nb_samples, nb_tokens_to_generate, starting_input = None,
device = torch.device('cpu')
):
- first = 0
results = torch.zeros(
nb_samples, nb_tokens_to_generate,
dtype = torch.int64, device = device
)
- if starting_input is not None:
+ if starting_input is None:
+ first = 0
+ else:
first = starting_input.size(1)
results = torch.cat((starting_input, results), 1)
- for input in results.split(self.batch_size):
+ for input in results.split(args.batch_size):
for s in tqdm.tqdm(range(first, input.size(1)), desc = 'synth'):
output = model(input)
logits = output[:, s]
nb_epochs_finished = 0
if args.no_checkpoint:
- log_string(f'Not trying to load checkpoint.')
+ log_string(f'not trying to load checkpoint.')
else:
try:
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.')
+ log_string(f'checkpoint loaded with {nb_epochs_finished} epochs finished.')
except FileNotFoundError:
- log_string('Starting from scratch.')
+ log_string('starting from scratch.')
except:
- log_string('Error when loading the checkpoint.')
+ log_string('error when loading the checkpoint.')
exit(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}')
+log_string(f'train set perplexity {train_set_perplexity}')
for k in range(nb_epochs_finished, nb_epochs):