From: François Fleuret Date: Fri, 25 Aug 2023 16:38:22 +0000 (+0200) Subject: Update. X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=6f61f9438799d65c980726e28546f8775bf83a60;p=culture.git Update. --- diff --git a/grid.py b/grid.py index 08ddc23..70f7739 100755 --- a/grid.py +++ b/grid.py @@ -118,7 +118,7 @@ class GridFactory: return properties - def generate_example(self): + def generate_scene_and_questions(self): while True: while True: scene = self.generate_scene() @@ -142,25 +142,51 @@ class GridFactory: if len(false) >= self.nb_questions: break + # print(f"{a=}") + if a < 10: break true = [true[k] for k in torch.randperm(len(true))[: self.nb_questions]] false = [false[k] for k in torch.randperm(len(false))[: self.nb_questions]] - true = [(q, "yes") for q in true] - false = [(q, "no") for q in false] + true = [" " + q + " " for q in true] + false = [" " + q + " " for q in false] union = true + false questions = [union[k] for k in torch.randperm(len(union))[: self.nb_questions]] - return scene, questions + result = " ".join( + [" " + x for x in self.grid_positions(scene)] + questions + ) + + return scene, result + + def generate_samples(self, nb, progress_bar=None): + result = [] + + r = range(nb) + if progress_bar is not None: + r = progress_bar(r) + + for _ in r: + result.append(self.generate_scene_and_questions()[1]) + + return result ###################################################################### if __name__ == "__main__": + import time + grid_factory = GridFactory() - scene, questions = grid_factory.generate_example() + + start_time = time.perf_counter() + samples = grid_factory.generate_samples(10000) + end_time = time.perf_counter() + print(f"{len(samples) / (end_time - start_time):.02f} samples per second") + + scene, questions = grid_factory.generate_scene_and_questions() grid_factory.print_scene(scene) print(questions) diff --git a/main.py b/main.py index ff831f4..00e19ac 100755 --- a/main.py +++ b/main.py @@ -33,7 +33,7 @@ parser.add_argument( "--task", type=str, default="twotargets", - help="byheart, learnop, guessop, twotargets, addition, picoclvr, mnist, maze, snake, stack, expr, rpl", + help="byheart, learnop, guessop, twotargets, addition, picoclvr, mnist, maze, snake, stack, expr, rpl, grid", ) parser.add_argument("--log_filename", type=str, default="train.log", help=" ") @@ -262,6 +262,13 @@ default_task_args = { "nb_train_samples": 25000, "nb_test_samples": 1000, }, + "grid": { + "model": "37M", + "nb_epochs": 25, + "batch_size": 25, + "nb_train_samples": 250000, + "nb_test_samples": 10000, + }, } if args.task in default_task_args: @@ -505,6 +512,17 @@ elif args.task == "rpl": device=device, ) +elif args.task == "grid": + task = tasks.Grid( + nb_train_samples=args.nb_train_samples, + nb_test_samples=args.nb_test_samples, + batch_size=args.batch_size, + height=args.picoclvr_height, + width=args.picoclvr_width, + logger=log_string, + device=device, + ) + elif args.task == "world": task = tasks.World( nb_train_samples=args.nb_train_samples, diff --git a/tasks.py b/tasks.py index 5019aed..c7348d5 100755 --- a/tasks.py +++ b/tasks.py @@ -1419,6 +1419,131 @@ class Expr(Task): ############################################################## +###################################################################### + +import grid + + +class Grid(Task): + # Make a tensor from a list of strings + def tensorize(self, descr): + token_descr = [s.strip().split(" ") for s in descr] + l = max([len(s) for s in token_descr]) + token_descr = [s + [""] * (l - len(s)) for s in token_descr] + id_descr = [[self.token2id[u] for u in s] for s in token_descr] + return torch.tensor(id_descr, device=self.device) + + # Make a list of strings from a tensor + def detensorize(self, x): + return [" ".join([self.id2token[t.item()] for t in r]) for r in x] + + # trim all the tensors in the tuple z to remove as much token from + # left and right in the first tensor. If z is a tuple, all its + # elements are trimed according to the triming for the first + def trim(self, z, token=""): + n = self.token2id[token] + if type(z) == tuple: + x = z[0] + i = (1 - (F.pad(x, (1, 1), value=n) == n).min(0).values.long()).cumsum(0) + a, b = (i == 0).nonzero().max(), (i == i.max()).nonzero().min() + return tuple([t[:, a:b] for t in z]) + else: + i = (1 - (F.pad(z, (1, 1), value=n) == n).min(0).values.long()).cumsum(0) + a, b = (i == 0).nonzero().max(), (i == i.max()).nonzero().min() + return z[:, a:b] + + ###################### + + def __init__( + self, + nb_train_samples, + nb_test_samples, + batch_size, + height, + width, + logger=None, + device=torch.device("cpu"), + ): + super().__init__() + + self.device = device + self.batch_size = batch_size + self.grid_factory = grid.GridFactory(height=height, width=width) + + if logger is not None: + logger( + f"generating {nb_train_samples+nb_test_samples} samples (can take some time)" + ) + + self.train_descr = self.grid_factory.generate_samples( + nb_train_samples, lambda r: tqdm.tqdm(r) + ) + self.test_descr = self.grid_factory.generate_samples( + nb_test_samples, lambda r: tqdm.tqdm(r) + ) + + # Build the tokenizer + tokens = {} + for d in [self.train_descr, self.test_descr]: + for s in d: + for t in s.strip().split(" "): + tokens.add(t) + # make this set a sorted list to get the same tensors given + # the same descr + tokens = list(tokens) + tokens.sort() + tokens = [""] + tokens + self.token2id = dict([(t, n) for n, t in enumerate(tokens)]) + self.id2token = dict([(n, t) for n, t in enumerate(tokens)]) + self.t_nul = self.token2id[""] + self.t_true = self.token2id[""] + self.t_false = self.token2id[""] + + # Tokenize the train and test sets + self.train_input = self.tensorize(self.train_descr) + self.test_input = self.tensorize(self.test_descr) + + def batches(self, split="train"): + assert split in {"train", "test"} + input = self.train_input if split == "train" else self.test_input + for batch in tqdm.tqdm( + input.split(self.batch_size), dynamic_ncols=True, desc=f"epoch-{split}" + ): + yield self.trim(batch) + + def vocabulary_size(self): + return len(self.token2id) + + def produce_results( + self, n_epoch, model, result_dir, logger, deterministic_synthesis + ): + correct = self.test_input[:1000] + result = correct.clone() + ar_mask = torch.logical_or(result == self.t_true, result == self.t_false).long() + result *= 1 - ar_mask + + for e in self.detensorize(result[:10]): + logger(f"test_before {e}") + + masked_inplace_autoregression( + model, + self.batch_size, + result, + ar_mask, + deterministic_synthesis, + device=self.device, + ) + + for e in self.detensorize(result[:10]): + logger(f"test_after {e}") + + nb_total = ar_mask.sum().item() + nb_correct = ((correct == result).long() * ar_mask).sum().item() + + logger(f"test_performance {nb_total=} {nb_correct=}") + logger(f"main_test_accuracy {nb_correct / nb_total}") + + ###################################################################### import world