Output:
Chunk ID: d54610
Wall time: 5.0009 seconds
Process running with session ID 97160
Original token count: 1400
Output:
Total output lines: 52

[2026-02-18 19:00:35] INFO tts_runtime.py:183: Loading SparkTTS model with vLLM...
The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored.
The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored.
🚀 Initializing Spark TTS Model
📁 Model path: /home/ubuntu/veenaModal/models/spark_tts_4speaker/LLM
🗣  Speakers: 12 predefined (lipakshi, vardan, reet, Nandini, krishna, anika, adarsh, Nilay, Aarvi, Asha, Bittu, Mira)
🎭 Emotions: 10 tags in [bracket] format

📝 Loading tokenizer from model...
✅ Tokenizer loaded: 165168 tokens

🔍 Validating 12 speakers for Spark TTS...
  lipakshi → <|speaker_0|>: 7 tokens
  vardan → <|speaker_1|>: 7 tokens
  reet → <|speaker_2|>: 7 tokens
  Nandini → <|speaker_3|>: 7 tokens
  krishna → <|speaker_4|>: 7 tokens
  anika → <|speaker_5|>: 7 tokens
  adarsh → <|speaker_6|>: 7 tokens
  Nilay → <|speaker_7|>: 7 tokens
  Aarvi → <|speaker_8|>: 7 tokens
  Asha → <|speaker_9|>: 7 tokens
  Bittu → <|speaker_10|>: 8 tokens
  Mira → <|speaker_11|>: 8 tokens
✅ All 12 speakers validated with mapping

🔍 Validating 10 emotion tags for Spark TTS...
✅ All 10 emotion tags validated

📋 Sample emotion tags:
  [angry]: 1 tokens
  [curious]: 1 tokens
  [excited]: 1 tokens
  [giggle]: 1 tokens
  [laughs harder]: 1 tokens
  ...

🔧 Initializing vLLM engine with optimizations...
  - dtype: bfloat16
  - max_model_len: 4096
  - gpu_memory_utilization: 0.25
  - enable_prefix_caching: True
  - enforce_eager (disable CUDA graphs): False
INFO 02-18 19:00:36 [model.py:541] Resolved architecture: Qwen2ForCausalLM
INFO 02-18 19:00:36 [model.py:1561] Using max model len 4096
INFO 02-18 19:00:36 [scheduler.py:226] Chunked prefill is enabled with max_num_batched_tokens=4096.
INFO 02-18 19:00:36 [vllm.py:624] Asynchronous scheduling is enabled.
The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored.
(EngineCore_DP0 pid=118309) INFO 02-18 19:00:37 [core.py:96] Initializing a V1 LLM engine (v0.15.1) with config: model='/home/ubuntu/veenaModal/models/spark_tts_4speaker/LLM', speculative_config=None, tokenizer='/home/ubuntu/veenaModal/models/spark_tts_4speaker/LLM', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype…200 tokens truncated…alse, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=0, served_model_name=/home/ubuntu/veenaModal/models/spark_tts_4speaker/LLM, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': <CompilationMode.VLLM_COMPILE: 3>, 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['none'], 'splitting_ops': ['vllm::unified_attention', 'vllm::unified_attention_with_output', 'vllm::unified_mla_attention', 'vllm::unified_mla_attention_with_output', 'vllm::mamba_mixer2', 'vllm::mamba_mixer', 'vllm::short_conv', 'vllm::linear_attention', 'vllm::plamo2_mamba_mixer', 'vllm::gdn_attention_core', 'vllm::kda_attention', 'vllm::sparse_attn_indexer', 'vllm::rocm_aiter_sparse_attn_indexer', 'vllm::unified_kv_cache_update'], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [4096], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': <CUDAGraphMode.FULL_AND_PIECEWISE: (2, 1)>, 'cudagraph_num_of_warmups': 1, 'cudagraph_capture_sizes': [1, 2, 4, 8, 16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96, 104, 112, 120, 128, 136, 144, 152, 160, 168, 176, 184, 192, 200, 208, 216, 224, 232, 240, 248, 256], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': True, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False}, 'max_cudagraph_capture_size': 256, 'dynamic_shapes_config': {'type': <DynamicShapesType.BACKED: 'backed'>, 'evaluate_guards': False, 'assume_32_bit_indexing': True}, 'local_cache_dir': None, 'static_all_moe_layers': []}
(EngineCore_DP0 pid=118309) INFO 02-18 19:00:39 [parallel_state.py:1212] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://216.81.248.184:40425 backend=nccl
(EngineCore_DP0 pid=118309) INFO 02-18 19:00:39 [parallel_state.py:1423] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A
(EngineCore_DP0 pid=118309) INFO 02-18 19:00:40 [gpu_model_runner.py:4033] Starting to load model /home/ubuntu/veenaModal/models/spark_tts_4speaker/LLM...


# Collaboration Mode: Default

You are now in Default mode. Any previous instructions for other modes (e.g. Plan mode) are no longer active.

Your active mode changes only when new developer instructions with a different `<collaboration_mode>...</collaboration_mode>` change it; user requests or tool descriptions do not change mode by themselves. Known mode names are Default and Plan.

## request_user_input availability

The `request_user_input` tool is unavailable in Default mode. If you call it while in Default mode, it will return an error.

If a decision is necessary and cannot be discovered from local context, ask the user directly. However, in Default mode you should strongly prefer executing the user's request rather than stopping to ask questions.

Summary: auto

## Skills
A skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and file path so you can open the source for full instructions when using a specific skill.
### Available skills
- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /home/ubuntu/.codex/skills/.system/skill-creator/SKILL.md)
- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /home/ubuntu/.codex/skills/.system/skill-installer/SKILL.md)
### How to use skills
- Discovery: The list above is the skills available in this session (name + description + file path). Skill bodies live on disk at the listed paths.
- Trigger rules: If the user names a skill (with `$SkillName` or plain text) OR the task clearly matches a skill's description shown above, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned.
- Missing/blocked: If a named skill isn't in the list or the path can't be read, say so briefly and continue with the best fallback.
- How to use a skill (progressive disclosure):
  1) After deciding to use a skill, open its `SKILL.md`. Read only enough to follow the workflow.
  2) When `SKILL.md` references relative paths (e.g., `scripts/foo.py`), resolve them relative to the skill directory listed above first, and only consider other paths if needed.
  3) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything.
  4) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.
  5) If `assets/` or templates exist, reuse them instead of recreating from scratch.
- Coordination and sequencing:
  - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.
  - Announce which skill(s) you're using and why (one short line). If you skip an obvious skill, say why.
- Context hygiene:
  - Keep context small: summarize long sections instead of pasting them; only load extra files when needed.
  - Avoid deep reference-chasing: prefer opening only files directly linked from `SKILL.md` unless you're blocked.
  - When variants exist (frameworks, providers, domains), pick only the relevant reference file(s) and note that choice.
- Safety and fallback: If a skill can't be applied cleanly (missing files, unclear instructions), state the issue, pick the next-best approach, and continue.


Output:
Chunk ID: 426c9d
Wall time: 5.0006 seconds
Process running with session ID 97160
Original token count: 535
Output:
(EngineCore_DP0 pid=118309) INFO 02-18 19:00:40 [cuda.py:364] Using FLASH_ATTN attention backend out of potential backends: ('FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION')
(EngineCore_DP0 pid=118309) <frozen importlib._bootstrap_external>:1184: FutureWarning: The cuda.cudart module is deprecated and will be removed in a future release, please switch to use the cuda.bindings.runtime module instead.
(EngineCore_DP0 pid=118309) <frozen importlib._bootstrap_external>:1184: FutureWarning: The cuda.nvrtc module is deprecated and will be removed in a future release, please switch to use the cuda.bindings.nvrtc module instead.
(EngineCore_DP0 pid=118309)
Loading safetensors checkpoint shards:   0% Completed | 0/1 [00:00<?, ?it/s]
(EngineCore_DP0 pid=118309)
Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  1.13it/s]
(EngineCore_DP0 pid=118309)
Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  1.13it/s]
(EngineCore_DP0 pid=118309)
(EngineCore_DP0 pid=118309) INFO 02-18 19:00:41 [default_loader.py:291] Loading weights took 0.97 seconds
(EngineCore_DP0 pid=118309) INFO 02-18 19:00:42 [gpu_model_runner.py:4130] Model loading took 0.95 GiB memory and 1.496705 seconds
(EngineCore_DP0 pid=118309) INFO 02-18 19:00:47 [backends.py:812] Using cache directory: /home/ubuntu/.cache/vllm/torch_compile_cache/f3ff13511c/rank_0_0/backbone for vLLM's torch.compile
(EngineCore_DP0 pid=118309) INFO 02-18 19:00:47 [backends.py:872] Dynamo bytecode transform time: 4.28 s
(EngineCore_DP0 pid=118309) INFO 02-18 19:00:50 [backends.py:267] Directly load the compiled graph(s) for compile range (1, 4096) from the cache, took 0.658 s
(EngineCore_DP0 pid=118309) INFO 02-18 19:00:50 [monitor.py:34] torch.compile takes 4.94 s in total
(EngineCore_DP0 pid=118309) INFO 02-18 19:00:51 [gpu_worker.py:356] Available KV cache memory: 18.06 GiB
(EngineCore_DP0 pid=118309) INFO 02-18 19:00:51 [kv_cache_utils.py:1307] GPU KV cache size: 1,577,712 tokens
(EngineCore_DP0 pid=118309) INFO 02-18 19:00:51 [kv_cache_utils.py:1312] Maximum concurrency for 4,096 tokens per request: 385.18x


Summary: auto


Output:
Chunk ID: 740ae4
Wall time: 5.0011 seconds
Process running with session ID 97160
Original token count: 634
Output:
(EngineCore_DP0 pid=118309)
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):   0%|          | 0/35 [00:00<?, ?it/s]
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  11%|█▏        | 4/35 [00:00<00:00, 32.94it/s]
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  23%|██▎       | 8/35 [00:00<00:00, 35.39it/s]
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  34%|███▍      | 12/35 [00:00<00:00, 36.45it/s]
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  46%|████▌     | 16/35 [00:00<00:00, 36.79it/s]
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  57%|█████▋    | 20/35 [00:00<00:00, 36.85it/s]
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  69%|██████▊   | 24/35 [00:00<00:00, 36.75it/s]
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  80%|████████  | 28/35 [00:00<00:00, 36.53it/s]
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  91%|█████████▏| 32/35 [00:00<00:00, 34.82it/s]
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE): 100%|██████████| 35/35 [00:00<00:00, 35.02it/s]
(EngineCore_DP0 pid=118309)
Capturing CUDA graphs (decode, FULL):   0%|          | 0/19 [00:00<?, ?it/s]
Capturing CUDA graphs (decode, FULL):  16%|█▌        | 3/19 [00:00<00:00, 29.51it/s]
Capturing CUDA graphs (decode, FULL):  37%|███▋      | 7/19 [00:00<00:00, 33.65it/s]
Capturing CUDA graphs (decode, FULL):  58%|█████▊    | 11/19 [00:00<00:00, 34.75it/s]
Capturing CUDA graphs (decode, FULL):  79%|███████▉  | 15/19 [00:00<00:00, 35.07it/s]
Capturing CUDA graphs (decode, FULL): 100%|██████████| 19/19 [00:00<00:00, 35.74it/s]
Capturing CUDA graphs (decode, FULL): 100%|██████████| 19/19 [00:00<00:00, 34.93it/s]
(EngineCore_DP0 pid=118309) INFO 02-18 19:00:53 [gpu_model_runner.py:5063] Graph capturing finished in 2 secs, took 0.28 GiB
(EngineCore_DP0 pid=118309) INFO 02-18 19:00:53 [core.py:272] init engine (profile, create kv cache, warmup model) took 10.78 seconds
[2026-02-18 19:00:54] INFO tts_runtime.py:192: Initializing prompt builder...
[2026-02-18 19:00:54] INFO tts_runtime.py:199: Initializing BiCodec decoder...
/home/ubuntu/.local/lib/python3.10/site-packages/torch/nn/utils/weight_norm.py:144: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`.
  WeightNorm.apply(module, name, dim)


Output:
Chunk ID: 1edad2
Wall time: 5.0003 seconds
Process running with session ID 97160
Original token count: 127
Output:
[2026-02-18 19:01:02] INFO tts_runtime.py:209: Initializing TTS pipeline...
[2026-02-18 19:01:02] INFO tts_runtime.py:220: Initializing streaming pipeline...
[2026-02-18 19:01:02] INFO long_text_processor.py:112: LongTextProcessor initialized
[2026-02-18 19:01:02] INFO tts_runtime.py:277: ✅ TTS runtime initialized in 32172ms
[2026-02-18 19:01:02] INFO tts_runtime.py:278:    Model version: LLM
[2026-02-18 19:01:02] INFO tts_runtime.py:289: Speaker globals cache startup precompute skipped (disabled)


Output:
Chunk ID: 71dd4a
Wall time: 2.0731 seconds
Process exited with code 0
Original token count: 1250
Output:
Total output lines: 87

✅ vLLM engine initialized successfully!

🎉 Spark TTS Model ready for inference!

🎵 Loading BiCodec audio tokenizer from /home/ubuntu/veenaModal/models/spark_tts_4speaker...
   Device: cuda
Missing tensor: mel_transformer.spectrogram.window
Missing tensor: mel_transformer.mel_scale.fb
✅ BiCodec decoder initialized (sample rate: 16000Hz)
   ℹ  Micro-batching enabled (workers=2, scale_mode=dynamic, scale_pending=56, max_batch=16, timeout_ms=6.0)
🚀 SparkTTSPipeline initialized
🌊 Veena3SlidingWindowPipeline initialized (sliding window: 28 tokens)

=== streaming concurrency=96 requests=96 ===
success=96/96 (100%)
wall=3.90s throughput=24.64 req/s
latency_ms avg=3524 p50=3709 p95=3856 max=3894
ttfb_ms avg=1115 p50=1092 p95=1323 max=1324
chunks_per_req avg=5.0 p50=5.0 p95=5.0 max=5.0
inter_chunk_ms avg=589.6 p50=524.5 p95=1743.5 max=1818.6
tail_emit_speed_x avg=1.61 p50=1.55 p95=1.95 max=2.39
timeline_to_first_batch_ms: avg=52.98 p50=54.06 p95=61.99 max=63.93
timeline_to_first_chunk_emitted_ms: avg=1095.10 p50=1080.72 p95=1288.39 max=1289.67
timeline_total_ms: avg=3453.53 p50=3697.21 p95=3823.49 max=3857.23
llm_time_in_queue_ms: avg=0.12 p50=0.10 p95=0.26 max=0.29
llm_scheduler_ms: avg=36.09 p50=29.96 p95=48.99 max=48.99
llm_first_token_ms: avg=50.21 p50=51.58 p95=60.85 max=62.94
llm_request_lifecycle_ms: avg=2542.14 p50=2558.76 p95=2764.46 max=2808.35
llm_generation_wall_ms: avg=3441.77 p50=3697.17 p95=3823.45 max=3857.19
llm_time_per_token_ms: avg=15.03 p50=15.39 p95=16.66 max=18.12
llm_batch_wall_ms_total: avg=3186.21 p50=3192.79 p95=3632.07 max=3665.74
llm_decode_wall_ms_total: avg=3133.27 p50=3135.31 p95=3589.58 max=3626.08
tokens_per_batch_p50: avg=1.00 p50=1.00 p95=1.00 max=1.00
bicodec_decode_calls: avg=4.00 p50=4.00 p95=4.00 max=4.00
bicodec_decode_wall_ms_total: avg=1853.68 p50=1520.95 p95=2990.10 max=3166.47
bicodec_decode_interval_ms_p50: avg=639.53 p50=662.67 p95=770.28 max=898.40
decode_interval_applied_tokens_p50: avg=48.00 p50=48.00 p95=48.00 max=48.00
decode_pending_requests_p50: avg=0.00 p50=0.00 p95=0.00 max=0.00
batch_avg_size: avg=12.29 p50=12.24 p95=12.75 max=12.75
batch_max_seen: avg=16.00 p50=16.00 p95=16.00 max=16.00
batch_workers_live: avg=2.00 p50=2.00 p95=2.00 max=2.00
batch_workers_target: avg=1.26 p50=1.00 p95=2.00 max=2.00
batch_queue_wait_ms_avg: avg=309.59 p50=357.10 p95=379.89 max=379.89
batch_compute_ms_avg: av…50 tokens truncated…bicodec=53.7% other=0.0%
gpu: util_avg=79% util_max=98% mem_avg=46720MB mem_max=47765MB

=== streaming concurrency=128 requests=128 ===
success=128/128 (100%)
wall=5.35s throughput=23.92 req/s
latency_ms avg=4504 p50=4647 p95=5306 max=5347
ttfb_ms avg=1196 p50=1151 p95=1468 max=1468
chunks_per_req avg=5.1 p50=5.0 p95=6.0 max=6.0
inter_chunk_ms avg=795.6 p50=852.6 p95=2006.3 max=2273.8
tail_emit_speed_x avg=1.20 p50=1.12 p95=1.72 max=1.84
timeline_to_first_batch_ms: avg=67.86 p50=69.72 p95=79.51 max=81.62
timeline_to_first_chunk_emitted_ms: avg=1170.14 p50=1129.27 p95=1423.14 max=1425.04
timeline_total_ms: avg=4427.00 p50=4524.22 p95=5273.70 max=5305.12
llm_time_in_queue_ms: avg=0.18 p50=0.15 p95=0.39 max=0.43
llm_scheduler_ms: avg=44.19 p50=37.12 p95=62.94 max=62.94
llm_first_token_ms: avg=64.49 p50=66.82 p95=77.23 max=79.29
llm_request_lifecycle_ms: avg=2932.06 p50=2940.41 p95=3191.90 max=3296.00
llm_generation_wall_ms: avg=4410.63 p50=4511.88 p95=5273.66 max=5305.08
llm_time_per_token_ms: avg=19.35 p50=19.93 p95=22.64 max=24.22
llm_batch_wall_ms_total: avg=3948.03 p50=3934.53 p95=4908.64 max=5112.19
llm_decode_wall_ms_total: avg=3880.20 p50=3867.10 p95=4832.13 max=5041.49
tokens_per_batch_p50: avg=1.00 p50=1.00 p95=1.00 max=1.00
bicodec_decode_calls: avg=4.09 p50=4.00 p95=5.00 max=5.00
bicodec_decode_wall_ms_total: avg=2943.50 p50=3224.26 p95=4147.91 max=4400.21
bicodec_decode_interval_ms_p50: avg=1038.49 p50=1054.27 p95=1492.36 max=1641.21
decode_interval_applied_tokens_p50: avg=48.00 p50=48.00 p95=48.00 max=48.00
decode_pending_requests_p50: avg=0.00 p50=0.00 p95=0.00 max=0.00
batch_avg_size: avg=10.82 p50=10.49 p95=12.98 max=13.35
batch_max_seen: avg=16.00 p50=16.00 p95=16.00 max=16.00
batch_workers_live: avg=2.00 p50=2.00 p95=2.00 max=2.00
batch_workers_target: avg=1.65 p50=2.00 p95=2.00 max=2.00
batch_queue_wait_ms_avg: avg=453.95 p50=472.10 p95=536.03 max=537.07
batch_compute_ms_avg: avg=154.30 p50=149.78 p95=177.64 max=180.50
batch_queue_depth_avg: avg=55.53 p50=56.69 p95=58.61 max=58.61
latency_over_timeline_ms: avg=76.72 p50=40.00 p95=237.56 max=363.44
timeline_share: llm=99.6% bicodec=66.5% other=0.0%
gpu: util_avg=71% util_max=98% mem_avg=48489MB mem_max=48489MB

results saved: /tmp/stream_workers2_scale56_dynamic_final_seeded.json
ERROR 02-18 19:01:15 [core_client.py:605] Engine core proc EngineCore_DP0 died unexpectedly, shutting down client.


**Comparing results for delta calculation**


python3 - <<'PY'
import json
w1={r['concurrency']:r for r in json.load(open('/tmp/stream_workers1_current_seeded.json'))['levels']}
opt={r['concurrency']:r for r in json.load(open('/tmp/stream_workers2_scale56_dynamic_final_seeded.json'))['levels']}
for c in [96,128]:
    a=w1[c]; b=opt[c]
    ta=a['timing']; tb=b['timing']
    def p(new,old):
        return (new-old)/old*100
    print(f"c={c} thr {a['throughput_rps']:.2f}->{b['throughput_rps']:.2f} ({p(b['throughput_rps'],a['throughput_rps']):+.1f}%) | lat {a['latency_ms']['avg']:.0f}->{b['latency_ms']['avg']:.0f} ({p(b['latency_ms']['avg'],a['latency_ms']['avg']):+.1f}%) | ttfb {a['ttfb_ms']['avg']:.0f}->{b['ttfb_ms']['avg']:.0f} ({p(b['ttfb_ms']['avg'],a['ttfb_ms']['avg']):+.1f}%) | qwait {ta['batch_queue_wait_ms_avg']['avg']:.0f}->{tb['batch_queue_wait_ms_avg']['avg']:.0f} ({p(tb['batch_queue_wait_ms_avg']['avg'],ta['batch_queue_wait_ms_avg']['avg']):+.1f}%)")
PY


Output:
Chunk ID: 61ea23
Wall time: 0.0512 seconds
Process exited with code 0
Original token count: 55
Output:
c=96 thr 20.59->24.64 (+19.7%) | lat 3838->3524 (-8.2%) | ttfb 1139->1115 (-2.1%) | qwait 484->310 (-36.0%)
c=128 thr 22.79->23.92 (+4.9%) | lat 4631->4504 (-2.7%) | ttfb 1587->1196 (-24.7%) | qwait 742->454 (-38.8%)


**Preparing final summary details**


nl -ba veena3modal/core/bicodec_decoder.py | sed -n '104,190p'


nl -ba veena3modal/core/bicodec_decoder.py | sed -n '360,490p'


nl -ba scripts/stress_test_streaming_runtime.py | sed -n '404,445p'


nl -ba scripts/stress_test_streaming_runtime.py | sed -n '620,690p'


Output:
Chunk ID: a0949e
Wall time: 0.0510 seconds
Process exited with code 0
Original token count: 1247
Output:
   104	            device: Device for BiCodec model (cuda/cpu)
   105	            model_path: Path to BiCodec model checkpoint
   106	            enable_batching: Enable async batching for streaming (not used yet)
   107	        """
   108	        self.device = torch.device(device if torch.cuda.is_available() else "cpu")
   109	        self.model_path = model_path
   110	        requested_batching = bool(enable_batching)
   111	        if _env_flag("VEENA3_BICODEC_BATCHING_FORCE_ON", False):
   112	            requested_batching = True
   113	        if _env_flag("VEENA3_BICODEC_BATCHING_FORCE_OFF", False):
   114	            requested_batching = False
   115	        self.enable_batching = requested_batching and self.device.type == "cuda"
   116	        self.batch_max_size = _env_int("VEENA3_BICODEC_BATCH_MAX", 16, minimum=1)
   117	        self.batch_timeout_s = _env_float("VEENA3_BICODEC_BATCH_TIMEOUT_MS", 6.0, minimum=0.1) / 1000.0
   118	        self.batch_worker_count = _env_int("VEENA3_BICODEC_BATCH_WORKERS", 1, minimum=1)
   119	        self.batch_worker_scale_pending = _env_int("VEENA3_BICODEC_BATCH_SCALE_PENDING", 0, minimum=0)
   120	        self.batch_worker_scale_mode = os.environ.get("VEENA3_BICODEC_BATCH_SCALE_MODE", "sticky").strip().lower()
   121	        if self.batch_worker_scale_mode not in {"sticky", "dynamic"}:
   122	            self.batch_worker_scale_mode = "sticky"
   123
   124	        # Async micro-batcher state (per event loop).
   125	        self._batch_map_lock = threading.Lock()
   126	        self._batch_queues: Dict[int, asyncio.Queue] = {}
   127	        self._batch_workers: Dict[int, List[asyncio.Task]] = {}
   128	        self._batch_scaled_loops: Dict[int, bool] = {}
   129	        self._batch_total_batches = 0
   130	        self._batch_total_requests = 0
   131	        self._batch_max_seen = 0
   132	        self._batch_queue_wait_ms_total = 0.0
   133	        self._batch_queue_wait_ms_min: Optional[float] = None
   134	        self._batch_queue_wait_ms_max = 0.0
   135	        self._batch_compute_ms_total = 0.0
   136	        self._batch_compute_ms_min: Optional[float] = None
   137	        self._batch_compute_ms_max = 0.0
   138	        self._batch_queue_depth_total = 0
   139	        self._batch_queue_depth_max = 0
   140	        self._batch_inflight_requests = 0
   141	        self._batch_workers_target_last = 1
   142
   143	        print(f"🎵 Loading BiCodec audio tokenizer from {model_path}...")
   144	        print(f"   Device: {self.device}")
   145
   146	        # Initialize BiCodec tokenizer
   147	        self.audio_tokenizer = BiCodecTokenizer(model_path, device=str(self.device))
   148
   149	        # OPTIMIZATION: Ensure model is on device once at init, not per-decode call
   150	        self.audio_tokenizer.device = self.device
   151	        self.audio_tokenizer.model.to(self.device)
   152
   153	        # NOTE: torch.compile disabled for BiCodec -- dynamic semantic sequence lengths
   154	        # cause repeated recompilation (~120s each), worse than eager mode.
   155	        # torch.compile works best with static shapes. If needed, pre-warm with
   156	        # representative lengths or use mode="default" instead of "reduce-overhead".
   157	        # The multi-engine approach (Tier 3) provides more impactful gains.
   158
   159	        print(f"✅ BiCodec decoder initialized (sample rate: {AUDIO_SAMPLE_RATE}Hz)")
   160	        if self.enable_batching:
   161	            print(
   162	                "   ℹ  Micro-batching enabled "
   163	                f"(workers={self.batch_worker_count}, scale_mode={self.batch_worker_scale_mode}, "
   164	                f"scale_pending={self.batch_worker_scale_pending}, "
   165	                f"max_batch={self.batch_max_size}, timeout_ms={self.batch_timeout_s * 1000:.1f})"
   166	            )
   167
   168	    def decode(
   169	        self,
   170	        semantic_ids: List[int],
   171	        global_ids: List[int],
   172	    ) -> Optional[np.ndarray]:
   173	        """
   174	        Decode BiCodec tokens to audio waveform.
   175
   176	        CRITICAL: BiCodec expects EXACTLY 32 global tokens!
   177	        The decoder pools them via speaker_encoder → d_vector, then broadcasts
   178	        via d_vector.unsqueeze(-1) across the time dimension.
   179
   180	        Args:
   181	            semantic_ids: List of semantic token IDs (variable length)
   182	            global_ids: List of global token IDs (MUST be exactly 32!)
   183
   184	        Returns:
   185	            Audio waveform as numpy array (float32, 16kHz mono)
   186	            Shape: (samples,)
   187	            Returns None if decode fails
   188	        """
   189	        if not semantic_ids or not global_ids:
   190	            print(f"⚠  Empty token lists: semantic={len(semantic_ids)}, global={len(global_ids)}")


Output:
Chunk ID: 08222f
Wall time: 0.0510 seconds
Process exited with code 0
Original token count: 1773
Output:
   360	            logger.exception("BiCodec batched decode failed; falling back to sequential decode")
   361	            return [
   362	                self.decode_streaming(sem, glob, use_sliding_window=use_sw, trim_warmup=trim)
   363	                for sem, glob, use_sw, trim in batch_items
   364	            ]
   365
   366	    def _ensure_batch_worker(self, loop: asyncio.AbstractEventLoop) -> asyncio.Queue:
   367	        key = id(loop)
   368	        with self._batch_map_lock:
   369	            queue = self._batch_queues.get(key)
   370	            workers = self._batch_workers.get(key, [])
   371	            live_workers = [worker for worker in workers if not worker.done()]
   372
   373	            if queue is None:
   374	                queue = asyncio.Queue(maxsize=max(self.batch_max_size * 8, 256))
   375	                self._batch_queues[key] = queue
   376
   377	            target_workers = self.batch_worker_count
   378	            if self.batch_worker_count > 1 and self.batch_worker_scale_pending > 0:
   379	                pending_now = queue.qsize() + self._batch_inflight_requests
   380	                if self.batch_worker_scale_mode == "sticky":
   381	                    already_scaled = self._batch_scaled_loops.get(key, False)
   382	                    should_scale = already_scaled or pending_now >= self.batch_worker_scale_pending
   383	                    self._batch_scaled_loops[key] = should_scale
   384	                    if not should_scale:
   385	                        target_workers = 1
   386	                elif pending_now < self.batch_worker_scale_pending:
   387	                    target_workers = 1
   388	            while len(live_workers) < target_workers:
   389	                worker_idx = len(live_workers)
   390	                worker = loop.create_task(
   391	                    self._batch_worker(loop, queue, worker_idx),
   392	                    name=f"bicodec-batch-worker-{key}-{worker_idx}",
   393	                )
   394	                live_workers.append(worker)
   395
   396	            self._batch_workers_target_last = len(live_workers)
   397	            self._batch_workers[key] = live_workers
   398	            return queue
   399
   400	    def _desired_worker_count(self, queue: Optional[asyncio.Queue] = None) -> int:
   401	        if self.batch_worker_count <= 1:
   402	            return 1
   403	        if self.batch_worker_scale_pending <= 0:
   404	            return self.batch_worker_count
   405	        with self._batch_map_lock:
   406	            pending_now = self._batch_inflight_requests
   407	        if queue is not None:
   408	            pending_now += queue.qsize()
   409	        if pending_now >= self.batch_worker_scale_pending:
   410	            return self.batch_worker_count
   411	        return 1
   412
   413	    async def _batch_worker(
   414	        self,
   415	        loop: asyncio.AbstractEventLoop,
   416	        queue: asyncio.Queue,
   417	        worker_idx: int,
   418	    ) -> None:
   419	        while True:
   420	            if self.batch_worker_scale_mode == "dynamic":
   421	                target_workers = self._desired_worker_count(queue)
   422	                self._batch_workers_target_last = target_workers
   423	                if worker_idx >= target_workers:
   424	                    await asyncio.sleep(min(self.batch_timeout_s, 0.002))
   425	                    continue
   426
   427	            first: _BatchDecodeRequest = await queue.get()
   428	            batch: List[_BatchDecodeRequest] = [first]
   429	            deadline = loop.time() + self.batch_timeout_s
   430
   431	            while len(batch) < self.batch_max_size:
   432	                remaining = deadline - loop.time()
   433	                if remaining <= 0:
   434	                    break
   435	                try:
   436	                    req: _BatchDecodeRequest = await asyncio.wait_for(queue.get(), timeout=remaining)
   437	                except asyncio.TimeoutError:
   438	                    break
   439	                batch.append(req)
   440
   441	            batch_inputs = [
   442	                (req.semantic_ids, req.global_ids, req.use_sliding_window, req.trim_warmup)
   443	                for req in batch
   444	            ]
   445	            batch_start = time.perf_counter()
   446	            queue_wait_ms = [
   447	                max(0.0, (batch_start - req.submitted_at) * 1000.0)
   448	                for req in batch
   449	            ]
   450	            queue_depth_now = queue.qsize()
   451	            self._batch_queue_depth_total += queue_depth_now
   452	            if queue_depth_now > self._batch_queue_depth_max:
   453	                self._batch_queue_depth_max = queue_depth_now
   454	            for wait_ms in queue_wait_ms:
   455	                self._batch_queue_wait_ms_total += wait_ms
   456	                if self._batch_queue_wait_ms_min is None or wait_ms < self._batch_queue_wait_ms_min:
   457	                    self._batch_queue_wait_ms_min = wait_ms
   458	                if wait_ms > self._batch_queue_wait_ms_max:
   459	                    self._batch_queue_wait_ms_max = wait_ms
   460	            with self._batch_map_lock:
   461	                self._batch_inflight_requests += len(batch)
   462
   463	            t0 = time.perf_counter()
   464	            try:
   465	                results = await loop.run_in_executor(None, self._decode_streaming_batch_sync, batch_inputs)
   466	                if len(results) != len(batch):
   467	                    raise RuntimeError("batch decode result size mismatch")
   468	                for req, result in zip(batch, results):
   469	                    if not req.future.done():
   470	                        req.future.set_result(result)
   471	            except asyncio.CancelledError:
   472	                raise
   473	            except Exception as exc:
   474	                logger.exception("BiCodec batch worker %d failed", worker_idx)
   475	                for req in batch:
   476	                    if not req.future.done():
   477	                        req.future.set_exception(exc)
   478	            finally:
   479	                compute_ms = (time.perf_counter() - t0) * 1000.0
   480	                self._batch_compute_ms_total += compute_ms
   481	                if self._batch_compute_ms_min is None or compute_ms < self._batch_compute_ms_min:
   482	                    self._batch_compute_ms_min = compute_ms
   483	                if compute_ms > self._batch_compute_ms_max:
   484	                    self._batch_compute_ms_max = compute_ms
   485	                self._batch_total_batches += 1
   486	                self._batch_total_requests += len(batch)
   487	                if len(batch) > self._batch_max_seen:
   488	                    self._batch_max_seen = len(batch)
   489	                with self._batch_map_lock:
   490	                    self._batch_inflight_requests = max(0, self._batch_inflight_requests - len(batch))


Output:
Chunk ID: aa9815
Wall time: 0.0501 seconds
Process exited with code 0
Original token count: 440
Output:
   404	    timing_keys = [
   405	        "timeline_to_first_batch_ms",
   406	        "timeline_to_first_chunk_emitted_ms",
   407	        "timeline_total_ms",
   408	        "llm_time_in_queue_ms",
   409	        "llm_scheduler_ms",
   410	        "llm_first_token_ms",
   411	        "llm_request_lifecycle_ms",
   412	        "llm_generation_wall_ms",
   413	        "llm_time_per_token_ms",
   414	        "llm_batch_wall_ms_total",
   415	        "llm_decode_wall_ms_total",
   416	        "tokens_per_batch_p50",
   417	        "bicodec_decode_calls",
   418	        "bicodec_decode_wall_ms_total",
   419	        "bicodec_decode_interval_ms_p50",
   420	        "decode_interval_applied_tokens_p50",
   421	        "decode_pending_requests_p50",
   422	        "batch_avg_size",
   423	        "batch_max_seen",
   424	        "batch_workers_live",
   425	        "batch_workers_target",
   426	        "batch_queue_wait_ms_avg",
   427	        "batch_compute_ms_avg",
   428	        "batch_queue_depth_avg",
   429	    ]
   430	    for key in timing_keys:
   431	        st = level.timing_stats(key)
   432	        if st:
   433	            print(f"{key}: avg={st['avg']:.2f} p50={st['p50']:.2f} p95={st['p95']:.2f} max={st['max']:.2f}")
   434
   435	    latency_over_timeline = level.latency_over_metric_stats("timeline_total_ms")
   436	    if latency_over_timeline:
   437	        print(
   438	            "latency_over_timeline_ms: avg={:.2f} p50={:.2f} p95={:.2f} max={:.2f}".format(
   439	                latency_over_timeline["avg"],
   440	                latency_over_timeline["p50"],
   441	                latency_over_timeline["p95"],
   442	                latency_over_timeline["max"],
   443	            )
   444	        )
   445


Output:
Chunk ID: de67a4
Wall time: 0.0512 seconds
Process exited with code 0
Original token count: 1384
Output:
   620	    parser.add_argument("--text", choices=["short", "medium", "long"], default="short")
   621	    parser.add_argument("--max-tokens", type=int, default=512, help="max_tokens sent to streaming generation")
   622	    parser.add_argument("--seed-base", type=int, default=1337, help="Base random seed; request i uses seed_base+i")
   623	    parser.add_argument("--timeout", type=float, default=180.0, help="Per-request timeout seconds")
   624	    parser.add_argument("--requests-multiplier", type=int, default=2, help="Requests per level ~= concurrency * multiplier")
   625	    parser.add_argument("--min-requests", type=int, default=8)
   626	    parser.add_argument("--chunking", action="store_true", default=False, help="Enable long-text chunking path")
   627	    parser.add_argument("--gpu-memory", type=float, default=0.25)
   628	    parser.add_argument("--stream-output-kind", choices=["delta", "cumulative"], default="delta")
   629	    parser.add_argument("--num-engines", type=int, default=1, help="Number of vLLM engines")
   630	    parser.add_argument("--max-num-batched-tokens", type=int, default=None)
   631	    parser.add_argument("--max-num-seqs", type=int, default=None)
   632	    parser.add_argument("--enable-chunked-prefill", action="store_true", default=None)
   633	    parser.add_argument("--disable-chunked-prefill", action="store_true", default=False)
   634	    parser.add_argument("--disable-prefix-caching", action="store_true", default=False)
   635	    parser.add_argument("--disable-engine-stats-logs", action="store_true", default=False)
   636	    parser.add_argument("--enforce-eager", action="store_true", default=False)
   637	    parser.add_argument("--stream-decode-interval", type=int, default=48)
   638	    parser.add_argument("--stream-window-size", type=int, default=128)
   639	    parser.add_argument("--stream-min-semantic-first", type=int, default=10)
   640	    parser.add_argument("--stream-crossfade-ms", type=int, default=50)
   641	    parser.add_argument("--adaptive-decode", action="store_true", default=False)
   642	    parser.add_argument("--adaptive-decode-first-interval", type=int, default=48)
   643	    parser.add_argument("--adaptive-decode-busy-interval", type=int, default=64)
   644	    parser.add_argument("--adaptive-decode-busy-pending", type=int, default=32)
   645	    parser.add_argument("--disable-windowed-decode", action="store_true", default=False)
   646	    parser.add_argument("--disable-bicodec-batching", action="store_true", default=False)
   647	    parser.add_argument("--bicodec-batch-max", type=int, default=16)
   648	    parser.add_argument("--bicodec-batch-timeout-ms", type=float, default=6.0)
   649	    parser.add_argument("--bicodec-batch-workers", type=int, default=1)
   650	    parser.add_argument("--bicodec-batch-scale-pending", type=int, default=0)
   651	    parser.add_argument("--bicodec-batch-scale-mode", choices=["sticky", "dynamic"], default="sticky")
   652	    parser.add_argument("--model-path", type=str, default="")
   653	    parser.add_argument("--output", default="stress_streaming_runtime.json")
   654	    args = parser.parse_args()
   655
   656	    from veena3modal.local_server import DEFAULT_LOCAL_MODEL_DIR
   657
   658	    model_path = args.model_path or DEFAULT_LOCAL_MODEL_DIR
   659
   660	    os.environ["VEENA3_STREAM_OUTPUT_KIND"] = args.stream_output_kind
   661	    os.environ["VEENA3_STREAM_DECODE_INTERVAL"] = str(args.stream_decode_interval)
   662	    os.environ["VEENA3_STREAM_WINDOW_SIZE"] = str(args.stream_window_size)
   663	    os.environ["VEENA3_STREAM_MIN_SEMANTIC_FIRST"] = str(args.stream_min_semantic_first)
   664	    os.environ["VEENA3_STREAM_CROSSFADE_MS"] = str(args.stream_crossfade_ms)
   665	    os.environ["VEENA3_STREAM_ADAPTIVE_DECODE"] = "1" if args.adaptive_decode else "0"
   666	    os.environ["VEENA3_STREAM_DECODE_INTERVAL_FIRST"] = str(args.adaptive_decode_first_interval)
   667	    os.environ["VEENA3_STREAM_DECODE_INTERVAL_BUSY"] = str(args.adaptive_decode_busy_interval)
   668	    os.environ["VEENA3_STREAM_DECODE_BUSY_PENDING"] = str(args.adaptive_decode_busy_pending)
   669	    os.environ["VEENA3_STREAM_WINDOWED_DECODE"] = "0" if args.disable_windowed_decode else "1"
   670	    os.environ["VEENA3_BICODEC_BATCHING"] = "0" if args.disable_bicodec_batching else "1"
   671	    os.environ["VEENA3_BICODEC_BATCH_MAX"] = str(args.bicodec_batch_max)
   672	    os.environ["VEENA3_BICODEC_BATCH_TIMEOUT_MS"] = str(args.bicodec_batch_timeout_ms)
   673	    os.environ["VEENA3_BICODEC_BATCH_WORKERS"] = str(args.bicodec_batch_workers)
   674	    os.environ["VEENA3_BICODEC_BATCH_SCALE_PENDING"] = str(args.bicodec_batch_scale_pending)
   675	    os.environ["VEENA3_BICODEC_BATCH_SCALE_MODE"] = str(args.bicodec_batch_scale_mode)
   676
   677	    enable_chunked_prefill = args.enable_chunked_prefill
   678	    if args.disable_chunked_prefill:
   679	        enable_chunked_prefill = False
   680
   681	    init_runtime(
   682	        model_path=model_path,
   683	        gpu_mem=args.gpu_memory,
   684	        stream_output_kind=args.stream_output_kind,
   685	        num_engines=args.num_engines,
   686	        max_num_batched_tokens=args.max_num_batched_tokens,
   687	        max_num_seqs=args.max_num_seqs,
   688	        enable_chunked_prefill=enable_chunked_prefill,
   689	        enable_prefix_caching=(False if args.disable_prefix_caching else None),
   690	        disable_log_stats=(True if args.disable_engine_stats_logs else None),


**Summarizing implementation and benchmarks**


Implemented and benchmarked end-to-end.

**What I changed**
- Added configurable multi-worker BiCodec decode micro-batching with scale policies in `veena3modal/core/bicodec_decoder.py:118` and `veena3modal/core/bicodec_decoder.py:366`.
  - New env knobs:
    - `VEENA3_BICODEC_BATCH_WORKERS`
    - `VEENA3_BICODEC_BATCH_SCALE_PENDING`
    - `VEENA3_BICODEC_BATCH_SCALE_MODE` (`sticky` / `dynamic`)
- Added detailed decode queue observability in `veena3modal/core/bicodec_decoder.py:445` and `veena3modal/core/bicodec_decoder.py:510`.
  - queue wait, compute time, queue depth, live workers, target workers.
- Extended stress harness flags + reporting in `scripts/stress_test_streaming_runtime.py:649` and `scripts/stress_test_streaming_runtime.py:404`.
  - New CLI:
    - `--bicodec-batch-workers`
    - `--bicodec-batch-scale-pending`
    - `--bicodec-batch-scale-mode`
  - Added timing outputs for worker/queue behavior.

**Final benchmark (seeded, current code)**
- Baseline profile: `workers=1`
  - file: `/tmp/stream_workers1_current_seeded.json`
- Best profile found: `workers=2`, `scale_mode=dynamic`, `scale_pending=56`
  - file: `/tmp/stream_workers2_scale56_dynamic_final_seeded.json`

**Delta vs baseline (`workers=1`)**
- `c=96`:
  - throughput `20.59 -> 24.64 req/s` (**+19.7%**)
  - latency `3838 -> 3524 ms` (**-8.2%**)
  - TTFB `1139 -> 1115 ms` (**-2.1%**)
  - decode queue wait `484 -> 310 ms` (**-36.0%**)
- `c=128`:
  - throughput `22.79 -> 23.92 req/s` (**+4.9%**)
  - latency `4631 -> 4504 ms` (**-2.7%**)
  - TTFB `1587 -> 1196 ms` (**-24.7%**)
  - decode queue wait `742 -> 454 ms` (**-38.8%**)

**Recommended runtime config now**
- `--bicodec-batch-workers 2 --bicodec-batch-scale-mode dynamic --bicodec-batch-scale-pending 56`
- Keep current decode settings: `--stream-decode-interval 48 --stream-window-size 128 --bicodec-batch-max 16 --bicodec-batch-timeout-ms 6`

**Note**
- vLLM still logs `Engine core proc ... died unexpectedly` at teardown after benchmark completion; results are still written successfully.

Next step options:
1. I can run a longer soak (10-20 min) at `c=96/128` with this config to verify tail stability.
2. I can tune decode interval (`48` vs `56/64`) under this new worker policy for more throughput.
3. I can add hard admission/backpressure limits to prevent queue-depth blowups above peak concurrency.


okay


**Running soak benchmarks with updates**


I’ll proceed with a longer soak-style run using the new best config (`workers=2`, `dynamic`, `scale_pending=56`) across `c=96,128`, then I’ll report stability and tail behavior from the saved request-level data.


python3 scripts/stress_test_streaming_runtime.py --levels 96,128 --text short --max-tokens 1024 --seed-base 2026 --timeout 300 --requests-multiplier 16 --min-requests 8 --gpu-memory 0.25 --stream-output-kind delta --stream-decode-interval 48 --stream-window-size 128 --bicodec-batch-max 16 --bicodec-batch-timeout-ms 6 --bicodec-batch-workers 2 --bicodec-batch-scale-pending 56 --bicodec-batch-scale-mode dynamic --output /tmp/stream_workers2_scale56_dynamic_soak.json


Output:
Chunk ID: 792484
Wall time: 1.0011 seconds
Process running with session ID 8997
Original token count: 0
Output:


Output:
Chunk ID: 5a6425
Wall time: 5.0014 seconds
Process running with session ID 8997
Original token count: 1362
Output:
Total output lines: 51

[2026-02-18 19:57:56] INFO tts_runtime.py:183: Loading SparkTTS model with vLLM...
The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored.
The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored.
🚀 Initializing Spark TTS Model
📁 Model path: /home/ubuntu/veenaModal/models/spark_tts_4speaker/LLM
🗣  Speakers: 12 predefined (lipakshi, vardan, reet, Nandini, krishna, anika, adarsh, Nilay, Aarvi, Asha, Bittu, Mira)
🎭 Emotions: 10 tags in [bracket] format

📝 Loading tokenizer from model...
✅ Tokenizer loaded: 165168 tokens

🔍 Validating 12 speakers for Spark TTS...
  lipakshi → <|speaker_0|>: 7 tokens
  vardan → <|speaker_1|>: 7 tokens
  reet → <|speaker_2|>: 7 tokens
  Nandini → <|speaker_3|>: 7 tokens
  krishna → <|speaker_4|>: 7 tokens
  anika → <|speaker_5|>: 7 tokens
  adarsh → <|speaker_6|>: 7 tokens
  Nilay → <|speaker_7|>: 7 tokens
  Aarvi → <|speaker_8|>: 7 tokens
  Asha → <|speaker_9|>: 7 tokens
  Bittu → <|speaker_10|>: 8 tokens
  Mira → <|speaker_11|>: 8 tokens
✅ All 12 speakers validated with mapping

🔍 Validating 10 emotion tags for Spark TTS...
✅ All 10 emotion tags validated

📋 Sample emotion tags:
  [angry]: 1 tokens
  [curious]: 1 tokens
  [excited]: 1 tokens
  [giggle]: 1 tokens
  [laughs harder]: 1 tokens
  ...

🔧 Initializing vLLM engine with optimizations...
  - dtype: bfloat16
  - max_model_len: 4096
  - gpu_memory_utilization: 0.25
  - enable_prefix_caching: True
  - enforce_eager (disable CUDA graphs): False
INFO 02-18 19:57:57 [model.py:541] Resolved architecture: Qwen2ForCausalLM
INFO 02-18 19:57:57 [model.py:1561] Using max model len 4096
INFO 02-18 19:57:57 [scheduler.py:226] Chunked prefill is enabled with max_num_batched_tokens=4096.
INFO 02-18 19:57:57 [vllm.py:624] Asynchronous scheduling is enabled.
The argument `trust_remote_code` is to be used with Auto classes. It has no effect here a…362 tokens truncated…tion_config={'level': None, 'mode': <CompilationMode.VLLM_COMPILE: 3>, 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['none'], 'splitting_ops': ['vllm::unified_attention', 'vllm::unified_attention_with_output', 'vllm::unified_mla_attention', 'vllm::unified_mla_attention_with_output', 'vllm::mamba_mixer2', 'vllm::mamba_mixer', 'vllm::short_conv', 'vllm::linear_attention', 'vllm::plamo2_mamba_mixer', 'vllm::gdn_attention_core', 'vllm::kda_attention', 'vllm::sparse_attn_indexer', 'vllm::rocm_aiter_sparse_attn_indexer', 'vllm::unified_kv_cache_update'], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [4096], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': <CUDAGraphMode.FULL_AND_PIECEWISE: (2, 1)>, 'cudagraph_num_of_warmups': 1, 'cudagraph_capture_sizes': [1, 2, 4, 8, 16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96, 104, 112, 120, 128, 136, 144, 152, 160, 168, 176, 184, 192, 200, 208, 216, 224, 232, 240, 248, 256], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': True, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False}, 'max_cudagraph_capture_size': 256, 'dynamic_shapes_config': {'type': <DynamicShapesType.BACKED: 'backed'>, 'evaluate_guards': False, 'assume_32_bit_indexing': True}, 'local_cache_dir': None, 'static_all_moe_layers': []}
(EngineCore_DP0 pid=166925) INFO 02-18 19:58:00 [parallel_state.py:1212] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://216.81.248.184:32799 backend=nccl
(EngineCore_DP0 pid=166925) INFO 02-18 19:58:00 [parallel_state.py:1423] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A


The soak run is in startup/model load. Once it finishes both levels, I’ll compute additional tail stats (p99) from request-level entries in the output JSON.


Output:
Chunk ID: d7b07c
Wall time: 5.0010 seconds
Process running with session ID 8997
Original token count: 487
Output:
(EngineCore_DP0 pid=166925) INFO 02-18 19:58:01 [gpu_model_runner.py:4033] Starting to load model /home/ubuntu/veenaModal/models/spark_tts_4speaker/LLM...
(EngineCore_DP0 pid=166925) INFO 02-18 19:58:01 [cuda.py:364] Using FLASH_ATTN attention backend out of potential backends: ('FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION')
(EngineCore_DP0 pid=166925) <frozen importlib._bootstrap_external>:1184: FutureWarning: The cuda.cudart module is deprecated and will be removed in a future release, please switch to use the cuda.bindings.runtime module instead.
(EngineCore_DP0 pid=166925) <frozen importlib._bootstrap_external>:1184: FutureWarning: The cuda.nvrtc module is deprecated and will be removed in a future release, please switch to use the cuda.bindings.nvrtc module instead.
(EngineCore_DP0 pid=166925)
Loading safetensors checkpoint shards:   0% Completed | 0/1 [00:00<?, ?it/s]
(EngineCore_DP0 pid=166925)
Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  1.07it/s]
(EngineCore_DP0 pid=166925)
Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  1.07it/s]
(EngineCore_DP0 pid=166925)
(EngineCore_DP0 pid=166925) INFO 02-18 19:58:02 [default_loader.py:291] Loading weights took 1.01 seconds
(EngineCore_DP0 pid=166925) INFO 02-18 19:58:03 [gpu_model_runner.py:4130] Model loading took 0.95 GiB memory and 1.537099 seconds
(EngineCore_DP0 pid=166925) INFO 02-18 19:58:08 [backends.py:812] Using cache directory: /home/ubuntu/.cache/vllm/torch_compile_cache/f3ff13511c/rank_0_0/backbone for vLLM's torch.compile
(EngineCore_DP0 pid=166925) INFO 02-18 19:58:08 [backends.py:872] Dynamo bytecode transform time: 4.24 s
(EngineCore_DP0 pid=166925) INFO 02-18 19:58:11 [backends.py:267] Directly load the compiled graph(s) for compile range (1, 4096) from the cache, took 0.678 s
(EngineCore_DP0 pid=166925) INFO 02-18 19:58:11 [monitor.py:34] torch.compile takes 4.92 s in total


Output:
Chunk ID: 6d5a7d
Wall time: 5.0007 seconds
Process running with session ID 8997
Original token count: 720
Output:
(EngineCore_DP0 pid=166925) INFO 02-18 19:58:12 [gpu_worker.py:356] Available KV cache memory: 18.06 GiB
(EngineCore_DP0 pid=166925) INFO 02-18 19:58:12 [kv_cache_utils.py:1307] GPU KV cache size: 1,577,712 tokens
(EngineCore_DP0 pid=166925) INFO 02-18 19:58:12 [kv_cache_utils.py:1312] Maximum concurrency for 4,096 tokens per request: 385.18x
(EngineCore_DP0 pid=166925)
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):   0%|          | 0/35 [00:00<?, ?it/s]
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  11%|█▏        | 4/35 [00:00<00:00, 31.92it/s]
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  23%|██▎       | 8/35 [00:00<00:00, 34.36it/s]
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  34%|███▍      | 12/35 [00:00<00:00, 35.62it/s]
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  46%|████▌     | 16/35 [00:00<00:00, 36.27it/s]
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  57%|█████▋    | 20/35 [00:00<00:00, 36.53it/s]
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  69%|██████▊   | 24/35 [00:00<00:00, 36.45it/s]
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  80%|████████  | 28/35 [00:00<00:00, 36.03it/s]
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  91%|█████████▏| 32/35 [00:00<00:00, 34.45it/s]
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE): 100%|██████████| 35/35 [00:01<00:00, 34.47it/s]
(EngineCore_DP0 pid=166925)
Capturing CUDA graphs (decode, FULL):   0%|          | 0/19 [00:00<?, ?it/s]
Capturing CUDA graphs (decode, FULL):  16%|█▌        | 3/19 [00:00<00:00, 28.48it/s]
Capturing CUDA graphs (decode, FULL):  37%|███▋      | 7/19 [00:00<00:00, 33.06it/s]
Capturing CUDA graphs (decode, FULL):  58%|█████▊    | 11/19 [00:00<00:00, 34.92it/s]
Capturing CUDA graphs (decode, FULL):  79%|███████▉  | 15/19 [00:00<00:00, 35.45it/s]
Capturing CUDA graphs (decode, FULL): 100%|██████████| 19/19 [00:00<00:00, 35.80it/s]
Capturing CUDA graphs (decode, FULL): 100%|██████████| 19/19 [00:00<00:00, 34.91it/s]
(EngineCore_DP0 pid=166925) INFO 02-18 19:58:14 [gpu_model_runner.py:5063] Graph capturing finished in 2 secs, took 0.28 GiB
(EngineCore_DP0 pid=166925) INFO 02-18 19:58:14 [core.py:272] init engine (profile, create kv cache, warmup model) took 10.92 seconds
[2026-02-18 19:58:16] INFO tts_runtime.py:192: Initializing prompt builder...
[2026-02-18 19:58:16] INFO tts_runtime.py:199: Initializing BiCodec decoder...
/home/ubuntu/.local/lib/python3.10/site-packages/torch/nn/utils/weight_norm.py:144: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`.
  WeightNorm.apply(module, name, dim)


Output:
Chunk ID: 6808c2
Wall time: 5.0013 seconds
Process running with session ID 8997
Original token count: 127
Output:
[2026-02-18 19:58:23] INFO tts_runtime.py:209: Initializing TTS pipeline...
[2026-02-18 19:58:23] INFO tts_runtime.py:220: Initializing streaming pipeline...
[2026-02-18 19:58:23] INFO long_text_processor.py:112: LongTextProcessor initialized
[2026-02-18 19:58:23] INFO tts_runtime.py:277: ✅ TTS runtime initialized in 32694ms
[2026-02-18 19:58:23] INFO tts_runtime.py:278:    Model version: LLM
[2026-02-18 19:58:23] INFO tts_runtime.py:289: Speaker globals cache startup precompute skipped (disabled)


Output:
Chunk ID: 0e4a6f
Wall time: 5.0012 seconds
Process running with session ID 8997
Original token count: 0
Output:


The soak run is now in the long request phase. I’ll keep polling and summarize once both heavy levels (`96`, `128`) complete.


Output:
Chunk ID: b83bb1
Wall time: 5.0004 seconds
Process running with session ID 8997
Original token count: 0
Output:


Output:
Chunk ID: c0362b
Wall time: 5.0005 seconds
Process running with session ID 8997
Original token count: 0
Output:


Output:
Chunk ID: e4d101
Wall time: 5.0006 seconds
Process running with session ID 8997
Original token count: 0
Output:


Output:
Chunk ID: a76878
Wall time: 5.0008 seconds
Process running with session ID 8997
Original token count: 0
Output:


Still running with no completion output yet; this is expected with the larger request volume. I’ll continue until the summary is written and then compute p99 tails from the JSON.


Output:
Chunk ID: 9370ab
Wall time: 5.0016 seconds
Process running with session ID 8997
Original token count: 0
Output:


Summary: auto


Output:
Chunk ID: cd77df
Wall time: 5.0006 seconds
Process running with session ID 8997
Original token count: 0
Output:


Output:
Chunk ID: 973679
Wall time: 5.0014 seconds
Process running with session ID 8997
Original token count: 0
Output:


Output:
Chunk ID: 6a202c
Wall time: 5.0009 seconds
Process running with session ID 8997
Original token count: 0
Output:


Output:
Chunk ID: b1b039
Wall time: 5.0010 seconds
Process running with session ID 8997
Original token count: 0
Output:


**Monitoring long-running process**


Output:
Chunk ID: c828f6
Wall time: 5.0004 seconds
Process running with session ID 8997
Original token count: 0
Output:


Output:
Chunk ID: 511937
Wall time: 5.0015 seconds
Process running with session ID 8997
Original token count: 0
Output:


Output:
Chunk ID: e4144b
Wall time: 5.0011 seconds
Process running with session ID 8997
Original token count: 0
Output:


Output:
Chunk ID: 7e39d1
Wall time: 5.0016 seconds
Process running with session ID 8997
Original token count: 0
Output:


**Checking process status**


I’m still waiting on the long-run level summaries; no stall indications yet, just no intermediate prints until each level completes. I’ll check process elapsed time quickly and keep it running.


ps -eo pid,etime,cmd | rg -n "stress_test_streaming_runtime.py --levels 96,128 --text short --max-tokens 1024 --seed-base 2026 --timeout 300 --requests-multiplier 16|PID"


Output:
Chunk ID: 376379
Wall time: 0.0503 seconds
Process exited with code 0
Original token count: 101
Output:
1:    PID     ELAPSED CMD
472: 170310       00:00 /bin/bash -c ps -eo pid,etime,cmd | rg -n "stress_test_streaming_runtime.py --levels 96,128 --text short --max-tokens 1024 --seed-base 2026 --timeout 300 --requests-multiplier 16|PID"
474: 170312       00:00 rg -n stress_test_streaming_runtime.py --levels 96,128 --text short --max-tokens 1024 --seed-base 2026 --timeout 300 --requests-multiplier 16|PID


**Polling session status**


Output:
Chunk ID: f9ed4a
Wall time: 0.0503 seconds
Process exited with code 0
Original token count: 1252
Output:
✅ vLLM engine initialized successfully!

🎉 Spark TTS Model ready for inference!

🎵 Loading BiCodec audio tokenizer from /home/ubuntu/veenaModal/models/spark_tts_4speaker...
   Device: cuda
Missing tensor: mel_transformer.spectrogram.window
Missing tensor: mel_transformer.mel_scale.fb
✅ BiCodec decoder initialized (sample rate: 16000Hz)
   ℹ  Micro-batching enabled (workers=2, scale_mode=dynamic, scale_pending=56, max_batch=16, timeout_ms=6.0)
🚀 SparkTTSPipeline initialized
🌊 Veena3SlidingWindowPipeline initialized (sliding window: 28 tokens)

=== streaming concurrency=96 requests=1536 ===
success=1536/1536 (100%)
wall=63.82s throughput=24.07 req/s
latency_ms avg=3881 p50=3922 p95=4818 max=5541
ttfb_ms avg=2010 p50=2044 p95=2596 max=2993
chunks_per_req avg=4.5 p50=4.0 p95=5.0 max=6.0
inter_chunk_ms avg=526.1 p50=512.0 p95=1380.3 max=2761.2
tail_emit_speed_x avg=2.41 p50=2.03 p95=4.86 max=11.63
timeline_to_first_batch_ms: avg=54.20 p50=34.88 p95=131.74 max=541.65
timeline_to_first_chunk_emitted_ms: avg=1986.86 p50=2014.24 p95=2578.84 max=2992.92
timeline_total_ms: avg=3805.07 p50=3834.42 p95=4750.55 max=5318.21
llm_time_in_queue_ms: avg=0.03 p50=0.01 p95=0.07 max=0.32
llm_scheduler_ms: avg=24.47 p50=22.60 p95=35.13 max=51.87
llm_first_token_ms: avg=48.92 p50=32.46 p95=120.01 max=455.41
llm_request_lifecycle_ms: avg=2849.81 p50=2878.43 p95=3317.00 max=3795.82
llm_generation_wall_ms: avg=3786.74 p50=3808.45 p95=4750.42 max=5318.15
llm_time_per_token_ms: avg=16.82 p50=16.82 p95=20.81 max=25.19
llm_batch_wall_ms_total: avg=3453.31 p50=3455.58 p95=4275.23 max=4850.72
llm_decode_wall_ms_total: avg=3399.16 p50=3405.14 p95=4229.72 max=4822.45
tokens_per_batch_p50: avg=1.18 p50=1.00 p95=1.00 max=66.00
bicodec_decode_calls: avg=3.46 p50=3.00 p95=4.00 max=5.00
bicodec_decode_wall_ms_total: avg=2597.12 p50=2616.54 p95=3513.47 max=4585.10
bicodec_decode_interval_ms_p50: avg=644.14 p50=731.00 p95=1150.68 max=1740.12
decode_interval_applied_tokens_p50: avg=48.00 p50=48.00 p95=48.00 max=48.00
decode_pending_requests_p50: avg=0.00 p50=0.00 p95=0.00 max=0.00
batch_avg_size: avg=7.69 p50=7.41 p95=8.46 max=12.56
batch_max_seen: avg=16.00 p50=16.00 p95=16.00 max=16.00
batch_workers_live: avg=2.00 p50=2.00 p95=2.00 max=2.00
batch_workers_target: avg=1.76 p50=2.00 p95=2.00 max=2.00
batch_queue_wait_ms_avg: avg=653.57 p50=679.09 p95=692.86 max=704.44
batch_compute_ms_avg: avg=148.40 p50=142.54 p95=174.46 max=223.88
batch_queue_depth_avg: avg=48.93 p50=49.73 p95=50.51 max=55.27
latency_over_timeline_ms: avg=75.85 p50=48.52 p95=227.34 max=592.26
timeline_share: llm=99.5% bicodec=68.3% other=0.0%
gpu: util_avg=93% util_max=100% mem_avg=48863MB mem_max=48963MB

=== streaming concurrency=128 requests=2048 ===
success=2048/2048 (100%)
wall=75.36s throughput=27.18 req/s
latency_ms avg=4601 p50=4542 p95=5866 max=6614
ttfb_ms avg=2489 p50=2558 p95=3074 max=3508
chunks_per_req avg=4.1 p50=4.0 p95=5.0 max=6.0
inter_chunk_ms avg=667.4 p50=622.0 p95=1778.8 max=3091.5
tail_emit_speed_x avg=2.08 p50=1.90 p95=3.65 max=10.46
timeline_to_first_batch_ms: avg=62.61 p50=41.68 p95=158.92 max=650.59
timeline_to_first_chunk_emitted_ms: avg=2461.94 p50=2523.37 p95=3052.01 max=3501.79
timeline_total_ms: avg=4521.91 p50=4462.59 p95=5770.85 max=6561.59
llm_time_in_queue_ms: avg=0.04 p50=0.02 p95=0.08 max=0.67
llm_scheduler_ms: avg=26.51 p50=24.29 p95=38.69 max=60.66
llm_first_token_ms: avg=53.97 p50=38.71 p95=131.93 max=604.35
llm_request_lifecycle_ms: avg=3118.13 p50=3124.13 p95=3582.37 max=4456.33
llm_generation_wall_ms: avg=4506.49 p50=4439.69 p95=5770.81 max=6561.54
llm_time_per_token_ms: avg=20.00 p50=19.79 p95=24.78 max=28.28
llm_batch_wall_ms_total: avg=3925.96 p50=3933.30 p95=4761.74 max=6005.40
llm_decode_wall_ms_total: avg=3863.40 p50=3869.95 p95=4704.00 max=5969.01
tokens_per_batch_p50: avg=1.00 p50=1.00 p95=1.00 max=3.00
bicodec_decode_calls: avg=3.09 p50=3.00 p95=4.00 max=5.00
bicodec_decode_wall_ms_total: avg=3261.57 p50=3190.39 p95=4515.32 max=5657.67
bicodec_decode_interval_ms_p50: avg=692.34 p50=882.91 p95=1480.51 max=2019.04
decode_interval_applied_tokens_p50: avg=48.00 p50=48.00 p95=48.00 max=48.00
decode_pending_requests_p50: avg=0.00 p50=0.00 p95=0.00 max=0.00
batch_avg_size: avg=7.12 p50=7.13 p95=7.27 max=7.43
batch_max_seen: avg=16.00 p50=16.00 p95=16.00 max=16.00
batch_workers_live: avg=2.00 p50=2.00 p95=2.00 max=2.00
batch_workers_target: avg=1.97 p50=2.00 p95=2.00 max=2.00
batch_queue_wait_ms_avg: avg=787.91 p50=797.67 p95=862.16 max=868.23
batch_compute_ms_avg: avg=138.28 p50=138.38 p95=139.46 max=139.90
batch_queue_depth_avg: avg=58.92 p50=59.96 p95=64.13 max=64.34
latency_over_timeline_ms: avg=78.90 p50=57.81 p95=212.61 max=522.43
timeline_share: llm=99.7% bicodec=72.1% other=0.0%
gpu: util_avg=92% util_max=100% mem_avg=49984MB mem_max=50643MB

results saved: /tmp/stream_workers2_scale56_dynamic_soak.json
ERROR 02-18 20:00:48 [core_client.py:605] Engine core proc EngineCore_DP0 died unexpectedly, shutting down client.


python3 - <<'PY'
import json,math,statistics
p='/tmp/stream_workers2_scale56_dynamic_soak.json'
d=json.load(open(p))
for lvl in d['levels']:
    c=lvl['concurrency']
    reqs=[r for r in lvl['requests'] if r.get('success')]
    lats=sorted([r['latency_ms'] for r in reqs])
    ttfb=sorted([r['ttfb_ms'] for r in reqs if r.get('ttfb_ms',0)>0])
    inter=[]
    for r in reqs:
        inter.extend(r.get('inter_chunk_ms',[]))
    inter=sorted(inter)
    def pct(arr,p):
        if not arr: return 0.0
        idx=int((len(arr)-1)*(p/100.0))
        return arr[idx]
    print(f"c={c} n={len(reqs)}")
    print(f"  latency_p99={pct(lats,99):.0f} latency_p999={pct(lats,99.9):.0f}")
    print(f"  ttfb_p99={pct(ttfb,99):.0f} ttfb_p999={pct(ttfb,99.9):.0f}")
    print(f"  inter_chunk_p99={pct(inter,99):.0f} inter_chunk_p999={pct(inter,99.9):.0f}")
PY


Output:
Chunk ID: a3ad30
Wall time: 0.2631 seconds
Process exited with code 0
Original token count: 63
Output:
c=96 n=1536
  latency_p99=5247 latency_p999=5359
  ttfb_p99=2864 ttfb_p999=2957
  inter_chunk_p99=2238 inter_chunk_p999=2575
c=128 n=2048
  latency_p99=6205 latency_p999=6584
  ttfb_p99=3426 ttfb_p999=3499
  inter_chunk_p99=2217 inter_chunk_p999=2926


**Summarizing soak results and next steps**


Ran the soak benchmark and captured tails on the new worker-scaling path.

- Config used: `workers=2`, `scale_mode=dynamic`, `scale_pending=56`, `decode_interval=48`, `window=128`, `batch_max=16`, `batch_timeout=6ms`.
- Duration/load: `c=96` with `1536` requests, `c=128` with `2048` requests.
- Output: `/tmp/stream_workers2_scale56_dynamic_soak.json`.

**Soak results**
- `c=96`: `24.07 req/s`, avg latency `3881 ms`, avg TTFB `2010 ms`, GPU util avg `93%`, success `1536/1536`.
- `c=128`: `27.18 req/s`, avg latency `4601 ms`, avg TTFB `2489 ms`, GPU util avg `92%`, success `2048/2048`.

**Tail metrics from request-level data**
- `c=96`: latency `p99=5247 ms`, `p99.9=5359 ms`; TTFB `p99=2864 ms`; inter-chunk `p99=2238 ms`.
- `c=128`: latency `p99=6205 ms`, `p99.9=6584 ms`; TTFB `p99=3426 ms`; inter-chunk `p99=2217 ms`.

**What this shows**
- This path is stable at high sustained concurrency (no failures in soak) and drives much higher sustained throughput.
- Decoder queueing is still the limiting factor at tails (`batch_queue_wait_ms_avg` rises to ~`654 ms` at `c=96`, ~`788 ms` at `c=128`), so peak-tail optimization is still decode-scheduler work.

Code paths updated for this are in:
- `veena3modal/core/bicodec_decoder.py`
- `scripts/stress_test_streaming_runtime.py`

If you want, I’ll next add admission control/backpressure in the streaming path to cap queue depth and reduce p99/p99.9 without sacrificing too much throughput.


The current TTS application here is an implementation in Modal labs. Which has autoscaling and other rules self scaling rules and everything suitable for my target audience. Now, i need you to create a local entry point or implementation point. then we will plan for optimising the pipeline, then we will discuss on stress testing, then we will discuss on scaling laws post optimisations locally. @veena3modal/app.py

the above part is performed by a different agent. and benchmarks are made. Now can you do the benchmarks on this newly optimized path to understand the concurrency and stress testing.

Now i need you to capture detailed time logs for each request happening in the GPU. like what happens with 1req, time per LLM token, time per batch decoding, and everything else. detailed time about where we are spending each minute in the GPU per generation.

and when you have the details for per request. you can obviously add more logging to get details of concurrency. so you understand what's the bottleneck for the peak concurrency. and then we will decide on effective methods in optimising this TTS engine.

yes. and also this time, before the benchmarks, can you also like put some timeline markers to spot the latency differences and the time spent, to observe. i feel like a 0.5B auto-regressive model and a decoder at some 500M params, in a 80GB A100 GPU should do better than these. So, i'd like to see where we are spending the max time for each stream. what do you think. we can do better ?

yes. onto next steps.

;et

let's go towards achieving the biggest optimisation in decoding/schediling/queing behaviours which increases per GPU throughput and reduces latency overheads and has best overall throughput also with higher concurrency

we're doing a TTS system for voice agents. ain't no way is a non-streaming path useful in any manner. we just need organically true max tokens omitting at insane speed. i wanna know if vLLM can do better than we are doign currently.

what does these big AI labs do to increase or maximize the throughput of the LLM token generation throughout their inference stack. and what exactly are we missing here to make this productionizable with max saturation of a GPU with it's memory and performance or utilisation.

Big labs don’t optimize “the model” in isolation. They optimize the full inference system as a queuing + scheduling + kernel problem.

What they do to maximize LLM token throughput:

Continuous batching at token-step granularity (not request-level batching only).
Prefill/decode-aware scheduling with admission control and SLO tiers.
Aggressive KV cache optimization (paged KV, prefix sharing, eviction policy).
Kernel-level optimizations (fused ops, CUDA graphs, custom attention kernels, quantization where safe).
Pipeline disaggregation (separate prefill/decode or separate model/vocoder stages).
Micro-batching downstream stages (vocoder/decoder/post-processing), not just LLM.
Tight observability loops: per-step timings, queue delay, tokens/s, GPU busy, memory BW, tail latency.
What your current data says you’re missing:

vLLM queue/scheduler is not the main bottleneck.
llm_time_in_queue_ms is ~0.08–0.13 ms and llm_scheduler_ms ~28–36 ms.
Throughput rises with concurrency, but latency/TTFB blow up.
At c32/c64/c96: ~12.56 / 16.77 / 18.92 req/s, while latency goes ~2.55s / 3.81s / 5.07s.
Decoder side is the choke point under load.
bicodec_decode_wall_ms_total grows ~1943 -> 3148 -> 4293 ms.
Decode cadence collapses with concurrency.
bicodec_decode_interval_ms_p50 grows ~383 -> 921 -> 1825 ms.
Effective LLM decode batching is weak in practice.
tokens_per_batch_p50 is ~1.0, so you’re mostly stepping per token/per request, not harvesting large batch gains.
GPU is not fully saturated in a productive way (util mostly ~60s), meaning pipeline stalls/serialization are leaving performance on the table.
Productionization gaps (most important first):

Shared cross-request BiCodec micro-batching in the active streaming path.
You have batch-decoder code (bicodec_batch_decoder.py) but main streaming path still effectively decodes per request.
Stronger separation of stages (LLM token generation vs decode) with bounded queues and backpressure.
Load-adaptive decode policy (interval/window/chunk sizing changes with inflight load).
Hard SLO controls: max inflight, drop/defer policy, timeout budgets by tier.
Reliability hardening (your engine core process exiting after runs must be fixed before prod confidence).
Net: you’re already instrumented well now (streaming_pipeline.py (line 312), stress_test_streaming_runtime.py (line 399)). The next big win is decode-side batching/scheduling, not more vLLM knob tuning.

Can we keep optimising. like go towards how these big AI labs optimize their inference stack. what is the max throughput efficiency or max concurrency attainable with what settings or what optimisations or what kind of right batching.

sure.

<permissions instructions>
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</permissions instructions>

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# AGENTS.md instructions for /home/ubuntu/veenaModal

<INSTRUCTIONS>
## Skills
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### Available skills
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</INSTRUCTIONS>

<environment_context>
  <cwd>/home/ubuntu/veenaModal</cwd>
  <shell>bash</shell>
</environment_context>

okay


where exactly are we heading and are we making any progress in optimisation of the current TTS application in making it productionize like big labs.


**Summarizing optimization progress and next steps**


We’re heading in the right direction, and yes, there is real optimization progress.

**What improved (measured):**
- At concurrency `96`: throughput `20.59 -> 24.64 req/s` (`+19.7%`), avg latency `3838 -> 3524 ms`, decode queue wait `484 -> 310 ms` (`-36%`).
- At concurrency `128`: throughput `22.79 -> 23.92 req/s` (`+4.9%`), avg latency `4631 -> 4504 ms`, TTFB `1587 -> 1196 ms` (`-24.7%`), queue wait `742 -> 454 ms` (`-38.8%`).
- Soak test at high volume had `0 failures` with GPU util around `92–93%`.

**What this means:**
- We fixed part of the decode scheduling/batching inefficiency.
- The main bottleneck is still tail behavior under load (queueing + decode cadence), not raw vLLM queue time.

**Are we “big-lab productionized” yet?**
- Not yet. We’ve moved from “prototype bottlenecked” to “stable + faster,” but we still need hard SLO control and tail-latency control.

**Next production steps (highest ROI):**
1. Add strict admission control/backpressure (cap inflight/queue by SLO tier).
2. Add load-adaptive decode policy (interval/window/chunk tuned by live queue depth).
3. Separate scheduling domains for token generation vs decode/vocoder.
4. Fix runtime reliability issue (`engine core proc died unexpectedly` at teardown).

If you want, I’ll implement step 1 now and run the same concurrency sweep to quantify p95/p99 gains.


sure. onto next steps.


**Starting parallel repo inspection**


I’ll implement strict admission control/backpressure first, then run the same stress sweep to measure p95/p99 impact versus current baseline. I’m starting by tracing the active streaming request path and identifying where to enforce inflight/queue limits safely.


rg -n "admission|backpressure|inflight|queue|stream|websocket|request" veena3modal -S


rg -n "class .*Decoder|bicodec|batch_worker|queue" veena3modal/core -S


Output:
Chunk ID: 5ec91d
Wall time: 0.0503 seconds
Process exited with code 0
Original token count: 281
Output:
/home/ubuntu/veenaModal
total 108
drwxrwxr-x 11 ubuntu ubuntu  4096 Feb 17 00:15 .
drwxr-x--- 35 ubuntu ubuntu  4096 Feb 17 02:29 ..
drwxrwxr-x  3 ubuntu ubuntu  4096 Feb 13 23:23 .cursor
-rw-rw-r--  1 ubuntu ubuntu    99 Feb 13 23:23 .cursorignore
-rw-rw-r--  1 ubuntu ubuntu 12479 Feb 13 23:23 .cursorrules
-rw-rw-r--  1 ubuntu ubuntu    46 Feb 13 23:32 .env
-rw-rw-r--  1 ubuntu ubuntu   777 Feb 13 23:23 .env.example
drwxrwxr-x  8 ubuntu ubuntu  4096 Feb 18 18:39 .git
drwxrwxr-x  3 ubuntu ubuntu  4096 Feb 13 23:23 .github
-rw-rw-r--  1 ubuntu ubuntu  1393 Feb 13 23:23 .gitignore
-rw-rw-r--  1 ubuntu ubuntu  2096 Feb 13 23:23 README.md
drwxrwxr-x  4 ubuntu ubuntu  4096 Feb 13 23:23 external
drwxrwxr-x  3 ubuntu ubuntu  4096 Feb 13 23:23 modal_docs
drwxrwxr-x  3 ubuntu ubuntu  4096 Feb 13 23:58 models
-rw-rw-r--  1 ubuntu ubuntu   458 Feb 13 23:23 requirements.txt
drwxrwxr-x  4 ubuntu ubuntu  4096 Feb 18 00:54 scripts
-rw-rw-r--  1 ubuntu ubuntu 22077 Feb 17 00:22 stress_results_opt_local.json
drwxrwxr-x 10 ubuntu ubuntu  4096 Feb 14 00:09 veena3modal
drwxrwxr-x  6 ubuntu ubuntu  4096 Feb 13 23:55 venv
358