type = int, default = 0)
parser.add_argument('--nb_epochs',
- type = int, default = 100)
+ type = int, default = -1)
parser.add_argument('--batch_size',
type = int, default = 25)
parser.add_argument('--synthesis_sampling',
action='store_true', default = True)
+parser.add_argument('--no_checkpoint',
+ action='store_true', default = False)
+
parser.add_argument('--checkpoint_name',
type = str, default = 'checkpoint.pth')
######################################################################
+def produce_results(
+ self,
+ model, nb_samples, nb_tokens_to_generate, starting_input = None,
+ device = 'cpu'
+):
+ results = torch.zeros(nb_samples, nb_tokens_to_generate, dtype = torch.int64, device = device)
+ for input in results.split(self.batch_size):
+ for s in tqdm.tqdm(range(input.size(1) - 1), desc = 'synth'):
+ output = model(input)
+ logits = output[:, s]
+ if args.synthesis_sampling:
+ dist = torch.distributions.categorical.Categorical(logits = logits)
+ t = dist.sample()
+ else:
+ t = logits.argmax(1)
+ input[:, s + 1] = t
+
+######################################################################
+
class Task:
def batches(self, split = 'train'):
pass
self.train_descr = generate_descr((nb * 4) // 5)
self.test_descr = generate_descr((nb * 1) // 5)
+ # Build the tokenizer
tokens = set()
for d in [ self.train_descr, self.test_descr ]:
for s in d:
img = [ picoclvr.descr2img(d, height = self.height, width = self.width) for d in descr ]
img = torch.cat(img, 0)
- file_name = f'result_picoclvr_{n_epoch:04d}.png'
+ image_name = f'result_picoclvr_{n_epoch:04d}.png'
torchvision.utils.save_image(
img / 255.,
- file_name, nrow = nb_per_primer, pad_value = 0.8
+ image_name, nrow = nb_per_primer, pad_value = 0.8
)
- log_string(f'wrote {file_name}')
+ log_string(f'wrote {image_name}')
nb_missing = sum( [
x[2] for x in picoclvr.nb_missing_properties(
def produce_results(self, n_epoch, model, nb_samples = 64):
results = torch.zeros(nb_samples, 28 * 28, dtype = torch.int64, device = self.device)
for input in results.split(self.batch_size):
- for s in tqdm.tqdm(range(input.size(1) - 1), desc = 'synth'):
+ for s in tqdm.tqdm(range(input.size(1)), desc = 'synth'):
output = model(input)
logits = output[:, s]
if args.synthesis_sampling:
t = dist.sample()
else:
t = logits.argmax(1)
- input[:, s + 1] = t
+ input[:, s] = t
image_name = f'result_mnist_{n_epoch:04d}.png'
torchvision.utils.save_image(1 - results.reshape(-1, 1, 28, 28) / 255.,
######################################################################
-def check_causality(model):
- #m = model[1:]
- input = torch.rand(1, 5, dim_model).requires_grad_()
- output = m(input)
- a = torch.zeros(output.size(1), input.size(1))
- for k in range(output.size(1)):
- for d in range(output.size(2)):
- g, = torch.autograd.grad(output[0, k, d], input, retain_graph = True)
- a[k] += g.squeeze(0).pow(2).sum(1)
- print(a)
-
-######################################################################
-
log_string(f'device {device}')
if args.data == 'wiki103':
+ nb_epochs_default = 10
task = TaskWiki103(batch_size = args.batch_size, device = device)
elif args.data == 'mnist':
+ nb_epochs_default = 25
task = TaskMNIST(batch_size = args.batch_size, device = device)
elif args.data == 'picoclvr':
+ nb_epochs_default = 10
task = TaskPicoCLVR(batch_size = args.batch_size,
height = args.picoclvr_height,
width = args.picoclvr_width,
nb_epochs_finished = 0
-try:
- checkpoint = torch.load(args.checkpoint_name, map_location = device)
- nb_epochs_finished = checkpoint['nb_epochs_finished']
- model.load_state_dict(checkpoint['model_state'])
- optimizer.load_state_dict(checkpoint['optimizer_state'])
- print(f'Checkpoint loaded with {nb_epochs_finished} epochs finished.')
+if args.no_checkpoint:
+ log_string(f'Not trying to load checkpoint.')
+
+else:
+ try:
+ checkpoint = torch.load(args.checkpoint_name, map_location = device)
+ nb_epochs_finished = checkpoint['nb_epochs_finished']
+ model.load_state_dict(checkpoint['model_state'])
+ optimizer.load_state_dict(checkpoint['optimizer_state'])
+ log_string(f'Checkpoint loaded with {nb_epochs_finished} epochs finished.')
-except FileNotFoundError:
- print('Starting from scratch.')
+ except FileNotFoundError:
+ log_string('Starting from scratch.')
-except:
- print('Error when loading the checkpoint.')
- exit(1)
+ except:
+ log_string('Error when loading the checkpoint.')
+ exit(1)
######################################################################
-for k in range(nb_epochs_finished, args.nb_epochs):
+nb_epochs = args.nb_epochs if args.nb_epochs > 0 else nb_epochs_default
+
+token_count = 0
+for input in task.batches(split = 'train'):
+ token_count += F.one_hot(input, num_classes = task.vocabulary_size()).sum((0, 1))
+token_probas = token_count / token_count.sum()
+h = -torch.xlogy(token_probas, token_probas).sum()
+train_set_perplexity = math.exp(h)
+log_string(f'Train set perplexity {train_set_perplexity}')
+
+for k in range(nb_epochs_finished, nb_epochs):
model.train()
for input in task.batches(split = 'train'):
input = input.to(device)
output = model(input)
- loss = F.cross_entropy(output[:, :-1].transpose(1, 2), input[:, 1:])
+ loss = F.cross_entropy(output.transpose(1, 2), input)
acc_train_loss += loss.item() * input.size(0)
nb_train_samples += input.size(0)
for input in task.batches(split = 'test'):
input = input.to(device)
output = model(input)
- loss = F.cross_entropy(output[:, :-1].transpose(1, 2), input[:, 1:])
+ loss = F.cross_entropy(output.transpose(1, 2), input)
acc_test_loss += loss.item() * input.size(0)
nb_test_samples += input.size(0)
train_perplexity = math.exp(min(100, acc_train_loss/nb_train_samples))
test_perplexity = math.exp(min(100, acc_test_loss/nb_test_samples))
- log_string(f'perplexity {k+1} train {train_perplexity} test {test_perplexity}')
+ log_string(f'perplexity {k} train {train_perplexity} test {test_perplexity}')
task.produce_results(k, model)
projects / mygpt.git / blobdiff