Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.

Inline comments:
In
`@cpp/tensorrt_llm/kernels/trtllmGenKernels/blockScaleMoe/routing/RoutingKernel.h`:
- Around line 152-154: Initialize mNumFusedSharedExperts and
mTotalExpertsPerToken in KernelParamsBase with explicit zero defaults, matching
the existing scalar member initialization pattern and ensuring safe no-fusion
behavior before setBaseParams() runs.

In `@cpp/tensorrt_llm/thop/fp8BlockScaleMoe.cpp`:
- Around line 143-149: In the enclosing function, add an upfront TORCH_CHECK
that num_fused_shared_experts.value_or(0) is non-negative, then replace the
repeated > 0 ternaries in the num_total_experts, total_experts_per_token, and
num_total_local_experts calculations with num_fused_shared_experts.value_or(0).
Apply the same simplification at the duplicated call sites in getValidConfigs
and run, including the args.num_fused_shared_experts assignment, while
preserving consistent totals across all paths.
- Around line 215-216: Update the external routing-buffer validation in the FP8
MoE routing setup to account for total_experts_per_token, which includes
num_fused_shared_experts, rather than validating only against top_k. Ensure
topk_weights and topk_ids are rejected unless they contain enough entries for
every token consumed by the fused shared-experts path, preventing downstream
out-of-bounds access.

In `@tensorrt_llm/_torch/modules/fused_moe/fused_moe_trtllm_gen.py`:
- Around line 339-354: Update the shared-expert fusion gate in TRTLLMGenFusedMoE
to also require model_config.mapping.moe_ep_size == 1, alongside the existing
backend, data-parallel, and quantization checks. Keep fusion disabled for EP
configurations while preserving the current behavior for supported non-EP
configurations.

In `@tests/microbenchmarks/bench_moe/build.py`:
- Around line 287-309: The shared-expert MLP is currently created only for the
unfused mode, leaving fused shared-expert weight slots uninitialized. Update the
conditional around GatedMLP construction to build and initialize shared_mlp
whenever model.n_shared_experts > 0, preserve the _UnfusedSharedMoE wrapping for
unfused mode, and call moe.fuse_shared_expert(shared_mlp) for fused mode.
- Around line 294-300: Update the MoE construction around the routed branch and
shared_mlp so both TTP branches use consistent reduction semantics before their
outputs are summed. Prefer enabling reduction for shared_mlp to match the routed
MoE’s reduce_results=True, or keep both outputs local and add one reduction
after summation; ensure the baseline timing includes the required multi-GPU
communication.
- Around line 121-160: Add complete type annotations to every new function in
the affected sites: in tests/microbenchmarks/bench_moe/build.py lines 121-160,
annotate __init__ with a None return, __getattr__ parameters and return, and
forward parameters and return; in
tests/unittest/_torch/modules/moe/test_moe_backend.py lines 1177-1218, annotate
routing-helper parameters and returns plus _write_fused_shared_expert_slots;
lines 1231-1370, annotate expert_info, the test return, and nested run_moe; and
lines 1377-1489, add the test’s -> None return annotation. Preserve existing
behavior and use appropriate concrete or existing project typing symbols.
- Around line 181-205: Update the shared-expert validation near the existing
parallel-layout guard to reject or skip fused candidates unless they use the
TRTLLM backend with FP8_BLOCK_SCALES quantization. Apply this only when
model.n_shared_experts > 0 and shared_expert_mode is not "unfused"; preserve
unfused behavior and ensure unsupported candidates cannot proceed while
recording n_shared_experts > 0.

In `@tests/microbenchmarks/bench_moe/specs.py`:
- Around line 111-116: Update ModelSpec’s shared_expert_mode annotation to
Literal["fused", "unfused"] and extend its validation to reject n_shared_experts
values below zero. Ensure invalid modes and negative shared-expert counts fail
explicitly rather than being silently interpreted or disabled, while preserving
valid configurations.

---

Outside diff comments:
In
`@cpp/tensorrt_llm/kernels/trtllmGenKernels/blockScaleMoe/routing/RoutingDeepSeek.cu`:
- Around line 193-200: Update the mPtrExpertCounts initialization in the routing
setup to clear the full post-expansion expert-count range, including the fused
shared-expert tail used by routingIndicesHistogramKernel and
routingIndicesOffsetsKernel. Base the initArr length on the expanded expert
count rather than only 2 * params.mNumExperts, while preserving the existing
guarded initialization and launch-stride behavior.

In `@cpp/tensorrt_llm/kernels/trtllmGenKernels/blockScaleMoe/runner.cu`:
- Around line 128-231: Add an explicit TLLM_CHECK_WITH_INFO(numFusedSharedExpert
== 0, ...) guard to both the SigmoidRenorm and MiniMax2 branches before
constructing routingCustom::Data, matching the existing Llama4 and
Renormalize/Default handling. Reject non-zero fused shared experts rather than
silently proceeding without fusion.

In `@tensorrt_llm/_torch/custom_ops/trtllm_gen_custom_ops.py`:
- Around line 857-862: Update the MoE runner’s unique_id() to include
num_fused_shared_experts alongside the existing tactic-validity fields, ensuring
get_cache_key() distinguishes fused and unfused configurations while preserving
the current tuple-based cache key behavior.

---

Nitpick comments:
In `@cpp/tensorrt_llm/thop/fp8BlockScaleMoe.cpp`:
- Around line 401-405: Extract the shared TLLM_MOE_FUSED_MIN_TILEN getenv/atoi
logic and default value into a single free function or constant, then update
both fusedMinTileN and fusedMinTileNFallback to reuse it. Remove the duplicated
parsing while preserving the current environment-variable name and fallback
value.

In `@tests/unittest/_torch/modules/moe/test_moe_backend.py`:
- Around line 1231-1370: Extend benchmark-path regression coverage by adding
tests in tests/microbenchmarks/bench_moe/test_shared_experts.py for unsupported
backend or quantization rejection, fused-slot initialization, and multi-GPU
reduction semantics. Keep the existing TRTLLM backend accuracy and fused-layout
coverage in tests/unittest/_torch/modules/moe/test_moe_backend.py, which is
sufficient for those behaviors; use this benchmark test file for the changed
benchmark wiring.