self, n_epoch, model, result_dir, deterministic_synthesis, nmax=1000
):
def compute_accuracy(input, log_prefix=None):
+ input = input.to(self.device)
ar_mask = self.make_ar_mask(input)
result = input.clone() * (1 - ar_mask)
seq_logproba = torch.empty(input.size(0), device=self.device)
backward_nb_total = correct[n_backward].size(0)
self.logger(
- f"{log_prefix}_forward_accuracy {n_epoch} model {model.id} nb_correct {forward_nb_correct} / {forward_nb_total} ({forward_nb_correct*100/forward_nb_total} %)"
- )
-
- self.logger(
- f"{log_prefix}_backward_accuracy {n_epoch} model {model.id} nb_correct {backward_nb_correct} / {backward_nb_total} ({backward_nb_correct*100/backward_nb_total} %)"
+ f"{log_prefix}_accuracy {n_epoch} model {model.id} forward {forward_nb_correct} / {forward_nb_total} backward {backward_nb_correct} / {backward_nb_total}"
)
return result, correct
input[:-nb] = input[nb:].clone()
fresh_w_quizzes = self.generate_token_sequences(nb)
self.reverse_random_half_in_place(fresh_w_quizzes)
- input[-nb:] = fresh_w_quizzes.to(self.device)
+ input[-nb:] = fresh_w_quizzes.to("cpu")
######################################################################
def store_c_quizzes(self, new_c_quizzes, for_train=True):
with self.LOCK_C_QUIZZES:
if for_train:
- self.train_c_quizzes.append(new_c_quizzes)
+ self.train_c_quizzes.append(new_c_quizzes.to("cpu"))
else:
- self.test_c_quizzes.append(new_c_quizzes)
+ self.test_c_quizzes.append(new_c_quizzes.to("cpu"))
######################################################################
def logproba_of_solutions(self, models, c_quizzes):
- logproba = c_quizzes.new_zeros(c_quizzes.size(0), len(models))
+ logproba = c_quizzes.new_zeros(
+ c_quizzes.size(0), len(models), device=self.device, dtype=torch.float32
+ )
for model in models:
- for input, l in zip(
- c_quizzes.split(self.batch_size), logproba.split(self.batch_size)
- ):
- ar_mask = self.make_ar_mask(input)
- output = model(mygpt.BracketedSequence(input)).x
- ce = (
- F.cross_entropy(output.transpose(1, 2), input, reduction="none")
- * ar_mask
- )
- l[:, model.id] = -ce.sum(dim=-1)
-
- return logproba
+ with torch.autograd.no_grad():
+ t = model.training
+ model.eval()
+
+ for input, l in zip(
+ c_quizzes.split(self.batch_size), logproba.split(self.batch_size)
+ ):
+ input = input.to(self.device)
+ ar_mask = self.make_ar_mask(input)
+ output = model(mygpt.BracketedSequence(input)).x
+ ce = (
+ F.cross_entropy(output.transpose(1, 2), input, reduction="none")
+ * ar_mask
+ )
+ l[:, model.id] = -ce.sum(dim=-1)
+
+ model.train(t)
+
+ return logproba.to("cpu")
###############################################################
device=self.device,
)
- return c_quizzes
+ return c_quizzes.to("cpu")