add qwen3

vllm-v0.6.2/tests/models/test_initialization.py

@@ -0,0 +1,55 @@
+from unittest.mock import patch
+
+import pytest
+import transformers
+from transformers import PretrainedConfig
+
+from vllm import LLM
+
+from .registry import HF_EXAMPLE_MODELS
+
+
+@pytest.mark.parametrize("model_arch", HF_EXAMPLE_MODELS.get_supported_archs())
+def test_can_initialize(model_arch):
+    if (model_arch == "Idefics3ForConditionalGeneration"
+            and transformers.__version__ < "4.46.0"):
+        pytest.skip(reason="Model introduced in HF >= 4.46.0")
+
+    model_info = HF_EXAMPLE_MODELS.get_hf_info(model_arch)
+    if not model_info.is_available_online:
+        pytest.skip("Model is not available online")
+
+    # Avoid OOM
+    def hf_overrides(hf_config: PretrainedConfig) -> PretrainedConfig:
+        if hasattr(hf_config, "text_config"):
+            text_config: PretrainedConfig = hf_config.text_config
+        else:
+            text_config = hf_config
+
+        text_config.update({
+            "num_layers": 1,
+            "num_hidden_layers": 1,
+            "num_experts": 2,
+            "num_experts_per_tok": 2,
+            "num_local_experts": 2,
+        })
+
+        return hf_config
+
+    # Avoid calling model.forward()
+    def _initialize_kv_caches(self) -> None:
+        self.cache_config.num_gpu_blocks = 0
+        self.cache_config.num_cpu_blocks = 0
+
+    with patch.object(LLM.get_engine_class(), "_initialize_kv_caches",
+                      _initialize_kv_caches):
+        LLM(
+            model_info.default,
+            tokenizer=model_info.tokenizer,
+            tokenizer_mode=model_info.tokenizer_mode,
+            speculative_model=model_info.speculative_model,
+            num_speculative_tokens=1 if model_info.speculative_model else None,
+            trust_remote_code=model_info.trust_remote_code,
+            load_format="dummy",
+            hf_overrides=hf_overrides,
+        )