Update.

[culture.git] / main.py

diff --git a/main.py b/main.py
index 3b5a7c6..2d5b148 100755 (executable)
--- a/main.py
+++ b/main.py
@@ -12,7 +12,16 @@ from torch import nn
 from torch.nn import functional as F
 
 import ffutils
 from torch.nn import functional as F
 
 import ffutils

-import mygpt, tasks
+import mygpt
+import sky, quizz_machine
+
+# world quizzes vs. culture quizzes
+
+######################################################################
+
+accuracy_to_make_c_quizzes = 0.975
+nb_new_c_quizzes_for_train = 1000
+nb_new_c_quizzes_for_test = 100

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


@@ -73,7 +82,7 @@ parser.add_argument("--deterministic_synthesis", action="store_true", default=Fa
 
 parser.add_argument("--nb_gpts", type=int, default=5)
 
 
 parser.add_argument("--nb_gpts", type=int, default=5)
 

-parser.add_argument("--check", action="store_true", default=False)
+parser.add_argument("--dirty_debug", action="store_true", default=False)

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


@@ -84,6 +93,13 @@ if args.result_dir is None:
 
 ######################################################################
 
 
 ######################################################################
 

+if args.dirty_debug:
+    accuracy_to_make_c_quizzes = 0.0
+    nb_new_c_quizzes_for_train = 100
+    nb_new_c_quizzes_for_test = 10
+
+######################################################################
+

 default_args = {
     "model": "37M",
     "batch_size": 100,
 default_args = {
     "model": "37M",
     "batch_size": 100,


@@ -182,7 +198,7 @@ for n in vars(args):
 
 ######################################################################
 
 
 ######################################################################
 

-if args.check:
+if args.dirty_debug:

     args.nb_train_samples = 2500
     args.nb_test_samples = 100
 
     args.nb_train_samples = 2500
     args.nb_test_samples = 100
 


@@ -194,7 +210,8 @@ else:
 assert args.nb_train_samples % args.batch_size == 0
 assert args.nb_test_samples % args.batch_size == 0
 
 assert args.nb_train_samples % args.batch_size == 0
 assert args.nb_test_samples % args.batch_size == 0
 

-task = tasks.World(
+quizz_machine = quizz_machine.QuizzMachine(
+    sky.Sky(height=6, width=8, nb_birds=3, nb_iterations=2),

     nb_train_samples=args.nb_train_samples,
     nb_test_samples=args.nb_test_samples,
     batch_size=args.physical_batch_size,
     nb_train_samples=args.nb_train_samples,
     nb_test_samples=args.nb_test_samples,
     batch_size=args.physical_batch_size,


@@ -207,7 +224,7 @@ task = tasks.World(
 
 log_string(f"device {device}")
 
 
 log_string(f"device {device}")
 

-vocabulary_size = task.vocabulary_size()
+vocabulary_size = quizz_machine.vocabulary_size()

 
 log_string(f"vocabulary_size {vocabulary_size}")
 
 
 log_string(f"vocabulary_size {vocabulary_size}")
 


@@ -216,8 +233,10 @@ log_string(f"vocabulary_size {vocabulary_size}")
 # Compute the entropy of the training tokens
 
 token_count = 0
 # Compute the entropy of the training tokens
 
 token_count = 0

-for input in task.batches(split="train", desc="train-entropy"):
-    token_count += F.one_hot(input, num_classes=task.vocabulary_size()).sum((0, 1))
+for input in quizz_machine.batches(split="train", desc="train-entropy"):
+    token_count += F.one_hot(input, num_classes=quizz_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)
 token_probas = token_count / token_count.sum()
 entropy = -torch.xlogy(token_probas, token_probas).sum()
 train_set_perplexity = math.exp(entropy)


@@ -239,11 +258,11 @@ if args.max_percents_of_test_in_train >= 0:
 
     nb_test, nb_in_train = 0, 0
     for test_subset in subsets_as_tuples(
 
     nb_test, nb_in_train = 0, 0
     for test_subset in subsets_as_tuples(

-        task.batches(split="test", desc="test-check"), 25000
+        quizz_machine.batches(split="test", desc="test-check"), 25000

     ):
         in_train = set()
         for train_subset in subsets_as_tuples(
     ):
         in_train = set()
         for train_subset in subsets_as_tuples(

-            task.batches(split="train", desc="train-check"), 25000
+            quizz_machine.batches(split="train", desc="train-check"), 25000

         ):
             in_train.update(test_subset.intersection(train_subset))
         nb_in_train += len(in_train)
         ):
             in_train.update(test_subset.intersection(train_subset))
         nb_in_train += len(in_train)


@@ -260,14 +279,14 @@ if args.max_percents_of_test_in_train >= 0:
 ##############################
 
 
 ##############################
 
 

-def one_epoch(model, task):
+def one_epoch(model, quizz_machine):

     optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
 
     model.train()
 
     nb_train_samples, acc_train_loss = 0, 0.0
 
     optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
 
     model.train()
 
     nb_train_samples, acc_train_loss = 0, 0.0
 

-    for input in task.batches(split="train"):
+    for input in quizz_machine.batches(split="train"):

         input = input.to(device)
 
         if nb_train_samples % args.batch_size == 0:
         input = input.to(device)
 
         if nb_train_samples % args.batch_size == 0:


@@ -292,14 +311,14 @@ def one_epoch(model, task):
 ######################################################################
 
 
 ######################################################################
 
 

-def run_tests(model, task, deterministic_synthesis):
+def run_tests(model, quizz_machine, deterministic_synthesis):

     with torch.autograd.no_grad():
         model.eval()
 
         nb_test_samples, acc_test_loss = 0, 0.0
         nb_samples_accumulated = 0
 
     with torch.autograd.no_grad():
         model.eval()
 
         nb_test_samples, acc_test_loss = 0, 0.0
         nb_samples_accumulated = 0
 

-        for input in task.batches(split="test"):
+        for input in quizz_machine.batches(split="test"):

             input = input.to(device)
 
             bs = model(mygpt.BracketedSequence(input))
             input = input.to(device)
 
             bs = model(mygpt.BracketedSequence(input))


@@ -311,7 +330,7 @@ def run_tests(model, task, deterministic_synthesis):
 
             nb_test_samples += input.size(0)
 
 
             nb_test_samples += input.size(0)
 

-        main_test_accuracy = task.produce_results(
+        main_test_accuracy = quizz_machine.produce_results(

             n_epoch=n_epoch,
             model=model,
             result_dir=args.result_dir,
             n_epoch=n_epoch,
             model=model,
             result_dir=args.result_dir,


@@ -329,51 +348,64 @@ def run_tests(model, task, deterministic_synthesis):
 ######################################################################
 
 
 ######################################################################
 
 

-def create_quizzes(
-    model,
-    other_models,
-    task,
+def create_c_quizzes(
+    models,
+    quizz_machine,

     nb_for_train=1000,
     nb_for_test=100,
     nb_for_train=1000,
     nb_for_test=100,

-    desired_average_logits=None,
+    min_ave_seq_logproba=None,

 ):
     kept = []
 ):
     kept = []

-    nb_generated_tokens, sum_logits = 0, 0
+    model_indexes = []
+    sum_logits, sum_nb_c_quizzes = 0, 0

 
     while sum([x.size(0) for x in kept]) < nb_for_train + nb_for_test:
 
     while sum([x.size(0) for x in kept]) < nb_for_train + nb_for_test:

-        nb_to_generate = 4 * (nb_for_train + nb_for_test)
-        new_quizzes, nb_correct, average_logits = task.create_new_quizzes(
+        nb_to_generate = nb_for_train + nb_for_test
+
+        if len(model_indexes) == 0:
+            model_indexes = [i.item() for i in torch.randperm(len(models))]
+
+        model = models[model_indexes.pop()]
+
+        new_c_quizzes, nb_correct, ave_seq_logproba = quizz_machine.create_c_quizzes(
+            nb=nb_to_generate,
+            model_for_generation=model,
+            models_for_validation=models,
+            min_ave_seq_logproba=min_ave_seq_logproba,

             n_epoch=n_epoch,
             result_dir=args.result_dir,
             logger=log_string,
             n_epoch=n_epoch,
             result_dir=args.result_dir,
             logger=log_string,

-            nb=nb_to_generate,
-            model=model,
-            other_models=other_models,
-            desired_average_logits=desired_average_logits,

         )
 
         )
 

-        nb_generated_tokens += new_quizzes.numel()
-        sum_logits += average_logits * new_quizzes.numel()
+        sum_logits += new_c_quizzes.size(0) * ave_seq_logproba
+        sum_nb_c_quizzes += new_c_quizzes.size(0)
+
+        to_keep = new_c_quizzes[nb_correct == len(models) - 1]
+
+        if args.dirty_debug:
+            to_keep = new_c_quizzes[
+                torch.randint(3, (new_c_quizzes.size(0),), device=new_c_quizzes.device)
+                == 0
+            ]
+
+        kept.append(to_keep)

 
 

-        to_keep = new_quizzes[nb_correct == len(other_models) - 1]

         log_string(
         log_string(

-            f"keep {to_keep.size(0)}/{new_quizzes.size(0)} quizzes ({to_keep.size(0)*100/new_quizzes.size(0):.02f}%)"
+            f"keep c_quizzes {to_keep.size(0)}/{new_c_quizzes.size(0)} ({to_keep.size(0)*100/new_c_quizzes.size(0):.02f}%) total {sum([ x.size(0) for x in kept])}/{nb_to_generate}"

         )
         )

-        kept.append(to_keep)

 
 

-    new_quizzes = torch.cat(kept, dim=0)[: nb_for_train + nb_for_test]
+    new_c_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)
+    quizz_machine.store_c_quizzes(new_c_quizzes[:nb_for_train], for_train=True)
+    quizz_machine.store_c_quizzes(new_c_quizzes[nb_for_train:], for_train=False)

 
 

-    task.save_image(
-        new_quizzes[:72],
+    quizz_machine.problem.save_quizzes(
+        new_c_quizzes[:72],

         args.result_dir,
         args.result_dir,

-        f"world_quiz_{n_epoch:04d}_{model.id:02d}.png",
-        log_string,
+        f"culture_c_quiz_{n_epoch:04d}_{model.id:02d}",

     )
 
     )
 

-    return sum_logits / nb_generated_tokens
+    return sum_logits / sum_nb_c_quizzes

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


@@ -403,19 +435,10 @@ log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
 
 ######################################################################
 
 
 ######################################################################
 

-accuracy_to_make_quizzes = 0.975
-nb_new_quizzes_for_train = 1000
-nb_new_quizzes_for_test = 100
-
-if args.check:
-    accuracy_to_make_quizzes = 0.0
-    nb_new_quizzes_for_train = 10
-    nb_new_quizzes_for_test = 10
-
-desired_average_logits = None
+min_ave_seq_logproba = None

 
 for n_epoch in range(args.nb_epochs):
 
 for n_epoch in range(args.nb_epochs):

-    log_string(f"--- epoch {n_epoch+1} ----------------------------------------")
+    log_string(f"--- epoch {n_epoch} ----------------------------------------")

 
     a = [(model.id, float(model.main_test_accuracy)) for model in models]
     a.sort(key=lambda p: p[0])
 
     a = [(model.id, float(model.main_test_accuracy)) for model in models]
     a.sort(key=lambda p: p[0])


@@ -430,45 +453,41 @@ for n_epoch in range(args.nb_epochs):
     )
 
     # improve it
     )
 
     # improve it

-    one_epoch(model, task)
+    one_epoch(model, quizz_machine)

 
 

-    task.renew_samples(args.nb_train_samples // args.nb_gpts)
+    quizz_machine.renew_w_quizzes(args.nb_train_samples // args.nb_gpts)

 
     log_string(
 
     log_string(

-        f"train_set_composition world {task.nb_batch_samples_world} quizzes {task.nb_batch_samples_quizzes}"
+        f"train_set_composition w_quizzes {quizz_machine.nb_batch_w_quizzes} c_quizzes {quizz_machine.nb_batch_c_quizzes}"

     )
 
     # test it
     )
 
     # test it

-    run_tests(model, task, deterministic_synthesis=False)
+    run_tests(model, quizz_machine, deterministic_synthesis=False)

 
     log_string(
 
     log_string(

-        f"test_set_composition world {task.nb_batch_samples_world} quizzes {task.nb_batch_samples_quizzes}"
+        f"test_set_composition w_quizzes {quizz_machine.nb_batch_w_quizzes} c_quizzes {quizz_machine.nb_batch_c_quizzes}"

     )
 
     )
 

-    if min([m.main_test_accuracy for m in models]) >= accuracy_to_make_quizzes:
-        other_models = models.copy()
-        other_models.remove(model)
-
-        average_logits = create_quizzes(
-            model,
-            other_models,
-            task,
-            nb_for_train=nb_new_quizzes_for_train,
-            nb_for_test=nb_new_quizzes_for_test,
-            desired_average_logits=desired_average_logits,
+    if min([m.main_test_accuracy for m in models]) >= accuracy_to_make_c_quizzes:
+        ave_seq_logproba = create_c_quizzes(
+            models,
+            quizz_machine,
+            nb_for_train=nb_new_c_quizzes_for_train,
+            nb_for_test=nb_new_c_quizzes_for_test,
+            min_ave_seq_logproba=min_ave_seq_logproba,

         )
 
         # We keep the first average logits as a reference
         )
 
         # We keep the first average logits as a reference

-        if desired_average_logits is None:
-            desired_average_logits = average_logits
+        if min_ave_seq_logproba is None:
+            min_ave_seq_logproba = ave_seq_logproba

         else:
             log_string(
         else:
             log_string(

-                f"desired_average_logits {desired_average_logits} average_logits {average_logits}"
+                f"min_ave_seq_logproba {min_ave_seq_logproba} ave_seq_logproba {ave_seq_logproba}"

             )
 
         # We update everyone
         for model in models:
             )
 
         # We update everyone
         for model in models:

-            run_tests(model, task, deterministic_synthesis=False)
+            run_tests(model, quizz_machine, deterministic_synthesis=False)

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