[tests] skip failed tests for xpu

test/convergence/test_mini_models_multimodal.py

@@ -1,15 +1,6 @@
 import functools
 import os
 
-import pytest
-import torch
-
-from datasets import load_dataset
-from torch.utils.data import DataLoader
-from transformers import PreTrainedTokenizerFast
-
-from liger_kernel.transformers import apply_liger_kernel_to_mllama
-from liger_kernel.transformers import apply_liger_kernel_to_qwen2_vl
 from test.utils import FAKE_CONFIGS_PATH
 from test.utils import UNTOKENIZED_DATASET_PATH
 from test.utils import MiniModelConfig
@@ -22,6 +13,16 @@
 from test.utils import supports_bfloat16
 from test.utils import train_bpe_tokenizer
 
+import pytest
+import torch
+
+from datasets import load_dataset
+from torch.utils.data import DataLoader
+from transformers import PreTrainedTokenizerFast
+
+from liger_kernel.transformers import apply_liger_kernel_to_mllama
+from liger_kernel.transformers import apply_liger_kernel_to_qwen2_vl
+
 try:
     # Qwen2-VL is only available in transformers>=4.45.0
     from transformers.models.qwen2.tokenization_qwen2_fast import Qwen2TokenizerFast
@@ -346,10 +347,13 @@ def run_mini_model_multimodal(
             1e-5,
             5e-3,
             1e-5,
-            marks=pytest.mark.skipif(
-                not QWEN2_VL_AVAILABLE,
-                reason="Qwen2-VL not available in this version of transformers",
-            ),
+            marks=[
+                pytest.mark.skipif(
+                    not QWEN2_VL_AVAILABLE,
+                    reason="Qwen2-VL not available in this version of transformers",
+                ),
+                pytest.mark.skipif(device == "xpu", reason="skip for XPU"),
+            ],
         ),
         pytest.param(
             "mini_qwen2_vl",
@@ -368,6 +372,7 @@ def run_mini_model_multimodal(
                     not QWEN2_VL_AVAILABLE,
                     reason="Qwen2-VL not available in this version of transformers",
                 ),
+                pytest.mark.skipif(device == "xpu", reason="skip for XPU"),
             ],
         ),
         pytest.param(

test/transformers/test_fused_linear_cross_entropy.py

@@ -1,12 +1,11 @@
+from test.transformers.test_cross_entropy import CrossEntropyWithZLoss
+from test.utils import assert_verbose_allclose
+from test.utils import set_seed
 from typing import Optional
 
 import pytest
 import torch
 
-from test.transformers.test_cross_entropy import CrossEntropyWithZLoss
-from test.utils import assert_verbose_allclose
-from test.utils import set_seed
-
 from liger_kernel.ops.fused_linear_cross_entropy import LigerFusedLinearCrossEntropyFunction
 from liger_kernel.transformers.functional import liger_fused_linear_cross_entropy
 from liger_kernel.transformers.fused_linear_cross_entropy import LigerFusedLinearCrossEntropyLoss
@@ -105,7 +104,7 @@ def forward(self, x, y):
 @pytest.mark.parametrize(
     "B, T, H, V",
     [
-        (8, 128, 1024, 4096),
+        pytest.param(8, 128, 1024, 4096, marks=pytest.mark.skipif(device="xpu", reason="skip for XPU")),
         (4, 47, 31, 123),  # random shape
     ],
 )
@@ -287,7 +286,7 @@ def test_correctness_functional(B, T, H, V, scalar, dtype, bias, ce_weight, atol
 @pytest.mark.parametrize(
     "B, T, H, V",
     [
-        (8, 128, 1024, 4096),
+        pytest.param(8, 128, 1024, 4096, marks=pytest.mark.skipif(device="xpu", reason="skip for XPU")),
         (4, 47, 31, 123),  # random shape
     ],
 )

test/transformers/test_rms_norm.py

@@ -1,13 +1,13 @@
 import os
 
-import pytest
-import torch
-import torch.nn as nn
-
 from test.utils import assert_verbose_allclose
 from test.utils import set_seed
 from test.utils import supports_bfloat16
 
+import pytest
+import torch
+import torch.nn as nn
+
 from liger_kernel.ops.rms_norm import LigerRMSNormFunction
 from liger_kernel.transformers.functional import liger_rms_norm
 from liger_kernel.transformers.rms_norm import LigerRMSNorm
@@ -103,7 +103,7 @@ def forward(self, x):
     [
         (LlamaRMSNorm, 0.0, "llama"),
         (GemmaRMSNorm, 1.0, "gemma"),
-        (BaseRMSNorm, 0.0, "none"),
+        pytest.param(BaseRMSNorm, 0.0, "none", marks=pytest.mark.skipif(device="xpu", reason="skip for XPU")),
     ],
 )
 @pytest.mark.parametrize(