[Edge][WP2] Finalize edge profile init knobs from init_profile; WP4 CPU emulation results
init_profile 20260909-112200 (1.7B, L20X, warm): default 152.0 s, eager_stage1 149.9 s, eager_both 110.9 s (first TTFA 15 s, no graphs), parallel_stage_init 94.6 s, edge 145.7 s. Edge profile now carries `orchestrator: parallel_stage_init: true`, applied by the deploy_profile selector (explicit values win); graphs stay on. hw_emulation 20260909-110229 (CPU venv, 0.6B, deploy_profile=auto): x86_cpu_avx2 (4c, fp32) TTFA 2.65 s RTF 11.9; x86_cpu_avx512 (8c) TTFA 0.68 s RTF 2.45; x86_cpu_amx (16c) TTFA 0.51 s RTF 1.72; arm64 logic-only same as avx2. Emulation rows now record the auto-detected hardware class. Adds tests/e2e/offline_inference/test_qwen3_tts_edge_profile.py (L3, one GPU). Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
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"engine_core_init": 15.890456199645996,
|
||||
"init_device": 3.3224265575408936,
|
||||
"load_model": 4.298788547515869,
|
||||
"load_weights": 4.256226301193237,
|
||||
"talker_mtp_init": 0.02964472770690918,
|
||||
"attn_prewarm": 4.0531158447265625e-06
|
||||
},
|
||||
"1": {
|
||||
"g3_device_lock": 9.393692016601562e-05,
|
||||
"g2_launch_lock_wait": 1.6689300537109375e-06,
|
||||
"g2_spawn_and_ready": 58.47568416595459,
|
||||
"proc_entry": 0.0,
|
||||
"engine_core_init": 11.900378704071045,
|
||||
"init_device": 2.9200496673583984,
|
||||
"load_model": 1.929183006286621,
|
||||
"load_weights": 1.9225208759307861,
|
||||
"code2wav_cudagraph": 7.700920104980469e-05,
|
||||
"talker_mtp_init": 4.291534423828125e-06,
|
||||
"attn_prewarm": 2.86102294921875e-06
|
||||
}
|
||||
},
|
||||
"total_s": 108.74093842506409
|
||||
},
|
||||
{
|
||||
"config": "parallel_stage_init",
|
||||
"returncode": 0,
|
||||
"wall_s": 153.031150653027,
|
||||
"cmd": [
|
||||
"/data/zhoutaichang/embedding_infer/.venvs/omni-cuda/bin/python",
|
||||
"/data/zhoutaichang/embedding_infer/vllm-omni/benchmarks/tts/stream_latency_bench.py",
|
||||
"--init-only",
|
||||
"--warmup",
|
||||
"1",
|
||||
"--model",
|
||||
"Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice",
|
||||
"--deploy-config",
|
||||
"/data/zhoutaichang/embedding_infer/vllm-omni/vllm_omni/deploy/qwen3_tts.yaml",
|
||||
"--output-dir",
|
||||
"/data/zhoutaichang/embedding_infer/vllm-omni/benchmarks/tts/edge_results/init_profile/20260909-112200/parallel_stage_init/bench",
|
||||
"--tag",
|
||||
"parallel_stage_init",
|
||||
"--parallel-stage-init",
|
||||
"--init-timeout",
|
||||
"1500",
|
||||
"--stage-init-timeout",
|
||||
"1200"
|
||||
],
|
||||
"deploy_config": "/data/zhoutaichang/embedding_infer/vllm-omni/vllm_omni/deploy/qwen3_tts.yaml",
|
||||
"init_s": 94.58078751433641,
|
||||
"ttfa_ms": 520.1878198422492,
|
||||
"gpu_mib": 42552,
|
||||
"phases": {
|
||||
"stages_init": 92.42109775543213,
|
||||
"g2_spawn": 0.01141667366027832,
|
||||
"g2_ready_wait": 135.4159436225891,
|
||||
"proc_entry": 0.0,
|
||||
"engine_core_init": 75.3825170993805,
|
||||
"init_device": 7.742107152938843,
|
||||
"load_model": 10.576136589050293,
|
||||
"load_weights": 10.54127049446106,
|
||||
"code2wav_cudagraph": 3.708811044692993,
|
||||
"talker_mtp_init": 0.0017044544219970703,
|
||||
"attn_prewarm": 0.018396377563476562,
|
||||
"cudagraph_capture": 2.863327980041504,
|
||||
"talker_mtp_capture": 26.89637327194214,
|
||||
"predictor_setup_compile": 12.268435001373291,
|
||||
"predictor_warmup": 12.259008407592773
|
||||
},
|
||||
"phases_by_stage": {
|
||||
"-": {
|
||||
"stages_init": 92.42109775543213
|
||||
},
|
||||
"0": {
|
||||
"g2_spawn": 0.005066633224487305,
|
||||
"g2_ready_wait": 87.45320534706116,
|
||||
"proc_entry": 0.0,
|
||||
"engine_core_init": 57.449283838272095,
|
||||
"init_device": 3.862210988998413,
|
||||
"load_model": 4.203946352005005,
|
||||
"load_weights": 4.183117389678955,
|
||||
"talker_mtp_init": 0.0016989707946777344,
|
||||
"attn_prewarm": 0.010425806045532227,
|
||||
"cudagraph_capture": 2.863327980041504,
|
||||
"talker_mtp_capture": 26.89637327194214,
|
||||
"predictor_setup_compile": 12.268435001373291,
|
||||
"predictor_warmup": 12.259008407592773
|
||||
},
|
||||
"1": {
|
||||
"g2_spawn": 0.006350040435791016,
|
||||
"g2_ready_wait": 47.962738275527954,
|
||||
"proc_entry": 0.0,
|
||||
"engine_core_init": 17.9332332611084,
|
||||
"init_device": 3.8798961639404297,
|
||||
"load_model": 6.372190237045288,
|
||||
"load_weights": 6.3581531047821045,
|
||||
"code2wav_cudagraph": 3.708811044692993,
|
||||
"talker_mtp_init": 5.4836273193359375e-06,
|
||||
"attn_prewarm": 0.007970571517944336
|
||||
}
|
||||
},
|
||||
"total_s": 92.42548394203186
|
||||
},
|
||||
{
|
||||
"config": "edge",
|
||||
"returncode": 0,
|
||||
"wall_s": 209.84598700003698,
|
||||
"cmd": [
|
||||
"/data/zhoutaichang/embedding_infer/.venvs/omni-cuda/bin/python",
|
||||
"/data/zhoutaichang/embedding_infer/vllm-omni/benchmarks/tts/stream_latency_bench.py",
|
||||
"--init-only",
|
||||
"--warmup",
|
||||
"1",
|
||||
"--model",
|
||||
"Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice",
|
||||
"--deploy-config",
|
||||
"/data/zhoutaichang/embedding_infer/vllm-omni/vllm_omni/deploy/edge/qwen3_tts.yaml",
|
||||
"--output-dir",
|
||||
"/data/zhoutaichang/embedding_infer/vllm-omni/benchmarks/tts/edge_results/init_profile/20260909-112200/edge/bench",
|
||||
"--tag",
|
||||
"edge",
|
||||
"--init-timeout",
|
||||
"1500",
|
||||
"--stage-init-timeout",
|
||||
"1200"
|
||||
],
|
||||
"deploy_config": "/data/zhoutaichang/embedding_infer/vllm-omni/vllm_omni/deploy/edge/qwen3_tts.yaml",
|
||||
"init_s": 145.6688683652319,
|
||||
"ttfa_ms": 511.6138099692762,
|
||||
"gpu_mib": 40938,
|
||||
"phases": {
|
||||
"stages_init": 143.48157787322998,
|
||||
"g3_device_lock": 0.0003292560577392578,
|
||||
"g2_launch_lock_wait": 8.344650268554688e-06,
|
||||
"g2_spawn_and_ready": 137.77231001853943,
|
||||
"proc_entry": 0.0,
|
||||
"engine_core_init": 51.35414981842041,
|
||||
"init_device": 7.537596225738525,
|
||||
"load_model": 12.739789247512817,
|
||||
"load_weights": 12.701488971710205,
|
||||
"talker_mtp_init": 0.0006916522979736328,
|
||||
"attn_prewarm": 0.02305459976196289,
|
||||
"cudagraph_capture": 1.7507147789001465,
|
||||
"talker_mtp_capture": 7.243081092834473,
|
||||
"predictor_setup_compile": 5.125781774520874,
|
||||
"predictor_warmup": 5.118142604827881,
|
||||
"code2wav_cudagraph": 5.841585874557495
|
||||
},
|
||||
"phases_by_stage": {
|
||||
"-": {
|
||||
"stages_init": 143.48157787322998
|
||||
},
|
||||
"0": {
|
||||
"g3_device_lock": 0.0001480579376220703,
|
||||
"g2_launch_lock_wait": 6.4373016357421875e-06,
|
||||
"g2_spawn_and_ready": 63.12420177459717,
|
||||
"proc_entry": 0.0,
|
||||
"engine_core_init": 30.913914680480957,
|
||||
"init_device": 3.751918315887451,
|
||||
"load_model": 4.1414031982421875,
|
||||
"load_weights": 4.120999574661255,
|
||||
"talker_mtp_init": 0.0006844997406005859,
|
||||
"attn_prewarm": 0.012444496154785156,
|
||||
"cudagraph_capture": 1.7507147789001465,
|
||||
"talker_mtp_capture": 7.243081092834473,
|
||||
"predictor_setup_compile": 5.125781774520874,
|
||||
"predictor_warmup": 5.118142604827881
|
||||
},
|
||||
"1": {
|
||||
"g3_device_lock": 0.0001811981201171875,
|
||||
"g2_launch_lock_wait": 1.9073486328125e-06,
|
||||
"g2_spawn_and_ready": 74.64810824394226,
|
||||
"proc_entry": 0.0,
|
||||
"engine_core_init": 20.440235137939453,
|
||||
"init_device": 3.785677909851074,
|
||||
"load_model": 8.59838604927063,
|
||||
"load_weights": 8.58048939704895,
|
||||
"code2wav_cudagraph": 5.841585874557495,
|
||||
"talker_mtp_init": 7.152557373046875e-06,
|
||||
"attn_prewarm": 0.010610103607177734
|
||||
}
|
||||
},
|
||||
"total_s": 143.48623204231262
|
||||
}
|
||||
],
|
||||
"table": "| config | init_s | ttfa_ms | gpu_mib | stages_init | g2_spawn_and_ready | engine_core_init | init_device | load_model | load_weights | predictor_setup_compile | predictor_warmup | cudagraph_capture | talker_mtp_capture | code2wav_cudagraph |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| default | 152.0 | 646.4 | 46272 | 149.9 | 144.5 | 66.2 | 6.1 | 9.0 | 8.9 | 11.6 | 11.6 | 2.8 | 27.4 | 3.6 |\n| eager_stage1 | 149.9 | 781.4 | 44148 | 147.8 | 142.8 | 67.1 | 5.9 | 6.5 | 6.4 | 11.4 | 11.4 | 4.4 | 29.1 | 0.0 |\n| eager_both | 110.9 | 14956.1 | 43938 | 108.7 | 103.5 | 27.8 | 6.2 | 6.2 | 6.2 | | | | | 0.0 |\n| parallel_stage_init | 94.6 | 520.2 | 42552 | 92.4 | | 75.4 | 7.7 | 10.6 | 10.5 | 12.3 | 12.3 | 2.9 | 26.9 | 3.7 |\n| edge | 145.7 | 511.6 | 40938 | 143.5 | 137.8 | 51.4 | 7.5 | 12.7 | 12.7 | 5.1 | 5.1 | 1.8 | 7.2 | 5.8 |"
|
||||
}
|
||||
@@ -29,6 +29,7 @@ import argparse
|
||||
import datetime as _dt
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
import shutil
|
||||
import subprocess
|
||||
import sys
|
||||
@@ -191,7 +192,13 @@ def run_case(case: Case, args: argparse.Namespace, out_dir: Path, passthrough: l
|
||||
rc = proc.returncode
|
||||
except subprocess.TimeoutExpired:
|
||||
rc = -1
|
||||
row: dict[str, Any] = {"case": case.name, "note": case.note, "returncode": rc, "cmd": cmd, "hardware_class": None}
|
||||
row: dict[str, Any] = {
|
||||
"case": case.name,
|
||||
"note": case.note,
|
||||
"returncode": rc,
|
||||
"cmd": cmd,
|
||||
"hardware_class": parse_hardware_class(case_dir / "stdout.log"),
|
||||
}
|
||||
files = sorted((case_dir / "bench").rglob("*.json"))
|
||||
if files:
|
||||
doc = json.loads(files[-1].read_text())
|
||||
@@ -208,6 +215,19 @@ def run_case(case: Case, args: argparse.Namespace, out_dir: Path, passthrough: l
|
||||
return row
|
||||
|
||||
|
||||
_AUTO_PROFILE_RE = re.compile(r"\[auto_profile\] (\S+) -> ")
|
||||
|
||||
|
||||
def parse_hardware_class(log_path: Path) -> str | None:
|
||||
"""Hardware class chosen by ``deploy_profile=auto`` as logged by auto_profile."""
|
||||
try:
|
||||
text = log_path.read_text(errors="replace")
|
||||
except OSError:
|
||||
return None
|
||||
m = _AUTO_PROFILE_RE.search(text)
|
||||
return m.group(1) if m else None
|
||||
|
||||
|
||||
def render_table(rows: list[dict[str, Any]]) -> str:
|
||||
cols = [
|
||||
"case",
|
||||
|
||||
@@ -0,0 +1,43 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
|
||||
"""E2E: Qwen3-TTS CustomVoice under the ``edge`` deploy profile.
|
||||
|
||||
The edge profile (``vllm_omni/deploy/edge/qwen3_tts.yaml``) runs both stages on
|
||||
one device with small batches, a ramped codec chunk schedule and concurrent
|
||||
stage init. This test drives the same profile YAML the ``deploy_profile="edge"``
|
||||
selector resolves to (the runner fixture always passes ``deploy_config``) and
|
||||
checks that audio is produced end to end. The chunk schedule itself is
|
||||
measured by ``benchmarks/tts/stream_latency_bench.py`` (stall_at_ttfa == 0).
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
|
||||
|
||||
import pytest
|
||||
|
||||
from tests.helpers.mark import hardware_test
|
||||
from tests.helpers.stage_config import get_deploy_config_path
|
||||
from vllm_omni.entrypoints.utils import profile_orchestrator_defaults, resolve_deploy_profile_path
|
||||
|
||||
MODEL = "Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice"
|
||||
EDGE_YAML = get_deploy_config_path("edge/qwen3_tts.yaml")
|
||||
|
||||
tts_edge_params = [
|
||||
pytest.param(
|
||||
(MODEL, EDGE_YAML, {"parallel_stage_init": True, "init_timeout": 1500, "stage_init_timeout": 900}),
|
||||
id="edge_profile",
|
||||
)
|
||||
]
|
||||
|
||||
|
||||
@pytest.mark.advanced_model
|
||||
@pytest.mark.tts
|
||||
@hardware_test(res={"cuda": "L4"}, num_cards=1)
|
||||
@pytest.mark.parametrize("omni_runner", tts_edge_params, indirect=True)
|
||||
def test_edge_profile_text_to_audio(omni_runner, offline_client) -> None:
|
||||
# The selector resolves the same file the runner was started with and
|
||||
# carries the profile's orchestrator defaults.
|
||||
assert os.path.realpath(resolve_deploy_profile_path(MODEL, "edge")) == os.path.realpath(EDGE_YAML)
|
||||
assert profile_orchestrator_defaults(EDGE_YAML).get("parallel_stage_init") is True
|
||||
offline_client.send_audio_speech_request({"input": "Edge profile end to end check.", "voice": "vivian"})
|
||||
@@ -15,11 +15,20 @@
|
||||
# * Both stages share logical device "0" (one GPU). Memory fractions are
|
||||
# what the two 0.6B/1.7B stages actually need with these limits, not the
|
||||
# server defaults; raise stage 1 if the KV-profile log reports OOM.
|
||||
# * ``enforce_eager`` / ``parallel_stage_init`` are intentionally not set
|
||||
# here yet; benchmarks/tts/init_profile.py results decide them (WP2).
|
||||
# * Init-time knobs come from benchmarks/tts/init_profile.py run
|
||||
# 20260909-112200 (1.7B, L20X, warm caches): default 152.0 s,
|
||||
# eager_stage1 149.9 s, eager_both 110.9 s but first-request TTFA 15 s
|
||||
# (no talker/predictor graphs), parallel_stage_init 94.6 s with unchanged
|
||||
# TTFA. So graphs stay on (``enforce_eager`` untouched) and both stages
|
||||
# initialise concurrently on the shared device.
|
||||
base_config: ../qwen3_tts.yaml
|
||||
async_chunk: true
|
||||
|
||||
# Orchestrator arguments carried by the profile (applied by the ``deploy_profile``
|
||||
# selector; explicit CLI/engine values win). Not part of the stage schema.
|
||||
orchestrator:
|
||||
parallel_stage_init: true
|
||||
|
||||
connectors:
|
||||
connector_of_shared_memory:
|
||||
name: SharedMemoryConnector
|
||||
|
||||
@@ -337,11 +337,41 @@ def apply_deploy_profile(model: str, args_dict: dict[str, Any]) -> dict[str, Any
|
||||
edge_path = deploy_path_for_profile(Path(default_path).stem, "edge")
|
||||
base = str(edge_path) if edge_path is not None else default_path
|
||||
args_dict["deploy_config"], _ = materialize_auto_deploy(model, base)
|
||||
_apply_profile_orchestrator_defaults(base, args_dict)
|
||||
return args_dict
|
||||
args_dict["deploy_config"] = resolve_deploy_profile_path(model, profile)
|
||||
_apply_profile_orchestrator_defaults(args_dict["deploy_config"], args_dict)
|
||||
return args_dict
|
||||
|
||||
|
||||
# Orchestrator-level settings a deploy profile may carry under a top-level
|
||||
# ``orchestrator:`` block. They are engine/CLI arguments, not stage config, so
|
||||
# ``load_deploy_config`` ignores the block and the profile selector applies it
|
||||
# here (explicit user values win; only unset/False defaults are replaced).
|
||||
PROFILE_ORCHESTRATOR_KEYS = frozenset({"parallel_stage_init", "init_timeout", "stage_init_timeout"})
|
||||
|
||||
|
||||
def profile_orchestrator_defaults(deploy_path: str | Path) -> dict[str, Any]:
|
||||
"""Return the ``orchestrator:`` block of a deploy YAML (only known keys)."""
|
||||
try:
|
||||
import yaml
|
||||
|
||||
with open(deploy_path, encoding="utf-8") as f:
|
||||
doc = yaml.safe_load(f) or {}
|
||||
except (OSError, ValueError):
|
||||
return {}
|
||||
block = doc.get("orchestrator") if isinstance(doc, dict) else None
|
||||
if not isinstance(block, dict):
|
||||
return {}
|
||||
return {k: v for k, v in block.items() if k in PROFILE_ORCHESTRATOR_KEYS}
|
||||
|
||||
|
||||
def _apply_profile_orchestrator_defaults(deploy_path: str | Path, args_dict: dict[str, Any]) -> None:
|
||||
for key, value in profile_orchestrator_defaults(deploy_path).items():
|
||||
if args_dict.get(key) in (None, False):
|
||||
args_dict[key] = value
|
||||
|
||||
|
||||
def resolve_model_config_path(model: str) -> str | None:
|
||||
"""Resolve the stage/deploy config file path from the model name.
|
||||
|
||||
|
||||
Reference in new issue
Block a user