Enable float16 ops on ops that don't care about dtype or call copy that both back-end support.

theano/compile/ops.py

@@ -50,6 +50,7 @@ class ViewOp(gof.Op):
     # the output variable is %(oname)s.
     c_code_and_version = {}
     __props__ = ()
+    _f16_ok = True
 
     def make_node(self, x):
         return gof.Apply(self, [x], [x.type()])
@@ -151,6 +152,7 @@ class DeepCopyOp(gof.Op):
 
     check_input = False
     __props__ = ()
+    _f16_ok = True
 
     def __init__(self):
         pass
@@ -659,6 +661,7 @@ class Rebroadcast(gof.Op):
 
     check_input = False
     __props__ = ("axis",)
+    _f16_ok = True
 
     def __init__(self, *axis):
         # Sort them to make sure we merge all possible case.
@@ -820,6 +823,7 @@ class SpecifyShape(gof.Op):
     # the output variable is %(oname)s.
     c_code_and_version = {}
     __props__ = ()
+    _f16_ok = True
 
     def make_node(self, x, shape):
         if not isinstance(x, gof.Variable):

theano/gpuarray/tests/test_type.py

@@ -7,7 +7,7 @@ import numpy as np
 import theano
 from theano.compat import PY3
 from theano import config
-from theano.compile import DeepCopyOp
+from theano.compile import DeepCopyOp, Rebroadcast, ViewOp
 from theano.misc.pkl_utils import CompatUnpickler
 
 # Disabled for now
@@ -21,8 +21,9 @@ import pygpu
 
 
 def test_deep_copy():
-    a = rand_gpuarray(20, dtype='float32')
-    g = GpuArrayType(dtype='float32', broadcastable=(False,))('g')
+    for dtype in ['float16', 'float32']:
+        a = rand_gpuarray(20, dtype=dtype)
+        g = GpuArrayType(dtype=dtype, broadcastable=(False,))('g')
 
         f = theano.function([g], g)
 
@@ -33,6 +34,34 @@ def test_deep_copy():
         assert GpuArrayType.values_eq(res, a)
 
 
+def test_view():
+    for dtype in ['float16', 'float32']:
+        a = rand_gpuarray(20, dtype=dtype)
+        g = GpuArrayType(dtype=dtype, broadcastable=(False,))('g')
+
+        f = theano.function([g], ViewOp()(g))
+
+        assert isinstance(f.maker.fgraph.toposort()[0].op, ViewOp)
+
+        res = f(a)
+
+        assert GpuArrayType.values_eq(res, a)
+
+
+def test_rebroadcast():
+    for dtype in ['float16', 'float32']:
+        a = rand_gpuarray(1, dtype=dtype)
+        g = GpuArrayType(dtype=dtype, broadcastable=(False,))('g')
+
+        f = theano.function([g], Rebroadcast((0, True))(g))
+
+        assert isinstance(f.maker.fgraph.toposort()[0].op, Rebroadcast)
+
+        res = f(a)
+
+        assert GpuArrayType.values_eq(res, a)
+
+
 def test_values_eq_approx():
     a = rand_gpuarray(20, dtype='float32')
     assert GpuArrayType.values_eq_approx(a, a)
@@ -45,8 +74,9 @@ def test_values_eq_approx():
 
 
 def test_specify_shape():
-    a = rand_gpuarray(20, dtype='float32')
-    g = GpuArrayType(dtype='float32', broadcastable=(False,))('g')
+    for dtype in ['float16', 'float32']:
+        a = rand_gpuarray(20, dtype=dtype)
+        g = GpuArrayType(dtype=dtype, broadcastable=(False,))('g')
         f = theano.function([g], theano.tensor.specify_shape(g, [20]))
         f(a)