Merge pull request #3076 from nouiz/tests

theano/sandbox/cuda/opt.py

@@ -1770,7 +1770,9 @@ def local_gpu_downsample_factor_max_grad(node):
         node.op.ds == node.op.st):
         assert node.op.__props__ == ('ds', 'ignore_border', 'st', 'padding',
                                      'mode')
-        if node.op.padding != (0, 0) or node.op.mode != 'max':
+        if (node.op.padding != (0, 0) or
+            node.op.mode != 'max' or
+            node.op.st != node.op.ds):
             return
         x, z, gz = node.inputs
         if (x.owner and isinstance(x.owner.op, HostFromGpu)):
@@ -1785,8 +1787,10 @@ def local_gpu_downsample_factor_max_grad(node):
 @local_optimizer([downsample.DownsampleFactorMaxGradGrad])
 def local_gpu_downsample_factor_max_grad_grad(node):
     if isinstance(node.op, downsample.DownsampleFactorMaxGradGrad):
-        assert node.op.__props__ == ('ds', 'ignore_border', 'st')
-
+        assert node.op.__props__ == ('ds', 'ignore_border', 'st',
+                                     'padding', 'mode')
+        if node.op.padding != (0, 0) or node.op.mode != 'max':
+            return
         x, z, gx = node.inputs
         if (x.owner and isinstance(x.owner.op, HostFromGpu)):
             op = GpuDownsampleFactorMaxGradGrad(node.op.ds,

theano/sandbox/cuda/tests/test_basic_ops.py

@@ -1069,7 +1069,7 @@ class T_subtensor(theano.tensor.tests.test_subtensor.T_subtensor):
             val = numpy.asarray(val)
             good = data[idx]
             self.assertTrue(val.ndim == data.ndim)
-            self.assertTrue(numpy.allclose(val, good), (val, good))
+            utt.assert_allclose(val, good)
 
             # Test with input strided
             t = self.adv_sub1()(n[::-1], idx)
@@ -1082,7 +1082,7 @@ class T_subtensor(theano.tensor.tests.test_subtensor.T_subtensor):
             val = numpy.asarray(val)
             good = data[::-1][idx]
             self.assertTrue(val.ndim == data.ndim)
-            self.assertTrue(numpy.allclose(val, good), (val, good))
+            utt.assert_allclose(val, good)
 
 
 def test_advinc_subtensor1():
@@ -1103,7 +1103,7 @@ def test_advinc_subtensor1():
         rval = f(yval)
         rep = xval.copy()
         rep[[0, 2]] += yval
-        assert numpy.allclose(rval, rep)
+        utt.assert_allclose(rval, rep)
 
 
 def test_inc_subtensor():
@@ -1121,8 +1121,8 @@ def test_inc_subtensor():
     assert sum([isinstance(node.op, cuda.GpuIncSubtensor) and
                 node.op.set_instead_of_inc == False
                 for node in f.maker.fgraph.toposort()]) == 1
-    assert numpy.allclose(f(xval, yval), [[1., 12., 13.],
-                                          [4., 15., 16.], [7., 18., 19.]])
+    utt.assert_allclose(f(xval, yval), [[1., 12., 13.],
+                                        [4., 15., 16.], [7., 18., 19.]])
 
 
 def test_set_subtensor():
@@ -1180,11 +1180,11 @@ def test_many_arg_elemwise():
                     if mode is mode_with_gpu:
                         assert any([isinstance(node.op, cuda.GpuElemwise)
                                     for node in f.maker.fgraph.apply_nodes])
-                    assert numpy.allclose(out, outputs[-1])
+                    utt.assert_allclose(out, outputs[-1])
 
                 results_gpu, results_cpu = outputs
 
-                assert numpy.allclose(results_gpu, results_cpu)
+                utt.assert_allclose(results_gpu, results_cpu)
 
 
 def test_duplicate_arg_elemwise():
@@ -1196,7 +1196,7 @@ def test_duplicate_arg_elemwise():
     Aval = numpy.random.RandomState([1, 2, 3]).randn(5, 5).astype('float32')
     Bval = Aval + Aval
 
-    assert numpy.allclose(Bval, f(Aval))
+    utt.assert_allclose(Bval, f(Aval))
 
 
 def test_shared_float32():
@@ -1235,7 +1235,7 @@ def test_gpueye():
                             B.as_cuda_ndarray_variable(out),
                             mode=mode_with_gpu)
         result = numpy.asarray(f(N, M))
-        assert numpy.allclose(result, numpy.eye(N, M_, dtype=dtype))
+        utt.assert_allclose(result, numpy.eye(N, M_, dtype=dtype))
         assert result.dtype == numpy.dtype(dtype)
         assert any([isinstance(node.op, B.GpuEye)
                     for node in f.maker.fgraph.toposort()])

theano/sandbox/test_multinomial.py

@@ -18,6 +18,8 @@ def get_mode(gpu):
                               'local_gpu_multinomial')
     if isinstance(mode.linker, theano.gof.PerformLinker):
         mode.linker = predefined_linkers['c|py']
+    if hasattr(mode.linker, 'c_thunks'):
+        mode.linker.c_thunks = True
     return mode
 
 

theano/tensor/tests/test_raw_random.py

@@ -1189,12 +1189,15 @@ class T_random_function(utt.InferShapeTester):
         # binomial was created by calling RandomFunction on a string,
         # random_integers by calling it on a function.
         rng_r = random_state_type()
+        mode = None
+        if theano.config.mode in ["DEBUG_MODE", "DebugMode"]:
+            mode = 'FAST_COMPILE'
         post_bin_r, bin_sample = binomial(rng_r, (3, 5), 1, .3)
-        f = theano.function([rng_r], [post_bin_r, bin_sample])
+        f = theano.function([rng_r], [post_bin_r, bin_sample], mode=mode)
         pkl_f = pickle.dumps(f)
 
         post_int_r, int_sample = random_integers(rng_r, (3, 5), -1, 8)
-        g = theano.function([rng_r], [post_int_r, int_sample])
+        g = theano.function([rng_r], [post_int_r, int_sample], mode=mode)
         pkl_g = pickle.dumps(g)