feat(hs-connectors): add FP8-quantized hidden-states backend - #1028
feat(hs-connectors): add FP8-quantized hidden-states backend#1028shubhra wants to merge 5 commits into
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📝 WalkthroughWalkthroughChangesThe change adds FP8 hidden-state quantization and dequantization, safetensors persistence, backend configuration, transfer support, end-to-end validation, unit tests, and ablation documentation. FP8 hidden-state pipeline
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1-52: 📐 Maintainability & Code Quality | 🟡 Minor | ⚡ Quick winFormat this Markdown file before merge.
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docs/fp8_ablation/dflash_bf16_acceptance.csvis excluded by!**/*.csvdocs/fp8_ablation/dflash_fp8_acceptance.csvis excluded by!**/*.csvdocs/fp8_ablation/dspark_bf16_acceptance.csvis excluded by!**/*.csvdocs/fp8_ablation/dspark_fp8_acceptance.csvis excluded by!**/*.csvdocs/fp8_ablation/eagle3_bf16_acceptance.csvis excluded by!**/*.csvdocs/fp8_ablation/eagle3_fp8_acceptance.csvis excluded by!**/*.csv
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docs/fp8_ablation/RESULTS_SUMMARY.mdhs_connectors/src/hs_connectors/__init__.pyhs_connectors/src/hs_connectors/fp8_hidden_states_connector.pyhs_connectors/src/hs_connectors/fp8_utils.pyhs_connectors/src/hs_connectors/transfer.pytests/e2e/hs_connectors/test_fp8_roundtrip.pytests/e2e/utils.pytests/unit/hs_connectors/test_backend_args_roundtrip.pytests/unit/hs_connectors/test_fp8_transfer.pytests/unit/hs_connectors/test_fp8_utils.py
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@shubhra I pushed a commit so that we can use the same |
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Generally looks good but left a couple comments regarding the docs.
Also it seems like we get good acceptance rates but do you have any results on training speed/data usage. Presumably for offline/hybrid training we can expect data that is roughly half the size, but is there any speed up (either for offline or online)?
Revives the intent of #491 (FP8HiddenStatesConnector), which predates two major refactors of the hidden-states transfer layer (save_kv_layer -> async _write_tensors hook, and the file/mooncake HiddenStatesBackend plugin abstraction) and can no longer be rebased as-is. Adds a new "fp8" backend following the same plugin pattern as the existing file/mooncake backends: - FP8HiddenStatesConnector overrides only vLLM's ExampleHiddenStatesConnector ._write_tensors staticmethod to quantize hidden states to float8_e4m3fn with per-token scaling (same granularity as the original #491 design) before writing to safetensors. All scheduler-side bookkeeping, async DtoH copy, and file-locking is inherited unchanged. - FP8Transfer (a FileTransfer subclass) transparently dequantizes on read, so consumers like ArrowDataset need no changes. - FP8Backend registers "fp8" with HiddenStatesBackend, using distinct --fp8-hidden-states-path flags to avoid colliding with FileBackend's train/launch args in the shared argparse parsers. Validated with unit tests (quantization round-trip, FP8Transfer dequant, backend arg registration) and a live e2e test that launches a real vLLM server with --hidden-states-backend fp8 and confirms the on-disk payload is genuinely FP8-quantized and dequantizes correctly. Co-authored-by: Cursor <cursoragent@cursor.com> Signed-off-by: Shubhra Pandit <shubhra@h100-02.nemg-001.lab.rdu2.dc.redhat.com>
Adds the per-model, per-precision guidellm acceptance.csv breakdowns (eagle3/dflash/dspark x bf16/fp8, 9 RedHatAI/speculator_benchmarks subsets each) plus the results summary referenced in the PR description, so reviewers don't have to rely on a separately-packaged archive. RESULTS_SUMMARY.md is mdformat-clean (ruff/mdformat/mypy all pass with the project's pinned dev deps; the earlier make-quality failure was a missing mdformat reflow on this one new file). Co-authored-by: Cursor <cursoragent@cursor.com> Signed-off-by: Shubhra Pandit <shubhra@h100-02.nemg-001.lab.rdu2.dc.redhat.com>
Signed-off-by: Fynn Schmitt-Ulms <fschmitt@redhat.com>
Adds real data-gen-run and isolated-microbenchmark numbers for the hidden-states write path itself (file size, write latency, end-to-end throughput), complementing the existing quality-parity ablation (val_loss, guidellm acceptance). Confirms an exact 50% on-disk size reduction with FP8 (real 300-sample run: 14.24GB -> 7.12GB) and no measurable generation-throughput regression, since the write is async and orders of magnitude smaller than per-sample GPU generation time. Signed-off-by: Shubhra Pandit <shubhra@h100-02.nemg-001.lab.rdu2.dc.redhat.com> Co-authored-by: Cursor <cursoragent@cursor.com>
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Drop the CSV outputs and summary markdown so the PR only carries the connector implementation and tests. Co-authored-by: Cursor <cursoragent@cursor.com> Signed-off-by: Shubhra Pandit <shubhra@h100-02.nemg-001.lab.rdu2.dc.redhat.com> Co-authored-by: Cursor <cursoragent@cursor.com>
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Summary
Revives the intent of #491 (FP8HiddenStatesConnector), which predates two major refactors of the hidden-states transfer layer (
save_kv_layer-> async_write_tensorshook, and the file/mooncakeHiddenStatesBackendplugin abstraction) and can no longer be rebased as-is.Adds a new
fp8backend following the same plugin pattern as the existing file/mooncake backends:FP8HiddenStatesConnectoroverrides only vLLM'sExampleHiddenStatesConnector._write_tensorsstaticmethod to quantize hidden states tofloat8_e4m3fnwith per-token scaling (same granularity as the original Feat/fp8 connector #491 design) before writing to safetensors. All scheduler-side bookkeeping, async DtoH copy, and file-locking is inherited unchanged.FP8Transfer(aFileTransfersubclass) transparently dequantizes on read, so the training pipeline consumes bf16/fp32 tensors identically regardless of backend.test_fp8_utils.py,test_fp8_transfer.py), backend-args roundtrip coverage, and an e2e roundtrip test (test_fp8_roundtrip.py).Test plan / results
29/29 unit tests pass locally (
test_fp8_utils.py,test_fp8_transfer.py,test_backend_args_roundtrip.py).Beyond unit tests, ran a full bf16-vs-FP8 ablation: trained
Qwen/Qwen3-8Bspeculators (eagle3, dflash, dspark) on 5K magpie + 5K ultrachat samples (frominference-optimization/Dataset-Qwen3-235B-Instruct), once with the existing bf16 file backend and once with this FP8 backend, then compared val_loss and ranguidellmthroughput evals (weighted across all 9RedHatAI/speculator_benchmarkssubsets) for both.acceptance_length= tokens actually produced per verification round (drives wall-clock speedup);acceptance_rate= fraction of individually proposed tokens accepted (isolates draft-model quality from block size). Format:len / rate.bf16-vs-fp8 gap is within run-to-run noise on both
lenandrate, for every subset and every architecture — no measurable quality regression from FP8 hidden-states transfer.Per-subset breakdown for all 6 runs (one row per
RedHatAI/speculator_benchmarkssubset) is committed indocs/fp8_ablation/in this branch —{model}_{precision}_acceptance.csvplusRESULTS_SUMMARY.md.Storage/write-speed numbers for the hidden-states transfer itself
The table above is quality-parity (does FP8 hurt the trained speculator?). Separately measured the thing FP8 actually speeds up — the write path:
Real data-gen run (eagle3 config, 300 samples via
scripts/data_generation_offline.py, otherwise-identical vLLM server,--concurrency 32):End-to-end throughput is identical within noise — the write (2-3 ms) is ~3 orders of magnitude smaller than per-sample GPU generation time (~1.1 s) and runs async off the critical path, so the 50% smaller payload doesn't show up as a local generation-throughput win. The real benefit is disk footprint and any bandwidth-constrained transfer (e.g. the
mooncakebackend, or a slower/network filesystem).Isolated CPU-only microbenchmark (synthetic tensors, same shapes, tmpfs) confirms the same exact 50% size reduction at every scale (128–8192 token chunks), with the quantization compute roughly a wash to ~2x slower than a plain write at small/medium chunk sizes on fast local storage, netting out as a write-time win only at very large single writes. Full numbers in
docs/fp8_ablation/RESULTS_SUMMARY.md.Notes
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