Remove the useless theano.tensor.blas_scipy.optimization_enabled mechanism.

theano/sandbox/cuda/tests/test_blas.py

@@ -264,14 +264,11 @@ class TestGpuGemv(TestCase, BaseGemv,
 class TestGpuGer(TestGer):
     def setUp(self):
         self.mode = mode_with_gpu
-        self.mode = self.mode.excluding('c_blas')
         dtype = self.dtype = 'float32'  # optimization isn't dtype-dependent
         self.A = tensor.tensor(dtype=dtype, broadcastable=(False, False))
         self.a = tensor.tensor(dtype=dtype, broadcastable=())
         self.x = tensor.tensor(dtype=dtype, broadcastable=(False,))
         self.y = tensor.tensor(dtype=dtype, broadcastable=(False,))
-        self.origval = theano.tensor.blas_scipy.optimizations_enabled
-        theano.tensor.blas_scipy.optimizations_enabled = False
         self.ger = gpu_ger_no_inplace
         self.ger_destructive = gpu_ger_inplace
         self.gemm = tcn.blas.gpu_gemm_no_inplace

theano/tensor/blas_scipy.py

@@ -23,10 +23,9 @@ try:
             numpy.dtype('complex64'):scipy.linalg.blas.fblas.cgeru,
             numpy.dtype('complex128'):scipy.linalg.blas.fblas.zgeru,
             }
-    optimizations_enabled = True
 except ImportError, e:
     have_fblas = False
-    optimizations_enabled = False
+
 
 class ScipyGer(Ger):
 
@@ -62,13 +61,11 @@ class ScipyGer(Ger):
 
 @local_optimizer([ger, ger_destructive])
 def use_scipy_ger(node):
-    if not optimizations_enabled: return
     if node.op == ger:
         return [ScipyGer(False)(*node.inputs)]
 
 @local_optimizer([ScipyGer(False)])
 def make_ger_destructive(node):
-    if not optimizations_enabled: return
     if node.op == ScipyGer(False):
         return [ScipyGer(True)(*node.inputs)]
 

theano/tensor/tests/test_blas.py

@@ -1284,21 +1284,16 @@ class TestGer(TestCase, unittest_tools.TestOptimizationMixin):
 
     def setUp(self):
         self.mode = theano.compile.get_default_mode().including('fast_run')
-        self.mode = self.mode.excluding('c_blas')
+        self.mode = self.mode.excluding('c_blas', 'scipy_blas')
         dtype = self.dtype = 'float64'  # optimization isn't dtype-dependent
         self.A = T.tensor(dtype=dtype, broadcastable=(False, False))
         self.a = T.tensor(dtype=dtype, broadcastable=())
         self.x = T.tensor(dtype=dtype, broadcastable=(False,))
         self.y = T.tensor(dtype=dtype, broadcastable=(False,))
-        self.origval = theano.tensor.blas_scipy.optimizations_enabled
-        theano.tensor.blas_scipy.optimizations_enabled = False
         self.ger = ger
         self.ger_destructive = ger_destructive
         self.gemm = gemm_no_inplace
 
-    def tearDown(self):
-        theano.tensor.blas_scipy.optimizations_enabled = self.origval
-
     def function(self, inputs, outputs, updates={}):
         return theano.function(inputs, outputs, self.mode, updates=updates)
 

theano/tensor/tests/test_blas_scipy.py

@@ -21,7 +21,7 @@ class TestScipyGer(TestCase, TestOptimizationMixin):
         self.Aval = numpy.ones((2,3), dtype=dtype)
         self.xval = numpy.asarray([1,2], dtype=dtype)
         self.yval = numpy.asarray([1.5,2.7,3.9], dtype=dtype)
-        if not theano.tensor.blas_scipy.optimizations_enabled:
+        if not theano.tensor.blas_scipy.have_fblas:
             self.SkipTest()