[xxi-nv]

Description

moe_config.backend names a family of MoE implementations. The family picks one of its implementations for each layer, and a layer none of them can serve runs on the CUTLASS fallback. A run can therefore name a family but not the kernel it wants, and a layer the family cannot serve ends up on another implementation instead of failing.

This PR adds moe_config.tactic, which names the implementations themselves by their canonical id provider.technique.kernel_name.quant, or by any dotted subset of it, and never falls back. speculative_config.moe_tactic does the same for draft models and MTP layers. moe_config.backend is unchanged, and both new fields are status="prototype". The field descriptions recommend a tactic over a backend other than AUTO, which is planned for deprecation, and point to the implementation catalog in the MoE perf guide for the values to set.

moe_config:
  tactic: cutedsl.grouped_gemm   # leave backend at AUTO, the default

	moe_config.backend: CUTEDSL	moe_config.tactic: cutedsl.grouped_gemm
Names	a backend family	every implementation whose id has these segments
Checked when the config is parsed	the value is a known backend	each segment against the implementation registry: a malformed or unknown segment, or a tactic matching nothing, fails before the model loads
A layer none of the candidates can serve	runs on the CUTLASS fallback	fails to build, and the error lists each candidate's reason
Draft models and MTP layers	speculative_config.moe_backend	speculative_config.moe_tactic; moe_config.tactic applies to the target only

To find the id of what a run uses today, run it once without a tactic: each MoE implementation a model builds is now logged once, as MoE layers run <class>, MoE implementation id <id> (moe_backend <family>).

How a tactic resolves

1. When the config is parsed, LlmArgs stores the tactic stripped and lowercased, and checks it with the model loader's own rules by asking MoE resolution (moe_resolution.moe_impl_request) for the implementations it names. A malformed or unknown segment, more than four segments, a field set twice, a wildcard-only tactic and a tactic matching nothing are rejected, and so is a tactic next to any backend but AUTO. Under per-layer dynamic EPLB the tactic must match an implementation that consumes the plans.
2. When the model loads, the matches are narrowed to the format of the checkpoint's MoE experts and must then belong to one backend family, because model code outside selection still branches on the moe_backend literal. ModelConfig.pinned_moe_backend sets that literal from the tactic; a tactic that still names several families is refused, with a narrower tactic suggested for each family.
3. When each MoE layer is built, resolve_moe_impl reads the tactic from ModelConfig.moe_tactic, so every resolution call site honors it. The candidates are tried in catalog priority order and the first that can serve the layer runs. If none can, the layer fails to build; the error names the field that set the tactic, then each candidate's reason.

The MoE perf guide walks tactic: trtllm_gen through these steps and lists every implementation with the shortest tactic that pins it.

What else changes

• One conversion point. moe_resolution.moe_impl_request is the one place a backend literal or a tactic becomes implementations. resolve_moe_impl, ModelConfig.pinned_moe_backend and the MXFP4 run-format rule all read the request it returns. A literal requests its BACKEND_FAMILY entry followed by the CUTLASS fallback, as before.
• Drafts. moe_config.tactic does not reach drafts, because a draft's format can differ from the target's: the MTP layer of the DeepSeek-R1 NVFP4 checkpoints is unquantized. modeling_speculative._draft_moe_selection decides for every one-engine draft (embedded and replacement-head MTP, Eagle3, external drafters, DSpark): speculative_config.moe_tactic pins, else speculative_config.moe_backend selects. With both unset, a draft inherits the target's backend, or resolves AUTO for its own weights when moe_config.tactic pins the target. Nemotron-H embedded MTP may take a selection of another family but must run VANILLA exactly when the target does. DSpark resolves the draft selection against its draft stages only, and the MiniMax-M3 sm_103 and sm_107 targets load their drafters through it.
• One selection value. model_config.MoESelection carries the configured choice, a literal or a tactic but never both. ModelConfig.copy_with makes the config copies that re-point it, changing moe_backend and moe_tactic together and refusing one without the other.
• Unquantized expert layers. A tactic applies to every MoE layer of the target, including layers whose experts the checkpoint excludes from quantization, which run as BF16, and it never falls back. For such a checkpoint the tactic has to name implementations of both formats in one family (trtllm_gen or trtllm.cutlass.grouped_gemm for NVFP4 and BF16 on SM100, cutlass for W4A16_NVFP4 and BF16 on SM90); the error names the tactics that would, or says to select by backend when none exists. Qwen3-Next and MuseSpark Open are exceptions in model code: their excluded target layers run on CUTLASS, or on the model's backend (TRTLLM under a MegaMoE family), with one warning that names the tactic. An excluded MTP layer is a draft layer, so speculative_config.moe_tactic pins it, and its format is read with the exclusion applied.
• modeling_v2 targets read no tactic, so each declares the implementation ids it honors (moe_impls_honored, draft_moe_impls_honored; ANY_MOE_IMPL honors any, an undeclared target honors none; the modeling_v2 README documents both). A target that does not honor the tactic does not claim the deployment: modeling_v2='auto' builds the built-in model, and 'require' fails, naming the tactic and the target. GLM-5.2 sm_107 and Kimi K3 honor only the TRTLLM-Gen id their decode kernels read.
• B12x identities. The two B12x leaves get ids, flashinfer.cutedsl.b12x.nvfp4 and flashinfer.cutedsl.b12x.w4a16_nvfp4, under the class names FlashinferCutedslNvfp4Impl and FlashinferCutedslW4a16Nvfp4Impl. The old CuteDslB12x*FusedMoE names stay as aliases.
• Weight sharing. The backend fingerprint covers both fields when they are set, in canonical form, since a tactic picks the implementation within one family, and speculative_config.moe_backend when it is set. A config that sets none of them keeps its digest; no speculative-decoding path shares weights today.
• DWDP. Expert DWDP accepts a tactic that names an implementation supporting it (cutedsl.grouped_gemm.nvfp4, fused_fc12, the B12x leaves) and refuses any other tactic at launch, naming it. Without a tactic the backend check is unchanged.
• Why backend keeps AUTO. backend predates tactic, and defaulting it to None to mean "unset" would change what existing configs and callers read back, so AUTO doubles as "no backend chosen" and a tactic is accepted only next to it. The check reads the value rather than model_fields_set, because model defaults are applied by rebuilding the config from a full model_dump(). Model code still branches on the literal, so loading derives it from the tactic (MoEImplRequest.backend_for); that derivation goes away with backend.
• Docs, schemas, skill. The MoE perf guide gains a tactic section and an implementation catalog generated from the registry by scripts/generate_moe_impl_catalog.py. The MoE developer guide describes the selection, and its File Map and a new Implementation Families section say where each kind of code lives. The trtllm-moe-develop skill follows both. The serve schemas are regenerated with both fields (generate_trtllm_serve_schemas.py --check passes). The telemetry golden manifest is unchanged (generate_llm_args_golden_manifest.py --check passes), since free-form text is never captured: moe_config.tactic and speculative_config.moe_tactic are new Optional[str] fields with no capture policy. Because no file under tensorrt_llm/usage/ changes, CODEOWNERS does not request the telemetry team, so their review is requested explicitly; whether to capture these fields, for example as a categorical allowlist of registered ids, is their call.

Behavior changes for configurations without a tactic

• MXFP4 with moe_backend: TRITON on SM100 and later. An MXFP4 checkpoint now runs as the SM's most preferred of the formats the request's implementations publish, so TRITON gets the SM's first choice as the other literals do: W4A8_MXFP4_MXFP8 instead of W4A8_MXFP4_FP8. TritonFusedMoE serves SM90 only, so these layers ran on the CUTLASS fallback before and still do, but on another implementation. On SM100, SM103 and SM107, trtllm.cutlass.grouped_gemm.w4a8_mxfp4_mxfp8 (dynamic MXFP8 activations) replaces trtllm.cutlass.grouped_gemm.w4a8_mxfp4_fp8 (static per-tensor FP8 activations), so the numerics change. On SM120 and SM121, which the FP8 implementation does not support, these layers now build as under the other literals instead of failing.
• speculative_config.moe_backend: AUTO under per-layer dynamic EPLB. A draft whose MoE layers register into the target's load balancer (MTP layers, the embedded DSpark draft, the Mistral Eagle3 draft) now resolves AUTO to MEGAMOE_CUTEDSL, the rule MoeConfig already applied to the target. Both go through llm_args.auto_moe_backend_for_load_balancer.
• speculative_config.moe_backend for replacement-head MTP checkpoints is now accepted and selects for the heads, which load through their own quant scope and weight mapper. It used to be rejected, with the heads inheriting the target's backend; they still inherit it when both draft fields are unset.
• AUTO for embedded MTP layers whose experts are unquantized. The MTP experts' format now applies exclude_modules before per-layer entries, and reads an MTP layer a MIXED_PRECISION checkpoint does not list as unquantized, so AUTO resolves for the format the layer is built in. That changes the backend only on a W4A16_NVFP4 or FP8 block-scale checkpoint that excludes its MTP experts, which no checkpoint in the CI model library does; for the other formats both resolve alike.
• Logging. Each MoE implementation a model builds is logged once at INFO, with its id.

Size

65 files, +6657/−724, with no C++ or CUDA change:

Part	Files	Lines
Code	30	+1971/−379
Tests, including the test-db lists	25	+3028/−96
Serve schemas (generated)	2	+892/−26
Docs (MoE guides, modeling_v2 README) and the trtllm-moe-develop skill	5	+519/−223
Catalog module, catalog generator, schema generator	3	+247/−0

The code is one change because a tactic is only safe once every place that chooses a MoE implementation either honors it or deliberately does not apply it. A layer in a format the tactic names no implementation of fails to build, so a target tactic that reached drafts or unquantized expert layers would fail to build them. The serve schemas must be regenerated for a new serving field, and the field descriptions point to the catalog for their values.

Related

• [TRTLLM-14961][refactor] Give the CuteDSL grouped-GEMM MoE implementations canonical identities #20929 gave the CuteDSL grouped-GEMM MoE implementations canonical identities.
• [TRTLLM-14973][fix] Refuse or honour every input on the B12x MoE path #21057 made the B12x leaves refuse or honor every input; this PR gives them identities.

Test Coverage

136 new tests and 3 changed ones, by what they cover:

• Parsing and validation
  • tests/unittest/llmapi/test_llm_args.py (20 new): stored stripped and lowercased; malformed ids and ids naming no implementation rejected at construction; a concrete backend next to a tactic rejected, an explicit AUTO accepted; per-layer dynamic EPLB leaves the backend to the tactic and rejects a tactic that cannot consume it; YAML round trip and a rebuild from its own dump; the tactic reaches the model config, and a user tactic supersedes a model's backend default.
  • tests/unittest/llmapi/test_trtllm_serve_config_schemas.py (2 new): the schemas of both fields match runtime validation, down to the whitespace str.strip() removes.
• Resolution and the catalog
  • tests/unittest/_torch/moe/test_moe_impl.py (45 new): pinning across techniques by priority; an explicit impl_id overrides the tactic; refusals for a tactic naming no implementation, a layer format it does not name (with what serves that format and a widened tactic), a tactic outside the model's family, and a pin spanning families (with a tactic per family); moe_impl_request for literals, tactics and AUTO; MXFP4 run formats; every registered id and every segment value is a valid tactic, and the config accepts a tactic exactly when the registry does; the B12x identities; shortest_query; a layer in another format is told the tactics of one family that serve every format; the families that consume per-layer dynamic EPLB plans are the one AUTO picks under it.
  • tests/unittest/_torch/moe/test_moe_impl_catalog.py (new file, 3): the perf guide embeds the current catalog, and its tactic examples resolve as it says.
  • tests/unittest/_torch/moe/test_moe_module.py (1 new, GPU): a real CUTLASS BF16 layer built through create_moe under a tactic runs the implementation the tactic names. Listed in l0_b200.yml.
  • test_cute_dsl_b12x_moe_backend.py, test_moe_backend.py, moe_test_utils.py: the B12x class names.
• Model load: tests/unittest/_torch/test_model_config.py (15 new): the backend a tactic sets for the checkpoint, narrowing to the expert formats (Kimi K3's included), a tactic spanning families failing at load, a tactic serving no expert format building for AUTO, MXFP4 run formats and a tactic naming none of them, the Mistral loader, and copy_with.
• Drafts
  • tests/unittest/_torch/modeling/test_modeling_speculative.py (17 new, 1 changed): external drafters, embedded and replacement-head MTP, and Nemotron-H embedded MTP; a pinned target's draft resolves AUTO, a draft tactic pins and a draft backend selects, AUTO under per-layer dynamic EPLB, MXFP4 draft experts, and the MTP experts' format with exclusion and MIXED_PRECISION applied.
  • tests/unittest/_torch/speculative/hw_agnostic/test_dspark_eplb_config.py (3 new; the file is now cpu_only) and test_dspark_v41.py (2 new): DSpark's draft selection under the target's EPLB and against its draft stages.
  • tests/unittest/_torch/speculative/hw_agnostic/test_mtp_separate_checkpoint.py (1 changed, moved from test_mtp.py, which does not import without a GPU, and marked cpu_only): replacement heads with shared KV now accept moe_backend and moe_tactic.
  • tests/unittest/_torch/modeling/test_modeling_deepseekv4.py: passes the request instead of a literal.
• Unquantized expert layers: tests/unittest/_torch/models/test_qwen3_next_moe_quant.py (6 new, including an excluded MTP layer keeping or refusing the draft tactic), tests/unittest/_torch/modeling/test_musespark_open_moe_finalize_hook.py (3 new, 1 changed).
• modeling_v2
  • tests/unittest/_torch/modeling_v2/test_modeling_v2_routing.py (11 new): targets that do and do not honor a tactic or a draft tactic, the GLM-5.2 sm_107, Kimi K3 and MiniMax-M3 declarations, and K3's honored ids and default routed format against the loader's. test_modeling_v2_claims.py counts ANY_MOE_IMPL among the imports that compute nothing.
  • tests/unittest/_torch/modeling_v2/models/glm_moe_dsa/glm_5_2_nvfp4__sm_107__low_latency/spec/test_modeling_v2_mtp_draft_moe.py (new file, 5): the GLM-5.2 MTP draft's MoE selection. Added to l0_cpu.yml.
• Weight sharing: tests/unittest/_torch/weight_sharing/test_source_identity.py (1 new: tactics in canonical form, and the draft's moe_backend), with _source_identity_fakes.py.
• DWDP: tests/unittest/_torch/modules/dwdp/test_dwdp_creator.py (2 new): the expert DWDP gate accepts a tactic naming an implementation that supports DWDP and refuses any other.

Local runs on an H100 (SM90), with this branch's Python code over the compiled code of main's post-merge build 3057 (d782229); this PR changes no compiled code.

• generate_trtllm_serve_schemas.py --check, generate_moe_impl_catalog.py --check and generate_llm_args_golden_manifest.py --check pass.
• The changed test files that run on one GPU or none: 14 failed, 2484 passed, 73 skipped, 1 error. The failures are environmental. The 12 cases of the two GLM-5.2 sm_107 tests in test_modeling_v2_routing.py and the collection of test_modeling_v2_mtp_draft_moe.py stop because build 3057's libth_modeling_v2_glm_moe_dsa.so does not carry the sm_107 target; CI already runs sibling tests of that directory through l0_cpu.yml. The two test_llm_args.py failures (test_runtime_sizes, test_serve_set_grpc_openengine_uses_multiple_frontends) fail the same way without this PR.
• The new GPU test, test_a_tactic_builds_the_implementation_it_names: 3 passed.
• With no GPU visible (CUDA_VISIBLE_DEVICES=), -m cpu_only over the newly marked files and the changed CPU-only ones: 1076 passed.
• -m cpu_only over whole directories: modeling_v2 (without the qwen4_exp target) 14 failed, 1226 passed, 29 skipped, 3 errors; speculative/hw_agnostic 751 passed; moe 936 passed; modeling 4 failed, 1713 passed, 59 skipped, 1 error. Apart from the GLM-5.2 sm_107 cases above, every failure is in a file this PR does not touch and fails the same way without it.

l0_cpu.yml gains the new MTP draft test and l0_b200.yml the new GPU test; every other changed test file already has an entry in the test-db lists.

PR Checklist

Please review the following before submitting your PR:

• PR description clearly explains what and why. If using CodeRabbit's summary, please make sure it makes sense.

• PR Follows TRT-LLM CODING GUIDELINES to the best of your knowledge.

• Test cases are provided for new code paths (see test instructions)

• If PR introduces API changes, an appropriate PR label is added - either api-compatible or api-breaking. For api-breaking, include BREAKING in the PR title.

• Any new dependencies have been scanned for license and vulnerabilities

• CODEOWNERS updated if ownership changes

• Documentation updated as needed

• Update tava architecture diagram if there is a significant design change in PR.

• The reviewers assigned automatically/manually are appropriate for the PR.

• Please check this after reviewing the above items as appropriate for this PR.

GitHub Bot Help

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Dev Engineer Review

• Adds prototype moe_config.tactic and speculative_config.moe_tactic fields. Tactics select registered MoE implementations by canonical ID or dotted subset. Resolution narrows candidates by expert format and pins the selected implementation without fallback.
• Tactics require the corresponding backend to be AUTO. The change also updates draft selection, dynamic EPLB, MTP and replacement-head behavior, quantization decisions, and expert DWDP checks.
• Registers B12x leaves under FlashInfer CuTe DSL implementation IDs and retains aliases for the previous class names. It updates implementation catalogs, schemas, documentation, weight-sharing fingerprints, and modeling_v2 declarations of honored implementations.
• Review focus: verify consistent selection across model loading, serving, and per-layer resolution. Check target and draft selection independently, especially with dynamic EPLB, MTP, and quantization-excluded layers. The changes span several model-specific paths and backend-selection APIs.
• Reported behavior changes include MXFP4 format selection under TRITON, draft backend selection under per-layer dynamic EPLB, and replacement-head MTP backend handling. Reported local checks include 14 failures and 1 error among changed files; the author attributes these to build or environment limitations or unchanged files. Current-head completion status for the reported schema, catalog, and telemetry checks is not available.

QA Engineer Review

• Test changes cover tactic parsing and resolution, expert-format narrowing, backend conflicts, model defaults, draft/MTP selection, dynamic EPLB, DWDP, weight-sharing fingerprints, serving schemas, modeling_v2 routing, and B12x implementation identities.
• test_mtp_moe_backend_rejected_for_shared_kv_replacement_heads was removed. A separate-checkpoint test now checks retention of the configured backend or tactic for replacement heads with shared KV cache.
• l0_b200.yml adds test_a_tactic_builds_the_implementation_it_names. l0_cpu.yml adds test_modeling_v2_mtp_draft_moe.py. Both tests are in the corresponding test-db/ CI lists.
• Reported checks include 3 passes for the new GPU test and 1,076 passes for CPU-only tests over newly marked and changed files. The supplied CI reports two unstable runs and one failed run. Current-head completion status for these checks is not available.
• Coverage verdict: needs follow-up. Confirm the reported failures and CI results on a compatible build and current head.

Per-File QA Perspective

Source, schema, documentation, and tooling files

• .claude/skills/trtllm-moe-develop/SKILL.md — Updates catalog and schema regeneration guidance. Verify the checklist matches the required maintenance workflow.
• .claude/skills/trtllm-moe-develop/references/moe-canonical-code-examples.md — Updates backend-selection and B12x identity examples. Verify examples use current implementation identities.
• docs/source/developer-guide/moe-perf-guide.md — Adds tactic guidance and a generated implementation catalog. Verify matching, failure, and draft-selection rules match runtime behavior.
• scripts/generate_moe_impl_catalog.py — Adds catalog generation and check mode. Verify check mode reports stale content without modifying files.
• scripts/generate_trtllm_serve_schemas.py — Adds tactic schema generation and validation. Verify generated schemas accept supported tactics and reject invalid syntax or backend combinations.
• tensorrt_llm/_torch/_experimental/modeling_v2/README.md — Documents honored-implementation declarations and routing. Verify guidance distinguishes create_moe execution from model-owned kernels.
• tensorrt_llm/_torch/_experimental/modeling_v2/_router_index.py — Adds tactic compatibility checks for targets and drafts. Verify unsupported tactics produce the intended fallback or error.
• tensorrt_llm/_torch/_experimental/modeling_v2/models/glm_moe_dsa/glm_5_2_nvfp4__sm_107__low_latency/_mtp.py — Uses the MTP backend-selection helper. Verify draft construction receives the resolved backend.
• tensorrt_llm/_torch/_experimental/modeling_v2/models/glm_moe_dsa/glm_5_2_nvfp4__sm_107__low_latency/_mtp_config.py — Adds MTP backend resolution and clears the tactic on a copied draft config. Verify target tactics and dynamic EPLB produce the intended draft config.
• tensorrt_llm/_torch/_experimental/modeling_v2/models/glm_moe_dsa/glm_5_2_nvfp4__sm_107__low_latency/modeling.py — Declares honored target and draft implementations. Verify routing accepts only the declared target implementation.
• tensorrt_llm/_torch/_experimental/modeling_v2/models/kimi_k3_vl/kimi_k3_mxfp4__sm_100__tp16_moetp16ep1/modeling.py — Declares honored target and draft implementations. Verify routing matches supported expert formats.
• tensorrt_llm/_torch/_experimental/modeling_v2/models/kimi_k3_vl/kimi_k3_mxfp4__sm_100__tp16_moetp4ep4/modeling.py — Declares honored target and draft implementations. Verify tactic routing uses the intended expert formats.
• tensorrt_llm/_torch/_experimental/modeling_v2/models/minimax_m3/minimax_m3_nvfp4__sm_103__tp4/modeling.py — Changes draft selection and declares draft compatibility. Verify the draft loader receives the intended selection.
• tensorrt_llm/_torch/_experimental/modeling_v2/models/minimax_m3/minimax_m3_nvfp4__sm_103__tp8/modeling.py — Changes draft selection and declares draft compatibility. Verify the draft loader receives the intended selection.
• tensorrt_llm/_torch/_experimental/modeling_v2/models/minimax_m3/minimax_m3_nvfp4__sm_107__tp2/modeling.py — Changes draft selection and declares draft compatibility. Verify declared support matches runtime behavior.
• tensorrt_llm/_torch/_experimental/modeling_v2/models/minimax_m3/minimax_m3_nvfp4__sm_107__tp4/modeling.py — Changes draft selection and declares draft compatibility. Verify declared support matches runtime behavior.
• tensorrt_llm/_torch/_experimental/modeling_v2/models/minimax_m3/minimax_m3_nvfp4__sm_107__tp8/modeling.py — Changes draft selection and declares draft compatibility. Verify declared support matches runtime behavior.
• tensorrt_llm/_torch/_experimental/modeling_v2/models/minimax_m3/modeling_support.py — Replaces a backend-only draft interface with selection helpers. Verify implementations pass consistent loader arguments.
• tensorrt_llm/_torch/model_config.py — Adds MoESelection, tactic-aware quantization loading, and copy_with. Verify backend and tactic changes remain paired and format narrowing uses loaded checkpoint data.
• tensorrt_llm/_torch/models/checkpoints/mistral/config_loader.py — Resolves a backend from a configured tactic. Verify behavior when architecture or quantization metadata is missing.
• tensorrt_llm/_torch/models/modeling_dspark.py — Passes MoESelection through draft construction. Verify draft-stage quantization and load-balancer handling.
• tensorrt_llm/_torch/models/modeling_dspark_v41.py — Uses draft selection for construction and MXFP4 quantization. Verify draft-stage quantization matches the selected implementation.
• tensorrt_llm/_torch/models/modeling_kimi_linear.py — Uses a shared Kimi routed-quantization default. Verify layers without explicit quantization retain the intended default.
• tensorrt_llm/_torch/models/modeling_musespark_open.py — Adds quantization-excluded layer handling and tactic-skip warnings. Verify tactic clearing is limited to excluded layers and does not mutate the original config.
• tensorrt_llm/_torch/models/modeling_qwen3_next.py — Changes backend selection for excluded layers, MTP, and NVFP4 drafts. Verify pinned tactics are not silently overridden and unsupported combinations fail clearly.
• tensorrt_llm/_torch/models/modeling_speculative.py — Adds draft selection, load-balancer-aware resolution, and MTP quantization handling. Verify embedded and separate drafts use the intended configuration.
• tensorrt_llm/_torch/moe/fused_moe/MOE_DEVELOPER_GUIDE.md — Documents selectors, implementation identities, and backend constraints. Verify examples match registered behavior.
• tensorrt_llm/_torch/moe/fused_moe/__init__.py — Exports the FlashInfer CuTe DSL implementations. Verify imports remain compatible with retained aliases.
• tensorrt_llm/_torch/moe/fused_moe/fused_moe_cute_dsl_b12x.py — Registers B12x leaves under new identities and retains aliases. Verify eligibility and dispatch select the intended leaf.
• tensorrt_llm/_torch/moe/fused_moe/impl_catalog.py — Renders and splices the implementation catalog. Verify registry changes appear in the catalog and malformed markers fail clearly.
• tensorrt_llm/_torch/moe/fused_moe/impl_identity.py — Adds compact tactic rendering and shortest unique queries. Verify canonicalization and uniqueness.
• tensorrt_llm/_torch/moe/fused_moe/moe_resolution.py — Adds pinned tactic resolution and MXFP4 format selection. Verify no-fallback behavior and backend-family conflict errors.
• tensorrt_llm/_torch/pyexecutor/model_loader.py — Passes the configured tactic into model loading. Verify the runtime config and checkpoint loader receive the same value.
• tensorrt_llm/_torch/pyexecutor/py_executor_creator.py — Adds tactic compatibility checks for expert DWDP. Verify supported implementations proceed and unsupported matches fail before manager creation.
• tensorrt_llm/_torch/speculative/utils.py — Removes a backend-compatibility validation call. Verify remaining validation covers resolved MTP cases.
• tensorrt_llm/_torch/weight_sharing/source_identity.py — Adds canonicalized target and draft selections to backend fingerprints. Verify equivalent spellings share identity and changed selections do not.
• tensorrt_llm/llmapi/llm_args.py — Adds target and draft tactic fields, validation, and dynamic EPLB backend resolution. Verify normalization, inheritance, and backend/tactic constraints.
• tensorrt_llm/llmapi/llm_utils.py — Prevents model backend defaults from conflicting with a user tactic and passes tactics into MXFP4 selection. Verify unrelated defaults still apply.
• tensorrt_llm/schemas/trtllm-serve-config.schema.json — Adds tactic fields and constraints. Verify applicable target and draft paths match runtime validation.
• tensorrt_llm/schemas/trtllm-serve-disagg-config.schema.json — Adds tactic fields and constraints to disaggregated schemas. Verify all applicable configuration paths expose them consistently.

Test and test-list files

• tests/integration/test_lists/test-db/l0_b200.yml — Adds test_a_tactic_builds_the_implementation_it_names to B200 CI.
• tests/integration/test_lists/test-db/l0_cpu.yml — Adds the GLM MTP draft MoE test to CPU CI.
• tests/unittest/_torch/modeling/test_modeling_deepseekv4.py — Updates routed-MoE quantization tests to pass an implementation request. Covers regular and MTP quantization.
• tests/unittest/_torch/modeling/test_modeling_speculative.py — Adds draft selection, backend, load-balancer, and quantization cases. Covers embedded, separate-checkpoint, and replacement-MTP paths.
• tests/unittest/_torch/modeling/test_musespark_open_moe_finalize_hook.py — Adds quantization-excluded layer and tactic-warning cases. Covers config copying and quantization overrides.
• tests/unittest/_torch/modeling_v2/models/glm_moe_dsa/glm_5_2_nvfp4__sm_107__low_latency/spec/test_modeling_v2_mtp_draft_moe.py — Adds CPU-only MTP draft selection cases for target tactics, replacement heads, and dynamic EPLB. Listed in l0_cpu.yml.
• tests/unittest/_torch/modeling_v2/test_modeling_v2_claims.py — Exempts ANY_MOE_IMPL from compute-import claims. Verify the exemption does not mask other undeclared imports.
• tests/unittest/_torch/modeling_v2/test_modeling_v2_routing.py — Adds target and draft tactic compatibility tests for Kimi, MiniMax, and GLM. Covers accepted and refused routing.
• tests/unittest/_torch/models/test_qwen3_next_moe_quant.py — Adds tactic cases for excluded layers, MTP, and NVFP4 drafts. Covers incompatible-selection errors.
• tests/unittest/_torch/modules/dwdp/test_dwdp_creator.py — Adds DWDP cases for supported and unsupported tactics. Covers acceptance and error reporting.
• tests/unittest/_torch/moe/moe_test_utils.py — Updates B12x test lookup for the new leaf identities. Covers quantization-to-implementation mapping.
• tests/unittest/_torch/moe/test_cute_dsl_b12x_moe_backend.py — Retargets B12x tests to the new classes. Covers eligibility, dispatch, and input scaling.
• tests/unittest/_torch/moe/test_moe_backend.py — Retargets backend tests to the new B12x classes. Covers prefill, decode, and multi-chunk cases.
• tests/unittest/_torch/moe/test_moe_impl.py — Adds identity, tactic, resolution, format, and dynamic EPLB tests. Covers malformed queries and pinned-selection failures.
• tests/unittest/_torch/moe/test_moe_impl_catalog.py — Adds catalog consistency and tactic-example tests. Covers marker validation and documented resolution.
• tests/unittest/_torch/moe/test_moe_module.py — Adds tests that tactics build the named implementation. Listed in l0_b200.yml.
• tests/unittest/_torch/speculative/hw_agnostic/test_dspark_eplb_config.py — Adds embedded and standalone DSpark load-balancer tests. Covers backend selection and tactic inheritance.
• tests/unittest/_torch/speculative/hw_agnostic/test_dspark_v41.py — Adds draft quantization and selection tests. Covers NVFP4 format resolution and mixed-family errors.
• tests/unittest/_torch/speculative/hw_agnostic/test_mtp.py — Removes the replacement-head backend rejection test. Verify replacement-head behavior is covered by the separate-checkpoint test.
• tests/unittest/_torch/speculative/hw_agnostic/test_mtp_separate_checkpoint.py — Adds shared-KV replacement-head tests for backend and tactic retention.
• tests/unittest/_torch/test_model_config.py — Adds MoESelection, tactic-driven backend and quantization, and copy_with tests. Covers immutability and partial-selection errors.
• tests/unittest/_torch/weight_sharing/_source_identity_fakes.py — Extends fakes with target and draft tactic settings. Verify defaults represent absent draft configuration.
• tests/unittest/_torch/weight_sharing/test_source_identity.py — Tests tactic-sensitive backend identity and canonical equivalence. Covers backend versus shard fingerprints.
• tests/unittest/llmapi/test_llm_args.py — Adds tactic parsing, validation, YAML, default-merging, and load-balancer tests.
• tests/unittest/llmapi/test_trtllm_serve_config_schemas.py — Compares schema and runtime validation for tactics and draft selections. Covers runtime-only validation restrictions.

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Walkthrough

This change adds MoE tactics for selecting registered implementations. Tactics now flow through serving validation, checkpoint quantization, speculative draft construction, modeling-v2 routing, and generated catalog documentation.

Changes

MoE Tactic Selection

Layer / File(s)	Summary
Implementation identity and resolution tensorrt_llm/_torch/moe/fused_moe/*, tests/unittest/_torch/moe/*, tests/unittest/_torch/modules/dwdp/test_dwdp_creator.py	MoE resolution now accepts backend literals or tactics. Tactics pin matching implementations, and resolution checks layer quantization formats and backend-family consistency. The B12x NVFP4 leaves now have FlashInfer CuTe DSL identities; the previous class names remain aliases.
Model and serving configuration tensorrt_llm/_torch/model_config.py, tensorrt_llm/llmapi/*, tensorrt_llm/schemas/*, scripts/generate_trtllm_serve_schemas.py, tensorrt_llm/_torch/pyexecutor/model_loader.py, tensorrt_llm/_torch/models/checkpoints/mistral/config_loader.py, tensorrt_llm/_torch/weight_sharing/source_identity.py	MoE and speculative configurations add tactic fields and validation. Checkpoint loading uses implementation requests to derive backends and quantization formats. Serve schemas expose tactic fields and reject incompatible backend/tactic combinations. Backend fingerprints now include canonicalized target and draft tactics.
Draft selection and model-specific handling tensorrt_llm/_torch/models/modeling_speculative.py, tensorrt_llm/_torch/models/modeling_dspark*.py, tensorrt_llm/_torch/models/modeling_musespark_open.py, tensorrt_llm/_torch/models/modeling_qwen3_next.py, tensorrt_llm/_torch/_experimental/modeling_v2/models/minimax_m3/*, tensorrt_llm/_torch/_experimental/modeling_v2/models/glm_moe_dsa/*, tests/unittest/_torch/modeling/*, tests/unittest/_torch/speculative/*	Draft construction now passes MoE selections through checkpoint and embedded-draft loaders. MTP resolution uses draft expert quantization and load-balancer settings. Model-specific handling updates quantization-excluded layers and preserves or clears tactics in copied configurations.
Modeling-v2 route compatibility tensorrt_llm/_torch/_experimental/modeling_v2/_router_index.py, tensorrt_llm/_torch/_experimental/modeling_v2/README.md, tensorrt_llm/_torch/_experimental/modeling_v2/models/*/modeling.py, tests/unittest/_torch/modeling_v2/*, tests/unittest/_torch/modeling_v2/models/glm_moe_dsa/*	Routing checks that a candidate target declares every implementation that the configured main or draft tactic can select. Unsupported tactics enter the existing no-target handling, where require raises and other enabled modes can fall back to the built-in model.
Catalog and documentation tensorrt_llm/_torch/moe/fused_moe/impl_catalog.py, scripts/generate_moe_impl_catalog.py, docs/source/developer-guide/moe-perf-guide.md, tensorrt_llm/_torch/moe/fused_moe/MOE_DEVELOPER_GUIDE.md, .claude/skills/trtllm-moe-develop/*, tests/unittest/_torch/moe/test_moe_impl_catalog.py	Adds catalog rendering and a script to regenerate or check the generated catalog. The guides describe tactic matching, implementation families, and B12x identities. Catalog tests check generated content and tactic examples.

Estimated code review effort: 5 (Critical) | ~120 minutes

Change: Feature

Merge Risk: Moderate · previous review 9c9ca

Some replacement-head MTP models cannot initialize with the requested draft tactic. Correct expert format selection before merging.

Security architecture risk: Low · up to 9c9ca

The new selection is restricted to registered implementations and retains compatibility checks. No concrete security regression was established in the inspected paths. Risk remains low rather than minimal because recovery after partial model construction and some affected surfaces remain incompletely covered.

Retained concerns
No architecture-level concerns identified.

Security review details

Security Blast Radius

• inferred — Within the inspected path, control of model configuration grants selection among registered in-process implementations and can cause construction to fail for incompatible layers. The tactic is not interpreted as a module path or executable expression. Serving authorization and tenant-level exposure were not established by the supplied evidence.

Trust Boundaries and Controls

• observed — The string-to-implementation boundary rejects malformed segments, unknown tokens, duplicate fields, empty queries, and queries matching no implementation. Resolution additionally checks load-balancing and LoRA capabilities and each candidate's layer/deployment eligibility before choosing a winner.

Resilience and Maintainability Implications

• observed — Checkpoint sessions finalize on normal completion and exceptions, and target materialization precedes draft materialization. Replacement configuration shares extra_attrs in both the base and head, so that alias is not newly introduced. Whole-model rollback, concurrent reuse, and retry isolation after partial failure remain unproven; this is a coverage limitation, not an established security regression.

Pre-merge checks: 4 · 1

Failed checks: 1 warning

Check name	Status	Explanation	Resolution
Docstring Coverage		Docstring coverage is 73.95% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documenta…	Write docstrings for the functions missing them to satisfy the coverage threshold.

Passed checks: 4 passed

Check name	Status	Explanation
Title check		The title names the main change: a new MoE tactic option (MoeConfig.tactic) that pins a MoE implementation by its id. It follows the required format with a JIRA ticket and the [feat] type. The changes…
Description check		The description is thorough. It explains the problem, the new moe_config.tactic and speculative_config.moe_tactic fields, resolution steps, behavior changes, size, and detailed test coverage with loca…
Linked Issues check		Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check		Check skipped because no linked issues were found for this pull request.

Full details: Title check

Explanation

The title names the main change: a new MoE tactic option (MoeConfig.tactic) that pins a MoE implementation by its id. It follows the required format with a JIRA ticket and the [feat] type. The changeset adds moe_config.tactic and speculative_config.moe_tactic, which matches the title.

Full details: Description check

Explanation

The description is thorough. It explains the problem, the new moe_config.tactic and speculative_config.moe_tactic fields, resolution steps, behavior changes, size, and detailed test coverage with local run results. It does not follow the repository template exactly: the Description section is present, but the title format is not used in the description itself and the PR checklist is filled in. The template sections (Description, Test Coverage, PR Checklist) are all present, so the description is mostly complete.

Full details: Docstring Coverage

Explanation

Docstring coverage is 73.95% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documentation-only edits and deletions within surviving functions are included; unchanged diff context and fully deleted functions are excluded. Analyzed 119 functions across 46 files.

Resolution

Write docstrings for the functions missing them to satisfy the coverage threshold.

Finishing Touches

Generate unit tests (beta)

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Files selected for processing: 12

• tensorrt_llm/_torch/_experimental/modeling_v2/models/glm_moe_dsa/glm_5_2_nvfp4__sm_107__low_latency/_mtp.py
• tensorrt_llm/_torch/_experimental/modeling_v2/models/glm_moe_dsa/glm_5_2_nvfp4__sm_107__low_latency/_speculative.py
• tensorrt_llm/_torch/_experimental/modeling_v2/models/glm_moe_dsa/glm_5_2_nvfp4__sm_107__low_latency/modeling.py
• tensorrt_llm/_torch/pyexecutor/model_loader.py
• tensorrt_llm/llmapi/llm_args.py
• tensorrt_llm/schemas/trtllm-serve-config.schema.json
• tensorrt_llm/schemas/trtllm-serve-disagg-config.schema.json
• tests/integration/test_lists/test-db/l0_cpu.yml
• tests/unittest/_torch/modeling_v2/models/glm_moe_dsa/glm_5_2_nvfp4__sm_107__low_latency/spec/test_modeling_v2_mtp_draft_moe.py
• tests/unittest/_torch/modeling_v2/test_modeling_v2_routing.py
• tests/unittest/llmapi/test_llm_args.py
• tests/unittest/llmapi/test_trtllm_serve_config_schemas.py

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