seq_logproba,
temperature=1.0,
deterministic_synthesis=False,
- forbidden_tokens=None,
- forced_biases=None,
):
to_generate = (ar_mask.sum(0) > 0).nonzero()
logits = (logits / temperature).log_softmax(dim=-1)
- if forbidden_tokens is not None:
- logits = logits.masked_fill(forbidden_tokens, float("-inf"))
-
- if forced_biases is not None:
- logits = logits + forced_biases[None, :]
-
if deterministic_synthesis:
t_next = logits.argmax(-1)
else:
seq_logproba=seq_logproba,
temperature=temperature,
deterministic_synthesis=deterministic_synthesis,
- forbidden_tokens=forbidden_tokens,
- forced_biases=logit_biases,
)
model.train(t)
)
def save_quizzes(self, result_dir, filename_prefix, quizzes, prediction=False):
- print(f"DEBUG {quizzes.size()=}")
l = (quizzes.size(1) - 1) // 2
forward = (quizzes[:, 0] == self.token_forward).long()
backward = (quizzes[:, 0] == self.token_backward).long()
)
def compute_correctness(
- self, c_quizzes, models_for_validation, both_directions=True
+ self, c_quizzes, models_for_validation, both_directions=False
):
reversed_c_quizzes = self.reverse_time(c_quizzes)
ar_mask = self.make_ar_mask(c_quizzes)
- seq_logproba = torch.empty(ar_mask.size(0), device=self.device)
+ seq_logproba = torch.zeros(
+ c_quizzes.size(0),
+ max([m.id for m in models_for_validation]) + 1,
+ device=self.device,
+ )
# Check how many of models can solve the quizzes in both directions
for model in models_for_validation:
result = c_quizzes.clone()
+ seq_logproba[...] = 0.0
+
masked_inplace_autoregression(
model=model,
batch_size=self.batch_size,
input=result,
ar_mask=ar_mask,
- seq_logproba=seq_logproba,
+ seq_logproba=seq_logproba[:, model.id],
temperature=1.0,
deterministic_synthesis=True,
# progress_bar_desc="solving c_quizzes",
batch_size=self.batch_size,
input=reversed_result,
ar_mask=ar_mask,
- seq_logproba=seq_logproba,
+ seq_logproba=seq_logproba[:, model.id],
temperature=1.0,
deterministic_synthesis=True,
# progress_bar_desc="solving reversed c_quizzes",
nb_correct += correct
- return nb_correct
+ return nb_correct, seq_logproba
###############################################################
- def generate_quizzes(self, nb, model_for_generation, reverse_cleanup=False):
+ def generate_quizzes(self, nb, model_for_generation):
c_quizzes = torch.empty(
nb, self.train_w_quizzes.size(1), device=self.device, dtype=torch.int64
)
- c_quizzes[:, 0] = self.token_forward
-
ar_mask_first = torch.zeros(c_quizzes.size(), device=self.device)
ar_mask_first[:, : ar_mask_first.size(1) // 2 + 1] = 1
ar_mask_second = 1 - ar_mask_first
ar_mask_first[:, 0] = 0
ar_mask_second[:, 0] = 0
- seq_logproba = torch.empty(ar_mask_first.size(0), device=self.device)
+ seq_logproba = torch.zeros(ar_mask_first.size(0), device=self.device)
- if reverse_cleanup:
- warnings.warn("very high temperature with reversed cleanup", RuntimeWarning)
- temperature = 10.0
- else:
- temperature = 1.0
+ temperature = 10.0
- # warnings.warn("noise injection", RuntimeWarning)
- # noise_std = torch.rand(1).item()
- # self.logger(f"{noise_std=}")
+ # First, we generate the answer at high temperature
- # mygpt.set_noise_injection(model_for_generation, noise_std)
+ c_quizzes[:, 0] = self.token_backward
masked_inplace_autoregression(
model=model_for_generation,
device=self.device,
)
- # mygpt.set_noise_injection(model_for_generation, 0.0)
-
- ave_seq_logproba = seq_logproba.mean()
+ # Then, we generate the prompt deterministically
masked_inplace_autoregression(
model=model_for_generation,
input=c_quizzes,
ar_mask=ar_mask_second,
seq_logproba=seq_logproba,
- temperature=temperature,
+ temperature=1.0,
deterministic_synthesis=True,
device=self.device,
)
- if reverse_cleanup:
- c_quizzes = self.reverse_time(c_quizzes)
+ # Then we return the quizz, and re-generate the response, now
+ # deterministically
- masked_inplace_autoregression(
- model=model_for_generation,
- batch_size=self.batch_size,
- input=c_quizzes,
- ar_mask=ar_mask_second,
- seq_logproba=seq_logproba,
- temperature=temperature,
- deterministic_synthesis=True,
- device=self.device,
- )
+ c_quizzes = self.reverse_time(c_quizzes)
- c_quizzes = self.reverse_time(c_quizzes)
-
- masked_inplace_autoregression(
- model=model_for_generation,
- batch_size=self.batch_size,
- input=c_quizzes,
- ar_mask=ar_mask_second,
- seq_logproba=seq_logproba,
- temperature=temperature,
- deterministic_synthesis=True,
- device=self.device,
- )
+ masked_inplace_autoregression(
+ model=model_for_generation,
+ batch_size=self.batch_size,
+ input=c_quizzes,
+ ar_mask=ar_mask_second,
+ seq_logproba=seq_logproba,
+ temperature=temperature,
+ deterministic_synthesis=True,
+ device=self.device,
+ )
- return c_quizzes, seq_logproba.mean()
+ return c_quizzes