diff --git a/thinc/tests/layers/test_tensorflow_wrapper.py b/thinc/tests/layers/test_tensorflow_wrapper.py
index 948e1f035..1c10b8242 100644
--- a/thinc/tests/layers/test_tensorflow_wrapper.py
+++ b/thinc/tests/layers/test_tensorflow_wrapper.py
@@ -64,11 +64,12 @@ def model(tf_model):
 def test_tensorflow_wrapper_roundtrip_conversion():
     import tensorflow as tf
 
-    xp_tensor = numpy.zeros((2, 3), dtype="f")
+    ops = get_current_ops()
+    xp_tensor = ops.alloc2f(2, 3, zeros=True)
     tf_tensor = xp2tensorflow(xp_tensor)
     assert isinstance(tf_tensor, tf.Tensor)
-    new_xp_tensor = tensorflow2xp(tf_tensor)
-    assert numpy.array_equal(xp_tensor, new_xp_tensor)
+    new_xp_tensor = tensorflow2xp(tf_tensor, ops=ops)
+    assert ops.xp.array_equal(xp_tensor, new_xp_tensor)
 
 
 @pytest.mark.skipif(not has_tensorflow, reason="needs TensorFlow")
@@ -99,8 +100,12 @@ def test_tensorflow_wrapper_predict(model, X):
 @pytest.mark.skipif(not has_tensorflow, reason="needs TensorFlow")
 def test_tensorflow_wrapper_train_overfits(model, X, Y, answer):
     optimizer = Adam()
+    ops = get_current_ops()
     for i in range(100):
         guesses, backprop = model(X, is_train=True)
+        # Ensure that the tensor is type-compatible with the current backend.
+        guesses = ops.asarray(guesses)
+
         d_guesses = (guesses - Y) / guesses.shape[0]
         backprop(d_guesses)
         model.finish_update(optimizer)
@@ -114,8 +119,12 @@ def test_tensorflow_wrapper_accumulate_gradients(model, X, Y, answer):
 
     optimizer = Adam()
     gradients = []
+    ops = get_current_ops()
     for i in range(3):
         guesses, backprop = model(X, is_train=True)
+        # Ensure that the tensor is type-compatible with the current backend.
+        guesses = ops.asarray(guesses)
+
         d_guesses = (guesses - Y) / guesses.shape[0]
         backprop(d_guesses)
         shim_grads = [tf.identity(var) for var in model.shims[0].gradients]
@@ -173,6 +182,9 @@ def call(self, inputs) -> tf.Tensor:
     optimizer = Adam()
     for i in range(50):
         guesses, backprop = model(X, is_train=True)
+        # Ensure that the tensor is type-compatible with the current backend.
+        guesses = ops.asarray(guesses)
+
         d_guesses = (guesses - Y) / guesses.shape[0]
         backprop(d_guesses)
         model.finish_update(optimizer)
@@ -323,10 +335,14 @@ def test_tensorflow_wrapper_from_bytes(model, X):
 @pytest.mark.skipif(not has_tensorflow, reason="needs TensorFlow")
 def test_tensorflow_wrapper_use_params(model, X, Y, answer):
     optimizer = Adam()
+    ops = get_current_ops()
     with model.use_params(optimizer.averages):
         assert model.predict(X).argmax() is not None
     for i in range(10):
         guesses, backprop = model.begin_update(X)
+        # Ensure that the tensor is type-compatible with the current backend.
+        guesses = ops.asarray(guesses)
+
         d_guesses = (guesses - Y) / guesses.shape[0]
         backprop(d_guesses)
         model.finish_update(optimizer)
diff --git a/thinc/tests/util.py b/thinc/tests/util.py
index 867e4839e..7440a4b6e 100644
--- a/thinc/tests/util.py
+++ b/thinc/tests/util.py
@@ -5,6 +5,7 @@
 from thinc.api import Linear, Ragged, Padded, ArgsKwargs
 import numpy
 import pytest
+from thinc.util import has_cupy, is_cupy_array, is_numpy_array
 
 
 @contextlib.contextmanager
@@ -95,18 +96,24 @@ def check_input_converters(Y, backprop, data, n_args, kwargs_keys, type_):
     assert all(isinstance(arg, type_) for arg in Y.args)
     assert all(isinstance(arg, type_) for arg in Y.kwargs.values())
     dX = backprop(Y)
+
+    def is_supported_backend_array(arr):
+        return is_cupy_array(arr) or is_numpy_array(arr)
+
     input_type = type(data) if not isinstance(data, list) else tuple
-    assert isinstance(dX, input_type)
+    assert isinstance(dX, input_type) or is_supported_backend_array(dX)
+
     if isinstance(data, dict):
         assert list(dX.keys()) == kwargs_keys
-        assert all(isinstance(arr, numpy.ndarray) for arr in dX.values())
+        assert all(is_supported_backend_array(arr) for arr in dX.values())
     elif isinstance(data, (list, tuple)):
         assert isinstance(dX, tuple)
-        assert all(isinstance(arr, numpy.ndarray) for arr in dX)
+        assert all(is_supported_backend_array(arr) for arr in dX)
     elif isinstance(data, ArgsKwargs):
         assert len(dX.args) == n_args
         assert list(dX.kwargs.keys()) == kwargs_keys
-        assert all(isinstance(arg, numpy.ndarray) for arg in dX.args)
-        assert all(isinstance(arg, numpy.ndarray) for arg in dX.kwargs.values())
+
+        assert all(is_supported_backend_array(arg) for arg in dX.args)
+        assert all(is_supported_backend_array(arg) for arg in dX.kwargs.values())
     elif not isinstance(data, numpy.ndarray):
         pytest.fail(f"Bad data type: {dX}")