[MLA][nvfp4] Per-layer outer-scale calibration + fp8-RoPE KV-cell for the nvfp4 MLA KV cache - #95
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Two opt-in improvements to the nvfp4 MLA KV cache: - Calibrated per-layer outer-scale on the compressed latent (KLD 0.184 -> 0.152). - fp8-RoPE KV-cell: store the decoupled RoPE key as fp8-e4m3 + amax scale, record 432 -> 368 B (KV pool +15.8%, genuine 1M single-request). Validated at 64K: needle retrieval 15/15 = 15/15, KL(fp8||bf16) below the boot-noise floor. Both gated (VLLM_NVFP4_MLA_SCALES_FILE / KV_FP8_ROPE); off = byte-identical. Kernel side (b12x) is a companion PR to lukealonso/b12x. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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📝 WalkthroughWalkthroughAdds ChangesNVFP4 MLA cache contracts
Estimated code review effort: 4 (Complex) | ~60 minutes Sequence Diagram(s)sequenceDiagram
participant MultiHeadLatentAttentionWrapper
participant MLAAttention
participant B12xMLASparseImpl
participant FP8RopeWriter
participant B12xKernels
MultiHeadLatentAttentionWrapper->>MLAAttention: pass scaled cache latent KV
MLAAttention->>B12xMLASparseImpl: invoke cache update or attention execution
B12xMLASparseImpl->>FP8RopeWriter: write FP8 RoPE cache when enabled
B12xMLASparseImpl->>B12xKernels: pass latent_scale and scale_format
B12xKernels->>MLAAttention: return outputs and optional decode LSE
Possibly related PRs
Suggested reviewers: 🚥 Pre-merge checks | ✅ 5✅ Passed checks (5 passed)
✨ Finishing Touches🧪 Generate unit tests (beta)
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Actionable comments posted: 3
🧹 Nitpick comments (4)
vllm/model_executor/layers/mla.py (1)
211-230: 📐 Maintainability & Code Quality | 🔵 Trivial | ⚡ Quick winSilent no-op when
VLLM_NVFP4_MLA_SCALES_FILEis set but backend/dtype don't match.If an operator sets the scale file but the runtime backend isn't
B12X_MLA_SPARSEorkv_cache_dtypeisn'tnvfp4_ds_mla, the calibration is silently skipped (_nvfp4_mla_outer_scalestays1.0) with no log message, making misconfiguration hard to notice.♻️ Proposed fix
scale_file = os.getenv(_NVFP4_MLA_SCALES_ENV, "").strip() - if scale_file and ( - self.mla_attn.kv_cache_dtype == "nvfp4_ds_mla" - and self.mla_attn.attn_backend.get_name() == "B12X_MLA_SPARSE" - ): + scales_applicable = ( + self.mla_attn.kv_cache_dtype == "nvfp4_ds_mla" + and self.mla_attn.attn_backend.get_name() == "B12X_MLA_SPARSE" + ) + if scale_file and not scales_applicable: + logger.warning_once( + "%s is set but backend/dtype do not support NVFP4 MLA " + "outer-scale calibration; ignoring.", + _NVFP4_MLA_SCALES_ENV, + ) + if scale_file and scales_applicable: match = _NVFP4_MLA_LAYER_RE.search(prefix)🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In `@vllm/model_executor/layers/mla.py` around lines 211 - 230, Update the initialization logic around _NVFP4_MLA_SCALES_ENV so that when a non-empty scale file is configured but kv_cache_dtype is not "nvfp4_ds_mla" or attn_backend.get_name() is not "B12X_MLA_SPARSE", emit a clear warning or informational log describing the ignored calibration file. Preserve the existing scale-loading behavior for matching configurations and the identity default otherwise.vllm/v1/attention/backends/mla/b12x_mla_sparse.py (3)
287-296: 📐 Maintainability & Code Quality | 🔵 Trivial | ⚡ Quick winDuplicated record-size constants (368/432/656) across three sites.
The NVFP4/FP8-RoPE byte-layout constants are hardcoded independently in
get_kv_cache_shape(368/432),__init__(_kv_record_bytes: 368/432/656), anddo_kv_cache_update's validation (368). If the layout ever changes, all three must be updated in lockstep or the allocator and the writer silently disagree on record size.♻️ Suggested consolidation
+_FP8_DS_MLA_RECORD_BYTES = 656 +_NVFP4_STOCK_RECORD_BYTES = 432 +_NVFP4_FP8_ROPE_RECORD_BYTES = 368 + ... if cache_dtype_str == "nvfp4_ds_mla": return ( num_blocks, block_size, - 368 if _kv_fp8_rope_enabled() else 432, + _NVFP4_FP8_ROPE_RECORD_BYTES + if _kv_fp8_rope_enabled() + else _NVFP4_STOCK_RECORD_BYTES, )Reuse the same constants in
__init__'s_kv_record_bytescomputation and indo_kv_cache_update'skv_u8.shape[-1] != 368check.Also applies to: 681-685, 858-862
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In `@vllm/v1/attention/backends/mla/b12x_mla_sparse.py` around lines 287 - 296, Consolidate the NVFP4/FP8-RoPE record-size values into shared constants near the existing layout logic, then reuse them in get_kv_cache_shape, __init__ when computing _kv_record_bytes, and do_kv_cache_update when validating kv_u8.shape[-1]. Preserve the existing 368, 432, and 656-byte selections and the current validation behavior.
602-617: 📐 Maintainability & Code Quality | 🔵 Trivial | ⚡ Quick winPer-layer
logger.warning/logger.infocalls will spam logs across every attention layer.
B12xMLASparseImpl.__init__runs once per MLA layer. The twologger.warningcalls (lines 606-609, 611-615) and thelogger.infocall (lines 686-692) will each print once per layer — for models with dozens of layers this produces repetitive, hard-to-scan startup logs.mla_attention.pyalready useslogger.warning_once/logger.info_oncefor comparable per-layer messages.♻️ Suggested fix
- logger.warning( + logger.warning_once( "KV_FP8_ROPE=1 ignored: compact MLA records are restricted to " "model_type=glm_moe_dsa and its associated MTP draft" ) ... - logger.warning( + logger.warning_once( "KV_FP8_ROPE=1 has no effect for kv_cache_dtype=%s; the compact " ... - logger.info( + logger.info_once( "B12X GLM MLA KV format: KV_FP8_ROPE=%d kv_gmem_stride=%d "Also applies to: 686-692
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In `@vllm/v1/attention/backends/mla/b12x_mla_sparse.py` around lines 602 - 617, Replace the per-layer logger.warning and logger.info calls in B12xMLASparseImpl.__init__ with the corresponding once-only logging APIs, including the message around the _kv_fp8_rope setup at lines 606-615 and the logger.info call around lines 686-692. Preserve each existing message and arguments while ensuring each startup notice is emitted only once across all MLA layers.
675-680: 📐 Maintainability & Code Quality | 🔵 Trivial | ⚡ Quick winHardcoded
scale_formatliteral2instead of a named constant/enum.
self._b12x_scale_format = 2encodesScaleFormat.NVFP4_E4M3as a bare int per the comment; this value drives kernel record parsing at multiple call sites (scratch-plan caps, warmup, and all four decode/extend calls). If b12x renumbers or renames this enum, this silently breaks with no type/compile check.Please confirm whether
b12x.integration.sparse_mla_scratch(or a related module) exports aScaleFormatenum that can be imported and referenced by name instead of the literal2.🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In `@vllm/v1/attention/backends/mla/b12x_mla_sparse.py` around lines 675 - 680, Replace the hardcoded value assigned in the b12x scale-format initialization with the exported ScaleFormat.NVFP4_E4M3 enum or named constant from b12x.integration.sparse_mla_scratch (or its related defining module), adding the necessary import and preserving the existing None behavior for non-nvfp4_dsmla caches.
🤖 Prompt for all review comments with AI agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
Inline comments:
In `@vllm/model_executor/layers/attention/mla_attention.py`:
- Around line 1491-1566: Remove the duplicate definitions of _v_up_proj_bmm and
_v_up_proj_bmm_chunked from the later block, preserving the earlier
implementations in the class and leaving their call sites unchanged.
- Around line 1342-1388: Add the missing os import at module scope in
mla_attention.py so both os.environ.get calls in get_kv_cache_spec resolve
correctly; do not alter the existing environment-variable logic.
In `@vllm/v1/attention/backends/mla/b12x_mla_sparse.py`:
- Around line 819-834: Update the do_kv_cache_update docstring with Google-style
Args entries for kv_c_normed, k_pe, kv_cache, slot_mapping, kv_cache_dtype, and
k_scale, and add a Raises section documenting the RuntimeError cases raised by
the method. Keep the existing behavior and summary unchanged; no Returns section
is needed because the method returns None.
---
Nitpick comments:
In `@vllm/model_executor/layers/mla.py`:
- Around line 211-230: Update the initialization logic around
_NVFP4_MLA_SCALES_ENV so that when a non-empty scale file is configured but
kv_cache_dtype is not "nvfp4_ds_mla" or attn_backend.get_name() is not
"B12X_MLA_SPARSE", emit a clear warning or informational log describing the
ignored calibration file. Preserve the existing scale-loading behavior for
matching configurations and the identity default otherwise.
In `@vllm/v1/attention/backends/mla/b12x_mla_sparse.py`:
- Around line 287-296: Consolidate the NVFP4/FP8-RoPE record-size values into
shared constants near the existing layout logic, then reuse them in
get_kv_cache_shape, __init__ when computing _kv_record_bytes, and
do_kv_cache_update when validating kv_u8.shape[-1]. Preserve the existing 368,
432, and 656-byte selections and the current validation behavior.
- Around line 602-617: Replace the per-layer logger.warning and logger.info
calls in B12xMLASparseImpl.__init__ with the corresponding once-only logging
APIs, including the message around the _kv_fp8_rope setup at lines 606-615 and
the logger.info call around lines 686-692. Preserve each existing message and
arguments while ensuring each startup notice is emitted only once across all MLA
layers.
- Around line 675-680: Replace the hardcoded value assigned in the b12x
scale-format initialization with the exported ScaleFormat.NVFP4_E4M3 enum or
named constant from b12x.integration.sparse_mla_scratch (or its related defining
module), adding the necessary import and preserving the existing None behavior
for non-nvfp4_dsmla caches.
🪄 Autofix (Beta)
Fix all unresolved CodeRabbit comments on this PR:
- Push a commit to this branch (recommended)
- Create a new PR with the fixes
ℹ️ Review info
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Configuration used: Organization UI
Review profile: CHILL
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Run ID: 7c05cb0f-2e2c-4f47-8e80-01c66e7bf53f
📒 Files selected for processing (4)
vllm/model_executor/layers/attention/mla_attention.pyvllm/model_executor/layers/mla.pyvllm/v1/attention/backends/mla/b12x_mla_sparse.pyvllm/v1/kv_cache_interface.py
| def do_kv_cache_update( | ||
| self, | ||
| kv_c_normed: torch.Tensor, | ||
| k_pe: torch.Tensor, | ||
| kv_cache: torch.Tensor, | ||
| slot_mapping: torch.Tensor, | ||
| kv_cache_dtype: str, | ||
| k_scale: torch.Tensor, | ||
| ) -> None: | ||
| """Write the post-RoPE key using the selected runtime cache format. | ||
|
|
||
| The disabled branch delegates to the shipped implementation unchanged, | ||
| including its stock 432-byte NVFP4 writer. The enabled branch calls a | ||
| separate operator so loading this overlay cannot replace or perturb the | ||
| stock operator used by KV_FP8_ROPE=0. | ||
| """ |
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📐 Maintainability & Code Quality | 🟡 Minor | ⚡ Quick win
Docstring missing Args/Raises sections.
do_kv_cache_update takes 6 parameters and raises RuntimeError from 3 distinct sites, but the docstring documents neither. As per coding guidelines, "Use Google-style docstrings in Python code, with Args:/Returns:/Raises: sections instead of reStructuredText/Sphinx fields".
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@vllm/v1/attention/backends/mla/b12x_mla_sparse.py` around lines 819 - 834,
Update the do_kv_cache_update docstring with Google-style Args entries for
kv_c_normed, k_pe, kv_cache, slot_mapping, kv_cache_dtype, and k_scale, and add
a Raises section documenting the RuntimeError cases raised by the method. Keep
the existing behavior and summary unchanged; no Returns section is needed
because the method returns None.
Source: Coding guidelines
…ile boot fix) get_current_vllm_config() raises during KV-cache shape resolution / cudagraph compilation in a worker before the config context is set. Wrap in try/except, cache the resolved value, fall back to the explicit KV_FP8_ROPE request. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…proj_bmm block Both were merge artifacts: os.environ is used in get_kv_cache_spec (NameError without the import), and _v_up_proj_bmm/_v_up_proj_bmm_chunked were duplicated. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
6c2f31e
into
local-inference-lab:dev/fathomless-firmament
2 participants
PR — Outer-scale calibration + fp8-RoPE KV-cell for the nvfp4 MLA KV cache
Targets: vllm side →
local-inference-lab/vllm@dev/fathomless-firmament(not yet mergedupstream); kernel side →
lukealonso/b12x. Both stack on the nvfp4 MLA KV already present indev/fathomless-firmament.Title
[MLA][nvfp4] Per-layer outer-scale calibration + fp8-RoPE KV-cell for the nvfp4 MLA KV cacheSummary
Two independent, opt-in improvements to the nvfp4 MLA KV cache:
A. Calibrated per-layer outer-scale — the writer previously discarded the scale tensor and
quantized the 512-D latent at outer-scale 1.0. Loading a calibrated per-layer
s_l, writingkv_c_normed / s_l, and restorings_lin the reader recovers quantization loss at zero cost:teacher-forced KLD 0.184 → 0.152.
B. fp8-RoPE KV-cell — store the 64-D decoupled RoPE key as fp8-e4m3 + per-token amax scale
instead of 128 B bf16, shrinking the record 432 → 368 B: KV pool +15.8% (>1.08M tokens),
enabling genuine 1,048,576 single-request context. Runtime KV format only (no re-quantization);
the nvfp4 latent and the outer-scale (A) are untouched.
Validation
off(within the 0.0075 boot-noise range)Shipped in
verdictai/vllm-glm52-tr3-hybrid:v4-fp8rope.The change
vllm (
local-inference-lab/vllm@dev/fathomless-firmament):model_executor/layers/mla.py— (A) outer-scale loader +kv_c_normed / s_l(+103/−2); (B) the fp8-rope write hook (+42/−1).v1/attention/backends/mla/b12x_mla_sparse.py— (B) the 368 B record layout +scale_format(+149/−6).v1/kv_cache_interface.py+model_executor/layers/attention/mla_attention.py— (B) the 368 B allocator ABI so page accounting / cache shape / writer / readers agree.b12x (
lukealonso/b12x):attention/mla/{api,decode_math,kernel,prefill,prefill_mg}.py— (A) in-kernels_lrestore (scale_format==2); (B) the fp8-rope pack on write + dequant on read (~200 lines).attention/mla/traits.py— (B) the gated 368 B GLM record variant.concat_and_cache_nvfp4_mla_fp8_rope(the fp8 rope writer).Opt-in / no default change
s_l = 1.0= prior behavior. The shipped per-layer scales are calibratedoffline (per-layer max-abs of
kv_c_normed÷ 6·448) —nvfp4_ds_mla_outer_scale_v1JSON,loaded via
VLLM_NVFP4_MLA_SCALES_FILE.KV_FP8_ROPEunset/0→ byte-identical 432 B record;=1selects 368 B. GLM path only(V3.2 + DSV4 records untouched).
Summary by CodeRabbit
New Features
Bug Fixes