[Model] Consolidate MOSS-TTS Local 1.5 deploy configuration

docs/configuration/stage_configs.md

@@ -626,52 +626,32 @@ the intended GPU, and warm the complete pipeline before measuring performance.
 
 ## MOSS-TTS Local 1.5 with Model Runner V2
 
-`OpenMOSS-Team/MOSS-TTS-Local-Transformer-v1.5` defaults to CUDA MRV2 using
-the shared runtime introduced for Qwen3-TTS. To select the C64 profile without
-MPS explicitly:
+`OpenMOSS-Team/MOSS-TTS-Local-Transformer-v1.5` uses one default deployment,
+`moss_tts_local.yaml`. Omitting `--deploy-config` selects the same file:
 
 ```bash
 vllm serve OpenMOSS-Team/MOSS-TTS-Local-Transformer-v1.5 --omni \
-  --deploy-config vllm_omni/deploy/moss_tts_local_mrv2.yaml
+  --deploy-config vllm_omni/deploy/moss_tts_local.yaml
 ```
 
-This profile inherits the batching, codec graph buckets and 1-frame/15-frame
-chunk geometry from `moss_tts_local_v1.yaml`, and selects V2 for both the Local
-Talker and codec stages. The Local depth predictor exposes the MRV2 `mtp`
-capabilities while retaining its V1 `talker_mtp` implementation, sampling
-defaults and explicit request-seed handling. Codec chunks use the native data
-plane; the internal Talker retains its final-only orchestrator output policy.
-
-On CUDA, the profile bounds stage-0 prefill work to 512 tokens per iteration.
-The codec retains its own CUDA Graph capture/replay, including batch buckets
-through 64, but disables Inductor compilation (`compilation_config.mode: 0`)
-of the stateful decoder to avoid lengthy compilation at startup. This is
-independent of `enforce_eager`; the profile keeps `enforce_eager: false`.
-It explicitly selects `cudagraph_mode: FULL`, which works without Inductor;
-the default `FULL_AND_PIECEWISE` would otherwise normalize to `NONE` and clear
-the codec's capture buckets when compilation is disabled.
-
-For sustained C128 serving on a large-memory CUDA GPU, select the
-separate `moss_tts_local_mrv2_high_concurrency.yaml` profile. It uses 128
-stream slots per stage, a 32 GiB Talker KV budget, and the codec's
-`triton_slot` backend with Inductor compilation and codec-owned CUDA graphs.
-The bounded KV budget leaves room for codec state and graphs; it does not
-guarantee that 128 maximum-length prompts fit simultaneously. See the
-[MOSS recipe](gh-file:recipes/OpenMOSS/MOSS-TTS.md#local-15-mrv2-and-slot-attention)
-for activation, backend comparisons, memory requirements and benchmark commands.
-
-Omitting `--deploy-config` selects the C128 system profile on CUDA GPUs with
-at least 140 GiB memory and MPS available. Smaller GPUs or a failed memory
-query use C64 with utilization-based memory budgets; missing MPS selects
-C64 without MPS. Selecting `moss_tts_local.yaml` explicitly uses C64 with MPS.
-Use `moss_tts_local_v1.yaml` for the previous V1 deployment.
-NPU, XPU, ROCm and MUSA overrides retain V1. These profiles do not enable
-MRV2 for MOSS Delay, Realtime or Nano. Local 1.5 outputs 48 kHz stereo audio;
-set `VLLM_OMNI_BENCH_AUDIO_SAMPLE_RATE=48000` and
-`VLLM_OMNI_BENCH_AUDIO_CHANNELS=2` when benchmarking raw PCM.
-
-Event-driven orchestration remains independently selectable with
-`VLLM_OMNI_EVENT_DRIVEN_ORCH=0` or `1`. Keep the runner and deployment identical
-when comparing these modes. Model-runner selection does not change the
-orchestration default. MPS, codec dispatch and graph settings belong to the
-deployment profile; the automatic CUDA default selects MPS when available.
+On CUDA, both stages use MRV2 and 128 stream slots on one GPU, with full-quota
+private MPS, a 32 GiB Talker KV budget, prefix caching and Triton attention.
+The Talker bounds prefill to 512 tokens and uses FULL CUDA graphs. The codec
+uses `triton_slot`, Inductor compilation, codec-owned graphs through batch
+128 and dispatch coalescing at 16 rows or 6 ms. Initial and steady chunks
+remain 1 and 15 codec frames; the Stage0 first-frame path follows the Talker
+capture sizes and safe-request/executor requirements.
+
+These CUDA settings are validated on H200 and require memory for both stages
+and their graph/state pools, plus `nvidia-cuda-mps-control` on `PATH`. Smaller
+GPUs and services without MPS should provide deployment overrides for the
+capacities, capture sizes, KV budget and `platforms.cuda.cuda_mps`. The
+pipeline no longer changes configuration based on a GPU-memory or MPS probe.
+
+Other platforms use V1 in the same file; NPU keeps its codec graph override.
+The 2/3-NPU files retain their distinct multi-device topology. These changes
+do not enable MRV2 for MOSS Delay, Realtime or Nano. Local 1.5 emits 48 kHz
+stereo; benchmark raw PCM with `VLLM_OMNI_BENCH_AUDIO_SAMPLE_RATE=48000` and
+`VLLM_OMNI_BENCH_AUDIO_CHANNELS=2`. See the
+[MOSS recipe](gh-file:recipes/OpenMOSS/MOSS-TTS.md#local-15-unified-deployment-and-slot-attention)
+for the execution path and configuration overrides.

examples/online_serving/text_to_speech/README.md

@@ -569,15 +569,15 @@ python examples/online_serving/text_to_speech/ming_flash_omni_tts/speech_client.
 
 ## MOSS-TTS Local Transformer v1.5
 
-For a single H200, the optional
-[`moss_tts_local_h200.yaml`](../../../vllm_omni/deploy/moss_tts_local_h200.yaml)
+For a single H200, the default
+[`moss_tts_local.yaml`](../../../vllm_omni/deploy/moss_tts_local.yaml)
 deployment places both the talker and codec on logical GPU 0:
 
 ```bash
 CUDA_VISIBLE_DEVICES=0 VLLM_OMNI_EVENT_DRIVEN_ORCH=1 \
     vllm serve OpenMOSS-Team/MOSS-TTS-Local-Transformer-v1.5 \
     --omni --trust-remote-code \
-    --deploy-config vllm_omni/deploy/moss_tts_local_h200.yaml \
+    --deploy-config vllm_omni/deploy/moss_tts_local.yaml \
     --disable-log-stats
 ```
 
@@ -586,18 +586,18 @@ Run from the repository root and select an available physical GPU with
 disables detailed per-request statistics logging to reduce CPU overhead at
 high concurrency. Remove `--disable-log-stats` when those statistics are needed;
 keep these settings identical when comparing performance.
-This preset configures 256 request slots per stage,
+This preset configures 128 request slots per stage,
 a 32 GiB talker KV cache, talker CUDA Graph buckets through 512 scheduled tokens,
-and codec batch buckets through 256. It requires more than 80 GiB of GPU memory;
-request capacity also depends on input and generated lengths. The default
-[`moss_tts_local.yaml`](../../../vllm_omni/deploy/moss_tts_local.yaml) remains
-available for smaller deployments.
-
-The preset enables the optional codec backend with
-`hf_overrides.codec_attention_backend: triton` on stage 1. It preserves the
-streaming ring-cache mask and uses BF16 attention with 64-dimensional heads;
-other attention shapes use PyTorch SDPA. Set the backend to `sdpa` to use the
-default attention implementation. Codec terminal tails share execution only
+and codec batch buckets through 128. It requires room for both stages and
+their graph/state pools, plus `nvidia-cuda-mps-control` on `PATH`.
+For smaller deployments, override the capacities, graph buckets, KV budget
+and MPS setting in a custom deploy file.
+
+The preset enables the codec backend with
+`hf_overrides.codec_attention_backend: triton_slot` on stage 1, accessing
+request-slot ring state directly with BF16 attention. Set the backend to
+`sdpa` in a custom deploy to compare with the PyTorch attention path.
+Codec terminal tails share execution only
 when they already map to the same padded CUDA Graph; returned audio retains
 each request's actual length.