perf(tts): Magpie TTS - CUDA/CPU fast path - #51
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Included review availability: This review used your included allowance. Your plan provides up to 12 included reviews per hour; 10 remain after this review. 📝 WalkthroughWalkthroughThe pull request changes TTS conversion defaults to Q8_0, adds fused CUDA and persistent streaming paths for MagpieTTS and NanoCodec, expands GGML support, and adds task-based CLI benchmarks for ASR, TTS, diarization, and translation. ChangesSpeech conversion and runtime
Multi-workload benchmarking
Priority: ➖ Normal Estimated code review effort: 5 (Critical) | ~120 minutes Change: Feature Sequence Diagram(s)sequenceDiagram
participant MagpieGeneration
participant PrefetchWorker
participant MagpieEncoder
participant MagpieDecoder
MagpieGeneration->>PrefetchWorker: Start prefill for the next chunk
PrefetchWorker->>MagpieEncoder: Encode the next text window
PrefetchWorker->>MagpieDecoder: Build paired decoder prefill
MagpieDecoder-->>PrefetchWorker: Return prefetched state
PrefetchWorker-->>MagpieGeneration: Provide prefetched state
MagpieGeneration->>MagpieDecoder: Adopt matching state or run normal decoding
Merge Risk: ⚪ Minimal · up to The test-link change supplies the CUDA driver stub only when the CUDA fusion test is enabled. The previously identified converter-import and persistent-pointer risks are resolved in the current code; no actionable merge risk remains. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
Full details: Docstring CoverageExplanation Docstring coverage is 14.64% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 280 functions across 41 files. (1 skipped: 1 unsupported.)
✨ Finishing Touches🧪 Generate unit tests (beta)
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This PR additionally fixes #8 |
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Inline comments:
In `@conversion/tts.py`:
- Line 26: Update the declared gguf minimum version so it provides
gguf.quants.quantize used by the import in conversion.tts, or switch that import
to a quantization API available in the currently supported versions.
In `@docs/tts/models.md`:
- Line 111: Update the Q8_0 precision description to state that rank-one
floating-point tensors, including norms, and biases remain F32 while other
non-projection tensors remain F16; correct the matching precision comment in the
conversion logic as well.
In `@ggml-patches/0014-cuda-fused-attention-extensions.patch`:
- Around line 357-369: Treat a split-KV partition whose maximum is negative
infinity as empty: skip exponentiation and set its weights and local sum to
zero. In the split-combination loop, skip partitions with a negative-infinity
maximum before accumulating their sums or context, preventing masked partitions
from propagating NaNs.
In `@ggml-patches/0023-cuda-conv1d-fused.patch`:
- Around line 669-673: The ggml_conv1d_fused wrapper accepts cache lengths that
do not match the kernel’s required history and permits a missing cache when
padding is nonzero. Compute the padding once, require a supplied cache to
contain exactly that many columns, and require a cache when padding is nonzero;
reuse the value when calculating t_out. Update the corresponding header comment
to document the exact cache-length contract.
- Around line 479-497: Update conv1d_fused_get_scratch so scratch storage is
owned per actual CUDA stream, with lazy initialization protected against races.
Ensure concurrent streams cannot share partials or counters; if retaining
device-global scratch, serialize each launch through kernel completion across
all users.
In `@src/tts/magpietts/decoder.cpp`:
- Around line 717-718: Update `matches()` to record and compare the identities
of the bound cross-cache K/V tensors, not just `cross_kv` and its capacity,
before reusing the runtime. Return false when the cache has been reset and
rebuilt with different tensor allocations, even if it is the same cache object
at the same capacity.
- Around line 2232-2233: Update the unconditional-cache fill path around
`uncond_cache->ready` so the first successful allocation also copies the K/V and
hidden-state results into the cache and publishes `ready` while holding
`fill_mutex`; retain the existing reuse behavior for an already-ready cache.
- Line 1198: When `evalCachedPair()` switches away from persistent decoding,
update the `persistent_owns_kv_` transition so eager decoding does not treat
stale KV buffers as current: restore the runtime’s latest K/V rows into
`cond_kv` and `uncond_kv`, or clear both caches so eager decoding refills them
from `audio_codes`.
In `@src/tts/magpietts/magpietts_cuda_sampling_device.cuh`:
- Around line 77-81: Update descending_key so it normalizes either signed zero
to the same value before constructing the radix key. Preserve the existing
ordering for nonzero logits so equal logits continue to follow the ascending-ID
tie rule.
- Line 199: Clamp the Gumbel uniform draw below one before it reaches __logf, so
the maximum random-bit value cannot produce an infinite Gumbel sample. Preserve
the existing uniform-draw calculation for values already below one.
In `@src/tts/magpietts/magpietts_decoder_fused.cu`:
- Around line 246-250: Update the cross-query prefetch and processing path
around ltf_q8_prefetch so each slot handles every row in rows_per, including
widths above 128, rather than only the first two rows per warp; preserve the
existing supported-width checks.
In `@src/tts/magpietts/magpietts_lt_fused.cu`:
- Around line 1270-1273: Update magpietts_lt_fused_capture_round’s argument
validation to reject codebook indices outside the valid round range and reject
missing codes when the index is greater than zero; preserve the existing
invalid-argument error and return behavior.
In `@src/tts/magpietts/magpietts.cpp`:
- Around line 1292-1294: Update the prefetch_enabled condition to require
h.dec_kernel == 1 before enabling prefetch. This keeps models whose decoder
kernel cannot use prefillPair on the synchronous path and avoids duplicate
encoder work.
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conversion/registry.pyconversion/tts.pydocs/tts/models.mdggml-patches/0014-cuda-fused-attention-extensions.patchggml-patches/0017-cuda-stream-interop.patchggml-patches/0022-cuda-im2col-1d-tiled.patchggml-patches/0023-cuda-conv1d-fused.patchggml-patches/0024-cuda-backend-graphs-toggle.patchggml-patches/0025-cuda-block-reduce-barrier.patchggml-patches/0026-cuda-conv1d-preactivation.patchggml-patches/README.mdsrc/tts/magpietts/CMakeLists.txtsrc/tts/magpietts/decoder.cppsrc/tts/magpietts/decoder.hsrc/tts/magpietts/encoder.cppsrc/tts/magpietts/encoder.hsrc/tts/magpietts/graph.hsrc/tts/magpietts/lt.cppsrc/tts/magpietts/magpietts.cppsrc/tts/magpietts/magpietts_chain_common.cuhsrc/tts/magpietts/magpietts_cuda_sampling.cusrc/tts/magpietts/magpietts_cuda_sampling.hsrc/tts/magpietts/magpietts_cuda_sampling_device.cuhsrc/tts/magpietts/magpietts_decoder_fused.cusrc/tts/magpietts/magpietts_decoder_fused.hsrc/tts/magpietts/magpietts_lt_fused.cusrc/tts/magpietts/magpietts_lt_fused.hsrc/tts/magpietts/model.cppsrc/tts/magpietts/model.hsrc/tts/nanocodec/CMakeLists.txtsrc/tts/nanocodec/model.cpptests/conversion/converter_contract_test.py
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Inline comments:
In `@app/bench_asr.cpp`:
- Around line 111-119: Update summarize_run to calculate audio_seconds from the
durations of the inputs actually processed, rather than scaling corpus_seconds_
by the average run count. Use the actual processed utterance count and audio
duration for the reported throughput metrics so uneven per-stream runs produce
accurate values.
In `@app/bench_diarize.cpp`:
- Around line 99-105: Update summarize_run so audio_seconds reflects the actual
audio duration represented by the results when --per-stream yields a count that
is not a multiple of the inputs; avoid scaling corpus_seconds_ by the averaged
item count, and compute rtfx from the corrected audio_seconds.
In `@app/bench_translate.cpp`:
- Around line 87-91: Update the input-bytes calculation in summarize_run to
account for the actual inputs processed under --per-stream, rather than scaling
corpus_bytes_ by items.size() / texts_.size(). Preserve the correct total-byte
throughput for counts that are not multiples of the line count.
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BENCHMARK.mdREADME.mdapp/CMakeLists.txtapp/bench.cppapp/bench.happ/bench_asr.cppapp/bench_diarize.cppapp/bench_translate.cppapp/bench_tts.cppapp/commands.happ/main.cppapp/synthesize.cppconversion/tts.pydocs/cli.mddocs/tts/models.mdggml-patches/0024-cuda-im2col-1d-tiled.patchggml-patches/0025-cuda-conv1d-fused.patchggml-patches/0026-cuda-backend-graphs-toggle.patchggml-patches/0027-cuda-block-reduce-barrier.patchggml-patches/0028-cuda-conv1d-preactivation.patchggml-patches/README.mdsrc/core/engine_registry.hsrc/tts/magpietts/decoder.cppsrc/tts/magpietts/decoder.hsrc/tts/magpietts/magpietts.cppsrc/tts/magpietts/magpietts_chain_common.cuhsrc/tts/magpietts/magpietts_decoder_fused.cusrc/tts/magpietts/magpietts_decoder_fused.hsrc/tts/magpietts/magpietts_lt_fused.cusrc/tts/magpietts/magpietts_lt_fused.hsrc/tts/nanocodec/model.cppsrc/tts/nanocodec/model.htest_files/tts/bench/long.txttest_files/tts/bench/medium.txttest_files/tts/bench/short.txttest_files/tts/ljs_audio_text_test_filelist_small.txttests/conversion/converter_contract_test.py
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Inline comments:
In `@src/tts/magpietts/decoder.cpp`:
- Line 708: Update DecoderCrossKvCache to maintain a generation token that
changes when its backing allocation is reset or rebuilt, and ensure its move
operations transfer that token with the cache. Record the token in
PersistentDecoderRuntime and have matches() compare it to the current cache
generation instead of relying on raw tensor or device-data addresses.
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app/bench.cppapp/bench.happ/bench_asr.cppapp/bench_diarize.cppapp/bench_translate.cppconversion/tts.pydocs/tts/models.mdggml-patches/0014-cuda-fused-attention-extensions.patchggml-patches/0025-cuda-conv1d-fused.patchrequirements.txtsrc/tts/magpietts/decoder.cppsrc/tts/magpietts/magpietts.cppsrc/tts/magpietts/magpietts_cuda_sampling_device.cuhsrc/tts/magpietts/magpietts_lt_fused.cusrc/tts/nanocodec/model.cpptests/cpp/tts/test_magpietts_cached_attention.cpp
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Inline comments:
Review comments at @BENCHMARK.md:
- Line 11: Clarify in BENCHMARK.md how each table p99 is derived across the
three trials: specify whether it averages the three per-run p99 values or
recomputes p99 from pooled samples. Include the per-trial results or a
reproducible aggregation command so the calculation can be verified.
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@ggml-patches/0029-cuda-skinny-q8-history-independent-dispatch.patch:
- Around line 130-148: Add a regression test for the narrow-MMVQ path in
skq8_run that runs the same call before and after a wider call triggers
repacking, then asserts the outputs are bitwise equal for N <=
MMVQ_MAX_BATCH_SIZE. Use an exact comparison rather than the usual numerical
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- Persistent LT chain kernel: all rounds in one launch with in-kernel sampling (Q8_0 proj) - Fused tensor-core conv1d op for the NanoCodec decoder, replacing im2col + cuBLAS - Persistent decoder runtime reused across text chunks - Converter: --outtype q8_0 for MagpieTTS, now the default
… prefetch path - prefetch the next chunk's encoder pass, cross K/V and baked-context prefill on a side backend and seed the persistent decoder from it - cache the constant unconditional-lane prefill; defer the alignment readback past the sampler launch - LT sampler: Gumbel-max with exact top-k acceptance, deterministic and distribution-equivalent - keep the prefetch thread off the driver lock: allocator reuse, grow-only device tensors, no CUDA graph capture on the side backend, codec stream priority above side streams - ggml: per-backend CUDA graph toggle (0026) and a barrier in block_reduce fixing a soft_max race under SM sharing (0027)
- build on stock ggml: patched-only ops and CUDA interop behind NEMO_SPEECH_GGML_PATCHED - gate the fast kernels to sm_80+ at runtime with arch-safe fallbacks; enable them on any GPU with 64+ SMs - remove the runtime switches; paths are chosen from backend, GPU and model - fix sampler argmax, stale sampler graph on top-k/CFG changes, prefill-cache race and lt-fp32 with q8_0 models
- select persistent kernel tier at runtime from SM count - skip cp.async L2 hint on Hopper+ (ptxas miscompile) - L2-prefetch upcoming weights when the cache fits them - build the persistent runtime once; fix first-request and first-audio latency - generic bench command; bench tts reports TTFA, inter-chunk latency, RTFx - add BENCHMARK.md and README performance section
- N <= 8 always runs MMVQ (planar after a repack), wider calls always skinny-Q8 (patch 0029) - fixes outputs changing with earlier requests, e.g. MagpieTTS audio after a short sentence - remove GGML_SKINNY_Q8_OUTER_BATCH; match the cached-F16 check in nn.cpp - add RTX 4090 MagpieTTS results to BENCHMARK.md and README
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Inline comments:
Review comments at @app/bench.cpp:
- Around line 398-399: Update the `--save` filename generation in the
`names[index]` output path so recursive ASR inputs with identical basenames
cannot overwrite each other. Derive filenames from paths relative to the input
root, or detect a collision and fail before overwriting; preserve the existing
output behavior for unique inputs.
Review comments at @docs/tts/models.md:
- Line 96: Update the Q8_0 quality statement in the MagpieTTS v2607 section to
specify the metric, test set, and measured result supporting the claim; if those
details are unavailable, narrow the statement to the result the evaluation
actually measured.
- Around line 97-98: Update the hardware-capability description to distinguish
the fused decoder from the fused local-transformer kernels: describe
local-transformer selection as requiring CFG in addition to applicable model and
GPU checks, and avoid implying the hardware gate alone enables both. Preserve
the automatic-selection qualification for the fused decoder.
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- enable llamafile tinyBLAS GEMM by default; stock ggml leaves CPU matmuls on per-output dot products - NanoCodec on CPU: zero-pad conv input channels to a multiple of 16 so the tiled GEMM applies; run transposed convs as GEMM + overlap-add with weights laid out once at load - ggml: hardware FP32->FP16 conversion on x86 and row-parallel im2col (patch 0030) - MagpieTTS: serve the unconditional-lane prefill from its cache on CPU too
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Inline comments:
Review comments at @ggml-patches/README.md:
- Around line 228-229: Qualify or remove the “bit-identical” and “1.7x faster on
CPU” claims in the NanoCodec README passage; retain them only if the README
provides reproducible before-and-after output and benchmark evidence with the
CPU, compiler and build settings, thread count, model, workload, and baseline
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BENCHMARK.mdCMakeLists.txtREADME.mdapp/bench.cppapp/bench_asr.cppggml-patches/0030-cpu-fp16-conversion-im2col.patchggml-patches/README.mdsrc/asr/recognizer.cppsrc/asr/recognizer.hsrc/runtime/ggml/runtime.hsrc/runtime/ggml/session.cppsrc/tts/magpietts/decoder.cppsrc/tts/nanocodec/model.cpp
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…rce builds, clarify DCO - ASR and TTS CPU threads default to min(8, hardware threads) instead of 4 - README and install docs: recommend native source builds, describe the installer's checksum accurately - CONTRIBUTING.md: state that signing off means agreeing to the DCO
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Inline comments:
Review comments at @CONTRIBUTING.md:
- Line 80: Update the sign-off sentence to state that signing off certifies one
of certifications (a), (b), or (c), together with certification (d), rather than
requiring all four certifications.
Review comments at @README.md:
- Around line 78-80: Update the README guidance for --source to say source
builds use main by default, NEMO_SPEECH_SOURCE_REF can select another branch or
tag, and a local checkout builds its current branch; remove the claim that
--source always builds from main.
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