class QuizzMachine:
- def make_ar_mask(self, input, first, nb):
- i = torch.arange(input.size(1), device=input.device)
- b = torch.logical_and(i >= first, i < first + nb)
- return b.long()[None, :].expand_as(input)
+ def indices_forward_and_backward(self, quizzes):
+ i_forward = quizzes[:, 0] == self.token_forward
+ j_forward = quizzes[:, 1 + self.prompt_len] == self.token_forward
+ i_backward = quizzes[:, 0] == self.token_backward
+ j_backward = quizzes[:, 1 + self.answer_len] == self.token_backward
+ assert torch.logical_or(
+ torch.logical_and(i_forward, j_forward),
+ torch.logical_and(i_backward, j_backward),
+ ).all()
+ return i_forward, i_backward
+
+ def reverse_time(self, quizzes):
+ i_forward, i_backward = self.indices_forward_and_backward(quizzes)
+
+ forward_to_backward = torch.cat(
+ [
+ quizzes[:, 0:1],
+ quizzes[:, 2 + self.prompt_len :],
+ quizzes[:, 1 + self.prompt_len : 2 + self.prompt_len],
+ quizzes[:, 1 : 1 + self.prompt_len],
+ ],
+ dim=1,
+ )
+ forward_to_backward[:, 0] = self.token_backward
+ forward_to_backward[:, 1 + self.answer_len] = self.token_backward
+
+ backward_to_forward = torch.cat(
+ [
+ quizzes[:, 0:1],
+ quizzes[:, 2 + self.answer_len :],
+ quizzes[:, 1 + self.answer_len : 2 + self.answer_len],
+ quizzes[:, 1 : 1 + self.answer_len],
+ ],
+ dim=1,
+ )
+
+ backward_to_forward[:, 0] = self.token_forward
+ backward_to_forward[:, 1 + self.prompt_len] = self.token_forward
+
+ m = i_forward.long()[:, None]
+
+ return m * forward_to_backward + (1 - m) * backward_to_forward
+
+ def make_ar_mask(self, quizzes, first=False):
+ i_forward, i_backward = self.indices_forward_and_backward(quizzes)
+
+ t = torch.arange(quizzes.size(1), device=quizzes.device)
+
+ if first:
+ m_forward = (t >= 1).long() * (t < 1 + self.prompt_len).long()
+ m_backward = (t >= 1).long() * (t < 1 + self.answer_len).long()
+ else:
+ m_forward = (t >= 2 + self.prompt_len).long()
+ m_backward = (t >= 2 + self.answer_len).long()
+
+ m = i_forward.long()[:, None]
+
+ return m * m_forward + (1 - m) * m_backward
def generate_token_sequences(self, nb):
prompts, answers = self.problem.generate_prompts_and_answers(nb)
result_dir, "culture_w_quizzes", self.train_w_quizzes[:72]
)
+ # toto = self.reverse_time(self.train_w_quizzes[:72])
+ # self.save_quizzes(result_dir, "toto", toto)
+ # exit(0)
+
def save_quizzes(self, result_dir, filename_prefix, quizzes, prediction=False):
- l = (quizzes.size(1) - 1) // 2
- forward = (quizzes[:, 0] == self.token_forward).long()
- backward = (quizzes[:, 0] == self.token_backward).long()
- assert forward.equal(1 - backward)
- first = quizzes[:, 1 : 1 + l]
- second = quizzes[:, 1 + l : 1 + 2 * l]
- prompts = forward[:, None] * first + backward[:, None] * second
- answers = forward[:, None] * second + backward[:, None] * first
+ forward = quizzes[quizzes[:, 0] == self.token_forward]
+ ib = quizzes[:, 0] == self.token_backward
+ backward = quizzes[ib]
+ assert forward.size(0) + backward.size(0) == quizzes.size(0)
+ quizzes[ib] = self.reverse_time(quizzes[ib])
if prediction:
- predicted_prompts = backward
- predicted_answers = forward
+ predicted_prompts = ib
+ predicted_answers = torch.logical_not(ib)
else:
predicted_prompts = None
predicted_answers = None
self.problem.save_quizzes(
result_dir,
filename_prefix,
- prompts,
- answers,
+ quizzes[:, 1 : 1 + self.prompt_len],
+ quizzes[:, 2 + self.prompt_len :],
predicted_prompts,
predicted_answers,
)
):
def compute_accuracy(input):
input = input[:nmax]
- ar_mask = self.make_ar_mask(input, 2 + self.prompt_len, self.answer_len)
+ ar_mask = self.make_ar_mask(input)
result = input.clone() * (1 - ar_mask)
seq_logproba = torch.empty(input.size(0), device=self.device)
##############################
input = self.test_w_quizzes[:96]
- ar_mask = self.make_ar_mask(input, 2 + self.prompt_len, self.answer_len)
+ ar_mask = self.make_ar_mask(input)
result = input.clone() * (1 - ar_mask)
seq_logproba = torch.empty(input.size(0), device=self.device)
else:
self.test_c_quizzes.append(new_c_quizzes)
- def forward_to_backward(self, c_quizzes):
- prompts = c_quizzes[:, 1 : 1 + self.prompt_len]
- answers = c_quizzes[:, 2 + self.prompt_len :]
- return torch.cat(
- [
- c_quizzes.new_full((c_quizzes, 1), self.token_backward),
- answers,
- c_quizzes.new_full((c_quizzes, 1), self.token_backward),
- prompts,
- ],
- dim=1,
- )
-
- def backward_to_forward(self, c_quizzes):
- answers = c_quizzes[:, 1 : 1 + self.answer_len :]
- prompts = c_quizzes[:, 2 + self.answer_len :]
- return torch.cat(
- [
- c_quizzes.new_full((c_quizzes.size(0), 1), self.token_forward),
- prompts,
- c_quizzes.new_full((c_quizzes.size(0), 1), self.token_forward),
- answers,
- ],
- dim=1,
- )
-
def compute_correctness(
self,
c_quizzes,
seq_logproba[...] = 0.0
- ar_mask = self.make_ar_mask(result, 2 + self.prompt_len, self.answer_len)
+ ar_mask = self.make_ar_mask(result)
masked_inplace_autoregression(
model=model,
if bidirectional_validation:
backward_result = backward_c_quizzes.clone()
- ar_mask = self.make_ar_mask(
- backward_result, 2 + self.answer_len, self.prompt_len
- )
+ ar_mask = self.make_ar_mask(backward_result)
masked_inplace_autoregression(
model=model,
nb, self.train_w_quizzes.size(1), device=self.device, dtype=torch.int64
)
- 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.zeros(ar_mask_first.size(0), device=self.device)
+ seq_logproba = torch.zeros(nb, device=self.device)
# First, we generate the answer at high temperature
c_quizzes[:, 0] = self.token_backward
+ c_quizzes[:, 1 + self.answer_len] = self.token_backward
masked_inplace_autoregression(
model=model_for_generation,
batch_size=self.batch_size,
input=c_quizzes,
- ar_mask=ar_mask_first,
+ ar_mask=self.make_ar_mask(c_quizzes, first=True),
seq_logproba=seq_logproba,
temperature=temperature,
deterministic_synthesis=False,
model=model_for_generation,
batch_size=self.batch_size,
input=c_quizzes,
- ar_mask=ar_mask_second,
+ ar_mask=self.make_ar_mask(c_quizzes),
seq_logproba=seq_logproba,
temperature=1 / temperature,
deterministic_synthesis=False,
# Then we return the quizz, and re-generate the response, now
# at low temperature
- c_quizzes = self.backward_to_forward(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,
+ ar_mask=self.make_ar_mask(c_quizzes),
seq_logproba=seq_logproba,
temperature=1 / temperature,
deterministic_synthesis=False,
# Written by Francois Fleuret <francois@fleuret.org>
-import math, sys, tqdm, os
+import math, sys, tqdm, os, warnings
import torch, torchvision
if predicted_answers is None:
predicted_answers = 255
- def add_frame(x, c, margin):
- y = x.new_full(
- (x.size(0), x.size(1), x.size(2) + 2 * margin, x.size(3) + 2 * margin),
- 0,
- )
+ def add_frame(x, c, margin, bottom=False):
+ if bottom:
+ h, w, di, dj = x.size(2) + margin, x.size(3), 0, 0
+ else:
+ h, w, di, dj = (
+ x.size(2) + 2 * margin,
+ x.size(3) + 2 * margin,
+ margin,
+ margin,
+ )
+
+ y = x.new_full((x.size(0), x.size(1), h, w), 0)
+
if type(c) is int:
y[...] = c
else:
c = c.long()[:, None]
- c = c * torch.tensor([192, 192, 192], device=c.device) + (
+ c = c * torch.tensor([0, 0, 0], device=c.device) + (
1 - c
) * torch.tensor([255, 255, 255], device=c.device)
y[...] = c[:, :, None, None]
- y[:, :, margin:-margin, margin:-margin] = x
+
+ y[:, :, di : di + x.size(2), dj : dj + x.size(3)] = x
+
return y
margin = 4
- img_prompts = add_frame(self.frame2img(prompts.to("cpu")), 0, 1)
- img_answers = add_frame(self.frame2img(answers.to("cpu")), 0, 1)
+ img_prompts = add_frame(self.frame2img(prompts.to("cpu")), c=0, margin=1)
+ img_answers = add_frame(self.frame2img(answers.to("cpu")), c=0, margin=1)
- # img_prompts = add_frame(img_prompts, 255, margin)
- # img_answers = add_frame(img_answers, 255, margin)
+ img_prompts = add_frame(img_prompts, c=255, margin=margin, bottom=True)
+ img_answers = add_frame(img_answers, c=255, margin=margin, bottom=True)
- img_prompts = add_frame(img_prompts, predicted_prompts, margin)
- img_answers = add_frame(img_answers, predicted_answers, margin)
+ img_prompts = add_frame(
+ img_prompts, c=predicted_prompts, margin=margin, bottom=True
+ )
+ img_answers = add_frame(
+ img_answers, c=predicted_answers, margin=margin, bottom=True
+ )
+
+ marker_size = 8
separator = img_prompts.new_full(
- (img_prompts.size(0), img_prompts.size(1), img_prompts.size(2), margin), 255
+ (
+ img_prompts.size(0),
+ img_prompts.size(1),
+ img_prompts.size(2),
+ marker_size,
+ ),
+ 255,
)
- img = torch.cat([img_prompts, img_answers], dim=3)
+ for k in range(2, 2 * marker_size - 3):
+ i = k + 1 - marker_size
+ j = marker_size - 2 - abs(k - marker_size + 1)
+ separator[:, :, separator.size(2) // 2 + i, j] = 0
+ separator[:, :, separator.size(2) // 2 + i + 1, j] = 0
+
+ img = torch.cat([img_prompts, separator, img_answers], dim=3)
image_name = os.path.join(result_dir, filename)
torchvision.utils.save_image(
def generate_prompts_and_answers(self, nb):
frame_sequences = self.generate_frame_sequences(nb)
frame_sequences = torch.cat([x[None] for x in frame_sequences], dim=0)
+
prompts = frame_sequences[:, : frame_sequences.size(1) // 2].flatten(1)
+
answers = frame_sequences[:, frame_sequences.size(1) // 2 :].flatten(1)
+ # warnings.warn("dirty test with longer answer", RuntimeWarning)
+ # answers = torch.cat(
+ # [
+ # frame_sequences[:, frame_sequences.size(1) // 2 :],
+ # frame_sequences[:, frame_sequences.size(1) // 2 :],
+ # ],
+ # dim=3,
+ # ).flatten(1)
+
return prompts, answers
def save_quizzes(
projects / culture.git / commitdiff