X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=tasks.py;h=73f61bf102dd46387bde2ecd99f0d7a74c2d7250;hb=a3211f96c7426a613b82a2de87d4dd70640e8f46;hp=5ac78cbbbbf698d8a6c6afc5087fa14b684968e6;hpb=1be1638f9906a1071dc82ebc6f35f8fc0eb91a3d;p=picoclvr.git

diff --git a/tasks.py b/tasks.py
index 5ac78cb..73f61bf 100755
--- a/tasks.py
+++ b/tasks.py
@@ -87,29 +87,29 @@ class ProblemLevel0(Problem):
 
 
 class ProblemLevel1(Problem):
-    def __init__(self, nb_operators=100, len_prompt=5, len_result=8):
-        self.len_prompt = len_prompt
+    def __init__(self, nb_operators=100, len_source=5, len_result=8):
+        self.len_source = len_source
         self.len_result = len_result
         self.len_nb_operator = int(math.log(nb_operators) / math.log(10)) + 1
         self.operators = F.one_hot(
-            torch.rand(nb_operators, len_result, len_prompt).argmax(-1),
-            num_classes=len_prompt,
+            torch.rand(nb_operators, len_result, len_source).argmax(-1),
+            num_classes=len_source,
         )
 
     def generate_sequences(self, nb):
-        a = self.len_nb_operator
-        b = a + 1 + self.len_prompt
-        sequences = torch.empty(nb, b + 1 + self.len_result, dtype=torch.int64)
         nb_operators = torch.randint(self.operators.size(0), (nb,))
-        sequences[:, :a] = (nb_operators[:, None] / 10 ** torch.arange(a-1,-1,-1)) % 10
-        sequences[:, a] = 10
-        sequences[:, a + 1 : b] = torch.randint(10, (nb, b - a - 1))
-        sequences[:, b] = 11
-
-        o = self.operators[nb_operators]
-        p = sequences[:, a + 1 : b]
-        print(f"{o.size()=} {p.size()=} {sequences[:,b+1:].size()=}")
-        sequences[:, b + 1 :] = o.bmm(p[:, :, None]).squeeze(-1)
+        operators = self.operators[nb_operators]
+        nb_operators = (
+            nb_operators[:, None]
+            // 10 ** torch.arange(self.len_nb_operator - 1, -1, -1)
+        ) % 10
+        marker1 = torch.full((nb, 1), 10)
+        source = torch.randint(10, (nb, self.len_source))
+        marker2 = torch.full((nb, 1), 11)
+        result = operators.bmm(source[:, :, None]).squeeze(-1)
+        print(f"{nb_operators.dtype=} {marker1.dtype=}")
+        sequences = torch.cat((nb_operators, marker1, source, marker2, result), 1)
+        print(f"{sequences.size()=}")
         ar_mask = (sequences == 11).long()
         ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1)
         return sequences, ar_mask
@@ -118,6 +118,35 @@ class ProblemLevel1(Problem):
         return "".join("0123456789|>"[x.item()] for x in seq)
 
 
+class ProblemLevel2(Problem):
+    def __init__(self, len_source=5, len_result=8):
+        self.len_source = len_source
+        self.len_result = len_result
+
+    def generate_sequences(self, nb):
+        operators = F.one_hot(
+            torch.rand(nb, self.len_result, self.len_source).argmax(-1),
+            num_classes=self.len_source,
+        )
+        source1 = torch.randint(10, (nb, self.len_source))
+        marker1 = torch.full((nb, 1), 10)
+        result1 = operators.bmm(source1[:, :, None]).squeeze(-1)
+        marker2 = torch.full((nb, 1), 11)
+        source2 = torch.randint(10, (nb, self.len_source))
+        marker3 = torch.full((nb, 1), 12)
+        result2 = operators.bmm(source2[:, :, None]).squeeze(-1)
+
+        sequences = torch.cat(
+            (source1, marker1, result1, marker2, source2, marker3, result2), 1
+        )
+        ar_mask = (sequences == 12).long()
+        ar_mask = (ar_mask.cumsum(1) - ar_mask).clamp(max=1)
+        return sequences, ar_mask
+
+    def seq2str(self, seq):
+        return "".join("0123456789>|~"[x.item()] for x in seq)
+
+
 ####################