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vllm-omni/tests/dfx/perf/scripts/run_benchmark.py
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
import json
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import math
import os
import threading
from collections.abc import Callable
from contextlib import AbstractContextManager, ExitStack, contextmanager
from pathlib import Path
from typing import Any
import pytest
from tests.dfx.conftest import (
create_paired_omni_benchmark_pytest_params,
create_test_parameter_mapping,
get_benchmark_params_for_server,
get_runtime_resource_label,
is_diffusion_perf_config,
load_benchmark_configs,
run_benchmark,
)
from tests.helpers.runtime import OmniServer
# Optional JSON field ``mark`` is applied as pytest marks via
# ``create_paired_omni_benchmark_pytest_params`` (e.g. ``"mark": [{"hardware_marks":
# {"res": {"cuda": "H100"}, "num_cards": 2}}, "full_model", "omni"]``).
os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
def _get_config_file_from_argv() -> str | None:
"""Read ``--test-config-file`` from ``sys.argv`` at import time so parametrization can use it."""
import sys
for i, arg in enumerate(sys.argv):
if arg == "--test-config-file" and i + 1 < len(sys.argv):
return sys.argv[i + 1]
if arg.startswith("--test-config-file="):
return arg.split("=", 1)[1]
return None
_PERF_TESTS_DIR = Path(__file__).resolve().parent.parent / "tests"
CONFIG_FILE_PATH = _get_config_file_from_argv()
if CONFIG_FILE_PATH is None:
_all_configs = load_benchmark_configs(config_dir=_PERF_TESTS_DIR)
BENCHMARK_CONFIGS = [cfg for cfg in _all_configs if not is_diffusion_perf_config(cfg)]
print(
f"No --test-config-file: loaded {len(BENCHMARK_CONFIGS)} omni/tts case(s) from "
f"{_PERF_TESTS_DIR}/*.json (skipped {len(_all_configs) - len(BENCHMARK_CONFIGS)} diffusion; "
f"use -m to filter, e.g. -m tts)"
)
else:
BENCHMARK_CONFIGS = load_benchmark_configs(CONFIG_FILE_PATH)
DEPLOY_CONFIGS_DIR = Path(__file__).parent.parent / "deploy"
server_to_benchmark_mapping = create_test_parameter_mapping(BENCHMARK_CONFIGS)
paired_benchmark_params = create_paired_omni_benchmark_pytest_params(BENCHMARK_CONFIGS, DEPLOY_CONFIGS_DIR)
_omni_server_lock = threading.Lock()
class _SingleActiveContext:
"""Reuse one active context while its configuration key is unchanged."""
def __init__(self) -> None:
self._key: Any = None
self._stack: ExitStack | None = None
self._value: Any = None
def acquire(self, key: Any, factory: Callable[[], AbstractContextManager[Any]]) -> Any:
if self._stack is not None and key == self._key:
return self._value
self.close()
stack = ExitStack()
value = stack.enter_context(factory())
self._key = key
self._stack = stack
self._value = value
return value
def close(self) -> None:
stack = self._stack
self._key = None
self._stack = None
self._value = None
if stack is not None:
stack.close()
@contextmanager
def _start_omni_server(server_param):
test_name, model, stage_config_path, stage_overrides, extra_cli_args, use_omni = server_param
print(f"Starting OmniServer with test: {test_name}, model: {model}")
server_args: list[str] = []
if use_omni:
server_args += ["--stage-init-timeout", "600", "--init-timeout", "900"]
# --deploy-config and --stage-overrides compose at the CLI (see vllm_omni/entrypoints/utils.py):
# deploy-config sets the base; stage-overrides are applied on top. Both can be set.
if stage_config_path:
server_args = ["--deploy-config", stage_config_path] + server_args
if stage_overrides:
server_args = ["--stage-overrides", stage_overrides] + server_args
if extra_cli_args:
server_args = list(extra_cli_args) + server_args
with OmniServer(model, server_args, use_omni=use_omni) as server:
server.test_name = test_name
print("OmniServer started successfully")
yield server
print("OmniServer stopping...")
print("OmniServer stopped")
@pytest.fixture(scope="module")
def omni_server_context():
"""Start vLLM-Omni server as a subprocess with actual model weights.
Reuse it for adjacent benchmark cases with the same server configuration.
Multi-stage initialization can take 10-20+ minutes.
"""
with _omni_server_lock:
active_context = _SingleActiveContext()
try:
yield active_context
finally:
active_context.close()
@pytest.fixture
def omni_server(request, omni_server_context):
return omni_server_context.acquire(request.param, lambda: _start_omni_server(request.param))
@pytest.fixture
def benchmark_params(request):
"""Benchmark parameters fixture; paired with ``omni_server`` via parametrization."""
test_name, param_index = request.param
all_params = get_benchmark_params_for_server(test_name, server_to_benchmark_mapping)
if not all_params:
raise ValueError(f"No benchmark parameters found for test: {test_name}")
if param_index >= len(all_params):
raise ValueError(f"No benchmark parameters found for index {param_index} in test: {test_name}")
current = param_index + 1
total = len(all_params)
print(f"\n Running benchmark {current}/{total} for {test_name}")
return {
"test_name": test_name,
"params": all_params[param_index],
}
def _resolve_num_warmups(params: dict[str, Any], *, default: int) -> int:
value = params.get("num_warmups")
if value is None:
return default
if not isinstance(value, int) or isinstance(value, bool) or value < 0:
raise ValueError("num_warmups must be a non-negative integer")
return value
def assert_result(result, params, num_prompt) -> None:
assert result["completed"] == num_prompt, "Request failures exist"
if params.get("dataset_name") == "omniinteract":
summary = result.get("omniinteract")
assert isinstance(summary, dict), "OmniInteract summary is missing"
assert (summary.get("total"), summary.get("success"), summary.get("failed")) == (
num_prompt,
num_prompt,
0,
), "OmniInteract requests did not all succeed"
assert summary.get("artifacts_complete") is True, "OmniInteract artifacts are incomplete"
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baseline = params.get("baseline")
hardware = result.get("Hardware")
hardware_baseline = baseline.get(hardware) if isinstance(baseline, dict) and isinstance(hardware, str) else None
if isinstance(hardware_baseline, dict) and "mean_tpot_ms" in hardware_baseline:
num_tpot_samples = result.get("num_tpot_samples")
mean_tpot_ms = result.get("mean_tpot_ms")
assert isinstance(num_tpot_samples, int) and not isinstance(num_tpot_samples, bool) and num_tpot_samples > 0, (
"TPOT baseline is configured, but no measurable TPOT samples were produced"
)
assert (
isinstance(mean_tpot_ms, int | float) and not isinstance(mean_tpot_ms, bool) and math.isfinite(mean_tpot_ms)
), "TPOT baseline is configured, but mean_tpot_ms is not finite"
expected_audio_turns = params.get("expected_duplex_audio_turns_per_session")
if expected_audio_turns is not None:
session_metrics = result.get("duplex_session_metrics")
assert isinstance(session_metrics, list), "Duplex session metrics are missing"
assert len(session_metrics) == num_prompt, (
f"Expected {num_prompt} duplex session metric rows, got {len(session_metrics)}"
)
assert all(
isinstance(metric, dict) and metric.get("audio_turn_count") == expected_audio_turns
for metric in session_metrics
), f"Not every duplex session emitted {expected_audio_turns} audio turns"
@pytest.mark.benchmark
@pytest.mark.parametrize(
"omni_server,benchmark_params",
paired_benchmark_params,
indirect=["omni_server", "benchmark_params"],
)
def test_performance_benchmark(omni_server, benchmark_params):
test_name = benchmark_params["test_name"]
params = benchmark_params["params"]
dataset_name = params.get("dataset_name", "")
host = omni_server.host
port = omni_server.port
model = omni_server.model
print(f"Running benchmark for model: {model}")
print(f"Benchmark parameters: {benchmark_params}")
resource_label = get_runtime_resource_label()
def to_list(value, default=None):
if value is None:
return [] if default is None else [default]
return [value] if not isinstance(value, (list, tuple)) else list(value)
qps_list = to_list(params.get("request_rate"))
num_prompt_list = to_list(params.get("num_prompts"))
max_concurrency_list = to_list(params.get("max_concurrency"))
max_len = max(len(qps_list), len(max_concurrency_list))
if len(num_prompt_list) == 1 and max_len > 1:
num_prompt_list = num_prompt_list * max_len
elif max_len == 1 and len(num_prompt_list) > 1:
if len(qps_list) == 1:
qps_list = qps_list * len(num_prompt_list)
if len(max_concurrency_list) == 1:
max_concurrency_list = max_concurrency_list * len(num_prompt_list)
max_len = max(len(qps_list), len(max_concurrency_list))
elif len(num_prompt_list) != max_len and max_len > 0:
raise ValueError("The number of prompts does not match the QPS or max_concurrency")
args = ["--host", host, "--port", str(port)]
exclude_keys = {
"request_rate",
"baseline",
"num_prompts",
"max_concurrency",
"num_warmups",
"task",
"enabled",
"eval_phase",
"trust_remote_code",
"expected_duplex_audio_turns_per_session",
}
for key, value in params.items():
if key in exclude_keys or value is None:
continue
arg_name = f"--{key.replace('_', '-')}"
if isinstance(value, bool) and value:
args.append(arg_name)
elif isinstance(value, dict):
json_str = json.dumps(value, ensure_ascii=False, separators=(",", ":"))
args.extend([arg_name, json_str])
elif not isinstance(value, bool):
args.extend([arg_name, str(value)])
for config in BENCHMARK_CONFIGS:
if config.get("test_name") != test_name:
continue
server_params = config.get("server_params") or {}
if server_params.get("trust_remote_code") or params.get("trust_remote_code"):
args.append("--trust-remote-code")
break
# QPS / request-rate sweep
for sweep_index, (qps, num_prompt) in enumerate(zip(qps_list, num_prompt_list)):
args = args + ["--request-rate", str(qps), "--num-prompts", str(num_prompt)]
result = run_benchmark(
args=args,
test_name=test_name,
flow=qps,
dataset_name=dataset_name,
num_prompt=num_prompt,
baseline_config=params.get("baseline"),
sweep_index=sweep_index,
random_input_len=params.get("random_input_len"),
random_output_len=params.get("random_output_len"),
resource_label=resource_label,
num_warmups=_resolve_num_warmups(params, default=2),
)
assert_result(result, params, num_prompt)
# concurrency test
for sweep_index, (concurrency, num_prompt) in enumerate(zip(max_concurrency_list, num_prompt_list)):
args = args + ["--max-concurrency", str(concurrency), "--num-prompts", str(num_prompt), "--request-rate", "inf"]
result = run_benchmark(
args=args,
test_name=test_name,
flow=concurrency,
dataset_name=dataset_name,
num_prompt=num_prompt,
baseline_config=params.get("baseline"),
sweep_index=sweep_index,
random_input_len=params.get("random_input_len"),
random_output_len=params.get("random_output_len"),
resource_label=resource_label,
num_warmups=_resolve_num_warmups(params, default=max(2, int(concurrency))),
)
assert_result(result, params, num_prompt)