Update.
authorFrançois Fleuret <francois@fleuret.org>
Sat, 13 Jul 2024 10:57:08 +0000 (12:57 +0200)
committerFrançois Fleuret <francois@fleuret.org>
Sat, 13 Jul 2024 10:57:08 +0000 (12:57 +0200)
main.py
quiz_machine.py

diff --git a/main.py b/main.py
index 9599cf3..957fd85 100755 (executable)
--- a/main.py
+++ b/main.py
@@ -370,77 +370,94 @@ def standard_validity(logproba):
     )
 
 
-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 valid_quizzes_and_logprobas(recorded, criteria):
+    validated_quizzes, validated_logprobas = [], []
+    for q, lp in recorded:
+        validated_indices = criteria(lp)
+        validated_quizzes.append(q[validated_indices])
+        validated_logprobas.append(lp[validated_indices])
+
+    if len(validated_quizzes) > 0:
+        return torch.cat(validated_quizzes, dim=0), torch.cat(
+            validated_logprobas, dim=0
+        )
+    else:
+        return None, None
 
 
 ######################################################################
 
 
-def create_c_quizzes(
-    models,
-    quiz_machine,
-    nb_for_train=1000,
-    nb_for_test=100,
-):
-    quizzes_and_logproba_records = []
-
+def create_c_quizzes(models, quiz_machine, nb_for_train=1000, nb_for_test=100):
     nb_to_create = nb_for_train + nb_for_test
 
-    # ------------------------------------------------------------
+    recorded_quizzes_logprobas = []
 
-    file_name = os.path.join(args.result_dir, f"culture_c_quiz_{n_epoch:04d}_logp.dat")
+    nb_validated = 0
 
-    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
+    while nb_validated < nb_to_create:
+        model_for_generation = models[torch.randint(len(models), (1,))]
 
-            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 = 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)]
 
-            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)
+            recorded_quizzes_logprobas.append((c_quizzes, logproba))
 
-            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))
+            validated_quizzes, validated_logprobas = valid_quizzes_and_logprobas(
+                recorded_quizzes_logprobas, standard_validity
+            )
 
-            nb_validated = valid_c_quizzes(
-                quizzes_and_logproba_records, standard_validity
-            ).size(0)
+            if validated_quizzes is not None:
+                nb_validated = validated_quizzes.size(0)
 
-            log_string(
-                f"keep c_quizzes model {model_for_generation.id} nb_accumulated {nb_validated} / {nb_to_create}"
-            )
+        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(validated_quizzes)
+    quiz_machine.store_c_quizzes(validated_quizzes[:nb_for_train], for_train=True)
+    quiz_machine.store_c_quizzes(
+        validated_quizzes[nb_for_train:nb_to_create], for_train=False
+    )
+
+    ######################################################################
+    # save the log probas
+
+    file_name = os.path.join(
+        args.result_dir, f"culture_c_quiz_all_{n_epoch:04d}_logp.dat"
+    )
 
-    quiz_machine.reverse_random_half_in_place(new_c_quizzes)
+    with open(file_name, "w") as logp_file:
+        for _, ll in recorded_quizzes_logprobas:
+            for l in ll:
+                s = " ".join([str(x.item()) for x in l])
+                logp_file.write(s + "\n")
 
-    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 images with their logprobas
 
-    # save images
+    vq = validated_quizzes[:72]
+    vl = validated_logprobas[:72]
 
-    q = new_c_quizzes[:72]
+    if vq.size(0) > 0:
+        prefix = f"culture_c_quiz_{n_epoch:04d}"
 
-    if q.size(0) > 0:
-        quiz_machine.save_quiz_illustrations(
-            args.result_dir, f"culture_c_quiz_{n_epoch:04d}", q
-        )
+        file_name = os.path.join(args.result_dir, prefix + "_logp.dat")
+        with open(file_name, "w") as logp_file:
+            for l in vl:
+                s = " ".join([str(x.item()) for x in l])
+                logp_file.write(s + "\n")
+
+        quiz_machine.save_quiz_illustrations(args.result_dir, prefix, vq)
 
 
 ######################################################################
@@ -478,9 +495,9 @@ if args.resume:
             filename = f"gpt_{model.id:03d}.pth"
 
             try:
-                model.load_state_dict(
-                    torch.load(os.path.join(args.result_dir, filename))
-                )
+                d = torch.load(os.path.join(args.result_dir, filename))
+                model.load_state_dict(d[0])
+                model.main_test_accuracy = d[1]
                 log_string(f"successfully loaded {filename}")
             except FileNotFoundError:
                 log_string(f"cannot find {filename}")
@@ -604,19 +621,17 @@ for n_epoch in range(args.nb_epochs):
     for t in threads:
         t.join()
 
+    # Save the models to disk
+
     for model in weakest_models:
         filename = f"gpt_{model.id:03d}.pth"
-        torch.save(model.state_dict(), os.path.join(args.result_dir, filename))
+        torch.save(
+            (model.state_dict(), model.main_test_accuracy),
+            os.path.join(args.result_dir, filename),
+        )
         log_string(f"wrote {filename}")
 
-    ##################################################
-    # 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
+    # Renew the training samples
 
     for model in weakest_models:
         quiz_machine.renew_w_quizzes(model, args.nb_train_samples)
@@ -633,6 +648,8 @@ for n_epoch in range(args.nb_epochs):
             nb_for_test=nb_new_c_quizzes_for_test,
         )
 
-        quiz_machine.save_c_quizzes(os.path.join(args.result_dir, "c_quizzes.pth"))
+        filename = "c_quizzes.pth"
+        quiz_machine.save_c_quizzes(os.path.join(args.result_dir, filename))
+        log_string(f"wrote {filename}")
 
 ######################################################################
index eab41dc..ef766c4 100755 (executable)
@@ -5,7 +5,7 @@
 
 # Written by Francois Fleuret <francois@fleuret.org>
 
-import math, os, tqdm, warnings
+import math, os, tqdm, warnings, sys
 
 import torch, torchvision
 
@@ -17,6 +17,36 @@ from mygpt import BracketedSequence
 
 import threading
 
+######################################################################
+# if output is log(P(X=y)) and target is Y, returns -log P(X=Y) + H(X
+# | X != Y)
+
+
+# output is NxCxT and target is NxT
+def confusion(output, target, reduction="mean"):
+    N, C, T = output.shape
+    output = output.permute(0, 2, 1).reshape(-1, C)
+    target = target.flatten()
+    all_t = torch.arange(N * T, device=output.device)
+    output = output.log_softmax(dim=-1)
+    result = -output[all_t, target]
+
+    output[all_t, target] = float("-inf")
+    output = output.log_softmax(dim=-1)
+    e = output.exp()
+    output[all_t, target] = 0
+    result = result - (output * e).sum(-1)
+
+    if reduction == "none":
+        return result.reshape(N, T)
+    elif reduction == "mean":
+        return result.reshape(N, T).mean()
+    elif reduction == "sum":
+        return result.reshape(N, T).sum()
+    else:
+        raise ValueError(f"unknown reduction '{reduction}'.")
+
+
 ######################################################################
 
 # ar_mask is a tensor with 0s and 1s, of same shape as input, with
@@ -189,6 +219,9 @@ class QuizMachine:
     def generate_token_sequences(self, nb):
         prompts, answers = self.problem.generate_prompts_and_answers(nb)
 
+        print(f"DEBUG {prompts.size()=} {answers.size()=}")
+        sys.stdout.flush()
+
         if self.prompt_len is None:
             self.prompt_len = prompts.size(1)