fix: Fix LoRA test failures by using ops to do numpy conversion

keras/src/layers/convolutional/conv_test.py

@@ -9,6 +9,7 @@
 from keras.src import constraints
 from keras.src import layers
 from keras.src import models
+from keras.src import ops
 from keras.src import saving
 from keras.src import testing
 
@@ -777,7 +778,7 @@ def test_enable_lora_with_alpha(self):
         expected_effective_kernel = base_kernel + scaling * delta
 
         # Compare the effective kernel computed via the property.
-        actual_effective_kernel = layer.kernel.numpy()
+        actual_effective_kernel = ops.convert_to_numpy(layer.kernel)
         self.assertAllClose(actual_effective_kernel, expected_effective_kernel)
 
     @pytest.mark.requires_trainable_backend

keras/src/layers/core/dense_test.py

@@ -285,9 +285,10 @@ def test_enable_lora_with_alpha(self):
         # Manually compute the expected effective kernel:
         # effective_kernel = base_kernel +
         # (lora_alpha / lora_rank) * (lora_kernel_a @ lora_kernel_b)
-        base_kernel = layer._kernel.numpy()
+        base_kernel = ops.convert_to_numpy(layer._kernel)
         lora_update = np.matmul(
-            layer.lora_kernel_a.numpy(), layer.lora_kernel_b.numpy()
+            ops.convert_to_numpy(layer.lora_kernel_a.numpy()),
+            ops.convert_to_numpy(layer.lora_kernel_b.numpy()),
         )
         effective_kernel_expected = base_kernel + (3.0 / 2) * lora_update
 

keras/src/layers/core/einsum_dense_test.py

@@ -400,7 +400,7 @@ def test_enable_lora_with_alpha(self):
         expected_kernel = base_kernel + expected_delta
 
         # Verify that the effective kernel property returns the expected value.
-        actual_kernel = layer.kernel.numpy()
+        actual_kernel = ops.convert_to_numpy(layer.kernel)
         self.assertAllClose(actual_kernel, expected_kernel)
 
     @pytest.mark.requires_trainable_backend

keras/src/layers/core/embedding_test.py

@@ -210,7 +210,7 @@ def test_enable_lora_with_alpha(self):
         expected_embeddings = base_emb + effective_delta
 
         # Verify that the effective embeddings match expectation.
-        actual_embeddings = layer.embeddings.numpy()
+        actual_embeddings = ops.convert_to_numpy(layer.embeddings)
         self.assertAllClose(actual_embeddings, expected_embeddings)
 
     @pytest.mark.requires_trainable_backend