improve code

* add device param * all in torch Co-authored-by: Albert Zeyer <[email protected]>
rwth-i6 · Dec 19, 2024 · 0d8a9e3 · 0d8a9e3
1 parent 3825aeb
commit 0d8a9e3
Showing 1 changed file with 5 additions and 6 deletions.
diff --git a/i6_models/samplers/log_uniform.py b/i6_models/samplers/log_uniform.py
@@ -8,20 +8,19 @@
 
 
 class LogUniformSampler(nn.Module):
-    def __init__(self, num_classes):
+    def __init__(self, num_classes: int, *, device: Optional[torch.device] = None):
         super().__init__()
 
         # assumes count-sorted vocabulary, descending
         self.num_classes = num_classes
 
         # approximately zipf distribution
-        self._distribution = [
-            (math.log1p(w + 1) - math.log1p(w)) / math.log1p(self.num_classes) for w in range(self.num_classes)
-        ]
-        self._distribution = torch.tensor(self._distribution).clamp(min=1e-10)
+        ws = torch.arange(self.num_classes, dtype=torch.get_default_dtype(), device=device)
+        self._distribution = (torch.log1p(ws + 1) - torch.log1p(ws)) / torch.log1p(torch.tensor(self.num_classes))
+        self._distribution.clamp_(min=1e-10)
         self._distribution /= self._distribution.sum()
 
-        self._cat_sampler = torch.distributions.categorical.Categorical(probs=self._distribution.cuda())
+        self._cat_sampler = torch.distributions.categorical.Categorical(probs=self._distribution)
 
     def sample(self, num_samples):
         return self._cat_sampler.sample(torch.Size([num_samples]))