Resolved #687: fixed output dtype of uniform and normal random samples

theano/tensor/raw_random.py

@@ -396,30 +396,15 @@ def uniform(random_state, size=None, low=0.0, high=1.0, ndim=None, dtype='floatX
     """
     if dtype == 'floatX':
         dtype = theano.config.floatX
-    # Handle special case of untyped Python int / float: we cast them into
-    # `dtype` to ensure they cannot upcast the end result accidentally.
-    def as_tensor(x):
-        if isinstance(x, int) or isinstance(x, float):
-            return tensor.constant(x, dtype=dtype)
-        else:
-            return tensor.as_tensor_variable(x)
-    low = as_tensor(low)
-    high = as_tensor(high)
+    low = tensor.cast(tensor.as_tensor_variable(low), dtype)
+    high = tensor.cast(tensor.as_tensor_variable(high), dtype)
     ndim, size, bcast = _infer_ndim_bcast(ndim, size, low, high)
-    out_dtype = tensor.scal.upcast(dtype, low.dtype, high.dtype)
-    # It would be confusing if the resulting dtype was not the same as the one
-    # passed as argument to the function.
-    assert out_dtype == dtype, (
-            "Output dtype should be %s, but it would be %s given the provided "
-            "low (%s) and high (%s) arguments: cast those into a more "
-            "appropriate type to solve this issue" %
-            (dtype, out_dtype, low.dtype, high.dtype))
     op = RandomFunction('uniform',
-            tensor.TensorType(dtype=out_dtype, broadcastable=bcast) )
+            tensor.TensorType(dtype=dtype, broadcastable=bcast) )
     return op(random_state, size, low, high)
 
 
-def normal(random_state, size=None, avg=0.0, std=1.0, ndim=None, dtype=theano.config.floatX):
+def normal(random_state, size=None, avg=0.0, std=1.0, ndim=None, dtype='floatX'):
     """
     Sample from a normal distribution centered on avg with
     the specified standard deviation (std).
@@ -430,10 +415,11 @@ def normal(random_state, size=None, avg=0.0, std=1.0, ndim=None, dtype=theano.co
     If size is None, the output shape will be determined by the shapes
     of avg and std.
     """
-    avg = tensor.as_tensor_variable(avg)
-    std = tensor.as_tensor_variable(std)
+    if dtype == 'floatX':
+        dtype = theano.config.floatX
+    avg = tensor.cast(tensor.as_tensor_variable(avg), dtype)
+    std = tensor.cast(tensor.as_tensor_variable(std), dtype)
     ndim, size, bcast = _infer_ndim_bcast(ndim, size, avg, std)
-    dtype = tensor.scal.upcast(dtype, avg.dtype, std.dtype)
     op = RandomFunction('normal',
             tensor.TensorType(dtype=dtype, broadcastable=bcast))
     return op(random_state, size, avg, std)