Do not introduce 0 strides for broadcastable dimensions in DimShuffle

pytensor/tensor/c_code/dimshuffle.c

@@ -33,12 +33,17 @@ int APPLY_SPECIFIC(cpu_dimshuffle)(PyArrayObject *input, PyArrayObject **res, PA
     npy_intp original_size = PyArray_SIZE(input);
     npy_intp new_size = 1;
     for (npy_intp i = 0; i < nd_out; ++i) {
+        // We set the strides of length 1 dimensions to PyArray_ITEMSIZE(input).
+        // The value is arbitrary, because there is never a next element.
+        // np.expand_dims(x, 0) and x[None] do different things here.
+        // I would prefer zero, but there are some poorly implemented BLAS operations
+        // That don't handle zero strides correctly. At least they won't fail because of DimShuffle.
         if (new_order[i] != -1) {
             dimensions[i] = PyArray_DIMS(input)[new_order[i]];
-            strides[i] = PyArray_DIMS(input)[new_order[i]] == 1 ? 0 : PyArray_STRIDES(input)[new_order[i]];
+            strides[i] = PyArray_DIMS(input)[new_order[i]] == 1 ? PyArray_ITEMSIZE(input) : PyArray_STRIDES(input)[new_order[i]];
         } else {
             dimensions[i] = 1;
-            strides[i] = 0;
+            strides[i] = PyArray_ITEMSIZE(input);
         }
         new_size *= dimensions[i];
     }

tests/tensor/test_elemwise.py

@@ -185,14 +185,13 @@ class TestDimShuffle(unittest_tools.InferShapeTester):
         # as the broadcasted value; that way, we'll be able to tell that we're getting
         # junk data from a poorly constructed array view.
         x_val = np.broadcast_to(2039, (5000,))
-        expected_x_val = x_val[None]
         for i in range(1):
             inputs[0].storage[0] = x_val
             thunk()
             # Make sure it's a view of the original data
             assert np.shares_memory(x_val, outputs[0].storage[0])
             # Confirm the right strides
-            assert outputs[0].storage[0].strides == expected_x_val.strides
+            assert outputs[0].storage[0].strides[-1] == 0
             # Confirm the broadcasted value in the output
             assert np.array_equiv(outputs[0].storage[0], 2039)