chore: import upstream snapshot with attribution

tests/test_regression.py

@@ -0,0 +1,78 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Containing tests that check for regressions in vLLM's behavior.
+
+It should include tests that are reported by users and making sure they
+will never happen again.
+
+"""
+
+import gc
+
+import pytest
+import torch
+
+from tests.utils import large_gpu_mark
+from vllm import LLM, SamplingParams
+from vllm.platforms import current_platform
+
+
+@pytest.mark.parametrize(
+    "model",
+    [
+        pytest.param(
+            "distilbert/distilgpt2",
+            marks=[
+                *([large_gpu_mark(min_gb=80)] if current_platform.is_rocm() else []),
+            ],
+        ),
+    ],
+)
+def test_max_tokens_none(model):
+    sampling_params = SamplingParams(temperature=0.01, top_p=0.1, max_tokens=None)
+    llm = LLM(
+        model=model,
+        max_num_batched_tokens=4096,
+        tensor_parallel_size=1,
+    )
+    prompts = ["Just say hello!"]
+    outputs = llm.generate(prompts, sampling_params=sampling_params)
+
+    assert len(prompts) == len(outputs)
+
+
+def test_gc():
+    llm = LLM(model="distilbert/distilgpt2", enforce_eager=True)
+    del llm
+
+    gc.collect()
+    torch.accelerator.empty_cache()
+
+    # The memory allocated for model and KV cache should be released.
+    # The memory allocated for PyTorch and others should be less than 50MB.
+    # Usually, it's around 10MB.
+    allocated = torch.accelerator.memory_allocated()
+    assert allocated < 50 * 1024 * 1024
+
+
+def test_model_from_modelscope(monkeypatch: pytest.MonkeyPatch):
+    # model: https://www.modelscope.ai/models/qwen/Qwen1.5-0.5B-Chat
+    with monkeypatch.context() as m:
+        m.setenv("VLLM_USE_MODELSCOPE", "True")
+        m.setenv("MODELSCOPE_DOMAIN", "www.modelscope.ai")
+        # Don't use HF_TOKEN for ModelScope repos, otherwise it will fail
+        # with 400 Client Error: Bad Request.
+        m.setenv("HF_TOKEN", "")
+        attn_backend = "TRITON_ATTN" if current_platform.is_rocm() else "auto"
+        llm = LLM(model="qwen/Qwen1.5-0.5B-Chat", attention_backend=attn_backend)
+
+        prompts = [
+            "Hello, my name is",
+            "The president of the United States is",
+            "The capital of France is",
+            "The future of AI is",
+        ]
+        sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+
+        outputs = llm.generate(prompts, sampling_params)
+        assert len(outputs) == 4