[TRTLLM-14957][refactor] split the MoE base class by responsibility and converge the loader owner gate - #17777
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WalkthroughThe change centralizes MoE weight ownership in reusable mixins and adds ChangesMoE weight ownership refactor
Estimated code review effort: 4 (Complex) | ~45 minutes Merge Risk: ⚪ Minimal · up to This change separates MoE responsibilities and broadens loader recognition to support both owner bases; the supplied evidence shows no resulting runtime or weight-loading defect. Remaining follow-up is limited to annotations and test-list registration, so no actionable merge-blocking risk remains after normal checks. Suggested reviewers: 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
✨ Finishing Touches 💡 1🛠️ Fix failing CI checks 💡
🧪 Generate unit tests (beta)
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In `@tests/unittest/_torch/modules/moe/test_moe_weight_owner.py`:
- Around line 126-375: Annotate every function and method in
test_moe_weight_owner.py, including helpers, fake module methods, and all test
functions, with complete parameter and return types. Add this test module to the
applicable QA list referenced by the l0_cpu test database so its coverage runs
in CI. Preserve all existing tests and behavior.
Apply the same fix in
`@tests/unittest/_torch/modules/moe/test_moe_weight_owner.py` around lines 50 -
61.
Apply the same fix in `@tensorrt_llm/_torch/modules/fused_moe/impl_blocks.py`
around lines 53 - 87: The same annotation requirement applies to the moved
weight-processing methods and properties.
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- Create a new PR with the fixes
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📒 Files selected for processing (17)
tensorrt_llm/_torch/models/checkpoints/hf/afmoe_weight_mapper.pytensorrt_llm/_torch/models/checkpoints/hf/exaone_moe_weight_mapper.pytensorrt_llm/_torch/models/checkpoints/hf/qwen2_moe_weight_mapper.pytensorrt_llm/_torch/models/checkpoints/hf/qwen3_5_weight_mapper.pytensorrt_llm/_torch/models/checkpoints/hf/qwen3vl_moe_weight_mapper.pytensorrt_llm/_torch/models/modeling_deepseekv3.pytensorrt_llm/_torch/models/modeling_deepseekv4.pytensorrt_llm/_torch/models/modeling_glm.pytensorrt_llm/_torch/models/modeling_gpt_oss.pytensorrt_llm/_torch/models/modeling_hunyuan_moe.pytensorrt_llm/_torch/models/modeling_utils.pytensorrt_llm/_torch/modules/fused_moe/__init__.pytensorrt_llm/_torch/modules/fused_moe/impl_base.pytensorrt_llm/_torch/modules/fused_moe/impl_blocks.pytensorrt_llm/_torch/modules/fused_moe/interface.pytensorrt_llm/_torch/modules/fused_moe/weight_owner.pytests/unittest/_torch/modules/moe/test_moe_weight_owner.py
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…nd converge the loader owner gate MoE stated the complete-layer contract and the expert-weight-owner contract in one class, so MoEImplBase could not reuse the weight-owner half without also inheriting forward and layer registration. Move the two blocks an expert-weight owner needs -- execution and the weight lifecycle, and the static EPLB layout -- into impl_blocks.py, and have both MoE and MoEImplBase include them. Nothing moves for existing backends: every member stays reachable at the same name. The loader identified weight owners through 18 isinstance(module, MoE) checks spread over 11 files. Those checks do not decide whether to load; they strip .backend so state_dict keys match checkpoints that predate the wrapper. A backend switching its base class would have turned them False, leaving expert weights silently unloaded. Converge them on is_moe_weight_owner(), which accepts both bases. Signed-off-by: xxi <xxi@nvidia.com>
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Summary
Stacked on #17532 (already merged), preparing for the backend base-class swap (TRTLLM-14958).
MoEstated the complete-layer contract and the expert-weight-owner contract in one class, soMoEImplBasecould not reuse the weight-owner half without also inheritingforwardand layer registration. Move the two blocks an expert-weight owner needs — execution plus weight lifecycle (MoEWeightOwnerMixin) and the static EPLB layout (MoEEplbWeightLayoutMixin) — intoimpl_blocks.py, and have bothMoEandMoEImplBaseinclude them. Mixins carry concrete shared methods only; the abstract contracts stay restated onMoE/MoEImplBase. Nothing moves for existing backends: every member stays reachable at the same name.isinstance(module, MoE)checks spread over 11 files. Those checks do not decide whether to load — they strip.backendso state_dict keys match checkpoints that predate the wrapper. A backend switching its base class would have turned themFalse, leaving expert weights silently unloaded. Converge them onis_moe_weight_owner(), which accepts both bases.Test Coverage
New:
tests/unittest/_torch/modules/moe/test_moe_weight_owner.py— coversis_moe_weight_owner()over both bases, the wrapper/backend pair, and the mixin member surface.Runs below were done on the pre-rebase commit; the rebase onto main was a clean replay with an identical diff.
test_moe_weight_owner.py(43 passed)test_moe_module.py(666 passed, 1908 skipped)TestDeepSeekV3Lite::test_bfloat16_flashinfer[enable_chunked_prefill=False]TestDeepSeekV4Flash::test_auto_dtypeTestGPTOSS::test_w4_1gpu[v2_kv_cache-True-True-cutlass-auto]TestQwen3_30B_A3B::test_nvfp4[latency_moe_trtllm-torch_compile=False]TestQwen3_5_35B_A3B::test_bf16[tp1-CUTLASS]GitHub Bot Help
To see a list of available CI bot commands, please comment
/bot help.Dev Engineer Review
MoEandMoEImplBasewhile preserving abstract contracts and member access.is_moe_weight_owner()to support both MoE layers and backend implementations.QA Engineer Review
tests/unittest/_torch/modules/moe/test_moe_weight_owner.py.MoEImplBaseexecution contracts.test-db/orqa/entries are listed.