[BugFix] fix trunc normal device

test/test_distributions.py

@@ -140,38 +140,30 @@ def test_tanhnormal(self, min, max, vecs, upscale, shape, device):
 class TestTruncatedNormal:
     def test_truncnormal(self, min, max, vecs, upscale, shape, device):
         torch.manual_seed(0)
-        min, max, vecs, upscale, shape = _map_all(
-            min, max, vecs, upscale, shape, device=device
+        *vecs, min, max, vecs, upscale = torch.utils._pytree.tree_map(
+            lambda t: torch.as_tensor(t, device=device),
+            (*vecs, min, max, vecs, upscale),
         )
+        assert all(t.device == device for t in vecs)
         d = TruncatedNormal(
             *vecs,
             upscale=upscale,
             min=min,
             max=max,
         )
+        assert d.device == device
         for _ in range(100):
             a = d.rsample(shape)
+            assert a.device == device
             assert a.shape[: len(shape)] == shape
             assert (a >= d.min).all()
             assert (a <= d.max).all()
             lp = d.log_prob(a)
             assert torch.isfinite(lp).all()
-        assert not torch.isfinite(
-            d.log_prob(
-                torch.as_tensor(d.min, device=device).expand(
-                    (*d.batch_shape, *d.event_shape)
-                )
-                - 1e-2
-            )
-        ).any()
-        assert not torch.isfinite(
-            d.log_prob(
-                torch.as_tensor(d.max, device=device).expand(
-                    (*d.batch_shape, *d.event_shape)
-                )
-                + 1e-2
-            )
-        ).any()
+        oob_min = d.min.expand((*d.batch_shape, *d.event_shape)) - 1e-2
+        assert not torch.isfinite(d.log_prob(oob_min)).any()
+        oob_max = d.max.expand((*d.batch_shape, *d.event_shape)) + 1e-2
+        assert not torch.isfinite(d.log_prob(oob_max)).any()
 
     def test_truncnormal_mode(self, min, max, vecs, upscale, shape, device):
         torch.manual_seed(0)

torchrl/modules/distributions/continuous.py

@@ -194,6 +194,8 @@ class TruncatedNormal(D.Independent):
 
     num_params: int = 2
 
+    base_dist: _TruncatedNormal
+
     arg_constraints = {
         "loc": constraints.real,
         "scale": constraints.greater_than(1e-6),
@@ -231,20 +233,10 @@ def __init__(
         self.tanh_loc = tanh_loc
 
         self.device = loc.device
-        self.upscale = (
-            upscale
-            if not isinstance(upscale, torch.Tensor)
-            else upscale.to(self.device)
-        )
+        self.upscale = torch.as_tensor(upscale, device=self.device)
 
-        if isinstance(max, torch.Tensor):
-            max = max.to(self.device)
-        else:
-            max = torch.as_tensor(max, device=self.device)
-        if isinstance(min, torch.Tensor):
-            min = min.to(self.device)
-        else:
-            min = torch.as_tensor(min, device=self.device)
+        max = torch.as_tensor(max, device=self.device)
+        min = torch.as_tensor(min, device=self.device)
         self.min = min
         self.max = max
         self.update(loc, scale)
@@ -258,7 +250,11 @@ def update(self, loc: torch.Tensor, scale: torch.Tensor) -> None:
         self.scale = scale
 
         base_dist = _TruncatedNormal(
-            loc, scale, self.min.expand_as(loc), self.max.expand_as(scale)
+            loc,
+            scale,
+            self.min.expand_as(loc),
+            self.max.expand_as(scale),
+            device=self.device,
         )
         super().__init__(base_dist, 1, validate_args=False)
 

torchrl/modules/distributions/truncated_normal.py

@@ -33,8 +33,10 @@ class TruncatedStandardNormal(Distribution):
     has_rsample = True
     eps = 1e-6
 
-    def __init__(self, a, b, validate_args=None):
+    def __init__(self, a, b, validate_args=None, device=None):
         self.a, self.b = broadcast_all(a, b)
+        self.a = self.a.to(device)
+        self.b = self.b.to(device)
         if isinstance(a, Number) and isinstance(b, Number):
             batch_shape = torch.Size()
         else:
@@ -139,9 +141,11 @@ class TruncatedNormal(TruncatedStandardNormal):
 
     has_rsample = True
 
-    def __init__(self, loc, scale, a, b, validate_args=None):
+    def __init__(self, loc, scale, a, b, validate_args=None, device=None):
         scale = scale.clamp_min(self.eps)
         self.loc, self.scale, a, b = broadcast_all(loc, scale, a, b)
+        a = a.to(device)
+        b = b.to(device)
         self._non_std_a = a
         self._non_std_b = b
         a = (a - self.loc) / self.scale