from torch import nn
from torch.nn import functional as F
+import mygpt
from mygpt import BracketedSequence
######################################################################
-
-class Gang(nn.Module):
- def __init__(self, models, nb_models_for_generation, mode="groupthink"):
- super().__init__()
- self.models = models
- self.nb_models_for_generation = nb_models_for_generation
- self.mode = mode
-
- def forward(self, bs):
- # If first = 0, we are re-starting an auto-regressive process,
- # that's the right moment to randomize who gonna do it
- if bs.first == 0:
- self.models_to_use = [
- self.models[k]
- for k in torch.randperm(len(self.models))[
- : self.nb_models_for_generation
- ]
- ]
-
- all_the_logits = torch.cat(
- [model(bs).x[None] for model in self.models_to_use], dim=0
- )
-
- if self.mode == "groupthink":
- y = all_the_logits.mean(dim=0)
- elif self.mode == "groupwork":
- m = torch.rand(all_the_logits.size(), device=all_the_logits.device)
- m = (m.sort(dim=0).indices == 0).long()
- y = (y * m).sum(dim=0)
- else:
- raise ValueError(f"Invalid mode {self.mode}")
-
- return BracketedSequence(y, bs.first, bs.nb)
-
-
-######################################################################
-
# ar_mask is a tensor with 0s and 1s, of same shape as input, with
# 1s where tokens should be generated. The others are kept
# unchanged.
b = torch.arange(input.size(1), device=input.device) > input.size(1) // 2
return b.long()[None, :].expand_as(input)
+ def generate_token_sequences(self, nb):
+ prompts, answers = self.problem.generate_prompts_and_answers(nb)
+ result = []
+
+ for prompt, answer in zip(prompts, answers):
+ if torch.rand(1) < 0.5:
+ a = [torch.tensor([self.token_forward]), prompt, answer]
+ else:
+ a = [torch.tensor([self.token_backward]), answer, prompt]
+
+ result.append(torch.cat(a, dim=0)[None, :])
+
+ return torch.cat(result, dim=0)
+
def __init__(
self,
problem,
nb_train_samples,
nb_test_samples,
batch_size,
- result_dir=None,
- logger=None,
+ result_dir,
+ logger,
device=torch.device("cpu"),
):
super().__init__()
+ v = problem.nb_token_values()
+ self.token_forward = v
+ self.token_backward = v + 1
+ self.nb_token_values = v + 2
+
self.problem = problem
self.batch_size = batch_size
self.device = device
+ self.logger = logger
- self.train_w_quizzes = self.problem.generate_token_sequences(
- nb_train_samples
- ).to(device)
- self.test_w_quizzes = self.problem.generate_token_sequences(nb_test_samples).to(
+ self.train_w_quizzes = self.generate_token_sequences(nb_train_samples).to(
device
)
- self.nb_codes = max(self.train_w_quizzes.max(), self.test_w_quizzes.max()) + 1
+ self.test_w_quizzes = self.generate_token_sequences(nb_test_samples).to(device)
self.train_c_quizzes = []
self.test_c_quizzes = []
if result_dir is not None:
- self.problem.save_quizzes(
- self.train_w_quizzes[:72], result_dir, "culture_w_quizzes"
+ self.save_quizzes(
+ result_dir, "culture_w_quizzes", self.train_w_quizzes[:72]
)
+ 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()
+ 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
+
+ if prediction:
+ predicted_prompts = backward
+ predicted_answers = forward
+ else:
+ predicted_prompts = None
+ predicted_answers = None
+
+ self.problem.save_quizzes(
+ result_dir,
+ filename_prefix,
+ prompts,
+ answers,
+ predicted_prompts,
+ predicted_answers,
+ )
+
def batches(self, split="train", desc=None):
assert split in {"train", "test"}
if split == "train":
yield batch
def vocabulary_size(self):
- return self.nb_codes
+ return self.nb_token_values
def produce_results(
- self, n_epoch, model, result_dir, logger, deterministic_synthesis, nmax=1000
+ self, n_epoch, model, result_dir, deterministic_synthesis, nmax=1000
):
- def compute_accuracy(input, logger=None):
+ def compute_accuracy(input):
input = input[:nmax]
ar_mask = self.make_ar_mask(input)
result = input.clone() * (1 - ar_mask)
train_nb_total, train_nb_correct = compute_accuracy(self.train_w_quizzes)
- logger(
+ self.logger(
f"accuracy_train {n_epoch} nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
)
- test_nb_total, test_nb_correct = compute_accuracy(self.test_w_quizzes, logger)
+ test_nb_total, test_nb_correct = compute_accuracy(self.test_w_quizzes)
- logger(
+ self.logger(
f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
)
main_test_accuracy = test_nb_correct / test_nb_total
- logger(f"main_test_accuracy {n_epoch} {main_test_accuracy}")
+ self.logger(f"main_test_accuracy {n_epoch} {main_test_accuracy}")
##############################
device=self.device,
)
- self.problem.save_quizzes(
- result[:72], result_dir, f"culture_prediction_{n_epoch:04d}_{model.id:02d}"
+ self.save_quizzes(
+ result_dir,
+ f"culture_prediction_{n_epoch:04d}_{model.id:02d}",
+ quizzes=result[:72],
+ prediction=True,
)
return main_test_accuracy
input = self.train_w_quizzes if for_train else self.test_w_quizzes
nb = min(nb, input.size(0))
input[:-nb] = input[nb:].clone()
- input[-nb:] = self.problem.generate_token_sequences(nb).to(self.device)
+ input[-nb:] = self.generate_token_sequences(nb).to(self.device)
def store_c_quizzes(self, new_c_quizzes, for_train=True):
if for_train:
else:
self.test_c_quizzes.append(new_c_quizzes)
- def comput_correctness(self, c_quizzes, models_for_validation):
- ###############################################################
- # Create the reverse quizzes
-
- token_forward, token_backward = self.problem.direction_tokens()
-
+ def reverse_time(self, c_quizzes):
l = (c_quizzes.size(1) - 1) // 2
- direction = c_quizzes[:, l : l + 1]
- direction = self.problem.token_forward * (
- direction == self.problem.token_backward
- ) + self.problem.token_backward * (direction == self.problem.token_forward)
- reverse_c_quizzes = torch.cat(
- [c_quizzes[:, l + 1 :], direction, c_quizzes[:, :l]], dim=1
+ direction = c_quizzes[:, 0:1]
+ direction = self.token_forward * (
+ direction == self.token_backward
+ ) + self.token_backward * (direction == self.token_forward)
+
+ return torch.cat(
+ [direction, c_quizzes[:, l + 1 :], c_quizzes[:, 1 : l + 1]], dim=1
)
+ def compute_correctness(
+ 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)
- ###############################################################
- # Check how many of the other models can solve them in both
- # directions
+ # Check how many of models can solve the quizzes in both directions
- nb_correct = []
+ nb_correct = 0
for model in models_for_validation:
result = c_quizzes.clone()
correct = (c_quizzes == result).long().min(dim=-1).values
- reverse_result = reverse_c_quizzes.clone()
+ if both_directions:
+ reversed_result = reversed_c_quizzes.clone()
- masked_inplace_autoregression(
- model=model,
- batch_size=self.batch_size,
- input=reverse_result,
- ar_mask=ar_mask,
- seq_logproba=seq_logproba,
- temperature=1.0,
- deterministic_synthesis=True,
- # progress_bar_desc="solving reversed c_quizzes",
- device=self.device,
- )
+ masked_inplace_autoregression(
+ model=model,
+ batch_size=self.batch_size,
+ input=reversed_result,
+ ar_mask=ar_mask,
+ seq_logproba=seq_logproba,
+ temperature=1.0,
+ deterministic_synthesis=True,
+ # progress_bar_desc="solving reversed c_quizzes",
+ device=self.device,
+ )
- reverse_correct = (
- (reverse_c_quizzes == reverse_result).long().min(dim=-1).values
- )
+ reversed_correct = (
+ (reversed_c_quizzes == reversed_result).long().min(dim=-1).values
+ )
+
+ correct *= reversed_correct
- nb_correct.append((correct * reverse_correct)[None, :])
+ # endif
- return torch.cat(nb_correct, dim=0).sum(dim=0)
+ nb_correct += correct
- def generate_quizzes(self, nb, model_for_generation, min_ave_seq_logproba):
- ###############################################################
- # Generate quizzes with model
+ return nb_correct
+ ###############################################################
+
+ 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
)
- ar_mask = torch.full(c_quizzes.size(), 1, device=self.device)
- seq_logproba = torch.empty(ar_mask.size(0), device=self.device)
+ 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
- # bracketing of the temperature to get the target logproba
+ seq_logproba = torch.empty(ar_mask_first.size(0), device=self.device)
- temperature = 1
- d_temperature = 1 / 3
+ temperature = 10.0
- while True:
- seq_logproba[...] = 0
+ # First, we generate the answer at high temperature
- masked_inplace_autoregression(
- model=model_for_generation,
- batch_size=self.batch_size,
- input=c_quizzes,
- ar_mask=ar_mask,
- seq_logproba=seq_logproba,
- temperature=temperature,
- deterministic_synthesis=False,
- # progress_bar_desc="sampling c_quizzes",
- device=self.device,
- )
-
- ave_seq_logproba = seq_logproba.mean()
-
- # If we do not have target logprobs, get out now
- if min_ave_seq_logproba is None:
- break
-
- # Oh man that's ugly
- if ave_seq_logproba < min_ave_seq_logproba:
- if d_temperature > 0:
- d_temperature *= -1 / 3
- temperature += d_temperature
- elif ave_seq_logproba > min_ave_seq_logproba * 0.99:
- if d_temperature < 0:
- d_temperature *= -1 / 3
- temperature += d_temperature
- else:
- break
+ c_quizzes[:, 0] = self.token_backward
- logger(f"changing temperature to {temperature}")
+ masked_inplace_autoregression(
+ model=model_for_generation,
+ batch_size=self.batch_size,
+ input=c_quizzes,
+ ar_mask=ar_mask_first,
+ seq_logproba=seq_logproba,
+ temperature=temperature,
+ deterministic_synthesis=False,
+ device=self.device,
+ )
- return c_quizzes, seq_logproba.mean()
+ ave_seq_logproba = seq_logproba.mean()
- ######################################################################
+ # Then, we generate the prompt deterministically
- def create_c_quizzes(
- self,
- nb,
- model_for_generation,
- models_for_validation,
- min_ave_seq_logproba,
- n_epoch,
- result_dir,
- logger,
- ):
- c_quizzes, ave_seq_logproba = self.generate_quizzes(
- nb, model_for_generation, min_ave_seq_logproba
+ 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=1.0,
+ deterministic_synthesis=True,
+ device=self.device,
)
- nb_correct = self.comput_correctness(c_quizzes, models_for_validation)
+ # Then we return the quizz, and re-generate the response, now
+ # deterministically
- return c_quizzes, nb_correct, ave_seq_logproba
+ c_quizzes = self.reverse_time(c_quizzes)
- ######################################################################
-
- def gang_create_c_quizzes(
- self,
- nb,
- nb_models_for_generation,
- models,
- mode,
- min_ave_seq_logproba,
- n_epoch,
- result_dir,
- logger,
- ):
- model_for_generation = Gang(models, nb_models_for_generation, mode)
- models_for_validation = models
- return self.create_c_quizzes(
- nb,
- model_for_generation,
- models_for_validation,
- min_ave_seq_logproba,
- n_epoch,
- result_dir,
- logger,
+ 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()
projects / culture.git / blobdiff