Update.

[picoclvr.git] / tasks.py

diff --git a/tasks.py b/tasks.py
index ea10d7c..44599f7 100755 (executable)
--- a/tasks.py
+++ b/tasks.py
@@ -1555,7 +1555,6 @@ import qmlp
 
 
 class QMLP(Task):
-
     ######################
 
     def __init__(
@@ -1563,6 +1562,7 @@ class QMLP(Task):
         nb_train_samples,
         nb_test_samples,
         batch_size,
+        result_dir,
         logger=None,
         device=torch.device("cpu"),
     ):
@@ -1570,39 +1570,34 @@ class QMLP(Task):
 
         self.device = device
         self.batch_size = batch_size
+        self.nb_samples_per_mlp = 256
 
         if logger is not None:
             logger(
                 f"generating {nb_train_samples+nb_test_samples} samples (can take some time)"
             )
 
-        self.train_descr = self.grid_factory.generate_samples(
-            nb_train_samples, lambda r: tqdm.tqdm(r)
-        )
-        self.test_descr = self.grid_factory.generate_samples(
-            nb_test_samples, lambda r: tqdm.tqdm(r)
+        seq, q_test_set, test_error = qmlp.generate_sequence_and_test_set(
+            nb_mlps=nb_train_samples + nb_test_samples,
+            nb_samples=self.nb_samples_per_mlp,
+            device=self.device,
+            batch_size=64,
+            nb_epochs=250,
+            nb_mlps_per_batch=1024,
         )
 
-        # Build the tokenizer
-        tokens = set()
-        for d in [self.train_descr, self.test_descr]:
-            for s in d:
-                for t in s.strip().split(" "):
-                    tokens.add(t)
-        # make this set a sorted list to get the same tensors given
-        # the same descr
-        tokens = list(tokens)
-        tokens.sort()
-        tokens = ["#"] + tokens
-        self.token2id = dict([(t, n) for n, t in enumerate(tokens)])
-        self.id2token = dict([(n, t) for n, t in enumerate(tokens)])
-        self.t_nul = self.token2id["#"]
-        self.t_true = self.token2id["true"]
-        self.t_false = self.token2id["false"]
+        self.train_input = seq[:nb_train_samples]
+        self.train_q_test_set = q_test_set[:nb_train_samples]
+        self.test_input = seq[nb_train_samples:]
+        self.test_q_test_set = q_test_set[nb_train_samples:]
+        self.ref_test_errors = test_error
 
-        # Tokenize the train and test sets
-        self.train_input = self.str2tensor(self.train_descr)
-        self.test_input = self.str2tensor(self.test_descr)
+        filename = os.path.join(result_dir, f"test_errors_ref.dat")
+        with open(filename, "w") as f:
+            for e in self.ref_test_errors:
+                f.write(f"{e}\n")
+
+        self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
 
     def batches(self, split="train"):
         assert split in {"train", "test"}
@@ -1610,24 +1605,23 @@ class QMLP(Task):
         for batch in tqdm.tqdm(
             input.split(self.batch_size), dynamic_ncols=True, desc=f"epoch-{split}"
         ):
-            yield self.trim(batch)
+            yield batch
 
     def vocabulary_size(self):
-        return len(self.token2id)
+        return self.nb_codes
 
     def produce_results(
         self, n_epoch, model, result_dir, logger, deterministic_synthesis
     ):
         correct = self.test_input[:1000]
         result = correct.clone()
-        ar_mask = torch.logical_or(result == self.t_true, result == self.t_false).long()
+        ar_mask = (
+            torch.arange(result.size(1), device=result.device)
+            > self.nb_samples_per_mlp * 3 + 1
+        ).long()[None, :]
+        ar_mask = ar_mask.expand_as(result)
         result *= 1 - ar_mask  # paraaaaanoiaaaaaaa
 
-        logger(f"----------------------------------------------------------")
-
-        for e in self.tensor2str(result[:10]):
-            logger(f"test_before {e}")
-
         masked_inplace_autoregression(
             model,
             self.batch_size,
@@ -1637,18 +1631,14 @@ class QMLP(Task):
             device=self.device,
         )
 
-        logger(f"----------------------------------------------------------")
-
-        for e in self.tensor2str(result[:10]):
-            logger(f"test_after  {e}")
-
-        logger(f"----------------------------------------------------------")
-
-        nb_total = ar_mask.sum().item()
-        nb_correct = ((correct == result).long() * ar_mask).sum().item()
+        q_train_set = result[:, : self.nb_samples_per_mlp * 3]
+        q_params = result[:, self.nb_samples_per_mlp * 3 + 1 :]
+        error_test = qmlp.evaluate_q_params(q_params, self.test_q_test_set)
 
-        logger(f"test_performance {n_epoch} {nb_total=} {nb_correct=}")
-        logger(f"main_test_accuracy {n_epoch} {nb_correct / nb_total}")
+        filename = os.path.join(result_dir, f"test_errors_{n_epoch:04d}.dat")
+        with open(filename, "w") as f:
+            for e in error_test:
+                f.write(f"{e}\n")
 
 
 ######################################################################