Update.
authorFrançois Fleuret <francois@fleuret.org>
Thu, 11 Jul 2024 13:31:56 +0000 (15:31 +0200)
committerFrançois Fleuret <francois@fleuret.org>
Thu, 11 Jul 2024 13:31:56 +0000 (15:31 +0200)
main.py

diff --git a/main.py b/main.py
index 5956be5..0a266a8 100755 (executable)
--- a/main.py
+++ b/main.py
@@ -16,12 +16,182 @@ import ffutils
 import mygpt
 import sky, grids, quiz_machine
 
-import threading
+import torch.multiprocessing as mp
+
+# mp.set_start_method('spawn')
 
 # world quizzes vs. culture quizzes
 
 ######################################################################
 
+
+def log_string(s):
+    t = time.strftime("%Y%m%d-%H:%M:%S ", time.localtime())
+
+    if log_file is not None:
+        log_file.write(t + s + "\n")
+        log_file.flush()
+
+    print(t + s)
+    sys.stdout.flush()
+
+
+######################################################################
+
+
+def run_tests(model, quiz_machine, deterministic_synthesis, local_device=None):
+    if local_device is None:
+        local_device = device
+
+    with torch.autograd.no_grad():
+        model.eval().to(local_device)
+
+        nb_test_samples, acc_test_loss = 0, 0.0
+        nb_samples_accumulated = 0
+
+        for input in quiz_machine.batches(model, split="test"):
+            input = input.to(local_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"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, local_device=None):
+    if local_device is None:
+        local_device = device
+
+    optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
+
+    model.to(local_device).train()
+
+    nb_train_samples, acc_train_loss = 0, 0.0
+
+    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()
+
+        output = model(mygpt.BracketedSequence(input)).x
+        loss = F.cross_entropy(output.transpose(1, 2), input)
+        acc_train_loss += loss.item() * input.size(0)
+
+        nb_train_samples += input.size(0)
+
+        loss.backward()
+
+        if nb_train_samples % args.batch_size == 0:
+            optimizer.step()
+
+    train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
+
+    log_string(f"train_perplexity {n_epoch} model {model.id} {train_perplexity}")
+
+    run_tests(model, quiz_machine, deterministic_synthesis=False)
+
+
+######################################################################
+
+
+def standard_validity(logproba):
+    l = logproba.sort(dim=-1).values
+    return (l[:, 0] < math.log(0.5)) & (l[:, 1] > math.log(0.99))
+    # warnings.warn("TEST!!!", RuntimeWarning)
+    # print(l.exp())
+    # return (l[:, 0] < math.log(0.99))
+
+
+def valid_c_quizzes(recorded, criteria):
+    result = [q[criteria(lp)] for q, lp in recorded]
+    return torch.cat(result, dim=0) if len(result) > 0 else torch.tensor([])
+
+
+######################################################################
+
+
+def create_c_quizzes(
+    models,
+    quiz_machine,
+    nb_for_train=1000,
+    nb_for_test=100,
+):
+    quizzes_and_logproba_records = []
+
+    nb_to_create = nb_for_train + nb_for_test
+
+    # ------------------------------------------------------------
+
+    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_logproba_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,
+            )
+
+            c_quizzes = c_quizzes[quiz_machine.non_trivial(c_quizzes)]
+
+            if c_quizzes.size(0) > 0:
+                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")
+                quizzes_and_logproba_records.append((c_quizzes, logproba))
+
+            nb_validated = valid_c_quizzes(
+                quizzes_and_logproba_records, standard_validity
+            ).size(0)
+
+            log_string(
+                f"keep c_quizzes model {model_for_generation.id} nb_accumulated {nb_validated} / {nb_to_create}"
+            )
+
+    # store the new c_quizzes which have been validated
+
+    new_c_quizzes = valid_c_quizzes(quizzes_and_logproba_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
+
+    q = new_c_quizzes[:72]
+
+    if q.size(0) > 0:
+        quiz_machine.save_quizzes(args.result_dir, f"culture_c_quiz_{n_epoch:04d}", q)
+
+
+######################################################################
+
 if torch.cuda.is_available():
     device = torch.device("cuda")
     torch.backends.cuda.matmul.allow_tf32 = True
@@ -189,6 +359,11 @@ except FileExistsError:
 
 log_file = open(os.path.join(args.result_dir, args.log_filename), "a")
 
+log_string(f"argv {' '.join(sys.argv)}")
+
+for n in vars(args):
+    log_string(f"args.{n} {getattr(args, n)}")
+
 if args.seed >= 0:
     # torch.backends.cudnn.deterministic = True
     # torch.backends.cudnn.benchmark = False
@@ -199,26 +374,6 @@ if args.seed >= 0:
 
 ######################################################################
 
-
-def log_string(s):
-    t = time.strftime("%Y%m%d-%H:%M:%S ", time.localtime())
-
-    if log_file is not None:
-        log_file.write(t + s + "\n")
-        log_file.flush()
-
-    print(t + s)
-    sys.stdout.flush()
-
-
-log_string(f"argv {' '.join(sys.argv)}")
-
-for n in vars(args):
-    log_string(f"args.{n} {getattr(args, n)}")
-
-
-######################################################################
-
 if args.dirty_debug:
     args.nb_train_samples = 2500
     args.nb_test_samples = 100
@@ -276,165 +431,6 @@ log_string(f"vocabulary_size {vocabulary_size}")
 
 ######################################################################
 
-
-######################################################################
-
-
-def run_tests(model, quiz_machine, deterministic_synthesis, local_device=None):
-    if local_device is None:
-        local_device = device
-
-    with torch.autograd.no_grad():
-        model.eval().to(local_device)
-
-        nb_test_samples, acc_test_loss = 0, 0.0
-        nb_samples_accumulated = 0
-
-        for input in quiz_machine.batches(model, split="test"):
-            input = input.to(local_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"test_perplexity {n_epoch} {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, local_device=None):
-    if local_device is None:
-        local_device = device
-
-    optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
-
-    model.to(local_device).train()
-
-    nb_train_samples, acc_train_loss = 0, 0.0
-
-    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()
-
-        output = model(mygpt.BracketedSequence(input)).x
-        loss = F.cross_entropy(output.transpose(1, 2), input)
-        acc_train_loss += loss.item() * input.size(0)
-
-        nb_train_samples += input.size(0)
-
-        loss.backward()
-
-        if nb_train_samples % args.batch_size == 0:
-            optimizer.step()
-
-    train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
-
-    log_string(f"train_perplexity {n_epoch} {train_perplexity}")
-
-    run_tests(model, quiz_machine, deterministic_synthesis=False)
-
-    model.TRAINING_LOCK.release()
-
-
-######################################################################
-
-
-def standard_validity(logproba):
-    l = logproba.sort(dim=-1).values
-    return (l[:, 0] < math.log(0.5)) & (l[:, 1] > math.log(0.99))
-    # warnings.warn("TEST!!!", RuntimeWarning)
-    # print(l.exp())
-    # return (l[:, 0] < math.log(0.99))
-
-
-def valid_c_quizzes(recorded, criteria):
-    result = [q[criteria(lp)] for q, lp in recorded]
-    return torch.cat(result, dim=0) if len(result) > 0 else torch.tensor([])
-
-
-######################################################################
-
-
-def create_c_quizzes(
-    models,
-    quiz_machine,
-    nb_for_train=1000,
-    nb_for_test=100,
-):
-    quizzes_and_logproba_records = []
-
-    nb_to_create = nb_for_train + nb_for_test
-
-    # ------------------------------------------------------------
-
-    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_logproba_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,
-            )
-
-            c_quizzes = c_quizzes[quiz_machine.non_trivial(c_quizzes)]
-
-            if c_quizzes.size(0) > 0:
-                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")
-                quizzes_and_logproba_records.append((c_quizzes, logproba))
-
-            nb_validated = valid_c_quizzes(
-                quizzes_and_logproba_records, standard_validity
-            ).size(0)
-
-            log_string(
-                f"keep c_quizzes model {model_for_generation.id} nb_accumulated {nb_validated} / {nb_to_create}"
-            )
-
-    # store the new c_quizzes which have been validated
-
-    new_c_quizzes = valid_c_quizzes(quizzes_and_logproba_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
-
-    q = new_c_quizzes[:72]
-
-    if q.size(0) > 0:
-        quiz_machine.save_quizzes(args.result_dir, f"culture_c_quiz_{n_epoch:04d}", q)
-
-
-######################################################################
-
 models = []
 
 for k in range(args.nb_gpts):
@@ -452,7 +448,6 @@ for k in range(args.nb_gpts):
 
     model.main_test_accuracy = 0.0
     model.id = k
-    model.TRAINING_LOCK = threading.Lock()
 
     model.train_w_quizzes = quiz_machine.generate_token_sequences(
         args.nb_train_samples
@@ -554,20 +549,24 @@ for n_epoch in range(args.nb_epochs):
 
     weakest_models = ranked_models[: args.nb_gpus]
 
-    for gpu_id, model in enumerate(weakest_models):
-        model.TRAINING_LOCK.acquire()
+    processes = []
 
+    for gpu_id, model in enumerate(weakest_models):
         log_string(
             f"training model {model.id} main_test_accuracy {model.main_test_accuracy}"
         )
 
-        threading.Thread(
-            target=one_epoch, daemon=True, args=(model, quiz_machine, f"cuda:{gpu_id}")
-        ).start()
+        process = mp.Process(
+            target=one_epoch, args=(model, quiz_machine, f"cuda:{gpu_id}")
+        )
 
-    for model in weakest_models:
-        model.TRAINING_LOCK.acquire()
-        model.TRAINING_LOCK.release()
+        processes.append(process)
+
+    for process in processes:
+        process.start()
+
+    for process in processes:
+        process.join()
 
     ##################################################
     # Renew the train sets