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fix: unify DSA projection exclusion parsing and drop synthetic gate quant variants - #653
One exclusion parser for every backend.attention_projection_exclusions drops its precise_module_paths switch and always matches projection module paths. SGLang, TRT-LLM and the MLA/task paths now read native GLM FP8 checkpoints the way vLLM already did (since fix: apply vLLM DSA projection precision and index reuse #407): their attention projections are block FP8, not BF16.
The prediction gate stops forcing quant modes.QUANT_VARIANTS keeps only default; the fp8 (all systems) and nvfp4 (SM100) overrides are removed with NVFP4_SYSTEMS, NVFP4_VARIANT and quant_variants_for.
Why
zai-org/GLM-5-FP8, GLM-5.1-FP8, GLM-5.2-FP8 and zai-org/GLM-5.3 are quant_method: fp8 with weight_block_size: [128, 128]. Their exclusion lists name only norms and indexers_proj (self_attn.q_a_layernorm, kv_a_layernorm, indexer.k_norm, ...). The legacy parser read any self_attn pattern without a projection name as a whole-block exclusion, so non-vLLM backends priced these projections as BF16. SGLang (_module_path_match) and TRT-LLM (modules_to_not_convert → exclude_modules) skip only the listed module or its descendants, so they run the projections quantized.
No bundled DSA checkpoint uses per-tensor FP8: the FP8 ones are block FP8, and the NVFP4 ones are ModelOpt with self_attn* excluded. The gate's fp8 override still forced per-tensor FP8 onto every model. Those rows were the only consumer of per-tensor vLLM DSA tables; the 540 expected-data-miss cases in fix: apply vLLM DSA projection precision and index reuse #407 were all quant=fp8, and fix: replace vLLM DSA profiles for remaining GPUs #409 recollected six GPUs to satisfy them. The nvfp4 override pairs block-FP8 checkpoints with NVFP4 attention projections that no NVFP4 checkpoint ships.
Keeping only the checkpoint's own quantization tests what is actually served. 26 bundled NVFP4 or ModelOpt mixed-precision checkpoints and 26 FP8 checkpoints still exercise those tables.
Changes
File
Change
sdk/models/helpers.py
Single precise parser; the indexer markers are its projections (wq_b, wk, weights_proj), not any indexer substring
Replace the "other consumers keep the legacy interpretation" test; the GLM-5.2-FP8 projection test now covers all three backends; new test: native GLM FP8 checkpoints key fp8_block on every backend without an override
Among the 95 bundled configs, only the four GLM FP8 checkpoints parse differently. MLA models (DeepSeek-V3/R1, Kimi) are unchanged.
Prediction impact
The official gate on base 7980e9500 vs head was collected locally (collect_static.py + report.py, 28 combos):
Category
Rows
Gate
REGRESSION
0
blocking
DRIFT
360
report-only
ROWS_REMOVED
102,600
report-only (the removed fp8/nvfp4 variants)
All 360 drifts are the four GLM FP8 checkpoints at tp1/adp8/ep8 (the only layout these models predict in the grid): SGLang 0.5.21 on all six NVIDIA DSA systems and TRT-LLM 1.3.0rc29 on B200/B300/GB200/GB300. Whole-model ratio new/old: min 0.891, median 0.964, max 1.156.
Removing the overrides is what makes the parser fix pass the gate. With them kept, the same parser change produces 36 blocking OK -> DATA_MISS rows per Blackwell system for each:
SGLang + nvfp4: SGLang has no GLM NVFP4 DSA rows;
TRT-LLM + fp8: there are no per-tensor GLM DSA rows.
Full -m unit: 12,974 passed. Three failures reproduce on unmodified base:
two test_support_cli.py path assertions under /private/tmp;
test_fpm_learned.py, because sklearn is missing locally.
The e2e collection errors come only from running with -p no:timeout locally.
test_engine_step_parity.py 304 passed and test_compile_engine_parity.py 85 passed; the goldens need no refresh. Their GLM cases (GLM-5 BF16, GLM-5.2-NVFP4, GLM-5.3-Flash) parse identically.
ruff check / ruff format --check pass on the changed files.
…uant variants
Native GLM FP8 checkpoints (zai-org/GLM-5-FP8, GLM-5.1-FP8, GLM-5.2-FP8,
GLM-5.3) ship block-FP8 attention projections and exclude only norms and
indexers_proj. The legacy exclusion parser read any self_attn pattern without
a projection name as a whole-block exclusion, so SGLang, TRT-LLM and the MLA
paths priced those projections as BF16; only vLLM used the precise parser.
Use the precise module-path parser for every consumer. Among the 95 bundled
configs only those four checkpoints change, now keyed fp8_block on every
backend.
The prediction gate forced per-tensor fp8 and nvfp4 GEMM/MoE modes onto every
bundled model, pairing quantized checkpoints with formats they do not ship and
turning those rows into perf-data obligations. Keep only each checkpoint's own
quantization; bundled FP8 and NVFP4 checkpoints still cover those precisions.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Signed-off-by: Tianhao Xu <tianhaox@nvidia.com>
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Applicability reference: AGENTS.md: # AGENTS Before changing a performance model, its configuration, or a caller in Rust, Python, CLI, Sweeper, Replay, or Planner, MUST read and follow [`perfmodel-api.md`](python/aisimulat...
Source excerpt: Binary parquet changes require a machine-readable summary of keys, shapes, units, row counts, coverage, and anomalies plus proof that a real consumer reaches the new data.
Frozen helper already uses the one-argument API and precise projection matching. [::ai-dynamo/aiconfigurator::aic-core/src/aiconfigurator_core/sdk/models/helpers.py:87-103]
DeepSeek-V3.2 consumers preserve per-projection exclusions, including q/kv/indexer while quantizing o_proj. [::ai-dynamo/aiconfigurator::aic-core/src/aiconfigurator_core/sdk/models/deepseek_v32.py:150-155] [::ai-dynamo/aiconfigurator::tests/unit/sdk/models/test_model_config.py:1759-1762]
The task consumer intentionally excludes indexer from its mixed-attention identity check. [::ai-dynamo/aiconfigurator::src/aiconfigurator/sdk/task_v2.py:1624-1634]
Frozen gate code still emits synthetic fp8 and nvfp4 variants, so its policy intentionally differs from this PR. [::ai-dynamo/aiconfigurator::tools/prediction_regression_gate/grid.py:110-134]
Backend facts explicitly model GLM DSA with both BF16 and FP8-block GEMM modes across SGLang/TRT-LLM. [::ai-dynamo/aiconfigurator::collector/op_backend_facts.yaml:264-277]
ai-dynamo/dynamo
GLM deployment recipes independently select NVFP4 for B200 and FP8 for H200. [::ai-dynamo/dynamo::recipes/glm-5.3/README.md:19] [::ai-dynamo/dynamo::recipes/glm-5.3/README.md:39-40]
No references to the changed helper or prediction-grid APIs were found in Dynamo.
Risk: Medium. The three areas that need human attention are projection-path matching across backends, GLM FP8 and ModelOpt mixed-precision cases, and prediction-gate coverage after removing forced quantization variants.
Changed behavior and contracts
attention_projection_exclusions(raw_config) now uses precise projection-module matching for every backend. Whole-attention-block exclusions still cover all projection groups. Named child exclusions, such as norms, do not exclude projection GEMMs. Targets-only ModelOpt exports leave projection groups without a qualifying quantized_layers entry unquantized, subject to exclusion globs.
DeepSeekV32Model.create now calls the shared helper without backend-specific behavior.
grid.QUANT_VARIANTS now contains only default, which uses each model config’s quantization. The public quant_variants_for function was removed. This changes the prediction gate’s sampled rows and removes forced fp8 and nvfp4 variants.
The precise_module_paths keyword was removed from attention_projection_exclusions. Callers that pass it must be updated.
Evidence and remaining gaps
Current source confirms the helper behavior, the unified model call, and the default-only quantization grid. Tests cover projection exclusions, ModelOpt targets-only handling, and GLM checkpoint quantization modes across backends.
The PR description reports 2,392 focused tests and 12,974 unit tests passed. It reports three failures as reproducible on base. It also reports 304 engine-step parity tests, 85 compile-engine parity tests, and Ruff checks passed. These results were not independently run here.
The PR description reports zero blocking gate regressions, 360 report-only drifts, and 102,600 removed report-only rows. These comparison results were not independently verified.
Review finding severity counts are unavailable because no current review findings were supplied.
Technical quality: Source and test coverage support the intended behavior changes. Merge readiness: Not established. The test results, gate comparison, and reported base-branch failures remain unverified here.
The model helper now applies precise projection-module matching across backends. GLM tests check projection GEMM modes across backends. Prediction regression collection now uses only each checkpoint’s default quantization variant.
The grid retains only the default quantization variant and removes system-specific variant selection. collect_combo uses the shared variant list for every system.
ai-dynamo/aisimulate#610: Adds targets-only ModelOpt MIXED_PRECISION parsing used by the same exclusion resolver.
ai-dynamo/aisimulate#615: Adds positive-target parsing for ModelOpt MIXED_PRECISION DSA exports, which use the same projection quantization contract.
ai-dynamo/aisimulate#614: Parses ModelOpt MIXED_PRECISION metadata for SGLang MLA collection, which depends on the same export metadata.
Merge Risk: Minimal · up to 5965e
The change is mergeable with no established actionable risk in the reviewed paths.
Pre-merge checks: 5 · 1 · 2
Failed checks: 1 warning · 2 inconclusive
Check name
Status
Explanation
Resolution
Cross-Layer Contract
The parser coverage gap from the prior failure is addressed: test_native_glm_fp8_checkpoint_prices_block_fp8_projections now includes zai-org/GLM-5.1-FP8 and covers all three backends and both pha…
Add a focused prediction-gate unit test that asserts grid.QUANT_VARIANTS == [("default", None, None)] and verifies collect_combo emits only quant="default" through the collector path. Keep the added GLM-5.1 parser coverage.
Title check
The title names the two changes: unifying DSA projection exclusion parsing and dropping the synthetic gate quant variants. The diff matches both. It does not state the behavioral change precisely. It …
Restate the behavior change more precisely. For example, name the affected backends and the matching rule: "fix: match DSA projection module paths for all backends and drop forced gate quant variants". Avoid vague terms and keep the title f…
Review Evidence
The visible PR description names relevant commands and results, reports negative and boundary outcomes, and distinguishes local gate and parity evidence. However, the supplied description is truncated…
Provide the complete PR description, including its omitted ending, so the review can verify that it does not cite workflows below python/aisimulate as hosted-CI evidence and that all validation provenance is correctly labeled.
Passed checks: 5 passed
Check name
Status
Explanation
Description check
The description follows the repository template and is thorough. It covers the problem, the behavior change, the changed files, and evidence with exact commands and counts. The Review map lacks a risk…
Linked Issues check
Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check
Check skipped because no linked issues were found for this pull request.
Modeling And Data Evidence
The PR supplies reproducible before/after evidence for its selection and prediction changes. The description identifies base revision 7980e9500, the collect_static.py and report.py procedure, 28…
Compatibility Boundaries
No compatibility-boundary failure is introduced. The reviewed range changes only six AISimulate Python implementation/test/tool files; it does not change Cargo manifests, the Python manifest, schemas,…
Full details: Description check
Explanation
The description follows the repository template and is thorough. It covers the problem, the behavior change, the changed files, and evidence with exact commands and counts. The Review map lacks a risk level, a Start with entry, and a statement on public or serialized contract changes. Evidence lacks a CodeRabbit reviewed commit, a Codex reviewed commit (required for medium or high risk), and a Modeling or data provenance section. Tracking lists related PRs but leaves Closes empty.
Full details: Modeling And Data Evidence
Explanation
The PR supplies reproducible before/after evidence for its selection and prediction changes. The description identifies base revision 7980e9500, the collect_static.py and report.py procedure, 28 compared combos, and a categorized result: 0 blocking regressions, 360 explained drifts, and 102,600 expected removed rows. It also explains the affected four GLM FP8 checkpoints and the removed synthetic variants. The repository report compares base and head CSV snapshots, uses explicit anomaly categories and a relative-drift threshold, and blocks only REGRESSION (python/aisimulate/tools/prediction_regression_gate/compare.py:3-18,28-34; report.py:40-44,176-212). No golden, CSV, snapshot, or binary refresh appears in the reviewed diff.
Full details: Compatibility Boundaries
Explanation
No compatibility-boundary failure is introduced. The reviewed range changes only six AISimulate Python implementation/test/tool files; it does not change Cargo manifests, the Python manifest, schemas, bindings, versions, or Rust code. The public aisimulate_core.sdk facade remains unchanged, and the modified helpers are internal model modules rather than facade exports. The new parser is self-contained and DeepSeekV32Model still passes the same exclusion data into the existing DSA operation path (helpers.py:98-126, deepseek_v32.py:275-284). The gate documents and applies the intentional default-only quantization policy (grid.py:10-14,105-116, collect_static.py:116-125). The changed implementation files import AISimulate namespaces only, with no Dynamo dependency. The existing packaging contract remains configured as one maturin wheel containing the Python packages and one aisimulate-core crate (python/aisimulate/pyproject.toml:105-123, Cargo.toml:4-9, crates/core/Cargo.toml:4-18).
Full details: Cross-Layer Contract
Explanation
The parser coverage gap from the prior failure is addressed: test_native_glm_fp8_checkpoint_prices_block_fp8_projections now includes zai-org/GLM-5.1-FP8 and covers all three backends and both phases (python/aisimulate/tests/unit/sdk/models/test_model_config.py:2010-2024). The prediction-gate contract remains untested. The PR changes QUANT_VARIANTS to one default entry (python/aisimulate/tools/prediction_regression_gate/grid.py:105-116) and changes collect_combo to consume that axis (collect_static.py:116-117), but test_prediction_gate_grid.py:4-119 only tests directory enumeration and does not assert the single-default axis or collector quant output. Repository search also shows no remaining test of QUANT_VARIANTS; the removed quant_variants_for path has no current caller.
Resolution
Add a focused prediction-gate unit test that asserts grid.QUANT_VARIANTS == [("default", None, None)] and verifies collect_combo emits only quant="default" through the collector path. Keep the added GLM-5.1 parser coverage.
Full details: Title check
Explanation
The title names the two changes: unifying DSA projection exclusion parsing and dropping the synthetic gate quant variants. The diff matches both. It does not state the behavioral change precisely. It does not say which backends change or what the parser now matches, so the title falls short of the stated requirement.
Resolution
Restate the behavior change more precisely. For example, name the affected backends and the matching rule: "fix: match DSA projection module paths for all backends and drop forced gate quant variants". Avoid vague terms and keep the title focused on what changes for consumers.
Full details: Review Evidence
Explanation
The visible PR description names relevant commands and results, reports negative and boundary outcomes, and distinguishes local gate and parity evidence. However, the supplied description is truncated, and the repository contains no full PR-body copy. The omitted text could contain an inactive nested-workflow claim, which is an explicit failure condition, so absence cannot be verified.
Resolution
Provide the complete PR description, including its omitted ending, so the review can verify that it does not cite workflows below python/aisimulate as hosted-CI evidence and that all validation provenance is correctly labeled.
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Actionable comments posted: 1
🪄 Fix comments: Fix CodeRabbit comments on this PR
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Inline comments:
Review comments at
@python/aisimulate/tests/unit/sdk/models/test_model_config.py:
- Line 2018: Add `zai-org/GLM-5.1-FP8` to the `model_path` parameter list so
this parameterized test checks its checkpoint-specific projection exclusion set
and default DSA mode.
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Preserve the Rust single oracle: Python may describe operations, load raw data, orchestrate, and present results, but must not compute per-op performance values.
Applicability reference: AGENTS.md: # AGENTS Before changing a performance model, its configuration, or a caller in Rust, Python, CLI, Sweeper, Replay, or Planner, MUST read and follow [`perfmodel-api.md`](python/aisimulat...
Source excerpt: Binary parquet changes require a machine-readable summary of keys, shapes, units, row counts, coverage, and anomalies plus proof that a real consumer reaches the new data.
The frozen helper defines the same one-argument API and matches whole-attention blocks versus individual projection markers. [::ai-dynamo/aiconfigurator::aic-core/src/aiconfigurator_core/sdk/models/helpers.py:87-103]
Its tests expect DeepSeek-V3.2-NVFP4 exclusions of {q, kv, indexer} while keeping o_proj quantized. [::ai-dynamo/aiconfigurator::tests/unit/sdk/models/test_model_config.py:1759-1762]
The task consumer intentionally masks exclusions to {q, kv, o} when determining mixed attention identity; indexer exclusions are handled separately by model-specific parameters. [::ai-dynamo/aiconfigurator::src/aiconfigurator/sdk/task_v2.py:1624-1634]
The frozen prediction gate still generates default, fp8, and system-specific nvfp4 variants. This is an intentional compatibility-policy divergence from the PR’s removal of synthetic variants and should remain scoped to the PR’s gate implementation. [::ai-dynamo/aiconfigurator::tools/prediction_regression_gate/grid.py:107-134] [::ai-dynamo/aiconfigurator::tools/prediction_regression_gate/collect_static.py:116-125]
ai-dynamo/dynamo
Dynamo has no references to the changed helper or prediction-grid APIs. Its GLM recipes independently select NVFP4 checkpoints for B200 and FP8 checkpoints for H200, so those deployment contracts are not directly affected by the gate change. [::ai-dynamo/dynamo::docs/fern/pages/recipes/model-recipes/glm-5-2.mdx:12-47]
The removed rows are synthetic, not required API coverage.
cli_estimate supports explicit quantization inputs, but that does not make every model, backend, and quantization pairing a supported deployment. The cited GLM/SGLang/NVFP4 combination is not generally valid because GLM-5.3-Flash requires its checkpoint’s native FP8/NVFP4 precision partition. The gate intentionally exercises checkpoint-native quantization, so removing blanket overrides does not establish a functional defect.
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Approving. What I checked:
I ran the old and new parsers over all 95 bundled model configs. Only zai-org/GLM-5-FP8, GLM-5.1-FP8, GLM-5.2-FP8 and GLM-5.3 change: the legacy parse gave {q, kv, o, indexer} and the new one gives nothing, the same as vLLM's precise parse already did. MLA, Kimi and DeepSeek configs parse the same as before, so for bundled models neither deepseek.py nor the mixed-identity check in task_v2 changes.
The gate only samples the latest version of each backend, so I checked data coverage for the older ones separately. Every SGLang (0.5.14, 0.5.21) and TRT-LLM (rc20, rc29) DSA table on all six systems has GlmMoeDsaForCausalLMfp8_block rows. They cover the same num_heads / tp_size cells as the bf16 rows, so the older versions should not turn into DATA_MISS either. I could not run the predictions end to end locally because my native build is stale, so this is a check on the tables, not a prediction run.
The SGLang collector already labels native block-FP8 GLM checkpoints fp8_block (collect_mla_module.py), which agrees with the new parse.
No files overlap with #649, and the branch merges cleanly into main.
One non-blocking comment on the gate grid, inline. Full CI has not run on 5965e6f yet.
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Summary
attention_projection_exclusionsdrops itsprecise_module_pathsswitch and always matches projection module paths. SGLang, TRT-LLM and the MLA/task paths now read native GLM FP8 checkpoints the way vLLM already did (since fix: apply vLLM DSA projection precision and index reuse #407): their attention projections are block FP8, not BF16.QUANT_VARIANTSkeeps onlydefault; thefp8(all systems) andnvfp4(SM100) overrides are removed withNVFP4_SYSTEMS,NVFP4_VARIANTandquant_variants_for.Why
zai-org/GLM-5-FP8,GLM-5.1-FP8,GLM-5.2-FP8andzai-org/GLM-5.3arequant_method: fp8withweight_block_size: [128, 128]. Their exclusion lists name only norms andindexers_proj(self_attn.q_a_layernorm,kv_a_layernorm,indexer.k_norm, ...). The legacy parser read anyself_attnpattern without a projection name as a whole-block exclusion, so non-vLLM backends priced these projections as BF16. SGLang (_module_path_match) and TRT-LLM (modules_to_not_convert→exclude_modules) skip only the listed module or its descendants, so they run the projections quantized.self_attn*excluded. The gate'sfp8override still forced per-tensor FP8 onto every model. Those rows were the only consumer of per-tensor vLLM DSA tables; the 540 expected-data-miss cases in fix: apply vLLM DSA projection precision and index reuse #407 were allquant=fp8, and fix: replace vLLM DSA profiles for remaining GPUs #409 recollected six GPUs to satisfy them. Thenvfp4override pairs block-FP8 checkpoints with NVFP4 attention projections that no NVFP4 checkpoint ships.Changes
sdk/models/helpers.pywq_b,wk,weights_proj), not anyindexersubstringsdk/models/deepseek_v32.pybackend_name == "vllm"switchtools/prediction_regression_gate/grid.py,collect_static.pydefaultquant varianttests/unit/sdk/models/test_model_config.pyfp8_blockon every backend without an overridetests/unit/sdk/models/test_modelopt_mixed_precision_targets.pyAmong the 95 bundled configs, only the four GLM FP8 checkpoints parse differently. MLA models (DeepSeek-V3/R1, Kimi) are unchanged.
Prediction impact
The official gate on base
7980e9500vs head was collected locally (collect_static.py+report.py, 28 combos):fp8/nvfp4variants)All 360 drifts are the four GLM FP8 checkpoints at
tp1/adp8/ep8(the only layout these models predict in the grid): SGLang 0.5.21 on all six NVIDIA DSA systems and TRT-LLM 1.3.0rc29 on B200/B300/GB200/GB300. Whole-model ratio new/old: min 0.891, median 0.964, max 1.156.Removing the overrides is what makes the parser fix pass the gate. With them kept, the same parser change produces 36 blocking
OK -> DATA_MISSrows per Blackwell system for each:nvfp4: SGLang has no GLM NVFP4 DSA rows;fp8: there are no per-tensor GLM DSA rows.That is 288 rows in total.
Validation
pytest tests/unit/sdk/models tests/unit/tools: 2,392 passed.Full
-m unit: 12,974 passed. Three failures reproduce on unmodified base:test_support_cli.pypath assertions under/private/tmp;test_fpm_learned.py, becausesklearnis missing locally.The e2e collection errors come only from running with
-p no:timeoutlocally.test_engine_step_parity.py304 passed andtest_compile_engine_parity.py85 passed; the goldens need no refresh. Their GLM cases (GLM-5 BF16, GLM-5.2-NVFP4, GLM-5.3-Flash) parse identically.ruff check/ruff format --checkpass on the changed files.Related
fp8MLA/DSA module cases from the collection plan. Existing tables are kept.DsaReuseSolPolicyand anyfp8 -> fp8_blocktable alias.🤖 Generated with Claude Code