From ead4b8e4edd29578c01501d168e416b47fa4047b Mon Sep 17 00:00:00 2001 From: =?utf8?q?Fran=C3=A7ois=20Fleuret?= Date: Tue, 18 Jul 2023 17:26:17 +0200 Subject: [PATCH] Update. --- main.py | 3 +- tasks.py | 156 +++++++++++++++++++++++++++++++++++++++++++++---------- 2 files changed, 132 insertions(+), 27 deletions(-) diff --git a/main.py b/main.py index 3be3d55..213524e 100755 --- a/main.py +++ b/main.py @@ -266,7 +266,8 @@ picoclvr_pruner_eval = ( if args.task == "sandbox": task = tasks.SandBox( - tasks.ProblemByheart(), + tasks.ProblemLevel1(), + # tasks.ProblemAddition(zero_padded=False, inverted_result=False), nb_train_samples=args.nb_train_samples, nb_test_samples=args.nb_test_samples, batch_size=args.batch_size, diff --git a/tasks.py b/tasks.py index fb85576..332d6c5 100755 --- a/tasks.py +++ b/tasks.py @@ -67,13 +67,15 @@ class Problem: def generate_sequences(self, nb): pass - def log_performance(self, sequences, logger): - pass + def seq2str(self, seq): + return "[NOT IMPLEMENTED]" + + +#################### -class ProblemByheart(Problem): - def __init__(self): - nb_seq, len_prompt, len_result = 100, 5, 5 +class ProblemLevel0(Problem): + def __init__(self, nb_sentences=100, len_prompt=5, len_result=5): self.seq = torch.randint(10, (nb_seq, len_prompt + 1 + len_result)) self.seq[:, len_prompt] = 10 @@ -83,20 +85,104 @@ class ProblemByheart(Problem): ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1) return sequences, ar_mask - # problems = [ProblemByheart()] - # nb_common_codes = 100 - # def generate_sequences(nb_samples): - # problem_indexes = torch.randint(len(problems), (nb_samples,)) - # nb_samples_per_problem = torch.one_hot(problem_indexes).sum(0) - # print(f"{nb_samples_per_problem}") - # all_seq = [] - # for nb, p in zip(nb_samples_per_problem, problems): - # all_seq.append(p.generate_sequences(nb_samples_per_problem[nb])) - # return all_seq +class ProblemLevel1(Problem): + def __init__(self, nb_operators=100, len_prompt=5, len_result=8): + self.len_prompt = len_prompt + self.len_result = len_result + self.len_nb_operator = int(math.log(nb_operators) / math.log(10)) + 1 + self.operators = F.one_hot( + torch.rand(nb_operators, len_result, len_prompt).argmax(-1), + num_classes=len_prompt, + ) + + def generate_sequences(self, nb): + a = self.len_nb_operator + b = a + 1 + self.len_prompt + sequences = torch.empty(nb, b + 1 + self.len_result, dtype=torch.int64) + nb_operators = torch.randint(self.operators.size(0), (nb,)) + sequences[:, :a] = (nb_operators[:, None] / 10 ** torch.arange(a)) % 10 + sequences[:, a] = 10 + sequences[:, a + 1 : b] = torch.randint(10, (nb, b - a - 1)) + sequences[:, b] = 11 + + o = self.operators[nb_operators] + p = sequences[:, a + 1 : b] + print(f"{o.size()=} {p.size()=} {sequences[:,b+1:].size()=}") + sequences[:, b + 1 :] = o.bmm(p[:, :, None]).squeeze(-1) + ar_mask = (sequences == 11).long() + ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1) + return sequences, ar_mask + + def seq2str(self, seq): + return "".join(self.id2char[x.item()] for x in seq) - # for strain, stest in zip(train_seq, test_seq): - # s = torch.cat((strain, stest), 0) + +#################### + + +class ProblemAddition(Problem): + def __init__(self, nb_digits=10, zero_padded=False, inverted_result=False): + self.nb_digits = nb_digits + self.zero_padded = zero_padded + self.inverted_result = inverted_result + self.char2id = dict([(c, n) for n, c in enumerate("0123456789+=$")]) + self.id2char = dict([(n, c) for c, n in self.char2id.items()]) + + def tensorize(self, strings): + len_max = max([len(x) for x in strings]) + return torch.cat( + [ + torch.tensor( + [ + [self.char2id[c] for c in s + "$" * (len_max - len(s))] + for s in strings + ] + ) + ], + 0, + ) + + def generate_sequences(self, nb): + sequences = [] + for k in range(nb): + a, b = torch.randint(10**self.nb_digits, (2,)) + c = a + b + a, b, c = str(a.item()), str(b.item()), str(c.item()) + if self.zero_padded: + a = "0" * (self.nb_digits - len(a)) + a + b = "0" * (self.nb_digits - len(b)) + b + c = "0" * (self.nb_digits + 1 - len(c)) + c + if self.inverted_result: + c = c[::-1] + sequences.append(f"{a}+{b}={c}$") + + sequences = self.tensorize(sequences) + ar_mask = (sequences == self.char2id["="]).long() + ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1) + return sequences, ar_mask + + def seq2str(self, seq): + return "".join(self.id2char[x.item()] for x in seq) + + +# class ProblemUnion(Problem): +# problems = [ProblemByheart()] +# nb_common_codes = 100 + +# def generate_sequences(nb_samples): +# problem_indexes = torch.randint(len(problems), (nb_samples,)) +# nb_samples_per_problem = torch.one_hot(problem_indexes).sum(0) +# print(f"{nb_samples_per_problem}") +# all_seq = [] +# for nb, p in zip(nb_samples_per_problem, problems): +# all_seq.append(p.generate_sequences(nb_samples_per_problem[nb])) +# return all_seq + +# for strain, stest in zip(train_seq, test_seq): +# s = torch.cat((strain, stest), 0) + +#################### class SandBox(Task): @@ -114,11 +200,21 @@ class SandBox(Task): self.batch_size = batch_size self.device = device + self.problem = problem - self.train_input, self.train_ar_mask = problem.generate_sequences( + self.train_input, self.train_ar_mask = self.problem.generate_sequences( nb_train_samples ) - self.test_input, self.test_ar_mask = problem.generate_sequences(nb_test_samples) + self.test_input, self.test_ar_mask = self.problem.generate_sequences( + nb_test_samples + ) + + self.train_input, self.train_ar_mask = self.train_input.to( + device + ), self.train_ar_mask.to(device) + self.test_input, self.test_ar_mask = self.test_input.to( + device + ), self.test_ar_mask.to(device) self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1 @@ -147,10 +243,12 @@ class SandBox(Task): return self.nb_codes def produce_results( - self, n_epoch, model, result_dir, logger, deterministic_synthesis + self, n_epoch, model, result_dir, logger, deterministic_synthesis, nmax=1000 ): - def compute_accuracy(input, ar_mask): + def compute_accuracy(input, ar_mask, logger=None): + input, ar_mask = input[:nmax], ar_mask[:nmax] result = input.clone() * (1 - ar_mask) + masked_inplace_autoregression( model, self.batch_size, @@ -161,6 +259,15 @@ class SandBox(Task): device=self.device, ) + if logger is not None: + for sp, st in zip(result[:10], input[:10]): + logger( + f"test_sequences {n_epoch} prediction {self.problem.seq2str(sp)}" + ) + logger( + f" {n_epoch} ground truth {self.problem.seq2str(st)}" + ) + nb_total = ar_mask.sum().item() nb_correct = ((result == input).long() * ar_mask).sum().item() @@ -175,7 +282,7 @@ class SandBox(Task): ) test_nb_total, test_nb_correct = compute_accuracy( - self.test_input, self.test_ar_mask + self.test_input, self.test_ar_mask, logger ) logger( @@ -1119,8 +1226,6 @@ class World(Task): device_storage=device_storage, ) - print(f"{train_action_seq.size()=}") - train_frame_seq = self.frame2seq(train_frames).to(device_storage) test_frame_seq = self.frame2seq(test_frames).to(device_storage) @@ -1132,7 +1237,7 @@ class World(Task): self.nb_codes = nb_frame_codes + nb_action_codes train_frame_seq = train_frame_seq.reshape(train_frame_seq.size(0) // 2, 2, -1) - print(f"{train_action_seq.device=} {nb_frame_codes.device=}") + train_action_seq += nb_frame_codes self.train_input = torch.cat( (train_frame_seq[:, 0, :], train_action_seq, train_frame_seq[:, 1, :]), 1 @@ -1191,7 +1296,6 @@ class World(Task): (seq_start[:, None, :], seq_end[:, None, :], seq_predicted[:, None, :]), 1 ) result = result.reshape(-1, result.size(-1)) - print(f"{result.size()=}") frames = self.seq2frame(result) image_name = os.path.join(result_dir, f"world_result_{n_epoch:04d}.png") -- 2.20.1