Fix test with mode=FAST_COMPILE in the new gpu back-end.

theano/gpuarray/tests/test_dnn.py

@@ -52,7 +52,8 @@ def test_dnn_conv_desc_merge():
     # CDataType is not DeepCopyable so this will crash if we don't use
     # borrow=True
     f = theano.function([], [theano.Out(desc1, borrow=True),
-                             theano.Out(desc2, borrow=True)])
+                             theano.Out(desc2, borrow=True)],
+                        mode=mode_with_gpu)
 
     d1, d2 = f()
 
@@ -997,9 +998,9 @@ def test_dnn_conv_grad():
         return dnn.GpuDnnConvGradW()(img, out, kern, desc, alpha=0.75,
                                      beta=-1.0)
 
-    utt.verify_grad(dconv, [img_val, kern_val, out_val], eps=1e-3)
-    utt.verify_grad(dconvi, [img_val, kern_val, out_val], eps=1e-3)
-    utt.verify_grad(dconvw, [img_val, kern_val, out_val], eps=1e-3)
+    utt.verify_grad(dconv, [img_val, kern_val, out_val], eps=1e-3, mode=mode_with_gpu)
+    utt.verify_grad(dconvi, [img_val, kern_val, out_val], eps=1e-3, mode=mode_with_gpu)
+    utt.verify_grad(dconvw, [img_val, kern_val, out_val], eps=1e-3, mode=mode_with_gpu)
 
 
 def get_conv3d_test_cases():

theano/gpuarray/tests/test_fft.py

@@ -54,12 +54,12 @@ class TestFFT(unittest.TestCase):
         def f_rfft(inp):
             return theano.gpuarray.fft.curfft(inp)
         inputs_val = np.random.random((1, N)).astype('float32')
-        utt.verify_grad(f_rfft, [inputs_val], eps=eps)
+        utt.verify_grad(f_rfft, [inputs_val], eps=eps, mode=mode_with_gpu)
 
         def f_irfft(inp):
             return theano.gpuarray.fft.cuirfft(inp)
         inputs_val = np.random.random((1, N // 2 + 1, 2)).astype('float32')
-        utt.verify_grad(f_irfft, [inputs_val], eps=eps)
+        utt.verify_grad(f_irfft, [inputs_val], eps=eps, mode=mode_with_gpu)
 
     def test_rfft(self):
         inputs_val = np.random.random((1, N, N)).astype('float32')
@@ -94,7 +94,7 @@ class TestFFT(unittest.TestCase):
         inputs = theano.shared(inputs_val)
 
         irfft = theano.gpuarray.fft.cuirfft(inputs)
-        f_irfft = theano.function([], irfft)
+        f_irfft = theano.function([], irfft, mode=mode_with_gpu)
         res_irfft = f_irfft()
         inputs_ref = inputs_val[..., 0] + inputs_val[..., 1] * 1j
 
@@ -164,22 +164,22 @@ class TestFFT(unittest.TestCase):
         def f_rfft(inp):
             return theano.gpuarray.fft.curfft(inp)
         inputs_val = np.random.random((1, N, N)).astype('float32')
-        utt.verify_grad(f_rfft, [inputs_val], eps=eps)
+        utt.verify_grad(f_rfft, [inputs_val], eps=eps, mode=mode_with_gpu)
 
         def f_irfft(inp):
             return theano.gpuarray.fft.cuirfft(inp)
         inputs_val = np.random.random((1, N, N // 2 + 1, 2)).astype('float32')
-        utt.verify_grad(f_irfft, [inputs_val], eps=eps)
+        utt.verify_grad(f_irfft, [inputs_val], eps=eps, mode=mode_with_gpu)
 
         def f_rfft(inp):
             return theano.gpuarray.fft.curfft(inp, norm='ortho')
         inputs_val = np.random.random((1, N, N)).astype('float32')
-        utt.verify_grad(f_rfft, [inputs_val], eps=eps)
+        utt.verify_grad(f_rfft, [inputs_val], eps=eps, mode=mode_with_gpu)
 
         def f_irfft(inp):
             return theano.gpuarray.fft.cuirfft(inp, norm='no_norm')
         inputs_val = np.random.random((1, N, N // 2 + 1, 2)).astype('float32')
-        utt.verify_grad(f_irfft, [inputs_val], eps=eps)
+        utt.verify_grad(f_irfft, [inputs_val], eps=eps, mode=mode_with_gpu)
 
     def test_odd(self):
         M = N - 1
@@ -224,22 +224,22 @@ class TestFFT(unittest.TestCase):
         def f_rfft(inp):
             return theano.gpuarray.fft.curfft(inp)
         inputs_val = np.random.random((1, M, M)).astype('float32')
-        utt.verify_grad(f_rfft, [inputs_val], eps=eps)
+        utt.verify_grad(f_rfft, [inputs_val], eps=eps, mode=mode_with_gpu)
 
         def f_irfft(inp):
             return theano.gpuarray.fft.cuirfft(inp, is_odd=True)
         inputs_val = np.random.random((1, M, M // 2 + 1, 2)).astype('float32')
-        utt.verify_grad(f_irfft, [inputs_val], eps=eps)
+        utt.verify_grad(f_irfft, [inputs_val], eps=eps, mode=mode_with_gpu)
 
         def f_rfft(inp):
             return theano.gpuarray.fft.curfft(inp, norm='ortho')
         inputs_val = np.random.random((1, M, M)).astype('float32')
-        utt.verify_grad(f_rfft, [inputs_val], eps=eps)
+        utt.verify_grad(f_rfft, [inputs_val], eps=eps, mode=mode_with_gpu)
 
         def f_irfft(inp):
             return theano.gpuarray.fft.cuirfft(inp, norm='no_norm', is_odd=True)
         inputs_val = np.random.random((1, M, M // 2 + 1, 2)).astype('float32')
-        utt.verify_grad(f_irfft, [inputs_val], eps=eps)
+        utt.verify_grad(f_irfft, [inputs_val], eps=eps, mode=mode_with_gpu)
 
     def test_params(self):
         inputs_val = np.random.random((1, N)).astype('float32')

theano/gpuarray/tests/test_gemmcorr.py

@@ -48,7 +48,7 @@ class TestCorrMM(unittest.TestCase):
             utt.verify_grad(GpuCorrMM(border_mode=border_mode,
                                       filter_dilation=filter_dilation,
                                       subsample=subsample),
-                            [inputs_val, filters_val])
+                            [inputs_val, filters_val], mode=mode_with_gpu)
 
     def test_valid(self):
         self.run_conv_valid(inputs_shape=(16, 20, 12, 1),

theano/gpuarray/tests/test_gemmcorr3d.py

@@ -47,7 +47,7 @@ class TestCorr3dMM(unittest.TestCase):
             utt.verify_grad(GpuCorr3dMM(border_mode=border_mode,
                                         filter_dilation=filter_dilation,
                                         subsample=subsample),
-                            [inputs_val, filters_val])
+                            [inputs_val, filters_val], mode=mode_with_gpu)
 
     def test_valid(self):
         self.run_conv_valid(inputs_shape=(16, 20, 12, 16, 1),

theano/gpuarray/tests/test_rng_mrg.py

@@ -129,7 +129,7 @@ def test_GPUA_full_fill():
                                               dtype='float32',
                                               size=size)
     rstate_gpu.default_update = new_rstate
-    f_gpu = theano.function([], sample)
+    f_gpu = theano.function([], sample, mode=mode)
 
     utt.assert_allclose(f_cpu(), f_gpu())