Fixes, and tests extended.

theano/gpuarray/c_code/dnn_redux.c

@@ -98,7 +98,7 @@ int APPLY_SPECIFIC(dnn_redux)(PyGpuArrayObject *input,
       return 1;
     }
 
-    if (rsz == 1 && cudnnGetVersion() <= 7004) {
+    if (rsz == 1) {
       /* We must reduce some dimensions which have all size 1.
        * cuDNN (up to 7004) does not support this case. Let's use GpuElemwise. */
       switch (params->red_op) {

theano/gpuarray/opt.py

@@ -1258,9 +1258,8 @@ def local_gpua_careduce(op, context_name, inputs, outputs):
             greduce = op2(
                 op.scalar_op,
                 axis=new_axis, reduce_mask=new_mask,
-                dtype=getattr(op, 'dtype', outputs[0].dtype),
-                acc_dtype=getattr(op, 'acc_dtype', None))
-
+                dtype=odtype,
+                acc_dtype=adtype)
             with inherit_stack_trace(outputs):
                 reshaped_x = x.reshape(tensor.stack(new_in_shp))
                 gpu_reshaped_x = as_gpuarray_variable(reshaped_x, context_name)

theano/gpuarray/tests/test_dnn.py

@@ -1614,26 +1614,40 @@ def test_dnn_reduction_absmax():
 def test_dnn_reduction_axis_size_one():
     if not dnn.dnn_available(test_ctx_name) or dnn.version(raises=False) < 6000:
         raise SkipTest(dnn.dnn_available.msg)
+
     for dtype in ('float16', 'float32', 'float64'):
-        for shape, axis in [[(1, 2, 3), 0], [(2, 1, 3), 1], [(2, 3, 1), 2]]:
-            x = theano.tensor.tensor3(dtype=dtype)
+        for shape, axis in [[(1, 2, 3), 0],
+                            [(2, 1, 3), 1],
+                            [(2, 3, 1), 2],
+                            [(1, 5, 1), (0, 2)],
+                            [(4, 1, 6, 1), (1, 3)]]:
+
+            x = theano.tensor.TensorType(dtype=dtype, broadcastable=[False] * len(shape))()
             sum = x.sum(axis=axis)
             sum_squares = (x**2).sum(axis=axis)
             sum_abs = abs(x).sum(axis=axis)
             absmax = abs(x).max(axis=axis)
+
             cpu_f = theano.function([x], [sum, sum_squares, sum_abs, absmax], mode=mode_without_gpu)
             f1 = theano.function([x], sum, mode=mode_with_gpu)
             f2 = theano.function([x], sum_squares, mode=mode_with_gpu)
             f3 = theano.function([x], sum_abs, mode=mode_with_gpu)
             f4 = theano.function([x], absmax, mode=mode_with_gpu)
+
             for fn, red_op in ((f1, 'add'), (f2, 'norm2'), (f3, 'norm1'), (f4, 'absmax')):
                 assert any(isinstance(node.op, dnn.GpuDnnReduction) and node.op.red_op == red_op
                            for node in fn.maker.fgraph.apply_nodes)
+
             xval = np.random.uniform(-10, -1, size=shape).astype(dtype)
-            xval_reshaped = xval.reshape(shape[:axis] + shape[(axis + 1):])
+            if isinstance(axis, int):
+                xval_reshaped = xval.reshape(shape[:axis] + shape[(axis + 1):])
+            else:
+                xval_reshaped = xval.reshape([n for i, n in enumerate(shape) if i not in axis])
             test_val = abs(xval_reshaped)
+
             val_sum, val_sum_squares, val_sum_abs, val_absmax = f1(xval), f2(xval), f3(xval), f4(xval)
             cpu_val_sum, cpu_val_sum_squares, cpu_val_sum_abs, cpu_val_absmax = cpu_f(xval)
+
             utt.assert_allclose(cpu_val_sum, val_sum)
             utt.assert_allclose(cpu_val_sum_squares, val_sum_squares)
             utt.assert_allclose(cpu_val_sum_abs, val_sum_abs)