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import torch.optim as optim
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
import json
import os
import numpy as np
import math
from generate import generate_data
from models.hybrid import HybridModel
from models.transformer import generate_mask
def get_ident_name(args):
dashed_task_name = "-".join(args.task_name.split("_"))
return "run_%s_%s_%s_w%d_d%d_nh%d_sd%d_nn%d_nv%d" % (dashed_task_name, args.layer1, args.layer2, args.window, args.embed_dim, \
args.num_heads, args.state_dim, args.num_numbers, args.num_vocab)
def get_task_dir_name(args):
return args.task_name + "/" + args.data_name + "/" + get_ident_name(args)
def make_dir(args):
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 args.task_name not in os.listdir(base_path + ''):
os.mkdir(base_path + '/' + args.task_name)
if args.data_name not in os.listdir(base_path + '/' + args.task_name):
os.mkdir(base_path + '/' + args.task_name + "/" + args.data_name)
if get_ident_name(args) not in os.listdir(base_path + '/' + args.task_name + "/" + args.data_name):
os.mkdir(base_path + '/' + args.task_name + "/" + args.data_name + "/" + get_ident_name(args))
class CustomDataset(Dataset):
def __init__(self, data_in, data_out):
self.data_in = data_in
self.data_out = data_out
def __len__(self):
return len(self.data_in)
def __getitem__(self, idx):
sample_in = self.data_in[idx]
sample_out = self.data_out[idx]
return sample_in, sample_out
def get_scheduler(args, optimizer):
total_steps = args.batches_per_epoch * args.num_epochs
warmup_steps = args.batches_per_epoch // 10 # Make the warmup be 10% of an epoch
def lr_lambda(step):
if step < warmup_steps:
return step / warmup_steps
progress = (step - warmup_steps) / (total_steps - warmup_steps)
return 0.5 * (1 + math.cos(math.pi * progress))
return optim.lr_scheduler.LambdaLR(optimizer, lr_lambda)
def train_epoch(model, optimizer, lr_scheduler, criterion, mask, train_loader, args, device="cuda"):
model.train()
avg_loss = 0
avg_acc = 0
# for itr in range(args.batches_per_epoch):
# print("Batch:", itr)
# input_seqs, target_seqs = generate_data(args)
for itr, (input_seqs, target_seqs) in enumerate(train_loader):
input_seqs = input_seqs.to(device)
target_seqs = target_seqs.type(torch.LongTensor).to(device)
outputs = model(input_seqs, mask) # (batch, seq-1, vocab)
# Masked loss, ignore positions which have null tokens (== vocab_size-1)
loss_mask = (target_seqs != args.vocab_size-1)
loss = loss_mask.reshape(-1) * criterion(outputs.view(-1, args.vocab_size), target_seqs.reshape(-1))
loss = loss.sum() / loss_mask.sum()
acc = torch.sum(loss_mask & ((torch.argmax(outputs, dim=-1) - target_seqs) == 0)).item()
acc /= loss_mask.sum()
optimizer.zero_grad()
loss.backward()
optimizer.step()
lr_scheduler.step()
avg_loss += loss.item()
avg_acc += acc
avg_loss /= args.batches_per_epoch
avg_acc /= args.batches_per_epoch
return avg_loss, avg_acc
def eval_epoch(model, criterion, mask, eval_loader, args, device="cuda"):
model.eval()
avg_loss = 0
avg_acc = 0
for itr, (input_seqs, target_seqs) in enumerate(eval_loader):
input_seqs = input_seqs.to(device)
target_seqs = target_seqs.type(torch.LongTensor).to(device)
outputs = model(input_seqs, mask) # (batch, seq-1, vocab)
# Masked loss, ignore positions which have null tokens (== vocab_size-1)
loss_mask = (target_seqs != args.vocab_size-1)
loss = loss_mask.reshape(-1) * criterion(outputs.view(-1, args.vocab_size), target_seqs.reshape(-1))
loss = loss.sum() / loss_mask.sum()
acc = torch.sum(loss_mask & ((torch.argmax(outputs, dim=-1) - target_seqs) == 0)).item()
acc /= loss_mask.sum()
avg_loss += loss.item()
avg_acc += acc
avg_loss /= args.batches_per_epoch
avg_acc /= args.batches_per_epoch
return avg_loss, avg_acc
def choose_lr(args):
# If found already, use what is cached
ident_name = get_ident_name(args)
with open('models/lrs.json') as f:
d = json.load(f)
if (not args.lr_no_save) and ident_name in d.keys():
return d[ident_name]
train_dataset = CustomDataset(*generate_data(args, all_at_once=True))
train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True)
assert args.window <= args.sequence_len
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("Starting to find lr for results/" + args.data_name + "/" + args.run_name)
lrs = np.geomspace(args.lr_low, args.lr_high, args.lr_num)
criterion = nn.CrossEntropyLoss(reduction='none')
all_losses = np.zeros_like(lrs)
for itr, lr in enumerate(lrs):
curr_losses = []
print("- Current learning rate: %.4f" % lr)
for run in range(3):
model = HybridModel(args).to(device)
optimizer = optim.AdamW(model.parameters(), lr=lr, weight_decay=1e-2)
lr_scheduler = get_scheduler(args, optimizer)
mask = generate_mask(args.sequence_len, window=args.window).to(device)
# Training loop
for epoch in range(args.lr_epochs):
loss, acc = train_epoch(model, optimizer, lr_scheduler, criterion, mask, train_loader, args, device)
print(f"- - Loss: {loss:.4f}, Acc: {acc:.4f}")
curr_losses.append(loss)
all_losses[itr] = np.median(np.array(curr_losses))
all_losses = np.nan_to_num(all_losses, nan=100) # Nan needs to be a big number
lr = lrs[np.argmin(all_losses)]
print("Loss array:", all_losses)
print("Determined a lr of", lr)
if not args.lr_no_save:
# Saving the lr
with open('models/lrs.json') as f:
d = json.load(f)
d[ident_name] = lr
with open('models/lrs.json', 'w') as f:
json.dump(d, f, indent=4)
return lr
def train(args):
assert args.window <= args.sequence_len
if args.save:
dir_name = get_task_dir_name(args)
if not args.force_learn:
if args.ood_eval:
if os.path.isdir("results_ood/" + dir_name) and "run%d.pt" % args.run_number in os.listdir("results_ood/" + dir_name):
return
else:
if os.path.isdir("results/" + dir_name) and "run%d.pt" % args.run_number in os.listdir("results/" + dir_name):
return
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
train_dataset = CustomDataset(*generate_data(args, all_at_once=True))
train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True)
print("Starting work for results/" + args.data_name + "/" + args.run_name)
model = HybridModel(args).to(device)
criterion = nn.CrossEntropyLoss(reduction='none')
optimizer = optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-2)
lr_scheduler = get_scheduler(args, optimizer)
mask = generate_mask(args.sequence_len, window=args.window).to(device)
# Training loop
losses = []
accs = []
for epoch in range(args.num_epochs):
loss, acc = train_epoch(model, optimizer, lr_scheduler, criterion, mask, train_loader, args, device)
losses.append(loss)
accs.append(acc)
print(f"Loss: {losses[-1]:.4f}, Acc: {accs[-1]:.4f}")
losses = torch.Tensor(losses)
accs = torch.Tensor(accs)
# Evaluation
if args.ood_eval:
args.num_vocab = args.eval_num_vocab
args.num_numbers = args.eval_num_numbers
args.p = args.eval_p
args.num_bits = args.eval_num_bits
eval_dataset = CustomDataset(*generate_data(args, all_at_once=True))
eval_loader = DataLoader(eval_dataset, batch_size=args.batch_size, shuffle=True)
eval_loss, eval_acc = eval_epoch(model, criterion, mask, eval_loader, args, device)
print(f"Eval - Loss: {eval_loss:.4f}, Acc: {eval_acc:.4f}")
# Save the model
if args.save and not args.train_only:
make_dir(args)
dir_name = get_task_dir_name(args)
if args.ood_eval:
run_filename = "results_ood/" + dir_name + "/run%d.pt" % args.run_number
else:
run_filename = "results/" + dir_name + "/run%d.pt" % args.run_number
# Count of the parameters
params = sum(p.numel() for p in model.parameters() if p.requires_grad)
torch.save({"args": args, "losses": losses, "accs": accs, "param_count": params, "eval_loss": eval_loss, "eval_acc": eval_acc}, run_filename)
print("Saved and finished for %s" % run_filename)
return model |