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