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[CI/Build] Resolve hardware-nested perf baselines per concurrency sweep (#5858)

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Signed-off-by: wangyu <410167048@qq.com>
Co-authored-by: amy-why-3459 <wuhaiyan17@huawei.com>
This commit is contained in:
wangyuandamy-why-3459 authored and GitHub committed 2026-08-07 16:54:38 +08:00
1 parent 1be47550d0
commit 0a12ac52ee
33 files changed
+1207 -978
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tests/dfx/perf/scripts/run_diffusion_benchmark.py
+32 -102
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@@ -13,8 +13,6 @@ A config JSON file may be passed via --test-config-file. If omitted, every ``*.j
pytest run_diffusion_benchmark.py -m "diffusion"
pytest run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_vllm_omni.json
Optional: ``--assert-baseline`` compares metrics to the ``baseline`` block in each benchmark entry (default: off).
Optional JSON field ``mark`` is applied as pytest marks on that case via
``pytest.param`` (e.g. ``"mark": [{"hardware_marks": {"res": {"cuda": "H100"}, "num_cards": 1}}, "full_model", "diffusion"]``).
@@ -46,6 +44,7 @@ from tests.dfx.conftest import (
get_runtime_resource_label,
hardware_json_value,
is_diffusion_perf_config,
resolve_baseline_for_sweep,
resolve_pytest_marks,
resource_label_for_filename,
)
@@ -274,11 +273,6 @@ def _write_result_record(record: dict[str, Any]) -> Path:
return target
def _append_to_aggregated_file(record: dict[str, Any]) -> None:
"""Backward-compatible wrapper; prefer :func:`_write_result_record`."""
_write_result_record(record)
_server_lock = threading.Lock()
# ---------------------------------------------------------------------------
@@ -355,6 +349,17 @@ def _kill_process_tree(pid: int) -> None:
# ---------------------------------------------------------------------------
def _resolve_offline_model(model: str) -> str:
"""Under HF_HUB_OFFLINE, resolve a HF repo id to its local snapshot dir."""
import huggingface_hub
if not model or os.path.isdir(model) or not huggingface_hub.constants.HF_HUB_OFFLINE:
return model
from huggingface_hub import snapshot_download
return snapshot_download(model, local_files_only=True)
class DiffusionServer:
"""Start a vLLM-Omni diffusion model server as a subprocess.
@@ -372,7 +377,7 @@ class DiffusionServer:
port: int | None = None,
) -> None:
self.server_cfg: dict[str, Any] = server_cfg
self.model = server_cfg["model"]
self.model = _resolve_offline_model(server_cfg["model"])
self.serve_args = server_cfg["serve_args"]
self.host = "127.0.0.1"
self.port = port if port is not None else get_open_port(self.host)
@@ -667,7 +672,7 @@ def run_benchmark(
with tempfile.NamedTemporaryFile(mode="w", suffix=".json", prefix="diffusion_bench_tmp_", delete=False) as tmp:
tmp_result_file = Path(tmp.name)
exclude_keys = {"baseline", "dataset", "task", "name", "skip-performance-assertion"}
exclude_keys = {"baseline", "dataset", "task", "name"}
cmd = [
sys.executable,
@@ -761,12 +766,17 @@ def run_benchmark(
completed = metrics.get("completed_requests", metrics.get("completed", 0))
failed = metrics.get("failed_requests", metrics.get("failed", 0))
# Persist sweep-resolved baseline from params (already narrowed in _build_run_params).
baseline = params.get("baseline") or {}
metrics["baseline"] = baseline
record: dict[str, Any] = {
"test_name": test_name,
"endpoint": endpoint,
"timestamp": timestamp,
"server_params": server_cfg.get("server_params"),
"benchmark_params": params,
"baseline": baseline,
"result": metrics,
"log_file": str(log_file),
"Model": model,
@@ -816,70 +826,14 @@ def run_benchmark(
# ---------------------------------------------------------------------------
def _resolve_baseline_value(
baseline_raw: Any,
*,
sweep_index: int | None,
max_concurrency: Any = None,
request_rate: Any = None,
) -> Any:
"""Pick the baseline threshold for this sweep step."""
if baseline_raw is None:
return 100000
if isinstance(baseline_raw, dict):
if max_concurrency is not None:
for key in (max_concurrency, str(max_concurrency)):
if key in baseline_raw:
return baseline_raw[key]
if request_rate is not None:
for key in (request_rate, str(request_rate)):
if key in baseline_raw:
return baseline_raw[key]
raise KeyError(
f"baseline dict has no key for max_concurrency={max_concurrency!r} "
f"or request_rate={request_rate!r}; keys={list(baseline_raw.keys())!r}"
)
if isinstance(baseline_raw, (list, tuple)):
if sweep_index is None:
raise ValueError("sweep_index is required when baseline is a list or tuple")
return baseline_raw[sweep_index]
return baseline_raw
def assert_result(
result: dict[str, Any],
params: dict[str, Any],
num_prompts: int,
*,
sweep_index: int | None = None,
max_concurrency: Any = None,
request_rate: Any = None,
assert_baseline: bool = True,
) -> None:
"""Assert that benchmark metrics satisfy the configured baselines."""
"""Assert that the expected number of requests completed."""
completed = result.get("completed_requests", result.get("completed", 0))
assert completed == num_prompts, f"Expected {num_prompts} completed requests, got {completed}"
if not assert_baseline:
return
if params.get("skip-performance-assertion", False):
print("Skipping performance assertions.")
return
for metric, baseline_raw in params.get("baseline", {}).items():
current = result.get(metric)
assert current is not None, f"Metric '{metric}' not found in result: {list(result.keys())}"
threshold = _resolve_baseline_value(
baseline_raw,
sweep_index=sweep_index,
max_concurrency=max_concurrency,
request_rate=request_rate,
)
if "throughput" in metric:
assert current >= threshold, f"{metric}: {current:.4f} < baseline {threshold}"
else:
assert current <= threshold, f"{metric}: {current:.4f} > baseline {threshold}"
def _default_benchmark_endpoint_for_task(task: str) -> str:
"""Return the default client-side benchmark endpoint for a diffusion task."""
@@ -921,15 +875,11 @@ def _build_run_params(
if max_concurrency is not None:
run_params["max-concurrency"] = max_concurrency
if "baseline" in params:
run_params["baseline"] = {
metric: _resolve_baseline_value(
baseline_raw,
sweep_index=sweep_index,
max_concurrency=max_concurrency,
request_rate=request_rate,
)
for metric, baseline_raw in params["baseline"].items()
}
# Keep all hardware buckets; pick the metric value for this sweep step only.
run_params["baseline"] = resolve_baseline_for_sweep(
params.get("baseline"),
sweep_index=sweep_index,
)
return run_params
@@ -952,36 +902,30 @@ def _iter_sweep_runs(params: dict[str, Any]) -> list[dict[str, Any]]:
sweep_runs: list[dict[str, Any]] = []
for i, (request_rate, num_prompts) in enumerate(zip(request_rate_list, num_prompt_list)):
for sweep_index, (request_rate, num_prompts) in enumerate(zip(request_rate_list, num_prompt_list)):
sweep_runs.append(
{
"params": _build_run_params(
params,
request_rate=request_rate,
num_prompts=num_prompts,
sweep_index=i,
sweep_index=sweep_index,
),
"num_prompts": num_prompts,
"sweep_index": i,
"request_rate": request_rate,
"max_concurrency": None,
}
)
for i, (max_concurrency, num_prompts) in enumerate(zip(max_concurrency_list, num_prompt_list)):
for sweep_index, (max_concurrency, num_prompts) in enumerate(zip(max_concurrency_list, num_prompt_list)):
sweep_runs.append(
{
"params": _build_run_params(
params,
max_concurrency=max_concurrency,
num_prompts=num_prompts,
sweep_index=i,
request_rate="inf",
sweep_index=sweep_index,
),
"num_prompts": num_prompts,
"sweep_index": i,
"request_rate": None,
"max_concurrency": max_concurrency,
}
)
@@ -992,11 +936,9 @@ def _iter_sweep_runs(params: dict[str, Any]) -> list[dict[str, Any]]:
"params": _build_run_params(
params,
num_prompts=default_num_prompts,
sweep_index=0,
),
"num_prompts": default_num_prompts,
"sweep_index": None,
"request_rate": None,
"max_concurrency": None,
}
)
@@ -1012,14 +954,12 @@ def _iter_sweep_runs(params: dict[str, Any]) -> list[dict[str, Any]]:
paired_benchmark_params,
indirect=["diffusion_server", "benchmark_params"],
)
def test_diffusion_performance_benchmark(diffusion_server, benchmark_params, request):
def test_diffusion_performance_benchmark(diffusion_server, benchmark_params):
"""Run the diffusion performance benchmark and verify request completion.
One server is started per unique parallel configuration (module scope).
For each server, all benchmark parameter sets defined in the config JSON
are executed sequentially; metrics are recorded to the aggregated result file.
Pass ``--assert-baseline`` to compare ``throughput_qps``, ``latency_mean``, etc. to the JSON ``baseline`` block.
"""
test_name = benchmark_params["test_name"]
params = benchmark_params["params"]
@@ -1062,14 +1002,4 @@ def test_diffusion_performance_benchmark(diffusion_server, benchmark_params, req
print(f"\n Aggregated results: {_aggregated_result_file_for_source(str(source))}")
print("=" * 60)
assert_baseline = request.config.getoption("--assert-baseline", default=False)
assert_result(
result,
params,
sweep_run["num_prompts"],
sweep_index=sweep_run["sweep_index"],
max_concurrency=sweep_run["max_concurrency"],
request_rate=sweep_run["request_rate"],
assert_baseline=assert_baseline,
)
assert_result(result, sweep_run["num_prompts"])