[CI] remove old quantization model (vllm-project#1003)

remove old quantization model, and new models will be added to testcase
later.

Signed-off-by: 22dimensions <waitingwind@foxmail.com>

Author: 22dimensions

tests/conftest.py

@@ -78,6 +78,7 @@ def __init__(
         enable_chunked_prefill: bool = False,
         swap_space: int = 4,
         enforce_eager: Optional[bool] = True,
+        quantization: Optional[str] = None,
         **kwargs,
     ) -> None:
         self.model = LLM(
@@ -94,6 +95,7 @@ def __init__(
             max_model_len=max_model_len,
             block_size=block_size,
             enable_chunked_prefill=enable_chunked_prefill,
+            quantization=quantization,
             **kwargs,
         )
 

tests/singlecard/test_offline_inference.py

@@ -25,6 +25,7 @@
 
 import pytest
 import vllm  # noqa: F401
+from modelscope import snapshot_download  # type: ignore[import-untyped]
 from vllm import SamplingParams
 from vllm.assets.image import ImageAsset
 
@@ -33,10 +34,13 @@
 
 MODELS = [
     "Qwen/Qwen2.5-0.5B-Instruct",
-    "vllm-ascend/Qwen2.5-0.5B-Instruct-w8a8",
     "Qwen/Qwen3-0.6B-Base",
 ]
 MULTIMODALITY_MODELS = ["Qwen/Qwen2.5-VL-3B-Instruct"]
+
+QUANTIZATION_MODELS = [
+    "vllm-ascend/Qwen2.5-0.5B-Instruct-W8A8-new",
+]
 os.environ["PYTORCH_NPU_ALLOC_CONF"] = "max_split_size_mb:256"
 
 
@@ -59,6 +63,27 @@ def test_models(model: str, dtype: str, max_tokens: int) -> None:
         vllm_model.generate_greedy(example_prompts, max_tokens)
 
 
+@pytest.mark.parametrize("model", QUANTIZATION_MODELS)
+@pytest.mark.parametrize("max_tokens", [5])
+def test_quantization_models(model: str, max_tokens: int) -> None:
+    prompt = "The following numbers of the sequence " + ", ".join(
+        str(i) for i in range(1024)) + " are:"
+    example_prompts = [prompt]
+
+    # NOTE: Using quantized model repo id from modelscope encounters an issue,
+    # this pr (https://github.com/vllm-project/vllm/pull/19212) fix the issue,
+    # after it is being merged, there's no need to download model explicitly.
+    model_path = snapshot_download(model)
+
+    with VllmRunner(model_path,
+                    max_model_len=8192,
+                    enforce_eager=True,
+                    dtype="auto",
+                    gpu_memory_utilization=0.7,
+                    quantization="ascend") as vllm_model:
+        vllm_model.generate_greedy(example_prompts, max_tokens)
+
+
 @pytest.mark.parametrize("model", MULTIMODALITY_MODELS)
 def test_multimodal(model, prompt_template, vllm_runner):
     image = ImageAsset("cherry_blossom") \