X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=tasks.py;h=42d912674641db0fec26a48ce5687b7040cba5cb;hb=291c38d093894d46fba6eb45f82e5b65a2a1cb8b;hp=a3d47f54451464470bd9d37710bda4f42c1ab4ce;hpb=c9dbc3abf436df8af1379d04ab51159e821496f1;p=picoclvr.git diff --git a/tasks.py b/tasks.py index a3d47f5..42d9126 100755 --- a/tasks.py +++ b/tasks.py @@ -1,5 +1,10 @@ #!/usr/bin/env python +# Any copyright is dedicated to the Public Domain. +# https://creativecommons.org/publicdomain/zero/1.0/ + +# Written by Francois Fleuret + import math, os, tqdm import torch, torchvision @@ -7,6 +12,13 @@ import torch, torchvision from torch import nn from torch.nn import functional as F +from mygpt import BracketedSequence + +try: + from graph import save_attention_image +except ImportError: + save_attention_image = None + ###################################################################### @@ -29,7 +41,7 @@ def masked_inplace_autoregression( batches, dynamic_ncols=True, desc=progress_bar_desc, - # total=input.size(0) // batch_size, + total=(input.size(0) + batch_size - 1) // batch_size, ) with torch.autograd.no_grad(): @@ -108,9 +120,7 @@ class ProblemLevel1(Problem): 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) - print(f"{nb_operators.dtype=} {marker1.dtype=}") sequences = torch.cat((nb_operators, marker1, source, marker2, result), 1) - print(f"{sequences.size()=}") ar_mask = (sequences == 11).long() ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1) return sequences, ar_mask @@ -130,7 +140,6 @@ class ProblemLevel2(Problem): num_classes=self.len_source, ) source1 = torch.rand(nb, 10).sort(dim=1).indices[:, : self.len_source] - # source1 = torch.randint(10, (nb, self.len_source)) marker1 = torch.full((nb, 1), 10) result1 = operators.bmm(source1[:, :, None]).squeeze(-1) marker2 = torch.full((nb, 1), 11) @@ -1042,26 +1051,50 @@ class RPL(Task): ) ], 0, - ).to(self.device) + ) + + def seq2str(self, seq): + return " ".join([self.id2token[i] for i in seq]) def __init__( self, nb_train_samples, nb_test_samples, batch_size, + nb_starting_values=3, + max_input=9, + prog_len=6, + nb_runs=5, + no_prog=False, + logger=None, device=torch.device("cpu"), ): super().__init__() self.batch_size = batch_size self.device = device + self.no_prog = no_prog train_sequences = [ - rpl.generate() + rpl.generate( + nb_starting_values=nb_starting_values, + nb_result_values_max=4 * nb_starting_values, + max_input=max_input, + prog_len=prog_len, + nb_runs=nb_runs, + ) for _ in tqdm.tqdm(range(nb_train_samples), desc="train-data") ] + test_sequences = [ - rpl.generate() for _ in tqdm.tqdm(range(nb_test_samples), desc="test-data") + rpl.generate( + nb_starting_values=nb_starting_values, + nb_result_values_max=4 * nb_starting_values, + max_input=max_input, + prog_len=prog_len, + nb_runs=nb_runs, + ) + for _ in tqdm.tqdm(range(nb_test_samples), desc="test-data") ] symbols = list( @@ -1071,15 +1104,55 @@ class RPL(Task): symbols = list(filter(lambda x: type(x) is str, symbols)) symbols.sort() symbols += [str(n) for n in range(val_max + 1)] - print(f"{val_max=}") self.token2id = dict([(c, n) for n, c in enumerate(symbols)]) self.id2token = dict([(n, c) for c, n in self.token2id.items()]) - self.t_nul, self.t_prog = self.token2id[""], self.token2id[""] + self.t_nul = self.token2id[""] + self.t_input = self.token2id[""] + self.t_output = self.token2id[""] + self.t_prog = self.token2id[""] + self.t_end = self.token2id[""] self.train_input = self.tensorize(train_sequences) self.test_input = self.tensorize(test_sequences) + if no_prog: + # Excise the program from every train and test example + k = torch.arange(self.train_input.size(1), device=self.train_input.device)[ + None, : + ] + p = ( + ((self.train_input == self.t_prog).long() * k) + .max(1, keepdim=True) + .values + ) + self.train_input = ( + self.train_input * (k <= p).long() + + self.t_end * (k == p + 1).long() + + self.t_nul * (k > p + 1).long() + ) + k = torch.arange(self.test_input.size(1), device=self.test_input.device)[ + None, : + ] + p = ( + ((self.test_input == self.t_prog).long() * k) + .max(1, keepdim=True) + .values + ) + self.test_input = ( + self.test_input * (k <= p).long() + + self.t_end * (k == p + 1).long() + + self.t_nul * (k > p + 1).long() + ) + + if logger is not None: + logger(f"value_max {val_max}") + for x in self.train_input[:25]: + end = (x != self.t_nul).nonzero().max().item() + 1 + seq = [self.id2token[i.item()] for i in x[:end]] + s = " ".join(seq) + logger(f"example_seq {s}") + self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1 def batches(self, split="train", nb_to_use=-1, desc=None): @@ -1093,7 +1166,7 @@ class RPL(Task): input.split(self.batch_size), dynamic_ncols=True, desc=desc ): last = (batch != self.t_nul).max(0).values.nonzero().max() + 3 - batch = batch[:, :last] + batch = batch[:, :last].to(self.device) yield batch def vocabulary_size(self): @@ -1102,7 +1175,8 @@ class RPL(Task): def produce_results( self, n_epoch, model, result_dir, logger, deterministic_synthesis ): - def compute_nb_errors(input, nb_to_log=0): + # -------------------------------------------------------------------- + def compute_nb_errors_prog(input, nb_to_log=0): result = input.clone() s = (result == self.t_prog).long() ar_mask = (s.cumsum(dim=1) - s).clamp(min=0, max=1) @@ -1117,27 +1191,130 @@ class RPL(Task): device=self.device, ) - if nb_to_log > 0: - for x in result[:nb_to_log]: - s = " ".join([self.id2token[i.item()] for i in x]) - logger(f"check {n_epoch} {s}") - nb_to_log -= min(nb_to_log, result.size(0)) + sum_nb_total, sum_nb_errors = 0, 0 + for one_input, one_result in zip(input, result): + seq = [self.id2token[i.item()] for i in one_result] + nb_total, nb_errors, prog, stacks = rpl.compute_nb_errors(seq) + sum_nb_total += 1 + sum_nb_errors += 0 if nb_errors == 0 else 1 + if nb_to_log > 0: + gt_seq = [self.id2token[i.item()] for i in one_input] + _, _, gt_prog, _ = rpl.compute_nb_errors(gt_seq) + gt_prog = " ".join([str(x) for x in gt_prog]) + prog = " ".join([str(x) for x in prog]) + comment = "*" if nb_errors == 0 else "-" + logger(f"{comment} PROG [{gt_prog}] PREDICTED [{prog}]") + for start_stack, target_stack, result_stack, correct in stacks: + comment = "*" if correct else "-" + start_stack = " ".join([str(x) for x in start_stack]) + target_stack = " ".join([str(x) for x in target_stack]) + result_stack = " ".join([str(x) for x in result_stack]) + logger( + f" {comment} [{start_stack}] -> [{target_stack}] PREDICTED [{result_stack}]" + ) + nb_to_log -= 1 + + return sum_nb_total, sum_nb_errors + + # -------------------------------------------------------------------- + def compute_nb_errors_output(input, nb_to_log=0): + result = input.clone() + k = torch.arange(result.size(1), device=result.device)[None, :] + last_output_idx = ( + ((result == self.t_output) * k).max(dim=1, keepdim=True).values + ) + first_prog_idx = ( + ((result == self.t_prog) * k).max(dim=1, keepdim=True).values + ) + ar_mask = (k > last_output_idx).long() * (k < first_prog_idx).long() + result = (1 - ar_mask) * result + ar_mask * self.t_nul + + masked_inplace_autoregression( + model, + self.batch_size, + result, + ar_mask, + deterministic_synthesis, + device=self.device, + ) sum_nb_total, sum_nb_errors = 0, 0 - for x in result: - seq = [self.id2token[i.item()] for i in x] - nb_total, nb_errors = rpl.check(seq) - sum_nb_total += nb_total - sum_nb_errors += nb_errors + for one_input, one_result, i, j in zip( + input, result, last_output_idx, first_prog_idx + ): + seq = [self.id2token[i.item()] for i in one_result] + sum_nb_total += 1 + correct = (one_input - one_result).abs().max() == 0 + sum_nb_errors += 0 if correct else 1 + if nb_to_log > 0: + result_stack = [ + self.id2token[i.item()] for i in one_result[i : j + 1] + ] + target_stack = [ + self.id2token[i.item()] for i in one_input[i : j + 1] + ] + comment = "*" if correct else "-" + result_stack = " ".join([str(x) for x in result_stack]) + target_stack = " ".join([str(x) for x in target_stack]) + logger( + f"output_test {comment} [{target_stack}] PREDICTED [{result_stack}]" + ) + nb_to_log -= 1 return sum_nb_total, sum_nb_errors - test_nb_total, test_nb_errors = compute_nb_errors(self.test_input, nb_to_log=10) + # -------------------------------------------------------------------- + + if not self.no_prog: + test_nb_total, test_nb_errors = compute_nb_errors_prog( + self.test_input[:1000].to(self.device), nb_to_log=10 + ) + + logger( + f"accuracy_prog_test {n_epoch} nb_total {test_nb_total} nb_errors {test_nb_errors} accuracy {100.0*(1-test_nb_errors/test_nb_total):.02f}%" + ) + + test_nb_total, test_nb_errors = compute_nb_errors_output( + self.test_input[:1000].to(self.device), nb_to_log=10 + ) logger( - f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_errors {test_nb_errors} accuracy {100.0*(1-test_nb_errors/test_nb_total):.02f}%" + f"accuracy_output_test {n_epoch} nb_total {test_nb_total} nb_errors {test_nb_errors} accuracy {100.0*(1-test_nb_errors/test_nb_total):.02f}%" ) + if save_attention_image is not None: + input = self.test_input[:1].clone() + last = (input != self.t_nul).max(0).values.nonzero().max() + 3 + input = input[:, :last].to(self.device) + + 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 = [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( + 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}") + ######################################################################