Add a tests for alpha and output merge and do some more fixes.

theano/sandbox/gpuarray/opt.py

@@ -624,13 +624,13 @@ def local_gpua_hgemm(node):
 
 
 @register_opt()
-@alpha_merge(GpuGemm, alpha_in=1, beta_in=2, nd=2)
+@alpha_merge(GpuGemm, alpha_in=1, beta_in=4, nd=2)
 def local_gpuagemm_alpha_merge(node, *inputs):
     return [gpugemm_no_inplace(*inputs)]
 
 
 @register_opt()
-@output_merge(GpuGemm, alpha_in=1, beta_in=2, out_in=0, nd=2)
+@output_merge(GpuGemm, alpha_in=1, beta_in=4, out_in=0, nd=2)
 def local_gpuagemm_output_merge(node, *inputs):
     return [gpugemm_no_inplace(*inputs)]
 

theano/sandbox/gpuarray/opt_util.py

@@ -73,7 +73,8 @@ def alpha_merge(cls, alpha_in, beta_in, nd):
                     lr = grab_cpu_scalar(node.inputs[0], nd=nd)
                 else:
                     lr = grab_cpu_scalar(node.inputs[1], nd=nd)
-                if lr is None or targ is None:
+                if (lr is None or targ is None or
+                        lr.dtype != targ.outputs[0].dtype):
                     return None
                 inputs = list(targ.inputs)
                 try:
@@ -110,6 +111,8 @@ def output_merge(cls, alpha_in, beta_in, out_in, nd):
                     W = node.inputs[0]
                 if targ is None:
                     return None
+                if W.dtype != targ.outputs[0].dtype:
+                    return None
                 if not is_equal(targ.inputs[beta_in], 0.0):
                     # other cases are too complex for now
                     return None

theano/sandbox/gpuarray/tests/test_blas.py

@@ -148,3 +148,19 @@ def test_hgemm_swap():
     on = numpy.dot(v1, v2)
 
     utt.assert_allclose(of, on)
+
+def test_hgemm_alpha_output_merge():
+    from theano.sandbox.cuda import nvcc_compiler
+    if nvcc_compiler.nvcc_version < '7.5':
+        raise SkipTest("SgemmEx is only avaialble on cuda 7.5+")
+
+    m1 = tensor.matrix(dtype='float16')
+    m2 = tensor.matrix(dtype='float16')
+
+    b = tensor.matrix(dtype='float16')
+
+    hgemm = numpy.asarray(0.05, dtype='float16') * (tensor.dot(m1, m2) + b)
+
+    f = theano.function([m1, m2, b], hgemm, mode=mode_with_gpu)
+    # there should be 3 gpu_from_host, 1 hgemm and 1 host_from_gpu
+    assert len(f.maker.fgraph.apply_nodes) == 5

theano/sandbox/gpuarray/type.py

@@ -36,7 +36,7 @@ class GpuArrayType(Type):
         return self.__class__(dtype=dtype, broadcastable=broadcastable,
                               name=self.name)
 
-    def __str__(self):
+    def __repr__(self):
         return "GpuArrayType(%s, %s)" % (self.dtype, self.broadcastable)
 
     def filter(self, data, strict=False, allow_downcast=None):