refactor test_blas for test_blas_c

theano/tensor/tests/test_blas.py

@@ -1018,6 +1018,8 @@ def matrixmultiply(a, b):
 
 
 class BaseGemv(object):
+    mode = mode_blas_opt  # can be overridden with self.mode
+
     def get_data(self,x_stride=1,y_stride=1):
         rng = numpy.random.RandomState(unittest_tools.fetch_seed())
         mult = array(1, dtype=self.dtype)
@@ -1036,10 +1038,10 @@ class BaseGemv(object):
 
         oy    = alpha * T.dot(a,x) + beta * y
 
-        oy_func = theano.function([], oy, mode = mode_blas_opt)
+        oy_func = theano.function([], oy, mode=self.mode)
 
         topo = oy_func.maker.env.toposort()
-        assert sum([isinstance(node.op, theano.tensor.blas.Gemv) for node in topo])==1
+        self.assertFunctionContains1(oy_func, self.gemv)
 
         oy_val = oy_func()
 
@@ -1056,22 +1058,9 @@ class BaseGemv(object):
 
         oy = T.dot(a,x)
 
-        oy_func = theano.function([], oy, mode = mode_blas_opt)
+        oy_func = theano.function([], oy, mode=self.mode)
 
-        topo = oy_func.maker.env.toposort()
-        # The only op in the graph is a dot.
-        # In the gemm case, we create a dot22 for that case
-        # There is no dot21.
-        # Creating one is not useful as this is not faster(in fact it would be slower!
-        # as more code would be in python, numpy.dot will call gemv itself)
-        # See ticket 594
-        """
->>> t0=time.time();x=scipy.linalg.blas.fblas.dgemv(1,a.T,b,1,z.T);t1=time.time();print t1-t0
-0.00192999839783
->>> t0=time.time();x=numpy.dot(a,b);t1=time.time();print t1-t0
-0.00158381462097
-"""
-        assert sum([isinstance(node.op, theano.tensor.blas.Gemv) for node in topo])==0
+        self.assertFunctionContains1(oy_func, self.gemv_inplace)
 
         oy_v = oy_func()
         assert_array_almost_equal(desired_oy, oy_v)
@@ -1086,10 +1075,9 @@ class BaseGemv(object):
 
         oy = alpha * T.dot(a.T,x)+beta*y
 
-        oy_func = theano.function([], oy, mode = mode_blas_opt)
+        oy_func = theano.function([], oy, mode=self.mode)
 
-        topo = oy_func.maker.env.toposort()
-        assert sum([isinstance(node.op, theano.tensor.blas.Gemv) for node in topo])==1
+        self.assertFunctionContains1(oy_func, self.gemv)
 
         oy_v = oy_func()
         assert_array_almost_equal(desired_oy, oy_v)
@@ -1103,10 +1091,9 @@ class BaseGemv(object):
 
         oy = alpha * T.dot(a,x[::2])+beta*y
 
-        oy_func = theano.function([], oy, mode = mode_blas_opt)
+        oy_func = theano.function([], oy, mode=self.mode)
 
-        topo = oy_func.maker.env.toposort()
-        assert sum([isinstance(node.op, theano.tensor.blas.Gemv) for node in topo])==1
+        self.assertFunctionContains1(oy_func, self.gemv)
 
         oy_v = oy_func()
         assert_array_almost_equal(desired_oy, oy_v)
@@ -1120,10 +1107,9 @@ class BaseGemv(object):
 
         oy = alpha * T.dot(a.T,x[::2])+beta*y
 
-        oy_func = theano.function([], oy, mode = mode_blas_opt)
+        oy_func = theano.function([], oy, mode=self.mode)
 
-        topo = oy_func.maker.env.toposort()
-        assert sum([isinstance(node.op, theano.tensor.blas.Gemv) for node in topo])==1
+        self.assertFunctionContains1(oy_func, self.gemv)
 
         oy_v = oy_func()
         assert_array_almost_equal(desired_oy, oy_v)
@@ -1137,10 +1123,9 @@ class BaseGemv(object):
 
         oy = alpha * T.dot(a,x)+beta*y[::2]
 
-        oy_func = theano.function([], oy, mode = mode_blas_opt)
+        oy_func = theano.function([], oy, mode=self.mode)
 
-        topo = oy_func.maker.env.toposort()
-        assert sum([isinstance(node.op, theano.tensor.blas.Gemv) for node in topo])==1
+        self.assertFunctionContains1(oy_func, self.gemv)
 
         oy_v = oy_func()
         assert_array_almost_equal(desired_oy, oy_v)
@@ -1154,21 +1139,24 @@ class BaseGemv(object):
 
         oy = alpha * T.dot(a.T,x)+beta*y[::2]
 
-        oy_func = theano.function([], oy, mode = mode_blas_opt)
+        oy_func = theano.function([], oy, mode=self.mode)
 
-        topo = oy_func.maker.env.toposort()
-        assert sum([isinstance(node.op, theano.tensor.blas.Gemv) for node in topo])==1
+        self.assertFunctionContains1(oy_func, self.gemv)
 
         oy_v = oy_func()
         assert_array_almost_equal(desired_oy, oy_v)
 
 
-
-class TestSgemv(TestCase, BaseGemv):
+class TestSgemv(TestCase, BaseGemv, unittest_tools.TestOptimizationMixin):
     dtype = float32
+    gemv = theano.tensor.blas.gemv_no_inplace
+    gemv_inplace = theano.tensor.blas.gemv_inplace
 
-class TestDgemv(TestCase, BaseGemv):
+
+class TestDgemv(TestCase, BaseGemv, unittest_tools.TestOptimizationMixin):
     dtype = float64
+    gemv = theano.tensor.blas.gemv_no_inplace
+    gemv_inplace = theano.tensor.blas.gemv_inplace
 
 #The optimization to put Gemv don't work for complex type for now.
 # See ticket 653.
@@ -1373,27 +1361,7 @@ class TestGer_make_thunk(TestCase):
     def test_c128_1_9(self): return self.given_dtype('complex128', 1, 9)
 
 
-# TODO: Refactor and add to this base class as we refactor test code.
-class TestOptimizationMixin(object):
-
-    def assertFunctionContains(self, f, op, min=1, max=sys.maxint):
-        toposort = f.maker.env.toposort()
-        matches = [node for node in toposort if node.op == op]
-        assert (min <= len(matches) <= max), toposort
-
-    def assertFunctionContains0(self, f, op):
-        return self.assertFunctionContains(f, op, min=0, max=0)
-
-    def assertFunctionContains1(self, f, op):
-        return self.assertFunctionContains(f, op, min=1, max=1)
-
-    def assertFunctionContainsN(self, f, op, N):
-        return self.assertFunctionContains(f, op, min=N, max=N)
-
-    def SkipTest(self):
-        raise Exception('how do I skip this test properly?')
-
-class TestGer_local_gemm_to_ger(TestCase, TestOptimizationMixin):
+class TestGer_local_gemm_to_ger(TestCase, unittest_tools.TestOptimizationMixin):
 
     def setUp(self):
         self.mode = theano.compile.get_default_mode().including('fast_run')

theano/tests/unittest_tools.py

@@ -5,11 +5,13 @@ import numpy
 import theano.tensor as T
 from theano.configparser import config, AddConfigVar, StrParam
 
+
 AddConfigVar('unittests.rseed',
         "Seed to use for randomized unit tests. Special value 'random' means using a seed of None.",
         StrParam(666),
         in_c_key=False)
 
+
 def fetch_seed(pseed=None):
     """
     Returns the seed to use for running the unit tests.
@@ -38,6 +40,7 @@ def fetch_seed(pseed=None):
 
     return seed
 
+
 def seed_rng(pseed=None):
     """
     Seeds numpy's random number generator with the value returned by fetch_seed.
@@ -51,6 +54,7 @@ def seed_rng(pseed=None):
     numpy.random.seed(seed)
     return seed
 
+
 def verify_grad(op, pt, n_tests=2, rng=None, *args, **kwargs):
     """
     Wrapper for tensor/basic.py:verify_grad
@@ -72,3 +76,24 @@ def verify_grad(op, pt, n_tests=2, rng=None, *args, **kwargs):
 #     raise
 #
 verify_grad.E_grad = T.verify_grad.E_grad
+
+
+class TestOptimizationMixin(object):
+    def assertFunctionContains(self, f, op, min=1, max=sys.maxint):
+        toposort = f.maker.env.toposort()
+        matches = [node for node in toposort if node.op == op]
+        assert (min <= len(matches) <= max), toposort
+
+    def assertFunctionContains0(self, f, op):
+        return self.assertFunctionContains(f, op, min=0, max=0)
+
+    def assertFunctionContains1(self, f, op):
+        return self.assertFunctionContains(f, op, min=1, max=1)
+
+    def assertFunctionContainsN(self, f, op, N):
+        return self.assertFunctionContains(f, op, min=N, max=N)
+
+    def SkipTest(self):
+        raise Exception('how do I skip this test properly?')
+
+