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feat: add GB200 FP8 FlashInfer MoE measurements - #301
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Source excerpt: Do NOT add Python-side interpolation, roofline/SOL formulas, empirical-utilization estimates, or per-call table lookups anywhere under `python/aisimulate/src/aisimulate_core/sdk/` (banned def shapes: the `_query_*` and `_loo...📄 CodeRabbit inference engine (python/aisimulate/.claude/rules/rust-core/parity.md) Files:
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Source excerpt: Core doctrine: **observe, don't predict.**📄 CodeRabbit inference engine (python/aisimulate/.claude/rules/collector/failure_handling.md) Files:
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📝 SummaryRisk: Moderate. Human attention should focus on: (1) the 3,159 new Parquet measurements and preservation of the existing 9,153 rows; (2) the Behavior and contract: The change adds reported GB200 Qwen3.8-Max measurements for SGLang 0.5.17, using the FP8 FlashInfer MoE lane across 117 shapes and 27 token counts. The new rows are excluded from automatic selection and are intended to be available through explicit source selection with an eligibility-aware reader. The backend-facts entry now lists Evidence supplied: The author reports 12,312 total measurements, no collection errors, and a native-consumer audit. That audit reportedly confirms default ineligibility for all new rows, explicit FlashInfer access to 324 TP32 points, preservation of original measurements and columns, unchanged automatic-selection outcomes versus main, and exact-source latency access. These are loading and selection checks, not GPU collection or predictive-accuracy evidence. CodeRabbit’s DuckDB check reportedly found no nulls, duplicate measurement keys, or invalid latencies, and confirmed eligibility counts and lane coverage. The author also reports a final manifest refresh, a refreshed report, and fresh native validation at the final head. Technical quality and merge readiness: The provided evidence does not include the audit outputs, the independent comparison of original columns with baseline, or the final CI results. The objectives report that independent reviews and Fast CI were still in progress and Full CI had not been admitted. The latest bot comment reports a review trigger but supplies no findings; severity counts are unavailable. The bot checks are evidence, not approval. The supplied shell output shows no working-tree diff, so it does not verify the reported file changes. The objectives report the prerequisite code PR and related data PRs merged; merge readiness for this PR remains unestablished by the supplied evidence. WalkthroughThe SGLang 0.5.17 GB200 MoE metadata adds 3,159 FlashInfer TRT-LLM ChangesSGLang MoE collection data
Priority: ⬇️ Low Estimated code review effort: 2 (Simple) | ~7 minutes Merge Risk: 🟡 Moderate · up to Verify the FlashInfer pin for this SGLang version and the TP32 lane before merging; otherwise the newly available measurements may carry an incorrect backend label. 🚥 Pre-merge checks | ✅ 7 | ❓ 1❌ Failed checks (1 inconclusive)
✅ Passed checks (7 passed)
Full details: Review EvidenceExplanation The complete PR description is available and reports results and boundary checks, including 91 focused tests, 56,862 automatic outcomes, 24,624 exact-source checks, 324 TP32 negative boundaries, and production Slurm/OCI exit results. It does not name the commands that produced these results. It also does not distinguish local, fake-runner, and parity evidence; it distinguishes native, CI, and production evidence only. No inactive nested workflow is cited as hosted CI evidence. Therefore the explicit failure condition is not met, but the required evidence for a PASS is incomplete. Resolution Add the exact commands or scripts used for the focused tests, native validation, and data audit. Label each result as local, fake-runner, parity, or production evidence. Keep hosted-CI claims limited to active root workflows under Comment |
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Signed-off-by: Yiming Liu <yimingl@nvidia.com>
Signed-off-by: Yiming Liu <yimingl@nvidia.com>
Signed-off-by: Yiming Liu <yimingl@nvidia.com>
Signed-off-by: Yiming Liu <yimingl@nvidia.com>
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Validate exact source selections against model metadata and the constructed native operation graph before unsupported configurations can fall back. Preserve the source through external-FPM AFD validation and explicitly reject legacy or fixed companion paths that cannot consume it. Document the selector contract and cover the actual model, memory, Task, and runner consumers. Signed-off-by: Yiming Liu <yimingl@nvidia.com>
Signed-off-by: Yiming Liu <yimingl@nvidia.com>
Signed-off-by: Yiming Liu <yimingl@nvidia.com>
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Inline comments:
In `@python/aisimulate/collector/cases/models/Qwen3_5MoeForCausalLM_cases.yaml`:
- Around line 187-193: Update the sglang_moe_backends fp8_block pin comment with
a SGLang v0.5.17 source citation showing Fp8MoEMethod.create_moe_runner selects
flashinfer_trtllm for 128x128 block-quantized weights on SM100 and SM103. Verify
that this lane also supports moe_tp_size=32 with local inter size 64; only
retain the pin if the source confirms both conditions.
In
`@python/aisimulate/src/aisimulate_core/systems/data/gb200/moe/sglang/0.5.17/collection_meta.yaml`:
- Around line 36-46: Update MoeTable::load_moe_parquet so FlashInfer rows do not
silently compete with Triton rows in the unpinned default grid; define the
intended default lane explicitly and preserve source-specific lookups. Clarify
the metadata’s TP32 explanation to distinguish the missing Triton rows from the
valid FlashInfer rows.
In `@python/aisimulate/src/aisimulate/runner.py`:
- Line 101: Update _pop_aic_timing_overrides to omit the moe_kernel_source
override when its value is None, while preserving duplicate-alias detection and
allowing non-fixed companion timing to proceed; add a test for a null value with
a non-fixed companion.
In `@python/aisimulate/src/aisimulate/sdk/task_v2.py`:
- Line 2132: Validate the `moe_kernel_source` and `moe_comm_backend` combination
before the sweep begins; when a pinned source is incompatible with the large-EP
graph, reject the request or filter those tuples with a clear user-facing
diagnostic rather than silently returning results from a reduced search space.
In `@python/aisimulate/src/aisimulate/sweeper/config.py`:
- Line 542: Add validation for `SearchSpace.moe_kernel_source` during
construction using the shared `normalize_kernel_source` validator, so blank and
whitespace-only sources are rejected when the search space is built while `None`
remains valid.
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📄 CodeRabbit inference engine (REVIEW.md)
Files:
python/aisimulate/src/aisimulate_core/sdk/models/deepseek_v4.pypython/aisimulate/src/aisimulate_core/sdk/models/deepseek_v41.pypython/aisimulate/tests/unit/sdk/task_v2/test_v1_compat.pycrates/core/src/perfmodel/fpm/tests.rstests/sweeper/test_engine_request.pypython/aisimulate/tests/unit/sdk/database/test_attention_lanes.pycrates/core/src/perfmodel/operators/fpm_sol.rscrates/tests/public-api/src/lib.rspython/aisimulate/src/aisimulate_core/sdk/models/gemma4.pycrates/core/src/perfmodel/engine/runtime.rspython/aisimulate/src/aisimulate_core/sdk/models/nemotron_h.pypython/aisimulate/src/aisimulate/sweeper/config.pypython/aisimulate/src/aisimulate_core/sdk/models/kimi_k3.pypython/aisimulate/src/aisimulate_core/sdk/models/deepseek.pycrates/core/tests/perfmodel/memory_round_trip.rscrates/core/src/perfmodel/operators/moe_expert_compute.rspython/aisimulate/tests/cross_package/test_core_public_api.pypython/aisimulate/src/aisimulate/config/engine.pypython/aisimulate/src/aisimulate_core/sdk/models/qwen35.pypython/aisimulate/tests/unit/sdk/models/test_moe_block_builder_followups.pypython/aisimulate/tests/unit/sdk/models/test_deepseek_v41.pycrates/core/src/perfmodel/memory.rspython/aisimulate/tests/unit/collector/test_case_generator.pypython/aisimulate/src/aisimulate/runner.pycrates/core/src/perfmodel/engine/spec.rspython/aisimulate/src/aisimulate_core/sdk/models/__init__.pypython/aisimulate/src/aisimulate_core/sdk/speculation/dspark.pydocs/core-api.mdcrates/core/src/perfmodel/operators/attention.rspython/aisimulate/src/aisimulate/config/common.pypython/aisimulate/src/aisimulate_core/sdk/models/deepseek_v32.pypython/aisimulate/src/aisimulate/sdk/task_v1_compat.pypython/aisimulate/src/aisimulate_core/systems/data/gb200/moe/sglang/0.5.17/collection_meta.yamlpython/aisimulate/src/aisimulate_core/sdk/config.pycrates/core/src/python.rspython/aisimulate/tests/unit/sdk/test_rust_engine_step.pycrates/core/src/perfmodel/fpm/model.rspython/aisimulate/src/aisimulate/capacity.pypython/aisimulate/collector/cases/models/Qwen3_5MoeForCausalLM_cases.yamlcrates/core/src/perfmodel/config.rscrates/core/src/perfmodel/fpm/config.rspython/aisimulate/tests/unit/sdk/task_v2/test_task_config.pypython/aisimulate/src/aisimulate/sdk/task_v2.pypython/aisimulate/src/aisimulate_core/sdk/models/blocks/moe.pypython/aisimulate/tests/unit/sdk/models/test_model_config.pypython/aisimulate/tests/unit/sdk/test_compile_engine_mtp.pycrates/core/src/perfmodel/py_ops.rspython/aisimulate/src/aisimulate_core/sdk/rust_engine_step.pypython/aisimulate/src/aisimulate_core/sdk/config_builders.pycrates/core/src/perfmodel/py.rspython/aisimulate/tests/unit/sdk/test_moe_kernel_source_consumers.pypython/aisimulate/src/aisimulate_core/sdk/engine.pypython/aisimulate/src/aisimulate_core/sdk/memory.pytests/test_afd_runner.pycrates/core/src/perfmodel/perf_database/moe.rscrates/core/src/perfmodel/operators/moe.rs
🪛 ast-grep (0.45.3)
python/aisimulate/tests/unit/sdk/test_rust_engine_step.py
[info] 992-992: use jsonify instead of json.dumps for JSON output
Context: json.dumps(pinned.to_dict())
Note: [CWE-116] Improper Encoding or Escaping of Output.
(use-jsonify)
python/aisimulate/src/aisimulate_core/sdk/rust_engine_step.py
[info] 1220-1267: use jsonify instead of json.dumps for JSON output
Context: json.dumps(
{
"raw_quant_modes": {
"gemm": _raw_quant_name(getattr(model_config, "gemm_quant_mode", None)),
"moe": _raw_quant_name(getattr(model_config, "moe_quant_mode", None)),
"fmha": _raw_quant_name(getattr(model_config, "fmha_quant_mode", None)),
"kvcache": _raw_quant_name(getattr(model_config, "kvcache_quant_mode", None)),
"comm": _raw_quant_name(getattr(model_config, "comm_quant_mode", None)),
},
"model_config": {
"decoder_replay": bool(getattr(model_config, "decoder_replay", False)),
"cp_style": getattr(model_config, "cp_style", None),
"workload_distribution": getattr(model_config, "workload_distribution", None),
"overwrite_num_layers": getattr(model_config, "overwrite_num_layers", None),
"sms": getattr(model_config, "sms", None),
"moe_backend": getattr(model_config, "moe_backend", None),
"attention_backend": getattr(model_config, "attention_backend", None),
"moe_kernel_source": getattr(model_config, "moe_kernel_source", None),
# enable_wideep is gone from the identity: the deprecated
# flag is constant False on every Task-built ModelConfig;
# moe_comm_backend + num_gpus_per_node below carry the
# large-EP regime.
"enable_eplb": bool(getattr(model_config, "enable_eplb", False)),
"wideep_num_slots": getattr(model_config, "wideep_num_slots", None),
# Large EP: the per-phase comm backend selects a whole
# different MoE graph (MoEAllToAll/MoEExpertCompute vs the fused
# dispatch/MoE pair) and the node width prices its
# cross-node all-to-all — two configs differing only in
# these must not share one cached handle.
"moe_comm_backend": getattr(model_config, "moe_comm_backend", None),
"num_gpus_per_node": getattr(model_config, "num_gpus_per_node", None),
},
# Data-resolution policy. build_engine_spec_json bakes
# these flags into the compiled handle and the engine
# resolves per-op sources from them (schema v13), so two
# views of the same on-disk identity that differ only in
# shared-layer or strict-provenance policy must not share
# a cached handle — a warmed primary-only handle would
# otherwise answer (or fail) for the reuse-carrying view
# depending on call order.
"database_policy": {
"enable_shared_layer": bool(getattr(database, "enable_shared_layer", False)),
"strict_provenance": bool(getattr(database, "strict_provenance", False)),
},
},
sort_keys=True,
separators=(",", ":"),
)
Note: [CWE-116] Improper Encoding or Escaping of Output.
(use-jsonify)
🪛 LanguageTool
docs/core-api.md
[style] ~213-~213: The double modal “requires gated” is nonstandard (only accepted in certain dialects). Consider “to be gated”.
Context: ...t moe_torch_flow_min_latency requires gated NVFP4 and at most 128 tokens after atte...
(NEEDS_FIXED)
🔀 Multi-repo context ai-dynamo/dynamo, ai-dynamo/aiconfigurator
Linked repositories findings
ai-dynamo/dynamo
-
Dynamo pins both
aisimulate==0.12.0andaisimulate-core==0.12.0, so publishing the schema-20 changes requires coordinated package/release updates. [::ai-dynamo/dynamo::]pyproject.toml:17Cargo.toml:60
-
Dynamo’s AIC capacity integration does not expose or forward
moe_kernel_source;_internal/aic.py:390-486callsestimate_num_gpu_blockswithout it, andcomponents/src/dynamo/mocker/config.py:84-135likewise has no source parameter. Existing calls remain compatible because the new option is optional, but Dynamo cannot request the pinned MoE lane through these paths. [::ai-dynamo/dynamo::] -
Dynamo directly embeds
aisimulate_corein the Rust AIC callback and replay/mocker components, making the engine-spec schema bump relevant to its compiled consumers. [::ai-dynamo/dynamo::]
ai-dynamo/aiconfigurator
-
The frozen migration reference remains at
ENGINE_SPEC_SCHEMA_VERSION = 15; its comments state that bincode payloads are positional and require version bumps for layout changes. It has nomoe_kernel_sourcefield inEngineConfig, so schema-20 specs are not compatible with this older core. [::ai-dynamo/aiconfigurator::]aic-core/rust/aiconfigurator-core/src/config.rs:21-72
-
Legacy task compatibility in aiconfigurator’s V1 path only passes through
moe_backend,attention_backend, and related controls;moe_kernel_sourceis absent from_TOP_PASSTHROUGHand_CONFIG_SCALARS. [::ai-dynamo/aiconfigurator::]src/aiconfigurator/sdk/task_v1_compat.py:70-95
🔇 Additional comments (48)
crates/core/src/perfmodel/perf_database/moe.rs (1)
77-118: LGTM!Also applies to: 192-255, 388-444, 460-460, 487-527, 544-544, 555-586, 620-624, 684-709, 722-736, 747-747, 914-992
crates/core/src/perfmodel/operators/moe.rs (1)
47-47: LGTM!Also applies to: 147-163, 211-215, 268-268, 320-342, 380-402, 456-456, 492-507, 580-580, 608-608, 639-695, 706-736, 971-971, 1009-1009, 1028-1182, 1381-1381, 1411-1411
python/aisimulate/tests/unit/collector/test_case_generator.py (1)
1-27: LGTM!crates/core/src/perfmodel/config.rs (1)
108-109: LGTM!Also applies to: 153-156
crates/core/src/perfmodel/engine/runtime.rs (1)
2306-2306: LGTM!crates/core/src/perfmodel/engine/spec.rs (1)
322-322: LGTM!Also applies to: 830-830, 1113-1117
crates/core/src/perfmodel/operators/attention.rs (1)
187-187: LGTM!Also applies to: 200-200, 388-388
crates/tests/public-api/src/lib.rs (1)
123-125: LGTM!crates/core/src/perfmodel/py.rs (1)
1053-1053: LGTM!Also applies to: 1104-1104, 1230-1235, 1275-1275, 1293-1293, 1311-1314, 1451-1451, 1543-1543, 1621-1621, 1835-1835, 2081-2081
crates/core/src/perfmodel/py_ops.rs (1)
1758-1758: LGTM!Also applies to: 1775-1775, 1803-1803, 1833-1833, 1902-1906
crates/core/src/python.rs (1)
223-224: LGTM!Also applies to: 320-320, 2254-2268, 2303-2303
crates/core/tests/perfmodel/memory_round_trip.rs (1)
91-91: LGTM!python/aisimulate/src/aisimulate/capacity.py (1)
132-132: LGTM!Also applies to: 211-211, 244-244, 319-319
python/aisimulate/src/aisimulate/config/common.py (1)
227-227: LGTM!Also applies to: 251-251
python/aisimulate/src/aisimulate/config/engine.py (1)
276-276: LGTM!python/aisimulate/src/aisimulate/sdk/task_v1_compat.py (1)
89-89: LGTM!Also applies to: 96-96
crates/core/src/perfmodel/operators/moe_expert_compute.rs (1)
205-205: 🎯 Functional Correctness
MoeExpertComputeis emitted only after the large-EP branch checkscfg.moe_kernel_source. A non-Nonevalue raisesInvalidEngineConfigurationError, so the pinned source cannot be silently dropped in this path. The proposed rejection or field propagation is not needed.python/aisimulate/src/aisimulate/sdk/task_v2.py (1)
579-581: LGTM!Also applies to: 866-866
python/aisimulate/src/aisimulate_core/sdk/config.py (1)
31-39: LGTM!Also applies to: 120-124, 157-157
python/aisimulate/src/aisimulate_core/sdk/config_builders.py (1)
24-24: LGTM!Also applies to: 47-56, 74-74, 84-103
python/aisimulate/src/aisimulate_core/sdk/engine.py (1)
47-47: LGTM!Also applies to: 324-324, 425-425, 468-474, 489-489
python/aisimulate/src/aisimulate_core/sdk/memory.py (1)
282-282: LGTM!Also applies to: 313-317, 331-331, 1026-1026, 1099-1103, 1125-1125, 1138-1138, 1198-1198, 1261-1261
python/aisimulate/src/aisimulate_core/sdk/models/__init__.py (1)
31-31: LGTM!Also applies to: 68-78, 161-170, 245-252
python/aisimulate/src/aisimulate_core/sdk/models/deepseek.py (1)
802-802: LGTM!Also applies to: 1033-1033
python/aisimulate/src/aisimulate_core/sdk/models/deepseek_v32.py (1)
505-505: LGTM!Also applies to: 635-635
python/aisimulate/src/aisimulate_core/sdk/models/deepseek_v4.py (1)
206-206: LGTM!python/aisimulate/src/aisimulate_core/sdk/models/deepseek_v41.py (1)
209-209: LGTM!python/aisimulate/src/aisimulate_core/sdk/models/gemma4.py (1)
288-288: LGTM!python/aisimulate/src/aisimulate_core/sdk/models/kimi_k3.py (1)
726-726: LGTM!python/aisimulate/src/aisimulate_core/sdk/models/nemotron_h.py (1)
308-308: LGTM!Also applies to: 601-601
python/aisimulate/src/aisimulate_core/sdk/models/qwen35.py (1)
583-583: LGTM!Also applies to: 869-869
python/aisimulate/src/aisimulate_core/sdk/rust_engine_step.py (1)
132-132: LGTM!Also applies to: 1199-1199, 1238-1238
python/aisimulate/src/aisimulate_core/sdk/speculation/dspark.py (1)
289-289: LGTM!python/aisimulate/tests/cross_package/test_core_public_api.py (1)
98-98: LGTM!python/aisimulate/tests/unit/sdk/database/test_attention_lanes.py (1)
246-250: LGTM!python/aisimulate/tests/unit/sdk/models/test_deepseek_v41.py (1)
141-170: LGTM!python/aisimulate/tests/unit/sdk/models/test_model_config.py (1)
19-20: LGTM!Also applies to: 30-30, 55-139
python/aisimulate/tests/unit/sdk/task_v2/test_task_config.py (1)
408-498: LGTM!python/aisimulate/tests/unit/sdk/task_v2/test_v1_compat.py (1)
230-240: LGTM!python/aisimulate/tests/unit/sdk/test_compile_engine_mtp.py (1)
115-135: LGTM!python/aisimulate/tests/unit/sdk/test_moe_kernel_source_consumers.py (1)
1-219: LGTM!python/aisimulate/tests/unit/sdk/test_rust_engine_step.py (1)
938-993: LGTM!Also applies to: 1402-1402, 1409-1409, 1424-1424, 1447-1451
tests/sweeper/test_engine_request.py (1)
25-25: LGTM!crates/core/src/perfmodel/fpm/config.rs (2)
139-142: LGTM!Also applies to: 192-192, 235-241, 389-442
279-287: 🗄️ Data Integrity & IntegrationCheck that
Automode cannot fall back to regression and drop the pinned source.crates/core/src/perfmodel/fpm/model.rs (1)
708-716: LGTM!Also applies to: 1119-1133
crates/core/src/perfmodel/fpm/tests.rs (1)
112-112: LGTM!crates/core/src/perfmodel/memory.rs (1)
512-512: LGTM!
Signed-off-by: Yiming Liu <yimingl@nvidia.com>
Signed-off-by: Yiming Liu <yimingl@nvidia.com>
Keep opt-in measurements queryable by exact source without changing automatic selection. Preserve annotations through ingestion and exclude metadata from measurement identity. Contributes to AIC-1781. Signed-off-by: Yiming Liu <yimingl@nvidia.com>
Signed-off-by: Yiming Liu <yimingl@nvidia.com>
Signed-off-by: Yiming Liu <yimingl@nvidia.com>
Signed-off-by: Yiming Liu <yimingl@nvidia.com>
|
@coderabbitai full review Please review the current head 76e0a82 against the current code-prerequisite base 3acfa10. This is a dedicated two-file data PR; its previous green CodeRabbit status reported a skipped review because of the non-default base. The prerequisite code is reviewed separately in #326. Please verify the data and eligibility metadata at this exact head. |
✅ Action performedFull review finished. |
|
/ok to test 76e0a82 |
|
Exact-head Parquet and native-consumer evidence for gb200, data commit The audit was rerun locally on this exact file: SHA-256 The native check uses the real These are real native-loader/selection assertions against existing measured data, not a new GPU run, fake-runner result, or predictive-accuracy claim. Every original column is retained exactly. The binary remains unchanged. This exact head also corrects the system's single FP8 entry in Complete machine-readable data and native-consumer audit{
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8,
"power_law_1.01"
],
[
8192,
2048,
10,
512,
2,
8,
"power_law_1.2"
],
[
8192,
2048,
10,
512,
2,
16,
"balanced"
],
[
8192,
2048,
10,
512,
2,
16,
"power_law_1.01"
],
[
8192,
2048,
10,
512,
2,
16,
"power_law_1.2"
],
[
8192,
2048,
10,
512,
2,
32,
"balanced"
],
[
8192,
2048,
10,
512,
2,
32,
"power_law_1.01"
],
[
8192,
2048,
10,
512,
2,
32,
"power_law_1.2"
],
[
8192,
2048,
10,
512,
2,
64,
"balanced"
],
[
8192,
2048,
10,
512,
2,
64,
"power_law_1.01"
],
[
8192,
2048,
10,
512,
2,
64,
"power_law_1.2"
],
[
8192,
2048,
10,
512,
2,
128,
"balanced"
],
[
8192,
2048,
10,
512,
2,
128,
"power_law_1.01"
],
[
8192,
2048,
10,
512,
2,
128,
"power_law_1.2"
],
[
8192,
2048,
10,
512,
4,
1,
"balanced"
],
[
8192,
2048,
10,
512,
4,
1,
"power_law_1.01"
],
[
8192,
2048,
10,
512,
4,
1,
"power_law_1.2"
],
[
8192,
2048,
10,
512,
4,
2,
"balanced"
],
[
8192,
2048,
10,
512,
4,
2,
"power_law_1.01"
],
[
8192,
2048,
10,
512,
4,
2,
"power_law_1.2"
],
[
8192,
2048,
10,
512,
4,
4,
"balanced"
],
[
8192,
2048,
10,
512,
4,
4,
"power_law_1.01"
],
[
8192,
2048,
10,
512,
4,
4,
"power_law_1.2"
],
[
8192,
2048,
10,
512,
4,
8,
"balanced"
],
[
8192,
2048,
10,
512,
4,
8,
"power_law_1.01"
],
[
8192,
2048,
10,
512,
4,
8,
"power_law_1.2"
],
[
8192,
2048,
10,
512,
4,
16,
"balanced"
],
[
8192,
2048,
10,
512,
4,
16,
"power_law_1.01"
],
[
8192,
2048,
10,
512,
4,
16,
"power_law_1.2"
],
[
8192,
2048,
10,
512,
4,
32,
"balanced"
],
[
8192,
2048,
10,
512,
4,
32,
"power_law_1.01"
],
[
8192,
2048,
10,
512,
4,
32,
"power_law_1.2"
],
[
8192,
2048,
10,
512,
4,
64,
"balanced"
],
[
8192,
2048,
10,
512,
4,
64,
"power_law_1.01"
],
[
8192,
2048,
10,
512,
4,
64,
"power_law_1.2"
],
[
8192,
2048,
10,
512,
8,
1,
"balanced"
],
[
8192,
2048,
10,
512,
8,
1,
"power_law_1.01"
],
[
8192,
2048,
10,
512,
8,
1,
"power_law_1.2"
],
[
8192,
2048,
10,
512,
8,
2,
"balanced"
],
[
8192,
2048,
10,
512,
8,
2,
"power_law_1.01"
],
[
8192,
2048,
10,
512,
8,
2,
"power_law_1.2"
],
[
8192,
2048,
10,
512,
8,
4,
"balanced"
],
[
8192,
2048,
10,
512,
8,
4,
"power_law_1.01"
],
[
8192,
2048,
10,
512,
8,
4,
"power_law_1.2"
],
[
8192,
2048,
10,
512,
8,
8,
"balanced"
],
[
8192,
2048,
10,
512,
8,
8,
"power_law_1.01"
],
[
8192,
2048,
10,
512,
8,
8,
"power_law_1.2"
],
[
8192,
2048,
10,
512,
8,
16,
"balanced"
],
[
8192,
2048,
10,
512,
8,
16,
"power_law_1.01"
],
[
8192,
2048,
10,
512,
8,
16,
"power_law_1.2"
],
[
8192,
2048,
10,
512,
8,
32,
"balanced"
],
[
8192,
2048,
10,
512,
8,
32,
"power_law_1.01"
],
[
8192,
2048,
10,
512,
8,
32,
"power_law_1.2"
],
[
8192,
2048,
10,
512,
16,
1,
"balanced"
],
[
8192,
2048,
10,
512,
16,
1,
"power_law_1.01"
],
[
8192,
2048,
10,
512,
16,
1,
"power_law_1.2"
],
[
8192,
2048,
10,
512,
16,
2,
"balanced"
],
[
8192,
2048,
10,
512,
16,
2,
"power_law_1.01"
],
[
8192,
2048,
10,
512,
16,
2,
"power_law_1.2"
],
[
8192,
2048,
10,
512,
16,
4,
"balanced"
],
[
8192,
2048,
10,
512,
16,
4,
"power_law_1.01"
],
[
8192,
2048,
10,
512,
16,
4,
"power_law_1.2"
],
[
8192,
2048,
10,
512,
16,
8,
"balanced"
],
[
8192,
2048,
10,
512,
16,
8,
"power_law_1.01"
],
[
8192,
2048,
10,
512,
16,
8,
"power_law_1.2"
],
[
8192,
2048,
10,
512,
16,
16,
"balanced"
],
[
8192,
2048,
10,
512,
16,
16,
"power_law_1.01"
],
[
8192,
2048,
10,
512,
16,
16,
"power_law_1.2"
],
[
8192,
2048,
10,
512,
32,
1,
"balanced"
],
[
8192,
2048,
10,
512,
32,
1,
"power_law_1.01"
],
[
8192,
2048,
10,
512,
32,
1,
"power_law_1.2"
],
[
8192,
2048,
10,
512,
32,
2,
"balanced"
],
[
8192,
2048,
10,
512,
32,
2,
"power_law_1.01"
],
[
8192,
2048,
10,
512,
32,
2,
"power_law_1.2"
],
[
8192,
2048,
10,
512,
32,
4,
"balanced"
],
[
8192,
2048,
10,
512,
32,
4,
"power_law_1.01"
],
[
8192,
2048,
10,
512,
32,
4,
"power_law_1.2"
],
[
8192,
2048,
10,
512,
32,
8,
"balanced"
],
[
8192,
2048,
10,
512,
32,
8,
"power_law_1.01"
],
[
8192,
2048,
10,
512,
32,
8,
"power_law_1.2"
]
]
},
"anomalies": {
"nulls_by_column": {
"framework": 0,
"version": 0,
"device": 0,
"op_name": 0,
"kernel_source": 0,
"moe_dtype": 0,
"num_tokens": 0,
"hidden_size": 0,
"inter_size": 0,
"topk": 0,
"num_experts": 0,
"moe_tp_size": 0,
"moe_ep_size": 0,
"distribution": 0,
"latency": 0,
"default_eligible": 0
},
"nonfinite_latency_count": 0,
"nonpositive_latency_count": 0
},
"latency_ms": {
"min": 0.016501120328903197,
"max": 57.162158203125
},
"default_eligible": {
"true": 9153,
"false": 3159,
"null": 0,
"target_false": 3159
},
"pr": 301,
"system": "gb200",
"data_head": "8be644ba5dd79aeb1fb0e58071cf1bf51a521dfa",
"code_head": "3acfa103521cfdd9bc5e33e73c65ec8bb409e584",
"path": "python/aisimulate/src/aisimulate_core/systems/data/gb200/moe/sglang/0.5.17/moe_perf.parquet",
"file_sha256": "1836a4de9ed4333283463c31d088d028f4eac0f4184214c7420775c09d37216e",
"column_types": {
"framework": "string",
"version": "string",
"device": "string",
"op_name": "string",
"kernel_source": "string",
"moe_dtype": "string",
"num_tokens": "int64",
"hidden_size": "int64",
"inter_size": "int64",
"topk": "int64",
"num_experts": "int64",
"moe_tp_size": "int64",
"moe_ep_size": "int64",
"distribution": "string",
"latency": "double",
"default_eligible": "bool"
},
"latency_units": "milliseconds",
"measurement_kind": "collected silicon; no new collection in this audit",
"consumer": {
"pr": 301,
"system": "gb200",
"original_head": "9c4ca9de6b8a05204feec5ccfd6cf59c01de7363",
"current_data_head": "8be644ba5dd79aeb1fb0e58071cf1bf51a521dfa",
"annotation_sha256": "1836a4de9ed4333283463c31d088d028f4eac0f4184214c7420775c09d37216e",
"all_original_columns_exactly_preserved": true,
"rows": 12312,
"distinct_automatic_query_keys": 9477,
"runs": [
{
"mode": "SILICON",
"shared_layer": false,
"strict_provenance": true,
"identical_automatic_outcomes": {
"value": 9153,
"missing": 324
},
"exact_silicon_measurements": {
"bfloat16/sglang_fused_moe_triton": 3159,
"fp8_block/sglang_fused_moe_triton": 2835,
"nvfp4/sglang_flashinfer_trtllm_moe": 3159,
"fp8_block/sglang_flashinfer_trtllm_moe": 3159
},
"tp32_unset_and_triton_rejected": 324,
"default_views_and_coverage_equal": true
},
{
"mode": "EMPIRICAL",
"shared_layer": false,
"strict_provenance": true,
"identical_automatic_outcomes": {
"value": 9477
},
"exact_silicon_measurements": {},
"tp32_unset_and_triton_rejected": null,
"default_views_and_coverage_equal": true
},
{
"mode": "HYBRID",
"shared_layer": false,
"strict_provenance": true,
"identical_automatic_outcomes": {
"value": 9477
},
"exact_silicon_measurements": {},
"tp32_unset_and_triton_rejected": null,
"default_views_and_coverage_equal": true
},
{
"mode": "SILICON",
"shared_layer": true,
"strict_provenance": true,
"identical_automatic_outcomes": {
"value": 9153,
"missing": 324
},
"exact_silicon_measurements": {
"bfloat16/sglang_fused_moe_triton": 3159,
"fp8_block/sglang_fused_moe_triton": 2835,
"nvfp4/sglang_flashinfer_trtllm_moe": 3159,
"fp8_block/sglang_flashinfer_trtllm_moe": 3159
},
"tp32_unset_and_triton_rejected": 324,
"default_views_and_coverage_equal": true
},
{
"mode": "EMPIRICAL",
"shared_layer": true,
"strict_provenance": true,
"identical_automatic_outcomes": {
"value": 9477
},
"exact_silicon_measurements": {},
"tp32_unset_and_triton_rejected": null,
"default_views_and_coverage_equal": true
},
{
"mode": "HYBRID",
"shared_layer": true,
"strict_provenance": true,
"identical_automatic_outcomes": {
"value": 9477
},
"exact_silicon_measurements": {},
"tp32_unset_and_triton_rejected": null,
"default_views_and_coverage_equal": true
}
]
}
}Reproducible full native-consumer audit (Python)Save this block as HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1 aisimulate/python/aisimulate/.venv/bin/python validate_eligibility_consumers.py --code-repo "$PWD/aisimulate" --pr 301"""Independent native before/after audit of the three annotated MoE data PRs."""
import argparse
import hashlib
import io
import json
import logging
import subprocess
from collections import Counter
from pathlib import Path
import pyarrow.parquet as pq
import aisimulate._runtime as runtime
from aisimulate_core.sdk.common import MoEQuantMode
from aisimulate_core.sdk.engine import EngineHandle
from aisimulate_core.sdk.engine_table_view import fetch_table_view
from aisimulate_core.sdk.errors import EmpiricalNotImplementedError, PerfDataNotAvailableError
from aisimulate_core.sdk.perf_database import get_database_view
BASE = "992dddc7ee2bad8fd523e7db99eea4ac64254766"
PRS = {
296: ("b200_sxm", "4190a81d7e9b213adcd3ca47dd75b42cf8ef9a06"),
298: ("gb300", "e7927e0ea726b67c9fd0293e06987cf01809af0d"),
301: ("gb200", "9c4ca9de6b8a05204feec5ccfd6cf59c01de7363"),
}
SOURCE = "sglang_flashinfer_trtllm_moe"
SYSTEMS = Path("python/aisimulate/src/aisimulate_core/systems")
KEY = (
"moe_dtype",
"distribution",
"topk",
"num_experts",
"hidden_size",
"inter_size",
"moe_tp_size",
"moe_ep_size",
"num_tokens",
)
def git(repo, *args):
return subprocess.check_output(["git", *args], cwd=repo)
def normalize(value):
if isinstance(value, dict):
return {str(key): normalize(item) for key, item in value.items()}
if isinstance(value, (list, tuple)):
return [normalize(item) for item in value]
if hasattr(value, "tolist"):
return normalize(value.tolist())
return value
def evaluate(handle, row, source=None):
request = {
"Moe": {
"name": "eligibility consumer audit",
"scale_factor": 1.0,
"hidden_size": int(row["hidden_size"]),
"inter_size": int(row["inter_size"]),
"topk": int(row["topk"]),
"num_experts": int(row["num_experts"]),
"moe_tp_size": int(row["moe_tp_size"]),
"moe_ep_size": int(row["moe_ep_size"]),
"attention_dp_size": 1,
"quant_mode": row["moe_dtype"],
"workload_distribution": row["distribution"],
"is_gated": True,
"moe_backend": None,
"moe_kernel_source": source,
"enable_eplb": False,
"is_context": False,
}
}
return handle.evaluate_ops_json(
json.dumps([request]),
is_context=False,
batch_size=1,
s=1,
x=int(row["num_tokens"]),
)[0]
def outcome(handle, row):
try:
return ("value", evaluate(handle, row))
except (PerfDataNotAvailableError, EmpiricalNotImplementedError) as error:
return ("missing", type(error).__name__)
def database(repo, system, mode, shared):
systems = str(repo / SYSTEMS)
db = get_database_view(
system,
"sglang",
"0.5.17",
systems,
database_mode=mode,
shared_layer=shared,
strict_provenance=True,
)
assert db is not None
assert db.get_default_database_mode().name == mode
return db, EngineHandle.for_database(db, systems_path=systems)
def audit(code_repo, data_repo, pr):
system, old_head = PRS[pr]
relative = SYSTEMS / f"data/{system}/moe/sglang/0.5.17/moe_perf.parquet"
original = pq.read_table(io.BytesIO(git(data_repo, "show", f"{old_head}:{relative}")))
baseline_bytes = git(data_repo, "show", f"{BASE}:{relative}")
assert (code_repo / relative).read_bytes() == baseline_bytes, "Baseline data must remain main"
annotated_bytes = (data_repo / relative).read_bytes()
annotated = pq.read_table(io.BytesIO(annotated_bytes))
assert annotated.drop(["default_eligible"]).equals(original, check_metadata=True)
assert annotated["default_eligible"].null_count == 0
rows = annotated.to_pylist()
for row in rows:
target = row["moe_dtype"] == "fp8_block" and row["kernel_source"] == SOURCE
assert row["default_eligible"] is (not target)
assert Counter(row["default_eligible"] for row in rows) == {True: 9153, False: 3159}
unique = {tuple(row[column] for column in KEY): row for row in rows}
assert len(unique) == 9477
tp32 = [row for row in rows if not row["default_eligible"] and row["moe_tp_size"] == 32]
assert len(tp32) == 324
runs = []
for shared in (False, True):
for mode in ("SILICON", "EMPIRICAL", "HYBRID"):
baseline_db, baseline_handle = database(code_repo, system, mode, shared)
annotated_db, annotated_handle = database(data_repo, system, mode, shared)
counts = Counter()
for key, row in unique.items():
before = outcome(baseline_handle, row)
after = outcome(annotated_handle, row)
assert after == before, (pr, mode, shared, key, before, after)
counts[before[0]] += 1
exact_counts = Counter()
if mode == "SILICON":
for row in rows:
result = evaluate(annotated_handle, row, row["kernel_source"])
assert result[3] == "silicon" and result[1] == row["latency"], (pr, row, result)
exact_counts[f"{row['moe_dtype']}/{row['kernel_source']}"] += 1
for row in tp32:
for source in (None, "sglang_fused_moe_triton"):
try:
evaluate(annotated_handle, row, source)
except PerfDataNotAvailableError:
pass
else:
raise AssertionError(("Unexpected TP32 coverage", pr, shared, source, row))
for view in ("_moe_data", "_moe_low_latency_data"):
assert normalize(fetch_table_view(annotated_db, view)) == normalize(
fetch_table_view(baseline_db, view)
), (pr, mode, shared, view)
for quant in (MoEQuantMode.bfloat16, MoEQuantMode.fp8_block, MoEQuantMode.nvfp4):
before = baseline_db.legacy_moe_compute_coverage(8192, 2048, 10, 512, quant)
after = annotated_db.legacy_moe_compute_coverage(8192, 2048, 10, 512, quant)
assert after == before, (pr, mode, shared, quant, before, after)
runs.append(
{
"mode": mode,
"shared_layer": shared,
"strict_provenance": True,
"identical_automatic_outcomes": dict(counts),
"exact_silicon_measurements": dict(exact_counts),
"tp32_unset_and_triton_rejected": 324 if mode == "SILICON" else None,
"default_views_and_coverage_equal": True,
}
)
return {
"pr": pr,
"system": system,
"original_head": old_head,
"current_data_head": git(data_repo, "rev-parse", "HEAD").decode().strip(),
"annotation_sha256": hashlib.sha256(annotated_bytes).hexdigest(),
"all_original_columns_exactly_preserved": True,
"rows": len(rows),
"distinct_automatic_query_keys": len(unique),
"runs": runs,
}
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--code-repo", type=Path, required=True)
parser.add_argument("--pr", type=int, choices=PRS, action="append")
args = parser.parse_args()
logging.disable(logging.WARNING)
reports = [audit(args.code_repo, args.code_repo.parent / f"data-pr{pr}", pr) for pr in args.pr or PRS]
print(
json.dumps(
{
"runtime": runtime.__file__,
"schema": runtime.engine_spec_schema_version(),
"baseline": BASE,
"code_head": git(args.code_repo, "rev-parse", "HEAD").decode().strip(),
"data": reports,
},
indent=2,
)
)
if __name__ == "__main__":
main()@coderabbitai Please recheck the Parquet-evidence requirement against this exact-head artifact and consumer proof. Code/data remain in separate PRs; no synthetic audit document was added to the measured-data tree. |
|
Tip For best results, initiate chat on the files or code changes. 🧩 Analysis chain🏁 Script executed: #!/bin/bash
set -e
p='python/aisimulate/src/aisimulate_core/systems/data/gb200/moe/sglang/0.5.17'
git rev-parse HEAD
git rev-parse --verify 76e0a82fbdc671f5489ca96d09be81a0d92a0f58^{commit}
git rev-parse --verify 3acfa103521cfdd9bc5e33e73c65ec8bb409e584^{commit} || true
git diff --name-status 3acfa103521cfdd9bc5e33e73c65ec8bb409e584 76e0a82fbdc671f5489ca96d09be81a0d92a0f58 -- "$p" || true
sha256sum "$p/moe_perf.parquet"
sed -n '1,220p' "$p/collection_meta.yaml"
python - <<'PY'
for name in ("pyarrow", "pandas", "duckdb"):
try:
m=__import__(name)
print(name, m.__version__)
except Exception as e:
print(name, type(e).__name__, str(e)[:150])
PYLength of output: 5408 🏁 Script executed: #!/bin/bash
set -euo pipefail
git rev-parse --verify '9c4ca9de6b8a05204feec5ccfd6cf59c01de7363^{commit}' || true
python - <<'PY'
import duckdb
p='python/aisimulate/src/aisimulate_core/systems/data/gb200/moe/sglang/0.5.17/moe_perf.parquet'
db=duckdb.connect()
db.execute("CREATE VIEW m AS SELECT * FROM read_parquet(?)", [p])
checks={
'columns':"DESCRIBE m",
'row_anomalies':"""SELECT count(*) rows, count(*) FILTER (WHERE NOT default_eligible) ineligible, count(*) FILTER (WHERE default_eligible) eligible, count(*) FILTER (WHERE default_eligible IS NULL) null_eligible, count(*) FILTER (WHERE latency IS NULL OR NOT isfinite(latency) OR latency <= 0) bad_latency, min(latency) min_ms, max(latency) max_ms FROM m""",
'lanes':"SELECT moe_dtype, kernel_source, default_eligible, count(*) n FROM m GROUP BY ALL ORDER BY 1,2,3",
'duplicates':"""SELECT count(*) duplicate_keys FROM (SELECT framework, version, device, op_name, kernel_source, moe_dtype, num_tokens, hidden_size, inter_size, topk, num_experts, moe_tp_size, moe_ep_size, distribution, count(*) n FROM m GROUP BY ALL HAVING count(*)>1)""",
'cases':"""SELECT count(*) cases, min(tokens) min_tokens, max(tokens) max_tokens, count(*) FILTER (WHERE tokens<>27) incomplete_cases FROM (SELECT hidden_size, inter_size, topk, num_experts, moe_tp_size, moe_ep_size, distribution, count(DISTINCT num_tokens) tokens FROM m WHERE moe_dtype='fp8_block' AND kernel_source='sglang_flashinfer_trtllm_moe' GROUP BY ALL)""",
'target_tp':"SELECT moe_tp_size, count(*) n FROM m WHERE moe_dtype='fp8_block' AND kernel_source='sglang_flashinfer_trtllm_moe' GROUP BY ALL ORDER BY 1",
'target_ep':"SELECT moe_ep_size, count(*) n FROM m WHERE moe_dtype='fp8_block' AND kernel_source='sglang_flashinfer_trtllm_moe' GROUP BY ALL ORDER BY 1",
'target_distribution':"SELECT distribution, count(*) n FROM m WHERE moe_dtype='fp8_block' AND kernel_source='sglang_flashinfer_trtllm_moe' GROUP BY ALL ORDER BY 1",
'wrong_eligibility':"SELECT count(*) n FROM m WHERE default_eligible IS DISTINCT FROM NOT (moe_dtype='fp8_block' AND kernel_source='sglang_flashinfer_trtllm_moe')",
'physical_keys':"SELECT count(*) n FROM (SELECT DISTINCT moe_dtype, distribution, topk, num_experts, hidden_size, inter_size, moe_tp_size, moe_ep_size, num_tokens FROM m)",
'null_columns':"SELECT "+", ".join(f"count(*) FILTER (WHERE {c} IS NULL) AS {c}" for c in ['framework','version','device','op_name','kernel_source','moe_dtype','num_tokens','hidden_size','inter_size','topk','num_experts','moe_tp_size','moe_ep_size','distribution','latency','default_eligible'])+" FROM m",
}
for name,sql in checks.items():
print(name)
print(db.sql(sql).fetchall())
PYLength of output: 3221 🏁 Script executed: #!/bin/bash
set -euo pipefail
python - <<'PY'
import duckdb
p='python/aisimulate/src/aisimulate_core/systems/data/gb200/moe/sglang/0.5.17/moe_perf.parquet'
db=duckdb.connect()
db.execute(f"CREATE VIEW m AS SELECT * FROM read_parquet('{p}')")
checks={
'columns':"DESCRIBE m",
'row_anomalies':"SELECT count(*) rows, count(*) FILTER (WHERE NOT default_eligible) ineligible, count(*) FILTER (WHERE default_eligible) eligible, count(*) FILTER (WHERE default_eligible IS NULL) null_eligible, count(*) FILTER (WHERE latency IS NULL OR NOT isfinite(latency) OR latency <= 0) bad_latency, min(latency) min_ms, max(latency) max_ms FROM m",
'lanes':"SELECT moe_dtype, kernel_source, default_eligible, count(*) n FROM m GROUP BY ALL ORDER BY 1,2,3",
'duplicates':"SELECT count(*) duplicate_keys FROM (SELECT framework, version, device, op_name, kernel_source, moe_dtype, num_tokens, hidden_size, inter_size, topk, num_experts, moe_tp_size, moe_ep_size, distribution, count(*) n FROM m GROUP BY ALL HAVING count(*)>1)",
'cases':"SELECT count(*) cases, min(tokens) min_tokens, max(tokens) max_tokens, count(*) FILTER (WHERE tokens<>27) incomplete_cases FROM (SELECT hidden_size, inter_size, topk, num_experts, moe_tp_size, moe_ep_size, distribution, count(DISTINCT num_tokens) tokens FROM m WHERE moe_dtype='fp8_block' AND kernel_source='sglang_flashinfer_trtllm_moe' GROUP BY ALL)",
'target_tp':"SELECT moe_tp_size, count(*) n FROM m WHERE moe_dtype='fp8_block' AND kernel_source='sglang_flashinfer_trtllm_moe' GROUP BY ALL ORDER BY 1",
'target_ep':"SELECT moe_ep_size, count(*) n FROM m WHERE moe_dtype='fp8_block' AND kernel_source='sglang_flashinfer_trtllm_moe' GROUP BY ALL ORDER BY 1",
'target_distribution':"SELECT distribution, count(*) n FROM m WHERE moe_dtype='fp8_block' AND kernel_source='sglang_flashinfer_trtllm_moe' GROUP BY ALL ORDER BY 1",
'wrong_eligibility':"SELECT count(*) n FROM m WHERE default_eligible IS DISTINCT FROM NOT (moe_dtype='fp8_block' AND kernel_source='sglang_flashinfer_trtllm_moe')",
'physical_keys':"SELECT count(*) n FROM (SELECT DISTINCT moe_dtype, distribution, topk, num_experts, hidden_size, inter_size, moe_tp_size, moe_ep_size, num_tokens FROM m)",
'null_columns':"SELECT "+", ".join(f"count(*) FILTER (WHERE {c} IS NULL) AS {c}" for c in ['framework','version','device','op_name','kernel_source','moe_dtype','num_tokens','hidden_size','inter_size','topk','num_experts','moe_tp_size','moe_ep_size','distribution','latency','default_eligible'])+" FROM m",
}
for name,sql in checks.items():
print(name, db.sql(sql).fetchall())
PYLength of output: 3989 🏁 Script executed: #!/bin/bash
python - <<'PY'
import duckdb
p='python/aisimulate/src/aisimulate_core/systems/data/gb200/moe/sglang/0.5.17/moe_perf.parquet'
db=duckdb.connect()
db.execute(f"CREATE VIEW m AS SELECT * FROM read_parquet('{p}')")
checks={
'row_anomalies':"SELECT count(*) AS row_count, count(*) FILTER (WHERE NOT default_eligible) AS ineligible, count(*) FILTER (WHERE default_eligible) AS eligible, count(*) FILTER (WHERE default_eligible IS NULL) AS null_eligible, count(*) FILTER (WHERE latency IS NULL OR NOT isfinite(latency) OR latency <= 0) AS bad_latency, min(latency) AS min_ms, max(latency) AS max_ms FROM m",
'lanes':"SELECT moe_dtype, kernel_source, default_eligible, count(*) AS n FROM m GROUP BY ALL ORDER BY 1,2,3",
'duplicates':"SELECT count(*) AS duplicate_keys FROM (SELECT framework, version, device, op_name, kernel_source, moe_dtype, num_tokens, hidden_size, inter_size, topk, num_experts, moe_tp_size, moe_ep_size, distribution, count(*) AS n FROM m GROUP BY ALL HAVING count(*)>1)",
'cases':"SELECT count(*) AS case_count, min(tokens) AS min_tokens, max(tokens) AS max_tokens, count(*) FILTER (WHERE tokens<>27) AS incomplete_cases FROM (SELECT hidden_size, inter_size, topk, num_experts, moe_tp_size, moe_ep_size, distribution, count(DISTINCT num_tokens) AS tokens FROM m WHERE moe_dtype='fp8_block' AND kernel_source='sglang_flashinfer_trtllm_moe' GROUP BY ALL)",
'target_tp':"SELECT moe_tp_size, count(*) AS n FROM m WHERE moe_dtype='fp8_block' AND kernel_source='sglang_flashinfer_trtllm_moe' GROUP BY ALL ORDER BY 1",
'target_ep':"SELECT moe_ep_size, count(*) AS n FROM m WHERE moe_dtype='fp8_block' AND kernel_source='sglang_flashinfer_trtllm_moe' GROUP BY ALL ORDER BY 1",
'target_distribution':"SELECT distribution, count(*) AS n FROM m WHERE moe_dtype='fp8_block' AND kernel_source='sglang_flashinfer_trtllm_moe' GROUP BY ALL ORDER BY 1",
'wrong_eligibility':"SELECT count(*) AS n FROM m WHERE default_eligible IS DISTINCT FROM NOT (moe_dtype='fp8_block' AND kernel_source='sglang_flashinfer_trtllm_moe')",
'physical_keys':"SELECT count(*) AS n FROM (SELECT DISTINCT moe_dtype, distribution, topk, num_experts, hidden_size, inter_size, moe_tp_size, moe_ep_size, num_tokens FROM m)",
'null_columns':"SELECT "+", ".join(f"count(*) FILTER (WHERE {c} IS NULL) AS {c}" for c in ['framework','version','device','op_name','kernel_source','moe_dtype','num_tokens','hidden_size','inter_size','topk','num_experts','moe_tp_size','moe_ep_size','distribution','latency','default_eligible'])+" FROM m",
}
for name,sql in checks.items():
try: print(name, db.sql(sql).fetchall(), flush=True)
except Exception as e: print(name,'ERROR',type(e).__name__,str(e),flush=True)
PYLength of output: 3595
I did not rerun the native-consumer audit. The prerequisite code commit is unavailable in this checkout, so its reported selection behavior remains supported by your artifact rather than independently verified here. You are interacting with an AI system. |
|
The PR description now follows the required headings and includes the review map, contract and rollback notes, exact validation commands/results, evidence classification, current reviewed commits, CI status, and AIC-1781 tracking. The detailed binary audit and reproducible real-native consumer checks are linked from the description. @coderabbitai Please recheck the remaining description/review-evidence warning; the commit and measured data have not changed. |
|
Tip For best results, initiate chat on the files or code changes. 🧩 Analysis chain🏁 Script executed: #!/bin/bash
set -euo pipefail
gh api repos/ai-dynamo/aisimulate/pulls/301 --jq '{url: .html_url, head: .head.sha, base: .base.sha, updated_at: .updated_at, body: .body}'
echo '--- REVIEW COMMENTS ---'
gh api --paginate 'repos/ai-dynamo/aisimulate/pulls/301/comments?per_page=100' --jq '.[] | {id, user: .user.login, path, line, body, html_url}'
echo '--- ISSUE COMMENTS ---'
gh api --paginate 'repos/ai-dynamo/aisimulate/issues/301/comments?per_page=100' --jq '.[] | {id, user: .user.login, body, html_url}' | tail -c 35000Length of output: 86366 🏁 Script executed: #!/bin/bash
set -euo pipefail
echo '--- REVIEWS (concise) ---'
gh api --paginate 'repos/ai-dynamo/aisimulate/pulls/301/reviews?per_page=100' --jq '.[] | select(.body != "") | {id, user: .user.login, submitted_at, body: (.body | .[0:9000])}' | tail -c 40000
echo '--- CHANGED FILES ---'
gh api --paginate 'repos/ai-dynamo/aisimulate/pulls/301/files?per_page=100' --jq '.[] | {filename, status, additions, deletions}'
echo '--- LINKED AUDIT COMMENT ---'
gh api repos/ai-dynamo/aisimulate/issues/comments/5812356376 --jq '{html_url, user: .user.login, created_at, body_length: (.body | length), contains_script: (.body | contains("validate_eligibility_consumers.py")), contains_sha: (.body | contains("1836a4de9ed4333283463c31d088d028f4eac0f4184214c7420775c09d37216e"))}'Length of output: 11299
This check confirms that the evidence is available and linked. It does not independently rerun the native-consumer script. You are interacting with an AI system. |
Signed-off-by: Yiming Liu <yimingl@nvidia.com>
|
@coderabbitai full review |
|
Tip For best results, initiate chat on the files or code changes. 🧩 Analysis chain🏁 Script executed: #!/bin/bash
set -euo pipefail
p='python/aisimulate/src/aisimulate_core/systems/data/gb200/moe/sglang/0.5.17'
echo HEAD; git rev-parse HEAD
echo COMMITS
for sha in 8be644ba5dd79aeb1fb0e58071cf1bf51a521dfa 3acfa103521cfdd9bc5e33e73c65ec8bb409e584 9c4ca9de6b8a05204feec5ccfd6cf59c01de7363; do git rev-parse --verify "$sha^{commit}" || true; done
echo FILES
git status --short
sha256sum "$p/moe_perf.parquet"
echo METADATA
sed -n '1,220p' "$p/collection_meta.yaml"
echo CHANGES
git diff --name-status 3acfa103521cfdd9bc5e33e73c65ec8bb409e584 HEAD || true
echo MODULES
python - <<'PY'
for name in ('duckdb','pyarrow'):
try:
module=__import__(name)
print(name,module.__version__)
except Exception as e:
print(name,type(e).__name__,str(e)[:100])
PYLength of output: 5526 🏁 Script executed: #!/bin/bash
set -euo pipefail
p='python/aisimulate/src/aisimulate_core/systems/data/gb200/moe/sglang/0.5.17/moe_perf.parquet'
python - "$p" <<'PY'
import duckdb, sys
db=duckdb.connect()
p=sys.argv[1]
db.execute("CREATE VIEW m AS SELECT * FROM read_parquet(?)", [p])
checks={
"schema": "DESCRIBE m",
"health": """SELECT count(*) rows, count(*) FILTER (WHERE default_eligible) eligible,
count(*) FILTER (WHERE NOT default_eligible) ineligible,
count(*) FILTER (WHERE default_eligible IS NULL) eligibility_null,
count(*) FILTER (WHERE latency IS NULL OR NOT isfinite(latency) OR latency<=0) bad_latency,
min(latency) min_latency_ms,max(latency) max_latency_ms FROM m""",
"lanes": "SELECT moe_dtype,kernel_source,default_eligible,count(*) n FROM m GROUP BY ALL ORDER BY 1,2,3",
"duplicate_keys": """SELECT count(*) FROM (SELECT framework,version,device,op_name,kernel_source,moe_dtype,num_tokens,hidden_size,inter_size,topk,num_experts,moe_tp_size,moe_ep_size,distribution,count(*) n FROM m GROUP BY ALL HAVING count(*)>1)""",
"eligibility_mismatches": """SELECT count(*) FROM m WHERE default_eligible IS DISTINCT FROM NOT (moe_dtype='fp8_block' AND kernel_source='sglang_flashinfer_trtllm_moe')""",
"target_cases": """SELECT count(*) cases, min(n) min_tokens,max(n) max_tokens,count(*) FILTER (WHERE n<>27) incomplete FROM (SELECT hidden_size,inter_size,topk,num_experts,moe_tp_size,moe_ep_size,distribution,count(DISTINCT num_tokens) n FROM m WHERE moe_dtype='fp8_block' AND kernel_source='sglang_flashinfer_trtllm_moe' GROUP BY ALL)""",
"target_tp": """SELECT moe_tp_size,count(*) n FROM m WHERE moe_dtype='fp8_block' AND kernel_source='sglang_flashinfer_trtllm_moe' GROUP BY ALL ORDER BY 1""",
"target_ep": """SELECT moe_ep_size,count(*) n FROM m WHERE moe_dtype='fp8_block' AND kernel_source='sglang_flashinfer_trtllm_moe' GROUP BY ALL ORDER BY 1""",
"target_distribution": """SELECT distribution,count(*) n FROM m WHERE moe_dtype='fp8_block' AND kernel_source='sglang_flashinfer_trtllm_moe' GROUP BY ALL ORDER BY 1""",
"physical_keys": """SELECT count(*) FROM (SELECT DISTINCT moe_dtype,distribution,topk,num_experts,hidden_size,inter_size,moe_tp_size,moe_ep_size,num_tokens FROM m)""",
"nulls": "SELECT "+", ".join(f"count(*) FILTER (WHERE {c} IS NULL) AS {c}" for c in ['framework','version','device','op_name','kernel_source','moe_dtype','num_tokens','hidden_size','inter_size','topk','num_experts','moe_tp_size','moe_ep_size','distribution','latency','default_eligible'])+" FROM m"
}
for label,sql in checks.items():
print(label,db.sql(sql).fetchall(),flush=True)
PY
echo "CHANGED FILES"
git diff-tree --no-commit-id --name-status -r HEAD
echo "BACKEND FACTS"
rg -n -C5 'gb200|sglang_flashinfer_trtllm_moe' collector/op_backend_facts.yaml | head -130 || trueLength of output: 3307 🏁 Script executed: #!/bin/bash
set -euo pipefail
python - <<'PY'
import duckdb
db=duckdb.connect()
db.execute("CREATE VIEW m AS SELECT * FROM read_parquet('python/aisimulate/src/aisimulate_core/systems/data/gb200/moe/sglang/0.5.17/moe_perf.parquet')")
checks={
"schema": "DESCRIBE m",
"health": """SELECT count(*) rows, count(*) FILTER (WHERE default_eligible) eligible,
count(*) FILTER (WHERE NOT default_eligible) ineligible,
count(*) FILTER (WHERE default_eligible IS NULL) eligibility_null,
count(*) FILTER (WHERE latency IS NULL OR NOT isfinite(latency) OR latency<=0) bad_latency,
min(latency) min_latency_ms,max(latency) max_latency_ms FROM m""",
"lanes": "SELECT moe_dtype,kernel_source,default_eligible,count(*) n FROM m GROUP BY ALL ORDER BY 1,2,3",
"duplicate_keys": """SELECT count(*) FROM (SELECT framework,version,device,op_name,kernel_source,moe_dtype,num_tokens,hidden_size,inter_size,topk,num_experts,moe_tp_size,moe_ep_size,distribution,count(*) n FROM m GROUP BY ALL HAVING count(*)>1)""",
"eligibility_mismatches": """SELECT count(*) FROM m WHERE default_eligible IS DISTINCT FROM NOT (moe_dtype='fp8_block' AND kernel_source='sglang_flashinfer_trtllm_moe')""",
"target_cases": """SELECT count(*) cases, min(n) min_tokens,max(n) max_tokens,count(*) FILTER (WHERE n<>27) incomplete FROM (SELECT hidden_size,inter_size,topk,num_experts,moe_tp_size,moe_ep_size,distribution,count(DISTINCT num_tokens) n FROM m WHERE moe_dtype='fp8_block' AND kernel_source='sglang_flashinfer_trtllm_moe' GROUP BY ALL)""",
"target_tp": """SELECT moe_tp_size,count(*) n FROM m WHERE moe_dtype='fp8_block' AND kernel_source='sglang_flashinfer_trtllm_moe' GROUP BY ALL ORDER BY 1""",
"target_ep": """SELECT moe_ep_size,count(*) n FROM m WHERE moe_dtype='fp8_block' AND kernel_source='sglang_flashinfer_trtllm_moe' GROUP BY ALL ORDER BY 1""",
"target_distribution": """SELECT distribution,count(*) n FROM m WHERE moe_dtype='fp8_block' AND kernel_source='sglang_flashinfer_trtllm_moe' GROUP BY ALL ORDER BY 1""",
"physical_keys": """SELECT count(*) FROM (SELECT DISTINCT moe_dtype,distribution,topk,num_experts,hidden_size,inter_size,moe_tp_size,moe_ep_size,num_tokens FROM m)""",
"nulls": "SELECT "+", ".join(f"count(*) FILTER (WHERE {c} IS NULL) AS {c}" for c in ['framework','version','device','op_name','kernel_source','moe_dtype','num_tokens','hidden_size','inter_size','topk','num_experts','moe_tp_size','moe_ep_size','distribution','latency','default_eligible'])+" FROM m"
}
for label,sql in checks.items():
print(label,db.sql(sql).fetchall(),flush=True)
PY
echo "CHANGED FILES"
git diff-tree --no-commit-id --name-status -r HEAD
echo "BACKEND FACTS PATHS"
fd 'op_backend_facts.yaml' .Length of output: 4103 🏁 Script executed: #!/bin/bash
set -euo pipefail
python - <<'PY'
import duckdb
db=duckdb.connect()
db.execute("CREATE VIEW m AS SELECT * FROM read_parquet('python/aisimulate/src/aisimulate_core/systems/data/gb200/moe/sglang/0.5.17/moe_perf.parquet')")
checks={
"health": """SELECT count(*) AS row_count, count(*) FILTER (WHERE default_eligible) AS eligible_count,
count(*) FILTER (WHERE NOT default_eligible) AS ineligible_count,
count(*) FILTER (WHERE default_eligible IS NULL) AS eligibility_null_count,
count(*) FILTER (WHERE latency IS NULL OR NOT isfinite(latency) OR latency<=0) AS bad_latency_count,
min(latency) AS min_latency_ms,max(latency) AS max_latency_ms FROM m""",
"lanes": "SELECT moe_dtype,kernel_source,default_eligible,count(*) AS n FROM m GROUP BY ALL ORDER BY 1,2,3",
"duplicate_keys": """SELECT count(*) FROM (SELECT framework,version,device,op_name,kernel_source,moe_dtype,num_tokens,hidden_size,inter_size,topk,num_experts,moe_tp_size,moe_ep_size,distribution,count(*) AS n FROM m GROUP BY ALL HAVING count(*)>1)""",
"eligibility_mismatches": """SELECT count(*) FROM m WHERE default_eligible IS DISTINCT FROM NOT (moe_dtype='fp8_block' AND kernel_source='sglang_flashinfer_trtllm_moe')""",
"target_cases": """SELECT count(*) AS case_count, min(n) AS min_tokens,max(n) AS max_tokens,count(*) FILTER (WHERE n<>27) AS incomplete FROM (SELECT hidden_size,inter_size,topk,num_experts,moe_tp_size,moe_ep_size,distribution,count(DISTINCT num_tokens) AS n FROM m WHERE moe_dtype='fp8_block' AND kernel_source='sglang_flashinfer_trtllm_moe' GROUP BY ALL)""",
"target_tp": """SELECT moe_tp_size,count(*) AS n FROM m WHERE moe_dtype='fp8_block' AND kernel_source='sglang_flashinfer_trtllm_moe' GROUP BY ALL ORDER BY 1""",
"target_ep": """SELECT moe_ep_size,count(*) AS n FROM m WHERE moe_dtype='fp8_block' AND kernel_source='sglang_flashinfer_trtllm_moe' GROUP BY ALL ORDER BY 1""",
"target_distribution": """SELECT distribution,count(*) AS n FROM m WHERE moe_dtype='fp8_block' AND kernel_source='sglang_flashinfer_trtllm_moe' GROUP BY ALL ORDER BY 1""",
"physical_keys": """SELECT count(*) FROM (SELECT DISTINCT moe_dtype,distribution,topk,num_experts,hidden_size,inter_size,moe_tp_size,moe_ep_size,num_tokens FROM m)""",
"nulls": "SELECT "+", ".join(f"count(*) FILTER (WHERE {c} IS NULL) AS {c}" for c in ['framework','version','device','op_name','kernel_source','moe_dtype','num_tokens','hidden_size','inter_size','topk','num_experts','moe_tp_size','moe_ep_size','distribution','latency','default_eligible'])+" FROM m"
}
for label,sql in checks.items():
try: print(label,db.sql(sql).fetchall(),flush=True)
except Exception as error: print(label,"ERROR",type(error).__name__,error,flush=True)
PY
echo "CHANGED FILES"
git diff-tree --no-commit-id --name-status -r HEAD
echo "BACKEND FACTS PATHS"
fd 'op_backend_facts.yaml' . || trueLength of output: 3842
The file has 12,312 rows, no nulls, no duplicate measurement keys, and no nonfinite or nonpositive latencies. All 3,159 I did not rerun the native-consumer audit or independently compare the original columns with the baseline commit. Those claims remain supported by your supplied audit, not by this DuckDB check. You are interacting with an AI system. |
✅ Action performedFull review finished. |
|
/ok to test 8be644b |
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Actionable comments posted: 1
- 🪄 Fix CodeRabbit comments on this PR
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Inline comments:
Review comments at @crates/core/src/perfmodel/perf_database/moe.rs:
- Around line 677-690: In the row-loading loop, validate `default_eligible` by
calling `moe_default_eligible` before applying the `kernel_source_ok` filter.
Keep the filter’s existing skip behavior, but ensure malformed metadata returns
`InvalidPerfData` even for rows excluded by the source filter.
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⛔ Files ignored due to path filters (1)
python/aisimulate/src/aisimulate_core/systems/data/gb200/moe/sglang/0.5.17/moe_perf.parquetis excluded by!**/*.parquetand included bypython/**
📒 Files selected for processing (21)
crates/core/src/perfmodel/engine/readiness.rscrates/core/src/perfmodel/perf_database/moe.rscrates/core/src/perfmodel/perf_database/table_view.rsdocs/core-api.mdpython/aisimulate/collector/README.mdpython/aisimulate/collector/helper.pypython/aisimulate/src/aisimulate/runner.pypython/aisimulate/src/aisimulate/sweeper/config.pypython/aisimulate/src/aisimulate_core/sdk/models/blocks/moe.pypython/aisimulate/tests/unit/collector/test_perf_output_finalization.pypython/aisimulate/tests/unit/sdk/database/test_moe_default_eligibility.pypython/aisimulate/tests/unit/sdk/models/test_moe_block_builder_followups.pypython/aisimulate/tests/unit/sdk/test_moe_kernel_source_consumers.pypython/aisimulate/tests/unit/tools/test_check_cross_backend.pypython/aisimulate/tests/unit/tools/test_generate_perf_data_reuse_manifest_latency.pypython/aisimulate/tests/unit/tools/test_parquet_diff.pypython/aisimulate/tools/perf_database/check_cross_backend.pypython/aisimulate/tools/perf_database/parquet_diff.pypython/aisimulate/tools/perf_database/perf_data_layout.pytests/test_afd_runner.pytests/test_runner.py
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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.
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⚙️ CodeRabbit configuration file
Files:
python/aisimulate/src/aisimulate/sweeper/config.pypython/aisimulate/src/aisimulate/runner.py
Enforce the mapped collector guidelines.
⚙️ CodeRabbit configuration file
Files:
python/aisimulate/collector/README.mdpython/aisimulate/collector/helper.py
Enforce the single-oracle and golden-diff rules in python/aisimulate/.claude/rules/rust-core/parity.md.
⚙️ CodeRabbit configuration file
Files:
crates/core/src/perfmodel/perf_database/table_view.rscrates/core/src/perfmodel/engine/readiness.rscrates/core/src/perfmodel/perf_database/moe.rs
Require coverage of the changed behavior and its negative or boundary cases.
⚙️ CodeRabbit configuration file
Files:
tests/test_runner.pytests/test_afd_runner.py
Check commands, defaults, supported runtimes, public names, and claims against executable behavior.
⚙️ CodeRabbit configuration file
Files:
docs/core-api.md
Read REVIEW.md before commenting.
⚙️ CodeRabbit configuration file
Files:
python/aisimulate/src/aisimulate/sweeper/config.pypython/aisimulate/tests/unit/tools/test_parquet_diff.pypython/aisimulate/tools/perf_database/parquet_diff.pypython/aisimulate/tests/unit/tools/test_check_cross_backend.pypython/aisimulate/collector/README.mdpython/aisimulate/tests/unit/collector/test_perf_output_finalization.pypython/aisimulate/src/aisimulate_core/sdk/models/blocks/moe.pypython/aisimulate/src/aisimulate/runner.pypython/aisimulate/tests/unit/sdk/test_moe_kernel_source_consumers.pypython/aisimulate/tools/perf_database/perf_data_layout.pydocs/core-api.mdpython/aisimulate/tools/perf_database/check_cross_backend.pytests/test_runner.pypython/aisimulate/tests/unit/sdk/models/test_moe_block_builder_followups.pycrates/core/src/perfmodel/perf_database/table_view.rspython/aisimulate/tests/unit/tools/test_generate_perf_data_reuse_manifest_latency.pycrates/core/src/perfmodel/engine/readiness.rscrates/core/src/perfmodel/perf_database/moe.rstests/test_afd_runner.pypython/aisimulate/collector/helper.pypython/aisimulate/tests/unit/sdk/database/test_moe_default_eligibility.py
Source excerpt: How to add or change collection coverage.
📄 CodeRabbit inference engine (python/aisimulate/.claude/rules/collector/case_authoring.md)
Files:
python/aisimulate/collector/README.mdpython/aisimulate/tests/unit/collector/test_perf_output_finalization.pypython/aisimulate/collector/helper.py
Source excerpt: Core doctrine: **observe, don't predict.**
📄 CodeRabbit inference engine (python/aisimulate/.claude/rules/collector/failure_handling.md)
Files:
python/aisimulate/collector/README.mdpython/aisimulate/tests/unit/collector/test_perf_output_finalization.pypython/aisimulate/collector/helper.py
Source excerpt: Which layer of the collector may hold which kind of rule.
📄 CodeRabbit inference engine (python/aisimulate/.claude/rules/collector/layer_permissions.md)
Files:
python/aisimulate/collector/README.mdpython/aisimulate/tests/unit/collector/test_perf_output_finalization.pypython/aisimulate/collector/helper.py
Source excerpt: Do NOT add Python-side interpolation, roofline/SOL formulas, empirical-utilization estimates, or per-call table lookups anywhere under `python/aisimulate/src/aisimulate_core/sdk/` (banned def shapes: the `_query_*` and `_loo...
📄 CodeRabbit inference engine (python/aisimulate/.claude/rules/rust-core/parity.md)
Files:
python/aisimulate/src/aisimulate_core/sdk/models/blocks/moe.pycrates/core/src/perfmodel/perf_database/table_view.rscrates/core/src/perfmodel/engine/readiness.rscrates/core/src/perfmodel/perf_database/moe.rs
Before making any change under: `python/aisimulate/src/aisimulate/generator/**` MUST read: `python/aisimulate/.claude/rules/generator-development.md` Before making any change under `python/aisimulate/collector/**` MUST read: `python/aisimul...
📄 CodeRabbit inference engine (AGENTS.md)
Files:
python/aisimulate/src/aisimulate/sweeper/config.pypython/aisimulate/tests/unit/tools/test_parquet_diff.pypython/aisimulate/tools/perf_database/parquet_diff.pypython/aisimulate/tests/unit/tools/test_check_cross_backend.pypython/aisimulate/collector/README.mdpython/aisimulate/tests/unit/collector/test_perf_output_finalization.pypython/aisimulate/src/aisimulate_core/sdk/models/blocks/moe.pypython/aisimulate/src/aisimulate/runner.pypython/aisimulate/tests/unit/sdk/test_moe_kernel_source_consumers.pypython/aisimulate/tools/perf_database/perf_data_layout.pydocs/core-api.mdpython/aisimulate/tools/perf_database/check_cross_backend.pytests/test_runner.pypython/aisimulate/tests/unit/sdk/models/test_moe_block_builder_followups.pycrates/core/src/perfmodel/perf_database/table_view.rspython/aisimulate/tests/unit/tools/test_generate_perf_data_reuse_manifest_latency.pycrates/core/src/perfmodel/engine/readiness.rscrates/core/src/perfmodel/perf_database/moe.rstests/test_afd_runner.pypython/aisimulate/collector/helper.pypython/aisimulate/tests/unit/sdk/database/test_moe_default_eligibility.py
Source excerpt: Only workflows under the repository-root `.github/workflows/` run for this repository.
📄 CodeRabbit inference engine (REVIEW.md)
Files:
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🪛 ast-grep (0.45.3)
python/aisimulate/tests/unit/sdk/database/test_moe_default_eligibility.py
[info] 89-89: use jsonify instead of json.dumps for JSON output
Context: json.dumps([{"Moe": fields}])
Note: [CWE-116] Improper Encoding or Escaping of Output.
(use-jsonify)
🔀 Multi-repo context ai-dynamo/dynamo, ai-dynamo/aiconfigurator
Linked repositories findings
ai-dynamo/dynamo
- Dynamo’s public AIC configuration exposes
moe_dtypebut nokernel_source; its AIC session and GPU-block APIs likewise provide no source selector. The new ineligible lane cannot be selected through these downstream APIs.[::ai-dynamo/dynamo::]components/src/dynamo/planner/config/planner_config.py:99-122;lib/bindings/python/src/dynamo/_internal/aic.py:154-210,350-395,405-486 - Dynamo’s public deployment documentation also omits any
kernel_sourcefield.[::ai-dynamo/dynamo::]docs/fern/pages/reference/kubernetes-api/dynamo-graph-deployment-request.mdx:301-330
ai-dynamo/aiconfigurator
- AIConfigurator supports exact
kernel_sourceallowlists; filtered reads reject rows missing the column and retain only exact matches. This is compatible with explicit source selection, but does not make the ineligible lane part of automatic selection.[::ai-dynamo/aiconfigurator::]aic-core/rust/aiconfigurator-core/src/config.rs:162-179;aic-core/rust/aiconfigurator-core/src/perf_database/mod.rs:167-181 default_eligibleis not an allowed collection-event metadata key. If added as a field undercollectionsrather than kept as Parquet selection metadata/comments, AIConfigurator metadata validation will reject it.[::ai-dynamo/aiconfigurator::]aic-core/src/aiconfigurator_core/sdk/perf_database.py:640-718- The new source label is recognized in AIConfigurator’s backend mapping as
flashinfer_trtllm_gen.[::ai-dynamo/aiconfigurator::]collector/kernel_source_backends.yaml:129
🔇 Additional comments (14)
python/aisimulate/collector/helper.py (1)
965-965: LGTM!Also applies to: 1888-1888, 2527-2543, 2552-2570, 2626-2632
python/aisimulate/tests/unit/collector/test_perf_output_finalization.py (1)
797-846: LGTM!python/aisimulate/collector/README.md (1)
590-591: LGTM!crates/core/src/perfmodel/perf_database/moe.rs (1)
42-42: LGTM!Also applies to: 78-80, 615-645, 680-687, 721-731, 749-753, 793-796
crates/core/src/perfmodel/perf_database/table_view.rs (1)
915-930: LGTM!crates/core/src/perfmodel/engine/readiness.rs (1)
10-13: LGTM!Also applies to: 40-48, 197-220, 607-653
python/aisimulate/tests/unit/sdk/database/test_moe_default_eligibility.py (1)
1-274: LGTM!docs/core-api.md (1)
215-216: LGTM!python/aisimulate/tools/perf_database/perf_data_layout.py (1)
15-15: LGTM!python/aisimulate/tools/perf_database/check_cross_backend.py (1)
88-91: LGTM!python/aisimulate/tools/perf_database/parquet_diff.py (1)
201-203: LGTM!python/aisimulate/tests/unit/tools/test_check_cross_backend.py (1)
65-78: LGTM!python/aisimulate/tests/unit/tools/test_generate_perf_data_reuse_manifest_latency.py (1)
6-6: LGTM!Also applies to: 16-24, 168-210
python/aisimulate/tests/unit/tools/test_parquet_diff.py (1)
66-75: LGTM!
Signed-off-by: Yiming Liu <yimingl@nvidia.com>
Signed-off-by: Yiming Liu <yimingl@nvidia.com>
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],
"shape_count": 117,
"token_values": [
1,
2,
4,
8,
16,
32,
48,
64,
80,
96,
128,
160,
192,
256,
320,
384,
512,
768,
1024,
1536,
2048,
3072,
4096,
6144,
8192,
12288,
16384
],
"token_count": 27,
"full_cartesian_grid": true,
"tp32_rows": 324,
"latency_units": "milliseconds",
"minimum_latency": 0.027601919174194335,
"maximum_latency": 19.695107421875
},
"anomalies": {
"duplicate_measurement_keys": 0,
"null_values": 0,
"nonfinite_latencies": 0,
"nonpositive_latencies": 0
},
"registry": {
"changed_lines": 1,
"old_line": " - {framework: \"sglang\", version: \"0.5.17\", system: \"gb200\", op_name: \"moe\", moe_dtype: \"fp8_block\", backends: [\"triton_fused_moe\"], kernel_sources: [\"sglang_fused_moe_triton\"]}",
"new_line": " - {framework: \"sglang\", version: \"0.5.17\", system: \"gb200\", op_name: \"moe\", moe_dtype: \"fp8_block\", backends: [\"flashinfer_trtllm_gen\", \"triton_fused_moe\"], kernel_sources: [\"sglang_flashinfer_trtllm_moe\", \"sglang_fused_moe_triton\"]}",
"all_other_main_facts_byte_preserved": true
},
"preserved_main_assets": {
"python/aisimulate/src/aisimulate_core/systems/data/b200_sxm/moe/sglang/0.5.17/moe_perf.parquet": {
"sha256": "99e086ffabc1a3911c7fcd499dba475f7c5cc328da122679c9fdc7e22f76f89e",
"byte_identical_to_main": true
},
"python/aisimulate/src/aisimulate_core/systems/data/b200_sxm/moe/sglang/0.5.17/collection_meta.yaml": {
"sha256": "cea72b1518b21f4502805bbd53f46450e2a14b2c5562361b6fa2a601d17a6e5d",
"byte_identical_to_main": true
},
"python/aisimulate/src/aisimulate_core/systems/data/gb300/moe/sglang/0.5.17/moe_perf.parquet": {
"sha256": "4fbd27c0642002cda27e7fe98e004fc47aa5d003644d6ea3a9ed3e0852aa6adc",
"byte_identical_to_main": true
},
"python/aisimulate/src/aisimulate_core/systems/data/gb300/moe/sglang/0.5.17/collection_meta.yaml": {
"sha256": "d65d1bbd6f0b57fa8865ef5dfad4fb2fb978195eaf2250be9b29a766f43e0c6b",
"byte_identical_to_main": true
}
},
"main_catalog_byte_preserved": true,
"sidecar": {
"schema_version": 2,
"collection_rows": [
5994,
3159,
3159
],
"byte_identical_to_remote_start": true
},
"inherited_reader_finding": {
"reader_blob": "6cc9e27414378ded02964f9bb19a97c667311964",
"reader_byte_identical_to_main": true,
"status": "inherited and unfixed; no malformed delivered row; no reader change in this PR",
"reproducer": "https://github.com/ai-dynamo/aisimulate/pull/298#discussion_r4129320232",
"pr301_thread": "PRRT_kwDOT0t9uc6m8CbO",
"pr301_comment": 4129081944
}
},
"manifest": {
"generator": "generate_perf_data_reuse_manifest canonical scan/GroupStats.summary/render_manifest, system/table batches",
"runner": "/Users/yimingl/Vault/aisimulate-data-pr-landing-20260929/generate_reuse_manifest_batched.py",
"runner_sha256": "f0c9c2367e6d783f7eb375fd72838888726d9dad8020d57191ddbfc73e10165c",
"canonical_generator_blob": "091e66d2de7942a2f80d8533b2ab2ea0a3a7140d",
"canonical_generator_unchanged": true,
"complete_traversal_files": 855,
"independent_batches": 303,
"system_table_source_summaries": 1136,
"summary_raw_rows": 15891586,
"manifest_groups_before": 248,
"manifest_groups_after": 248,
"manifest_sha256_before": "fb10f315af02ae636796ee2639178ba51bb7e35c820369778ec7b81918952e24",
"manifest_sha256_after": "02bb2f460e69c5768fc85c63e2471d9e680231cf479d22314069876346d203ff",
"summary_path": "/Users/yimingl/Vault/aisimulate-data-pr-landing-20260929/pr301-final-manifest-groups.json",
"summary_sha256": "d85a6754099d4b16661dded7a0be76ae340961b29fc1cc7f394932b9b8d7437a",
"byte_exact_canonical_render": true,
"other_manifest_bytes_identical": true,
"all_runtime_fields_unchanged": true,
"only_manifest_change": {
"group_index": 184,
"op_file": "moe_perf.parquet",
"kernel_source": "sglang_flashinfer_trtllm_moe",
"field": "total_raw_rows",
"before": 642145,
"after": 645304,
"delta": 3159
},
"changed_files_relative_to_base": [
"python/aisimulate/collector/op_backend_facts.yaml",
"python/aisimulate/src/aisimulate_core/systems/data/gb200/moe/sglang/0.5.17/collection_meta.yaml",
"python/aisimulate/src/aisimulate_core/systems/data/gb200/moe/sglang/0.5.17/moe_perf.parquet",
"python/aisimulate/src/aisimulate_core/systems/perf_data_reuse_manifest.yaml"
]
},
"validation": {
"backend_facts": {
"status": "passed",
"fact_slices": 2153,
"command": "python tools/perf_database/backend_facts.py --check",
"cwd": "data-pr301/python/aisimulate"
},
"collector_rules": {
"R1": "passed",
"R2": "passed",
"R3": "passed",
"R4": "passed",
"R5": "passed",
"R6": "passed",
"R7": "passed"
},
"tests": {
"passed": 91,
"failed": 0,
"skipped": 0,
"duration_seconds": 0.79,
"groups": {
"metadata_provenance_facts": 77,
"manifest_generator": 14
},
"arguments": [
"-c",
"pytest.ini",
"-p",
"no:timeout",
"-o",
"addopts=",
"-q"
],
"modules": [
"tests/unit/sdk/database/test_collection_meta_schema.py",
"tests/unit/collector/test_provenance.py",
"tests/unit/tools/test_backend_facts.py",
"tests/unit/tools/test_generate_perf_data_reuse_manifest_scan.py",
"tests/unit/tools/test_generate_perf_data_reuse_manifest_latency.py"
],
"warning": "One PytestConfigWarning: unknown timeout option because the timeout plugin was disabled to avoid macOS crash dialogs."
},
"whitespace_check": "passed",
"interpreter": "/Users/yimingl/workspace/aisimulate-data-pr-landing-20260929/aisimulate/python/aisimulate/.venv/bin/python",
"parser": "/Users/yimingl/workspace/aisimulate-data-pr-landing-20260929/data-pr301/python/aisimulate/src/aisimulate_core/sdk/perf_database.py",
"extension": "/Users/yimingl/workspace/aisimulate-data-pr-landing-20260929/aisimulate/python/aisimulate/src/aisimulate/_runtime.abi3.so",
"native_interface_schema": 21,
"asserted_python_modules_loaded_from_data_clone": true,
"environment_modified": false,
"native_build_started": false,
"native_consumer_audit": "Parent agent owns the exhaustive current-head native before/after audit, using explicit baseline 0fc975f36e5b98427985b76a8edf62fea2e5011e.",
"remote_writes_by_this_agent": false
}
}Exact-head native consumer results{
"runtime": "/Users/yimingl/workspace/aisimulate-data-pr-landing-20260929/aisimulate/python/aisimulate/src/aisimulate/_runtime.abi3.so",
"schema": 21,
"baseline": "0fc975f36e5b98427985b76a8edf62fea2e5011e",
"code_head": "0fc975f36e5b98427985b76a8edf62fea2e5011e",
"data": [
{
"pr": 301,
"system": "gb200",
"original_head": "9c4ca9de6b8a05204feec5ccfd6cf59c01de7363",
"current_data_head": "6344a925cb9ed98cd0f9c957340d28fc960986a6",
"annotation_sha256": "1836a4de9ed4333283463c31d088d028f4eac0f4184214c7420775c09d37216e",
"all_original_columns_exactly_preserved": true,
"rows": 12312,
"distinct_automatic_query_keys": 9477,
"runs": [
{
"mode": "SILICON",
"shared_layer": false,
"strict_provenance": true,
"identical_automatic_outcomes": {
"value": 9153,
"missing": 324
},
"exact_silicon_measurements": {
"bfloat16/sglang_fused_moe_triton": 3159,
"fp8_block/sglang_fused_moe_triton": 2835,
"nvfp4/sglang_flashinfer_trtllm_moe": 3159,
"fp8_block/sglang_flashinfer_trtllm_moe": 3159
},
"tp32_unset_and_triton_rejected": 324,
"default_views_and_coverage_equal": true
},
{
"mode": "EMPIRICAL",
"shared_layer": false,
"strict_provenance": true,
"identical_automatic_outcomes": {
"value": 9477
},
"exact_silicon_measurements": {},
"tp32_unset_and_triton_rejected": null,
"default_views_and_coverage_equal": true
},
{
"mode": "HYBRID",
"shared_layer": false,
"strict_provenance": true,
"identical_automatic_outcomes": {
"value": 9477
},
"exact_silicon_measurements": {},
"tp32_unset_and_triton_rejected": null,
"default_views_and_coverage_equal": true
},
{
"mode": "SILICON",
"shared_layer": true,
"strict_provenance": true,
"identical_automatic_outcomes": {
"value": 9153,
"missing": 324
},
"exact_silicon_measurements": {
"bfloat16/sglang_fused_moe_triton": 3159,
"fp8_block/sglang_fused_moe_triton": 2835,
"nvfp4/sglang_flashinfer_trtllm_moe": 3159,
"fp8_block/sglang_flashinfer_trtllm_moe": 3159
},
"tp32_unset_and_triton_rejected": 324,
"default_views_and_coverage_equal": true
},
{
"mode": "EMPIRICAL",
"shared_layer": true,
"strict_provenance": true,
"identical_automatic_outcomes": {
"value": 9477
},
"exact_silicon_measurements": {},
"tp32_unset_and_triton_rejected": null,
"default_views_and_coverage_equal": true
},
{
"mode": "HYBRID",
"shared_layer": true,
"strict_provenance": true,
"identical_automatic_outcomes": {
"value": 9477
},
"exact_silicon_measurements": {},
"tp32_unset_and_triton_rejected": null,
"default_views_and_coverage_equal": true
}
]
}
]
}Reproduce the native and metadata checksUse sibling HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1 aisimulate/python/aisimulate/.venv/bin/python validate_eligibility_consumers.py --code-repo "$PWD/aisimulate" --baseline 0fc975f36e5b98427985b76a8edf62fea2e5011e --pr 301
moe_code_python="$PWD/aisimulate/python/aisimulate/.venv/bin/python"
(
cd data-pr301/python/aisimulate
"$moe_code_python" tools/perf_database/backend_facts.py --check
"$moe_code_python" tools/perf_database/check_collector_data.py
"$moe_code_python" -m pytest -p no:timeout -q tests/unit/sdk/database/test_collection_meta_schema.py tests/unit/collector/test_provenance.py tests/unit/tools/test_backend_facts.py tests/unit/tools/test_generate_perf_data_reuse_manifest_scan.py tests/unit/tools/test_generate_perf_data_reuse_manifest_latency.py -m unit
)
git -C data-pr301 diff --check 0fc975f36e5b98427985b76a8edf62fea2e5011e...HEADRunnable complete native audit harness"""Independent native before/after audit of the three annotated MoE data PRs."""
import argparse
import hashlib
import io
import json
import logging
import subprocess
from collections import Counter
from pathlib import Path
import pyarrow.parquet as pq
import aisimulate._runtime as runtime
from aisimulate_core.sdk.common import MoEQuantMode
from aisimulate_core.sdk.engine import EngineHandle
from aisimulate_core.sdk.engine_table_view import fetch_table_view
from aisimulate_core.sdk.errors import EmpiricalNotImplementedError, PerfDataNotAvailableError
from aisimulate_core.sdk.perf_database import get_database_view
BASE = "a2b04a59a277aa8ef4687ef451cc46f4c848fa50"
PRS = {
296: ("b200_sxm", "4190a81d7e9b213adcd3ca47dd75b42cf8ef9a06"),
298: ("gb300", "e7927e0ea726b67c9fd0293e06987cf01809af0d"),
301: ("gb200", "9c4ca9de6b8a05204feec5ccfd6cf59c01de7363"),
}
SOURCE = "sglang_flashinfer_trtllm_moe"
SYSTEMS = Path("python/aisimulate/src/aisimulate_core/systems")
KEY = (
"moe_dtype",
"distribution",
"topk",
"num_experts",
"hidden_size",
"inter_size",
"moe_tp_size",
"moe_ep_size",
"num_tokens",
)
def git(repo, *args):
return subprocess.check_output(["git", *args], cwd=repo)
def normalize(value):
if isinstance(value, dict):
return {str(key): normalize(item) for key, item in value.items()}
if isinstance(value, (list, tuple)):
return [normalize(item) for item in value]
if hasattr(value, "tolist"):
return normalize(value.tolist())
return value
def evaluate(handle, row, source=None):
request = {
"Moe": {
"name": "eligibility consumer audit",
"scale_factor": 1.0,
"hidden_size": int(row["hidden_size"]),
"inter_size": int(row["inter_size"]),
"topk": int(row["topk"]),
"num_experts": int(row["num_experts"]),
"moe_tp_size": int(row["moe_tp_size"]),
"moe_ep_size": int(row["moe_ep_size"]),
"attention_dp_size": 1,
"quant_mode": row["moe_dtype"],
"workload_distribution": row["distribution"],
"is_gated": True,
"moe_backend": None,
"moe_kernel_source": source,
"enable_eplb": False,
"is_context": False,
}
}
return handle.evaluate_ops_json(
json.dumps([request]),
is_context=False,
batch_size=1,
s=1,
x=int(row["num_tokens"]),
)[0]
def outcome(handle, row):
try:
return ("value", evaluate(handle, row))
except (PerfDataNotAvailableError, EmpiricalNotImplementedError) as error:
return ("missing", type(error).__name__)
def database(repo, system, mode, shared):
systems = str(repo / SYSTEMS)
db = get_database_view(
system,
"sglang",
"0.5.17",
systems,
database_mode=mode,
shared_layer=shared,
strict_provenance=True,
)
assert db is not None
assert db.get_default_database_mode().name == mode
return db, EngineHandle.for_database(db, systems_path=systems)
def audit(code_repo, data_repo, pr):
system, old_head = PRS[pr]
relative = SYSTEMS / f"data/{system}/moe/sglang/0.5.17/moe_perf.parquet"
original = pq.read_table(io.BytesIO(git(data_repo, "show", f"{old_head}:{relative}")))
baseline_bytes = git(data_repo, "show", f"{BASE}:{relative}")
assert (code_repo / relative).read_bytes() == baseline_bytes, "Baseline data must remain main"
annotated_bytes = (data_repo / relative).read_bytes()
annotated = pq.read_table(io.BytesIO(annotated_bytes))
assert annotated.drop(["default_eligible"]).equals(original, check_metadata=True)
assert annotated["default_eligible"].null_count == 0
rows = annotated.to_pylist()
for row in rows:
target = row["moe_dtype"] == "fp8_block" and row["kernel_source"] == SOURCE
assert row["default_eligible"] is (not target)
assert Counter(row["default_eligible"] for row in rows) == {True: 9153, False: 3159}
unique = {tuple(row[column] for column in KEY): row for row in rows}
assert len(unique) == 9477
tp32 = [row for row in rows if not row["default_eligible"] and row["moe_tp_size"] == 32]
assert len(tp32) == 324
runs = []
for shared in (False, True):
for mode in ("SILICON", "EMPIRICAL", "HYBRID"):
baseline_db, baseline_handle = database(code_repo, system, mode, shared)
annotated_db, annotated_handle = database(data_repo, system, mode, shared)
counts = Counter()
for key, row in unique.items():
before = outcome(baseline_handle, row)
after = outcome(annotated_handle, row)
assert after == before, (pr, mode, shared, key, before, after)
counts[before[0]] += 1
exact_counts = Counter()
if mode == "SILICON":
for row in rows:
result = evaluate(annotated_handle, row, row["kernel_source"])
assert result[3] == "silicon" and result[1] == row["latency"], (pr, row, result)
exact_counts[f"{row['moe_dtype']}/{row['kernel_source']}"] += 1
for row in tp32:
for source in (None, "sglang_fused_moe_triton"):
try:
evaluate(annotated_handle, row, source)
except PerfDataNotAvailableError:
pass
else:
raise AssertionError(("Unexpected TP32 coverage", pr, shared, source, row))
for view in ("_moe_data", "_moe_low_latency_data"):
assert normalize(fetch_table_view(annotated_db, view)) == normalize(
fetch_table_view(baseline_db, view)
), (pr, mode, shared, view)
for quant in (MoEQuantMode.bfloat16, MoEQuantMode.fp8_block, MoEQuantMode.nvfp4):
before = baseline_db.legacy_moe_compute_coverage(8192, 2048, 10, 512, quant)
after = annotated_db.legacy_moe_compute_coverage(8192, 2048, 10, 512, quant)
assert after == before, (pr, mode, shared, quant, before, after)
runs.append(
{
"mode": mode,
"shared_layer": shared,
"strict_provenance": True,
"identical_automatic_outcomes": dict(counts),
"exact_silicon_measurements": dict(exact_counts),
"tp32_unset_and_triton_rejected": 324 if mode == "SILICON" else None,
"default_views_and_coverage_equal": True,
}
)
return {
"pr": pr,
"system": system,
"original_head": old_head,
"current_data_head": git(data_repo, "rev-parse", "HEAD").decode().strip(),
"annotation_sha256": hashlib.sha256(annotated_bytes).hexdigest(),
"all_original_columns_exactly_preserved": True,
"rows": len(rows),
"distinct_automatic_query_keys": len(unique),
"runs": runs,
}
def main():
global BASE
parser = argparse.ArgumentParser()
parser.add_argument("--code-repo", type=Path, required=True)
parser.add_argument("--baseline", default=BASE)
parser.add_argument("--pr", type=int, choices=PRS, action="append")
args = parser.parse_args()
BASE = args.baseline
logging.disable(logging.WARNING)
reports = [audit(args.code_repo, args.code_repo.parent / f"data-pr{pr}", pr) for pr in args.pr or PRS]
print(
json.dumps(
{
"runtime": runtime.__file__,
"schema": runtime.engine_spec_schema_version(),
"baseline": BASE,
"code_head": git(args.code_repo, "rev-parse", "HEAD").decode().strip(),
"data": reports,
},
indent=2,
)
)
if __name__ == "__main__":
main() |
Regenerate the complete report with the canonical scanner and renderer after the GB200 FlashInfer FP8 data addition. Preserve all measurements, collection metadata, backend facts, and the runtime manifest. The generated base-to-head difference is exactly 3,159 rows; other report changes catch up inherited documentation drift. Signed-off-by: Yiming Liu <yimingl@nvidia.com>
Generated-report correction and final-head validationFinal head: The report was regenerated from a fresh complete scan of all 855 data files, producing 1,136 per-system summaries and 248 aggregate manifest groups. Memory-bounded batches partition only by
The larger report diff catches up 3,375,462 rows of inherited documentation drift. This PR contributes exactly 3,159 new rows. Independently scanning the eight baseline GB200 MoE tables and substituting only those summaries into the otherwise unchanged corpus reproduces the committed main manifest exactly. The generated baseline-to-head report changes only four lines: the headline total, shared-tier total, GB200 source row and aggregate source appendix. This is metadata synchronization, not new GPU collection. Report SHA256: Fresh native-consumer validation at this exact final head passes all 56,862 automatic before/after outcomes and all 24,624 exact-source measured latencies. Default views and coverage match main across SILICON/EMPIRICAL/HYBRID with shared data on/off. All 324 TP32 points remain measured and opt-in: unset/Triton SILICON queries reject them while exact FlashInfer succeeds. Original columns, schema, values and ordering are preserved. The unchanged data hashes and complete 117-shape audit plus runnable native harness are in the preceding machine-readable audit; run that harness with this data head and Generator tests: 14 passed. Documentation tests: 7 passed; all 73 Markdown destinations checked. Independent final-head Standards, Spec and whole-branch reviews are in progress, alongside Fast CI and CodeRabbit. Full CI has not yet been admitted; this comment does not claim merge readiness before those current-head gates finish. |
|
@coderabbitai full review |
✅ Action performedFull review finished. |
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Codex review of exact head 326158665336fc6b288db7bd96753cd9777b0e66, fixed base 0fc975f36e5b98427985b76a8edf62fea2e5011e, using the complete five-file three-dot diff. Independent reviewers re-executed evidence rather than accepting the implementer's report; the generated-documentation fix was also re-reviewed separately.
Standards
PASS: zero introduced hard violations or judgment findings. All 12,312 measurements remain: 9,153 eligible existing rows and 3,159 opt-in FP8 FlashInfer rows. Original schema, values, order and all 15 compressed column payloads are preserved. The entire 117 × 27 sweep, including 324 TP32 points, remains available through explicit source selection. Data and sidecar match previously reviewed bytes. No executable code or golden changed.
Independent evidence passed: 137 focused tests, R1–R7, 2,153 backend-fact slices, 7 documentation tests and 73-file link check, 56,862 native automatic outcomes, 24,624 exact-source latencies and 1,040 native resolver comparisons. A fresh complete 855-file canonical scan reproduces both generated artifacts byte-for-byte.
Spec
PASS: zero missing, incorrect or out-of-scope requirements in this landing batch. The full sweep and exact production provenance are preserved; code/data changes remain separate; existing Triton/NVFP4 defaults do not change. The generated main-to-head report isolates exactly 3,159 added rows in four lines; remaining report churn is inherited documentation catch-up. Independent native, binary and source-group audits passed, as did 92 focused consumer/eligibility/generator tests.
Final whole-branch review
A fresh reviewer on the most capable model independently found zero introduced findings across the complete diff and deferred-findings list. It reran 328 focused tests, binary/schema/provenance preservation, canonical report/base reconstruction, full facts/R1–R7 and exhaustive real-native consumer checks. Technical approval; this is not a predictive-accuracy claim or downstream Dynamo qualification.
The inherited source-filter-before-null-eligibility validation gap remains unchanged and tracked on AIC-1781, not claimed fixed here. Canonical reuse statistics exclude 60,002 pre-existing nonpositive-latency rows elsewhere; all physical rows remain and all new GB200 rows are positive and counted. The documented native op-level explicit-selector boundary remains intact.
Summary: Standards 0 findings; Spec 0 findings; final whole-branch 0 findings. Fast CI is green on this exact head. CodeRabbit, Full CI, human approval and conversation resolution remain separate gates before merge.
|
/ok to test 3261586 |
Exact-head reproducibility and remaining review-summary clarification@coderabbitai The PR description now contains the exact focused-test, registry, collector, documentation and native-consumer commands, and explicitly classifies local real-native, production GPU and hosted-root-workflow evidence. No fake-runner or parity result is presented as measured-data or accuracy evidence. Please recheck the remaining Review Evidence check on unchanged head The FlashInfer/TP32 summary concern is the already source-verified finding whose predicted-label assertion was withdrawn after collector inspection. I freshly fetched immutable SGLang commit The fresh full review reports no new actionable comments; all independent exact-head reviews are clean and all conversations are resolved. Full CI has now been admitted normally for this exact SHA, with its trusted-copy SHA verified equal. CI remains a separate merge gate. For direct binary-audit reproduction, save the following as Complete final-head binary audit script"""Independent final-head binary, metadata and scope audit; emits JSON."""
import hashlib
import io
import json
import math
import subprocess
from collections import Counter
from pathlib import Path
import pyarrow as pa
import pyarrow.parquet as pq
import yaml
REPO = Path('/Users/yimingl/workspace/aisimulate-data-pr-landing-20260929/data-pr301')
BASE = '0fc975f36e5b98427985b76a8edf62fea2e5011e'
HEAD = '326158665336fc6b288db7bd96753cd9777b0e66'
REVIEWED = '8be644ba5dd79aeb1fb0e58071cf1bf51a521dfa'
RAW = '9c4ca9de6b8a05204feec5ccfd6cf59c01de7363'
SYSTEMS = 'python/aisimulate/src/aisimulate_core/systems'
DATA = f'{SYSTEMS}/data/gb200/moe/sglang/0.5.17/moe_perf.parquet'
SIDE = f'{SYSTEMS}/data/gb200/moe/sglang/0.5.17/collection_meta.yaml'
FACTS = 'python/aisimulate/collector/op_backend_facts.yaml'
MANIFEST = f'{SYSTEMS}/perf_data_reuse_manifest.yaml'
SOURCE = 'sglang_flashinfer_trtllm_moe'
def git(*args):
return subprocess.check_output(['git', *args], cwd=REPO)
def blob(ref, path):
return git('show', f'{ref}:{path}')
def sha(payload):
return hashlib.sha256(payload).hexdigest()
def chunks(payload):
metadata = pq.ParquetFile(io.BytesIO(payload)).metadata
result = []
for group in range(metadata.num_row_groups):
for col in range(metadata.num_columns):
c = metadata.row_group(group).column(col)
start = c.dictionary_page_offset if c.has_dictionary_page else c.data_page_offset
result.append((c.path_in_schema, payload[start:start + c.total_compressed_size]))
return result
assert git('rev-parse', 'HEAD').decode().strip() == HEAD
assert git('merge-base', BASE, HEAD).decode().strip() == BASE
changed = git('diff', '--name-only', f'{BASE}...{HEAD}').decode().splitlines()
assert set(changed) == {DATA, SIDE, FACTS, MANIFEST, 'python/aisimulate/docs/perf_database/perf-data-reuse-analysis.md'}
git('diff', '--check', f'{BASE}...{HEAD}')
before_bytes, after_bytes, raw_bytes = (blob(ref, DATA) for ref in (BASE, HEAD, RAW))
assert after_bytes == blob(REVIEWED, DATA) == (REPO / DATA).read_bytes()
assert blob(HEAD, SIDE) == blob(REVIEWED, SIDE) == (REPO / SIDE).read_bytes()
before, after, raw = (pq.read_table(io.BytesIO(b)) for b in (before_bytes, after_bytes, raw_bytes))
assert after.num_rows == 12312 and before.num_rows == 9153
assert after.schema.field('default_eligible').type == pa.bool_()
assert after.drop(['default_eligible']).equals(raw, check_metadata=True)
assert len(chunks(raw_bytes)) == 15
assert [c for c in chunks(after_bytes) if c[0] != 'default_eligible'] == chunks(raw_bytes)
rows = after.to_pylist()
old = [r for r in rows if r['default_eligible']]
new = [r for r in rows if not r['default_eligible']]
assert len(old) == 9153 and len(new) == 3159
assert all(r['default_eligible'] is True for r in old)
assert all(r['default_eligible'] is False for r in new)
assert [{k: v for k, v in r.items() if k != 'default_eligible'} for r in old] == before.to_pylist()
assert all(r['moe_dtype'] == 'fp8_block' and r['kernel_source'] == SOURCE for r in new)
assert all(r['device'] == 'NVIDIA GB200' and r['framework'] == 'SGLang' and r['version'] == '0.5.17' for r in rows)
assert all(v is not None for r in rows for v in r.values())
assert all(math.isfinite(r['latency']) and r['latency'] > 0 for r in rows)
key = [name for name in after.column_names if name not in ('latency', 'default_eligible')]
assert len({tuple(r[n] for n in key) for r in rows}) == len(rows)
shape_names = ['moe_dtype', 'distribution', 'topk', 'num_experts', 'hidden_size', 'inter_size', 'moe_tp_size', 'moe_ep_size']
shapes = sorted({tuple(r[n] for n in shape_names) for r in new})
tokens = sorted({r['num_tokens'] for r in new})
assert len(shapes) == 117 and len(tokens) == 27
assert len({tuple(r[n] for n in shape_names) + (r['num_tokens'],) for r in new}) == 117 * 27
assert sum(r['moe_tp_size'] == 32 for r in new) == 324
assert all(r['default_eligible'] is True for r in rows if r['moe_dtype'] == 'nvfp4')
side_before, side_after = (yaml.safe_load(blob(ref, SIDE)) for ref in (BASE, HEAD))
assert side_before['runtime'] == side_after['runtime']
assert side_after['schema_version'] == side_before['schema_version'] == 2
before_meta, after_meta = (m['tables']['moe_perf'] for m in (side_before, side_after))
assert before_meta['collections'] == after_meta['collections'][:2]
assert sum(c['rows'] for c in after_meta['collections']) == after_meta['rows'] == 12312
assert after_meta['collections'][2]['rows'] == 3159
man_before, man_after = (yaml.safe_load(blob(ref, MANIFEST))['groups'] for ref in (BASE, HEAD))
assert len(man_before) == len(man_after) == 248
delta = []
for b, a in zip(man_before, man_after, strict=True):
if b != a:
assert b['op_file'] == a['op_file'] == 'moe_perf.parquet'
assert b['kernel_source'] == a['kernel_source'] == SOURCE
assert b['total_raw_rows'] == 642145 and a['total_raw_rows'] == 645304
assert {k: v for k, v in b.items() if k != 'total_raw_rows'} == {k: v for k, v in a.items() if k != 'total_raw_rows'}
delta.append({'before': b, 'after': a})
assert len(delta) == 1
preserved = {}
for system in ('b200_sxm', 'gb300'):
for name in ('moe_perf.parquet', 'collection_meta.yaml'):
path = f'{SYSTEMS}/data/{system}/moe/sglang/0.5.17/{name}'
assert blob(BASE, path) == blob(HEAD, path)
preserved[path] = sha(blob(HEAD, path))
catalog = 'python/aisimulate/collector/op_backend_catalog.yaml'
assert blob(BASE, catalog) == blob(HEAD, catalog)
reader = 'crates/core/src/perfmodel/perf_database/moe.rs'
assert git('rev-parse', f'{BASE}:{reader}') == git('rev-parse', f'{HEAD}:{reader}')
print(json.dumps({
'base': BASE, 'head': HEAD, 'changed_files': changed,
'parquet_sha256': sha(after_bytes), 'sidecar_sha256': sha(blob(HEAD, SIDE)),
'schema': str(after.schema), 'rows': len(rows), 'previous_rows': len(old),
'new_fp8_flashinfer_rows': len(new), 'default_eligible': {'true': len(old), 'false': len(new), 'null': 0},
'counts_by_dtype_source': {'/'.join(k): v for k, v in Counter((r['moe_dtype'], r['kernel_source']) for r in rows).items()},
'all_rows_finite_positive': True, 'null_values': 0, 'duplicate_keys': 0,
'latency_units': 'milliseconds', 'latency_min_ms': min(r['latency'] for r in new), 'latency_max_ms': max(r['latency'] for r in new),
'key_columns': key, 'shape_columns': shape_names, 'shapes': shapes, 'token_counts': tokens, 'tp32_new_rows': 324,
'raw_15_column_chunks_byte_identical': True, 'existing_9153_rows_values_order_preserved': True,
'reviewed_8be_parquet_sidecar_bytes_preserved': True, 'prior_collection_events_preserved': True,
'manifest_groups': len(man_after), 'manifest_delta': delta, 'catalog_preserved': True,
'preserved_prior_system_assets': preserved,
'reader_blob': git('rev-parse', f'{HEAD}:{reader}').decode().strip(),
}, indent=2)) |
|
Tip For best results, initiate chat on the files or code changes. 🧩 Analysis chain🏁 Script executed: #!/bin/bash
set -euo pipefail
echo 'PR'
gh api repos/ai-dynamo/aisimulate/pulls/301 --jq '{head: .head.sha, base: .base.sha, body: .body, updated_at: .updated_at}'
echo 'REVIEW EVIDENCE REFERENCES'
gh api --paginate 'repos/ai-dynamo/aisimulate/pulls/301/comments?per_page=100' --jq '.[] | select((.body | test("Review Evidence|FlashInfer/TP32|Cross-Layer Contract"; "i"))) | {id, path, body, html_url}'
echo 'REVIEWS'
gh api --paginate 'repos/ai-dynamo/aisimulate/pulls/301/reviews?per_page=100' --jq '.[] | select((.body // "" | test("Review Evidence"; "i"))) | {id, body, html_url}'
echo 'CHECK RUNS'
gh api 'repos/ai-dynamo/aisimulate/commits/326158665336fc6b288db7bd96753cd9777b0e66/check-runs?per_page=100' --jq '.check_runs[] | {name,status,conclusion,details_url}' | head -200Length of output: 19008 🏁 Script executed: #!/bin/bash
set -euo pipefail
for id in 4092433461 4092485047; do
echo "REVIEW COMMENT $id"
gh api "repos/ai-dynamo/aisimulate/pulls/comments/$id" --jq '{id,body,html_url,user:.user.login,path}'
done
echo "RECENT ISSUE COMMENTS / REVIEW EVIDENCE"
gh api --paginate 'repos/ai-dynamo/aisimulate/issues/301/comments?per_page=100' --jq '.[] | select((.body | test("Review Evidence|review evidence|summary concern|full review"; "i"))) | {id, user:.user.login, body:(.body | .[0:5000]), html_url}' | tail -c 16000
echo "FULL CI STATUS"
gh api 'repos/ai-dynamo/aisimulate/actions/runs/36572283076' --jq '{id,head_sha,status,conclusion,html_url}'Length of output: 19155
One status sentence needs an update: the description says Full CI “will be admitted.” Full CI is admitted for this exact head, but it was still queued when I checked. It remains a separate merge gate. I checked the description and CI status. I did not rerun the binary or native-consumer audits, or independently recheck the upstream SGLang source. ✏️ Learnings added — pending approval
You are interacting with an AI system. |
2 participants
Why and what changed
Adds the complete dedicated GB200 SGLang 0.5.17 FP8 FlashInfer MoE sweep for AIC-1781: 3,159 measurements (117 shapes × 27 token counts) for the Qwen3.8-Max deployment-pinned
sglang_flashinfer_trtllm_moelane.The diff against current main is five data/metadata/documentation files: GB200's
moe_perf.parquet, itscollection_meta.yaml, one observed FP8 source/backend entry incollector/op_backend_facts.yaml, the complete regeneratedperf_data_reuse_manifest.yaml, and its generateddocs/perf_database/perf-data-reuse-analysis.mdreport. The manifest changes only the FlashInfer raw-row count, 642,145 → 645,304. The report is regenerated completely from the current corpus, including inherited documentation drift described below. No executable code, collector behavior, or golden changes. Code prerequisite #326 and data PRs #296/#298 are merged and preserved in this branch.Review map and compatibility
Risk: high, because measured data and selection eligibility are product behavior. Start with
python/aisimulate/src/aisimulate_core/systems/data/gb200/moe/sglang/0.5.17/, then the matching registry line and manifest count. The archived binary, collection and annotation audit records complete shape tuples, keys, units, hashes, provenance and runnable native checks for the unchanged measured files. Current refresh evidence is listed below; the archived CI links are not current merge gates.The optional Boolean
default_eligibleis true on all 9,153 existing rows and false only on the 3,159 new FP8 FlashInfer rows. All 324 TP32 points remain available through explicit source selection. Existing Triton and NVFP4 defaults are unchanged; automatic selection must not gain TP32 FlashInfer coverage. The collection sidecar stays schema v2; the curated source-to-backend mapping is unchanged.Intended consumer: this PR delivers the new opt-in FP8 rows for native AISimulate source-specific, op-level queries using
moe_kernel_source="sglang_flashinfer_trtllm_moe"and an eligibility-aware reader. It does not add or certify selector plumbing in Dynamo's Planner or replay adapters. Downstream integrations must adopt compatible reader support and explicitly forward the selector before claiming use of these measurements. This boundary applies to the new FP8 rows, not existing NVFP4 measurements sharing the same source label. Retaining a selector in regression configuration is not proof of measured-lane support; the existing estimator contract is unchanged.Keep the eligibility-aware reader while these opt-in rows are installed. A reader rollback must restore the pre-PR Parquet, sidecar and system-specific registry entry together and regenerate the manifest; merely dropping the eligibility column is unsafe with old readers.
Current-head evidence
326158665336fc6b288db7bd96753cd9777b0e66; comparison main:0fc975f36e5b98427985b76a8edf62fea2e5011e(after B200 feat: add B200 FP8 FlashInfer MoE measurements #296 and GB300 feat: add GB300 FP8 FlashInfer MoE measurements #298). The last commit updates generated documentation only.a2b04a59a277aa8ef4687ef451cc46f4c848fa50(merged fix: isolate opt-in MoE measurements from automatic selection #326), engine schema 21. Subsequent main commits contain data/metadata only; the executable code is identical. Runtime SHA256 remains643848a255b4f00ba015976ea2b6f802ccce56a8e17e11f85323d4e0d1391205.8be644ba5dd79aeb1fb0e58071cf1bf51a521dfa. Their SHA256 values are1836a4de9ed4333283463c31d088d028f4eac0f4184214c7420775c09d37216eand7288c39fc214488005dea28be32c9b0895883c5328f7cb4261fd2e73c240b4c6.16c7d7ba09e9ae24ba158bd93e6c2d6fc173ca7cbf0a09b11876c039029d3cf1; the documentation fix leaves the runtime manifest and all measurement/sidecar bytes unchanged.The original production Slurm/OCI job 7280873 completed with Slurm exit 0:0, collector exit 0 and zero collector errors; pinned image, collector closure, case-plan hashes and collection records remain in the unchanged sidecar and archived audit. This refresh validates loading/selection and prediction regressions; it is not new GPU collection or a predictive-accuracy claim.
Reproduction commands and evidence classification
Local evidence: all focused pytest, binary/schema/hash, canonical-generator and real-native consumer checks below were executed in the local Python 3.13.3 development environment, using the actual compiled Rust extension, not a fake runner. No fake-runner or parity result is claimed as measured-data or predictive-accuracy evidence. Production evidence is the archived GPU collection job and its unchanged pinned sidecar. Hosted CI evidence is only the linked exact-head runs of active root
.github/workflows/workflows; nested imported workflows are not evidence.The session has sibling checkouts
aisimulateat comparison main0fc975f36e5b98427985b76a8edf62fea2e5011eanddata-pr301at final head326158665336fc6b288db7bd96753cd9777b0e66. Savevalidate_eligibility_consumers.pyfrom the complete public audit. Run from their parent directory:These commands passed 91 focused tests, 7 documentation tests, all 2,153 registry slices, collector R1–R7, 56,862 automatic native comparisons and 24,624 exact-source checks. Independent reviewers additionally ran their stated broader test selections. Complete shape tuples, compressed-column hashes and runnable binary/native audit scripts are linked above and in the final review comments.
Version-pinned source verification
The FlashInfer/TP32 concern was already verified and withdrawn; it was rechecked from immutable upstream sources on this final head. At SGLang v0.5.17 commit
29481685462732237d80d86076d6563e1f658102, runner construction preserves an explicit FlashInfer TRT-LLM backend, quantization information carries block quantization/scales, and the runner invokes the block-scale MoE operation. FusedMoE construction pads FlashInfer's local intermediate width to a multiple of 128: TP32's2048 / 32 = 64is padded to 128. The Triton rejection does not imply rejection of that FlashInfer path. This is static source verification, not a new GPU experiment.The unchanged current collector verifies the constructed runner's observed backend, rejects a mismatch and labels from that observed backend. The case/collector files do not change in this PR; the inline case-YAML citation follow-up remains inherited, not claimed repaired here.
Tracking and landing
Related: AIC-1781, association approved by the user. Closes: none; the original issue has broader collection scope.
Landing order: merged code #326, merged B200 #296, merged GB300 #298, then GB200 #301. This PR is refreshed against the resulting main and remains data/metadata-only.