_cnn_module (bool): Whether to use convolution module.
|
||
cnn_module_kernel (int): Kernel size of convolution module.
|
||
causal (bool): whether to use causal convolution or not.
|
||
key_bias: whether use bias in attention.linear_k, False for whisper models.
|
||
"""
|
||
super().__init__()
|
||
self.output_size = output_size
|
||
self.embed = LinearEmbed(input_size, output_size, dropout_rate,
|
||
EspnetRelPositionalEncoding(output_size, positional_dropout_rate), dtype=dtype, device=device, operations=operations)
|
||
self.normalize_before = normalize_before
|
||
self.after_norm = operations.LayerNorm(output_size, eps=1e-5, dtype=dtype, device=device)
|
||
self.use_dynamic_chunk = use_dynamic_chunk
|
||
|
||
self.static_chunk_size = static_chunk_size
|
||
self.use_dynamic_chunk = use_dynamic_chunk
|
||
self.use_dynamic_left_chunk = use_dynamic_left_chunk
|
||
activation = ACTIVATION_CLASSES[activation_type]()
|
||
|
||
# self-attention module definition
|
||
encoder_selfattn_layer_args = (
|
||
attention_heads,
|
||
output_size,
|
||
attention_dropout_rate,
|
||
key_bias,
|
||
)
|
||
# feed-forward module definition
|
||
positionwise_layer_args = (
|
||
output_size,
|
||
linear_units,
|
||
dropout_rate,
|
||
activation,
|
||
)
|
||
# convolution module definition
|
||
convolution_layer_args = (output_size, cnn_module_kernel, activation,
|
||
cnn_module_norm, causal)
|
||
|
||
self.encoders = torch.nn.ModuleList([
|
||
ConformerEncoderLayer(
|
||
output_size,
|
||
RelPositionMultiHeadedAttention(
|
||
*encoder_selfattn_layer_args, dtype=dtype, device=device, operations=operations),
|
||
PositionwiseFeedForward(*positionwise_layer_args, dtype=dtype, device=device, operations=operations),
|
||
PositionwiseFeedForward(
|
||
*positionwise_layer_args, dtype=dtype, device=device, operations=operations) if macaron_style else None,
|
||
ConvolutionModule(
|
||
*convolution_layer_args, dtype=dtype, device=device, operations=operations) if use_cnn_module else None,
|
||
dropout_rate,
|
||
normalize_before, dtype=dtype, device=device, operations=operations
|
||
) for _ in range(num_blocks)
|
||
])
|
||
|
||
def forward_layers(self, xs: torch.Tensor, chunk_masks: torch.Tensor,
|
||
pos_emb: torch.Tensor,
|
||
mask_pad: torch.Tensor) -> torch.Tensor:
|
||
for layer in self.encoders:
|
||
xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad)
|
||
return xs
|
||
|
||
def forward(
|
||
self,
|
||
xs: torch.Tensor,
|
||
pad_mask: torch.Tensor,
|
||
decoding_chunk_size: int = 0,
|
||
num_decoding_left_chunks: int = -1,
|
||
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||
"""Embed positions in tensor.
|
||
|
||
Args:
|
||
xs: padded input tensor (B, T, D)
|
||
xs_lens: input length (B)
|
||
decoding_chunk_size: decoding chunk size for dynamic chunk
|
||
0: default for training, use random dynamic chunk.
|
||
<0: for decoding, use full chunk.
|
||
>0: for decoding, use fixed chunk size as set.
|
||
num_decoding_left_chunks: number of left chunks, this is for decoding,
|
||
the chunk size is decoding_chunk_size.
|
||
>=0: use num_decoding_left_chunks
|
||
<0: use all left chunks
|
||
Returns:
|
||
encoder output tensor xs, and subsampled masks
|
||
xs: padded output tensor (B, T' ~= T/subsample_rate, D)
|
||
masks: torch.Tensor batch padding mask after subsample
|
||
(B, 1, T' ~= T/subsample_rate)
|
||
NOTE(xcsong):
|
||
We pass the `__call__` method of the modules instead of `forward` to the
|
||
checkpointing API because `__call__` attaches all the hooks of the module.
|
||
https://discuss.pytorch.org/t/any-different-between-model-input-and-model-forward-input/3690/2
|
||
"""
|
||
masks = None
|
||
if pad_mask is not None:
|
||
masks = pad_mask.to(torch.bool).unsqueeze(1) # (B, 1, T)
|
||
xs, pos_emb = self.embed(xs)
|
||
mask_pad = masks # (B, 1, T/subsample_rate)
|
||
chunk_masks = add_optional_chunk_mask(xs, masks,
|
||
self.use_dynamic_chunk,
|
||
self.use_dynamic_left_chunk,
|
||
decoding_chunk_size,
|
||
self.static_chunk_size,
|
||
num_decoding_left_chunks)
|
||
|
||
xs = self.forward_layers(xs, chunk_masks, pos_emb, mask_pad)
|
||
if self.normalize_before:
|
||
xs = self.after_norm(xs)
|
||
# Here we assume the mask is not changed in encoder layers, so just
|
||
# return the masks before encoder layers, and the masks will be used
|
||
# for cross attention with decoder later
|
||
return xs, masks
|
||
|