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import torch
import torch.nn as nn
from torch.nn import functional as nnf
from torch.utils.data import Dataset, DataLoader
from enum import Enum
from transformers import GPT2LMHeadModel
from typing import Tuple, Optional, Union
def get_clapcap(name: str):
if name == "ClapCaption":
return ClapCaptionModel
else:
raise Exception('The ClapCap model {} is incorrect or not supported'.format(name))
class MappingType(Enum):
MLP = 'mlp'
Transformer = 'transformer'
class MLP(nn.Module):
def __init__(self, sizes: Tuple[int, ...], bias=True, act=nn.Tanh):
super(MLP, self).__init__()
layers = []
for i in range(len(sizes) - 1):
layers.append(nn.Linear(sizes[i], sizes[i + 1], bias=bias))
if i < len(sizes) - 2:
layers.append(act())
self.model = nn.Sequential(*layers)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.model(x)
class MlpTransformer(nn.Module):
def __init__(self, in_dim, h_dim, out_d: Optional[int] = None, act=nnf.relu, dropout=0.):
super().__init__()
out_d = out_d if out_d is not None else in_dim
self.fc1 = nn.Linear(in_dim, h_dim)
self.act = act
self.fc2 = nn.Linear(h_dim, out_d)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.dropout(x)
x = self.fc2(x)
x = self.dropout(x)
return x
class MultiHeadAttention(nn.Module):
def __init__(self, dim_self, dim_ref, num_heads, bias=True, dropout=0.):
super().__init__()
self.num_heads = num_heads
head_dim = dim_self // num_heads
self.scale = head_dim ** -0.5
self.to_queries = nn.Linear(dim_self, dim_self, bias=bias)
self.to_keys_values = nn.Linear(dim_ref, dim_self * 2, bias=bias)
self.project = nn.Linear(dim_self, dim_self)
self.dropout = nn.Dropout(dropout)
def forward(self, x, y=None, mask=None):
y = y if y is not None else x
b, n, c = x.shape
_, m, d = y.shape
# b n h dh
queries = self.to_queries(x).reshape(b, n, self.num_heads, c // self.num_heads)
# b m 2 h dh
keys_values = self.to_keys_values(y).reshape(b, m, 2, self.num_heads, c // self.num_heads)
keys, values = keys_values[:, :, 0], keys_values[:, :, 1]
attention = torch.einsum('bnhd,bmhd->bnmh', queries, keys) * self.scale
if mask is not None:
if mask.dim() == 2:
mask = mask.unsqueeze(1)
attention = attention.masked_fill(mask.unsqueeze(3), float("-inf"))
attention = attention.softmax(dim=2)
out = torch.einsum('bnmh,bmhd->bnhd', attention, values).reshape(b, n, c)
out = self.project(out)
return out, attention
class TransformerLayer(nn.Module):
def forward_with_attention(self, x, y=None, mask=None):
x_, attention = self.attn(self.norm1(x), y, mask)
x = x + x_
x = x + self.mlp(self.norm2(x))
return x, attention
def forward(self, x, y=None, mask=None):
x = x + self.attn(self.norm1(x), y, mask)[0]
x = x + self.mlp(self.norm2(x))
return x
def __init__(self, dim_self, dim_ref, num_heads, mlp_ratio=4., bias=False, dropout=0., act=nnf.relu,
norm_layer: nn.Module = nn.LayerNorm):
super().__init__()
self.norm1 = norm_layer(dim_self)
self.attn = MultiHeadAttention(dim_self, dim_ref, num_heads, bias=bias, dropout=dropout)
self.norm2 = norm_layer(dim_self)
self.mlp = MlpTransformer(dim_self, int(dim_self * mlp_ratio), act=act, dropout=dropout)
class Transformer(nn.Module):
def __init__(self, dim_self: int, num_heads: int, num_layers: int, dim_ref: Optional[int] = None,
mlp_ratio: float = 2., act=nnf.relu, norm_layer: nn.Module = nn.LayerNorm, enc_dec: bool = False):
super(Transformer, self).__init__()
dim_ref = dim_ref if dim_ref is not None else dim_self
self.enc_dec = enc_dec
if enc_dec:
num_layers = num_layers * 2
layers = []
for i in range(num_layers):
if i % 2 == 0 and enc_dec: # cross
layers.append(TransformerLayer(dim_self, dim_ref, num_heads, mlp_ratio, act=act, norm_layer=norm_layer))
elif enc_dec: # self
layers.append(TransformerLayer(dim_self, dim_self, num_heads, mlp_ratio, act=act, norm_layer=norm_layer))
else: # self or cross
layers.append(TransformerLayer(dim_self, dim_ref, num_heads, mlp_ratio, act=act, norm_layer=norm_layer))
self.layers = nn.ModuleList(layers)
def forward_with_attention(self, x, y=None, mask=None):
attentions = []
for layer in self.layers:
x, att = layer.forward_with_attention(x, y, mask)
attentions.append(att)
return x, attentions
def forward(self, x, y=None, mask=None):
for i, layer in enumerate(self.layers):
if i % 2 == 0 and self.enc_dec: # cross
x = layer(x, y)
elif self.enc_dec: # self
x = layer(x, x, mask)
else: # self or cross
x = layer(x, y, mask)
return x
class TransformerMapper(nn.Module):
def __init__(self, dim_clip: int, dim_embedding: int, prefix_length: int, clip_length: int, num_layers: int = 8):
super(TransformerMapper, self).__init__()
self.clip_length = clip_length
self.transformer = Transformer(dim_embedding, 8, num_layers)
self.linear = nn.Linear(dim_clip, clip_length * dim_embedding)
self.prefix_const = nn.Parameter(torch.randn(prefix_length, dim_embedding), requires_grad=True)
def forward(self, x):
x = self.linear(x).view(x.shape[0], self.clip_length, -1)
prefix = self.prefix_const.unsqueeze(0).expand(x.shape[0], *self.prefix_const.shape)
prefix = torch.cat((x, prefix), dim=1)
out = self.transformer(prefix)[:, self.clip_length:]
return out
class ClapCaptionModel(nn.Module):
def __init__(self, clap, text_decoder: str, prefix_length: int, clip_length: Optional[int] = None, prefix_size: int = 512,
num_layers: int = 8, normalize_prefix: bool = True, mapping_type: str = None,\
freeze_audio_encoder_weights: bool = True, freeze_gpt_weights: bool = True):
super(ClapCaptionModel, self).__init__()
self.clap = clap.audio_encoder
self.prefix_length = prefix_length
self.normalize_prefix = normalize_prefix
self.gpt = GPT2LMHeadModel.from_pretrained(text_decoder)
self.gpt_embedding_size = self.gpt.transformer.wte.weight.shape[1]
if mapping_type == 'mlp':
self.clap_project = MLP((prefix_size, (self.gpt_embedding_size * prefix_length) // 2,
self.gpt_embedding_size * prefix_length))
else:
self.clap_project = TransformerMapper(prefix_size, self.gpt_embedding_size, prefix_length,
clip_length, num_layers)
# Freeze all CLAP parameters
if freeze_audio_encoder_weights:
for p in self.clap.parameters():
p.requires_grad = False
if freeze_gpt_weights:
for p in self.gpt.parameters():
p.requires_grad = False
def get_dummy_token(self, batch_size: int, device: torch.device) -> torch.Tensor:
return torch.zeros(batch_size, self.prefix_length, dtype=torch.int64, device=device)
def forward(self, audios: torch.Tensor, tokens: torch.Tensor, mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None):
# get audio embeddings
prefix, _ = self.clap(audios)
# normalize prefix (audio embedding)
if self.normalize_prefix:
prefix = prefix / prefix.norm(2, -1).reshape(-1,1)
embedding_text = self.gpt.transformer.wte(tokens['input_ids'])
prefix_projections = self.clap_project(prefix).view(-1, self.prefix_length, self.gpt_embedding_size)
embedding_cat = torch.cat((prefix_projections, embedding_text), dim=1)
if labels is not None:
dummy_token = self.get_dummy_token(tokens['input_ids'].shape[0], tokens['input_ids'].device)
labels = torch.cat((dummy_token, tokens), dim=1)
out = self.gpt(inputs_embeds=embedding_cat, labels=labels, attention_mask=mask)
return out |