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import math
import logging
import torch
import torch.nn as nn
from torch.nn import functional as F
# logger = logging.getLogger(__name__)
from SCMG.config import varables
from torch.autograd import Variable
class PositionalEncoder(nn.Module):
def __init__(self, config):
super(PositionalEncoder, self).__init__()
self.Dropout = nn.Dropout(p=config[varables.RATE_DROPOUT])
max_len = config[varables.SIZE_BLOCK]
pe = torch.zeros(max_len, config[varables.DIM_EMBEDDING])
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, config[varables.DIM_EMBEDDING], 2).float() * (-math.log(10000.0) / config[varables.DIM_EMBEDDING]))
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0)
self.register_buffer('pe', pe)
def forward(self, T):
x = self.Dropout(self.pe[:,:T, :])
return x
class Attention(nn.Module):
def __init__(self, config):
super().__init__()
assert config[varables.DIM_ATTENTION] % config[varables.NUM_HEADS] == 0
self.Key = nn.Linear(config[varables.DIM_EMBEDDING], config[varables.DIM_ATTENTION])
self.Query = nn.Linear(config[varables.DIM_EMBEDDING], config[varables.DIM_ATTENTION])
self.Value = nn.Linear(config[varables.DIM_EMBEDDING], config[varables.DIM_ATTENTION])
self.Dropout_Attention = nn.Dropout(config[varables.RATE_DROPOUT])
self.Dropout_Residue = nn.Dropout(config[varables.RATE_DROPOUT])
self.Projection = nn.Linear(config[varables.DIM_ATTENTION], config[varables.DIM_EMBEDDING])
self.NumberOfHeads = config[varables.NUM_HEADS]
self.DimHead = config[varables.DIM_ATTENTION] // self.NumberOfHeads
self.DimAttention = config[varables.DIM_ATTENTION]
def forward(self, X_1,X_2, mask=None):
if X_2 is None:
X_2 = X_1
BatchSize, T_Encoder, _ = X_1.size()
BatchSize, T_Decoder, _ = X_2.size()
K = self.Key( X_1).view(BatchSize, T_Encoder, self.NumberOfHeads,self.DimHead).transpose(1, 2)
Q = self.Query(X_2).view(BatchSize, T_Decoder, self.NumberOfHeads,self.DimHead).transpose(1, 2)
V = self.Value(X_1).view(BatchSize, T_Encoder, self.NumberOfHeads,self.DimHead).transpose(1, 2)
# k,q,v dimension: (BatchSize, SequenceSize, NumberOfHeads, HeadDimension) 3,4,5,16
ScoreAttention = (Q @ K.transpose(-2, -1)) / math.sqrt(self.DimHead)
ScoreAttention = ScoreAttention.masked_fill(mask==0, -1e9)
ScoreAttention = F.softmax(ScoreAttention, dim=-1)
ScoreAttention = self.Dropout_Attention(ScoreAttention)
# k.transpose(-2,-1): 3,4,16,5
# (q@(k.transpose(-2,-1))): 3,4,5,5
Z = ScoreAttention @ V
# y dimension: 3,4,5,16
Z = Z.transpose(1, 2).contiguous().view(BatchSize, T_Decoder, self.DimAttention)
# y dimension: 3,5,64
Z = self.Dropout_Residue(self.Projection(Z))
return Z
class FeedForward(nn.Module):
def __init__(self, config):
super().__init__()
if config[varables.DIM_FEEDFORWARD] == 0:
Dim_FeedForward = config[varables.DIM_ATTENTION] *4
else:
Dim_FeedForward = config[varables.DIM_FEEDFORWARD]
self.Linear1 = nn.Linear(config[varables.DIM_EMBEDDING], Dim_FeedForward)
self.GELU = nn.GELU()
self.Linear2 = nn.Linear(Dim_FeedForward, config[varables.DIM_EMBEDDING])
self.Dropout = nn.Dropout(config[varables.RATE_DROPOUT])
def forward(self,x):
x = self.Linear1(x)
x = self.GELU (x)
x = self.Dropout(x)
x = self.Linear2(x)
return x
class EncoderBlock(nn.Module):
def __init__(self, config):
super().__init__()
self.LayerNorm1 = nn.LayerNorm(config[varables.DIM_EMBEDDING])
self.LayerNorm2 = nn.LayerNorm(config[varables.DIM_EMBEDDING])
self.Dropout1 = nn.Dropout(config[varables.RATE_DROPOUT])
self.Dropout2 = nn.Dropout(config[varables.RATE_DROPOUT])
self.Attention = Attention( config)
self.FeedForward = FeedForward(config)
def forward(self, X_Encoder,Mask_Encoder):
X_Encoder = self.LayerNorm1(X_Encoder + self.Attention (self.Dropout1(X_Encoder), None, Mask_Encoder))
X_Encoder = self.LayerNorm2(X_Encoder + self.FeedForward(self.Dropout2(X_Encoder)))
return X_Encoder
class DecoderBlock(nn.Module):
def __init__(self, config):
super().__init__()
self.LayerNorm1 = nn.LayerNorm(config[varables.DIM_EMBEDDING])
self.LayerNorm2 = nn.LayerNorm(config[varables.DIM_EMBEDDING])
self.LayerNorm3 = nn.LayerNorm(config[varables.DIM_EMBEDDING])
self.Dropout1 = nn.Dropout(config[varables.RATE_DROPOUT])
self.Dropout2 = nn.Dropout(config[varables.RATE_DROPOUT])
self.Dropout3 = nn.Dropout(config[varables.RATE_DROPOUT])
self.AttentionMasked = Attention( config)
self.AttentionCross = Attention( config)
self.FeedForward = FeedForward(config)
def forward(self, X_Encoder,X_Decoder,Mask_Cross,Mask_Decoder):
X_Decoder = self.LayerNorm1(X_Decoder + self.AttentionMasked(self.Dropout1(X_Decoder), None, Mask_Decoder))
X_Decoder = self.LayerNorm2(X_Decoder + self.AttentionCross ( X_Encoder, self.Dropout2(X_Decoder), Mask_Cross ))
X_Decoder = self.LayerNorm3(X_Decoder + self.FeedForward (self.Dropout3(X_Decoder) ))
return X_Decoder
class Model(nn.Module):
def __init__(self, config):
super().__init__()
# Varables
self.Dim_Embedding = config[varables.DIM_EMBEDDING]
self.Token_Padding_Encoder = config["Token_Padding_Encoder"]
self.Token_Padding_Decoder = config["Token_Padding_Decoder"]
# Embedding and positional encoding layers
self.Embedding_Encoder = nn.Embedding(len(config["vocab_encoder"]), config[varables.DIM_EMBEDDING])
self.Embedding_Decoder = nn.Embedding(len(config["vocab_decoder"]), config[varables.DIM_EMBEDDING])
self.pos_emb = PositionalEncoder(config)
# Dropout and normalization layers
self.Dropout1 = nn.Dropout(config[varables.RATE_DROPOUT])
self.Dropout2 = nn.Dropout(config[varables.RATE_DROPOUT])
self.LayerNorm1 = nn.LayerNorm(config[varables.DIM_EMBEDDING])
self.LayerNorm2 = nn.LayerNorm(config[varables.DIM_EMBEDDING])
# Transformer layers
self.encoder_blocks = nn.ModuleList([EncoderBlock(config) for _ in range(config[varables.NUM_LAYERS])])
self.decoder_blocks = nn.ModuleList([DecoderBlock(config) for _ in range(config[varables.NUM_LAYERS])])
# Output layer
self.head = nn.Linear(config[varables.DIM_EMBEDDING], len(config["vocab_decoder"]), bias=False)
# Init
self.apply(self._init_weights)
self.optimizer = None
# logger.info("number of parameters: %e", sum(p.numel() for p in self.parameters()))
def _init_weights(self, module):
for p in module.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
# if isinstance(module, (nn.Linear, nn.Embedding)):
# module.weight.data.normal_(mean=0.0, std=0.02)
# if isinstance(module, nn.Linear) and module.bias is not None:
# module.bias.data.zero_()
# elif isinstance(module, nn.LayerNorm):
# module.bias.data.zero_()
# module.weight.data.fill_(1.0)
def init_optimizers(self,train_config):
optimizer = torch.optim.Adam(self.parameters(), lr=train_config[varables.RATE_LEARNING])
return optimizer
def init_scheduler(self,train_config):
scheduler = torch.optim.lr_scheduler.StepLR(self.optimizer, step_size=train_config[varables.SIZE_STEP], gamma=train_config[varables.GAMMA])
return scheduler
def get_collate_fn(self, vocab_encoder,vocab_decoder):
def collate(results):
X_Encoder = [a[0] for a in results]
X_Decoder = [a[1] for a in results]
boundary = -1
max_len_x = max([len(a) for a in X_Encoder])
max_len_y = max([len(a) for a in X_Decoder])
x = torch.tensor([(a+[vocab_encoder[varables.TOKEN_PAD]]*(max_len_x-len(a))) for a in X_Encoder],dtype=torch.long)
y = torch.tensor([(a+[vocab_decoder[varables.TOKEN_PAD]]*(max_len_y-len(a))) for a in X_Decoder],dtype=torch.long)
return x,y,boundary
return collate
def generate_masks(self,X_Encoder, X_Decoder):
# Generate encoder, decoder, cross masks
T = X_Decoder.shape[1]
Mask_Encoder = (X_Encoder != self.Token_Padding_Encoder).unsqueeze(-2).unsqueeze(-2)
Mask_Decoder = (X_Decoder != self.Token_Padding_Decoder).unsqueeze(-2).unsqueeze(-2).repeat(1,1,T,1)
Mask_Cross = (X_Encoder != self.Token_Padding_Encoder).unsqueeze(-2).unsqueeze(-2)
mask_tril = torch.tril(torch.ones(T, T)).view(1, 1, T, T).to(Mask_Decoder.device)
Mask_Decoder = Mask_Decoder.masked_fill(mask_tril==0,0)
return Mask_Encoder,Mask_Decoder,Mask_Cross
def forward(self, X_Encoder, X_Decoder, Y_Decoder_Ref=None,boundary=None):
Mask_Encoder, Mask_Decoder,Mask_Cross = self.generate_masks(X_Encoder, X_Decoder)
# preprocess
X_Encoder = self.Dropout1(self.Embedding_Encoder(X_Encoder) * math.sqrt(self.Dim_Embedding) + self.pos_emb(X_Encoder.size(1)))
X_Decoder = self.Dropout2(self.Embedding_Decoder(X_Decoder) * math.sqrt(self.Dim_Embedding) + self.pos_emb(X_Decoder.size(1)))
#### Now X_Encoder: BatchSize, SequenceLength, DimAttention
# Encoder blocks
for encoder_block in self.encoder_blocks:
X_Encoder = encoder_block(X_Encoder,Mask_Encoder)
# X_Encoder = self.LayerNorm1(X_Encoder)
# Decoder blocks
for decoder_block in self.decoder_blocks:
X_Decoder = decoder_block(X_Encoder,X_Decoder,Mask_Cross,Mask_Decoder)
# X_Decoder = self.LayerNorm2(X_Decoder)
Y_Decoder_Logits = self.head(X_Decoder)
loss = None
if Y_Decoder_Ref is not None:
# loss = F.cross_entropy(Y_Decoder_Logits.view(-1, Y_Decoder_Logits.size(-1)), Y_Decoder_Ref.view(-1),ignore_index=self.Token_Padding_Decoder)
loss = F.nll_loss(F.log_softmax(Y_Decoder_Logits,dim=-1).view(-1, Y_Decoder_Logits.size(-1)),Y_Decoder_Ref.view(-1),ignore_index=self.Token_Padding_Decoder)
return Y_Decoder_Logits, loss
# def generate_masks(self,X_Encoder, X_Decoder):
# # Generate encoder, decoder, cross masks
# Mask_Encoder = (X_Encoder != self.Token_Padding_Encoder).unsqueeze(-2).int().cpu()
# Mask_Decoder = (X_Decoder != self.Token_Padding_Decoder).unsqueeze(-2).int().cpu()
# Mask_Cross = Mask_Decoder.unsqueeze(-1) @ Mask_Encoder.unsqueeze(-2)
# Mask_Encoder = Mask_Encoder.unsqueeze(-1) @ Mask_Encoder.unsqueeze(-2)
# Mask_Decoder = Mask_Decoder.unsqueeze(-1) @ Mask_Decoder.unsqueeze(-2)
# T = X_Decoder.shape[1]
# mask_tril = torch.tril(torch.ones(T, T)).view(1, 1, T, T)
# Mask_Decoder = Mask_Decoder.masked_fill(mask_tril==0,0)
# Mask_Encoder = Mask_Encoder.to(X_Encoder.device)
# Mask_Decoder = Mask_Decoder.to(X_Decoder.device)
# Mask_Cross = Mask_Cross.to(X_Encoder.device)
# return Mask_Encoder,Mask_Decoder,Mask_Cross
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