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import torch
import math as m
import numpy as np
import math
import torch.nn.functional as F
import sys
sys.path.append("..")
"""
MUSIC TRANSFORMER
Multi use, can handle following conditioning methods:
none (vanilla), continuous_concat, discrete_token
CONTINUOUS CONCAT
Takes continuous conditions as a vector of length 2, embeds it and
then concatenates it with every input token
If d_condition <= 0, it become VANILLA music transformer
If d_condition <= 0 and discrete condition tokens are fed,
it becomes "DISCRETE TOKEN" music transformer
"""
def generate_mask(x, pad_token=None, batch_first=True):
batch_size = x.size(0)
seq_len = x.size(1)
subsequent_mask = torch.logical_not(torch.triu(torch.ones(seq_len, seq_len, device=x.device)).t()).unsqueeze(
-1).repeat(1, 1, batch_size)
pad_mask = x == pad_token
if batch_first:
pad_mask = pad_mask.t()
mask = torch.logical_or(subsequent_mask, pad_mask)
if batch_first:
mask = mask.permute(2, 0, 1)
return mask
class MusicTransformerMulti(torch.nn.Module):
def __init__(self, embedding_dim=None, d_inner=None, d_condition=None, vocab_size=None, num_layer=None, num_head=None,
max_seq=None, dropout=None, pad_token=None,
):
super().__init__()
self.max_seq = max_seq
self.num_layer = num_layer
self.embedding_dim = embedding_dim
self.vocab_size = vocab_size
self.pad_token = pad_token
d_condition = 0 if d_condition < 0 else d_condition
self.d_condition = d_condition
self.embedding = torch.nn.Embedding(num_embeddings=vocab_size,
embedding_dim=self.embedding_dim-self.d_condition,
padding_idx=pad_token)
if self.d_condition > 0:
self.fc_condition = torch.nn.Linear(2, self.d_condition)
self.pos_encoding = DynamicPositionEmbedding(self.embedding_dim, max_seq=max_seq)
self.enc_layers = torch.nn.ModuleList(
[EncoderLayer(embedding_dim, d_inner, dropout, h=num_head, additional=False, max_seq=max_seq)
for _ in range(num_layer)])
self.dropout = torch.nn.Dropout(dropout)
self.fc = torch.nn.Linear(self.embedding_dim, self.vocab_size)
self.init_weights()
def init_weights(self):
initrange = 0.1
self.embedding.weight.data.uniform_(-initrange, initrange)
self.fc.bias.data.zero_()
self.fc.weight.data.uniform_(-initrange, initrange)
if self.d_condition > 0:
self.fc_condition.bias.data.zero_()
self.fc_condition.weight.data.uniform_(-initrange, initrange)
def forward(self, x, condition):
# no_conditioning = not torch.equal(condition, condition)
# assert (self.d_condition > 0) != no_conditioning
# takes batch first
# x.shape = [batch_size, sequence_length]
mask = generate_mask(x, self.pad_token)
# embed input
x = self.embedding(x) # (batch_size, input_seq_len, d_model)
x *= math.sqrt(self.embedding_dim-self.d_condition)
if self.d_condition > 0:
# embed condition using fully connected layer
condition = self.fc_condition(condition)
# tile to match input
condition = condition.unsqueeze(1).expand(-1, x.size(1), -1)
x = torch.cat([x, condition], dim=-1) # concatenate
x = self.pos_encoding(x)
x = self.dropout(x)
for i in range(len(self.enc_layers)):
x = self.enc_layers[i](x, mask)
x = self.fc(x)
return x
class EncoderLayer(torch.nn.Module):
def __init__(self, d_model, d_inner, rate=0.1, h=16, additional=False, max_seq=2048):
super(EncoderLayer, self).__init__()
self.d_model = d_model
self.rga = RelativeGlobalAttention(h=h, d=d_model, max_seq=max_seq, add_emb=additional)
self.FFN_pre = torch.nn.Linear(self.d_model, d_inner)
self.FFN_suf = torch.nn.Linear(d_inner, self.d_model)
self.layernorm1 = torch.nn.LayerNorm(self.d_model, eps=1e-6)
self.layernorm2 = torch.nn.LayerNorm(self.d_model, eps=1e-6)
self.dropout1 = torch.nn.Dropout(rate)
self.dropout2 = torch.nn.Dropout(rate)
def forward(self, x, mask=None):
attn_out = self.rga([x,x,x], mask)
attn_out = self.dropout1(attn_out)
out1 = self.layernorm1(attn_out+x)
ffn_out = F.relu(self.FFN_pre(out1))
ffn_out = self.FFN_suf(ffn_out)
ffn_out = self.dropout2(ffn_out)
out2 = self.layernorm2(out1+ffn_out)
return out2
def sinusoid(max_seq, embedding_dim):
return np.array([[
[
m.sin(
pos * m.exp(-m.log(10000) * i / embedding_dim) * m.exp(
m.log(10000) / embedding_dim * (i % 2)) + 0.5 * m.pi * (i % 2)
)
for i in range(embedding_dim)
]
for pos in range(max_seq)
]])
class DynamicPositionEmbedding(torch.nn.Module):
def __init__(self, embedding_dim, max_seq=2048):
super().__init__()
self.device = torch.device("cpu")
self.dtype = torch.float32
embed_sinusoid_list = sinusoid(max_seq, embedding_dim)
self.positional_embedding = torch.from_numpy(embed_sinusoid_list).to(
self.device, dtype=self.dtype)
def forward(self, x):
if x.device != self.device or x.dtype != self.dtype:
self.positional_embedding = self.positional_embedding.to(x.device, dtype=x.dtype)
x += self.positional_embedding[:, :x.size(1), :]
return x
class RelativeGlobalAttention(torch.nn.Module):
"""
from Music Transformer ( Huang et al, 2018 )
[paper link](https://arxiv.org/pdf/1809.04281.pdf)
"""
def __init__(self, h=4, d=256, add_emb=False, max_seq=2048):
super().__init__()
self.len_k = None
self.max_seq = max_seq
self.E = None
self.h = h
self.d = d
self.dh = d // h
self.Wq = torch.nn.Linear(self.d, self.d)
self.Wk = torch.nn.Linear(self.d, self.d)
self.Wv = torch.nn.Linear(self.d, self.d)
self.fc = torch.nn.Linear(d, d)
self.additional = add_emb
self.E = torch.nn.Parameter(torch.randn([self.max_seq, int(self.dh)]))
if self.additional:
self.Radd = None
def forward(self, inputs, mask=None):
"""
:param inputs: a list of tensors. i.e) [Q, K, V]
:param mask: mask tensor
:param kwargs:
:return: final tensor ( output of attention )
"""
q = inputs[0]
q = self.Wq(q)
q = torch.reshape(q, (q.size(0), q.size(1), self.h, -1))
q = q.permute(0, 2, 1, 3) # batch, h, seq, dh
k = inputs[1]
k = self.Wk(k)
k = torch.reshape(k, (k.size(0), k.size(1), self.h, -1))
k = k.permute(0, 2, 1, 3)
v = inputs[2]
v = self.Wv(v)
v = torch.reshape(v, (v.size(0), v.size(1), self.h, -1))
v = v.permute(0, 2, 1, 3)
self.len_k = k.size(2)
self.len_q = q.size(2)
E = self._get_left_embedding(self.len_q, self.len_k).to(q.device)
QE = torch.einsum('bhld,md->bhlm', [q, E])
QE = self._qe_masking(QE)
Srel = self._skewing(QE)
Kt = k.permute(0, 1, 3, 2)
QKt = torch.matmul(q, Kt)
logits = QKt + Srel
logits = logits / math.sqrt(self.dh)
if mask is not None:
mask = mask.unsqueeze(1)
new_mask = torch.zeros_like(mask, dtype=torch.float)
new_mask.masked_fill_(mask, float("-inf"))
mask = new_mask
logits += mask
attention_weights = F.softmax(logits, -1)
attention = torch.matmul(attention_weights, v)
out = attention.permute(0, 2, 1, 3)
out = torch.reshape(out, (out.size(0), -1, self.d))
out = self.fc(out)
return out
def _get_left_embedding(self, len_q, len_k):
starting_point = max(0,self.max_seq-len_q)
e = self.E[starting_point:,:]
return e
def _skewing(self, tensor: torch.Tensor):
padded = F.pad(tensor, [1, 0, 0, 0, 0, 0, 0, 0])
reshaped = torch.reshape(padded, shape=[padded.size(0), padded.size(1), padded.size(-1), padded.size(-2)])
Srel = reshaped[:, :, 1:, :]
if self.len_k > self.len_q:
Srel = F.pad(Srel, [0, 0, 0, 0, 0, 0, 0, self.len_k-self.len_q])
elif self.len_k < self.len_q:
Srel = Srel[:, :, :, :self.len_k]
return Srel
@staticmethod
def _qe_masking(qe):
mask = sequence_mask(
torch.arange(qe.size()[-1] - 1, qe.size()[-1] - qe.size()[-2] - 1, -1).to(qe.device),
qe.size()[-1])
mask = ~mask.to(mask.device)
return mask.to(qe.dtype) * qe
def sequence_mask(length, max_length=None):
"""Tensorflow의 sequence_mask를 구현"""
if max_length is None:
max_length = length.max()
x = torch.arange(max_length, dtype=length.dtype, device=length.device)
return x.unsqueeze(0) < length.unsqueeze(1) |