Remove deprecated tensor constructors in torch.distributions (#19979)

Summary:
This removes the deprecated `tensor.new_*` constructors (see #16770) from `torch.distributions` module.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19979

Differential Revision: D15195618

Pulled By: soumith

fbshipit-source-id: 46b519bfd32017265e90bd5c53f12cfe4a138021

torch/distributions/beta.py

@@ -55,10 +55,7 @@ class Beta(ExponentialFamily):
                 (total.pow(2) * (total + 1)))
 
     def rsample(self, sample_shape=()):
-        value = self._dirichlet.rsample(sample_shape).select(-1, 0)
-        if isinstance(value, Number):
-            value = self._dirichlet.concentration.new_tensor(value)
-        return value
+        return self._dirichlet.rsample(sample_shape).select(-1, 0)
 
     def log_prob(self, value):
         if self._validate_args:

torch/distributions/categorical.py

@@ -93,11 +93,11 @@ class Categorical(Distribution):
 
     @property
     def mean(self):
-        return self.probs.new_tensor(nan).expand(self._extended_shape())
+        return torch.full(self._extended_shape(), nan, dtype=self.probs.dtype, device=self.probs.device)
 
     @property
     def variance(self):
-        return self.probs.new_tensor(nan).expand(self._extended_shape())
+        return torch.full(self._extended_shape(), nan, dtype=self.probs.dtype, device=self.probs.device)
 
     def sample(self, sample_shape=torch.Size()):
         sample_shape = self._extended_shape(sample_shape)

torch/distributions/cauchy.py

@@ -47,11 +47,11 @@ class Cauchy(Distribution):
 
     @property
     def mean(self):
-        return self.loc.new_tensor(nan).expand(self._extended_shape())
+        return torch.full(self._extended_shape(), nan, dtype=self.loc.dtype, device=self.loc.device)
 
     @property
     def variance(self):
-        return self.loc.new_tensor(inf).expand(self._extended_shape())
+        return torch.full(self._extended_shape(), inf, dtype=self.loc.dtype, device=self.loc.device)
 
     def rsample(self, sample_shape=torch.Size()):
         shape = self._extended_shape(sample_shape)

torch/distributions/kl.py

@@ -116,7 +116,7 @@ def _infinite_like(tensor):
     """
     Helper function for obtaining infinite KL Divergence throughout
     """
-    return tensor.new_tensor(inf).expand_as(tensor)
+    return torch.full_like(tensor, inf)
 
 
 def _x_log_x(tensor):

torch/distributions/relaxed_categorical.py

@@ -77,7 +77,7 @@ class ExpRelaxedCategorical(Distribution):
         if self._validate_args:
             self._validate_sample(value)
         logits, value = broadcast_all(self.logits, value)
-        log_scale = (self.temperature.new_tensor(float(K)).lgamma() -
+        log_scale = (torch.full_like(self.temperature, float(K)).lgamma() -
                      self.temperature.log().mul(-(K - 1)))
         score = logits - value.mul(self.temperature)
         score = (score - score.logsumexp(dim=-1, keepdim=True)).sum(-1)

torch/distributions/transforms.py

@@ -434,7 +434,7 @@ class AffineTransform(Transform):
         shape = x.shape
         scale = self.scale
         if isinstance(scale, numbers.Number):
-            result = x.new_empty(shape).fill_(math.log(abs(scale)))
+            result = torch.full_like(x, math.log(abs(scale)))
         else:
             result = torch.abs(scale).log()
         if self.event_dim:

torch/distributions/utils.py

@@ -4,11 +4,6 @@ import torch
 import torch.nn.functional as F
 
 
-# promote numbers to tensors of dtype torch.get_default_dtype()
-def _default_promotion(v):
-    return torch.tensor(v, dtype=torch.get_default_dtype())
-
-
 def broadcast_all(*values):
     r"""
     Given a list of values (possibly containing numbers), returns a list where each
@@ -28,12 +23,13 @@ def broadcast_all(*values):
     if not all(torch.is_tensor(v) or isinstance(v, Number) for v in values):
         raise ValueError('Input arguments must all be instances of numbers.Number or torch.tensor.')
     if not all(map(torch.is_tensor, values)):
-        new_tensor = _default_promotion
+        options = dict(dtype=torch.get_default_dtype())
         for value in values:
             if torch.is_tensor(value):
-                new_tensor = value.new_tensor
+                options = dict(dtype=value.dtype, device=value.device)
                 break
-        values = [v if torch.is_tensor(v) else new_tensor(v) for v in values]
+        values = [v if torch.is_tensor(v) else torch.tensor(v, **options)
+                  for v in values]
     return torch.broadcast_tensors(*values)