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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 |" +} \ No newline at end of file diff --git a/benchmarks/tts/hw_emulation.py b/benchmarks/tts/hw_emulation.py index 24ba953e7..b18d06d06 100644 --- a/benchmarks/tts/hw_emulation.py +++ b/benchmarks/tts/hw_emulation.py @@ -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", diff --git a/tests/e2e/offline_inference/test_qwen3_tts_edge_profile.py b/tests/e2e/offline_inference/test_qwen3_tts_edge_profile.py new file mode 100644 index 000000000..46105b954 --- /dev/null +++ b/tests/e2e/offline_inference/test_qwen3_tts_edge_profile.py @@ -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"}) diff --git a/vllm_omni/deploy/edge/qwen3_tts.yaml b/vllm_omni/deploy/edge/qwen3_tts.yaml index fbff8b975..830d8fd0d 100644 --- a/vllm_omni/deploy/edge/qwen3_tts.yaml +++ b/vllm_omni/deploy/edge/qwen3_tts.yaml @@ -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 diff --git a/vllm_omni/entrypoints/utils.py b/vllm_omni/entrypoints/utils.py index 2bfd755e1..d2cbbbb1c 100644 --- a/vllm_omni/entrypoints/utils.py +++ b/vllm_omni/entrypoints/utils.py @@ -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.