X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;ds=sidebyside;f=main.py;h=b4ab473abb8e6f13c55265e8b8736d2f10856cac;hb=dc28a8c5cc4456cd9512c57947b104ac23338c8f;hp=57f79a32e6d9565a1b0ffea11a8c796801a7cf5b;hpb=f7a8cd141a39039048d3e9311220a33079f2cfc7;p=culture.git

diff --git a/main.py b/main.py
index 57f79a3..b4ab473 100755
--- a/main.py
+++ b/main.py
@@ -16,20 +16,13 @@ import ffutils
 import mygpt
 import sky, grids, quiz_machine
 
-# world quizzes vs. culture quizzes
+import threading
 
-######################################################################
-
-if torch.cuda.is_available():
-    device = torch.device("cuda")
-    torch.backends.cuda.matmul.allow_tf32 = True
-else:
-    device = torch.device("cpu")
+import torch.multiprocessing as mp
 
 ######################################################################
 
 parser = argparse.ArgumentParser(
-    description="An implementation of GPT with cache.",
     formatter_class=argparse.ArgumentDefaultsHelpFormatter,
 )
 
@@ -39,7 +32,7 @@ parser.add_argument("--result_dir", type=str, default=None)
 
 parser.add_argument("--seed", type=int, default=0)
 
-parser.add_argument("--max_percents_of_test_in_train", type=int, default=1)
+parser.add_argument("--max_percents_of_test_in_train", type=int, default=-1)
 
 ########################################
 
@@ -77,7 +70,9 @@ parser.add_argument("--deterministic_synthesis", action="store_true", default=Fa
 
 parser.add_argument("--problem", type=str, default="grids")
 
-parser.add_argument("--nb_threads", type=int, default=-1)
+parser.add_argument("--nb_threads", type=int, default=1)
+
+parser.add_argument("--gpus", type=str, default="all")
 
 parser.add_argument("--nb_gpts", type=int, default=5)
 
@@ -87,16 +82,29 @@ parser.add_argument("--max_to_validate", type=int, default=None)
 
 parser.add_argument("--accuracy_to_make_c_quizzes", type=float, default=0.975)
 
-parser.add_argument("--generation_temperature", type=float, default=2.0)
+parser.add_argument("--proba_understands", type=float, default=0.99)
 
-parser.add_argument("--deterministic_validation", action="store_true", default=False)
+parser.add_argument("--proba_not_understands", type=float, default=0.5)
 
-parser.add_argument("--bidirectional_validation", action="store_true", default=False)
+parser.add_argument("--generation_temperature", type=float, default=2.0)
 
 parser.add_argument("--dirty_debug", action="store_true", default=False)
 
 ######################################################################
 
+grids_tasks = ", ".join(
+    [x.__name__.removeprefix("task_") for x in grids.Grids().all_tasks]
+)
+
+parser.add_argument(
+    "--grids_tasks",
+    type=str,
+    default=None,
+    help="A comma-separated subset of: " + grids_tasks + ", or None for all.",
+)
+
+######################################################################
+
 parser.add_argument("--sky_height", type=int, default=6)
 
 parser.add_argument("--sky_width", type=int, default=8)
@@ -220,6 +228,19 @@ for n in vars(args):
 
 ######################################################################
 
+if args.gpus == "all":
+    gpus_idx = range(torch.cuda.device_count())
+else:
+    gpus_idx = [int(k) for k in args.gpus.split(",")]
+
+gpus = [torch.device(f"cuda:{n}") for n in gpus_idx]
+
+if torch.cuda.is_available():
+    main_device = gpus[0]
+else:
+    assert len(gpus) == 0
+    main_device = torch.device("cpu")
+
 if args.dirty_debug:
     args.nb_train_samples = 2500
     args.nb_test_samples = 100
@@ -239,21 +260,24 @@ if args.problem == "sky":
         nb_birds=args.sky_nb_birds,
         nb_iterations=args.sky_nb_iterations,
         speed=args.sky_speed,
-        max_nb_cached_chunks=args.nb_train_samples // 100,
+        max_nb_cached_chunks=len(gpus) * args.nb_train_samples // 100,
         chunk_size=100,
         nb_threads=args.nb_threads,
     )
     back_accuracy = False
 elif args.problem == "grids":
     problem = grids.Grids(
-        max_nb_cached_chunks=args.nb_train_samples // 100,
+        max_nb_cached_chunks=len(gpus) * args.nb_train_samples // 100,
         chunk_size=100,
         nb_threads=args.nb_threads,
+        tasks=args.grids_tasks,
     )
     back_accuracy = True
 else:
     raise ValueError
 
+problem.save_some_examples(args.result_dir)
+
 quiz_machine = quiz_machine.QuizMachine(
     problem=problem,
     nb_train_samples=args.nb_train_samples,
@@ -262,12 +286,12 @@ quiz_machine = quiz_machine.QuizMachine(
     batch_size=args.physical_batch_size,
     result_dir=args.result_dir,
     logger=log_string,
-    device=device,
+    device=main_device,
 )
 
 ######################################################################
 
-log_string(f"device {device}")
+log_string(f"main_device {main_device} gpus {[ str(g) for g in gpus]}")
 
 vocabulary_size = quiz_machine.vocabulary_size()
 
@@ -275,64 +299,47 @@ log_string(f"vocabulary_size {vocabulary_size}")
 
 ######################################################################
 
-# Compute the entropy of the training tokens
 
-token_count = 0
-for input in quiz_machine.batches(split="train", desc="train-entropy"):
-    token_count += F.one_hot(input, num_classes=quiz_machine.vocabulary_size()).sum(
-        (0, 1)
-    )
-token_probas = token_count / token_count.sum()
-entropy = -torch.xlogy(token_probas, token_probas).sum()
-train_set_perplexity = math.exp(entropy)
+def run_tests(model, quiz_machine, deterministic_synthesis, local_device=main_device):
+    with torch.autograd.no_grad():
+        model.eval().to(local_device)
 
-######################################################################
-# A bit of paranoia never hurts
+        nb_test_samples, acc_test_loss = 0, 0.0
+        nb_samples_accumulated = 0
 
-if args.max_percents_of_test_in_train >= 0:
+        for input in quiz_machine.batches(model, split="test"):
+            input = input.to(local_device)
 
-    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
+            bs = model(mygpt.BracketedSequence(input))
+            output = bs.x
 
-    nb_test, nb_in_train = 0, 0
-    for test_subset in subsets_as_tuples(
-        quiz_machine.batches(split="test", desc="test-check"), 25000
-    ):
-        in_train = set()
-        for train_subset in subsets_as_tuples(
-            quiz_machine.batches(split="train", desc="train-check"), 25000
-        ):
-            in_train.update(test_subset.intersection(train_subset))
-        nb_in_train += len(in_train)
-        nb_test += len(test_subset)
+            loss = F.cross_entropy(output.transpose(1, 2), input)
 
-    log_string(
-        f"data_check {nb_in_train*100/nb_test:.02f}% ({nb_in_train}/{nb_test}) of test samples are in the train set"
-    )
+            acc_test_loss += loss.item() * input.size(0)
 
-    assert (
-        nb_in_train <= args.max_percents_of_test_in_train * nb_test / 100
-    ), f"More than {args.max_percents_of_test_in_train}% of test samples are in the train set"
+            nb_test_samples += input.size(0)
+
+        test_perplexity = math.exp(min(100, acc_test_loss / nb_test_samples))
+
+        log_string(f"test_perplexity {n_epoch} model {model.id} {test_perplexity}")
 
-##############################
+        model.main_test_accuracy = quiz_machine.produce_results(
+            n_epoch=n_epoch,
+            model=model,
+            result_dir=args.result_dir,
+            deterministic_synthesis=deterministic_synthesis,
+        )
 
 
-def one_epoch(model, quiz_machine):
+def one_epoch(model, quiz_machine, local_device=main_device):
     optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
 
-    model.train()
+    model.to(local_device).train()
 
     nb_train_samples, acc_train_loss = 0, 0.0
 
-    for input in quiz_machine.batches(split="train"):
-        input = input.to(device)
+    for input in quiz_machine.batches(model, split="train"):
+        input = input.to(local_device)
 
         if nb_train_samples % args.batch_size == 0:
             optimizer.zero_grad()
@@ -350,41 +357,11 @@ def one_epoch(model, quiz_machine):
 
     train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
 
-    log_string(f"train_perplexity {n_epoch} {train_perplexity}")
-
-
-######################################################################
-
-
-def run_tests(model, quiz_machine, deterministic_synthesis):
-    with torch.autograd.no_grad():
-        model.eval()
-
-        nb_test_samples, acc_test_loss = 0, 0.0
-        nb_samples_accumulated = 0
-
-        for input in quiz_machine.batches(split="test"):
-            input = input.to(device)
-
-            bs = model(mygpt.BracketedSequence(input))
-            output = bs.x
-
-            loss = F.cross_entropy(output.transpose(1, 2), input)
-
-            acc_test_loss += loss.item() * input.size(0)
-
-            nb_test_samples += input.size(0)
-
-        test_perplexity = math.exp(min(100, acc_test_loss / nb_test_samples))
+    log_string(f"train_perplexity {n_epoch} model {model.id} {train_perplexity}")
 
-        log_string(f"test_perplexity {n_epoch} {test_perplexity}")
+    run_tests(model, quiz_machine, deterministic_synthesis=False)
 
-        model.main_test_accuracy = quiz_machine.produce_results(
-            n_epoch=n_epoch,
-            model=model,
-            result_dir=args.result_dir,
-            deterministic_synthesis=deterministic_synthesis,
-        )
+    model.to(main_device)
 
 
 ######################################################################
@@ -392,7 +369,9 @@ def run_tests(model, quiz_machine, deterministic_synthesis):
 
 def standard_validity(logproba):
     l = logproba.sort(dim=-1).values
-    return (l[:, 0] < math.log(0.5)) & (l[:, 1] > math.log(0.95))
+    return (l[:, 0] < math.log(args.proba_not_understands)) & (
+        l[:, 1] > math.log(args.proba_understands)
+    )
 
 
 def valid_c_quizzes(recorded, criteria):
@@ -435,7 +414,7 @@ def create_c_quizzes(
             c_quizzes = c_quizzes[quiz_machine.non_trivial(c_quizzes)]
 
             if c_quizzes.size(0) > 0:
-                logproba = quiz_machine.logproba_solution(models, c_quizzes)
+                logproba = quiz_machine.logproba_of_solutions(models, c_quizzes)
                 for l in logproba:
                     s = " ".join([str(x.item()) for x in l])
                     logp_file.write(s + "\n")
@@ -467,118 +446,12 @@ def create_c_quizzes(
         quiz_machine.save_quizzes(args.result_dir, f"culture_c_quiz_{n_epoch:04d}", q)
 
 
-######################################################################
-
-
-def create_c_quizzes_(
-    models,
-    quiz_machine,
-    nb_for_train=1000,
-    nb_for_test=100,
-):
-    quizzes_and_nb_correct_records = []
-
-    nb_to_create = nb_for_train + nb_for_test
-
-    # ------------------------------------------------------------
-
-    standard_validity = lambda nb_correct: torch.logical_and(
-        nb_correct >= args.min_to_validate, nb_correct <= args.max_to_validate
-    )
-
-    file_name = os.path.join(args.result_dir, f"culture_c_quiz_{n_epoch:04d}_logp.dat")
-
-    with open(file_name, "w") as logp_file:
-        while (
-            valid_c_quizzes(quizzes_and_nb_correct_records, standard_validity).size(0)
-            < nb_to_create
-        ):
-            # Select a model at random to generate the new quizzes
-
-            model_for_generation = models[torch.randint(len(models), (1,))]
-
-            c_quizzes = quiz_machine.generate_quizzes(
-                nb_to_create,
-                model_for_generation=model_for_generation,
-                temperature=args.generation_temperature,
-            )
-
-            # if args.prediction_correctness:
-
-            # else:
-            # logproba = quiz_machine.new(quiz_machine.size(0), len(models))
-            # for q,l in zip(quizzes.split(args.batch_size), logits.split(args.batch_size)):
-            # for model in models:
-            # l[...] = F.cross_entropy(model(q))
-
-            c_quizzes = c_quizzes[quiz_machine.non_trivial(c_quizzes)]
-
-            if c_quizzes.size(0) > 0:
-                nb_correct, seq_logproba = quiz_machine.compute_correctness(
-                    c_quizzes,
-                    models,
-                    bidirectional_validation=args.bidirectional_validation,
-                    deterministic_validation=args.deterministic_validation,
-                )
-
-                for n, l in zip(nb_correct, seq_logproba):
-                    s = " ".join([str(x.item()) for x in l])
-                    logp_file.write(f"{n} {s}\n")
-
-                if args.dirty_debug:
-                    nb_correct = torch.randint(
-                        len(models) + 1, nb_correct.size(), device=c_quizzes.device
-                    )
-
-                quizzes_and_nb_correct_records.append((c_quizzes, nb_correct))
-
-            nv = F.one_hot(nb_correct, num_classes=len(models) + 1).sum(0)
-            nv = " ".join([str(x.item()) for x in nv])
-
-            nb_validated = valid_c_quizzes(
-                quizzes_and_nb_correct_records, standard_validity
-            ).size(0)
-
-            log_string(
-                f"keep c_quizzes model {model_for_generation.id} kept {nv} nb_accumulated {nb_validated} / {nb_to_create}"
-            )
-
-    # store the new c_quizzes which have been validated
-
-    new_c_quizzes = valid_c_quizzes(quizzes_and_nb_correct_records, standard_validity)
-
-    quiz_machine.reverse_random_half_in_place(new_c_quizzes)
-
-    quiz_machine.store_c_quizzes(new_c_quizzes[:nb_for_train], for_train=True)
-    quiz_machine.store_c_quizzes(new_c_quizzes[nb_for_train:], for_train=False)
-
-    # save a bunch of images to investigate what quizzes with a
-    # certain nb of correct predictions look like
-
-    for n in range(len(models) + 1):
-        s = (
-            "_validated"
-            if n >= args.min_to_validate and n <= args.max_to_validate
-            else ""
-        )
-
-        q = valid_c_quizzes(
-            quizzes_and_nb_correct_records, criteria=lambda nb_correct: nb_correct == n
-        )[:72]
-
-        quiz_machine.reverse_random_half_in_place(q)
-
-        if q.size(0) > 0:
-            quiz_machine.save_quizzes(
-                args.result_dir, f"culture_c_quiz_{n_epoch:04d}_N{n}{s}", q
-            )
-
-
 ######################################################################
 
 models = []
 
 for k in range(args.nb_gpts):
+    log_string(f"creating model {k} and its w_quizzes")
     model = mygpt.MyGPT(
         vocabulary_size=vocabulary_size,
         dim_model=args.dim_model,
@@ -588,11 +461,16 @@ for k in range(args.nb_gpts):
         nb_blocks=args.nb_blocks,
         causal=True,
         dropout=args.dropout,
-    ).to(device)
+    ).to(main_device)
 
     model.main_test_accuracy = 0.0
     model.id = k
 
+    model.train_w_quizzes = quiz_machine.generate_token_sequences(args.nb_train_samples)
+    quiz_machine.reverse_random_half_in_place(model.train_w_quizzes)
+    model.test_w_quizzes = quiz_machine.generate_token_sequences(args.nb_test_samples)
+    quiz_machine.reverse_random_half_in_place(model.test_w_quizzes)
+
     models.append(model)
 
 
@@ -601,6 +479,54 @@ log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
 
 ######################################################################
 
+# Compute the entropy of the training tokens
+
+token_count = 0
+for input in quiz_machine.batches(models[0], split="train", desc="train-entropy"):
+    token_count += F.one_hot(input, num_classes=quiz_machine.vocabulary_size()).sum(
+        (0, 1)
+    )
+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
+
+if args.max_percents_of_test_in_train >= 0:
+
+    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
+
+    nb_test, nb_in_train = 0, 0
+    for test_subset in subsets_as_tuples(
+        quiz_machine.batches(models[0], split="test", desc="test-check"), 25000
+    ):
+        in_train = set()
+        for train_subset in subsets_as_tuples(
+            quiz_machine.batches(models[0], split="train", desc="train-check"), 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_in_train*100/nb_test:.02f}% ({nb_in_train}/{nb_test}) of test samples are in the train set"
+    )
+
+    assert (
+        nb_in_train <= args.max_percents_of_test_in_train * nb_test / 100
+    ), f"More than {args.max_percents_of_test_in_train}% of test samples are in the train set"
+
+######################################################################
+
 nb_new_c_quizzes_for_train = args.nb_train_samples // 50
 nb_new_c_quizzes_for_test = args.nb_test_samples // 50
 
@@ -616,6 +542,11 @@ if args.dirty_debug:
     nb_new_c_quizzes_for_train = 100
     nb_new_c_quizzes_for_test = 10
 
+    def standard_validity(logproba):
+        l = logproba.sort(dim=-1).values
+        return l[:, 0] < math.log(0.5)
+
+
 ######################################################################
 
 for n_epoch in range(args.nb_epochs):
@@ -627,23 +558,25 @@ for n_epoch in range(args.nb_epochs):
     ##################################################
     # Select, improve, and eval the worst model
 
-    weakest_model = min(models, key=lambda m: float(m.main_test_accuracy))
+    ranked_models = sorted(models, key=lambda m: float(m.main_test_accuracy))
 
-    log_string(
-        f"training model {weakest_model.id} main_test_accuracy {weakest_model.main_test_accuracy}"
-    )
+    weakest_models = ranked_models[: len(gpus)]
 
-    one_epoch(weakest_model, quiz_machine)
+    threads = []
 
-    log_string(
-        f"train_set_composition w_quizzes {quiz_machine.nb_batch_w_quizzes} c_quizzes {quiz_machine.nb_batch_c_quizzes}"
-    )
+    for gpu, model in zip(gpus, weakest_models):
+        log_string(f"training model {model.id}")
 
-    run_tests(weakest_model, quiz_machine, deterministic_synthesis=False)
+        t = threading.Thread(
+            target=one_epoch, daemon=True, args=(model, quiz_machine, gpu)
+        )
 
-    log_string(
-        f"test_set_composition w_quizzes {quiz_machine.nb_batch_w_quizzes} c_quizzes {quiz_machine.nb_batch_c_quizzes}"
-    )
+        threads.append(t)
+
+        t.start()
+
+    for t in threads:
+        t.join()
 
     ##################################################
     # Replace a fraction of the w_quizzes with fresh ones
@@ -651,7 +584,11 @@ for n_epoch in range(args.nb_epochs):
     log_string(
         f"cache_w_quizzes contains {quiz_machine.problem.nb_cached_quizzes()} quizzes"
     )
-    quiz_machine.renew_w_quizzes(args.nb_train_samples // args.nb_gpts)
+
+    # Renew entirely the train set
+
+    for model in weakest_models:
+        quiz_machine.renew_w_quizzes(model, args.nb_train_samples)
 
     ##################################################
     # If all the models are good enough, generate new quizzes and
@@ -665,8 +602,4 @@ for n_epoch in range(args.nb_epochs):
             nb_for_test=nb_new_c_quizzes_for_test,
         )
 
-        for model in models:
-            run_tests(model, quiz_machine, deterministic_synthesis=False)
-
-
 ######################################################################