Add GLM-5.3-Flash indexer scorer keyword arguments to vLLM fake-quant serving

[sychen52]

What does this PR do?

Type of change: new feature

Adds indexer_scorer_kwargs_quantizer to the GLM-5.3-Flash k-pool indexer in vLLM fake-quant serving. When a recipe enables it with backend: scorer_kwargs, its backend_extra_args become keyword arguments of that layer's DeepGEMM indexer scorer kernel: stock DeepGEMM's logits_dtype, or the numerics options of a DeepGEMM build that declares them in deep_gemm.MQA_LOGITS_NUMERICS, such as a DeepGEMM fork. ModelOpt ships no such build. register_indexer_scorer_kwargs_quantizer lets plugins add quantizers that turn their own configs into scorer arguments, without patching vLLM.

The scorer quantizers start disabled. They are TensorQuantizers so that recipes select layers with quantizer_name patterns and the setting is saved and restored with the quantizer state; the scorer_kwargs backend never runs on a tensor. The arguments apply only while the quantizer quantizes, so calibration runs the stock kernel. mtq.quantize now also raises when a recipe enables indexer_scorer_kwargs_quantizer but it quantizes no indexer, as it already does for the query and key quantizers. Quantizers that plugins add with register_indexer_scorer_kwargs_quantizer are checked the same way.

Every indexer forward (also during calibration) and every restore checks the scorer configs and that the loaded DeepGEMM takes the arguments (logits_dtype, or a value declared in MQA_LOGITS_NUMERICS). The scorer runs only for sequences longer than index_topk, which calibration and warmup may never reach, so a bad recipe or a missing DeepGEMM build fails at the first forward, not at the first long request.

Finding the calling layer

The arguments are per layer, but the scorer and the K-cache writer kernels get only tensors, so they cannot tell which layer calls them. The indexer op, sparse_attn_indexer_kpool, gets the name of its layer's K cache (k_cache_prefix) instead of the module, the convention of vLLM's layer ops: a torch.ops.vllm custom op cannot take a module argument.

The new modelopt/torch/quantization/plugins/vllm_layer_scope.py maps such a name back to the converted module:

• register_layer_owner(name, owner) maps a layer name to the converted module that acts for the layer, in a registry with weak values. Each converted GLM-5.3-Flash indexer registers itself under its K cache's prefix, the name its op gets.
• wrap_layer_op wraps the op so that the layer it names is the current layer (a ContextVar) while the op runs.
• current_layer() and current_layer_owner() give the functions the op calls that layer's name and owner; outside a wrapped op both are None.
• patch_function(module, name, wrapper) replaces a function so that each call runs wrapper(original, *args, **kwargs), once per wrapper.

Nothing in the module is specific to the indexer, and it does not import vLLM: any op that takes a layer name can use it, as vLLM's attention ops take layer_name. The registry and the patches are process-wide, for one converted model per process as in vLLM serving; they are permanent, and the first converted GLM-5.3-Flash indexer installs them. The indexer's wrappers of the K-cache writers and the scorers are plain functions that check the scope themselves.

The scorer wraps are on vLLM's vllm.utils.deep_gemm.fp8_fp4_mqa_logits (prefill, keys gathered per query range) and fp8_fp4_paged_mqa_logits (decode, paged K cache). The GLM-5.3-Flash op imports both from that module on every call, so wrapping the module attributes reaches it without patching vLLM, whichever DeepGEMM vLLM loaded. vLLM's wrappers do not pass extra keyword arguments on, so when arguments are set, the wrap calls the loaded DeepGEMM directly. DeepSeek-V4's op imports these functions at module level, which such a wrap does not reach, so the DeepSeek-V4 indexer rejects the scorer quantizers.

If vLLM renames the op, its k_cache_prefix argument, a K-cache writer or a scorer wrapper, converting a GLM-5.3-Flash indexer fails. If the GLM-5.3-Flash op starts importing the scorer at module level, as DeepSeek-V4's does, the scorer quantizers would be skipped silently; test_tiny_sparse_attn_indexer_quantize catches that on SM100 GPUs by checking that every layer's prefill and decode scorer calls went through them.

K-cache writers

The K-cache writers now find their indexer through the layer scope too, instead of comparing the written cache's data_ptr with the K cache of every converted indexer. Two cases now raise a RuntimeError before the kernel runs:

• a K-cache write outside the indexer op while any indexer_k_quantizer is enabled (previously re-quantized if its cache matched an indexer);
• a write inside the op to another layer's K cache while the calling layer's indexer_k_quantizer is enabled.

vLLM 0.30 writes the K cache only inside the op, and indexer_k_quantizer is new in the unreleased 0.48.0, so no released behavior changes. Without an enabled K or scorer quantizer, the wraps pass calls through to vLLM's functions.

Scorer output

• BF16 scores (logits_dtype: bfloat16) are widened to FP32 with the same strides: vLLM's top-k kernels read FP32 with the strides they are given.
• NaN scores become -inf before top-k. vLLM's prefill top-k returns out-of-range indices for rows with NaN, which the sparse attention then reads out of bounds. Reduced-precision sums in the scorer can overflow into NaN (inf - inf in the head sum); top-k handles +inf and -inf.

Both apply only on layers with an enabled scorer quantizer.

CUDA graphs

The op wrap goes under eager_break_during_capture. With breakable CUDA graphs (on by default for GLM-5.3-Flash), a piecewise-dispatched batch runs the indexer op eagerly between graph pieces, and replays call the undecorated function, so a wrap over the decorator would be skipped on every replay. A FULL graph (uniform decode batches) captures the op whole: the wraps run at capture, and replays repeat the kernels they launched. test_indexer_op_in_cuda_graph_replays checks both with a stand-in op: after the capture it changes the captured inputs and replays a FULL graph (torch.cuda.graph) and a breakable one (vLLM's BreakableCUDAGraphCapture), which must re-quantize the K-cache writes and score with each layer's arguments.

Usage

A recipe that sums the heads in BF16 with stock DeepGEMM (logits_dtype is available on SM100 GPUs):

# modelopt-schema: modelopt.recipe.config.ModelOptPTQRecipe
imports:
  base_disable_all: configs/ptq/units/base_disable_all

metadata:
  description: BF16 head sum in the indexer scorer.
quantize:
  algorithm: max
  quant_cfg:
    - $import: base_disable_all
    - quantizer_name: '*indexer_scorer_kwargs_quantizer'
      cfg:
        backend: scorer_kwargs
        backend_extra_args: {logits_dtype: bfloat16}

# Only for options other than logits_dtype: a DeepGEMM build that declares them in MQA_LOGITS_NUMERICS.
# vLLM imports an installed deep_gemm before its vendored copy.
pip install --no-deps --no-build-isolation <DeepGEMM source directory or git URL>

RECIPE_PATH=<recipe>.yaml python examples/vllm_serve/vllm_serve_fakequant.py <model_path> -tp 4

With such a build, its options go into backend_extra_args the same way, e.g. a DeepGEMM fork's post_relu. A plugin can instead map its own quantizer config to arguments:

from modelopt.torch.quantization.plugins.vllm_indexer import register_indexer_scorer_kwargs_quantizer


def post_relu_kwargs(quantizer):
    """The enabled quantizer's config as scorer arguments; raise for a config it cannot express."""
    fmt = (quantizer.backend_extra_args or {}).get("format")
    if fmt not in ("bf16", "fp16"):
        raise ValueError(f"indexer_post_relu_quantizer: unsupported format {fmt!r}")
    return {"post_relu": fmt}  # an option of a DeepGEMM fork


# Run in every vLLM worker before quantization, e.g. from a vLLM general plugin (entry-point group
# `vllm.general_plugins`). Indexers converted from then on get a disabled indexer_post_relu_quantizer,
# which a recipe enables by name:
#   - quantizer_name: '*indexer_post_relu_quantizer'
#     cfg: {backend: scorer_kwargs, backend_extra_args: {format: bf16}}
register_indexer_scorer_kwargs_quantizer("indexer_post_relu_quantizer", post_relu_kwargs)

Testing

On B200 with vLLM 0.30.0, torch 2.13 and CUDA 13.0, on this branch (the same set also passed on H200, where the SM100-only cases skip):

• tests/gpu_vllm/torch/quantization with vLLM's vendored DeepGEMM, as in CI: 78 passed, 2 skipped (the vendored DeepGEMM has no MQA_LOGITS_NUMERICS). One existing test failed for a reason of the test machine: test_tiny_deepseek_fp8_kv_quant_cfg could not start its engine: with the file's module-scoped tiny engines still alive, the B200 had 24.6 of 178 GiB free, less than the 20% each engine reserves (gpu_memory_utilization=0.2, unchanged here).
• test_vllm_indexer_quant.py with a DeepGEMM fork wheel first on PYTHONPATH: 31 passed.
• test_vllm_dynamic_modules.py -k tiny_sparse_attn, eager mode, with the fork wheel: 3 passed (tiny GLM-5.3-Flash with logits_dtype: bfloat16, tiny GLM-5.3-Flash with the fork's post_relu and mma_accum, tiny DeepSeek-V4). The logits_dtype: bfloat16 recipe of Usage ran only on the fork's DeepGEMM build.
• tests/unit/torch/quantization/plugins/test_vllm_layer_scope.py (new; needs neither vLLM nor a GPU): 5 passed.
• tests/unit/torch/quantization/test_quantize_cpu.py -k "indexer or moe_comm or config_only": 10 passed.
• pre-commit on the changed files: clean.

The new tests cover routing to the calling layer, the op wrap under eager_break_during_capture, the op in FULL and breakable CUDA graph replays, the rejected K-cache writes, the MQA_LOGITS_NUMERICS check, validation in forward, FP32 widening, the NaN mapping, the DeepSeek-V4 rejection, and an enabled quantizer that only carries a config (no amax) through calibration, the quantization summary, fold_weight and a restore.

End to end, on an earlier revision of this change, whose layer scope looked the owner up through vLLM's static_forward_context instead of its own registry: GLM-5.3-Flash (FP8 checkpoint), TP4 on B200, served through examples/vllm_serve/vllm_serve_fakequant.py with RECIPE_PATH, CUDA graphs on (vLLM's default here: FULL_AND_PIECEWISE with breakable CUDA graphs). The client scored the NLL of 6 long documents through prompt_logprobs (~96k scored tokens: 12,282 below position 2048, 83,712 from 2048 on), and sent 8 greedy decodes and chat and completion requests.

Case	DeepGEMM	ΔNLL vs stock
Stock, no recipe (NLL 0.61414)	vendored	0
indexer_k_quantizer NVFP4 (configs/ptq/units/indexer_k_nvfp4)	vendored	-0.00021
indexer_scorer_kwargs_quantizer with the fork's BF16 post-ReLU option	fork wheel	+0.00025
K NVFP4 and the scorer arguments above	fork wheel	+0.00031
Positive control: indexer_scorer_kwargs_quantizer with the fork's FP16 QK-accumulation option (NLL 1.01864)	fork wheel	+0.40450

• Run-to-run NLL noise is about ±0.0003, so the three middle cases are at the noise level. In all cases: no non-finite logprobs, 8/8 decodes and the API requests succeeded.
• Logprobs at positions below 2048 (index_topk), where every key is selected, are identical to stock in every case.
• From position 2048 on, K NVFP4 and stock give equal logprobs for 0.9% of tokens, against 44% between two stock runs, so the K re-quantization takes effect.
• The FP16-accumulation control raised NLL by +0.405, as an earlier run with the same option did (+0.406), so the scorer arguments reach the kernel.
• These NLLs come from prefill, and vLLM runs a step with more tokens than its largest CUDA-graph size (at most 1024 by default on B200) without a graph, so they are not evidence for graph replays. Decode steps run in CUDA graphs; the 8 greedy decodes completed in every case. Graph replays of the wrapped op are covered by test_indexer_op_in_cuda_graph_replays, with a stand-in op.

Before your PR is "Ready for review"

• Is this change backward compatible?: ✅ (the scorer quantizers are new and start disabled; the stricter K-cache write check only affects indexer_k_quantizer, which is new in the unreleased 0.48.0)
• If you copied code from any other sources or added a new PIP dependency, did you follow guidance in CONTRIBUTING.md: N/A (no new dependency; a DeepGEMM build with more options is the user's to install)
• Did you write any new necessary tests?: ✅
• Did you update Changelog?: ✅
• Did you get Claude approval on this PR?: ❌ (requested after opening)

Additional Information

Size: 522 added lines outside tests (476 in modelopt/, 46 in the README and CHANGELOG); the rest is tests.

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Summary by CodeRabbit

• New Features
  • Added opt-in support for configuring scorer-argument quantization for GLM-5.3-Flash indexers served with vLLM, including scorer logits dtype options.
  • Scorer NaN values are handled safely during ranking. Unsupported indexer configurations and incompatible settings are validated and reported.
• Documentation
  • Added a vLLM serving example with configuration guidance, setup instructions, and compatibility notes, including DeepSeek-V4 restrictions and DeepGEMM requirements.

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📒 Files selected for processing (8)

• examples/vllm_serve/README.md
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• modelopt/torch/quantization/plugins/vllm_indexer.py
• modelopt/torch/quantization/plugins/vllm_layer_scope.py
• tests/gpu_vllm/torch/quantization/test_vllm_dynamic_modules.py
• tests/gpu_vllm/torch/quantization/test_vllm_indexer_quant.py
• tests/unit/torch/quantization/plugins/test_vllm_layer_scope.py
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📝 Walkthrough

The vLLM indexer plugin adds per-layer scorer keyword quantization for GLM-5.3-Flash. It adds layer-scoped wrappers, validates scorer configurations, applies arguments to DeepGEMM scoring, and adds GPU and unit tests plus serving documentation.

Changes: vLLM Indexer Quantization

Layer / File(s)	Summary
Layer-owner scope and wrapper utilities modelopt/torch/quantization/plugins/vllm_layer_scope.py, modelopt/torch/quantization/plugins/__init__.py, tests/unit/torch/quantization/plugins/test_vllm_layer_scope.py	Adds weakly held layer-owner references and wrappers that set and restore owner context around operations and patched functions. Unit tests cover owner lookup, nested scopes, exceptions, wrapper behavior, and garbage collection.
Scorer quantizer configuration and validation modelopt/torch/quantization/plugins/vllm_indexer.py, modelopt/torch/quantization/model_quant.py, tests/unit/torch/quantization/test_quantize_cpu.py, tests/gpu_vllm/torch/quantization/test_vllm_indexer_quant.py	Adds scorer-kwargs quantizer registration, backend handling, format checks, and validation for unsupported indexer layouts. Tests cover configuration, restore and forward validation, and invalid settings.
GLM scorer and cache integration modelopt/torch/quantization/plugins/vllm_indexer.py, tests/gpu_vllm/torch/quantization/test_vllm_indexer_quant.py	Scopes GLM indexer operations and cache writes to their layer owner. Passes configured keyword arguments to prefill and paged DeepGEMM scoring, widens non-FP32 logits, and maps NaN scores to negative infinity. GPU tests cover routing, cache writes, scorer behavior, and numerics.
Serving-path validation and documentation tests/gpu_vllm/torch/quantization/test_vllm_dynamic_modules.py, examples/vllm_serve/README.md, CHANGELOG.rst	Adds GLM-5 Next serving tests for scorer quantizer configurations and records prefill and decode calls, logits dtype, NaN status, and DeepGEMM numerics. Documents configuration and adds a changelog entry.

Priority: ➖ Normal

Estimated code review effort: 4 (Complex) | ~45 minutes

Change: Feature

Sequence Diagram(s)

sequenceDiagram
  participant Indexer as GLM indexer op
  participant Scope as current layer scope
  participant Wrapper as DeepGEMM scorer wrapper
  participant DeepGEMM
  Indexer->>Scope: set active layer
  Indexer->>Wrapper: call prefill or paged scorer
  Wrapper->>Scope: read active layer owner
  Wrapper->>DeepGEMM: pass configured scorer kwargs
  DeepGEMM-->>Wrapper: return logits
  Wrapper-->>Indexer: widen logits and map NaN scores to -inf

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The inspected serving paths preserve layer routing and apply configured scorer arguments. No issue requiring a fix before merge was established.

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• modelopt/torch/quantization/plugins/vllm_indexer.py
• modelopt/torch/quantization/plugins/vllm_layer_scope.py
• tests/gpu_vllm/torch/quantization/test_vllm_dynamic_modules.py
• tests/gpu_vllm/torch/quantization/test_vllm_indexer_quant.py
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Codecov Report

❌ Patch coverage is 99.44134% with 1 line in your changes missing coverage. Please review.
✅ Project coverage is 78.08%. Comparing base (8101d12) to head (d5b4238).
⚠️ Report is 7 commits behind head on main.

Files with missing lines	Patch %	Lines
...odelopt/torch/quantization/plugins/vllm_indexer.py	99.22%	1 Missing ⚠️

Additional details and impacted files

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[cjluo-nv]

Bot review (bedrock-claude-opus-5-5) — DM the bot to share feedback.

Nudge: the code looks correct and well tested, but it adds a new process-wide patching layer (vllm_layer_scope) over vLLM internals, so a vLLM-plugin owner should sign off on the design.

Needs action:

• Confirm, as the vLLM plugin owner, that the new layer-scope mechanism is the right approach. It wraps sparse_attn_indexer_kpool under eager_break_during_capture and wraps vLLM's deep_gemm scorer attributes permanently. It also replaces data_ptr matching with hard RuntimeErrors for K-cache writes outside the op.
• Confirm that a TensorQuantizer with a backend that always raises (scorer_kwargs) is an acceptable way to carry config. The PR body's rationale is quantizer_name selection plus state save/restore. Check that no calibration, export or fold path calls or post-processes enabled quantizers that have no amax.
• Fix the _PLUGIN_QUANTIZERS hint for indexer_scorer_kwargs_quantizer in model_quant.py. It points users at indexer_*_nvfp4, but no such unit exists for the scorer (inline comment).

No action needed:

• Core logic is about 454 added lines, under the 500-line budget. The PR body explains why the design reuses vLLM's layer-name convention.
• The edited existing tests are justified: the data_ptr lookup was removed, so the writer test now checks the new rejection behaviour, and the CPU check test was parametrized without losing coverage.
• The new files' headers match LICENSE_HEADER.
• I read the tests but did not run them.

---

Additional comments (outside the PR diff):

• modelopt/torch/quantization/plugins/model_quant.py:243 — > Bot comment.

The third element is the hint to the recipe unit users should import, but there's no indexer_*_nvfp4 unit for the scorer quantizer. Point at the README section, or give a scorer-specific hint (e.g. backend: scorer_kwargs), so the error message doesn't send users to the wrong unit.

[cjluo-nv]

Bot review (bedrock-claude-opus-5-5) — DM the bot to share feedback.

Nudge: both concrete fixes from the last review are in, but the two design questions for the vLLM plugin owner are still open. This PR adds a permanent, process-wide patching layer over vLLM internals.

Needs action:

• Confirm, as the vLLM plugin owner, that the vllm_layer_scope approach is acceptable. It permanently wraps sparse_attn_indexer_kpool under eager_break_during_capture, and it wraps vLLM's deep_gemm scorer attributes. It also replaces data_ptr matching with hard RuntimeErrors for K-cache writes made outside the op.
• Confirm that carrying config in a TensorQuantizer whose scorer_kwargs backend always raises is acceptable. Check that no export, fold or amax-loading path touches enabled quantizers that have no amax. The end-to-end test covers calibration only.
• Update the PR body. It still describes wrap_layer_callee and an outside hook, but the code now has patch_function. It also gives the old location of test_vllm_layer_scope.py, which is now under tests/unit/torch/quantization/plugins/.

No action needed:

• ✔️ Resolved since the last review: the _PLUGIN_QUANTIZERS hint for the scorer now points at the README section, and a parametrized CPU test checks it. The README serve command now uses the repository-root launcher path.
• Core logic is about 476 added lines, under the 500-line budget. The new file's header matches the standard NVIDIA Apache header.
• The edits to existing tests are justified: the data_ptr lookup was removed, so the writer test now checks the new rejection behaviour, and the CPU check test was parametrized without losing coverage. I read the tests but did not run them.

[sychen52]

Thanks. Updates in 1f979e8, da61765, 0b4930c and f65bdbe:

• _PLUGIN_QUANTIZERS hint (da61765): the last column now says where the supported models are listed. For indexer_scorer_kwargs_quantizer that is the "Indexer scorer kernel arguments" section of examples/vllm_serve/README.md; the other quantizers' messages are unchanged. test_indexer_patterns_need_their_own_quantizer checks both hints.
• An enabled quantizer without amax: while calibrating, _if_quant is off, so the stock kernel runs and the scorer_kwargs backend is never called on a tensor. finish_stats_collection loads an amax only when the calibrator collected one, and the DP/EP/TP amax syncs skip quantizers without _amax. fold_weight only folds *weight_quantizer attributes, the vLLM fake-quant export and reload only read weight, input and K/V quantizers or the state dict (empty here), print_quant_summary prints dynamic without an amax, and modelopt_state save/restore keeps backend and backend_extra_args. The new test_config_only_quantizer_survives_calibration_fold_summary_and_restore (0b4930c) runs such a quantizer, with a backend that fails if called, through mtq.quantize, print_quant_summary, fold_weight and a restore.
• Layer scope: 1f979e8 made the module more general. It gives the functions an op calls only current_layer() and current_layer_owner() (renamed in f65bdbe), plus a plain patch_function; the indexer's checks of the K-cache writes and the scorer calls are ordinary code in the plugin. The sign-off on the mechanism is still for the vLLM plugin owner.
• PR body: updated for the new API, the test location and the new test results.

[cjluo-nv]

Bot review (bedrock-claude-opus-5-5) — DM the bot to share feedback.

Nudge: every concrete fix from the last review is in the diff. The one open item is the vLLM plugin owner's approval of the new process-wide patching layer over vLLM internals.

Needs action:

• 💬 Author replied: the mechanism was made generic in 1f979e8 and f65bdbe. It now exposes current_layer/current_layer_owner plus patch_function, and the indexer-specific checks live in the plugin. This still needs the vLLM plugin owner to approve three things:
  • the permanent wraps of sparse_attn_indexer_kpool (under eager_break_during_capture) and of vllm.utils.deep_gemm.fp8_fp4_*mqa_logits;
  • the weak-value owner registry, which is process-wide;
  • the hard RuntimeErrors that now replace data_ptr matching for K-cache writes.

No action needed:

• ✔️ Resolved since the last review: the PR body now describes the current API and the test locations. test_config_only_quantizer_survives_calibration_fold_summary_and_restore covers a config-only TensorQuantizer through calibration, print_quant_summary, fold_weight and restore. The _PLUGIN_QUANTIZERS hint for the scorer quantizer was fixed earlier, along with the README launcher path.
• Design gate: the PR body explains the choice. A torch.ops.vllm op can't take a module, so the indexer is looked up by its layer name, and the body names the earlier static_forward_context revision. No second in-repo system for this exists.
• About 476 added lines of core logic, under the 500-line budget. The new files' headers match the standard NVIDIA Apache header.
• The edits to existing tests are justified. The data_ptr lookup was removed, so the writer test now asserts the new rejections. The CPU hint test was parametrized without losing its indexer_q_quantizer case.
• I read the tests but did not run them.