[misc][ci] fix quant test (#8449)

tests/quantization/test_bitsandbytes.py

@@ -10,6 +10,8 @@ import torch
 
 from tests.quantization.utils import is_quant_method_supported
 
+from ..utils import fork_new_process_for_each_test
+
 models_4bit_to_test = [
     ('huggyllama/llama-7b', 'quantize model inflight'),
 ]
@@ -29,6 +31,7 @@ models_pre_quant_8bit_to_test = [
 @pytest.mark.skipif(not is_quant_method_supported("bitsandbytes"),
                     reason='bitsandbytes is not supported on this GPU type.')
 @pytest.mark.parametrize("model_name, description", models_4bit_to_test)
+@fork_new_process_for_each_test
 def test_load_4bit_bnb_model(hf_runner, vllm_runner, example_prompts,
                              model_name, description) -> None:
 
@@ -41,6 +44,7 @@ def test_load_4bit_bnb_model(hf_runner, vllm_runner, example_prompts,
                     reason='bitsandbytes is not supported on this GPU type.')
 @pytest.mark.parametrize("model_name, description",
                          models_pre_qaunt_4bit_to_test)
+@fork_new_process_for_each_test
 def test_load_pre_quant_4bit_bnb_model(hf_runner, vllm_runner, example_prompts,
                                        model_name, description) -> None:
 
@@ -52,6 +56,7 @@ def test_load_pre_quant_4bit_bnb_model(hf_runner, vllm_runner, example_prompts,
                     reason='bitsandbytes is not supported on this GPU type.')
 @pytest.mark.parametrize("model_name, description",
                          models_pre_quant_8bit_to_test)
+@fork_new_process_for_each_test
 def test_load_8bit_bnb_model(hf_runner, vllm_runner, example_prompts,
                              model_name, description) -> None:
 
@@ -77,18 +82,8 @@ def validate_generated_texts(hf_runner,
                              model_name,
                              hf_model_kwargs=None):
 
-    if hf_model_kwargs is None:
+    # NOTE: run vLLM first, as it requires a clean process
-        hf_model_kwargs = {}
+    # when using distributed inference
-
-    # Run with HF runner
-    with hf_runner(model_name, model_kwargs=hf_model_kwargs) as llm:
-        hf_outputs = llm.generate_greedy(prompts, 8)
-        hf_logs = log_generated_texts(prompts, hf_outputs, "HfRunner")
-
-    # Clean up the GPU memory for the next test
-    torch.cuda.synchronize()
-    gc.collect()
-    torch.cuda.empty_cache()
 
     #Run with vLLM runner
     with vllm_runner(model_name,
@@ -104,6 +99,19 @@ def validate_generated_texts(hf_runner,
     gc.collect()
     torch.cuda.empty_cache()
 
+    if hf_model_kwargs is None:
+        hf_model_kwargs = {}
+
+    # Run with HF runner
+    with hf_runner(model_name, model_kwargs=hf_model_kwargs) as llm:
+        hf_outputs = llm.generate_greedy(prompts, 8)
+        hf_logs = log_generated_texts(prompts, hf_outputs, "HfRunner")
+
+    # Clean up the GPU memory for the next test
+    torch.cuda.synchronize()
+    gc.collect()
+    torch.cuda.empty_cache()
+
     # Compare the generated strings
     for hf_log, vllm_log in zip(hf_logs, vllm_logs):
         hf_str = hf_log["generated_text"]

tests/quantization/utils.py

@@ -1,12 +1,10 @@
-import torch
-
 from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
 from vllm.platforms import current_platform
 
 
 def is_quant_method_supported(quant_method: str) -> bool:
     # Currently, all quantization methods require Nvidia or AMD GPUs
-    if not torch.cuda.is_available():
+    if not (current_platform.is_cuda() or current_platform.is_rocm()):
         return False
 
     capability = current_platform.get_device_capability()