add qwen3

vllm-v0.6.2/tests/compile/test_basic_correctness.py

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+import dataclasses
+from typing import Dict, List, Optional
+
+import pytest
+
+from vllm.compilation.levels import CompilationLevel
+from vllm.utils import cuda_device_count_stateless
+
+from ..utils import compare_all_settings
+
+
+@dataclasses.dataclass
+class TestSetting:
+    model: str
+    model_args: List[str]
+    pp_size: int
+    tp_size: int
+    attn_backend: str
+    method: str
+    fullgraph: bool
+
+
+# representative settings for testing
+test_settings = [
+    # basic llama model
+    TestSetting(
+        model="meta-llama/Llama-3.2-1B",
+        model_args=[],
+        pp_size=2,
+        tp_size=2,
+        attn_backend="FLASHINFER",
+        method="generate",
+        fullgraph=True,
+    ),
+    # llama model with quantization
+    TestSetting(
+        model="TheBloke/TinyLlama-1.1B-Chat-v0.3-GPTQ",
+        model_args=["--quantization", "gptq"],
+        pp_size=1,
+        tp_size=1,
+        attn_backend="FLASH_ATTN",
+        method="generate",
+        fullgraph=True,
+    ),
+    # MoE model
+    TestSetting(
+        model="ibm/PowerMoE-3b",
+        model_args=[],
+        pp_size=1,
+        tp_size=2,
+        attn_backend="FLASH_ATTN",
+        method="generate",
+        fullgraph=True,
+    ),
+    # embedding model
+    TestSetting(
+        model="BAAI/bge-multilingual-gemma2",
+        model_args=["--task", "embedding"],
+        pp_size=1,
+        tp_size=1,
+        attn_backend="FLASHINFER",
+        method="encode",
+        fullgraph=True,
+    ),
+    # vision language model
+    TestSetting(
+        model="microsoft/Phi-3.5-vision-instruct",
+        model_args=["--trust-remote-code", "--max-model-len", "2048"],
+        pp_size=2,
+        tp_size=1,
+        attn_backend="FLASH_ATTN",
+        method="generate_with_image",
+        fullgraph=False,
+    ),
+]
+
+
+# we cannot afford testing the full Catesian product
+# of all models and all levels
+@pytest.mark.parametrize("test_setting", test_settings)
+def test_compile_correctness(test_setting: TestSetting):
+    # this test is run under multiple suits, with different GPUs.
+    # make sure we only run the test with correct CUDA devices.
+    # don't use "<", as it will duplicate the tests.
+    model = test_setting.model
+    model_args = test_setting.model_args
+    pp_size = test_setting.pp_size
+    tp_size = test_setting.tp_size
+    attn_backend = test_setting.attn_backend
+    method = test_setting.method
+    fullgraph = test_setting.fullgraph
+    if cuda_device_count_stateless() != pp_size * tp_size:
+        pytest.skip("Not correct CUDA devices for the test.")
+    import os
+    os.environ["VLLM_ATTENTION_BACKEND"] = attn_backend
+    final_args = ["--enforce-eager"] + model_args + ["-pp", str(pp_size)] + \
+                ["-tp", str(tp_size)]
+
+    all_envs: List[Optional[Dict[str, str]]] = []
+
+    for level in [
+            CompilationLevel.NO_COMPILATION,
+            CompilationLevel.PIECEWISE,
+    ]:
+        all_envs.append({"VLLM_TORCH_COMPILE_LEVEL": str(level)})
+
+    # inductor will change the output, so we only compare if the output
+    # is close, not exactly the same.
+    compare_all_settings(
+        model, [final_args] * 2,
+        all_envs,
+        method=method if method != "generate" else "generate_close")
+    all_envs.clear()
+
+    for level in [
+            CompilationLevel.NO_COMPILATION,
+            CompilationLevel.DYNAMO_AS_IS,
+            CompilationLevel.DYNAMO_ONCE,
+    ]:
+        all_envs.append({"VLLM_TORCH_COMPILE_LEVEL": str(level)})
+        if level != CompilationLevel.DYNAMO_ONCE and not fullgraph:
+            # "DYNAMO_ONCE" will always use fullgraph
+            all_envs[-1][
+                "VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE"] = "0"  # type: ignore
+
+    compare_all_settings(model, [final_args] * 3, all_envs, method=method)