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models.py
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models.py
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import torch
from torch import nn
from torch.nn import functional as F
import math, copy
from torch.autograd import Variable
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
#device = torch.device("cpu")
import time
import sqlite3
from sqlite3 import OperationalError
import random
import joblib
import gensim
import numpy as np
import sys
from torch import optim
import pickle
from attention_model import make_model,PositionalEncoding
from utils import create_attention_mask_from_input_mask,create_embedding_weight, create_embedding_weight_model, create_embedding_weight_model_conc, create_embedding_weight_matrix
from global_vars import get_sql_path, get_dict_path, get_model_path
'''
class RelBERTJ(nn.Module):
# @staticmethod
# def __init__(self,count_dict,count_col,embedding_dim=100,arg_N=4,position_arg=True,
# load_pre_train_embeddings=False,entity_dict=None,non_trainable = False,
# embedding_path=get_model_path()+"actors_nam_data_clean.bin",tie_weights=True):
def __init__(self, tokensPerColumnCountDict, tableCount, embeddingDim=100, arg_N=4,
loadPretrainEmbeddings = False, entityDict = None, nonTrainable = False,
embeddingPath = get_model_path()+"actors_nam_data_clean.bin", embeddingMatrix = None):
super(RelBERTJ, self).__init__()
self.embeddingDim=embeddingDim
self.entityDict = entityDict
self.tokensPerColumnCountDict = tokensPerColumnCountDict
if loadPretrainEmbeddings:
self.embeddings = nn.ModuleList([
create_embedding_weight(embeddingPath, self.entityDict[s], embeddingDim, nonTrainable) #Retruns a token to embedding lookup table
for s in self.entityDict
])
else:
self.embeddings = embeddingMatrix
#self.embeddings = nn.ModuleList([
# nn.Embedding(self.perColumnTokenCountDict[columnHeader], embeddingDim)
# for columnHeader in self.perColumnTokenCountDict
# ])
#My recommendation :def create_embedding_weight_matrix(embedding_matrix,entity_dict, embedding_dim,non_trainable):
#self.embeddings = create_embedding_weight_matrix(embedding_matrix, entity_dict, embedding_dim, false)
if tableCount > 1:
self.tab_emb=nn.Embedding(tableCount, embeddingDim)
self.transformer=make_model( N=arg_N, d_model=embeddingDim, d_ff=4*embeddingDim, h=4)
self.cls=nn.Linear(embeddingDim, 2)
self.decoder = nn.ModuleList([
nn.Linear(embedding_dim, self.tokensPerColumnCountDict[s])
for s in self.tokensPerColumnCountDict
])
for k in range(len(self.tokensPerColumnCountDict)):
self.decoder[k].weight = self.embeddings[k].weight
def initialEmbeddingAssignment(NSPSentences, NSPTableSentences,
t1ColumnHeader, t2ColumnHeader):
NSPSentences = NSPSentences.to(device)
sentenceCount = NSPSentences.shape[0]
sentenceLength = NSPSentences.shape[1]
embeddingArray=[]
columnHeaderArray = ["spec"] + t1ColumnHeader + ["spec"] + t2ColumnHeader
columnHeaderArray += ["spec"] * (sentenceLength - len(columnHeaderList))
for tokenIndex in range(sentenceLength):
embeddingArray.append(self.tokenEmbeddings[columnHeaderArray[tokenIndex]](sentence[:,tokenIndex]).to(device).reshape(sentenceCount,-1,self.embeddingDim))
sentenceEmbeddingMatrix = torch.cat(embeddingArray,dim=1)
sentenceEmbeddingMatrix = (sentenceEmbeddingMatrix + self.tab_emb(NSPTableSentences)).to(device).reshape(sentenceCount,-1,self.embeddingDim)
return sentenceEmbeddingMatrix
def NSP(self, NSPEmbeddedSentences, maskSentences):
sentenceCount = len(NSPEmbeddedSentences)
output = self.transformer.forward(NSPEmbeddedSentences, maskSentences)
CLSEmbedding = self.cls(output[:,0])
return CLSEmbedding.reshape(sentenceCount,-1)
def MLM(self, sentences,index,non_index):
sentences = sentences.to(device)
sentenceCount=sentences.shape[0]
#B=sentences.shape[1]
x_array=[]
for k in self.tokensPerColumnCountDict:
x_array.append(self.embeddings[k](sentences[:,k]).to(device).reshape(sentenceCount,-1,self.embeddingDim))
x=torch.cat(x_array,dim=1)
if self.position_arg:
x=self.position(x)
mask = torch.ones_like(sentences).to(device)
mask[:,index]= torch.zeros(sentences.shape[0]).to(device)
mask1=torch.where(non_index==0,torch.zeros_like(sentences).to(device),mask)
mask1=mask1.unsqueeze(-2)
out=self.transformer.forward(x,mask1)
res=self.decoder[index](out[:,index])
return self.m(res.view(sentenceCount,-1))
def forward(self, sentence,index,non_index):
sentence = sentence.to(device)
BAT=sentence.shape[0]
B=sentence.shape[1]
x_array=[]
for k in self.count_dict:
x_array.append(self.embeddings[k](sentence[:,k]).to(device).reshape(BAT,-1,self.embedding_dim))
x=torch.cat(x_array,dim=1)
if self.position_arg:
x=self.position(x)
mask = torch.ones_like(sentence).to(device)
mask[:,index]= torch.zeros(sentence.shape[0]).to(device)
mask1=torch.where(non_index==0,torch.zeros_like(sentence).to(device),mask)
mask1=mask1.unsqueeze(-2)
out=self.transformer.forward(x,mask1)
res=self.decoder[index](out[:,index])
return self.m(res.view(BAT,-1))
'''
class Attention_Database(nn.Module):
# @staticmethod
def __init__(self,tokensPerColumnCountDict,columnList,embeddingDim=300,arg_N=4,position_arg=True,loadPretrainEmbeddings=False,entityDict=None,nonTrainable=False,embeddingPath=get_model_path()+"actors_nam_data_clean.bin",tie_weights=True):
super(Attention_Database, self).__init__()
self.tokensPerColumnCountDict = tokensPerColumnCountDict
self.entityDict = entityDict
self.embeddingDim = embeddingDim
self.position_arg = position_arg
self.outLength = 0
self.columnList = columnList
for col in tokensPerColumnCountDict:
if tokensPerColumnCountDict[col] > self.outLength:
self.outLength = tokensPerColumnCountDict[col]
if loadPretrainEmbeddings:
self.embeddings = nn.ModuleList([
create_embedding_weight(embeddingPath, self.entityDict[s], embeddingDim, nonTrainable)
for s in self.entityDict
#for s in self.columnList
])
else:
self.embeddings = nn.ModuleList([
nn.Embedding(self.tokensPerColumnCountDict[s], embeddingDim)
for s in self.tokensPerColumnCountDict
#for s in range(len(self.columnList))
])
self.position = PositionalEncoding(embeddingDim, 0)
self.transformer = make_model( N=arg_N, d_model=embeddingDim, d_ff=4*embeddingDim, h=4)
self.W1 = nn.Linear(embeddingDim, embeddingDim).to(device)
self.decoder = nn.ModuleList([
nn.Linear(embeddingDim,self.tokensPerColumnCountDict[s])
for s in self.tokensPerColumnCountDict
#for s in range(len(self.columnList))
])
if tie_weights:
for k in range(len(self.tokensPerColumnCountDict)):
self.decoder[k].weight = self.embeddings[k].weight
else:
for k in range(len(self.tokensPerColumnCountDict)):
self.decoder[k].load_state_dict({"weight": self.embeddings[k].weight.data,"bias": self.decoder[k].bias.data})
self.cls=nn.Linear(embeddingDim, 2)
self.m = nn.Softmax(dim=-1)
def forward(self, sentence, mask, maskedIndex):
sentence = sentence.to(device) #sentence is a np.array
BAT = sentence.shape[0] # number of sentences
B = sentence.shape[1] #length of the sentences
x_array = []
#adding for trail
mask = mask.to(device)
# for k in self.tokensPerColumnCountDict:
for k in range(len(self.columnList)):
x_array.append(self.embeddings[k](sentence[:,k]).to(device).reshape(BAT,-1,self.embeddingDim))
#x_array.append(self.embeddings[k](sentence[:,k]).reshape(BAT,-1,self.embeddingDim))
x = torch.cat(x_array,dim=1) #substituting tokens with their respective embedding
if self.position_arg:
x = self.position(x)
# print("Before size of mask and sentence: ",str(mask.size()), str(x.size()))
#mask = torch.ones_like(sentence).to(device)
#mask[:,index] = torch.zeros(sentence.shape[0]).to(device)
#mask1 = torch.where(non_index == 0, torch.zeros_like(sentence).to(device), mask)
#mask = torch.tensor(mask)
mask = mask.unsqueeze(-1)
# print("size of mask and sentence: ",str(mask.size()), str(x.size()))
out = self.transformer.forward(x,mask) #pass the input from the encoder
# print("output size: "+str(out.size()))
# print("MAx length: "+str(self.outLength))
i = 0
pred = []
for index in maskedIndex:
res = self.decoder[index](out[i, index])
i += 1
#if int(res.size(0)) != self.tokensPerColumnCountDict[int(index)]:
# print("Error!!")
#print("res size:"+ str(res.size(0))+"\t"+str(self.tokensPerColumnCountDict[int(index)]))
#if sum(res == 0.0).bool():
# print("Zero value in softmax!!\t"+str(index)+"\t"+str(sum(res == 1.0).bool()))
#softMaxed = self.m(res).to(device)
softMaxed = res.to(device)
#if sum(softMaxed == 0.0).bool():
# print("Zero value in softmax!!\t"+str(index)+"\t"+str(sum(softMaxed == 1.0).bool()))
# print("Res Max: "+str(torch.max(res))+"\tMin: "+str(torch.min(res)))
# for j in range(softMaxed.size(0)):
# if float(softMaxed[j].item()) == 0.0:
# print("Res val:"+str(res[j]))
#print("Res Val: "+res[])
# print("SoftMaxed size: "+ str(softMaxed.size()))
# tmp = torch.from_numpy(np.array([0.0]*(self.outLength - softMaxed.size(0)))).to(device)
neg_infinity = float('-inf')
tmp = torch.from_numpy(np.array([neg_infinity]*(self.outLength - softMaxed.size(0)))).to(device)
#softMaxed += [0.0] * (self.outLength - len(softMaxed))
softMaxed = torch.cat((softMaxed,tmp),0)
# print("SoftMaxed size after padding: "+ str(softMaxed.size()))
softMaxed = softMaxed.view(1,-1)
# print("SoftMaxed size after view: "+ str(softMaxed.size()))
#print("After concat SoftMaxed size:", str(softMaxed.size()))
pred.append(softMaxed)
# print("After concat SoftMaxed size: "+ str(len(pred))+ str(len(pred[0])))
predictions = torch.cat(pred,dim=0)
#res = self.decoder[index](out[:,index])
#return self.m(res.view(BAT,-1))
#print("PRed output: "+str(predictions.size()))
#return torch.from_numpy(np.array(pred))
return predictions
def NSP(self, sentence, mask, maskedIndex):
sentence = sentence.to(device) #sentence is a np.array
BAT = sentence.shape[0] # number of sentences
B = sentence.shape[1] #length of the sentences
x_array = []
#adding for trail
mask = mask.to(device)
for k in self.tokensPerColumnCountDict:
x_array.append(self.embeddings[k](sentence[:,k]).to(device).reshape(BAT,-1,self.embeddingDim))
#x_array.append(self.embeddings[k](sentence[:,k]).reshape(BAT,-1,self.embeddingDim))
x = torch.cat(x_array,dim=1) #substituting tokens with their respective embedding
if self.position_arg:
x = self.position(x)
# print("Before size of mask and sentence: ",str(mask.size()), str(x.size()))
mask = mask.unsqueeze(-1)
print("size of mask and sentence: ",str(mask.size()), str(x.size()))
out = self.transformer.forward(x,mask) #pass the input from the encoder
cls_out = self.cls(out[:,0]).to(device)
nsp_predictions = self.m(cls_out.view(BAT,-1)).to(device)
return nsp_predictions
def MLM_NSP(self, sentence, mask, maskedIndex):
sentence = sentence.to(device) #sentence is a np.array
BAT = sentence.shape[0] # number of sentences
B = sentence.shape[1] #length of the sentences
x_array = []
#adding for trail
mask = mask.to(device)
for k in self.tokensPerColumnCountDict:
x_array.append(self.embeddings[k](sentence[:,k]).to(device).reshape(BAT,-1,self.embeddingDim))
#x_array.append(self.embeddings[k](sentence[:,k]).reshape(BAT,-1,self.embeddingDim))
x = torch.cat(x_array,dim=1) #substituting tokens with their respective embedding
if self.position_arg:
x = self.position(x)
# print("Before size of mask and sentence: ",str(mask.size()), str(x.size()))
#mask = torch.ones_like(sentence).to(device)
#mask[:,index] = torch.zeros(sentence.shape[0]).to(device)
#mask1 = torch.where(non_index == 0, torch.zeros_like(sentence).to(device), mask)
#mask = torch.tensor(mask)
mask = mask.unsqueeze(-1)
out = self.transformer.forward(x,mask) #pass the input from the encoder
# nsp_out = self.cls(out[:,0]).to(device)
# nsp_predictions = self.m(nsp_out.view(BAT,-1)).to(device)
nsp_predictions = self.cls(out[:,0]).to(device)
i = 0
pred = []
for index in maskedIndex:
res = self.decoder[index](out[i, index])
i += 1
#print("res size:", str(res.size()))
#softMaxed = self.m(res).to(device)
softMaxed = res.to(device)
#print("SoftMaxed size:", str(softMaxed.size()))
neg_infinity = float('-inf')
tmp = torch.from_numpy(np.array([neg_infinity]*(self.outLength - softMaxed.size(0)))).to(device)
#softMaxed += [0.0] * (self.outLength - len(softMaxed))
softMaxed = torch.cat((softMaxed,tmp),0)
softMaxed = softMaxed.view(1,-1)
#print("After concat SoftMaxed size:", str(softMaxed.size()))
pred.append(softMaxed)
predictions = torch.cat(pred,dim=0)
#res = self.decoder[index](out[:,index])
#return self.m(res.view(BAT,-1))
# print("PRed output: ",str(predictions.size()))
#return torch.from_numpy(np.array(pred))
return predictions, nsp_predictions
#This class model takes NLP snetences constructed by templates as inputs
class Attention_Database_Sentences(nn.Module):
# @staticmethod
def __init__(self,tokensPerColumnCountDict,columnList,embeddingDim=300,arg_N=4,position_arg=True,loadPretrainEmbeddings=False,entityDict=None,nonTrainable=False,embeddingPath=get_model_path()+"actors_nam_data_clean.bin",tie_weights=True):
super(Attention_Database_Sentences, self).__init__()
self.tokensPerColumnCountDict = tokensPerColumnCountDict
self.entityDict = entityDict
self.embeddingDim = embeddingDim
self.position_arg = position_arg
self.outLength = 0
self.columnList = columnList
for col in tokensPerColumnCountDict:
if tokensPerColumnCountDict[col] > self.outLength:
self.outLength = tokensPerColumnCountDict[col]
self.embeddings_spec = create_embedding_weight(embeddingPath,self.entityDict['en'], embeddingDim,nonTrainable)
# self.entityDict.pop('spec')
print("\n\n***Length of token count dict: "+str(len(tokensPerColumnCountDict))+"\t length of columnList: "+str(len(columnList)))
if loadPretrainEmbeddings:
self.embeddings = nn.ModuleList()
#Construct embedding array taking into account special tokens
for col in range(len(self.columnList)):
key = self.columnList[col]
if key == 'en':
self.embeddings.append(self.embeddings_spec)
else:
self.embeddings.append(create_embedding_weight(embeddingPath,self.entityDict[key], embeddingDim,nonTrainable))
else:
self.embeddings = nn.ModuleList([
nn.Embedding(self.tokensPerColumnCountDict[s], embeddingDim)
for s in self.tokensPerColumnCountDict
#for s in range(len(self.columnList))
])
print("Size of embedding matrix: "+str(len(self.embeddings)))
self.position = PositionalEncoding(embeddingDim, 0)
self.transformer = make_model( N=arg_N, d_model=embeddingDim, d_ff=4*embeddingDim, h=4)
self.W1 = nn.Linear(embeddingDim, embeddingDim).to(device)
self.decoder = nn.ModuleList([
nn.Linear(embeddingDim,self.tokensPerColumnCountDict[s])
for s in self.tokensPerColumnCountDict
#for s in range(len(self.columnList))
])
if tie_weights:
for k in range(len(self.tokensPerColumnCountDict)):
self.decoder[k].weight = self.embeddings[k].weight
else:
for k in range(len(self.tokensPerColumnCountDict)):
self.decoder[k].load_state_dict({"weight": self.embeddings[k].weight.data,"bias": self.decoder[k].bias.data})
self.cls=nn.Linear(embeddingDim, 2)
self.m = nn.Softmax(dim=-1)
def forward(self, sentence, mask, maskedIndex):
sentence = sentence.to(device) #sentence is a np.array
BAT = sentence.shape[0] # number of sentences
B = sentence.shape[1] #length of the sentences
x_array = []
#adding for trail
mask = mask.to(device)
# for k in self.tokensPerColumnCountDict:
for k in range(len(self.columnList)):
x_array.append(self.embeddings[k](sentence[:,k]).to(device).reshape(BAT,-1,self.embeddingDim))
#x_array.append(self.embeddings[k](sentence[:,k]).reshape(BAT,-1,self.embeddingDim))
x = torch.cat(x_array,dim=1) #substituting tokens with their respective embedding
if self.position_arg:
x = self.position(x)
# print("Before size of mask and sentence: ",str(mask.size()), str(x.size()))
#mask = torch.ones_like(sentence).to(device)
#mask[:,index] = torch.zeros(sentence.shape[0]).to(device)
#mask1 = torch.where(non_index == 0, torch.zeros_like(sentence).to(device), mask)
#mask = torch.tensor(mask)
mask = mask.unsqueeze(-1)
# print("size of mask and sentence: ",str(mask.size()), str(x.size()))
out = self.transformer.forward(x,mask) #pass the input from the encoder
# print("output size: "+str(out.size()))
# print("MAx length: "+str(self.outLength))
i = 0
pred = []
for index in maskedIndex:
res = self.decoder[index](out[i, index])
i += 1
#if int(res.size(0)) != self.tokensPerColumnCountDict[int(index)]:
# print("Error!!")
#print("res size:"+ str(res.size(0))+"\t"+str(self.tokensPerColumnCountDict[int(index)]))
#if sum(res == 0.0).bool():
# print("Zero value in softmax!!\t"+str(index)+"\t"+str(sum(res == 1.0).bool()))
#softMaxed = self.m(res).to(device)
softMaxed = res.to(device)
#if sum(softMaxed == 0.0).bool():
# print("Zero value in softmax!!\t"+str(index)+"\t"+str(sum(softMaxed == 1.0).bool()))
# print("Res Max: "+str(torch.max(res))+"\tMin: "+str(torch.min(res)))
# for j in range(softMaxed.size(0)):
# if float(softMaxed[j].item()) == 0.0:
# print("Res val:"+str(res[j]))
#print("Res Val: "+res[])
# print("SoftMaxed size: "+ str(softMaxed.size()))
# tmp = torch.from_numpy(np.array([0.0]*(self.outLength - softMaxed.size(0)))).to(device)
neg_infinity = float('-inf')
tmp = torch.from_numpy(np.array([neg_infinity]*(self.outLength - softMaxed.size(0)))).to(device)
#softMaxed += [0.0] * (self.outLength - len(softMaxed))
softMaxed = torch.cat((softMaxed,tmp),0)
# print("SoftMaxed size after padding: "+ str(softMaxed.size()))
softMaxed = softMaxed.view(1,-1)
# print("SoftMaxed size after view: "+ str(softMaxed.size()))
#print("After concat SoftMaxed size:", str(softMaxed.size()))
pred.append(softMaxed)
# print("After concat SoftMaxed size: "+ str(len(pred))+ str(len(pred[0])))
predictions = torch.cat(pred,dim=0)
#res = self.decoder[index](out[:,index])
#return self.m(res.view(BAT,-1))
#print("PRed output: "+str(predictions.size()))
#return torch.from_numpy(np.array(pred))
return predictions
def NSP(self, sentence, mask, maskedIndex):
sentence = sentence.to(device) #sentence is a np.array
BAT = sentence.shape[0] # number of sentences
B = sentence.shape[1] #length of the sentences
x_array = []
#adding for trail
mask = mask.to(device)
for k in self.tokensPerColumnCountDict:
x_array.append(self.embeddings[k](sentence[:,k]).to(device).reshape(BAT,-1,self.embeddingDim))
#x_array.append(self.embeddings[k](sentence[:,k]).reshape(BAT,-1,self.embeddingDim))
x = torch.cat(x_array,dim=1) #substituting tokens with their respective embedding
if self.position_arg:
x = self.position(x)
# print("Before size of mask and sentence: ",str(mask.size()), str(x.size()))
mask = mask.unsqueeze(-1)
print("size of mask and sentence: ",str(mask.size()), str(x.size()))
out = self.transformer.forward(x,mask) #pass the input from the encoder
cls_out = self.cls(out[:,0]).to(device)
nsp_predictions = self.m(cls_out.view(BAT,-1)).to(device)
return nsp_predictions
def MLM_NSP(self, sentence, mask, maskedIndex):
sentence = sentence.to(device) #sentence is a np.array
BAT = sentence.shape[0] # number of sentences
B = sentence.shape[1] #length of the sentences
x_array = []
#adding for trail
mask = mask.to(device)
for k in self.tokensPerColumnCountDict:
x_array.append(self.embeddings[k](sentence[:,k]).to(device).reshape(BAT,-1,self.embeddingDim))
#x_array.append(self.embeddings[k](sentence[:,k]).reshape(BAT,-1,self.embeddingDim))
x = torch.cat(x_array,dim=1) #substituting tokens with their respective embedding
if self.position_arg:
x = self.position(x)
# print("Before size of mask and sentence: ",str(mask.size()), str(x.size()))
#mask = torch.ones_like(sentence).to(device)
#mask[:,index] = torch.zeros(sentence.shape[0]).to(device)
#mask1 = torch.where(non_index == 0, torch.zeros_like(sentence).to(device), mask)
#mask = torch.tensor(mask)
mask = mask.unsqueeze(-1)
out = self.transformer.forward(x,mask) #pass the input from the encoder
# nsp_out = self.cls(out[:,0]).to(device)
# nsp_predictions = self.m(nsp_out.view(BAT,-1)).to(device)
nsp_predictions = self.cls(out[:,0]).to(device)
i = 0
pred = []
for index in maskedIndex:
res = self.decoder[index](out[i, index])
i += 1
#print("res size:", str(res.size()))
#softMaxed = self.m(res).to(device)
softMaxed = res.to(device)
#print("SoftMaxed size:", str(softMaxed.size()))
neg_infinity = float('-inf')
tmp = torch.from_numpy(np.array([neg_infinity]*(self.outLength - softMaxed.size(0)))).to(device)
#softMaxed += [0.0] * (self.outLength - len(softMaxed))
softMaxed = torch.cat((softMaxed,tmp),0)
softMaxed = softMaxed.view(1,-1)
#print("After concat SoftMaxed size:", str(softMaxed.size()))
pred.append(softMaxed)
predictions = torch.cat(pred,dim=0)
#res = self.decoder[index](out[:,index])
#return self.m(res.view(BAT,-1))
# print("PRed output: ",str(predictions.size()))
#return torch.from_numpy(np.array(pred))
return predictions, nsp_predictions
class Attention_Database_NSP(nn.Module):
# @staticmethod
def __init__(self,tokensPerColumnCountDict,columnList,embeddingDim=300,arg_N=4,position_arg=True,loadPretrainEmbeddings=False,entityDict=None,nonTrainable=False,embeddingPath=get_model_path()+"actors_nam_data_clean.bin",tie_weights=True):
super(Attention_Database_NSP, self).__init__()
self.tokensPerColumnCountDict = tokensPerColumnCountDict
self.entityDict = entityDict
self.embeddingDim = embeddingDim
self.position_arg = position_arg
self.outLength = 0
self.columnList = columnList
print("CHECK keys of entity dict: "+str(self.entityDict.keys()) + "\t"+str(self.entityDict['spec']))
self.embeddings_spec = create_embedding_weight(embeddingPath,self.entityDict['spec'], embeddingDim,nonTrainable)
# self.entityDict.pop('spec')
for col in tokensPerColumnCountDict:
if tokensPerColumnCountDict[col] > self.outLength:
self.outLength = tokensPerColumnCountDict[col]
if loadPretrainEmbeddings:
self.embeddings = nn.ModuleList()
#Construct embedding array taking into account special tokens
for col in range(len(self.columnList)):
key = self.columnList[col]
if key == 'spec':
self.embeddings.append(self.embeddings_spec)
else:
self.embeddings.append(create_embedding_weight(embeddingPath,self.entityDict[key], embeddingDim,nonTrainable))
else:
self.embeddings = nn.ModuleList([
nn.Embedding(self.tokensPerColumnCountDict[s], embeddingDim)
for s in self.tokensPerColumnCountDict
#for s in range(len(self.columnList))
])
self.position = PositionalEncoding(embeddingDim, 0)
self.transformer = make_model( N=arg_N, d_model=embeddingDim, d_ff=4*embeddingDim, h=4)
self.W1 = nn.Linear(embeddingDim, embeddingDim).to(device)
#self.tab_emb=nn.Embedding(count_col, embedding_dim)
self.decoder = nn.ModuleList([
nn.Linear(embeddingDim,self.tokensPerColumnCountDict[s])
for s in self.tokensPerColumnCountDict
#for s in range(len(self.columnList))
])
if tie_weights:
for k in range(len(self.tokensPerColumnCountDict)):
self.decoder[k].weight = self.embeddings[k].weight
else:
for k in range(len(self.tokensPerColumnCountDict)):
self.decoder[k].load_state_dict({"weight": self.embeddings[k].weight.data,"bias": self.decoder[k].bias.data})
self.cls=nn.Linear(embeddingDim, 2)
self.m = nn.Softmax(dim=-1)
def NSP(self, sentence, mask, maskedIndex):
sentence = sentence.to(device) #sentence is a np.array
BAT = sentence.shape[0] # number of sentences
B = sentence.shape[1] #length of the sentences
x_array = []
#adding for trail
mask = mask.to(device)
colIndex = 0
for k in self.tokensPerColumnCountDict:
x_array.append(self.embeddings[k](sentence[:,k]).to(device).reshape(BAT,-1,self.embeddingDim))
#x_array.append(self.embeddings[k](sentence[:,k]).reshape(BAT,-1,self.embeddingDim))
x = torch.cat(x_array,dim=1) #substituting tokens with their respective embedding
if self.position_arg:
x = self.position(x)
# print("Before size of mask and sentence: ",str(mask.size()), str(x.size()))
mask = mask.unsqueeze(-1)
print("size of mask and sentence: ",str(mask.size()), str(x.size()))
out = self.transformer.forward(x,mask) #pass the input from the encoder
cls_out = self.cls(out[:,0]).to(device)
nsp_predictions = self.m(cls_out.view(BAT,-1)).to(device)
return nsp_predictions
def MLM_NSP(self, sentence, mask, maskedIndex):
sentence = sentence.to(device) #sentence is a np.array
BAT = sentence.shape[0] # number of sentences
B = sentence.shape[1] #length of the sentences
x_array = []
#adding for trail
mask = mask.to(device)
for k in self.tokensPerColumnCountDict:
x_array.append(self.embeddings[k](sentence[:,k]).to(device).reshape(BAT,-1,self.embeddingDim))
#x_array.append(self.embeddings[k](sentence[:,k]).reshape(BAT,-1,self.embeddingDim))
x = torch.cat(x_array,dim=1) #substituting tokens with their respective embedding
if self.position_arg:
x = self.position(x)
# print("Before size of mask and sentence: ",str(mask.size()), str(x.size()))
#mask = torch.ones_like(sentence).to(device)
#mask[:,index] = torch.zeros(sentence.shape[0]).to(device)
#mask1 = torch.where(non_index == 0, torch.zeros_like(sentence).to(device), mask)
#mask = torch.tensor(mask)
mask = mask.unsqueeze(-1)
# print("After size of mask and sentence: ",str(mask.size()), str(x.size()))
out = self.transformer.forward(x,mask) #pass the input from the encoder
# nsp_out = self.cls(out[:,0]).to(device)
# nsp_predictions = self.m(nsp_out.view(BAT,-1)).to(device)
nsp_predictions = self.cls(out[:,0]).to(device)
i = 0
pred = []
for index in maskedIndex:
res = self.decoder[index](out[i, index])
i += 1
#print("res size:", str(res.size()))
#softMaxed = self.m(res).to(device)
softMaxed = res.to(device)
#print("SoftMaxed size:", str(softMaxed.size()))
neg_infinity = float('-inf')
tmp = torch.from_numpy(np.array([neg_infinity]*(self.outLength - softMaxed.size(0)))).to(device)
#softMaxed += [0.0] * (self.outLength - len(softMaxed))
softMaxed = torch.cat((softMaxed,tmp),0)
softMaxed = softMaxed.view(1,-1)
#print("After concat SoftMaxed size:", str(softMaxed.size()))
pred.append(softMaxed)
predictions = torch.cat(pred,dim=0)
#res = self.decoder[index](out[:,index])
#return self.m(res.view(BAT,-1))
# print("PRed output: ",str(predictions.size()))
#return torch.from_numpy(np.array(pred))
return predictions, nsp_predictions
#For finetuning
def MLM(self, sentence, mask, maskedIndex):
sentence = sentence.to(device) #sentence is a np.array
BAT = sentence.shape[0] # number of sentences
B = sentence.shape[1] #length of the sentences
x_array = []
#adding for trail
mask = mask.to(device)
for k in self.tokensPerColumnCountDict:
x_array.append(self.embeddings[k](sentence[:,k]).to(device).reshape(BAT,-1,self.embeddingDim))
#x_array.append(self.embeddings[k](sentence[:,k]).reshape(BAT,-1,self.embeddingDim))
x = torch.cat(x_array,dim=1) #substituting tokens with their respective embedding
if self.position_arg:
x = self.position(x)
# print("Before size of mask and sentence: ",str(mask.size()), str(x.size()))
#mask = torch.ones_like(sentence).to(device)
#mask[:,index] = torch.zeros(sentence.shape[0]).to(device)
#mask1 = torch.where(non_index == 0, torch.zeros_like(sentence).to(device), mask)
#mask = torch.tensor(mask)
mask = mask.unsqueeze(-1)
# print("After size of mask and sentence: ",str(mask.size()), str(x.size()))
out = self.transformer.forward(x,mask) #pass the input from the encoder
# nsp_out = self.cls(out[:,0]).to(device)
# nsp_predictions = self.m(nsp_out.view(BAT,-1)).to(device)
# nsp_predictions = self.cls(out[:,0]).to(device)
i = 0
pred = []
for index in maskedIndex:
res = self.decoder[index](out[i, index])
i += 1
#print("res size:", str(res.size()))
#softMaxed = self.m(res).to(device)
softMaxed = res.to(device)
#print("SoftMaxed size:", str(softMaxed.size()))
neg_infinity = float('-inf')
tmp = torch.from_numpy(np.array([neg_infinity]*(self.outLength - softMaxed.size(0)))).to(device)
#softMaxed += [0.0] * (self.outLength - len(softMaxed))
softMaxed = torch.cat((softMaxed,tmp),0)
softMaxed = softMaxed.view(1,-1)
#print("After concat SoftMaxed size:", str(softMaxed.size()))
pred.append(softMaxed)
predictions = torch.cat(pred,dim=0)
#res = self.decoder[index](out[:,index])
#return self.m(res.view(BAT,-1))
# print("PRed output: ",str(predictions.size()))
#return torch.from_numpy(np.array(pred))
return predictions
def forward(self, sentence, mask, maskedIndex):
sentence = sentence.to(device) #sentence is a np.array
BAT = sentence.shape[0] # number of sentences
B = sentence.shape[1] #length of the sentences
x_array = []
#adding for trail
mask = mask.to(device)
# for k in self.tokensPerColumnCountDict:
for k in range(len(self.columnList)):
x_array.append(self.embeddings[k](sentence[:,k]).to(device).reshape(BAT,-1,self.embeddingDim))
#x_array.append(self.embeddings[k](sentence[:,k]).reshape(BAT,-1,self.embeddingDim))
x = torch.cat(x_array,dim=1) #substituting tokens with their respective embedding
if self.position_arg:
x = self.position(x)
mask = mask.unsqueeze(-1)
out = self.transformer.forward(x,mask) #pass the input from the encoder
i = 0
pred = []
for index in maskedIndex:
res = self.decoder[index](out[i, index])
i += 1
softMaxed = res.to(device)
neg_infinity = float('-inf')
tmp = torch.from_numpy(np.array([neg_infinity]*(self.outLength - softMaxed.size(0)))).to(device)
softMaxed = torch.cat((softMaxed,tmp),0)
softMaxed = softMaxed.view(1,-1)
pred.append(softMaxed)
predictions = torch.cat(pred,dim=0)
return predictions
#For single latent space handling
class Attention_Database_NSP_SS(nn.Module):
# @staticmethod
def __init__(self,tokensCount,embeddingDim=300,arg_N=4,position_arg=True,loadPretrainEmbeddings=False,entityDict=None,nonTrainable=False,embeddingPath=get_model_path()+"actors_nam_data_clean.bin",tie_weights=True):
super(Attention_Database_NSP_SS, self).__init__()
# self.tokensPerColumnCountDict = tokensPerColumnCountDict
self.entityDict = entityDict
self.embeddingDim = embeddingDim
self.position_arg = position_arg
self.outLength = 0
# print("CHECK keys of entity dict: "+str(self.entityDict.keys()) + "\t"+str(self.entityDict['spec']))
# self.embeddings_spec = create_embedding_weight(embeddingPath,self.entityDict['spec'], embeddingDim,nonTrainable)
# self.entityDict.pop('spec')
# for col in tokensPerColumnCountDict:
# if tokensPerColumnCountDict[col] > self.outLength:
# self.outLength = tokensPerColumnCountDict[col]
self.outLength = tokensCount;
if loadPretrainEmbeddings:
self.embeddings = create_embedding_weight(embeddingPath,entityDict, embeddingDim,nonTrainable) #fetch learned embedding from the embedding_path
else:
self.embeddings = nn.Embedding(len(entityDict), embedding_dim)
self.position = PositionalEncoding(embeddingDim, 0)
self.transformer = make_model( N=arg_N, d_model=embeddingDim, d_ff=4*embeddingDim, h=4)
self.W1 = nn.Linear(embeddingDim, embeddingDim).to(device)
#self.tab_emb=nn.Embedding(count_col, embedding_dim)
self.decoder = nn.Linear(embeddingDim, tokensCount)
self.decoder.weight = self.embeddings.weight
# if tie_weights:
# for k in range(len(self.columnList)):
# self.decoder[k].weight = self.embeddings[k].weight
# else:
# for k in range(len(self.columnList)):
# self.decoder[k].load_state_dict({"weight": self.embeddings[k].weight.data,"bias": self.decoder[k].bias.data})
self.cls=nn.Linear(embeddingDim, 2)
self.m = nn.Softmax(dim=-1)
def NSP(self, sentence, mask, maskedIndex):
sentence = sentence.to(device) #sentence is a np.array
BAT = sentence.shape[0] # number of sentences
B = sentence.shape[1] #length of the sentences
x_array = []
#adding for trail
mask = mask.to(device)
colIndex = 0
#for k in self.columnList:
# x_array.append(self.embeddings[k](sentence[:,k]).to(device).reshape(BAT,-1,self.embeddingDim))
#x_array.append(self.embeddings[k](sentence[:,k]).reshape(BAT,-1,self.embeddingDim))
#x = torch.cat(x_array,dim=1) #substituting tokens with their respective embedding
x=(self.embeddings(sentence)).to(device).reshape(BAT,-1,self.embeddingDim)
if self.position_arg:
x = self.position(x)
# print("Before size of mask and sentence: ",str(mask.size()), str(x.size()))
mask = mask.unsqueeze(-1)
print("size of mask and sentence: ",str(mask.size()), str(x.size()))
out = self.transformer.forward(x,mask) #pass the input from the encoder
cls_out = self.cls(out[:,0]).to(device)
nsp_predictions = self.m(cls_out.view(BAT,-1)).to(device)
return nsp_predictions
def MLM_NSP(self, sentence, mask, maskedIndex):
sentence = sentence.to(device) #sentence is a np.array
BAT = sentence.shape[0] # number of sentences
B = sentence.shape[1] #length of the sentences
x_array = []
#adding for trail
mask = mask.to(device)
#for k in self.columnList:
# x_array.append(self.embeddings[k](sentence[:,k]).to(device).reshape(BAT,-1,self.embeddingDim))
#x_array.append(self.embeddings[k](sentence[:,k]).reshape(BAT,-1,self.embeddingDim))
#x = torch.cat(x_array,dim=1) #substituting tokens with their respective embedding
x=(self.embeddings(sentence)).to(device).reshape(BAT,-1,self.embeddingDim)
if self.position_arg:
x = self.position(x)
# print("Before size of mask and sentence: ",str(mask.size()), str(x.size()))
#mask = torch.ones_like(sentence).to(device)
#mask[:,index] = torch.zeros(sentence.shape[0]).to(device)
#mask1 = torch.where(non_index == 0, torch.zeros_like(sentence).to(device), mask)
#mask = torch.tensor(mask)
mask = mask.unsqueeze(-1)
# print("After size of mask and sentence: ",str(mask.size()), str(x.size()))
out = self.transformer.forward(x,mask) #pass the input from the encoder