NeuralSeq/modules/commons/rel_transformer.py

import math
import torch
from torch import nn
from torch.nn import functional as F
from utils.hparams import hparams
from modules.commons.common_layers import Embedding
from utils.tts_utils import group_hidden_by_segs, expand_word2ph

import transformers

def convert_pad_shape(pad_shape):
 l = pad_shape[::-1]
 pad_shape = [item for sublist in l for item in sublist]
 return pad_shape


def shift_1d(x):
 x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
 return x


def sequence_mask(length, max_length=None):
 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)


class Encoder(nn.Module):
 def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0.,
 window_size=None, block_length=None, pre_ln=False, **kwargs):
 super().__init__()
 self.hidden_channels = hidden_channels
 self.filter_channels = filter_channels
 self.n_heads = n_heads
 self.n_layers = n_layers
 self.kernel_size = kernel_size
 self.p_dropout = p_dropout
 self.window_size = window_size
 self.block_length = block_length
 self.pre_ln = pre_ln

 self.drop = nn.Dropout(p_dropout)
 self.attn_layers = nn.ModuleList()
 self.norm_layers_1 = nn.ModuleList()
 self.ffn_layers = nn.ModuleList()
 self.norm_layers_2 = nn.ModuleList()
 for i in range(self.n_layers):
 self.attn_layers.append(
 MultiHeadAttention(hidden_channels, hidden_channels, n_heads, window_size=window_size,
 p_dropout=p_dropout, block_length=block_length))
 self.norm_layers_1.append(LayerNorm(hidden_channels))
 self.ffn_layers.append(
 FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
 self.norm_layers_2.append(LayerNorm(hidden_channels))
 if pre_ln:
 self.last_ln = LayerNorm(hidden_channels)

 def forward(self, x, x_mask):
 attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
 for i in range(self.n_layers):
 x = x * x_mask
 x_ = x
 if self.pre_ln:
 x = self.norm_layers_1[i](x)
 y = self.attn_layers[i](x, x, attn_mask)
 y = self.drop(y)
 x = x_ + y
 if not self.pre_ln:
 x = self.norm_layers_1[i](x)

 x_ = x
 if self.pre_ln:
 x = self.norm_layers_2[i](x)
 y = self.ffn_layers[i](x, x_mask)
 y = self.drop(y)
 x = x_ + y
 if not self.pre_ln:
 x = self.norm_layers_2[i](x)
 if self.pre_ln:
 x = self.last_ln(x)
 x = x * x_mask
 return x


class MultiHeadAttention(nn.Module):
 def __init__(self, channels, out_channels, n_heads, window_size=None, heads_share=True, p_dropout=0.,
 block_length=None, proximal_bias=False, proximal_init=False):
 super().__init__()
 assert channels % n_heads == 0

 self.channels = channels
 self.out_channels = out_channels
 self.n_heads = n_heads
 self.window_size = window_size
 self.heads_share = heads_share
 self.block_length = block_length
 self.proximal_bias = proximal_bias
 self.p_dropout = p_dropout
 self.attn = None

 self.k_channels = channels // n_heads
 self.conv_q = nn.Conv1d(channels, channels, 1)
 self.conv_k = nn.Conv1d(channels, channels, 1)
 self.conv_v = nn.Conv1d(channels, channels, 1)
 if window_size is not None:
 n_heads_rel = 1 if heads_share else n_heads
 rel_stddev = self.k_channels ** -0.5
 self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
 self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
 self.conv_o = nn.Conv1d(channels, out_channels, 1)
 self.drop = nn.Dropout(p_dropout)

 nn.init.xavier_uniform_(self.conv_q.weight)
 nn.init.xavier_uniform_(self.conv_k.weight)
 if proximal_init:
 self.conv_k.weight.data.copy_(self.conv_q.weight.data)
 self.conv_k.bias.data.copy_(self.conv_q.bias.data)
 nn.init.xavier_uniform_(self.conv_v.weight)

 def forward(self, x, c, attn_mask=None):
 q = self.conv_q(x)
 k = self.conv_k(c)
 v = self.conv_v(c)

 x, self.attn = self.attention(q, k, v, mask=attn_mask)

 x = self.conv_o(x)
 return x

 def attention(self, query, key, value, mask=None):
 # reshape [b, d, t] -> [b, n_h, t, d_k]
 b, d, t_s, t_t = (*key.size(), query.size(2))
 query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
 key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
 value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)

 scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.k_channels)
 if self.window_size is not None:
 assert t_s == t_t, "Relative attention is only available for self-attention."
 key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
 rel_logits = self._matmul_with_relative_keys(query, key_relative_embeddings)
 rel_logits = self._relative_position_to_absolute_position(rel_logits)
 scores_local = rel_logits / math.sqrt(self.k_channels)
 scores = scores + scores_local
 if self.proximal_bias:
 assert t_s == t_t, "Proximal bias is only available for self-attention."
 scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
 if mask is not None:
 scores = scores.masked_fill(mask == 0, -1e4)
 if self.block_length is not None:
 block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
 scores = scores * block_mask + -1e4 * (1 - block_mask)
 p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
 p_attn = self.drop(p_attn)
 output = torch.matmul(p_attn, value)
 if self.window_size is not None:
 relative_weights = self._absolute_position_to_relative_position(p_attn)
 value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
 output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
 output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
 return output, p_attn

 def _matmul_with_relative_values(self, x, y):
 """
 x: [b, h, l, m]
 y: [h or 1, m, d]
 ret: [b, h, l, d]
 """
 ret = torch.matmul(x, y.unsqueeze(0))
 return ret

 def _matmul_with_relative_keys(self, x, y):
 """
 x: [b, h, l, d]
 y: [h or 1, m, d]
 ret: [b, h, l, m]
 """
 ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
 return ret

 def _get_relative_embeddings(self, relative_embeddings, length):
 max_relative_position = 2 * self.window_size + 1
 # Pad first before slice to avoid using cond ops.
 pad_length = max(length - (self.window_size + 1), 0)
 slice_start_position = max((self.window_size + 1) - length, 0)
 slice_end_position = slice_start_position + 2 * length - 1
 if pad_length > 0:
 padded_relative_embeddings = F.pad(
 relative_embeddings,
 convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
 else:
 padded_relative_embeddings = relative_embeddings
 used_relative_embeddings = padded_relative_embeddings[:, slice_start_position:slice_end_position]
 return used_relative_embeddings

 def _relative_position_to_absolute_position(self, x):
 """
 x: [b, h, l, 2*l-1]
 ret: [b, h, l, l]
 """
 batch, heads, length, _ = x.size()
 # Concat columns of pad to shift from relative to absolute indexing.
 x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))

 # Concat extra elements so to add up to shape (len+1, 2*len-1).
 x_flat = x.view([batch, heads, length * 2 * length])
 x_flat = F.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [0, length - 1]]))

 # Reshape and slice out the padded elements.
 x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[:, :, :length, length - 1:]
 return x_final

 def _absolute_position_to_relative_position(self, x):
 """
 x: [b, h, l, l]
 ret: [b, h, l, 2*l-1]
 """
 batch, heads, length, _ = x.size()
 # padd along column
 x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]]))
 x_flat = x.view([batch, heads, length ** 2 + length * (length - 1)])
 # add 0's in the beginning that will skew the elements after reshape
 x_flat = F.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
 x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
 return x_final

 def _attention_bias_proximal(self, length):
 """Bias for self-attention to encourage attention to close positions.
 Args:
 length: an integer scalar.
 Returns:
 a Tensor with shape [1, 1, length, length]
 """
 r = torch.arange(length, dtype=torch.float32)
 diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
 return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)


class FFN(nn.Module):
 def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None):
 super().__init__()
 self.in_channels = in_channels
 self.out_channels = out_channels
 self.filter_channels = filter_channels
 self.kernel_size = kernel_size
 self.p_dropout = p_dropout
 self.activation = activation

 self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
 self.conv_2 = nn.Conv1d(filter_channels, out_channels, 1)
 self.drop = nn.Dropout(p_dropout)

 def forward(self, x, x_mask):
 x = self.conv_1(x * x_mask)
 if self.activation == "gelu":
 x = x * torch.sigmoid(1.702 * x)
 else:
 x = torch.relu(x)
 x = self.drop(x)
 x = self.conv_2(x * x_mask)
 return x * x_mask


class LayerNorm(nn.Module):
 def __init__(self, channels, eps=1e-4):
 super().__init__()
 self.channels = channels
 self.eps = eps

 self.gamma = nn.Parameter(torch.ones(channels))
 self.beta = nn.Parameter(torch.zeros(channels))

 def forward(self, x):
 n_dims = len(x.shape)
 mean = torch.mean(x, 1, keepdim=True)
 variance = torch.mean((x - mean) ** 2, 1, keepdim=True)

 x = (x - mean) * torch.rsqrt(variance + self.eps)

 shape = [1, -1] + [1] * (n_dims - 2)
 x = x * self.gamma.view(*shape) + self.beta.view(*shape)
 return x


class ConvReluNorm(nn.Module):
 def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
 super().__init__()
 self.in_channels = in_channels
 self.hidden_channels = hidden_channels
 self.out_channels = out_channels
 self.kernel_size = kernel_size
 self.n_layers = n_layers
 self.p_dropout = p_dropout
 assert n_layers > 1, "Number of layers should be larger than 0."

 self.conv_layers = nn.ModuleList()
 self.norm_layers = nn.ModuleList()
 self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size // 2))
 self.norm_layers.append(LayerNorm(hidden_channels))
 self.relu_drop = nn.Sequential(
 nn.ReLU(),
 nn.Dropout(p_dropout))
 for _ in range(n_layers - 1):
 self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size // 2))
 self.norm_layers.append(LayerNorm(hidden_channels))
 self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
 self.proj.weight.data.zero_()
 self.proj.bias.data.zero_()

 def forward(self, x, x_mask):
 x_org = x
 for i in range(self.n_layers):
 x = self.conv_layers[i](x * x_mask)
 x = self.norm_layers[i](x)
 x = self.relu_drop(x)
 x = x_org + self.proj(x)
 return x * x_mask


class RelTransformerEncoder(nn.Module):
 def __init__(self,
 n_vocab,
 out_channels,
 hidden_channels,
 filter_channels,
 n_heads,
 n_layers,
 kernel_size,
 p_dropout=0.0,
 window_size=4,
 block_length=None,
 prenet=True,
 pre_ln=True,
 ):

 super().__init__()

 self.n_vocab = n_vocab
 self.out_channels = out_channels
 self.hidden_channels = hidden_channels
 self.filter_channels = filter_channels
 self.n_heads = n_heads
 self.n_layers = n_layers
 self.kernel_size = kernel_size
 self.p_dropout = p_dropout
 self.window_size = window_size
 self.block_length = block_length
 self.prenet = prenet
 if n_vocab > 0:
 self.emb = Embedding(n_vocab, hidden_channels, padding_idx=0)

 if prenet:
 self.pre = ConvReluNorm(hidden_channels, hidden_channels, hidden_channels,
 kernel_size=5, n_layers=3, p_dropout=0)
 self.encoder = Encoder(
 hidden_channels,
 filter_channels,
 n_heads,
 n_layers,
 kernel_size,
 p_dropout,
 window_size=window_size,
 block_length=block_length,
 pre_ln=pre_ln,
 )

 def forward(self, x, x_mask=None):
 if self.n_vocab > 0:
 x_lengths = (x > 0).long().sum(-1)
 x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
 else:
 x_lengths = (x.abs().sum(-1) > 0).long().sum(-1)
 x = torch.transpose(x, 1, -1) # [b, h, t]
 x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)

 if self.prenet:
 x = self.pre(x, x_mask)
 x = self.encoder(x, x_mask)
 return x.transpose(1, 2)


class Pooler(nn.Module):
 """
 Parameter-free poolers to get the sentence embedding
 'cls': [CLS] representation with BERT/RoBERTa's MLP pooler.
 'cls_before_pooler': [CLS] representation without the original MLP pooler.
 'avg': average of the last layers' hidden states at each token.
 'avg_top2': average of the last two layers.
 'avg_first_last': average of the first and the last layers.
 """
 def __init__(self, pooler_type):
 super().__init__()
 self.pooler_type = pooler_type
 assert self.pooler_type in ["cls", "cls_before_pooler", "avg", "avg_top2", "avg_first_last"], "unrecognized pooling type %s" % self.pooler_type

 def forward(self, attention_mask, outputs):
 last_hidden = outputs.last_hidden_state
 pooler_output = outputs.pooler_output
 hidden_states = outputs.hidden_states

 if self.pooler_type in ['cls_before_pooler', 'cls']:
 return last_hidden[:, 0]
 elif self.pooler_type == "avg":
 return ((last_hidden * attention_mask.unsqueeze(-1)).sum(1) / attention_mask.sum(-1).unsqueeze(-1))
 elif self.pooler_type == "avg_first_last":
 first_hidden = hidden_states[0]
 last_hidden = hidden_states[-1]
 pooled_result = ((first_hidden + last_hidden) / 2.0 * attention_mask.unsqueeze(-1)).sum(1) / attention_mask.sum(-1).unsqueeze(-1)
 return pooled_result
 elif self.pooler_type == "avg_top2":
 second_last_hidden = hidden_states[-2]
 last_hidden = hidden_states[-1]
 pooled_result = ((last_hidden + second_last_hidden) / 2.0 * attention_mask.unsqueeze(-1)).sum(1) / attention_mask.sum(-1).unsqueeze(-1)
 return pooled_result
 else:
 raise NotImplementedError


class Similarity(nn.Module):
 """
 Dot product or cosine similarity
 """

 def __init__(self, temp):
 super().__init__()
 self.temp = temp
 self.cos = nn.CosineSimilarity(dim=-1)
 self.record = None
 self.pos_avg = 0.0
 self.neg_avg = 0.0

 def forward(self, x, y):
 sim = self.cos(x, y)
 self.record = sim.detach() # [64,64]
 min_size = min(self.record.shape[0], self.record.shape[1]) # 64
 num_item = self.record.shape[0] * self.record.shape[1] # 4096
 self.pos_avg = self.record.diag().sum() / min_size
 if num_item - min_size == 0:
 self.neg_avg = (self.record.sum() - self.record.diag().sum()) / 1
 return sim / self.temp
 if torch.any(torch.isnan(self.record)).item() is True:
 print("we got self.record has nan when compute neg_avg")
 if torch.any(torch.isnan(self.record.diag())).item() is True:
 print("we got self.record.diag() has nan when compute neg_avg")
 self.neg_avg = (self.record.sum() - self.record.diag().sum()) / (num_item - min_size)
 
 return sim / self.temp


class BertPredictionHeadTransform(nn.Module):
 def __init__(self, hidden_size):
 super().__init__()
 self.dense = nn.Linear(hidden_size, hidden_size)
 self.transform_act_fn = F.gelu
 self.LayerNorm = nn.LayerNorm(hidden_size, eps=1e-12)

 def forward(self, hidden_states):
 hidden_states = self.dense(hidden_states)
 hidden_states = self.transform_act_fn(hidden_states)
 hidden_states = self.LayerNorm(hidden_states)
 return hidden_states


class BertLMPredictionHead(nn.Module):
 def __init__(self, hid_dim, out_dim):
 super().__init__()
 self.transform = BertPredictionHeadTransform(hid_dim)
 self.decoder = nn.Linear(hid_dim, out_dim, bias=False)
 self.bias = nn.Parameter(torch.zeros(out_dim))
 self.decoder.bias = self.bias

 def forward(self, hidden_states):
 hidden_states = self.transform(hidden_states)
 hidden_states = self.decoder(hidden_states)
 return hidden_states


# V2_2
# change add to concat.
# now support finetune BERT
# grad_bert=0.1 & trainable_block_idx=0
class BERTRelTransformerEncoder(nn.Module):
 def __init__(self,
 n_vocab,
 out_channels,
 hidden_channels,
 filter_channels,
 n_heads,
 n_layers,
 kernel_size,
 p_dropout=0.0,
 window_size=4,
 block_length=None,
 prenet=True,
 pre_ln=True,
 ):

 super().__init__()

 self.n_vocab = n_vocab
 self.out_channels = out_channels
 self.hidden_channels = hidden_channels
 self.filter_channels = filter_channels
 self.n_heads = n_heads
 self.n_layers = n_layers
 self.kernel_size = kernel_size
 self.p_dropout = p_dropout
 self.window_size = window_size
 self.block_length = block_length
 self.prenet = prenet
 if n_vocab > 0:
 self.emb = Embedding(n_vocab, hidden_channels, padding_idx=0)

 if prenet:
 self.pre = ConvReluNorm(hidden_channels, hidden_channels, hidden_channels,
 kernel_size=5, n_layers=3, p_dropout=0)
 self.encoder1 = Encoder(
 hidden_channels,
 filter_channels,
 n_heads,
 n_layers//2,
 kernel_size,
 p_dropout,
 window_size=window_size,
 block_length=block_length,
 pre_ln=pre_ln,
 )

 self.encoder2 = Encoder(
 hidden_channels,
 filter_channels,
 n_heads,
 n_layers - n_layers//2,
 kernel_size,
 p_dropout,
 window_size=window_size,
 block_length=block_length,
 pre_ln=pre_ln,
 )

 if hparams['ds_name'] in ['ljspeech', 'libritts', 'librispeech']:
 model_name = 'bert-base-uncased'
 elif hparams['ds_name'] in ['biaobei', 'wenetspeech']:
 model_name = 'bert-base-chinese'
 else:
 raise NotImplementedError()
 
 self.tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
 config = transformers.AutoConfig.from_pretrained(model_name)
 if hparams.get("load_bert_from_pretrained", True):
 print("Load BERT from pretrained model ...")
 self.bert = transformers.AutoModel.from_pretrained(model_name,config=config)
 trainable_start_block = hparams.get("bert_trainable_start_block", 0)
 else:
 print("Initialize BERT from scratch!")
 self.bert = transformers.BertModel(config=config)
 trainable_start_block = 0
 
 for k, v in self.bert.named_parameters():
 if 'embeddings' in k:
 v.requires_grad = False
 elif 'encoder.layer' in k:
 block_idx = int(k.split(".")[2])
 if block_idx < trainable_start_block:
 v.requires_grad = False
 else:
 v.requires_grad = True
 elif 'cls' in k:
 v.requires_grad = True
 else:
 print("Unhandled key: {}, set to requires_grad...".format(k))
 v.requires_grad = True

 self.bert_combine = nn.Sequential(*[
 nn.Conv1d(768 + hidden_channels, hidden_channels, 3, 1, 1),
 nn.ReLU(),
 ])
 self.pooler = Pooler("avg")
 self.sim = Similarity(temp=0.05)
 
 def forward(self, x, x_mask=None, bert_feats=None, ph2word=None, **kwargs):
 if self.n_vocab > 0:
 x_lengths = (x > 0).long().sum(-1)
 x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
 else:
 x_lengths = (x.abs().sum(-1) > 0).long().sum(-1)
 x = torch.transpose(x, 1, -1) # [b, h, t]
 x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)

 if self.prenet:
 x = self.pre(x, x_mask)
 x = self.encoder1(x, x_mask)
 bert_outputs = self.bert(bert_feats['bert_input_ids'],
 attention_mask=bert_feats['bert_attention_mask'],
 token_type_ids=bert_feats['bert_token_type_ids'],
 output_hidden_states=True)
 bert_num_blocks = hparams.get("bert_num_blocks", 12) # total 1+12blocks in bert
 bert_embedding = bert_outputs['hidden_states'][bert_num_blocks]
 # bert_embedding = bert_outputs['last_hidden_state']
 grad_bert = hparams.get("grad_bert", 0.1)
 bert_embedding = bert_embedding.detach() * (1-grad_bert) + bert_embedding * grad_bert
 bert_word_embedding, _ = group_hidden_by_segs(bert_embedding, bert_feats['bert_token2word'], bert_feats['bert_token2word'].max().item())
 bert_ph_embedding = expand_word2ph(bert_word_embedding, ph2word)
 bert_ph_embedding = bert_ph_embedding.transpose(1,2)
 x = torch.cat([x, bert_ph_embedding], dim=1)
 x = self.bert_combine(x)
 x = self.encoder2(x, x_mask)
 return x.transpose(1, 2)