From: François Fleuret Date: Sun, 23 Jul 2023 18:29:08 +0000 (+0200) Subject: Update. X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=db7cefe4fefb381e56f1292d5bbe4a18c76afb47;p=culture.git Update. --- diff --git a/main.py b/main.py index 1b0d39a..0f1fbb5 100755 --- a/main.py +++ b/main.py @@ -12,7 +12,7 @@ from torch import nn from torch.nn import functional as F import ffutils -import mygpt, tasks +import mygpt, tasks, problems ###################################################################### @@ -335,19 +335,19 @@ picoclvr_pruner_eval = ( if args.task == "sandbox": if args.sandbox_level == 0: - problem = tasks.ProblemLevel0( + problem = problems.ProblemLevel0( nb_sentences=args.sandbox_levels_nb_items, len_prompt=args.sandbox_levels_len_source, len_result=args.sandbox_levels_len_result, ) elif args.sandbox_level == 1: - problem = tasks.ProblemLevel1( + problem = problems.ProblemLevel1( nb_operators=args.sandbox_levels_nb_items, len_source=args.sandbox_levels_len_source, len_result=args.sandbox_levels_len_result, ) elif args.sandbox_level == 2: - problem = tasks.ProblemLevel2( + problem = problems.ProblemLevel2( len_source=args.sandbox_levels_len_source, len_result=args.sandbox_levels_len_result, ) @@ -356,7 +356,7 @@ if args.task == "sandbox": task = tasks.SandBox( problem, - # tasks.ProblemAddition(zero_padded=False, inverted_result=False), + # problems.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/problems.py b/problems.py new file mode 100755 index 0000000..78bb64e --- /dev/null +++ b/problems.py @@ -0,0 +1,159 @@ +#!/usr/bin/env python + +import math + +import torch, torchvision + +from torch import nn +from torch.nn import functional as F + +###################################################################### + + +class Problem: + def generate_sequences(self, nb): + pass + + def seq2str(self, seq): + return "[NOT IMPLEMENTED]" + + +#################### + + +class ProblemLevel0(Problem): + def __init__(self, nb_sentences=100, len_prompt=5, len_result=5): + self.seq = torch.randint(10, (nb_sentences, len_prompt + 1 + len_result)) + self.seq[:, len_prompt] = 10 + + def generate_sequences(self, nb): + sequences = self.seq[torch.randint(self.seq.size(0), (nb,))] + ar_mask = (sequences == 10).long() + ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1) + return sequences, ar_mask + + +class ProblemLevel1(Problem): + def __init__(self, nb_operators=100, len_source=5, len_result=8): + self.len_source = len_source + 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_source).argmax(-1), + num_classes=len_source, + ) + + def generate_sequences(self, nb): + nb_operators = torch.randint(self.operators.size(0), (nb,)) + operators = self.operators[nb_operators] + nb_operators = ( + nb_operators[:, None] + // 10 ** torch.arange(self.len_nb_operator - 1, -1, -1) + ) % 10 + marker1 = torch.full((nb, 1), 10) + # source = torch.randint(10, (nb, self.len_source)) + source = torch.rand(nb, 10).sort(dim=1).indices[:, : self.len_source] + marker2 = torch.full((nb, 1), 11) + result = operators.bmm(source[:, :, None]).squeeze(-1) + sequences = torch.cat((nb_operators, marker1, source, marker2, result), 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("0123456789|>"[x.item()] for x in seq) + + +class ProblemLevel2(Problem): + def __init__(self, len_source=5, len_result=8): + self.len_source = len_source + self.len_result = len_result + + def generate_sequences(self, nb): + operators = F.one_hot( + torch.rand(nb, self.len_result, self.len_source).argmax(-1), + num_classes=self.len_source, + ) + source1 = torch.rand(nb, 10).sort(dim=1).indices[:, : self.len_source] + marker1 = torch.full((nb, 1), 10) + result1 = operators.bmm(source1[:, :, None]).squeeze(-1) + marker2 = torch.full((nb, 1), 11) + source2 = torch.randint(10, (nb, self.len_source)) + marker3 = torch.full((nb, 1), 12) + result2 = operators.bmm(source2[:, :, None]).squeeze(-1) + + sequences = torch.cat( + (source1, marker1, result1, marker2, source2, marker3, result2), 1 + ) + ar_mask = (sequences == 12).long() + ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1) + return sequences, ar_mask + + def seq2str(self, seq): + return "".join("0123456789>|~"[x.item()] for x in seq) + + +#################### + + +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) + diff --git a/tasks.py b/tasks.py index 17904d8..421aee4 100755 --- a/tasks.py +++ b/tasks.py @@ -72,158 +72,9 @@ class Task: pass -###################################################################### - - -class Problem: - def generate_sequences(self, nb): - pass - - def seq2str(self, seq): - return "[NOT IMPLEMENTED]" - - -#################### - - -class ProblemLevel0(Problem): - def __init__(self, nb_sentences=100, len_prompt=5, len_result=5): - self.seq = torch.randint(10, (nb_sentences, len_prompt + 1 + len_result)) - self.seq[:, len_prompt] = 10 - - def generate_sequences(self, nb): - sequences = self.seq[torch.randint(self.seq.size(0), (nb,))] - ar_mask = (sequences == 10).long() - ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1) - return sequences, ar_mask - - -class ProblemLevel1(Problem): - def __init__(self, nb_operators=100, len_source=5, len_result=8): - self.len_source = len_source - 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_source).argmax(-1), - num_classes=len_source, - ) - - def generate_sequences(self, nb): - nb_operators = torch.randint(self.operators.size(0), (nb,)) - operators = self.operators[nb_operators] - nb_operators = ( - nb_operators[:, None] - // 10 ** torch.arange(self.len_nb_operator - 1, -1, -1) - ) % 10 - marker1 = torch.full((nb, 1), 10) - # source = torch.randint(10, (nb, self.len_source)) - source = torch.rand(nb, 10).sort(dim=1).indices[:, : self.len_source] - marker2 = torch.full((nb, 1), 11) - result = operators.bmm(source[:, :, None]).squeeze(-1) - sequences = torch.cat((nb_operators, marker1, source, marker2, result), 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("0123456789|>"[x.item()] for x in seq) - - -class ProblemLevel2(Problem): - def __init__(self, len_source=5, len_result=8): - self.len_source = len_source - self.len_result = len_result - - def generate_sequences(self, nb): - operators = F.one_hot( - torch.rand(nb, self.len_result, self.len_source).argmax(-1), - num_classes=self.len_source, - ) - source1 = torch.rand(nb, 10).sort(dim=1).indices[:, : self.len_source] - marker1 = torch.full((nb, 1), 10) - result1 = operators.bmm(source1[:, :, None]).squeeze(-1) - marker2 = torch.full((nb, 1), 11) - source2 = torch.randint(10, (nb, self.len_source)) - marker3 = torch.full((nb, 1), 12) - result2 = operators.bmm(source2[:, :, None]).squeeze(-1) - - sequences = torch.cat( - (source1, marker1, result1, marker2, source2, marker3, result2), 1 - ) - ar_mask = (sequences == 12).long() - ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1) - return sequences, ar_mask - - def seq2str(self, seq): - return "".join("0123456789>|~"[x.item()] for x in seq) - - -#################### - - -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) - #################### +import problems class SandBox(Task): def __init__( @@ -1283,7 +1134,7 @@ class RPL(Task): ) if save_attention_image is not None: - ns=torch.randint(self.text_input.size(0),(1,)).item() + ns=torch.randint(self.test_input.size(0),(1,)).item() input = self.test_input[ns:ns+1].clone() last = (input != self.t_nul).max(0).values.nonzero().max() + 3 input = input[:, :last].to(self.device) @@ -1297,7 +1148,7 @@ class RPL(Task): ram = model.retrieve_attention() model.record_attention(False) - tokens_output = [self.id2token[i.item()] for i in input[ns]] + tokens_output = [self.id2token[i.item()] for i in input[0]] tokens_input = ["n/a"] + tokens_output[:-1] for n_head in range(ram[0].size(1)): filename = os.path.join(