add qwen3

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

@@ -0,0 +1,81 @@
+import os
+
+import pytest
+
+from vllm import LLM, PoolingParams, SamplingParams
+from vllm.assets.image import ImageAsset
+
+from ..utils import fork_new_process_for_each_test
+
+
+@fork_new_process_for_each_test
+def test_plugin(dummy_opt_path):
+    os.environ["VLLM_PLUGINS"] = ""
+    with pytest.raises(Exception) as excinfo:
+        LLM(model=dummy_opt_path, load_format="dummy")
+    assert "are not supported for now" in str(excinfo.value)
+
+
+@fork_new_process_for_each_test
+def test_oot_registration_text_generation(dummy_opt_path):
+    os.environ["VLLM_PLUGINS"] = "register_dummy_model"
+    prompts = ["Hello, my name is", "The text does not matter"]
+    sampling_params = SamplingParams(temperature=0)
+    llm = LLM(model=dummy_opt_path, load_format="dummy")
+    first_token = llm.get_tokenizer().decode(0)
+    outputs = llm.generate(prompts, sampling_params)
+
+    for output in outputs:
+        generated_text = output.outputs[0].text
+        # make sure only the first token is generated
+        rest = generated_text.replace(first_token, "")
+        assert rest == ""
+
+
+@fork_new_process_for_each_test
+def test_oot_registration_embedding(dummy_gemma2_embedding_path):
+    os.environ["VLLM_PLUGINS"] = "register_dummy_model"
+    prompts = ["Hello, my name is", "The text does not matter"]
+    sampling_params = PoolingParams()
+    llm = LLM(model=dummy_gemma2_embedding_path, load_format="dummy")
+    outputs = llm.encode(prompts, sampling_params)
+
+    for output in outputs:
+        assert all(v == 0 for v in output.outputs.embedding)
+
+
+image = ImageAsset("cherry_blossom").pil_image.convert("RGB")
+
+
+@fork_new_process_for_each_test
+def test_oot_registration_multimodal(dummy_llava_path):
+    os.environ["VLLM_PLUGINS"] = "register_dummy_model"
+    prompts = [{
+        "prompt": "What's in the image?<image>",
+        "multi_modal_data": {
+            "image": image
+        },
+    }, {
+        "prompt": "Describe the image<image>",
+        "multi_modal_data": {
+            "image": image
+        },
+    }]
+
+    sampling_params = SamplingParams(temperature=0)
+    llm = LLM(model=dummy_llava_path,
+              load_format="dummy",
+              max_num_seqs=1,
+              trust_remote_code=True,
+              gpu_memory_utilization=0.98,
+              max_model_len=4096,
+              enforce_eager=True,
+              limit_mm_per_prompt={"image": 1})
+    first_token = llm.get_tokenizer().decode(0)
+    outputs = llm.generate(prompts, sampling_params)
+
+    for output in outputs:
+        generated_text = output.outputs[0].text
+        # make sure only the first token is generated
+        rest = generated_text.replace(first_token, "")
+        assert rest == ""