chore: import upstream snapshot with attribution

2026-07-13 12:55:37 +08:00
commit 7ce4c8e27e
5900 changed files with 1668062 additions and 0 deletions
@@ -0,0 +1,104 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import subprocess
+
+import pytest
+
+from vllm.benchmarks.datasets import SampleRequest
+from vllm.benchmarks.throughput import (
+    _run_vllm_chat_requests,
+    add_cli_args,
+)
+from vllm.utils.argparse_utils import FlexibleArgumentParser
+
+MODEL_NAME = "meta-llama/Llama-3.2-1B-Instruct"
+
+
+@pytest.mark.benchmark
+def test_bench_throughput():
+    command = [
+        "vllm",
+        "bench",
+        "throughput",
+        "--model",
+        MODEL_NAME,
+        "--input-len",
+        "32",
+        "--output-len",
+        "1",
+        "--enforce-eager",
+        "--load-format",
+        "dummy",
+    ]
+    result = subprocess.run(command, capture_output=True, text=True)
+    print(result.stdout)
+    print(result.stderr)
+
+    assert result.returncode == 0, f"Benchmark failed: {result.stderr}"
+
+
+def test_bench_throughput_accepts_custom_audio_args():
+    parser = FlexibleArgumentParser()
+    add_cli_args(parser)
+
+    args = parser.parse_args(
+        [
+            "--dataset-name",
+            "custom_audio",
+            "--dataset-path",
+            "audio.jsonl",
+            "--no-oversample",
+            "--custom-output-len",
+            "32",
+            "--enable-multimodal-chat",
+        ]
+    )
+
+    assert args.dataset_name == "custom_audio"
+    assert args.no_oversample
+    assert args.custom_output_len == 32
+    assert args.enable_multimodal_chat
+
+
+def test_vllm_chat_requests_include_multimodal_content():
+    class FakeLLM:
+        def __init__(self):
+            self.prompts = None
+
+        def chat(self, prompts, sampling_params, use_tqdm):
+            del sampling_params, use_tqdm
+            self.prompts = prompts
+            return []
+
+    llm = FakeLLM()
+    audio_content = {
+        "type": "input_audio",
+        "input_audio": {"data": "abc", "format": "wav"},
+    }
+    request = SampleRequest(
+        prompt="Transcribe this audio.",
+        prompt_len=1,
+        expected_output_len=8,
+        multi_modal_data=audio_content,
+    )
+
+    _run_vllm_chat_requests(
+        llm,
+        [request],
+        n=1,
+        disable_detokenize=False,
+        do_profile=False,
+        prequeue_requests=False,
+    )
+
+    assert llm.prompts == [
+        [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "Transcribe this audio."},
+                    audio_content,
+                ],
+            }
+        ]
+    ]