Output:
Chunk ID: 797379
Wall time: 5.0018 seconds
Process running with session ID 99881
Original token count: 1277
Output:
[2026-02-17 04:36:02] INFO tts_runtime.py:154: 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 02-17 04:36:03 [model.py:541] Resolved architecture: Qwen2ForCausalLM
INFO 02-17 04:36:03 [model.py:1561] Using max model len 4096
INFO 02-17 04:36:03 [scheduler.py:226] Chunked prefill is enabled with max_num_batched_tokens=4096.
INFO 02-17 04:36:03 [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=3812375) INFO 02-17 04:36:04 [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=torch.bfloat16, max_seq_len=4096, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, 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': []}


# 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: da4bca
Wall time: 5.0014 seconds
Process running with session ID 99881
Original token count: 511
Output:
(EngineCore_DP0 pid=3812375) INFO 02-17 04:36:06 [parallel_state.py:1212] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://216.81.248.184:35389 backend=nccl
(EngineCore_DP0 pid=3812375) INFO 02-17 04:36:06 [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=3812375) INFO 02-17 04:36:07 [gpu_model_runner.py:4033] Starting to load model /home/ubuntu/veenaModal/models/spark_tts_4speaker/LLM...
(EngineCore_DP0 pid=3812375) INFO 02-17 04:36:07 [cuda.py:364] Using FLASH_ATTN attention backend out of potential backends: ('FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION')
(EngineCore_DP0 pid=3812375) <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=3812375) <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=3812375)
Loading safetensors checkpoint shards:   0% Completed | 0/1 [00:00<?, ?it/s]
(EngineCore_DP0 pid=3812375)
Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  1.84it/s]
(EngineCore_DP0 pid=3812375)
Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  1.83it/s]
(EngineCore_DP0 pid=3812375)
(EngineCore_DP0 pid=3812375) INFO 02-17 04:36:08 [default_loader.py:291] Loading weights took 0.61 seconds
(EngineCore_DP0 pid=3812375) INFO 02-17 04:36:09 [gpu_model_runner.py:4130] Model loading took 0.95 GiB memory and 1.151869 seconds
(EngineCore_DP0 pid=3812375) INFO 02-17 04:36:13 [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=3812375) INFO 02-17 04:36:13 [backends.py:872] Dynamo bytecode transform time: 4.31 s


Summary: auto


This high-concurrency run is still in cold-start/init; after that it should produce the `16/32/64` level summaries quickly. I’ll report where throughput flattens and latency spikes.


Output:
Chunk ID: efce5a
Wall time: 5.0010 seconds
Process running with session ID 99881
Original token count: 762
Output:
(EngineCore_DP0 pid=3812375) INFO 02-17 04:36:16 [backends.py:267] Directly load the compiled graph(s) for compile range (1, 4096) from the cache, took 0.647 s
(EngineCore_DP0 pid=3812375) INFO 02-17 04:36:16 [monitor.py:34] torch.compile takes 4.96 s in total
(EngineCore_DP0 pid=3812375) INFO 02-17 04:36:17 [gpu_worker.py:356] Available KV cache memory: 18.06 GiB
(EngineCore_DP0 pid=3812375) INFO 02-17 04:36:17 [kv_cache_utils.py:1307] GPU KV cache size: 1,577,712 tokens
(EngineCore_DP0 pid=3812375) INFO 02-17 04:36:17 [kv_cache_utils.py:1312] Maximum concurrency for 4,096 tokens per request: 385.18x
(EngineCore_DP0 pid=3812375)
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, 32.05it/s]
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  23%|██▎       | 8/35 [00:00<00:00, 34.85it/s]
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  34%|███▍      | 12/35 [00:00<00:00, 36.20it/s]
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  46%|████▌     | 16/35 [00:00<00:00, 36.59it/s]
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  57%|█████▋    | 20/35 [00:00<00:00, 36.81it/s]
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  69%|██████▊   | 24/35 [00:00<00:00, 36.90it/s]
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  80%|████████  | 28/35 [00:00<00:00, 36.68it/s]
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE):  91%|█████████▏| 32/35 [00:00<00:00, 35.35it/s]
Capturing CUDA graphs (mixed prefill-decode, PIECEWISE): 100%|██████████| 35/35 [00:00<00:00, 35.21it/s]
(EngineCore_DP0 pid=3812375)
Capturing CUDA graphs (decode, FULL):   0%|          | 0/19 [00:00<?, ?it/s]
Capturing CUDA graphs (decode, FULL):  21%|██        | 4/19 [00:00<00:00, 31.87it/s]
Capturing CUDA graphs (decode, FULL):  42%|████▏     | 8/19 [00:00<00:00, 34.78it/s]
Capturing CUDA graphs (decode, FULL):  63%|██████▎   | 12/19 [00:00<00:00, 35.78it/s]
Capturing CUDA graphs (decode, FULL):  84%|████████▍ | 16/19 [00:00<00:00, 36.25it/s]
Capturing CUDA graphs (decode, FULL): 100%|██████████| 19/19 [00:00<00:00, 35.94it/s]
(EngineCore_DP0 pid=3812375) INFO 02-17 04:36:19 [gpu_model_runner.py:5063] Graph capturing finished in 2 secs, took 0.28 GiB
(EngineCore_DP0 pid=3812375) INFO 02-17 04:36:19 [core.py:272] init engine (profile, create kv cache, warmup model) took 10.65 seconds
[2026-02-17 04:36:21] INFO tts_runtime.py:162: Initializing prompt builder...
[2026-02-17 04:36:21] INFO tts_runtime.py:169: 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: 043f92
Wall time: 5.0010 seconds
Process running with session ID 99881
Original token count: 5435
Output:
[2026-02-17 04:36:26] INFO tts_runtime.py:176: Initializing TTS pipeline...
[2026-02-17 04:36:26] INFO tts_runtime.py:187: Initializing streaming pipeline...
[2026-02-17 04:36:26] INFO tts_runtime.py:237: ✅ TTS runtime initialized in 28789ms
[2026-02-17 04:36:26] INFO tts_runtime.py:238:    Model version: LLM
[2026-02-17 04:36:26] INFO tts_runtime.py:249: Speaker globals cache startup precompute skipped (disabled)
[2026-02-17 04:36:26] INFO pipeline.py:182: perf_non_stream_start
[2026-02-17 04:36:26] INFO pipeline.py:236: perf_first_llm_batch
[2026-02-17 04:36:26] INFO pipeline.py:274: perf_llm_generation_done
[2026-02-17 04:36:26] INFO pipeline.py:297: perf_token_parse_done
[2026-02-17 04:36:27] INFO pipeline.py:397: perf_bicodec_decode_done
[2026-02-17 04:36:27] INFO pipeline.py:417: perf_non_stream_done
[2026-02-17 04:36:27] INFO tts_runtime.py:427: SR check: output_sample_rate='16khz', sr_service=False, is_loaded=False
[2026-02-17 04:36:27] INFO pipeline.py:182: perf_non_stream_start
[2026-02-17 04:36:27] INFO pipeline.py:182: perf_non_stream_start
[2026-02-17 04:36:27] INFO pipeline.py:182: perf_non_stream_start
[2026-02-17 04:36:27] INFO pipeline.py:182: perf_non_stream_start
[2026-02-17 04:36:27] INFO pipeline.py:182: perf_non_stream_start
[2026-02-17 04:36:27] INFO pipeline.py:182: perf_non_stream_start
[2026-02-17 04:36:27] INFO pipeline.py:182: perf_non_stream_start
[2026-02-17 04:36:27] INFO pipeline.py:182: perf_non_stream_start
[2026-02-17 04:36:27] INFO pipeline.py:182: perf_non_stream_start
[2026-02-17 04:36:27] INFO pipeline.py:182: perf_non_stream_start
[2026-02-17 04:36:27] INFO pipeline.py:182: perf_non_stream_start
[2026-02-17 04:36:27] INFO pipeline.py:182: perf_non_stream_start
[2026-02-17 04:36:27] INFO pipeline.py:182: perf_non_stream_start
[2026-02-17 04:36:27] INFO pipeline.py:182: perf_non_stream_start
[2026-02-17 04:36:27] INFO pipeline.py:182: perf_non_stream_start
[2026-02-17 04:36:27] INFO pipeline.py:182: perf_non_stream_start
[2026-02-17 04:36:27] INFO pipeline.py:236: perf_first_llm_batch
[2026-02-17 04:36:27] INFO pipeline.py:236: perf_first_llm_batch
[2026-02-17 04:36:27] INFO pipeline.py:236: perf_first_llm_batch
[2026-02-17 04:36:27] INFO pipeline.py:236: perf_first_llm_batch
[2026-02-17 04:36:27] INFO pipeline.py:236: perf_first_llm_batch
[2026-02-17 04:36:27] INFO pipeline.py:236: perf_first_llm_batch
[2026-02-17 04:36:27] INFO pipeline.py:236: perf_first_llm_batch
[2026-02-17 04:36:27] INFO pipeline.py:236: perf_first_llm_batch
[2026-02-17 04:36:27] INFO pipeline.py:236: perf_first_llm_batch
[2026-02-17 04:36:27] INFO pipeline.py:236: perf_first_llm_batch
[2026-02-17 04:36:27] INFO pipeline.py:236: perf_first_llm_batch
[2026-02-17 04:36:27] INFO pipeline.py:236: perf_first_llm_batch
[2026-02-17 04:36:27] INFO pipeline.py:236: perf_first_llm_batch
[2026-02-17 04:36:27] INFO pipeline.py:236: perf_first_llm_batch
[2026-02-17 04:36:27] INFO pipeline.py:236: perf_first_llm_batch
[2026-02-17 04:36:27] INFO pipeline.py:236: perf_first_llm_batch
[2026-02-17 04:36:28] INFO pipeline.py:274: perf_llm_generation_done
[2026-02-17 04:36:28] INFO pipeline.py:297: perf_token_parse_done
[2026-02-17 04:36:28] INFO pipeline.py:397: perf_bicodec_decode_done
[2026-02-17 04:36:28] INFO pipeline.py:417: perf_non_stream_done
[2026-02-17 04:36:28] INFO tts_runtime.py:427: SR check: output_sample_rate='16khz', sr_service=False, is_loaded=False
[2026-02-17 04:36:28] INFO pipeline.py:274: perf_llm_generation_done
[2026-02-17 04:36:28] INFO pipeline.py:297: perf_token_parse_done
[2026-02-17 04:36:28] INFO pipeline.py:397: perf_bicodec_decode_done
[2026-02-17 04:36:28] INFO pipeline.py:417: perf_non_stream_done
[2026-02-17 04:36:28] INFO tts_runtime.py:427: SR check: output_sample_rate='16khz', sr_service=False, is_loaded=False
[2026-02-17 04:36:28] INFO pipeline.py:274: perf_llm_generation_done
[2026-02-17 04:36:28] INFO pipeline.py:297: perf_token_parse_done
[2026-02-17 04:36:28] INFO pipeline.py:397: perf_bicodec_decode_done
[2026-02-17 04:36:28] INFO pipeline.py:417: perf_non_stream_done
[2026-02-17 04:36:28] INFO tts_runtime.py:427: SR check: output_sample_rate='16khz', sr_service=False, is_loaded=False
[2026-02-17 04:36:28] INFO pipeline.py:274: perf_llm_generation_done
[2026-02-17 04:36:28] INFO pipeline.py:297: perf_token_parse_done
[2026-02-17 04:36:28] INFO pipeline.py:397: perf_bicodec_decode_done
[2026-02-17 04:36:28] INFO pipeline.py:417: perf_non_stream_done
[2026-02-17 04:36:28] INFO tts_runtime.py:427: SR check: output_sample_rate='16khz', sr_service=False, is_loaded=False
[2026-02-17 04:36:28] INFO pipeline.py:274: perf_llm_generation_done
[2026-02-17 04:36:28] INFO pipeline.py:297: perf_token_parse_done
[2026-02-17 04:36:28] INFO pipeline.py:397: perf_bicodec_decode_done
[2026-02-17 04:36:28] INFO pipeline.py:417: perf_non_stream_done
[2026-02-17 04:36:28] INFO tts_runtime.py:427: SR check: output_sample_rate='16khz', sr_service=False, is_loaded=False
[2026-02-17 04:36:28] INFO pipeline.py:274: perf_llm_generation_done
[2026-02-17 04:36:28] INFO pipeline.py:297: perf_token_parse_done
[2026-02-17 04:36:28] INFO pipeline.py:397: perf_bicodec_decode_done
[2026-02-17 04:36:28] INFO pipeline.py:417: perf_non_stream_done
[2026-02-17 04:36:28] INFO tts_runtime.py:427: SR check: output_sample_rate='16khz', sr_service=False, is_loaded=False
[2026-02-17 04:36:28] INFO pipeline.py:274: perf_llm_generation_done
[2026-02-17 04:36:28] INFO pipeline.py:297: perf_token_parse_done
[2026-02-17 04:36:28] INFO pipeline.py:397: perf_bicodec_decode_done
[2026-02-17 04:36:28] INFO pipeline.py:417: perf_non_stream_done
[2026-02-17 04:36:28] INFO tts_runtime.py:427: SR check: output_sample_rate='16khz', sr_service=False, is_loaded=False
[2026-02-17 04:36:28] INFO pipeline.py:274: perf_llm_generation_done
[2026-02-17 04:36:28] INFO pipeline.py:297: perf_token_parse_done
[2026-02-17 04:36:28] INFO pipeline.py:397: perf_bicodec_decode_done
[2026-02-17 04:36:28] INFO pipeline.py:417: perf_non_stream_done
[2026-02-17 04:36:28] INFO tts_runtime.py:427: SR check: output_sample_rate='16khz', sr_service=False, is_loaded=False
[2026-02-17 04:36:28] INFO pipeline.py:274: perf_llm_generation_done
[2026-02-17 04:36:28] INFO pipeline.py:297: perf_token_parse_done
[2026-02-17 04:36:28] INFO pipeline.py:397: perf_bicodec_decode_done
[2026-02-17 04:36:28] INFO pipeline.py:417: perf_non_stream_done
[2026-02-17 04:36:28] INFO tts_runtime.py:427: SR check: output_sample_rate='16khz', sr_service=False, is_loaded=False
[2026-02-17 04:36:28] INFO pipeline.py:274: perf_llm_generation_done
[2026-02-17 04:36:28] INFO pipeline.py:297: perf_token_parse_done
[2026-02-17 04:36:28] INFO pipeline.py:397: perf_bicodec_decode_done
[2026-02-17 04:36:28] INFO pipeline.py:417: perf_non_stream_done
[2026-02-17 04:36:28] INFO tts_runtime.py:427: SR check: output_sample_rate='16khz', sr_service=False, is_loaded=False
[2026-02-17 04:36:28] INFO pipeline.py:274: perf_llm_generation_done
[2026-02-17 04:36:28] INFO pipeline.py:297: perf_token_parse_done
[2026-02-17 04:36:28] INFO pipeline.py:397: perf_bicodec_decode_done
[2026-02-17 04:36:28] INFO pipeline.py:417: perf_non_stream_done
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[2026-02-17 04:36:28] INFO pipeline.py:236: perf_first_llm_batch
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[2026-02-17 04:36:28] INFO pipeline.py:274: perf_llm_generation_done
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[2026-02-17 04:36:28] INFO pipeline.py:274: perf_llm_generation_done
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[2026-02-17 04:36:28] INFO pipeline.py:274: perf_llm_generation_done
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[2026-02-17 04:36:28] INFO pipeline.py:274: perf_llm_generation_done
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[2026-02-17 04:36:28] INFO pipeline.py:274: perf_llm_generation_done
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[2026-02-17 04:36:28] INFO pipeline.py:274: perf_llm_generation_done
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[2026-02-17 04:36:28] INFO tts_runtime.py:427: SR check: output_sample_rate='16khz', sr_service=False, is_loaded=False
[2026-02-17 04:36:28] INFO pipeline.py:274: perf_llm_generation_done
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[2026-02-17 04:36:28] INFO pipeline.py:397: perf_bicodec_decode_done
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[2026-02-17 04:36:28] INFO tts_runtime.py:427: SR check: output_sample_rate='16khz', sr_service=False, is_loaded=False
[2026-02-17 04:36:30] INFO pipeline.py:182: perf_non_stream_start
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[2026-02-17 04:36:30] INFO pipeline.py:182: perf_non_stream_start
[2026-02-17 04:36:30] INFO pipeline.py:236: perf_first_llm_batch
[2026-02-17 04:36:30] INFO pipeline.py:236: perf_first_llm_batch
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[2026-02-17 04:36:30] INFO pipeline.py:236: perf_first_llm_batch
[2026-02-17 04:36:30] INFO pipeline.py:236: perf_first_llm_batch
[2026-02-17 04:36:30] INFO pipeline.py:236: perf_first_llm_batch
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[2026-02-17 04:36:30] INFO pipeline.py:236: perf_first_llm_batch
[2026-02-17 04:36:30] INFO pipeline.py:236: perf_first_llm_batch
[2026-02-17 04:36:30] INFO pipeline.py:236: perf_first_llm_batch
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[2026-02-17 04:36:30] INFO pipeline.py:236: perf_first_llm_batch
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[2026-02-17 04:36:30] INFO pipeline.py:236: perf_first_llm_batch
[2026-02-17 04:36:30] INFO pipeline.py:236: perf_first_llm_batch
[2026-02-17 04:36:30] INFO pipeline.py:236: perf_first_llm_batch
[2026-02-17 04:36:30] INFO pipeline.py:236: perf_first_llm_batch
[2026-02-17 04:36:30] INFO pipeline.py:236: perf_first_llm_batch
[2026-02-17 04:36:30] INFO pipeline.py:236: perf_first_llm_batch
[2026-02-17 04:36:30] INFO pipeline.py:236: perf_first_llm_batch
[2026-02-17 04:36:30] INFO pipeline.py:236: perf_first_llm_batch
[2026-02-17 04:36:30] INFO pipeline.py:236: perf_first_llm_batch
[2026-02-17 04:36:30] INFO pipeline.py:236: perf_first_llm_batch


**Extending log polling duration**


Output:
Chunk ID: c06697
Wall time: 4.2943 seconds
Process exited with code 0
Original token count: 24520
Output:
Total output lines: 1335

[2026-02-17 04:36:31] INFO pipeline.py:274: perf_llm_generation_done
[2026-02-17 04:36:31] INFO pipeline.py:297: perf_token_parse_done
[2026-02-17 04:36:31] INFO pipeline.py:397: perf_bicodec_decode_done
[2026-02-17 04:36:31] INFO pipeline.py:417: perf_non_stream_done
[2026-02-17 04:36:31] INFO tts_runtime.py:427: SR check: output_sample_rate='16khz', sr_service=False, is_loaded=False
[2026-02-17 04:36:31] INFO pipeline.py:182: perf_non_stream_start
[2026-02-17 04:36:31] INFO pipeline.py:274: perf_llm_generation_done
[2026-02-17 04:36:31] INFO pipeline.py:297: perf_token_parse_done
[2026-02-17 04:36:31] INFO pipeline.py:397: perf_bicodec_decode_done
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[2026-02-17 04:36:31] INFO tts_runtime.py:427: SR check: output_sample_rate='16khz', sr_service=False, is_loaded=False
[2026-02-17 04:36:31] INFO pipeline.py:274: perf_llm_generation_done
[2026-02-17 04:36:31] INFO pipeline.py:297: perf_token_parse_done
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[2026-02-17 04:36:31] INFO tts_runtime.py:427: SR check: output_sample_rate='16khz', sr_service=False, is_loaded=False
[2026-02-17 04:36:31] INFO pipeline.py:274: perf_llm_generation_done
[2026-02-17 04:36:31] INFO pipeline.py:297: perf_token_parse_done
[2026-02-17 04:36:31] INFO pipeline.py:397: perf_bicodec_decode_done
[2026-02-17 04:36:31] INFO pipeline.py:417: perf_non_stream_done
[2026-02-17 04:36:31] INFO tts_runtime.py:427: SR check: output_sample_rate='16khz', sr_service=False, is_loaded=False
[2026-02-17 04:36:31] INFO pipeline.py:274: perf_llm_generation_done
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[2026-02-17 04:36:31] INFO tts_runtime.py:427: SR check: output_sample_rate='16khz', sr_service=False, is_loaded=False
[2026-02-17 04:36:31] INFO pipeline.py:274: perf_llm_generation_done
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[2026-02-17 04:36:31] INFO pipeline.py:397: perf_bicodec_decode_done
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[2026-02-17 04:36:31] INFO tts_runtime.py:427: SR check: output_sample_rate='16khz', sr_service=False, is_loaded=False
[2026-02-17 04:36:31] INFO pipeline.py:274: perf_llm_generation_done
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[2026-02-17 04:36:31] INFO tts_runtime.py:427: SR check: output_sample_rate='16khz', sr_service=False, is_loaded=False
[2026-02-17 04:36:31] INFO pipeline.py:274: perf_llm_generation_done
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[2026-02-17 04:36:31] INFO tts_runtime.py:427: SR check: output_sample_rate='16khz', sr_service=False, is_loaded=False
[2026-02-17 04:36:31] INFO pipeline.py:274: perf_llm_generation_done
[2026-02-17 04:36:31] INFO pipeline.py:297: perf_token_parse_done
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[2026-02-17 04:36:31] INFO tts_runtime.py:427: SR check: output_sample_rate='16khz', sr_service=False, is_loaded=False
[2026-02-17 04:36:31] INFO pipeline.py:274: perf_llm_generation_done
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[2026-02-17 04:36:32…12520 tokens truncated…', sr_service=False, is_loaded=False
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[2026-02-17 04:36:38] INFO pipeline.py:417: perf_non_stream_done
[2026-02-17 04:36:38] INFO tts_runtime.py:427: SR check: output_sample_rate='16khz', sr_service=False, is_loaded=False
[2026-02-17 04:36:38] INFO pipeline.py:274: perf_llm_generation_done
[2026-02-17 04:36:38] INFO pipeline.py:297: perf_token_parse_done
[2026-02-17 04:36:38] INFO pipeline.py:397: perf_bicodec_decode_done
[2026-02-17 04:36:38] INFO pipeline.py:417: perf_non_stream_done
[2026-02-17 04:36:38] INFO tts_runtime.py:427: SR check: output_sample_rate='16khz', sr_service=False, is_loaded=False
[2026-02-17 04:36:38] INFO pipeline.py:274: perf_llm_generation_done
[2026-02-17 04:36:38] INFO pipeline.py:297: perf_token_parse_done
[2026-02-17 04:36:38] INFO pipeline.py:397: perf_bicodec_decode_done
[2026-02-17 04:36:38] INFO pipeline.py:417: perf_non_stream_done
[2026-02-17 04:36:38] INFO tts_runtime.py:427: SR check: output_sample_rate='16khz', sr_service=False, is_loaded=False
[2026-02-17 04:36:38] INFO pipeline.py:274: perf_llm_generation_done
[2026-02-17 04:36:38] INFO pipeline.py:297: perf_token_parse_done
[2026-02-17 04:36:38] INFO pipeline.py:397: perf_bicodec_decode_done
[2026-02-17 04:36:38] INFO pipeline.py:417: perf_non_stream_done
[2026-02-17 04:36:38] INFO tts_runtime.py:427: SR check: output_sample_rate='16khz', sr_service=False, is_loaded=False
[2026-02-17 04:36:38] INFO pipeline.py:274: perf_llm_generation_done
[2026-02-17 04:36:38] INFO pipeline.py:297: perf_token_parse_done
[2026-02-17 04:36:38] INFO pipeline.py:397: perf_bicodec_decode_done
[2026-02-17 04:36:38] INFO pipeline.py:417: perf_non_stream_done
[2026-02-17 04:36:38] INFO tts_runtime.py:427: SR check: output_sample_rate='16khz', sr_service=False, is_loaded=False
[2026-02-17 04:36:38] INFO pipeline.py:274: perf_llm_generation_done
[2026-02-17 04:36:38] INFO pipeline.py:297: perf_token_parse_done
[2026-02-17 04:36:38] INFO pipeline.py:397: perf_bicodec_decode_done
[2026-02-17 04:36:38] INFO pipeline.py:417: perf_non_stream_done
[2026-02-17 04:36:38] INFO tts_runtime.py:427: SR check: output_sample_rate='16khz', sr_service=False, is_loaded=False
[2026-02-17 04:36:38] INFO pipeline.py:274: perf_llm_generation_done
[2026-02-17 04:36:38] INFO pipeline.py:297: perf_token_parse_done
[2026-02-17 04:36:38] INFO pipeline.py:397: perf_bicodec_decode_done
[2026-02-17 04:36:38] INFO pipeline.py:417: perf_non_stream_done
[2026-02-17 04:36:38] INFO tts_runtime.py:427: SR check: output_sample_rate='16khz', sr_service=False, is_loaded=False
[2026-02-17 04:36:38] INFO pipeline.py:274: perf_llm_generation_done
[2026-02-17 04:36:38] INFO pipeline.py:297: perf_token_parse_done
[2026-02-17 04:36:38] INFO pipeline.py:397: perf_bicodec_decode_done
[2026-02-17 04:36:38] INFO pipeline.py:417: perf_non_stream_done
[2026-02-17 04:36:38] INFO tts_runtime.py:427: SR check: output_sample_rate='16khz', sr_service=False, is_loaded=False
[2026-02-17 04:36:38] INFO pipeline.py:274: perf_llm_generation_done
[2026-02-17 04:36:38] INFO pipeline.py:297: perf_token_parse_done
[2026-02-17 04:36:38] INFO pipeline.py:397: perf_bicodec_decode_done
[2026-02-17 04:36:38] INFO pipeline.py:417: perf_non_stream_done
[2026-02-17 04:36:38] INFO tts_runtime.py:427: SR check: output_sample_rate='16khz', sr_service=False, is_loaded=False
[2026-02-17 04:36:38] INFO pipeline.py:274: perf_llm_generation_done
[2026-02-17 04:36:38] INFO pipeline.py:297: perf_token_parse_done
[2026-02-17 04:36:38] INFO pipeline.py:397: perf_bicodec_decode_done
[2026-02-17 04:36:38] INFO pipeline.py:417: perf_non_stream_done
[2026-02-17 04:36:38] INFO tts_runtime.py:427: SR check: output_sample_rate='16khz', sr_service=False, is_loaded=False
[2026-02-17 04:36:38] INFO pipeline.py:274: perf_llm_generation_done
[2026-02-17 04:36:38] INFO pipeline.py:297: perf_token_parse_done
[2026-02-17 04:36:38] INFO pipeline.py:397: perf_bicodec_decode_done
[2026-02-17 04:36:38] INFO pipeline.py:417: perf_non_stream_done
[2026-02-17 04:36:38] INFO tts_runtime.py:427: SR check: output_sample_rate='16khz', sr_service=False, is_loaded=False
[2026-02-17 04:36:38] INFO pipeline.py:274: perf_llm_generation_done
[2026-02-17 04:36:38] INFO pipeline.py:297: perf_token_parse_done
[2026-02-17 04:36:38] INFO pipeline.py:397: perf_bicodec_decode_done
[2026-02-17 04:36:38] INFO pipeline.py:417: perf_non_stream_done
[2026-02-17 04:36:38] INFO tts_runtime.py:427: SR check: output_sample_rate='16khz', sr_service=False, is_loaded=False
✅ 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)
🚀 SparkTTSPipeline initialized
🌊 Veena3SlidingWindowPipeline initialized (sliding window: 28 tokens)

=== concurrency=16 requests=32 ===
success=32/32 (100%)
wall=1.45s throughput=22.02 req/s
latency_ms avg=726 p50=712 p95=741 max=741
generation_ms: avg=641.47 p50=639.00 p95=710.00 max=731.00
llm_batch_wall_ms_total: avg=631.04 p50=628.61 p95=700.07 max=720.53
llm_decode_wall_ms_total: avg=610.01 p50=605.83 p95=680.65 max=702.12
llm_parse_ms: avg=0.06 p50=0.06 p95=0.07 max=0.09
llm_time_per_token_ms: avg=4.93 p50=4.91 p95=5.47 max=5.63
bicodec_decode_wall_ms: avg=10.34 p50=10.21 p95=11.70 max=12.18
bicodec_decode_gpu_ms: avg=10.25 p50=10.13 p95=11.50 max=12.10
gpu: util_avg=54% util_max=65% mem_avg=23601MB mem_max=23601MB

=== concurrency=32 requests=64 ===
success=64/64 (100%)
wall=2.07s throughput=30.95 req/s
latency_ms avg=1003 p50=985 p95=1079 max=1079
generation_ms: avg=834.36 p50=829.00 p95=1010.00 max=1060.00
llm_batch_wall_ms_total: avg=822.63 p50=816.45 p95=998.04 max=1047.66
llm_decode_wall_ms_total: avg=791.98 p50=793.03 p95=978.37 max=1029.81
llm_parse_ms: avg=0.07 p50=0.06 p95=0.08 max=0.20
llm_time_per_token_ms: avg=6.43 p50=6.38 p95=7.80 max=8.18
bicodec_decode_wall_ms: avg=11.72 p50=12.13 p95=13.79 max=14.66
bicodec_decode_gpu_ms: avg=11.63 p50=12.02 p95=13.56 max=14.56
gpu: util_avg=77% util_max=99% mem_avg=23601MB mem_max=23601MB
❌ BiCodec requires EXACTLY 32 global tokens, got 31
   └─ Padded to 32

=== concurrency=64 requests=128 ===
success=128/128 (100%)
wall=3.18s throughput=40.24 req/s
latency_ms avg=1499 p50=1502 p95=1672 max=1672
generation_ms: avg=1182.05 p50=1175.00 p95=1540.00 max=1623.00
llm_batch_wall_ms_total: avg=1170.15 p50=1161.78 p95=1527.62 max=1612.01
llm_decode_wall_ms_total: avg=1129.09 p50=1126.20 p95=1501.82 max=1589.41
llm_parse_ms: avg=0.06 p50=0.06 p95=0.08 max=0.09
llm_time_per_token_ms: avg=9.14 p50=9.08 p95=11.93 max=12.59
bicodec_decode_wall_ms: avg=11.82 p50=12.02 p95=13.79 max=33.33
bicodec_decode_gpu_ms: avg=11.73 p50=11.95 p95=13.70 max=33.24
gpu: util_avg=65% util_max=99% mem_avg=23601MB mem_max=23601MB

results saved: /tmp/stress_runtime_detailed_high.json
ERROR 02-17 04:36:38 [core_client.py:605] Engine core proc EngineCore_DP0 died unexpectedly, shutting down client.


**Planning detailed request metrics extraction**


python3 - <<'PY'
import json, statistics

low=json.load(open('/tmp/stress_runtime_detailed_fixed.json'))
high=json.load(open('/tmp/stress_runtime_detailed_high.json'))

# helper

def pick(levels, c):
    for l in levels:
        if l['concurrency']==c:
            return l

for src,name in [(low,'low'),(high,'high')]:
    for c in sorted({l['concurrency'] for l in src['levels']}):
        lvl=pick(src['levels'],c)
        succ=[r for r in lvl['requests'] if r['success']]
        if not succ:
            continue
        # choose median by latency
        succ_sorted=sorted(succ,key=lambda r:r['latency_ms'])
        req=succ_sorted[len(succ_sorted)//2]
        t=req['timing']
        print(f"{name} c={c} sample latency={req['latency_ms']:.1f}ms audio={req['audio_seconds']:.3f}s")
        keys=['generation_ms','llm_prompt_token_total','llm_token_total','llm_batch_count','llm_prefill_wall_ms','llm_decode_wall_ms_total','llm_batch_wall_ms_total','llm_batch_gpu_ms_total','llm_time_per_token_ms','bicodec_decode_wall_ms','bicodec_decode_gpu_ms','bicodec_decode_cpu_ms','pipeline_total_ms']
        for k in keys:
            if k in t:
                print(f"  {k}={t[k]}")
        break

# aggregate knee metrics
for c in [1,2,4,8,16,32,64]:
    lvl=pick(low['levels'],c) or pick(high['levels'],c)
    if lvl is None: continue
    succ=[r for r in lvl['requests'] if r['success']]
    gen=[r['timing']['generation_ms'] for r in succ if 'generation_ms' in r['timing']]
    tpt=[r['timing']['llm_time_per_token_ms'] for r in succ if 'llm_time_per_token_ms' in r['timing']]
    print(f"agg c={c} throughput={lvl['throughput_rps']:.2f} avg_gen={statistics.mean(gen):.1f} avg_tpt={statistics.mean(tpt):.3f}")
PY


Output:
Chunk ID: 9169cd
Wall time: 0.0514 seconds
Process exited with code 0
Original token count: 333
Output:
low c=1 sample latency=414.5ms audio=1.880s
  generation_ms=414
  llm_prompt_token_total=22
  llm_token_total=128
  llm_batch_count=128
  llm_prefill_wall_ms=11.458189226686954
  llm_decode_wall_ms_total=390.46588353812695
  llm_batch_wall_ms_total=401.9240727648139
  llm_time_per_token_ms=3.1401239466504194
  bicodec_decode_wall_ms=11.593984439969063
  bicodec_decode_gpu_ms=11.357151985168457
  bicodec_decode_cpu_ms=0.23683245480060577
  pipeline_total_ms=414.3243422731757
high c=16 sample latency=740.5ms audio=1.880s
  generation_ms=710
  llm_prompt_token_total=22
  llm_token_total=128
  llm_batch_count=127
  llm_prefill_wall_ms=19.42602638155222
  llm_decode_wall_ms_total=680.6489545851946
  llm_batch_wall_ms_total=700.0749809667468
  llm_time_per_token_ms=5.4694409045623615
  bicodec_decode_wall_ms=10.361206717789173
  bicodec_decode_gpu_ms=10.265503883361816
  bicodec_decode_cpu_ms=0.09570283442735672
  pipeline_total_ms=710.896959528327
agg c=1 throughput=2.27 avg_gen=440.7 avg_tpt=3.346
agg c=2 throughput=4.14 avg_gen=477.0 avg_tpt=3.632
agg c=4 throughput=7.97 avg_gen=483.4 avg_tpt=3.690
agg c=8 throughput=13.85 avg_gen=533.9 avg_tpt=4.079
agg c=16 throughput=22.26 avg_gen=630.1 avg_tpt=4.839
agg c=32 throughput=30.95 avg_gen=834.4 avg_tpt=6.427
agg c=64 throughput=40.24 avg_gen=1182.0 avg_tpt=9.142


**Analyzing concurrency bottleneck metrics**


python3 - <<'PY'
import json, statistics
files=['/tmp/stress_runtime_detailed_fixed.json','/tmp/stress_runtime_detailed_high.json']
levels=[]
for f in files:
    data=json.load(open(f))
    levels.extend(data['levels'])

# unique by concurrency keep first maybe low for 16 from fixed? we can combine by map prefer high for >=16 from high maybe
by={}
for l in levels:
    c=l['concurrency']
    if c in by:
        # choose larger sample size
        if l['total_requests']>by[c]['total_requests']:
            by[c]=l
    else:
        by[c]=l

for c in sorted(by):
    l=by[c]
    succ=[r for r in l['requests'] if r['success']]
    if not succ: continue
    wall=[r['timing'].get('llm_batch_wall_ms_total') for r in succ if r.get('timing',{}).get('llm_batch_wall_ms_total') is not None]
    gpu=[r['timing'].get('llm_batch_gpu_ms_total') for r in succ if r.get('timing',{}).get('llm_batch_gpu_ms_total') is not None]
    decw=[r['timing'].get('llm_decode_wall_ms_total') for r in succ if r.get('timing',{}).get('llm_decode_wall_ms_total') is not None]
    decg=[r['timing'].get('llm_decode_gpu_ms_total') for r in succ if r.get('timing',{}).get('llm_decode_gpu_ms_total') is not None]
    if wall:
        aw=statistics.mean(wall)
        ag=statistics.mean(gpu) if gpu else 0
        print(f"c={c:>2} batch_wall={aw:.1f}ms batch_gpu={ag:.1f}ms gpu_share={(ag/aw*100 if aw and ag else 0):.1f}%")
    if decw:
        aw=statistics.mean(decw)
        ag=statistics.mean(decg) if decg else 0
        print(f"    decode_wall={aw:.1f}ms decode_gpu={ag:.1f}ms gpu_share={(ag/aw*100 if aw and ag else 0):.1f}%")
PY


Output:
Chunk ID: bd1df2
Wall time: 0.0514 seconds
Process exited with code 0
Original token count: 195
Output:
c= 1 batch_wall=428.3ms batch_gpu=0.0ms gpu_share=0.0%
    decode_wall=416.7ms decode_gpu=0.0ms gpu_share=0.0%
c= 2 batch_wall=464.8ms batch_gpu=0.0ms gpu_share=0.0%
    decode_wall=448.9ms decode_gpu=0.0ms gpu_share=0.0%
c= 4 batch_wall=472.3ms batch_gpu=0.0ms gpu_share=0.0%
    decode_wall=454.8ms decode_gpu=0.0ms gpu_share=0.0%
c= 8 batch_wall=522.1ms batch_gpu=0.0ms gpu_share=0.0%
    decode_wall=500.9ms decode_gpu=0.0ms gpu_share=0.0%
c=16 batch_wall=619.3ms batch_gpu=0.0ms gpu_share=0.0%
    decode_wall=598.5ms decode_gpu=0.0ms gpu_share=0.0%
c=32 batch_wall=822.6ms batch_gpu=0.0ms gpu_share=0.0%
    decode_wall=792.0ms decode_gpu=0.0ms gpu_share=0.0%
c=64 batch_wall=1170.1ms batch_gpu=0.0ms gpu_share=0.0%
    decode_wall=1129.1ms decode_gpu=0.0ms gpu_share=0.0%


**Noting GPU metric limitations**


python3 - <<'PY'
import json
levels=[]
for p in ['/tmp/stress_runtime_detailed_fixed.json','/tmp/stress_runtime_detailed_high.json']:
    levels.extend(json.load(open(p))['levels'])

by={}
for l in levels:
    c=l['concurrency']
    if c not in by or l['total_requests']>by[c]['total_requests']:
        by[c]=l

print('c\tthroughput\tp95_lat\tgpu_avg\tgpu_max\tmem_mb')
for c in sorted(by):
    l=by[c]
    lat=l['latency_ms']
    gpu=l['gpu']
    print(f"{c}\t{l['throughput_rps']:.2f}\t{lat.get('p95',0):.0f}\t{gpu.get('gpu_util_pct',{}).get('avg',0):.0f}\t{gpu.get('gpu_util_pct',{}).get('max',0):.0f}\t{gpu.get('memory_mb',{}).get('max',0):.0f}")
PY


Output:
Chunk ID: b584e9
Wall time: 0.0511 seconds
Process exited with code 0
Original token count: 54
Output:
c	throughput	p95_lat	gpu_avg	gpu_max	mem_mb
1	2.27	439	57	69	23601
2	4.14	507	58	69	23601
4	7.97	529	70	71	23601
8	13.85	591	50	78	23601
16	22.26	732	49	67	23601
32	30.95	1079	77	99	23601
64	40.24	1672	65	99	23601


**Considering throughput gain factors**


python3 - <<'PY'
import json
levels=[]
for p in ['/tmp/stress_runtime_detailed_fixed.json','/tmp/stress_runtime_detailed_high.json']:
    levels.extend(json.load(open(p))['levels'])
by={}
for l in levels:
    c=l['concurrency']
    if c not in by or l['total_requests']>by[c]['total_requests']:
        by[c]=l
base=by[1]['throughput_rps']
prev=None
for c in sorted(by):
    thr=by[c]['throughput_rps']
    if prev is None:
        m='-'
    else:
        m=f"{thr/prev:.2f}x"
    print(c, f"thr={thr:.2f}", f"vs_c1={thr/base:.2f}x", f"step={m}")
    prev=thr
PY


Output:
Chunk ID: a974e1
Wall time: 0.0514 seconds
Process exited with code 0
Original token count: 61
Output:
1 thr=2.27 vs_c1=1.00x step=-
2 thr=4.14 vs_c1=1.83x step=1.83x
4 thr=7.97 vs_c1=3.52x step=1.93x
8 thr=13.85 vs_c1=6.11x step=1.74x
16 thr=22.26 vs_c1=9.82x step=1.61x
32 thr=30.95 vs_c1=13.66x step=1.39x
64 thr=40.24 vs_c1=17.75x step=1.30x


**Computing per-level token throughput**


python3 - <<'PY'
import json
levels=[]
for p in ['/tmp/stress_runtime_detailed_fixed.json','/tmp/stress_runtime_detailed_high.json']:
    levels.extend(json.load(open(p))['levels'])
by={}
for l in levels:
    c=l['concurrency']
    if c not in by or l['total_requests']>by[c]['total_requests']:
        by[c]=l
for c in sorted(by):
    thr=by[c]['throughput_rps']
    tps=thr*128
    print(f"c={c:>2} req/s={thr:.2f} est_tokens/s={tps:.0f}")
PY


Output:
Chunk ID: 521f9c
Wall time: 0.0503 seconds
Process exited with code 0
Original token count: 60
Output:
c= 1 req/s=2.27 est_tokens/s=290
c= 2 req/s=4.14 est_tokens/s=530
c= 4 req/s=7.97 est_tokens/s=1020
c= 8 req/s=13.85 est_tokens/s=1773
c=16 req/s=22.26 est_tokens/s=2850
c=32 req/s=30.95 est_tokens/s=3962
c=64 req/s=40.24 est_tokens/s=5151


**Planning HTTP metrics comparison**


python3 - <<'PY'
import json, statistics
hp=json.load(open('/tmp/stress_http_detailed_fixed.json'))
for lvl in hp:
    c=lvl['concurrency']
    succ=[r for r in lvl['requests'] if r['success']]
    if not succ: continue
    r=sorted(succ,key=lambda x:x['latency_ms'])[len(succ)//2]
    t=r['timing']
    print(f"c={c} sample latency={r['latency_ms']:.1f} request_inflight={r.get('request_inflight')}")
    for k in ['generation_ms','llm_prompt_token_total','llm_token_total','llm_batch_count','llm_prefill_wall_ms','llm_batch_wall_ms_total','llm_decode_wall_ms_total','llm_parse_ms','bicodec_decode_wall_ms','bicodec_decode_gpu_ms','bicodec_decode_cpu_ms','pipeline_total_ms']:
        if k in t:
            print(f"  {k}={t[k]}")
    break

# inflight percentiles at c16
for lvl in hp:
    if lvl['concurrency']==16:
        infl=[r['request_inflight'] for r in lvl['requests'] if r['success'] and isinstance(r.get('request_inflight'),int)]
        print('c16 inflight stats count',len(infl),'min',min(infl),'p50',sorted(infl)[len(infl)//2],'max',max(infl))
PY


Output:
Chunk ID: 193606
Wall time: 0.0515 seconds
Process exited with code 0
Original token count: 111
Output:
c=1 sample latency=427.7 request_inflight=1
  generation_ms=423
  llm_prompt_token_total=21
  llm_token_total=128
  llm_batch_count=127
  llm_prefill_wall_ms=11.229567229747772
  llm_batch_wall_ms_total=410.81
  llm_decode_wall_ms_total=399.581
  llm_parse_ms=0.103
  bicodec_decode_wall_ms=11.958
  bicodec_decode_gpu_ms=11.746
  bicodec_decode_cpu_ms=0.212
  pipeline_total_ms=423.6633684486151
c16 inflight stats count 48 min 1 p50 9 max 16


**Preparing HTTP sampling parameters**


python3 - <<'PY'
import json
hp=json.load(open('/tmp/stress_http_detailed_fixed.json'))
for lvl in hp:
    if lvl['concurrency']==16:
        succ=[r for r in lvl['requests'] if r['success']]
        # choose highest inflight sample
        r=max(succ,key=lambda x:x.get('request_inflight') or 0)
        print('c16 high inflight sample latency',r['latency_ms'],'inflight',r.get('request_inflight'))
        t=r['timing']
        for k in ['generation_ms','llm_batch_wall_ms_total','llm_decode_wall_ms_total','llm_time_per_token_ms','bicodec_decode_wall_ms','pipeline_total_ms']:
            print(k,t.get(k))
PY


Output:
Chunk ID: 81c3b2
Wall time: 0.0504 seconds
Process exited with code 0
Original token count: 61
Output:
c16 high inflight sample latency 756.7665576934814 inflight 16
generation_ms 705
llm_batch_wall_ms_total 695.107
llm_decode_wall_ms_total 675.715
llm_time_per_token_ms 5.5078125
bicodec_decode_wall_ms 10.184
pipeline_total_ms 705.7493850588799


Summary: auto


**Confirming no lingering processes**


ps -eo pid,ppid,cmd | rg "VLLM::EngineCore|stress_test_runtime_detailed.py|local_server" -n || true


Output:
Chunk ID: 7b28aa
Wall time: 0.0514 seconds
Process exited with code 0
Original token count: 56
Output:
310:3813021 3675569 /bin/bash -c ps -eo pid,ppid,cmd | rg "VLLM::EngineCore|stress_test_runtime_detailed.py|local_server" -n || true
312:3813023 3813021 rg VLLM::EngineCore|stress_test_runtime_detailed.py|local_server -n


**Planning minimal git diff review**


Question: time: {"utc_offset":"-05:00"}


**Determining modified files via git**


git status --short veena3modal/services/tts_runtime.py veena3modal/local_server.py scripts/stress_test_optimized_local.py scripts/stress_test_runtime_detailed.py


Output:
Chunk ID: 738a0d
Wall time: 0.0513 seconds
Process exited with code 0
Original token count: 39
Output:
 M veena3modal/services/tts_runtime.py
?? scripts/stress_test_optimized_local.py
?? scripts/stress_test_runtime_detailed.py
?? veena3modal/local_server.py


**Summarizing benchmarks and changes**


python3 - <<'PY'
import json, statistics
# consolidate runtime levels choose larger sample per c
levels=[]
for p in ['/tmp/stress_runtime_detailed_fixed.json','/tmp/stress_runtime_detailed_high.json']:
    levels.extend(json.load(open(p))['levels'])
by={}
for l in levels:
    c=l['concurrency']
    if c not in by or l['total_requests']>by[c]['total_requests']:
        by[c]=l

print('Runtime summary:')
print('c\tsuccess\treq/s\tp95_ms\tavg_gen_ms\tavg_llm_ms\tavg_bicodec_ms\tavg_tpt_ms')
for c in sorted(by):
    l=by[c]
    succ=[r for r in l['requests'] if r['success']]
    gen=[r['timing']['generation_ms'] for r in succ if 'generation_ms' in r['timing']]
    llm=[r['timing']['llm_batch_wall_ms_total'] for r in succ if 'llm_batch_wall_ms_total' in r['timing']]
    bdc=[r['timing']['bicodec_decode_wall_ms'] for r in succ if 'bicodec_decode_wall_ms' in r['timing']]
    tpt=[r['timing']['llm_time_per_token_ms'] for r in succ if 'llm_time_per_token_ms' in r['timing']]
    print(f"{c}\t{len(succ)}/{l['total_requests']}\t{l['throughput_rps']:.2f}\t{l['latency_ms'].get('p95',0):.0f}\t{statistics.mean(gen):.1f}\t{statistics.mean(llm):.1f}\t{statistics.mean(bdc):.1f}\t{statistics.mean(tpt):.3f}")

hp=json.load(open('/tmp/stress_http_detailed_fixed.json'))
print('\nHTTP summary:')
print('c\tsuccess\treq/s\tp95_ms\tavg_gen_ms\tavg_llm_ms\tpeak_inflight')
for l in hp:
    c=l['concurrency']
    succ=[r for r in l['requests'] if r['success']]
    gen=[r['timing'].get('generation_ms') for r in succ if isinstance(r.get('timing',{}).get('generation_ms'),(int,float))]
    llm=[r['timing'].get('llm_batch_wall_ms_total') for r in succ if isinstance(r.get('timing',{}).get('llm_batch_wall_ms_total'),(int,float))]
    infl=[r.get('request_inflight') for r in succ if isinstance(r.get('request_inflight'),int)]
    print(f"{c}\t{len(succ)}/{len(l['requests'])}\t{l['throughput_rps']:.2f}\t{l['latency_ms'].get('p95',0):.0f}\t{statistics.mean(gen):.1f}\t{statistics.mean(llm):.1f}\t{max(infl) if infl else 0}")
PY


Output:
Chunk ID: a23ab6
Wall time: 0.0504 seconds
Process exited with code 0
Original token count: 145
Output:
Runtime summary:
c	success	req/s	p95_ms	avg_gen_ms	avg_llm_ms	avg_bicodec_ms	avg_tpt_ms
1	6/6	2.27	439	440.7	428.3	11.8	3.346
2	6/6	4.14	507	477.0	464.8	11.6	3.632
4	12/12	7.97	529	483.4	472.3	11.0	3.690
8	24/24	13.85	591	533.9	522.1	11.6	4.079
16	48/48	22.26	732	630.1	619.3	10.7	4.839
32	64/64	30.95	1079	834.4	822.6	11.7	6.427
64	128/128	40.24	1672	1182.0	1170.1	11.8	9.142

HTTP summary:
c	success	req/s	p95_ms	avg_gen_ms	avg_llm_ms	peak_inflight
1	10/10	2.31	441	422.9	407.6	1
4	12/12	7.34	576	501.1	486.9	4
8	24/24	13.20	606	531.7	520.8	8
16	48/48	21.07	760	619.8	607.8	16


**Summarizing benchmark and fix outcomes**