Sync from v0.13

tests/entrypoints/llm/test_generate.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import weakref
+
+import pytest
+
+from vllm import LLM, SamplingParams
+from vllm.distributed import cleanup_dist_env_and_memory
+
+MODEL_NAME = "distilbert/distilgpt2"
+
+PROMPTS = [
+    "Hello, my name is",
+    "The president of the United States is",
+    "The capital of France is",
+    "The future of AI is",
+]
+
+TOKEN_IDS = [
+    [0],
+    [0, 1],
+    [0, 2, 1],
+    [0, 3, 1, 2],
+]
+
+
+@pytest.fixture(scope="module")
+def llm():
+    # pytest caches the fixture so we use weakref.proxy to
+    # enable garbage collection
+    llm = LLM(
+        model=MODEL_NAME,
+        max_num_batched_tokens=4096,
+        tensor_parallel_size=1,
+        gpu_memory_utilization=0.10,
+        enforce_eager=True,
+    )
+
+    yield weakref.proxy(llm)
+
+    del llm
+
+    cleanup_dist_env_and_memory()
+
+
+@pytest.mark.skip_global_cleanup
+def test_multiple_sampling_params(llm: LLM):
+    sampling_params = [
+        SamplingParams(temperature=0.01, top_p=0.95),
+        SamplingParams(temperature=0.3, top_p=0.95),
+        SamplingParams(temperature=0.7, top_p=0.95),
+        SamplingParams(temperature=0.99, top_p=0.95),
+    ]
+
+    # Multiple SamplingParams should be matched with each prompt
+    outputs = llm.generate(PROMPTS, sampling_params=sampling_params)
+    assert len(PROMPTS) == len(outputs)
+
+    # Exception raised, if the size of params does not match the size of prompts
+    with pytest.raises(ValueError):
+        outputs = llm.generate(PROMPTS, sampling_params=sampling_params[:3])
+
+    # Single SamplingParams should be applied to every prompt
+    single_sampling_params = SamplingParams(temperature=0.3, top_p=0.95)
+    outputs = llm.generate(PROMPTS, sampling_params=single_sampling_params)
+    assert len(PROMPTS) == len(outputs)
+
+    # sampling_params is None, default params should be applied
+    outputs = llm.generate(PROMPTS, sampling_params=None)
+    assert len(PROMPTS) == len(outputs)
+
+
+def test_multiple_priority(llm: LLM):
+    # Generate works when priority is None
+    outputs = llm.generate(PROMPTS, sampling_params=None, priority=None)
+    assert len(PROMPTS) == len(outputs)
+
+    # Generate works when length of priority is same as the len(PROMPTS)
+    outputs = llm.generate(PROMPTS, sampling_params=None, priority=[0] * len(PROMPTS))
+    assert len(PROMPTS) == len(outputs)
+
+    # Exception raised, if the length of priority does not match the length of prompts
+    with pytest.raises(ValueError):
+        outputs = llm.generate(
+            PROMPTS, sampling_params=None, priority=[0] * (len(PROMPTS) - 1)
+        )
+
+    # Exception raised, if the priority list is empty
+    with pytest.raises(ValueError):
+        outputs = llm.generate(PROMPTS, sampling_params=None, priority=[])
+
+
+def test_max_model_len():
+    max_model_len = 20
+    llm = LLM(
+        model=MODEL_NAME,
+        max_model_len=max_model_len,
+        gpu_memory_utilization=0.10,
+        enforce_eager=True,  # reduce test time
+    )
+    sampling_params = SamplingParams(max_tokens=max_model_len + 10)
+    outputs = llm.generate(PROMPTS, sampling_params)
+    for output in outputs:
+        num_total_tokens = len(output.prompt_token_ids) + len(
+            output.outputs[0].token_ids
+        )
+        # Total tokens must not exceed max_model_len.
+        # It can be less if generation finishes due to other reasons (e.g., EOS)
+        # before reaching the absolute model length limit.
+        assert num_total_tokens <= max_model_len
+
+
+def test_log_stats():
+    llm = LLM(
+        model=MODEL_NAME,
+        disable_log_stats=False,
+        gpu_memory_utilization=0.10,
+        enforce_eager=True,  # reduce test time
+    )
+    outputs = llm.generate(PROMPTS, sampling_params=None)
+
+    # disable_log_stats is False, every output should have metrics
+    assert all(output.metrics is not None for output in outputs)