+++ /dev/null
-#!/usr/bin/env python
-
-# Any copyright is dedicated to the Public Domain.
-# https://creativecommons.org/publicdomain/zero/1.0/
-
-# Written by Francois Fleuret <francois@fleuret.org>
-
-import math, os, tqdm, warnings
-
-import torch, torchvision
-
-from torch import nn
-from torch.nn import functional as F
-
-import mygpt
-from mygpt import BracketedSequence
-
-######################################################################
-
-# 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.
-
-
-def one_batch_masked_inplace_autoregression(
- model,
- input,
- ar_mask,
- seq_logproba,
- temperature,
- deterministic_synthesis,
-):
- to_generate = (ar_mask.sum(0) > 0).nonzero()
-
- if to_generate.min() > 0:
- model(
- BracketedSequence(input, 0, to_generate.min())
- ) # Needed to initialize the model's cache
- for s in range(to_generate.min(), to_generate.max() + 1):
- output = model(BracketedSequence(input, s, 1)).x
-
- logits = output[:, s]
-
- logits = (logits / temperature).log_softmax(dim=-1)
-
- if deterministic_synthesis:
- t_next = logits.argmax(-1)
- else:
- dist = torch.distributions.categorical.Categorical(logits=logits)
- t_next = dist.sample()
-
- all_n = torch.arange(t_next.size(0))
-
- seq_logproba += logits[all_n, t_next]
-
- input[:, s] = ar_mask[:, s] * t_next + (1 - ar_mask[:, s]) * input[:, s]
-
-
-def masked_inplace_autoregression(
- model,
- batch_size,
- input,
- ar_mask,
- seq_logproba,
- temperature,
- deterministic_synthesis,
- forbidden_tokens=None,
- logit_biases=None,
- progress_bar_desc=None,
- device=torch.device("cpu"),
-):
- assert input.size() == ar_mask.size()
-
- batches = zip(
- input.split(batch_size),
- ar_mask.split(batch_size),
- seq_logproba.split(batch_size),
- )
-
- if progress_bar_desc is not None:
- batches = tqdm.tqdm(
- batches,
- dynamic_ncols=True,
- desc=progress_bar_desc,
- total=(input.size(0) + batch_size - 1) // batch_size,
- )
-
- with torch.autograd.no_grad():
- t = model.training
- model.eval()
-
- for input, ar_mask, seq_logproba in batches:
- one_batch_masked_inplace_autoregression(
- model=model,
- input=input,
- ar_mask=ar_mask,
- seq_logproba=seq_logproba,
- temperature=temperature,
- deterministic_synthesis=deterministic_synthesis,
- )
-
- model.train(t)
-
-
-######################################################################
-
-
-class QuizMachine:
- 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 non_trivial(self, quizzes):
- quizzes = quizzes.clone()
- n_forward = quizzes[quizzes[:, 0] == self.token_forward]
- n_backward = quizzes[:, 0] == self.token_backward
- backward = quizzes[n_backward]
- quizzes[n_backward] = self.reverse_time(quizzes[n_backward])
- return torch.logical_not(
- self.problem.trivial_prompts_and_answers(
- quizzes[:, 1 : 1 + self.prompt_len],
- quizzes[:, 2 + self.prompt_len :],
- )
- )
-
- 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 : 2 + self.prompt_len + self.answer_len],
- quizzes[:, 1 + self.prompt_len : 1 + self.prompt_len + 1],
- 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 reverse_random_half_in_place(self, quizzes):
- i = torch.rand(quizzes.size(0)) < 0.5
- if i.any():
- quizzes[i] = self.reverse_time(quizzes[i])
-
- 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)
-
- if self.prompt_len is None:
- self.prompt_len = prompts.size(1)
-
- if self.answer_len is None:
- self.answer_len = answers.size(1)
-
- assert prompts.size(1) == self.prompt_len and answers.size(1) == self.answer_len
-
- result = []
-
- for prompt, answer in zip(prompts, answers):
- a = [
- torch.tensor([self.token_forward]),
- prompt,
- torch.tensor([self.token_forward]),
- answer,
- ]
-
- result.append(torch.cat(a, dim=0)[None, :])
-
- return torch.cat(result, dim=0)
-
- def __init__(
- self,
- problem,
- nb_train_samples,
- nb_test_samples,
- back_accuracy,
- batch_size,
- 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.back_accuracy = back_accuracy
- self.batch_size = batch_size
- self.device = device
- self.logger = logger
- self.prompt_len = None
- self.answer_len = None
-
- self.train_w_quizzes = self.generate_token_sequences(nb_train_samples)
- self.reverse_random_half_in_place(self.train_w_quizzes)
- self.train_w_quizzes = self.train_w_quizzes.to(device)
-
- self.test_w_quizzes = self.generate_token_sequences(nb_test_samples).to(device)
- self.reverse_random_half_in_place(self.test_w_quizzes)
- self.test_w_quizzes = self.test_w_quizzes.to(device)
-
- self.train_c_quizzes = []
- self.test_c_quizzes = []
-
- if result_dir is not None:
- self.save_quizzes(
- result_dir,
- "culture_w_quizzes",
- self.train_w_quizzes[:72],
- )
-
- def save_quizzes(
- self,
- result_dir,
- filename_prefix,
- quizzes,
- mistakes=None,
- ):
- quizzes = quizzes.clone().to("cpu")
- n_forward = quizzes[quizzes[:, 0] == self.token_forward]
- n_backward = quizzes[:, 0] == self.token_backward
- backward = quizzes[n_backward]
- assert n_forward.size(0) + backward.size(0) == quizzes.size(0)
- quizzes[n_backward] = self.reverse_time(quizzes[n_backward])
-
- predicted_prompts = n_backward.long()
- predicted_answers = 1 - predicted_prompts
- if mistakes is not None:
- # 0/-1/+1 ~ not-to-predict / predicted wrong / predicted correct
- predicted_prompts *= mistakes.to("cpu")
- predicted_answers *= mistakes.to("cpu")
- else:
- # 0/2 ~ not-to-predict / to predict
- predicted_prompts *= 2
- predicted_answers *= 2
-
- self.problem.save_quizzes(
- result_dir,
- filename_prefix,
- quizzes[:, 1 : 1 + self.prompt_len],
- quizzes[:, 2 + self.prompt_len :],
- predicted_prompts,
- predicted_answers,
- )
-
- def batches(self, split="train", desc=None):
- assert split in {"train", "test"}
- if split == "train":
- w_quizzes = self.train_w_quizzes
- c_quizzes = self.train_c_quizzes
- else:
- w_quizzes = self.test_w_quizzes
- c_quizzes = self.test_c_quizzes
-
- if len(c_quizzes) > 0:
- c_quizzes = torch.cat(c_quizzes, dim=0)
- if c_quizzes.size(0) > w_quizzes.size(0) // 2:
- i = torch.randperm(c_quizzes.size(0))[: w_quizzes.size(0) // 2]
- c_quizzes = c_quizzes[i]
-
- i = torch.randperm(w_quizzes.size(0))[
- : w_quizzes.size(0) - c_quizzes.size(0)
- ]
- w_quizzes = w_quizzes[i]
-
- self.nb_batch_w_quizzes = w_quizzes.size(0)
- self.nb_batch_c_quizzes = c_quizzes.size(0)
-
- input = torch.cat([w_quizzes, c_quizzes], dim=0)
- else:
- input = w_quizzes
- self.nb_batch_w_quizzes = w_quizzes.size(0)
- self.nb_batch_c_quizzes = 0
-
- # Shuffle
- input = input[torch.randperm(input.size(0))]
-
- if desc is None:
- desc = f"epoch-{split}"
- for batch in tqdm.tqdm(
- input.split(self.batch_size), dynamic_ncols=True, desc=desc
- ):
- yield batch
-
- def vocabulary_size(self):
- return self.nb_token_values
-
- def produce_results(
- self, n_epoch, model, result_dir, deterministic_synthesis, nmax=1000
- ):
- def compute_accuracy(input, log_prefix=None):
- ar_mask = self.make_ar_mask(input)
- result = input.clone() * (1 - ar_mask)
- seq_logproba = torch.empty(input.size(0), device=self.device)
-
- masked_inplace_autoregression(
- model=model,
- batch_size=self.batch_size,
- input=result,
- ar_mask=ar_mask,
- seq_logproba=seq_logproba,
- temperature=1.0,
- deterministic_synthesis=deterministic_synthesis,
- progress_bar_desc=None,
- device=self.device,
- )
-
- correct = torch.empty(input.size(0), dtype=torch.int64, device=input.device)
-
- n_forward = input[:, 0] == self.token_forward
- n_backward = input[:, 0] == self.token_backward
-
- correct[n_forward] = (
- (input[n_forward] == result[n_forward]).long().min(dim=1).values
- )
-
- if self.back_accuracy and n_backward.any():
- # accuracy of B->A*->B*=B instead of B->A*=A
- back_input = self.reverse_time(result[n_backward])
- back_input[:, 2 + self.prompt_len :] = input[
- n_backward, 1 : 1 + self.answer_len
- ]
- _, correct[n_backward] = compute_accuracy(back_input)
-
- if log_prefix is not None:
- forward_nb_correct = correct[n_forward].sum()
- forward_nb_total = correct[n_forward].size(0)
- backward_nb_correct = correct[n_backward].sum()
- 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} %)"
- )
-
- return result, correct
-
- compute_accuracy(self.train_w_quizzes[:nmax], log_prefix="train")
-
- test_result, test_correct = compute_accuracy(
- self.test_w_quizzes[:nmax], log_prefix="test"
- )
-
- main_test_accuracy = test_correct.sum() / test_correct.size(0)
- self.logger(f"main_test_accuracy {n_epoch} {main_test_accuracy}")
-
- ##############################
-
- self.save_quizzes(
- result_dir,
- f"culture_prediction_{n_epoch:04d}_{model.id:02d}",
- quizzes=test_result[:72],
- mistakes=test_correct[:72] * 2 - 1,
- )
-
- return main_test_accuracy
-
- def renew_w_quizzes(self, nb, for_train=True):
- input = self.train_w_quizzes if for_train else self.test_w_quizzes
- nb = min(nb, input.size(0))
- 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)
-
- def store_c_quizzes(self, new_c_quizzes, for_train=True):
- if for_train:
- self.train_c_quizzes.append(new_c_quizzes)
- else:
- self.test_c_quizzes.append(new_c_quizzes)
-
- def logproba_solution(self, models, c_quizzes):
- logproba = c_quizzes.new_zeros(c_quizzes.size(0), len(models))
-
- 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
-
- ###############################################################
-
- def compute_correctness(
- self,
- c_quizzes,
- models_for_validation,
- bidirectional_validation=False,
- deterministic_validation=True,
- ):
- if bidirectional_validation:
- backward_c_quizzes = self.forward_to_backward(c_quizzes)
-
- seq_logproba = torch.zeros(
- c_quizzes.size(0),
- max([m.id for m in models_for_validation]) + 1,
- device=self.device,
- )
-
- nb_correct = 0
-
- seq_logproba[...] = 0.0
-
- for model in models_for_validation:
- result = c_quizzes.clone()
-
- ar_mask = self.make_ar_mask(result)
-
- masked_inplace_autoregression(
- model=model,
- batch_size=self.batch_size,
- input=result,
- ar_mask=ar_mask,
- seq_logproba=seq_logproba[:, model.id],
- temperature=1.0,
- deterministic_synthesis=deterministic_validation,
- # progress_bar_desc="solving c_quizzes",
- device=self.device,
- )
-
- correct = (c_quizzes == result).long().min(dim=-1).values
-
- if bidirectional_validation:
- backward_result = backward_c_quizzes.clone()
-
- ar_mask = self.make_ar_mask(backward_result)
-
- masked_inplace_autoregression(
- model=model,
- batch_size=self.batch_size,
- input=backward_result,
- ar_mask=ar_mask,
- seq_logproba=seq_logproba[:, model.id],
- temperature=1.0,
- deterministic_synthesis=deterministic_validation,
- # progress_bar_desc="solving backward c_quizzes",
- device=self.device,
- )
-
- backward_correct = (
- (backward_c_quizzes == backward_result).long().min(dim=-1).values
- )
-
- correct *= backward_correct
-
- # endif
-
- nb_correct += correct
-
- return nb_correct, seq_logproba
-
- ###############################################################
-
- def generate_quizzes(self, nb, model_for_generation, temperature=1.0):
- c_quizzes = torch.empty(
- nb, self.train_w_quizzes.size(1), device=self.device, dtype=torch.int64
- )
-
- 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=self.make_ar_mask(c_quizzes, first=True),
- seq_logproba=seq_logproba,
- temperature=temperature,
- deterministic_synthesis=False,
- device=self.device,
- )
-
- # Then, we generate the prompt at low temperature
-
- masked_inplace_autoregression(
- model=model_for_generation,
- batch_size=self.batch_size,
- input=c_quizzes,
- ar_mask=self.make_ar_mask(c_quizzes),
- seq_logproba=seq_logproba,
- temperature=1 / temperature,
- deterministic_synthesis=False,
- device=self.device,
- )
-
- # Then we return the quizz, and re-generate the response, now
- # at low temperature
-
- c_quizzes = self.reverse_time(c_quizzes)
-
- masked_inplace_autoregression(
- model=model_for_generation,
- batch_size=self.batch_size,
- input=c_quizzes,
- ar_mask=self.make_ar_mask(c_quizzes),
- seq_logproba=seq_logproba,
- temperature=1 / temperature,
- deterministic_synthesis=False,
- device=self.device,
- )
-
- return c_quizzes
projects / culture.git / blobdiff