[CI/Build] Reduce LoRA model tests. (#42266)

Signed-off-by: Jee Jee Li <[email protected]>

.buildkite/test-amd.yaml

@@ -460,7 +460,7 @@ steps:
   - tests/lora
   - vllm/platforms/rocm.py
   commands:
-  - pytest -v -s lora --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --ignore=lora/test_chatglm3_tp.py --ignore=lora/test_llama_tp.py --ignore=lora/test_llm_with_multi_loras.py --ignore=lora/test_olmoe_tp.py --ignore=lora/test_deepseekv2_tp.py --ignore=lora/test_gptoss_tp.py --ignore=lora/test_qwen3moe_tp.py --ignore=lora/test_qwen35_densemodel_lora.py
+  - pytest -v -s lora --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --ignore=lora/test_chatglm3_tp.py --ignore=lora/test_llama_tp.py --ignore=lora/test_qwen3_with_multi_loras.py --ignore=lora/test_olmoe_tp.py --ignore=lora/test_deepseekv2_tp.py --ignore=lora/test_gptoss_tp.py --ignore=lora/test_qwen3moe_tp.py --ignore=lora/test_qwen35_densemodel_lora.py
 
 #------------------------------------------------------  mi250 · model_executor  -------------------------------------------------------#
 
@@ -1760,7 +1760,7 @@ steps:
   - export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
   - pytest -v -s -x lora/test_chatglm3_tp.py
   - pytest -v -s -x lora/test_llama_tp.py
-  - pytest -v -s -x lora/test_llm_with_multi_loras.py
+  - pytest -v -s -x lora/test_qwen3_with_multi_loras.py
   - pytest -v -s -x lora/test_olmoe_tp.py
   - pytest -v -s -x lora/test_gptoss_tp.py
   - pytest -v -s -x lora/test_qwen35_densemodel_lora.py

.buildkite/test_areas/lora.yaml

@@ -9,7 +9,7 @@ steps:
   - vllm/lora
   - tests/lora
   commands:
-    - pytest -v -s lora --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --ignore=lora/test_chatglm3_tp.py --ignore=lora/test_llama_tp.py --ignore=lora/test_llm_with_multi_loras.py --ignore=lora/test_olmoe_tp.py --ignore=lora/test_deepseekv2_tp.py --ignore=lora/test_gptoss_tp.py --ignore=lora/test_qwen3moe_tp.py --ignore=lora/test_qwen35_densemodel_lora.py 
+    - pytest -v -s lora --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --ignore=lora/test_chatglm3_tp.py --ignore=lora/test_llama_tp.py --ignore=lora/test_qwen3_with_multi_loras.py --ignore=lora/test_olmoe_tp.py --ignore=lora/test_deepseekv2_tp.py --ignore=lora/test_gptoss_tp.py --ignore=lora/test_qwen3moe_tp.py --ignore=lora/test_qwen35_densemodel_lora.py 
   parallelism: 4
 
 
@@ -31,7 +31,7 @@ steps:
     # requires multi-GPU testing for validation.
     - pytest -v -s -x lora/test_chatglm3_tp.py
     - pytest -v -s -x lora/test_llama_tp.py
-    - pytest -v -s -x lora/test_llm_with_multi_loras.py
+    - pytest -v -s -x lora/test_qwen3_with_multi_loras.py
     - pytest -v -s -x lora/test_olmoe_tp.py
     - pytest -v -s -x lora/test_gptoss_tp.py
     - pytest -v -s -x lora/test_qwen35_densemodel_lora.py

tests/lora/test_chatglm3_tp.py

@@ -1,9 +1,12 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import pytest
+
 import vllm
 import vllm.config
 from vllm.lora.request import LoRARequest
+from vllm.platforms import current_platform
 
 from ..utils import create_new_process_for_each_test, multi_gpu_test
 
@@ -50,6 +53,9 @@ def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> list[str]:
     return generated_texts
 
 
+@pytest.mark.skipif(
+    current_platform.is_cuda_alike(), reason="Skipping to avoid redundant model tests"
+)
 @create_new_process_for_each_test()
 def test_chatglm3_lora(chatglm3_lora_files):
     llm = vllm.LLM(
@@ -70,6 +76,9 @@ def test_chatglm3_lora(chatglm3_lora_files):
         assert output2[i] == EXPECTED_LORA_OUTPUT[i]
 
 
+@pytest.mark.skipif(
+    current_platform.is_cuda_alike(), reason="Skipping to avoid redundant model tests"
+)
 @multi_gpu_test(num_gpus=4)
 def test_chatglm3_lora_tp4(chatglm3_lora_files):
     llm = vllm.LLM(

tests/lora/test_default_mm_loras.py

@@ -11,6 +11,7 @@ import pytest
 from huggingface_hub import snapshot_download
 
 from vllm.lora.request import LoRARequest
+from vllm.platforms import current_platform
 
 from ..conftest import AudioTestAssets, VllmRunner
 from ..utils import create_new_process_for_each_test
@@ -76,6 +77,9 @@ def test_active_default_mm_lora(
     )
 
 
+@pytest.mark.skipif(
+    current_platform.is_cuda_alike(), reason="Skipping to avoid redundant model tests"
+)
 @create_new_process_for_each_test()
 def test_inactive_default_mm_lora(
     vllm_runner: type[VllmRunner],
@@ -92,6 +96,9 @@ def test_inactive_default_mm_lora(
     )
 
 
+@pytest.mark.skipif(
+    current_platform.is_cuda_alike(), reason="Skipping to avoid redundant model tests"
+)
 @create_new_process_for_each_test()
 def test_default_mm_lora_succeeds_with_redundant_lora_request(
     vllm_runner: type[VllmRunner],
@@ -107,6 +114,9 @@ def test_default_mm_lora_succeeds_with_redundant_lora_request(
     )
 
 
+@pytest.mark.skipif(
+    current_platform.is_cuda_alike(), reason="Skipping to avoid redundant model tests"
+)
 @create_new_process_for_each_test()
 def test_default_mm_lora_fails_with_overridden_lora_request(
     vllm_runner: type[VllmRunner],

tests/lora/test_llama_tp.py

@@ -10,6 +10,7 @@ import vllm.config
 from vllm import LLM
 from vllm.lora.request import LoRARequest
 from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
+from vllm.platforms import current_platform
 
 from ..utils import VLLM_PATH, create_new_process_for_each_test, multi_gpu_test
 
@@ -139,6 +140,9 @@ def test_llama_lora(llama32_lora_files, cudagraph_specialize_lora: bool):
     generate_and_test(llm, llama32_lora_files)
 
 
+@pytest.mark.skipif(
+    current_platform.is_cuda_alike(), reason="Skipping to avoid redundant model tests"
+)
 @multi_gpu_test(num_gpus=4)
 def test_llama_lora_tp4(llama32_lora_files):
     llm = vllm.LLM(

tests/lora/test_minicpmv_tp.py

@@ -68,6 +68,9 @@ def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> list[str]:
     return generated_texts
 
 
+@pytest.mark.skipif(
+    current_platform.is_cuda_alike(), reason="Skipping to avoid redundant model tests"
+)
 def test_minicpmv_lora(minicpmv_lora_files):
     llm = vllm.LLM(
         MODEL_PATH,

tests/lora/test_olmoe_tp.py

@@ -11,6 +11,7 @@ from safetensors.torch import load_file, save_file
 
 import vllm
 from vllm.lora.request import LoRARequest
+from vllm.platforms import current_platform
 
 from ..utils import multi_gpu_test
 
@@ -110,6 +111,9 @@ def generate_and_test(
         )
 
 
+@pytest.mark.skipif(
+    current_platform.is_cuda_alike(), reason="Skipping to avoid redundant model tests"
+)
 def test_olmoe_lora(olmoe_lora_files, maybe_enable_lora_dual_stream):
     # We enable enforce_eager=True here to reduce VRAM usage for lora-test CI,
     # Otherwise, the lora-test will fail due to CUDA OOM.
@@ -178,6 +182,9 @@ def test_olmoe_lora_mixed_random(
     assert outputs[0].outputs[0].text.strip().startswith(EXPECTED_LORA_OUTPUT[0])
 
 
+@pytest.mark.skipif(
+    current_platform.is_cuda_alike(), reason="Skipping to avoid redundant model tests"
+)
 @pytest.mark.parametrize("fully_sharded_loras", [False, True])
 @multi_gpu_test(num_gpus=2)
 def test_olmoe_lora_tp2(olmoe_lora_files, fully_sharded_loras):

tests/lora/test_qwen35_densemodel_lora.py

@@ -1,12 +1,14 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import pytest
 from transformers import AutoTokenizer
 
 import vllm
 import vllm.config
 from vllm.assets.image import ImageAsset
 from vllm.lora.request import LoRARequest
+from vllm.platforms import current_platform
 
 from ..utils import create_new_process_for_each_test, multi_gpu_test
 
@@ -311,6 +313,9 @@ def _assert_qwen35_text_vl_and_mixed_lora(
     )
 
 
+@pytest.mark.skipif(
+    current_platform.is_cuda_alike(), reason="Skipping to avoid redundant model tests"
+)
 @create_new_process_for_each_test()
 def test_qwen35_text_lora(
     qwen35_text_lora_files, qwen35_vl_lora_files, maybe_enable_lora_dual_stream

tests/lora/test_qwenvl.py

@@ -2,12 +2,14 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from dataclasses import dataclass
 
+import pytest
 from packaging.version import Version
 from transformers import __version__ as TRANSFORMERS_VERSION
 
 import vllm
 from vllm.assets.image import ImageAsset
 from vllm.lora.request import LoRARequest
+from vllm.platforms import current_platform
 from vllm.sampling_params import BeamSearchParams
 
 
@@ -206,6 +208,9 @@ def test_qwen2vl_lora_beam_search(qwen2vl_lora_files):
         )
 
 
+@pytest.mark.skipif(
+    current_platform.is_cuda_alike(), reason="Skipping to avoid redundant model tests"
+)
 def test_qwen25vl_lora(qwen25vl_lora_files):
     """Test Qwen 2.5 VL model with LoRA"""
     config = TestConfig(model_path=QWEN25VL_MODEL_PATH, lora_path=qwen25vl_lora_files)
@@ -216,6 +221,9 @@ def test_qwen25vl_lora(qwen25vl_lora_files):
         tester.run_test(TEST_IMAGES, expected_outputs=EXPECTED_OUTPUTS, lora_id=lora_id)
 
 
+@pytest.mark.skipif(
+    current_platform.is_cuda_alike(), reason="Skipping to avoid redundant model tests"
+)
 def test_qwen25vl_vision_lora(qwen25vl_vision_lora_files):
     config = TestConfig(
         model_path=QWEN25VL_MODEL_PATH,

tests/lora/test_whisper.py

@@ -124,30 +124,3 @@ def test_whisper_multi_lora(whisper_lora_files):
         f"Expected same outputs for same adapter with different IDs. "
         f"Got: {outputs_lora1} vs {outputs_lora2}"
     )
-
-
-@create_new_process_for_each_test()
-def test_whisper_with_and_without_lora(whisper_lora_files):
-    """Test that Whisper produces different outputs with and without LoRA.
-
-    This test verifies that the LoRA adapter actually affects the model output.
-    """
-    llm = create_whisper_llm(enable_lora=True)
-
-    # Run with LoRA
-    outputs_with_lora = run_whisper_inference(
-        llm, lora_path=whisper_lora_files, lora_id=1
-    )
-
-    # Run without LoRA (base model only)
-    outputs_without_lora = run_whisper_inference(llm, lora_path=None)
-
-    # Both should produce valid outputs
-    assert len(outputs_with_lora[0]) > 0
-    assert len(outputs_without_lora[0]) > 0
-
-    print(f"Output with LoRA: {outputs_with_lora[0]}")
-    print(f"Output without LoRA: {outputs_without_lora[0]}")
-
-    # Note: Outputs may or may not differ depending on the adapter
-    # The main verification is that both configurations work