X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=main.py;h=af94979e937d852912dc79c01ada589436de461f;hb=d2844d7a2d09ef38dc6f62d5e131059cccc872c5;hp=901b1d0529bb525b1cbca2b5c3bc91af7b12bf36;hpb=439c597d409c344283f8996f042daf79d3f24de2;p=picoclvr.git diff --git a/main.py b/main.py index 901b1d0..af94979 100755 --- a/main.py +++ b/main.py @@ -5,9 +5,6 @@ # Written by Francois Fleuret -# torch.backends.cuda.matmul.allow_tf23 -# torch.autocast(torch.bfloat16) - import math, sys, argparse, time, tqdm, os import torch, torchvision @@ -15,7 +12,7 @@ from torch import nn from torch.nn import functional as F import ffutils -import mygpt, tasks +import mygpt, tasks, problems ###################################################################### @@ -82,7 +79,20 @@ parser.add_argument("--overwrite_results", action="store_true", default=False) parser.add_argument("--checkpoint_name", type=str, default="checkpoint.pth") ############################## -# picoclvr options +# rpl options + +parser.add_argument("--rpl_nb_starting_values", type=int, default=5) + +parser.add_argument("--rpl_max_input", type=int, default=9) + +parser.add_argument("--rpl_prog_len", type=int, default=10) + +parser.add_argument("--rpl_nb_runs", type=int, default=8) + +parser.add_argument("--rpl_no_prog", action="store_true", default=False) + +############################## +# sandbox options parser.add_argument("--sandbox_level", type=int, default=0) @@ -325,19 +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, ) @@ -345,8 +355,9 @@ if args.task == "sandbox": raise ValueError(f"Unknown sandbox level {args.sandbox_level}") task = tasks.SandBox( - problem, - # tasks.ProblemAddition(zero_padded=False, inverted_result=False), + # problem, + # problems.ProblemAddition(zero_padded=False, inverted_result=False), + problems.ProblemLenId(len_max=args.sandbox_levels_len_source), nb_train_samples=args.nb_train_samples, nb_test_samples=args.nb_test_samples, batch_size=args.batch_size, @@ -430,6 +441,11 @@ elif args.task == "rpl": nb_train_samples=args.nb_train_samples, nb_test_samples=args.nb_test_samples, batch_size=args.batch_size, + nb_starting_values=args.rpl_nb_starting_values, + max_input=args.rpl_max_input, + prog_len=args.rpl_prog_len, + nb_runs=args.rpl_nb_runs, + no_prog=args.rpl_no_prog, logger=log_string, device=device, ) @@ -503,12 +519,12 @@ else: if args.task == "expr" and args.expr_input_file is not None: task.produce_results( - nb_epochs_finished, - model, - args.result_dir, - log_string, - args.deterministic_synthesis, - args.expr_input_file, + n_epoch=nb_epochs_finished, + model=model, + result_dir=args.result_dir, + logger=log_string, + deterministic_synthesis=args.deterministic_synthesis, + input_file=args.expr_input_file, ) exit(0) @@ -526,34 +542,37 @@ token_probas = token_count / token_count.sum() entropy = -torch.xlogy(token_probas, token_probas).sum() train_set_perplexity = math.exp(entropy) -############################## - +###################################################################### # A bit of paranoia never hurts -train_examples = {} +def subsets_as_tuples(batches, cs): + s = set() + for batch in batches: + for x in batch: + s.add(tuple([v.item() for v in x])) + if len(s) == cs: + yield s + s = set() + yield s -for input in task.batches(split="train"): - assert input.dim() == 2 and input.dtype == torch.int64 - for x in input: - train_examples[x.sum().item()] = x - -nb_total, nb_collisions = 0, 0 -for input in task.batches(split="test"): - assert input.dim() == 2 and input.dtype == torch.int64 - for x in input: - nb_total += 1 - y = train_examples.get(x.sum().item()) - if y is not None: - if x.size() == y.size() and (x - y).abs().sum() == 0: - nb_collisions += 1 - -del train_examples + +nb_test, nb_in_train = 0, 0 +for test_subset in subsets_as_tuples(task.batches(split="test"), 25000): + in_train = set() + for train_subset in subsets_as_tuples(task.batches(split="train"), 25000): + in_train.update(test_subset.intersection(train_subset)) + nb_in_train += len(in_train) + nb_test += len(test_subset) log_string( - f"data_check {nb_collisions*100/nb_total:.02f}% ({nb_collisions}/{nb_total}) of test samples are in the train set" + f"data_check {nb_in_train*100/nb_test:.02f}% ({nb_in_train}/{nb_test}) of test samples are in the train set" ) +assert ( + nb_in_train <= nb_test // 100 +), "More than 1% of test samples are in the train set" + ############################## if args.learning_rate_schedule == "cos": @@ -584,11 +603,11 @@ nb_samples_seen = 0 if nb_epochs_finished >= nb_epochs: task.produce_results( - nb_epochs_finished, - model, - args.result_dir, - log_string, - args.deterministic_synthesis, + n_epoch=nb_epochs_finished, + model=model, + result_dir=args.result_dir, + logger=log_string, + deterministic_synthesis=args.deterministic_synthesis, ) for n_epoch in range(nb_epochs_finished, nb_epochs): @@ -642,7 +661,11 @@ for n_epoch in range(nb_epochs_finished, nb_epochs): ) task.produce_results( - n_epoch, model, args.result_dir, log_string, args.deterministic_synthesis + n_epoch=n_epoch, + model=model, + result_dir=args.result_dir, + logger=log_string, + deterministic_synthesis=args.deterministic_synthesis, ) checkpoint = {