[Quantization] Layer re-quantization for linear layers through online quantization API (MXFP8 -> FP8 PTPC showcase) - #55684
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Documentation preview: https://vllm--55684.org.readthedocs.build/en/55684/ |
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📝 SummarySummary by CodeRabbit
WalkthroughThis change adds load-time re-quantization for serialized ModelOpt MXFP8 linear weights. The resolver composes checkpoint and online quantization methods, dequantizes weights to BF16, and applies the target method. Tests and documentation cover supported and unsupported combinations. ChangesOnline Re-quantization
Estimated code review effort: 3 (Moderate) | ~25 minutes Merge Risk: 🟡 Moderate · up to Online re-quantization of serialized ModelOpt MXFP8 linear layers can leave layers in their original checkpoint format during layerwise loading, causing the requested target precision not to be applied. This should be fixed before merge. Sequence Diagram(s)sequenceDiagram
participant Checkpoint
participant QuantizationResolver
participant ModelOptLinearMethod
participant OnlineLinearMethod
Checkpoint->>QuantizationResolver: Load serialized MXFP8 linear weights
QuantizationResolver->>OnlineLinearMethod: Set ModelOptLinearMethod as source
OnlineLinearMethod->>ModelOptLinearMethod: dequantize_weight(layer)
ModelOptLinearMethod-->>OnlineLinearMethod: Return BF16 weights
OnlineLinearMethod->>OnlineLinearMethod: Apply target quantization
Suggested reviewers: 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
Full details: Docstring CoverageExplanation Docstring coverage is 48.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 25 functions across 7 files. (1 skipped: 1 unsupported.)
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Co-authored-by: vllmellm <vllm.ellm@embeddedllm.com> Co-authored-by: Tan Pin Siang <tanpinsiang@gmail.com> Signed-off-by: Felix Marty <Felix.Marty@amd.com>
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Actionable comments posted: 1
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Inline comments:
In `@vllm/model_executor/layers/quantization/online/fp8.py`:
- Line 152: Update the early-return branch in the relevant layer setup flow to
call initialize_online_processing(layer) before returning, ensuring
online-processing loaders are installed and layerwise reload can invoke
process_weights_after_loading after weight and weight_scale load.
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📒 Files selected for processing (8)
docs/features/quantization/online.mdtests/quantization/test_online.pyvllm/model_executor/layers/quantization/base_config.pyvllm/model_executor/layers/quantization/modelopt.pyvllm/model_executor/layers/quantization/online/fp8.pyvllm/model_executor/layers/quantization/online/moe_base.pyvllm/model_executor/layers/quantization/online/mxfp4.pyvllm/model_executor/layers/quantization/online/mxfp8.py
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BowenBao
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LGTM with minor comment
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| weight_fp4, weight_scale = mxfp4_quantize(layer.weight.contiguous()) | ||
| weight = self.get_weight_for_quantization(layer) |
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We should remove the old scales after requantization since it's no longer used, is that done in this PR?
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Fixed the missing cleanup in online/fp8.py. Thanks for the hint!
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cc @tjtanaa , @dllehr-amd , @AndreasKaratzas |
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/ci run |
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✅ Triggered Buildkite CI #88192 for commit |
Co-authored-by: vllmellm <vllm.ellm@embeddedllm.com> Co-authored-by: Felix Marty <Felix.Marty@amd.com> Signed-off-by: Tan Pin Siang <tanpinsiang@gmail.com>
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Thanks @fxmarty-amd! I tested this PR with two small fixes and focused regression coverage:
The second fix addresses Bowen's question about removing old scales. The repaired candidate passed 9 focused tests, 13 real-model functional cases (including TP2 and AITER), and 8 loading-memory probes on MI355X. Measured loading peaks matched the native baseline within each model. For
GSM8K: lm-eval 0.4.12, 25-shot, all 1,319 questions, concurrency 256, temperature 0, max output 1,024 tokens; two complete runs per configuration. Performance: 512 requests, c=64, 512 input / 128 output tokens, one full warmup followed by two measured runs. |
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Disclosure
AI assistance was used. The changes were reviewed and tested manually.
Purpose
This PR allows re-quantizing linear layers to a different precision using online quantization API, as long as the
dequantize_weightmethod for relevantLinearMethodBasesubclass is implemented.Similar features have been requested previously in specialized cases:
and this feature exists on sglang side sgl-project/sglang#28291 / sgl-project/sglang#29328.
Addresses the third item of #52167. This PR is adapted from #48427, co-authored by @tanpinsiang.
This change lets
--quantization-config.linear fp8_per_channelconvert selected serialized ModelOpt MXFP8 linears to FP8 PTPC at load time.The original checkpoint quantization config remains responsible for unselected linears and MoE.
The composition is handled by the existing
resolve_quant_method, so model implementations continue to receive their original checkpoint quantization config type.Quantization methods expose an optional
dequantize_weightmethod, that:NotImplementedErrorby default, orThis draft implements it only for
ModelOptMxFp8LinearMethod.Such
dequantize_weightmethods implementation can be shared across model producers (quark, modelopt, compressed-tensors, etc.) based on weight quant key, no model producer-specific code should be required.Test Plan
vllm serve mmangkad/Qwen3-4B-Instruct-2507-MXFP8 --quantization-config.linear "fp8_per_channel"or
pytest tests/quantization/test_online.py-s -vvvvvTest Result
Unit test passing, end-to-end eval to be added.