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import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
import string
import pandas as pd
import numpy as np
from torch import nn
from sklearn.model_selection import train_test_split
# from gensim.models import Word2Vec
from torch.nn.utils.rnn import pack_padded_sequence
from pathlib import Path
import argparse
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, Trainer, TrainingArguments, AdamW, GPT2Tokenizer, GPT2Model, GPT2LMHeadModel
from transformers import GPTNeoForCausalLM, GPT2Tokenizer ,GPTNeoConfig
from transformers import BertConfig, EncoderDecoderConfig, EncoderDecoderModel,BertTokenizer
from transformers import GPT2TokenizerFast
# from peft import LoraModel, LoraConfig
from pathlib import Path
import datetime
from tqdm import tqdm
import random
from tqdm import tqdm
from torch.cuda.amp import autocast, GradScaler
import gc
import matplotlib.pyplot as plt
class Encoder(torch.nn.Module): #8,18,24 -> 8,40,24 (8x720 and 432x960)
def __init__(self,h=128,n=8, e=64, a=4, o=1280):
super(Encoder, self).__init__()
self.embed = nn.Embedding(50257,e)
# self.ip = nn.Sequential(
# nn.Linear(e,e//2),
# nn.ReLU(),
# nn.Linear(e//2,e)
# )
self.lstm = nn.LSTM(input_size=e,hidden_size=h,num_layers=n, batch_first=True, bidirectional=True)
self.sa = nn.MultiheadAttention(h*2, a, dropout=0.1, batch_first=True)
self.op = nn.Sequential(
nn.Linear(2*h, h//2),
nn.ReLU(),
nn.Linear(h//2 , o),
)
# self.__init_weights()
def forward(self, X):
emb = self.embed(X) #bs,seq ,e
# emb = self.ip(emb)
enc, (hidden, cell) = self.lstm(emb) #bs, seq, h #1,bs,h
query = enc #nn.MA expects ; seq, bs, h
atOp , atW = self.sa(query, query, query)
#convert back to bs,seq, h
# print(f'AtOp: {atOp.shape} | enc: {enc.shape}')
logits = self.op(atOp + enc)
# logits = self.op(enc)
return logits , hidden , cell
# def __init_weights(self):
# for module in [self.ip, self.op]:
# if isinstance(module, torch.nn.Linear):
# torch.nn.init.normal_(module.weight,mean = 0.0 , std=0.02)
# if module.bias is not None:
# torch.nn.init.zeros_(module.bias)
class Decoder(torch.nn.Module):
def __init__(self,h=128,n=8, e=64, a=4, o=50257):
super(Decoder, self).__init__()
self.embed = nn.Embedding(50257,e)
# self.ip = nn.Sequential(
# nn.Linear(e,e),
# nn.ReLU(),
# nn.Linear(e,e)
# )
self.lstm = nn.LSTM(input_size=e,hidden_size=h,num_layers=n, batch_first=True, bidirectional=True)
self.sa = nn.MultiheadAttention(h, a, dropout=0.1, batch_first=True)
self.op = nn.Sequential(
nn.Linear(2*h + e, h//2),
nn.ReLU(),
nn.Linear(h//2 , o),
)
# self.__init_weights()
def forward(self, ip, ho, co, enc, mask):
emb = self.embed(ip) #bs, seq_i, e
# emb = self.ip(emb)
dec, (ho, co) = self.lstm(emb, (ho, co)) #bs, seq_i, h #1,bs,h
query = emb #bs, seq_i, e
key = enc #bs, seq_e, o
value = enc #bs, seq_e, o
# print(f'Q:{query.shape} | K:{key.shape} | V:{value.shape}')
atOp , atW = self.sa(query, key, value, key_padding_mask=mask) #bs, seq_i, e
# print(f'Dec: {dec.shape} | atOp : {atOp.shape}')
op = torch.cat([dec.squeeze(dim=1), atOp.squeeze(dim=1)], dim=1) #bs, seq_i, 2*h + bs, seq_i, e -> bs, 2*h + r
# op = torch.cat([ho[-1], co[-1], atOp.reshape(atOp.size(0), -1)], dim=-1)
logits = self.op(op) #bs, o
return logits, ho ,co
# def __init_weights(self):
# for module in [self.ip, self.op]:
# if isinstance(module, torch.nn.Linear):
# torch.nn.init.normal_(module.weight,mean = 0.0 , std=0.02)
# if module.bias is not None:
# torch.nn.init.zeros_(module.bias)
def init_state(self, batch_size):
return (torch.zeros(2*self.n,batch_size, self.h).to(device),torch.zeros(2*self.n,batch_size, self.h).to(device))
class Seq2Seq(nn.Module):
def __init__(self, encoder, decoder):
super(Seq2Seq, self).__init__()
self.encoder = encoder
self.decoder = decoder
def forward(self, seq_ip, ip_mask, seq_tg):
enc, hidden, cell = self.encoder(seq_ip)
outputs = []
len_tg = seq_tg.shape[1]
dec_ip = seq_tg[:,0].unsqueeze(dim=-1)
# print('Target length: ')
for t in range(1, len_tg): # Teacher Forcing
op , hidden, cell = self.decoder(dec_ip, hidden, cell, enc, ip_mask)
outputs.append(op)
dec_ip = seq_tg[:,t].unsqueeze(dim=-1)
torch.stack(outputs, dim=1)
return outputs
def diverse_beam_search(decoder, encoder_output, ip_mask, hidden, cell, device, beam_width=5, diversity_penalty=0.7, max_len=100):
dec_ip = torch.tensor([50256]).type(torch.int64).to(device) # Start token
beams = [(0.0, [dec_ip.item()], hidden.clone(), cell.clone())] # (score, sequence, hidden, cell)
count = 0
for _ in range(max_len):
all_candidates = []
for score, seq, h, c in beams:
if seq[-1] == 50256 and count > 0: # EOS reached
all_candidates.append((score, seq, h, c))
continue
dec_out, h_new, c_new = decoder(
torch.tensor([seq[-1]]).unsqueeze(0).to(device), h, c, encoder_output, ip_mask
)
log_probs = torch.nn.functional.log_softmax(dec_out, dim=-1) # Shape: [1, vocab_size]
top_k_log_probs, top_k_tokens = torch.topk(log_probs, beam_width, dim=-1)
for i in range(beam_width):
new_score = score + top_k_log_probs[0, i].item() - (diversity_penalty * i) # Diversity penalty
new_seq = seq + [top_k_tokens[0, i].item()]
all_candidates.append((new_score, new_seq, h_new.clone(), c_new.clone()))
count = 1
# Select top beam_width candidates
beams = sorted(all_candidates, key=lambda x: x[0], reverse=True)[:beam_width]
if all(seq[-1] == 50256 for _, seq, _, _ in beams): # All beams ended
break
return beams[0][1] # Return highest-scoring sequence
def mbr_decoding(decoder, encoder_output, ip_mask, hidden, cell, device, num_candidates=10, max_len=100):
# Generate candidate sequences using top-k sampling
candidates = []
for _ in range(num_candidates):
dec_ip = torch.tensor([50256]).type(torch.int64).to(device)
seq = [dec_ip.item()]
h, c = hidden.clone(), cell.clone()
for _ in range(max_len):
dec_out, h, c = decoder(dec_ip.unsqueeze(0), h, c, encoder_output, ip_mask)
dec_ip = top_k_sampling(dec_out, k=5).unsqueeze(dim=0) # Use top-k for diversity
seq.append(dec_ip.item())
if dec_ip.item() == 50256:
break
candidates.append(seq)
# Score candidates by similarity (e.g., average overlap with others)
best_seq, best_score = None, float('-inf')
for i, cand in enumerate(candidates):
score = sum(sum(1 for t1, t2 in zip(cand, other) if t1 == t2)
for other in candidates if other != cand) / (len(candidates) - 1)
if score > best_score:
best_score, best_seq = score, cand
return best_seq
def top_k_sampling(logits, k=10, temperature=1.0):
logits = logits / temperature # Temperature scaling for diversity
probs = torch.nn.functional.softmax(logits, dim=-1)
top_k_probs, top_k_indices = torch.topk(probs, k, dim=-1)
sampled_idx = torch.multinomial(top_k_probs, num_samples=1)
return top_k_indices[0, sampled_idx.item()]
def genOp(encoder, decoder, device, ip, ip_mask, mode='greedy', temperature=1.0, k=13, beam_width=5, diversity_penalty=0.7, num_candidates=10, max_len=100):
encoder.eval()
decoder.eval()
# model.eval()
print(f'\n\n\n GENOP FX CALL \n\n\n')
with torch.no_grad():
enc, hidden, cell = encoder(ip)
print(f'Hidden : {hidden.shape} | Cell : {cell.shape}')
if mode == 'greedy':
outputs = []
dec_ip = torch.tensor([50256]).type(torch.int64).to(device)
count = 0
while True:
dec, hidden, cell = decoder(dec_ip.unsqueeze(dim=0), hidden, cell, enc, ip_mask)
dec_ip = torch.argmax(dec, dim=-1)
outputs.append(dec_ip.item())
count += 1
if count > max_len:
break
if dec_ip.item() == 50256:
print('Self terminated !!!')
break
return outputs
elif mode=='sample':
outputs = []
dec_ip = torch.tensor([50256]).type(torch.int64).to(device)
count = 0
while True:
dec, hidden, cell = decoder(dec_ip.unsqueeze(dim=0), hidden, cell, enc, ip_mask)
# print(dec)
dec = dec/temperature
dec = torch.nn.functional.softmax(dec, dim=-1)
dec_ip = torch.multinomial(input=dec, num_samples=1, replacement=True).squeeze(0)
outputs.append(dec_ip.item())
count += 1
if count > max_len:
break
if dec_ip.item() == 50256:
print('Self terminated !!!')
break
return outputs
elif mode=='top_k':
outputs = []
dec_ip = torch.tensor([50256]).type(torch.int64).to(device)
count = 0
while True:
dec, hidden, cell = decoder(dec_ip.unsqueeze(dim=0), hidden, cell, enc, ip_mask)
dec = torch.nn.functional.softmax(dec, dim=-1)
top_k_probs , top_k_indices = torch.topk(dec, k, dim=-1)
dec_ip = torch.multinomial(input=top_k_probs, num_samples=1, replacement=True).squeeze(0)
dec_ip = top_k_indices[0, dec_ip.item()].unsqueeze(dim=0)
outputs.append(dec_ip.item())
count += 1
if count > max_len:
break
if dec_ip.item() == 50256:
print('Self terminated !!!')
break
return outputs
elif mode=='diverse-beam-search':
outputs = diverse_beam_search(decoder, enc, ip_mask, hidden, cell, device, beam_width=beam_width, diversity_penalty=diversity_penalty)
# print(f'GenOP stack trace: {outputs}')
return outputs
elif mode=='min-bayes-risk':
outputs = mbr_decoding(decoder, enc, ip_mask, hidden, cell, device, num_candidates=num_candidates, max_len=max_len)
return outputs
# ip = torch.tensor([[50256, 11195, 318, 13837, 11, 8272, 318, 2688, 4345, 1578,
# 11, 4475, 318, 3909, 11, 3035, 767, 11, 1941, 318,
# 4793, 11, 2435, 357, 315, 66, 8, 318, 1478, 25,
# 405, 11, 1078, 437, 590, 318, 3126, 11, 2931, 23,
# 11, 4080, 318, 24880, 10499, 11, 3576, 11, 4492, 11,
# 19316, 318, 4793, 12, 12726, 37985, 9952, 4041, 11, 6057,
# 62, 13376, 318, 19446, 11, 30408, 448, 318, 10352, 11,
# 11195, 62, 26675, 318, 657, 11, 8272, 62, 26675, 318,
# 352, 11, 11195, 62, 79, 49809, 47, 310, 318, 5598,
# 7441, 8272, 62, 79, 49809, 47, 310, 318, 4570, 7441,
# 11195, 62, 20910, 22093, 318, 1542, 357, 1314, 828, 8272,
# 62, 20910, 22093, 318, 718, 357, 20, 828, 11195, 62,
# 69, 42033, 6935, 2175, 318, 838, 13, 15, 11, 8272,
# 62, 69, 42033, 6935, 2175, 318, 1315, 13, 15, 11,
# 11195, 62, 36022, 34, 1371, 318, 657, 13, 15, 11,
# 8272, 62, 36022, 34, 1371, 318, 352, 13, 15, 11,
# 11195, 62, 445, 34, 1371, 318, 657, 13, 15, 11,
# 8272, 62, 445, 34, 1371, 318, 657, 13, 15, 11,
# 11195, 62, 8210, 1460, 318, 657, 13, 15, 11, 8272,
# 62, 8210, 1460, 318, 604, 13, 15, 11, 11195, 62,
# 26502, 41389, 364, 318, 1478, 13, 15, 11, 8272, 62,
# 26502, 41389, 364, 318, 352, 13, 15, 11, 11195, 62,
# 82, 3080, 318, 642, 13, 15, 11, 8272, 62, 82,
# 3080, 318, 1596, 13, 15, 11, 11195, 62, 1161, 318,
# 16185, 11, 8272, 62, 1161, 318, 16185, 11, 24623, 318,
# 3594, 9952, 4041, 11, 16060, 62, 15592, 318, 449, 641,
# 29921, 9038, 11, 17121, 7096, 292, 11, 42, 14057, 9852,
# 2634, 11, 10161, 18713, 12119, 280, 2634, 11, 35389, 26689,
# 75, 1012, 488, 88, 11, 30847, 11979, 406, 73, 2150,
# 3900, 11, 13787, 292, 10018, 17479, 11, 40747, 32371, 23720,
# 11, 15309, 38142, 81, 367, 293, 65, 11, 34, 3798,
# 376, 24247, 65, 2301, 292, 11, 10161, 18713, 1215, 1765,
# 323, 273, 11, 5124, 2731, 978, 6199, 544, 11, 49680,
# 68, 311, 2194, 418, 11, 41, 21356, 48590, 18226, 12523,
# 11, 4826, 280, 6031, 3930, 11, 31579, 44871, 12104, 324,
# 13235, 11, 32, 1014, 62, 15592, 318, 5199, 3469, 11,
# 22946, 292, 3169, 359, 11, 20191, 44677, 11, 13217, 261,
# 44312, 11, 14731, 14006, 11, 24338, 9740, 9860, 11, 25372,
# 20017, 9557, 11, 45, 47709, 797, 78, 12, 34, 11020,
# 11, 9704, 20833, 11, 33, 11369, 38343, 5799, 11, 26886,
# 418, 1665, 33425, 11, 32027, 21298, 11, 31306, 6559, 19574,
# 1040, 11, 30365, 13058, 273, 11, 25596, 271, 3248, 64,
# 10788, 68, 11, 42, 538, 64, 11, 7575, 318, 4153,
# 6]])
# ip_mask = torch.tensor([[True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True, True,
# True, True, True, True, True, True, True, True, True, True, True]])
# device = 'cuda' if torch.cuda.is_available() else 'cpu'
# encoder = Encoder(h=64,n=2, e=64, a=4, o=64).to(device)
# decoder = Decoder(h=64,n=2, e=64, a=4, o=50257).to(device)
# model = Seq2Seq(encoder, decoder).to(device)
# # checkpoint = torch.load('./seq2seq_checkpoint.pt', weights_only=True, map_location=device)
# # model.load_state_dict(checkpoint['model_state_dict'])
# print(genOp(model.encoder, model.decoder, device, ip, ip_mask, mode='greedy', temperature=1.0, k=13, beam_width=5, diversity_penalty=0.7, num_candidates=10, max_len=100)) |