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4ca4e4c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 | from torch.nn import CrossEntropyLoss
from transformers import get_scheduler, AutoModel
from tqdm import tqdm
from pathlib import Path
from torch.optim import AdamW
import torch
import os
from accelerate import Accelerator
def ce_loss(inputs, logits, mask):
# Shift so that tokens < n predict n
if type(logits) != torch.Tensor:
logits = logits['logits']
shift_labels = inputs.contiguous()
shift_logits = logits.contiguous()
mask = mask.contiguous().view(-1)
# Calculate per-token loss
loss_fct = CrossEntropyLoss(reduction='none')
# loss_fct = CrossEntropyLoss(ignore_index=TO_TOKEN['*'], reduction='none')
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
return torch.sum(loss*mask)/torch.sum(mask)
def get_optimizer(model, args):
optimizer = AdamW(model.parameters(), lr=args.lr, weight_decay=0.1)
return optimizer
def custom_get_scheduler(optimizer, num_training_steps):
lr_scheduler = get_scheduler(
name="linear",
optimizer=optimizer,
num_warmup_steps=100,
num_training_steps=num_training_steps,
)
return lr_scheduler
def train(args, model, optimizer, tokenizer, train_dataset):
# optimizer = get_optimizer(model, args)
# Put model on GPU
accelerator = Accelerator()
model, optimizer = accelerator.prepare(model, optimizer)
num_train_epochs = args.epochs
num_update_steps_per_epoch = args.num_examples
num_training_steps = num_train_epochs * num_update_steps_per_epoch
num_log_steps = 50
lr_scheduler = custom_get_scheduler(optimizer,num_training_steps)
gradient_accumulation_steps = 1
model.train()
completed_steps = 0
num_train_epochs = 1
for epoch in range(num_train_epochs):
avg_loss = [0]
count = [0]
if args.print:
progress_bar = tqdm(
enumerate(train_dataset, start=1), total=num_training_steps,
desc=f'Epoch {epoch + 1}/{num_train_epochs}'
)
else:
progress_bar = enumerate(train_dataset, start=1)
for step, batch in progress_bar:
x = batch['input_ids'].to('cuda')
y = batch['output_ids'].to('cuda')
mask = batch['mask'].to('cuda')
attention_mask = torch.ones((x.shape[1], x.shape[1]))
attention_mask = (torch.triu(attention_mask, diagonal=0) - torch.triu(attention_mask, diagonal=args.window)).T.to('cuda')
# attention_mask = attention_mask.unsqueeze(0).repeat(x.shape[0], 1, 1)
if args.model=="lstm":
assert False # Untested
state = model.init_hidden(args.train_batch_size, 'cuda')
logits, state = model(x, state, attention_mask=attention_mask)
else:
logits = model(x, attention_mask=attention_mask, return_dict=True)['logits']
# if args.model=="mamba":
# print(logits)
# assert False
# logits = logits[0]
loss = ce_loss(y, logits, mask)
if (step+1) % num_log_steps == 0:
avg_loss.append(0)
count.append(0)
loss = loss / gradient_accumulation_steps
avg_loss[-1] += loss.item()
count[-1] += 1
accelerator.backward(loss)
if step % gradient_accumulation_steps == 0:
accelerator.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
completed_steps += 1
if step > num_training_steps:
break
# Update tqdm description with the current loss
if args.print:
progress_bar.set_postfix({'Loss': loss.item()})
def get_filepath(args):
if args.model.startswith("T") or args.model == "hybrid" or args.model == "hybrid_nope":
path = "./output_dir/"+f"model_{args.model}_layer_{args.layers}_hidden_{args.hidden_size}_heads_{args.heads}_train_{args.train_task}_lr_{args.lr}_epochs_{args.epochs}_steps_{args.num_examples}/"
elif args.model == "lstm" or args.model == "mamba":
path = "./output_dir/"+f"model_{args.model}_layer_{args.layers}_hidden_{args.hidden_size}_train_{args.train_task}_lr_{args.lr}_epochs_{args.epochs}_steps_{args.num_examples}/"
return path
def load_model(args, model):
path = get_filepath(args)
# Load model
# if args.model=="lstm" or args.model=="mamba":
if args.model=="lstm":
path += "model.pt"
model = torch.load(path, weights_only=False)
else:
model = model.from_pretrained(path)
return model
def save_model(args, model):
path = get_filepath(args)
if not os.path.exists(path):
Path(path).mkdir(parents=True, exist_ok=True)
# Save model
# if args.model=="lstm" or args.model=="mamba":
if args.model=="lstm":
path += "model.pt"
torch.save(model, path)
else:
model.save_pretrained(path)
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