huggingface
diff --git a/‎tests/test_common_gpu.py
Lines changed: 3 additions & 2 deletions b/‎tests/test_common_gpu.py
Lines changed: 3 additions & 2 deletions
diff --git a/‎tests/test_initialization.py
Lines changed: 2 additions & 1 deletion b/‎tests/test_initialization.py
Lines changed: 2 additions & 1 deletion
diff --git a/‎tests/testing_utils.py
Lines changed: 17 additions & 1 deletion b/‎tests/testing_utils.py
Lines changed: 17 additions & 1 deletion
@@ -60,6 +60,7 @@
     device_count,
     load_cat_image,
     require_bitsandbytes,
+    require_deterministic_for_xpu,
     require_multi_accelerator,
     require_non_cpu,
 )
@@ -1124,8 +1125,8 @@ def test_serialization_shared_tensors(self):
 
     @require_non_cpu
     @pytest.mark.single_gpu_tests
+    @require_deterministic_for_xpu
     @require_bitsandbytes
-    @pytest.mark.skipif(not torch.cuda.is_available(), reason="XPU have numerial errors")
     def test_4bit_dora_inference(self):
         # check for same result with and without DoRA when initializing with init_lora_weights=False
         bnb_config = BitsAndBytesConfig(
@@ -1164,8 +1165,8 @@ def test_4bit_dora_inference(self):
 
     @require_non_cpu
     @pytest.mark.single_gpu_tests
+    @require_deterministic_for_xpu
     @require_bitsandbytes
-    @pytest.mark.skipif(not torch.cuda.is_available(), reason="XPU have numerial errors")
     def test_8bit_dora_inference(self):
         # check for same result with and without DoRA when initializing with init_lora_weights=False
         model = AutoModelForCausalLM.from_pretrained(
 
@@ -64,7 +64,7 @@
 from peft.utils import infer_device
 from peft.utils.hotswap import hotswap_adapter, prepare_model_for_compiled_hotswap
 
-from .testing_utils import load_dataset_english_quotes
+from .testing_utils import load_dataset_english_quotes, require_deterministic_for_xpu
 
 
 class TestLoraInitialization:
@@ -1053,6 +1053,7 @@ def test_lora_rslora_scaling_pattern(self):
         assert model.embed.scaling["default"] == expected_scaling["embed"]
         assert model.conv2d.scaling["default"] == expected_scaling["conv2d"]
 
+    @require_deterministic_for_xpu
     def test_lora_use_dora_linear(self, data):
         # check that dora is a no-op when initialized
         torch.manual_seed(0)
 
@@ -13,7 +13,7 @@
 # limitations under the License.
 import unittest
 from contextlib import contextmanager
-from functools import lru_cache
+from functools import lru_cache, wraps
 
 import numpy as np
 import pytest
@@ -159,6 +159,22 @@ def require_torchao(test_case):
     return unittest.skipUnless(is_torchao_available(), "test requires torchao")(test_case)
 
 
+def require_deterministic_for_xpu(test_case):
+    @wraps(test_case)
+    def wrapper(*args, **kwargs):
+        if torch_device == "xpu":
+            original_state = torch.are_deterministic_algorithms_enabled()
+            try:
+                torch.use_deterministic_algorithms(True)
+                return test_case(*args, **kwargs)
+            finally:
+                torch.use_deterministic_algorithms(original_state)
+        else:
+            return test_case(*args, **kwargs)
+
+    return wrapper
+
+
 @contextmanager
 def temp_seed(seed: int):
     """Temporarily set the random seed. This works for python numpy, pytorch."""