testing more dtypes

theano/gpuarray/tests/test_multinomial.py

@@ -23,7 +23,9 @@ def test_multinomial_0():
     p = tensor.fmatrix()
     u = tensor.fvector()
 
-    m = theano.sandbox.multinomial.MultinomialFromUniform('auto')(p, u)
+    for dtype in ['int64', 'float32', 'auto']:
+
+        m = theano.sandbox.multinomial.MultinomialFromUniform(dtype)(p, u)
 
         # the m*2 allows the multinomial to reuse output
         f = function([p, u], m * 2, allow_input_downcast=True, mode=mode_with_gpu)
@@ -81,8 +83,8 @@ def test_multinomial_large():
 
 
 def test_gpu_opt_dtypes():
-    # Does have some overlap with test_multinomial_0
-    for dtype in ['float32', 'int64', 'float64']:
+    # Test if the returned samples are of the datatype specified
+    for dtype in ['uint32', 'float32', 'int64', 'float64']:
         p = tensor.fmatrix()
         u = tensor.fvector()
         m = theano.sandbox.multinomial.MultinomialFromUniform(dtype)(p, u)
@@ -104,8 +106,8 @@ def test_gpu_opt():
     # is moved to the gpu.
     p = tensor.fmatrix()
     u = tensor.fvector()
-    m = theano.sandbox.multinomial.MultinomialFromUniform('float32')(p, u)
-    # assert m.dtype == 'float32', m.dtype
+    m = theano.sandbox.multinomial.MultinomialFromUniform('auto')(p, u)
+    assert m.dtype == 'float32', m.dtype
 
     f = function([p, u], m, allow_input_downcast=True, mode=mode_with_gpu)
     assert any([type(node.op) is GPUAMultinomialFromUniform
@@ -115,18 +117,18 @@ def test_gpu_opt():
     uval = numpy.ones_like(pval[:, 0]) * 0.5
     f(pval, uval)
 
-    # # Test with a row, it was failing in the past.
-    # r = tensor.frow()
-    # m = theano.sandbox.multinomial.MultinomialFromUniform('auto')(r, u)
-    # assert m.dtype == 'float32', m.dtype
-
-    # f = function([r, u], m, allow_input_downcast=True, mode=mode_with_gpu)
-    # assert any([type(node.op) is GPUAMultinomialFromUniform
-    #             for node in f.maker.fgraph.toposort()])
-    # pval = numpy.arange(1 * 4, dtype='float32').reshape((1, 4)) + 0.1
-    # pval = pval / pval.sum(axis=1)[:, None]
-    # uval = numpy.ones_like(pval[:, 0]) * 0.5
-    # f(pval, uval)
+    # Test with a row, it was failing in the past.
+    r = tensor.frow()
+    m = theano.sandbox.multinomial.MultinomialFromUniform('auto')(r, u)
+    assert m.dtype == 'float32', m.dtype
+
+    f = function([r, u], m, allow_input_downcast=True, mode=mode_with_gpu)
+    assert any([type(node.op) is GPUAMultinomialFromUniform
+                for node in f.maker.fgraph.toposort()])
+    pval = numpy.arange(1 * 4, dtype='float32').reshape((1, 4)) + 0.1
+    pval = pval / pval.sum(axis=1)[:, None]
+    uval = numpy.ones_like(pval[:, 0]) * 0.5
+    f(pval, uval)
 
 
 class test_OP_wor(unittest.TestCase):