Update.

diff --git a/main.py b/main.py
index 2afe61b..ee4e9e5 100755 (executable)
--- a/main.py
+++ b/main.py
@@ -183,8 +183,8 @@ for n in vars(args):
 ######################################################################
 
 if args.check:
-    args.nb_train_samples = 2500
-    args.nb_test_samples = 100
+    args.nb_train_samples = 25000
+    args.nb_test_samples = 1000
 
 if args.physical_batch_size is None:
     args.physical_batch_size = args.batch_size
@@ -338,11 +338,13 @@ def create_quizzes(
     desired_average_logits=None,
 ):
     kept = []
-    nb_generated_tokens, sum_logits = 0, 0
+
+    sum_logits = 0
 
     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(
+
+        new_quizzes, nb_correct, _sum_logits = task.create_new_quizzes(
             n_epoch=n_epoch,
             result_dir=args.result_dir,
             logger=log_string,
@@ -352,8 +354,7 @@ def create_quizzes(
             desired_average_logits=desired_average_logits,
         )
 
-        nb_generated_tokens += new_quizzes.numel()
-        sum_logits += average_logits * new_quizzes.numel()
+        sum_logits += _sum_logits
 
         to_keep = new_quizzes[nb_correct == len(other_models) - 1]
         log_string(
@@ -373,7 +374,7 @@ def create_quizzes(
         log_string,
     )
 
-    return sum_logits / nb_generated_tokens
+    return sum_logits / new_quizzes.size(0)
 
 
 ######################################################################
@@ -409,7 +410,7 @@ 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_train = 100
     nb_new_quizzes_for_test = 10
 
 desired_average_logits = None

diff --git a/mygpt.py b/mygpt.py
index c58bea1..3e63567 100755 (executable)
--- a/mygpt.py
+++ b/mygpt.py
@@ -285,16 +285,19 @@ class MyGPT(nn.Module):
         forced_biases=None,
     ):
         sum_logits = 0
+
         to_generate = (ar_mask.sum(0) > 0).nonzero()
+
         if to_generate.min() > 0:
             self(
                 BracketedSequence(input, 0, to_generate.min())
             )  # Needed to initialize the model's cache
         for s in range(to_generate.min(), to_generate.max() + 1):
             output = self(BracketedSequence(input, s, 1)).x
+
             logits = output[:, s]
 
-            logits = logits.log_softmax(dim=-1) / temperature
+            logits = logits.log_softmax(dim=1) / temperature
 
             if forbidden_tokens is not None:
                 logits = logits.masked_fill(forbidden_tokens, float("-inf"))
@@ -307,9 +310,10 @@ class MyGPT(nn.Module):
             else:
                 dist = torch.distributions.categorical.Categorical(logits=logits)
                 t_next = dist.sample()
-                sum_logits += logits.log_softmax(dim=-1)[
+                sum_logits += logits.log_softmax(dim=1)[
                     torch.arange(t_next.size(0)), t_next
                 ].sum()
+
             input[:, s] = ar_mask[:, s] * t_next + (1 - ar_mask[:, s]) * input[:, s]
 
         return sum_logits

diff --git a/tasks.py b/tasks.py
index 64fe967..b3b56ad 100755 (executable)
--- a/tasks.py
+++ b/tasks.py
@@ -41,12 +41,12 @@ def masked_inplace_autoregression(
             total=(input.size(0) + batch_size - 1) // batch_size,
         )
 
+    sum_logits = 0
+
     with torch.autograd.no_grad():
         t = model.training
         model.eval()
 
-        sum_logits = 0
-
         for input, ar_mask in batches:
             sum_logits += model.masked_inplace_autoregression(
                 input=input,
@@ -59,7 +59,7 @@ def masked_inplace_autoregression(
 
         model.train(t)
 
-        return sum_logits
+    return sum_logits
 
 
 ######################################################################
@@ -264,31 +264,14 @@ class World(Task):
         quizzes = torch.empty(
             nb, self.height * self.width * 2 + 1, device=self.device, dtype=torch.int64
         )
-        ar_mask = torch.full(quizzes.size(), 1, device=self.device)
 
-        sum_logits = masked_inplace_autoregression(
-            model=model,
-            batch_size=self.batch_size,
-            input=quizzes,
-            ar_mask=ar_mask,
-            temperature=1.0,
-            deterministic_synthesis=False,
-            progress_bar_desc="creating quizzes",
-            device=self.device,
-        )
-
-        # Should not be necessary though, the autoregression is done
-        # in eval mode
-        sum_logits = sum_logits.detach()
-
-        average_logits = sum_logits / quizzes.numel()
+        ar_mask = torch.full(quizzes.size(), 1, device=self.device)
 
-        # It's a bit brutal to do it twice, we should probably have a
-        # moving average and apply it right away
+        temperature = 1
+        d_temperature = 1
 
-        if desired_average_logits is not None:
-            temperature = average_logits / desired_average_logits
-            masked_inplace_autoregression(
+        while True:
+            sum_logits = masked_inplace_autoregression(
                 model=model,
                 batch_size=self.batch_size,
                 input=quizzes,
@@ -299,6 +282,24 @@ class World(Task):
                 device=self.device,
             )
 
+            average_logits = sum_logits / quizzes.size(0)
+
+            logger(f"{average_logits=} {desired_average_logits=}")
+
+            if desired_average_logits is None:
+                break
+
+            # Oh man that's ugly
+            if average_logits > desired_average_logits:
+                if d_temperature < 0:
+                    d_temperature *= -0.5
+                temperature += d_temperature
+            else:
+                if d_temperature > 0:
+                    d_temperature *= -0.5
+                temperature += d_temperature
+            logger(f"chaging temperature to {temperature}")
+
         ###############################################################
         # Create the reverse quizzes
 
@@ -356,9 +357,9 @@ class World(Task):
 
         nb_correct = torch.cat(nb_correct, dim=0)
 
-        filename = os.path.join(result_dir, "correct_{n_epoch:04d}.dat")
-        with open(filename, "w") as f:
-            for k in nb_correct:
-                f.write(f"{k}\n")
+        # filename = os.path.join(result_dir, "correct_{n_epoch:04d}.dat")
+        # with open(filename, "w") as f:
+        # for k in nb_correct:
+        # f.write(f"{k}\n")
 
-        return quizzes, nb_correct.sum(dim=0), average_logits
+        return quizzes, nb_correct.sum(dim=0), sum_logits