add qwen3

vllm-v0.6.2/tests/models/decoder_only/language/test_big_models.py

@@ -0,0 +1,70 @@
+"""Compare the outputs of HF and vLLM when using greedy sampling.
+
+This tests bigger models and use half precision.
+
+Run `pytest tests/models/test_big_models.py`.
+"""
+import pytest
+import torch
+
+from ...utils import check_outputs_equal
+
+'''
+=============================
+Modify by vllm_mlu
+=============================
+@brief(MODELS): Only test Llama-2-7b-hf, disable gpt-j-6b.
+''' 
+MODELS = [
+    "meta-llama/Llama-2-7b-hf",
+    # "mistralai/Mistral-7B-v0.1",  # Tested by test_mistral.py
+    # "Deci/DeciLM-7b",  # Broken
+    # "tiiuae/falcon-7b",  # Broken
+    # "EleutherAI/gpt-j-6b",
+    # "mosaicml/mpt-7b",  # Broken
+    # "Qwen/Qwen1.5-0.5B"  # Broken,
+]
+
+#TODO: remove this after CPU float16 support ready
+target_dtype = "float"
+if torch.cuda.is_available():
+    target_dtype = "half"
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", [target_dtype])
+@pytest.mark.parametrize("max_tokens", [32])
+def test_models(
+    hf_runner,
+    vllm_runner,
+    example_prompts,
+    model: str,
+    dtype: str,
+    max_tokens: int,
+) -> None:
+    with hf_runner(model, dtype=dtype) as hf_model:
+        hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
+
+    with vllm_runner(model, dtype=dtype) as vllm_model:
+        vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
+
+    check_outputs_equal(
+        outputs_0_lst=hf_outputs,
+        outputs_1_lst=vllm_outputs,
+        name_0="hf",
+        name_1="vllm",
+    )
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", [target_dtype])
+def test_model_print(
+    vllm_runner,
+    model: str,
+    dtype: str,
+) -> None:
+    with vllm_runner(model, dtype=dtype) as vllm_model:
+        # This test is for verifying whether the model's extra_repr
+        # can be printed correctly.
+        print(vllm_model.model.llm_engine.model_executor.driver_worker.
+              model_runner.model)