Update.

diff --git a/README.txt b/README.txt
index 231d292..d4cb93d 100644 (file)
--- a/README.txt
+++ b/README.txt
@@ -14,4 +14,4 @@ For the arithmetic expressions experiments
 # 352M parameters / 2.5M samples, reaches 99.80% after 12 epochs, the
   learning rate schedule is obviously terrible
 
-./main.py --task=expr --nb_blocks=48 --result_dir=results_expr_48b_d1024_2.5M --dim_model=1024 --nb_train_samples=2500000
+./main.py --task=expr --nb_blocks=48 --dim_model=1024 --nb_train_samples=2500000 --result_dir=results_expr_48b_d1024_2.5M

diff --git a/main.py b/main.py
index 003028a..56b7e1c 100755 (executable)
--- a/main.py
+++ b/main.py
@@ -127,6 +127,8 @@ parser.add_argument("--expr_nb_variables", type=int, default=5)
 
 parser.add_argument("--expr_sequence_length", type=int, default=30)
 
+parser.add_argument("--expr_input_file", type=str, default=None)
+
 ######################################################################
 
 args = parser.parse_args()
@@ -366,6 +368,20 @@ else:
 
 ######################################################################
 
+if args.task == "expr" and args.expr_input_file is not None:
+    task.produce_results(
+        nb_epochs_finished,
+        model,
+        args.result_dir,
+        log_string,
+        args.deterministic_synthesis,
+        args.expr_input_file,
+    )
+
+    exit(0)
+
+######################################################################
+
 nb_epochs = args.nb_epochs if args.nb_epochs > 0 else nb_epochs_default
 
 # Compute the entropy of the training tokens

diff --git a/tasks.py b/tasks.py
index 62a8891..912b405 100755 (executable)
--- a/tasks.py
+++ b/tasks.py
@@ -776,6 +776,20 @@ import expr
 
 
 class Expr(Task):
+    def tensorize(self, sequences):
+        len_max = max([len(x) for x in sequences])
+        return torch.cat(
+            [
+                torch.tensor(
+                    [
+                        [self.char2id[c] for c in s + "#" * (len_max - len(s))]
+                        for s in sequences
+                    ]
+                )
+            ],
+            0,
+        ).to(self.device)
+
     def __init__(
         self,
         nb_train_samples,
@@ -800,43 +814,17 @@ class Expr(Task):
             nb_variables=nb_variables,
             length=sequence_length,
         )
-        self.char2id = dict(
-            [
-                (c, n)
-                for n, c in enumerate(
-                    set("#" + "".join(train_sequences + test_sequences))
-                )
-            ]
-        )
+
+        symbols = list(set("#" + "".join(train_sequences + test_sequences)))
+        symbols.sort()
+
+        self.char2id = dict([(c, n) for n, c in enumerate(symbols)])
         self.id2char = dict([(n, c) for c, n in self.char2id.items()])
 
         self.filler, self.space = self.char2id["#"], self.char2id[" "]
 
-        len_max = max([len(x) for x in train_sequences])
-        self.train_input = torch.cat(
-            [
-                torch.tensor(
-                    [
-                        [self.char2id[c] for c in s + "#" * (len_max - len(s))]
-                        for s in train_sequences
-                    ]
-                )
-            ],
-            0,
-        ).to(device)
-
-        len_max = max([len(x) for x in test_sequences])
-        self.test_input = torch.cat(
-            [
-                torch.tensor(
-                    [
-                        [self.char2id[c] for c in s + "#" * (len_max - len(s))]
-                        for s in test_sequences
-                    ]
-                )
-            ],
-            0,
-        ).to(device)
+        self.train_input = self.tensorize(train_sequences)
+        self.test_input = self.tensorize(test_sequences)
 
         self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
 
@@ -862,7 +850,13 @@ class Expr(Task):
         return "".join([self.id2char[k.item()] for k in s])
 
     def produce_results(
-        self, n_epoch, model, result_dir, logger, deterministic_synthesis
+        self,
+        n_epoch,
+        model,
+        result_dir,
+        logger,
+        deterministic_synthesis,
+        input_file=None,
     ):
         with torch.autograd.no_grad():
             t = model.training
@@ -931,7 +925,14 @@ class Expr(Task):
 
             ##############################################################
             # Log a few generated sequences
-            input = self.test_input[:10]
+            if input_file is None:
+                input = self.test_input[:10]
+            else:
+                with open(input_file, "r") as f:
+                    sequences = [e.strip() for e in f.readlines()]
+                    sequences = [s + " " + "#" * 50 for s in sequences]
+                    input = self.tensorize(sequences)
+
             result = input.clone()
             ar_mask = (result == self.space).long().cumsum(dim=1).clamp(max=1)
             result = (1 - ar_mask) * result + ar_mask * self.filler