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import torch
from torch import nn
import math
from functools import partial
from einops import rearrange, repeat
# from local_attention import LocalAttention
import torch.nn.functional as F
#import fast_transformers.causal_product.causal_product_cuda
class PCmer(nn.Module):
"""The encoder that is used in the Transformer model."""
def __init__(self,
num_layers,
num_heads,
dim_model,
dim_keys,
dim_values,
residual_dropout,
attention_dropout):
super().__init__()
self.num_layers = num_layers
self.num_heads = num_heads
self.dim_model = dim_model
self.dim_values = dim_values
self.dim_keys = dim_keys
self.residual_dropout = residual_dropout
self.attention_dropout = attention_dropout
self._layers = nn.ModuleList([_EncoderLayer(self) for _ in range(num_layers)])
# METHODS ########################################################################################################
def forward(self, phone, mask=None):
# apply all layers to the input
for (i, layer) in enumerate(self._layers):
phone = layer(phone, mask)
# provide the final sequence
return phone
# ==================================================================================================================== #
# CLASS _ E N C O D E R L A Y E R #
# ==================================================================================================================== #
class _EncoderLayer(nn.Module):
"""One layer of the encoder.
Attributes:
attn: (:class:`mha.MultiHeadAttention`): The attention mechanism that is used to read the input sequence.
feed_forward (:class:`ffl.FeedForwardLayer`): The feed-forward layer on top of the attention mechanism.
"""
def __init__(self, parent: PCmer):
"""Creates a new instance of ``_EncoderLayer``.
Args:
parent (Encoder): The encoder that the layers is created for.
"""
super().__init__()
self.conformer = ConformerConvModule(parent.dim_model)
self.norm = nn.LayerNorm(parent.dim_model)
self.dropout = nn.Dropout(parent.residual_dropout)
# selfatt -> fastatt: performer!
self.attn = Attention(dim = parent.dim_model,
heads = parent.num_heads,dim_head=32
# causal = False
)
# METHODS ########################################################################################################
def forward(self, phone, mask=None):
# compute attention sub-layer
phone = phone + (self.attn(self.norm(phone), mask=mask))
phone = phone + (self.conformer(phone))
return phone
def calc_same_padding(kernel_size):
pad = kernel_size // 2
return (pad, pad - (kernel_size + 1) % 2)
# helper classes
class Swish(nn.Module):
def forward(self, x):
return x * x.sigmoid()
class Transpose(nn.Module):
def __init__(self, dims):
super().__init__()
assert len(dims) == 2, 'dims must be a tuple of two dimensions'
self.dims = dims
def forward(self, x):
return x.transpose(*self.dims)
class GLU(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, x):
out, gate = x.chunk(2, dim=self.dim)
return out * gate.sigmoid()
class DepthWiseConv1d(nn.Module):
def __init__(self, chan_in, chan_out, kernel_size, padding):
super().__init__()
self.padding = padding
self.conv = nn.Conv1d(chan_in, chan_out, kernel_size, groups = chan_in)
def forward(self, x):
x = F.pad(x, self.padding)
return self.conv(x)
class ConformerConvModule(nn.Module):
def __init__(
self,
dim,
causal = False,
expansion_factor = 2,
kernel_size = 31,
dropout = 0.):
super().__init__()
inner_dim = dim * expansion_factor
padding = calc_same_padding(kernel_size) if not causal else (kernel_size - 1, 0)
self.net = nn.Sequential(
nn.LayerNorm(dim),
Transpose((1, 2)),
nn.Conv1d(dim, inner_dim * 2, 1),
GLU(dim=1),
DepthWiseConv1d(inner_dim, inner_dim, kernel_size = kernel_size, padding = padding),
#nn.BatchNorm1d(inner_dim) if not causal else nn.Identity(),
Swish(),
nn.Conv1d(inner_dim, dim, 1),
Transpose((1, 2)),
nn.Dropout(dropout)
)
def forward(self, x):
return self.net(x)
class Attention(nn.Module):
def __init__(self, dim, heads=4, dim_head=64, conditiondim=None):
super().__init__()
if conditiondim is None:
conditiondim = dim
self.scale = dim_head ** -0.5
self.heads = heads
hidden_dim = dim_head * heads
self.to_q = nn.Linear(dim, hidden_dim, bias=False)
self.to_kv = nn.Linear(conditiondim, hidden_dim * 2, bias=False)
self.to_out = nn.Sequential(nn.Linear(hidden_dim, dim, ),
)
def forward(self, q, kv=None, mask=None):
# b, c, h, w = x.shape
if kv is None:
kv = q
# q, kv = map(
# lambda t: rearrange(t, "b c t -> b t c", ), (q, kv)
# )
q = self.to_q(q)
k, v = self.to_kv(kv).chunk(2, dim=2)
q, k, v = map(
lambda t: rearrange(t, "b t (h c) -> b h t c", h=self.heads), (q, k, v)
)
if mask is not None:
mask = mask.unsqueeze(1).unsqueeze(1)
with torch.backends.cuda.sdp_kernel(#enable_math=False
):
out = F.scaled_dot_product_attention(q, k, v, attn_mask=mask)
out = rearrange(out, "b h t c -> b t (h c) ", h=self.heads, )
return self.to_out(out)
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