[Perf][Ming-TTS] Add initial latent chunk for lower streaming TTFP (#5011)

Signed-off-by: LHXuuu <xulianhao.xlh@antgroup.com>
This commit is contained in:
LHXuuu authored and GitHub committed 2026-07-13 06:45:28 +08:00
1 parent d3c47efc4f
commit a28ded243f
7 files changed
+135 -8

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@@ -22,6 +22,7 @@ from .constants import (
DEFAULT_SIGMA,
DEFAULT_TEMPERATURE,
HISTORY_PATCH_SIZE,
INITIAL_LATENT_CHUNK_SIZE,
KEY_CFG,
KEY_CHUNK_ID,
KEY_DECODE_STEP,
@@ -239,6 +240,7 @@ class MingTTSConfig:
max_decode_steps: int = MAX_DECODE_STEPS
latent_chunk_size: int = LATENT_CHUNK_SIZE
initial_latent_chunk_size: int = INITIAL_LATENT_CHUNK_SIZE
latent_left_context: int = LATENT_LEFT_CONTEXT
text_eos_token_id: int = TEXT_EOS_TOKEN_ID
@@ -58,6 +58,7 @@ DEFAULT_TEMPERATURE = 0.0
# Connector / Stage-2 streaming defaults (runtime tuning)
LATENT_CHUNK_SIZE = 25
INITIAL_LATENT_CHUNK_SIZE = 4
LATENT_LEFT_CONTEXT = 0
MAX_DECODE_STEPS = 200
@@ -172,6 +172,9 @@ def validate_ming_tts_config(cfg: Any) -> None:
if cfg.latent_chunk_size <= 0:
raise ValueError(f"latent_chunk_size must be > 0, got {cfg.latent_chunk_size}.")
initial_latent_chunk_size = getattr(cfg, "initial_latent_chunk_size", 0)
if initial_latent_chunk_size < 0:
raise ValueError(f"initial_latent_chunk_size must be >= 0, got {initial_latent_chunk_size}.")
if cfg.latent_left_context < 0:
raise ValueError(f"latent_left_context must be >= 0, got {cfg.latent_left_context}.")
if cfg.max_decode_steps <= 0:
@@ -12,6 +12,7 @@ from vllm.logger import init_logger
from vllm_omni.data_entry_keys import CodesStruct, MetaStruct, OmniPayloadStruct
from vllm_omni.inputs.data import OmniTokensPrompt
from vllm_omni.model_executor.models.ming_tts.config_ming_tts import (
INITIAL_LATENT_CHUNK_SIZE,
KEY_CHUNK_ID,
KEY_REQUEST_ID,
LATENT_CHUNK_SIZE,
@@ -116,7 +117,7 @@ def _decode_stop_reason(value: Any) -> str | None:
return MING_STOP_REASON_BY_CODE.get(int(value))
def _get_async_chunk_config(transfer_manager: Any) -> tuple[int, int]:
def _get_async_chunk_config(transfer_manager: Any) -> tuple[int, int, int]:
connector = getattr(transfer_manager, "connector", None)
raw_cfg = getattr(connector, "config", {}) or {}
cfg = raw_cfg.get("extra", raw_cfg) if isinstance(raw_cfg, Mapping) else {}
@@ -131,9 +132,19 @@ def _get_async_chunk_config(transfer_manager: Any) -> tuple[int, int]:
LATENT_LEFT_CONTEXT,
)
chunk_size = int(cfg.get("latent_chunk_size", LATENT_CHUNK_SIZE))
initial_chunk_size = int(cfg.get("initial_latent_chunk_size", INITIAL_LATENT_CHUNK_SIZE))
left_context = int(cfg.get("latent_left_context", LATENT_LEFT_CONTEXT))
if chunk_size <= 0:
raise ValueError(f"Invalid Ming latent_chunk_size={chunk_size}")
if initial_chunk_size < 0:
raise ValueError(f"Invalid Ming initial_latent_chunk_size={initial_chunk_size}")
if initial_chunk_size > chunk_size:
logger.warning(
"Ming initial_latent_chunk_size=%d > latent_chunk_size=%d, clamping to latent_chunk_size.",
initial_chunk_size,
chunk_size,
)
initial_chunk_size = chunk_size
# Stage-2 VAE caches past_key_values and stream_state by request_id.
# Replaying left-context latents would double-feed cached decoder state.
if left_context != 0:
@@ -142,7 +153,7 @@ def _get_async_chunk_config(transfer_manager: Any) -> tuple[int, int]:
"Ming boundary continuity is handled by per-request decoder state cache, not "
f"latent replay. Got latent_left_context={left_context}."
)
return chunk_size, left_context
return chunk_size, initial_chunk_size, left_context
def _build_chunk_observability(
@@ -196,7 +207,7 @@ def llm2audio_vae_async_chunk(
if patch is not None:
transfer_manager.code_prompt_token_ids[request_id].append(patch)
chunk_size, _ = _get_async_chunk_config(transfer_manager)
chunk_size, initial_chunk_size, _ = _get_async_chunk_config(transfer_manager)
patches = transfer_manager.code_prompt_token_ids[request_id]
seen_patch_len = int(state.get("seen_patch_len", 0))
@@ -225,11 +236,17 @@ def llm2audio_vae_async_chunk(
)
return None
chunk_length = length % chunk_size
if chunk_length != 0 and not finished:
return None
emit_count = chunk_length if chunk_length != 0 else chunk_size
use_first_chunk = 0 < initial_chunk_size < chunk_size and seen_patch_len == 0
if finished:
emit_count = length
elif use_first_chunk:
if length < initial_chunk_size:
return None
emit_count = initial_chunk_size
else:
if length < chunk_size:
return None
emit_count = chunk_size
emit_patches = list(new_patches[:emit_count])
state["seen_patch_len"] = seen_patch_len + len(emit_patches)
latent_patches = torch.stack(emit_patches, dim=0)