qwen3-tts-rs Profiling & Benchmarking

Run performance profiling and benchmarks for the qwen3-tts Rust TTS engine.

Prerequisites

• •Docker with --gpus all support
• •qwen3-tts:latest Docker image (has Rust toolchain + CUDA)
• •Model weights in test_data/models/ (1.7B-CustomVoice is the default)
• •tokenizer.json must be in the model directory

Docker Execution Pattern

The CUDA toolchain lives inside the Docker container. All cargo commands must run there. The workspace is bind-mounted at /workspace:

docker run --rm --gpus all --entrypoint /bin/bash \
  -v "$(pwd):/workspace" -w /workspace \
  qwen3-tts:latest \
  -c 'export PATH=/root/.rustup/toolchains/stable-aarch64-unknown-linux-gnu/bin:$PATH && <COMMAND>'

Profiling Modes

1. Chrome Trace (default — best for span hierarchy)

Produces trace.json for viewing in chrome://tracing or https://ui.perfetto.dev.

docker run --rm --gpus all --entrypoint /bin/bash \
  -v "$(pwd):/workspace" -w /workspace \
  qwen3-tts:latest \
  -c 'export PATH=/root/.rustup/toolchains/stable-aarch64-unknown-linux-gnu/bin:$PATH && \
      cargo run --profile=profiling --features=profiling,cuda,cli --bin e2e_bench -- \
        --model-dir test_data/models/1.7B-CustomVoice --iterations 1 --warmup 1'

Output: trace.json (~12MB for 3 sentences). Contains spans:

• •generate_frames — full generation loop
• •code_predictor / code_predictor_inner — per-frame acoustic code generation
• •talker_step — per-frame transformer forward pass
• •sampling / top_k / top_p — per-frame token sampling
• •gpu_sync trace events — marks every to_vec1() GPU→CPU sync

2. Per-Stage Timing (no profiling feature needed)

The e2e_bench binary reports stage breakdowns (prefill / generation / decode) even without the profiling feature:

docker run --rm --gpus all --entrypoint /bin/bash \
  -v "$(pwd):/workspace" -w /workspace \
  qwen3-tts:latest \
  -c 'export PATH=/root/.rustup/toolchains/stable-aarch64-unknown-linux-gnu/bin:$PATH && \
      cargo run --release --features=cuda,cli --bin e2e_bench -- \
        --model-dir test_data/models/1.7B-CustomVoice --iterations 3 --warmup 1'

3. Streaming TTFA (Time to First Audio)

# Add --streaming flag
... --bin e2e_bench -- --model-dir test_data/models/1.7B-CustomVoice \
    --iterations 3 --warmup 1 --streaming

4. JSON Output

... --bin e2e_bench -- --model-dir test_data/models/1.7B-CustomVoice \
    --json-output results.json --iterations 3

GPU Sync Audit

List all to_vec1() GPU→CPU synchronization points:

bash scripts/audit-gpu-syncs.sh

Interpreting Results

Stage Breakdown Table

Label  Words  Wall (ms)  Audio (s)  RTF    Tok/s  Mem (MB)  Prefill     Generate      Decode
short     13    5235.2      3.68   1.423    8.8      858   21ms (1%)  2724ms (71%)  1109ms (29%)
medium    53   23786.3     34.00   0.700   17.9      859   20ms (0%)  22694ms (95%)  1057ms (4%)
long     115   43797.4     60.96   0.718   17.4      864   19ms (0%)  41861ms (96%)  1886ms (4%)

Key metrics:

• •RTF < 1.0 = faster than real-time
• •Prefill: Should be <50ms on GPU. If high, check embedding/attention.
• •Generation: Dominates. ~18 GPU→CPU syncs per frame (16 code_predictor + 2 sampling).
• •Decode: ConvNeXt decoder. Scales with frame count. ~4% for long text.
• •Tok/s: Semantic tokens per second. Higher = better.

Chrome Trace Analysis

In Perfetto/chrome://tracing:

1. •Look for gaps between talker_step and code_predictor — that's CPU overhead
2. •Check if sampling (top_k + top_p) is significant vs model forward passes
3. •The gpu_sync events mark where GPU stalls waiting for CPU

Optimization Targets

The ~18 to_vec1() calls per frame are the main bottleneck:

• •16 in code_predictor (argmax per acoustic code group)
• •2 in sampling (read sampled token)

Batch these to reduce GPU→CPU round-trips.

Model Variants

Reference Baseline (1.7B-CustomVoice, CUDA)

From January 2025 on DGX (A100):

• •Short (13 words): RTF 1.42, 8.8 tok/s
• •Medium (53 words): RTF 0.70, 17.9 tok/s
• •Long (115 words): RTF 0.72, 17.4 tok/s
• •Prefill: ~20ms, Decode: ~1-2s, Generation: 71-96%