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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 | from torch.nn import CrossEntropyLoss
from transformers import get_scheduler
from tqdm import tqdm
from pathlib import Path
from torch.optim import AdamW
import torch
import os
import json
from test_utils import evaluation
from accelerate import Accelerator
def get_lrs(args):
if not os.path.exists("results"):
return {}
if not os.path.exists("results/" + args.train_task):
return {}
if not os.path.exists("results/" + args.train_task + "/lrs.json"):
return {}
with open("results/" + args.train_task + "/lrs.json") as f:
lrs = json.load(f)
return lrs
def add_lr(args, lr):
lrs = get_lrs(args)
ident_name = get_data_ident_name(args) + "_" + get_ident_name(args)
lrs[ident_name] = lr
if not os.path.exists("results"):
os.mkdir("results")
if not os.path.exists("results/" + args.train_task):
os.mkdir("results/" + args.train_task)
with open("results/" + args.train_task + "/lrs.json", "w") as f:
json.dump(lrs, f)
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 = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)).view(*shift_labels.shape)
return torch.sum(loss*mask)/torch.sum(mask)
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_epoch(model, epoch, accelerator, optimizer, lr_scheduler, attention_mask, train_dataset, args, device="cuda"):
avg_loss = 0
count = 0
if args.progress_bar:
# progress_bar = tqdm(
# enumerate(train_dataset, start=1), total=args.epochs * args.num_examples,
# desc=f'Epoch {epoch + 1}/{args.epochs}'
# )
pass
else:
progress_bar = enumerate(train_dataset, start=1)
progress_bar = enumerate(train_dataset)
for step, batch in progress_bar:
x = batch['input_ids'].to(device)
y = batch['output_ids'].to(device)
loss_mask = batch['mask'].to(device)
logits = model(x, attention_mask=attention_mask, return_dict=True)['logits']
loss = ce_loss(y, logits, loss_mask)
# if (step+1) % args.num_log_steps == 0:
# avg_loss.append(0)
# count.append(0)
loss = loss / args.gradient_accumulation_steps
avg_loss += loss.item()
count += 1
accelerator.backward(loss)
if step % args.gradient_accumulation_steps == 0:
accelerator.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# completed_steps += 1
# This should never happen, but just in case
if step > args.epochs * args.num_examples:
# if step > args.num_examples:
assert False, "This should never happen"
# break
if args.progress_bar:
# Update tqdm description with the current loss
progress_bar.set_postfix({'Loss': loss.item()})
return avg_loss / count
def train(args, model, tokenizer, train_dataset, one_epoch=False):
optimizer = AdamW(model.parameters(), lr=args.lr, weight_decay=0.1)
# Put model on GPU
accelerator = Accelerator()
model, optimizer = accelerator.prepare(model, optimizer)
lr_scheduler = custom_get_scheduler(optimizer, args.epochs * args.num_examples // args.gradient_accumulation_steps)
attention_mask = torch.ones((args.sequence_length, args.sequence_length))
attention_mask = (torch.triu(attention_mask, diagonal=0) - torch.triu(attention_mask, diagonal=args.window)).T.to('cuda')
losses = []
accs = []
for epoch in range(args.epochs):
model.train()
avg_loss = train_epoch(model, epoch, accelerator, optimizer, lr_scheduler, attention_mask, train_dataset, args, device="cuda")
losses.append(avg_loss)
print(epoch, optimizer.param_groups[0]["lr"], avg_loss)
model.eval()
_, _, char_accuracy_list = evaluation(args, model, tokenizer, do_print=False)
accs.append(char_accuracy_list[0])
if one_epoch:
break
# return losses
return accs, losses[-1]
##################################################################################################################################
# Saving helpers
def get_data_ident_name(args):
return "data_%d_%d_%d" % (args.sequence_length, args.num_numbers, args.num_vocab)
def get_ident_name(args):
dashed_task_name = "-".join(args.train_task.split("_"))
# Depth tests
if args.num_layers is not None:
return "run_%s_%s-%d_w%d_d%d_nh%d_sd%d" % (dashed_task_name, args.model, args.num_layers, args.window, args.hidden_size, \
args.heads, args.state_dim)
elif args.layer3 is not None:
return "run_%s_%s-%s-%s_w%d_d%d_nh%d_sd%d" % (dashed_task_name, args.layer1, args.layer2, args.layer3, args.window, args.hidden_size, \
args.heads, args.state_dim)
else:
if args.mixed:
return "run-mixed_%s_%s-%s_w%d_d%d_nh%d_sd%d" % (dashed_task_name, args.layer1, args.layer2, args.window, args.hidden_size, \
args.heads, args.state_dim)
else:
return "run_%s_%s-%s_w%d_d%d_nh%d_sd%d" % (dashed_task_name, args.layer1, args.layer2, args.window, args.hidden_size, \
args.heads, args.state_dim)
def get_task_dir_name(args):
return args.train_task + "/" + args.data_name + "/" + get_ident_name(args)
def make_dir(args, saving='results'):
if saving == 'results':
if args.ood_eval:
if 'results_ood' not in os.listdir('.'):
os.mkdir('results_ood')
base_path = 'results_ood'
else:
if 'results' not in os.listdir('.'):
os.mkdir('results')
base_path = 'results'
if saving == 'model_results':
if 'model_results' not in os.listdir('.'):
os.mkdir('model_results')
base_path = 'model_results'
if args.train_task not in os.listdir(base_path + ''):
os.mkdir(base_path + '/' + args.train_task)
if args.data_name not in os.listdir(base_path + '/' + args.train_task):
os.mkdir(base_path + '/' + args.train_task + "/" + args.data_name)
if get_ident_name(args) not in os.listdir(base_path + '/' + args.train_task + "/" + args.data_name):
os.mkdir(base_path + '/' + args.train_task + "/" + args.data_name + "/" + get_ident_name(args))
def load_model(args, model):
path = 'model_results/' + get_task_dir_name(args)
# Load model
model = model.from_pretrained(path)
return model
def save_model(args, model):
path = 'model_results/' + get_task_dir_name(args)
if not os.path.exists(path):
make_dir(args, saving='model_results')
# Save model
model.save_pretrained(path)
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