From: François Fleuret Date: Tue, 25 Jul 2023 18:22:39 +0000 (-1000) Subject: Update. X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=687d5b2d9f465577665991b84faec7c789685271;p=culture.git Update. --- diff --git a/graph.py b/graph.py index 2c7caf8..6db9ed7 100755 --- a/graph.py +++ b/graph.py @@ -110,7 +110,7 @@ def save_attention_image( x_advance, y_advance, ) = ctx.text_extents(s) - ctx.move_to(k * token_gap - width_t / 2, token_gap / 5 - y_bearing) + ctx.move_to(k * token_gap - width_t / 2, 2 * token_gap / 5) ctx.show_text(s) for k, t in enumerate(tokens_output): @@ -146,7 +146,7 @@ def save_attention_image( if __name__ == "__main__": import mygpt - tokens_output = ["", 2, 3, 4, ""] + tokens_output = ["", "-", 3, 4, ""] tokens_input = [""] + tokens_output[:-1] vocabulary_size = 3 diff --git a/main.py b/main.py index 68b946a..9c28e47 100755 --- a/main.py +++ b/main.py @@ -365,7 +365,8 @@ if args.task == "sandbox": task = tasks.SandBox( # problem, # problems.ProblemAddition(zero_padded=False, inverted_result=False), - problems.ProblemLenId(len_max=args.sandbox_levels_len_source), + # problems.ProblemLenId(len_max=args.sandbox_levels_len_source), + problems.ProblemTwoTargets(len_total=12, len_targets=4), 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 index 7b1d698..aa3acf0 100755 --- a/problems.py +++ b/problems.py @@ -22,32 +22,39 @@ class Problem: class ProblemTwoTargets(Problem): - def __init__(self, len_total=10, len_target=2): - assert len_total >= 3 * (2 + len_target) - 1 + def __init__(self, len_total=10, len_targets=3): + assert len_targets >= 3 + assert len_total >= 3 * len_targets - 1 self.len_total = len_total - self.len_target = len_target + self.len_targets = len_targets def generate_sequences(self, nb): k = torch.arange(self.len_total)[None, :] - l = torch.randint(self.len_total, (2, nb))[:, :, None] + 1 - i = torch.randint(10, (2, nb))[:, :, None] - a = l[0] - b = l[0] + 1 + l[1] - c = l[0] + 1 + l[1] + 1 + l[0] - sequences = ( - (k < a) * i[0] - + (k == a) * 10 - + (k > a) * (k < b) * i[1] - + (k == b) * 11 - + (k > b) * (k < c) * i[1] - + (k >= c) * 12 + s = torch.randint(10, (nb, self.len_total)) + l = torch.rand(nb, self.len_total) + l = l * (k <= self.len_total - self.len_targets).long() + k1 = l.argmax(dim=1, keepdim=True) + m = (k != k1).long() * (k != k1 + self.len_targets - 1).long() + s = s * m + 10 * (1 - m) + l = l * ( + 1 + - (k + self.len_targets - 1 >= k1).long() + * (k < k1 + self.len_targets).long() ) - ar_mask = (sequences == 11).long() + k2 = l.argmax(dim=1, keepdim=True) + m = (k != k2).long() * (k != k2 + self.len_targets - 1).long() + s = s * m + 11 * (1 - m) + a1 = s.gather(dim=1, index=k1 + 1 + torch.arange(self.len_targets - 2)[None, :]) + a2 = s.gather(dim=1, index=k2 + 1 + torch.arange(self.len_targets - 2)[None, :]) + sequences = torch.cat( + (s, torch.full((nb, 1), 12), a1, torch.full((nb, 1), 12), a2), 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) + return "".join("0123456789+-|"[x.item()] for x in seq) #################### @@ -212,18 +219,8 @@ class ProblemAddition(Problem): 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) +if __name__ == "__main__": + p = ProblemTwoTargets(12, 4) + s, m = p.generate_sequences(10) + for x in s: + print(p.seq2str(x)) diff --git a/tasks.py b/tasks.py index 038a8ac..0143ab2 100755 --- a/tasks.py +++ b/tasks.py @@ -182,36 +182,37 @@ class SandBox(Task): ) if save_attention_image is not None: - ns = torch.randint(self.test_input.size(0), (1,)).item() - input = self.test_input[ns : ns + 1].clone() - - with torch.autograd.no_grad(): - t = model.training - model.eval() - model.record_attention(True) - model(BracketedSequence(input)) - model.train(t) - ram = model.retrieve_attention() - model.record_attention(False) - - tokens_output = [c for c in self.problem.seq2str(input[0])] - tokens_input = ["n/a"] + tokens_output[:-1] - for n_head in range(ram[0].size(1)): - filename = os.path.join( - result_dir, f"rpl_attention_{n_epoch}_h{n_head}.pdf" - ) - attention_matrices = [m[0, n_head] for m in ram] - save_attention_image( - filename, - tokens_input, - tokens_output, - attention_matrices, - k_top=10, - # min_total_attention=0.9, - token_gap=12, - layer_gap=50, - ) - logger(f"wrote {filename}") + for k in range(10): + ns = torch.randint(self.test_input.size(0), (1,)).item() + input = self.test_input[ns : ns + 1].clone() + + with torch.autograd.no_grad(): + t = model.training + model.eval() + model.record_attention(True) + model(BracketedSequence(input)) + model.train(t) + ram = model.retrieve_attention() + model.record_attention(False) + + tokens_output = [c for c in self.problem.seq2str(input[0])] + tokens_input = ["n/a"] + tokens_output[:-1] + for n_head in range(ram[0].size(1)): + filename = os.path.join( + result_dir, f"sandbox_attention_{k}_h{n_head}.pdf" + ) + attention_matrices = [m[0, n_head] for m in ram] + save_attention_image( + filename, + tokens_input, + tokens_output, + attention_matrices, + k_top=10, + # min_total_attention=0.9, + token_gap=12, + layer_gap=50, + ) + logger(f"wrote {filename}") ######################################################################