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Add vLLM NVFP4 MLA KV-cache fake quant - #2620
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Included review availability: This review used your included allowance. Your plan provides up to 12 included reviews per hour; 8 remain after this review. 📝 WalkthroughWalkthroughThe changes add pass-through support for pre-quantized vLLM linear and fused-MoE layers, an NVFP4 recipe for MLA latent KV caches, and updates to serving configuration, documentation, and tests. ChangesvLLM fake-quant serving
Priority: ⬇️ Low Estimated code review effort: 3 (Moderate) | ~25 minutes Change: Feature Sequence Diagram(s)sequenceDiagram
participant FakeQuantWorker
participant MLAQuantizer
participant KVCache
participant Attention
FakeQuantWorker->>MLAQuantizer: Apply the NVFP4 KV preset
MLAQuantizer->>KVCache: Fake-quantize and write the latent
KVCache->>Attention: Provide the quantized latent for attention
Merge Risk: 🔵 Low · up to The serving recipe is ready on the inspected path, but running the GPU test module on a CPU-only host can fail rather than skip. This is a bounded test-workflow risk. 🚥 Pre-merge checks | ✅ 5 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (5 passed)
✨ Finishing Touches🧪 Generate unit tests (beta)
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Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
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+ Misses 21731 15757 -5974
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Review comments at
@tests/gpu_vllm/torch/quantization/test_vllm_dynamic_modules.py:
- Around line 884-907: Add a CUDA-availability skip guard to
test_kv_nvfp4_mla_quantizer_replays_in_cuda_graph so it is skipped when CUDA is
unavailable; leave its existing test logic unchanged.
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CHANGELOG.rstexamples/vllm_serve/README.mdtests/gpu_vllm/torch/quantization/test_vllm_dynamic_modules.py
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NVFP4 on MLAAttention's kv_c_bmm_quantizer (the cached latent) with the global scale fixed to 1, plus a README example recipe. Signed-off-by: Shiyang Chen <shiychen@nvidia.com>
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Approve: the recipe reuses existing NVFP4 configuration and MLA hooks, with focused tests for latent-only quantization and CUDA-graph replay.
No action needed.
The unit now also sets k_pe_bmm_quantizer to FP8 with scale 1, next to the NVFP4 latent, as in vLLM's nvfp4_ds_mla cache. NoPE models such as GLM-5.3-Flash have an empty k_pe, which passes through. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Signed-off-by: Shiyang Chen <shiychen@nvidia.com>
2 participants
What does this PR do?
Type of change: New feature
NVFP4 fake quant of the MLA latent KV cache (GLM-5.3-Flash, DeepSeek-V3) in vLLM fake-quant serving:
configs/ptq/units/kv_nvfp4_mlaunit: NVFP4 on*kv_c_bmm_quantizer(the cached latent) with the global scale fixed to 1. The existing KV units (*[kv]_bmm_quantizer) don't match it. The RoPE key part (k_pe, e.g. in DeepSeek-V3) stays unquantized.examples/vllm_serve/README.md.Usage
# recipe: examples/vllm_serve/README.md RECIPE_PATH=kv_nvfp4_mla_only.yaml python vllm_serve_fakequant.py zai-org/GLM-5.3-Flash -tp 4 --moe-backend autoTesting
B200, vLLM 0.30:
tests/gpu_vllm/torch/quantization: 51 passed, including the 2 new tests. The 1 error,test_tiny_deepseek_mla_quantize, already fails on vLLM 0.30.Before your PR is "Ready for review"
Make sure you read and follow Contributor guidelines and your commits are signed (
git commit -s -S).Make sure you read and follow the Security Best Practices (e.g. avoiding hardcoded
trust_remote_code=True,torch.load(..., weights_only=False),pickle, etc.).CONTRIBUTING.md: N/AAdditional Information
Related: #2552, #2539.
Summary by CodeRabbit
--enforce-eager.