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import torch
import torch.nn as nn
from comfy.ldm.modules.attention import optimized_attention_masked
class LayerNormConv(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, bias=False, dtype=None, device=None, operations=None):
super().__init__()
self.conv = operations.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, bias=bias, device=device, dtype=dtype)
self.layer_norm = operations.LayerNorm(out_channels, elementwise_affine=True, device=device, dtype=dtype)
def forward(self, x):
x = self.conv(x)
return torch.nn.functional.gelu(self.layer_norm(x.transpose(-2, -1)).transpose(-2, -1))
class ConvFeatureEncoder(nn.Module):
def __init__(self, conv_dim, dtype=None, device=None, operations=None):
super().__init__()
self.conv_layers = nn.ModuleList([
LayerNormConv(1, conv_dim, kernel_size=10, stride=5, bias=True, device=device, dtype=dtype, operations=operations),
LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=True, device=device, dtype=dtype, operations=operations),
LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=True, device=device, dtype=dtype, operations=operations),
LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=True, device=device, dtype=dtype, operations=operations),
LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=True, device=device, dtype=dtype, operations=operations),
LayerNormConv(conv_dim, conv_dim, kernel_size=2, stride=2, bias=True, device=device, dtype=dtype, operations=operations),
LayerNormConv(conv_dim, conv_dim, kernel_size=2, stride=2, bias=True, device=device, dtype=dtype, operations=operations),
])
def forward(self, x):
x = x.unsqueeze(1)
for conv in self.conv_layers:
x = conv(x)
return x.transpose(1, 2)
class FeatureProjection(nn.Module):
def __init__(self, conv_dim, embed_dim, dtype=None, device=None, operations=None):
super().__init__()
self.layer_norm = operations.LayerNorm(conv_dim, eps=1e-05, device=device, dtype=dtype)
self.projection = operations.Linear(conv_dim, embed_dim, device=device, dtype=dtype)
def forward(self, x):
x = self.layer_norm(x)
x = self.projection(x)
return x
class PositionalConvEmbedding(nn.Module):
def __init__(self, embed_dim=768, kernel_size=128, groups=16):
super().__init__()
self.conv = nn.Conv1d(
embed_dim,
embed_dim,
kernel_size=kernel_size,
padding=kernel_size // 2,
groups=groups,
)
self.conv = torch.nn.utils.parametrizations.weight_norm(self.conv, name="weight", dim=2)
self.activation = nn.GELU()
def forward(self, x):
x = x.transpose(1, 2)
x = self.conv(x)[:, :, :-1]
x = self.activation(x)
x = x.transpose(1, 2)
return x
class TransformerEncoder(nn.Module):
def __init__(
self,
embed_dim=768,
num_heads=12,
num_layers=12,
mlp_ratio=4.0,
dtype=None, device=None, operations=None
):
super().__init__()
self.pos_conv_embed = PositionalConvEmbedding(embed_dim=embed_dim)
self.layers = nn.ModuleList([
TransformerEncoderLayer(
embed_dim=embed_dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
device=device, dtype=dtype, operations=operations
)
for _ in range(num_layers)
])
self.layer_norm = operations.LayerNorm(embed_dim, eps=1e-05, device=device, dtype=dtype)
def forward(self, x, mask=None):
x = x + self.pos_conv_embed(x)
all_x = ()
for layer in self.layers:
all_x += (x,)
x = layer(x, mask)
x = self.layer_norm(x)
all_x += (x,)
return x, all_x
class Attention(nn.Module):
def __init__(self, embed_dim, num_heads, bias=True, dtype=None, device=None, operations=None):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.head_dim = embed_dim // num_heads
self.k_proj = operations.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype)
self.v_proj = operations.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype)
self.q_proj = operations.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype)
self.out_proj = operations.Linear(embed_dim, embed_dim, bias=bias, device=device, dtype=dtype)
def forward(self, x, mask=None):
assert (mask is None) # TODO?
q = self.q_proj(x)
k = self.k_proj(x)
v = self.v_proj(x)
out = optimized_attention_masked(q, k, v, self.num_heads)
return self.out_proj(out)
class FeedForward(nn.Module):
def __init__(self, embed_dim, mlp_ratio, dtype=None, device=None, operations=None):
super().__init__()
self.intermediate_dense = operations.Linear(embed_dim, int(embed_dim * mlp_ratio), device=device, dtype=dtype)
self.output_dense = operations.Linear(int(embed_dim * mlp_ratio), embed_dim, device=device, dtype=dtype)
def forward(self, x):
x = self.intermediate_dense(x)
x = torch.nn.functional.gelu(x)
x = self.output_dense(x)
return x
class TransformerEncoderLayer(nn.Module):
def __init__(
self,
embed_dim=768,
num_heads=12,
mlp_ratio=4.0,
dtype=None, device=None, operations=None
):
super().__init__()
self.attention = Attention(embed_dim, num_heads, device=device, dtype=dtype, operations=operations)
self.layer_norm = operations.LayerNorm(embed_dim, device=device, dtype=dtype)
self.feed_forward = FeedForward(embed_dim, mlp_ratio, device=device, dtype=dtype, operations=operations)
self.final_layer_norm = operations.LayerNorm(embed_dim, device=device, dtype=dtype)
def forward(self, x, mask=None):
residual = x
x = self.layer_norm(x)
x = self.attention(x, mask=mask)
x = residual + x
x = x + self.feed_forward(self.final_layer_norm(x))
return x
class Wav2Vec2Model(nn.Module):
"""Complete Wav2Vec 2.0 model."""
def __init__(
self,
embed_dim=1024,
final_dim=256,
num_heads=16,
num_layers=24,
dtype=None, device=None, operations=None
):
super().__init__()
conv_dim = 512
self.feature_extractor = ConvFeatureEncoder(conv_dim, device=device, dtype=dtype, operations=operations)
self.feature_projection = FeatureProjection(conv_dim, embed_dim, device=device, dtype=dtype, operations=operations)
self.masked_spec_embed = nn.Parameter(torch.empty(embed_dim, device=device, dtype=dtype))
self.encoder = TransformerEncoder(
embed_dim=embed_dim,
num_heads=num_heads,
num_layers=num_layers,
device=device, dtype=dtype, operations=operations
)
def forward(self, x, mask_time_indices=None, return_dict=False):
x = torch.mean(x, dim=1)
x = (x - x.mean()) / torch.sqrt(x.var() + 1e-7)
features = self.feature_extractor(x)
features = self.feature_projection(features)
batch_size, seq_len, _ = features.shape
x, all_x = self.encoder(features)
return x, all_x
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