X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;ds=sidebyside;f=main.py;h=b88cbc4b545c553e36629d43b174cfc056250bd9;hb=7c79c0b140c88a529962945ec5b482fe90c55581;hp=5956be5f91effe796d5b984e0447ddc7c4c46e67;hpb=07c065e77f1d2a775814ec402752a4a8eb6c7574;p=culture.git

diff --git a/main.py b/main.py
index 5956be5..b88cbc4 100755
--- a/main.py
+++ b/main.py
@@ -18,6 +18,8 @@ import sky, grids, quiz_machine
 
 import threading
 
+import torch.multiprocessing as mp
+
 # world quizzes vs. culture quizzes
 
 ######################################################################
@@ -88,7 +90,7 @@ parser.add_argument("--min_to_validate", type=int, default=None)
 
 parser.add_argument("--max_to_validate", type=int, default=None)
 
-parser.add_argument("--accuracy_to_make_c_quizzes", type=float, default=0.9)
+parser.add_argument("--accuracy_to_make_c_quizzes", type=float, default=0.975)
 
 parser.add_argument("--generation_temperature", type=float, default=2.0)
 
@@ -343,12 +345,10 @@ def one_epoch(model, quiz_machine, local_device=None):
 
     train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
 
-    log_string(f"train_perplexity {n_epoch} {train_perplexity}")
+    log_string(f"train_perplexity {n_epoch} model.id {model.id} {train_perplexity}")
 
     run_tests(model, quiz_machine, deterministic_synthesis=False)
 
-    model.TRAINING_LOCK.release()
-
 
 ######################################################################
 
@@ -356,9 +356,6 @@ def one_epoch(model, quiz_machine, local_device=None):
 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):
@@ -452,15 +449,10 @@ 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
-    ).to(device)
+    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
-    ).to(device)
+    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)
@@ -536,7 +528,7 @@ if args.dirty_debug:
 
     def standard_validity(logproba):
         l = logproba.sort(dim=-1).values
-        return l[:, 0] < math.log(0.99)
+        return l[:, 0] < math.log(0.5)
 
 
 ######################################################################
@@ -548,34 +540,37 @@ for n_epoch in range(args.nb_epochs):
     log_string(f"current_test_accuracies {cta}")
 
     ##################################################
-    # Select, improve, and eval the worst models
+    # Select, improve, and eval the worst model
 
     ranked_models = sorted(models, key=lambda m: float(m.main_test_accuracy))
 
     weakest_models = ranked_models[: args.nb_gpus]
 
+    threads = []
+
     for gpu_id, model in enumerate(weakest_models):
-        model.TRAINING_LOCK.acquire()
+        log_string(f"training model {model.id}")
 
-        log_string(
-            f"training model {model.id} main_test_accuracy {model.main_test_accuracy}"
+        t = threading.Thread(
+            target=one_epoch, daemon=True, args=(model, quiz_machine, f"cuda:{gpu_id}")
         )
 
-        threading.Thread(
-            target=one_epoch, daemon=True, args=(model, quiz_machine, f"cuda:{gpu_id}")
-        ).start()
+        threads.append(t)
 
-    for model in weakest_models:
-        model.TRAINING_LOCK.acquire()
-        model.TRAINING_LOCK.release()
+        t.start()
+
+    for t in threads:
+        t.join()
 
     ##################################################
-    # Renew the train sets
+    # Replace a fraction of the w_quizzes with fresh ones
 
     log_string(
         f"cache_w_quizzes contains {quiz_machine.problem.nb_cached_quizzes()} quizzes"
     )
 
+    # Renew entirely the train set
+
     for model in weakest_models:
         quiz_machine.renew_w_quizzes(model, args.nb_train_samples)