Update.

[culture.git] / main.py

diff --git a/main.py b/main.py
index ca0d152..22edf7b 100755 (executable)
--- a/main.py
+++ b/main.py
@@ -82,12 +82,6 @@ parser.add_argument("--dropout", type=float, default=0.1)
 
 parser.add_argument("--deterministic_synthesis", action="store_true", default=False)
 
 
 parser.add_argument("--deterministic_synthesis", action="store_true", default=False)
 

-parser.add_argument("--no_checkpoint", action="store_true", default=False)
-
-parser.add_argument("--resume", action="store_true", default=False)
-
-parser.add_argument("--checkpoint_name", type=str, default="checkpoint.pth")
-

 ##############################
 # filetask
 
 ##############################
 # filetask
 


@@ -207,6 +201,12 @@ if args.result_dir is None:
 ######################################################################
 
 default_task_args = {
 ######################################################################
 
 default_task_args = {

+    "world": {
+        "model": "37M",
+        "batch_size": 100,
+        "nb_train_samples": 250000,
+        "nb_test_samples": 10000,
+    },

     "file": {
         "model": "37M",
         "batch_size": 25,
     "file": {
         "model": "37M",
         "batch_size": 25,


@@ -219,12 +219,6 @@ default_task_args = {
         "nb_train_samples": 250000,
         "nb_test_samples": 10000,
     },
         "nb_train_samples": 250000,
         "nb_test_samples": 10000,
     },

-    "world": {
-        "model": "37M",
-        "batch_size": 25,
-        "nb_train_samples": 50000,
-        "nb_test_samples": 10000,
-    },

     "byheart": {
         "model": "37M",
         "batch_size": 25,
     "byheart": {
         "model": "37M",
         "batch_size": 25,


@@ -677,64 +671,28 @@ log_string(f"vocabulary_size {vocabulary_size}")
 
 ##############################
 
 
 ##############################
 

-model = mygpt.MyGPT(
-    vocabulary_size=vocabulary_size,
-    dim_model=args.dim_model,
-    dim_keys=args.dim_keys,
-    dim_hidden=args.dim_hidden,
-    nb_heads=args.nb_heads,
-    nb_blocks=args.nb_blocks,
-    causal=True,
-    dropout=args.dropout,
-)
+models = []
+
+for k in range(2):
+    models.append(
+        mygpt.MyGPT(
+            vocabulary_size=vocabulary_size,
+            dim_model=args.dim_model,
+            dim_keys=args.dim_keys,
+            dim_hidden=args.dim_hidden,
+            nb_heads=args.nb_heads,
+            nb_blocks=args.nb_blocks,
+            causal=True,
+            dropout=args.dropout,
+        ).to(device)
+    )

 
 

-model.to(device)

 
 

-nb_parameters = sum(p.numel() for p in model.parameters())
+nb_parameters = sum(p.numel() for p in models[0].parameters())

 log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
 
 ######################################################################
 
 log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
 
 ######################################################################
 

-nb_epochs_finished = 0
-
-if args.no_checkpoint:
-    log_string(f"not trying to load checkpoint.")
-
-else:
-    try:
-        checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
-        checkpoint = torch.load(checkpoint_name)
-        nb_epochs_finished = checkpoint["nb_epochs_finished"]
-        model.load_state_dict(checkpoint["model_state"])
-        torch.set_rng_state(checkpoint["rng_state"])
-        if torch.cuda.is_available():
-            torch.cuda.set_rng_state(checkpoint["cuda_rng_state"])
-
-        log_string(f"checkpoint loaded with {nb_epochs_finished} epochs finished.")
-
-    except FileNotFoundError:
-        log_string("starting from scratch.")
-
-    except:
-        log_string("error when loading the checkpoint.")
-        exit(1)
-
-######################################################################
-
-if args.task == "expr" and args.expr_input_file is not None:
-    task.produce_results(
-        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)
-
-######################################################################
-

 # Compute the entropy of the training tokens
 
 token_count = 0
 # Compute the entropy of the training tokens
 
 token_count = 0


@@ -803,17 +761,6 @@ else:
 
 log_string(f"learning_rate_schedule {learning_rate_schedule}")
 
 
 log_string(f"learning_rate_schedule {learning_rate_schedule}")
 

-##############################
-
-if nb_epochs_finished >= args.nb_epochs:
-    task.produce_results(
-        n_epoch=nb_epochs_finished,
-        model=model,
-        result_dir=args.result_dir,
-        logger=log_string,
-        deterministic_synthesis=args.deterministic_synthesis,
-    )
-

 time_pred_result = None
 
 ######################################################################
 time_pred_result = None
 
 ######################################################################


@@ -896,44 +843,64 @@ def run_tests(model, task, deterministic_synthesis):
 
 ######################################################################
 
 
 ######################################################################
 

-for n_epoch in range(nb_epochs_finished, args.nb_epochs):
-    learning_rate = learning_rate_schedule[n_epoch]
-
-    one_epoch(model, task, learning_rate)

 
 

-    test_accuracy = run_tests(model, task, deterministic_synthesis=False)
+def create_quizzes(
+    other_models,
+    task,
+    nb_for_train=1000,
+    nb_for_test=100,
+    nb_runs=10,
+    nb_min_correct=9,
+    nb_max_correct=9,
+):
+    kept = []

 
 

-    # --------------------------------------------
+    while sum([x.size(0) for x in kept]) < nb_for_train + nb_for_test:
+        new_quizzes, nb_correct = task.create_new_quizzes(
+            n_epoch=n_epoch,
+            result_dir=args.result_dir,
+            logger=log_string,
+            nb=4 * (nb_for_train + nb_for_test),
+            models=other_models,
+            nb_runs=nb_runs,
+        )

 
 

-    if test_accuracy >= 0.8:
-        nb_for_train, nb_for_test = 1000, 100
-        kept = []
-
-        while sum([x.size(0) for x in kept]) < nb_for_train + nb_for_test:
-            new_quizzes, nb_correct = task.create_new_quizzes(
-                n_epoch=n_epoch,
-                result_dir=args.result_dir,
-                logger=log_string,
-                nb=nb_required,
-                model=model,
-                nb_runs=10,
+        to_keep = new_quizzes[
+            torch.logical_and(
+                nb_correct >= nb_min_correct, nb_correct <= nb_max_correct

             )
             )

+        ]
+        log_string(f"keep {to_keep.size(0)} quizzes")
+        kept.append(to_keep)

 
 

-            to_keep = new_quizzes[torch.logical_and(nb_correct >= 8, nb_correct < 10)]
-            log_string(f"keep {to_keep.size(0)} quizzes")
-            kept.append(to_keep)
+    new_quizzes = torch.cat(kept, dim=0)[: nb_for_train + nb_for_test]

 
 

-        new_quizzes = torch.cat(kept, dim=0)[: nb_for_train + nb_for_test]
+    task.store_new_quizzes(new_quizzes[:nb_for_train], for_train=True)
+    task.store_new_quizzes(new_quizzes[nb_for_train:], for_train=False)

 
 

-        task.store_new_quizzes(new_quizzes[:nb_for_train], train=True)
-        task.store_new_quizzes(new_quizzes[nb_for_train:], train=False)
+    task.save_image(
+        new_quizzes[:96],
+        args.result_dir,
+        f"world_new_{n_epoch:04d}.png",
+        log_string,
+    )

 
 

-        task.save_image(
-            new_quizzes[:96],
-            args.result_dir,
-            f"world_new_{n_epoch:04d}.png",
-            log_string,
-        )
+
+######################################################################
+
+accuracy_to_make_quizzes = 0.95
+
+for n_epoch in range(nb_epochs_finished, args.nb_epochs):
+    learning_rate = learning_rate_schedule[n_epoch]
+
+    for m in models:
+        one_epoch(m, task, learning_rate)
+        test_accuracy = run_tests(m, task, deterministic_synthesis=False)
+
+    if test_accuracy >= accuracy_to_make_quizzes:
+        other_models = models.copy()
+        other_models.remove(model)
+        create_quizzes(other_models, task)

 
     # --------------------------------------------
 
 
     # --------------------------------------------
 


@@ -944,19 +911,4 @@ for n_epoch in range(nb_epochs_finished, args.nb_epochs):
         )
     time_pred_result = time_current_result
 
         )
     time_pred_result = time_current_result
 

-    # --------------------------------------------
-
-    checkpoint = {
-        "nb_epochs_finished": n_epoch + 1,
-        "model_state": model.state_dict(),
-        "rng_state": torch.get_rng_state(),
-    }
-
-    if torch.cuda.is_available():
-        checkpoint["cuda_rng_state"] = torch.cuda.get_rng_state()
-
-    checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
-    torch.save(checkpoint, checkpoint_name)
-    log_string(f"saved checkpoint {checkpoint_name}")
-

 ######################################################################
 ######################################################################