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Included review availability: Your plan provides up to 12 included reviews per hour; 10 remain after this review. WalkthroughKimi K3 NVFP4 now supports SiTU through CuteDSL, preserves explicit backend failures, adds resolver and kernel tests, and exposes backend selection in Python and Slurm launchers. ChangesKimi K3 SiTU backend support
Priority: ➖ Normal Estimated code review effort: 4 (Complex) | ~45 minutes Change: Feature Sequence Diagram(s)sequenceDiagram
participant KimiK3Launcher
participant LLM
participant BackendResolver
participant CuteDslFusedMoE
participant SiTUKernel
KimiK3Launcher->>LLM: pass MoeConfig backend override
LLM->>BackendResolver: resolve explicit MoE backend
BackendResolver->>CuteDslFusedMoE: validate NVFP4 SiTU eligibility
CuteDslFusedMoE->>SiTUKernel: pass SiTU beta parameters
SiTUKernel->>LLM: return fused MoE output
Merge Risk: 🔵 Low · up to Sequential SiTU beta configurations still lack verified regression coverage for kernel-cache separation. This is a bounded test-confidence risk that should be tracked before merge. 🚥 Pre-merge checks | ✅ 5✅ Passed checks (5 passed)
✨ Finishing Touches🧪 Generate unit tests (beta)
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Inline comments:
In `@examples/kimi_k3/quick_start_kimi_k3.sbatch`:
- Line 43: Update the usage() function to include the accepted --moe-backend
option as [--moe-backend BACKEND], leaving the existing argument parsing
unchanged.
In `@tensorrt_llm/_torch/custom_ops/cute_dsl_custom_ops.py`:
- Line 92: Update _canonicalize_situ_beta to return None only when situ_beta
equals the SITU_BETA_DISABLED sentinel, preserving explicit values such as 0.0
and -2.0. Add coverage for ActivationType.Swiglu confirming those values are
rejected by validation.
In
`@tensorrt_llm/_torch/cute_dsl_kernels/blackwell/blockscaled_contiguous_gather_grouped_gemm_act_fusion.py`:
- Around line 436-440: Update the SiTU beta validation in the constructor around
the existing situ_beta and situ_linear_beta check to reject any non-finite value
as well as zero or negative values, while preserving the current ValueError
behavior and message context. Add regression coverage in the Kimi SiTU MoE tests
for NaN, positive infinity, zero, and negative beta inputs.
In `@tensorrt_llm/_torch/models/modeling_kimi_linear.py`:
- Line 1141: Add a regression test in the Kimi K3 MoE runtime tests covering
explicit CUTEDSL selection when CUTEDSL is ineligible but CUTLASS is eligible.
Assert that KimiK3MoERuntime raises and includes the backend rejection trail,
rather than falling back to CUTLASS; keep existing allow-list and
configuration-preservation tests unchanged.
In `@tests/unittest/_torch/moe/test_kimi_k3_situ_moe.py`:
- Around line 1043-1044: Update the CUTEDSL test skip condition around
IS_CUTLASS_DSL_AVAILABLE to also require an SM100 or SM103 GPU, using the
existing device capability detection symbols in the test; skip before backend
resolution for all other architectures while preserving the current CuTe DSL
wheel check.
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examples/kimi_k3/eval_extra_llm_options_nvfp4_dep16.yamlexamples/kimi_k3/quick_start_kimi_k3.pyexamples/kimi_k3/quick_start_kimi_k3.sbatchtensorrt_llm/_torch/custom_ops/cute_dsl_custom_ops.pytensorrt_llm/_torch/cute_dsl_kernels/blackwell/blockscaled_contiguous_gather_grouped_gemm_act_fusion.pytensorrt_llm/_torch/models/modeling_kimi_linear.pytensorrt_llm/_torch/moe/fused_moe/fused_moe_cute_dsl.pytests/unittest/_torch/moe/test_kimi_k3_situ_moe.pytests/unittest/_torch/moe/test_moe_backend.py
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leslie-fang25
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We rarely use the CUTLASS backend for kimi k3, so could we keep this test focused on CUTEDSL and remove the CUTLASS case to avoid spending CI resources on it? thanks
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| @nvfp4_moe_supported | ||
| @pytest.mark.parametrize("moe_backend", ["CUTLASS", "TRTLLM"]) | ||
| @pytest.mark.parametrize("moe_backend", _NVFP4_SITU_BACKENDS) |
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CUTEDSL uses plain SwiGLU, but this test’s negative reference is SwigluBias(alpha=4, beta=25). Please compare against plain SwiGLU and assert an error bound; the current cosine ordering accepts both plain-SwiGLU output and incorrectly scaled SiTU output.
| [CutlassFusedMoE, CuteDslFusedMoE], | ||
| ids=["cutlass", "cutedsl"], | ||
| ) | ||
| def test_situ_survives_resolution_not_just_construction(backend_cls): |
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Please wire this test into CI. Existing GPU selectors do not select it, and CPU collection excludes this file because it has no pytest.mark.cpu_only marker.
| return float("inf") if swiglu_limit_scalar < 0 else swiglu_limit_scalar | ||
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| #: Sentinel for "this layer is not SiTU". A torch custom op schema cannot carry |
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PyTorch custom-op schemas support Optional[float] = None. Could we use that directly and remove the sentinel, canonicalization helper, and sentinel-only test?
| from tensorrt_llm._torch.moe.fused_moe.mega_moe.mega_moe_deepgemm import MegaMoEDeepGemm | ||
| from tensorrt_llm._torch.utils import ActivationType | ||
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| declares_situ = { |
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This dictionary duplicates the backend list and checks only inclusion, not the exact agreement claimed by the docstring. Consider simplifying this to the CUTEDSL admission regression or deriving candidates from the existing backend registry.
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Inline comments:
In `@tensorrt_llm/_torch/custom_ops/cute_dsl_custom_ops.py`:
- Around line 3460-3461: Add regression coverage in the SiTU MoE execution test
using the same CUTEDSL shape and tactic with two distinct beta pairs. Verify
each output against its corresponding SiTU reference and assert the outputs
differ, exercising beta-specific kernel-cache identity and preventing reuse
across soft-cap values.
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Kimi K3's routed experts use SiTU, which CUTLASS, TRTLLM-Gen and both
MegaMoE backends already implement. The CuteDSL grouped-GEMM act-fusion
kernel did not, so its SUPPORTED_ACTIVATION_TYPES stopped at
(Swiglu, Relu2) and a K3 layer could never reach it.
situ_gate = beta * tanh(g / beta) * sigmoid(g)
situ_up = linear_beta * tanh(u / linear_beta)
is the same expression the MegaMoE CuteDSL kernel and the CUTLASS
SiTuAdaptor evaluate, so the backends stay numerically comparable.
Two constants, not one: the branches are soft-capped independently and
both must be positive because the kernel divides by them. They are
per-model scalars, so they fold at trace time and belong in the
compiled-kernel cache key.
There is no packed tanh intrinsic, so the vectorized path uses
tanh(z) = 2*sigmoid(2z) - 1 -- the identity utils.gelu_tanh_f32 already
uses -- to stay on the packed f32x2 path.
A SwiGLU clamp is rejected alongside SiTU rather than silently ignored,
matching what both MegaMoE backends and DeepGEMM do.
Signed-off-by: Xin Guan <294044352+xguannv@users.noreply.github.com>
The Blackwell act-fusion kernel now implements SiTU, but nothing could reach it: the op in between had no parameters for the soft-caps. Both betas are trace-time constants -- the kernel folds them rather than reading them from memory -- so they are added to three places, not one: the runner constructor, unique_id, and the compile cache key. Missing either of the latter two would let a layer silently reuse a kernel compiled for different soft-caps, a wrong-numbers bug with no error attached to it. The op boundary carries SITU_BETA_DISABLED = -1.0 rather than None. Zero is not usable as the neutral value because the epilogue divides by both betas, and a negative soft-cap is already impossible (SiTuActivation rejects it at construction), so the negative range is free to reserve. _canonicalize_situ_beta maps the sentinel back to None at the runner boundary, mirroring _canonicalize_swiglu_limit_scalar directly above it. Signed-off-by: Xin Guan <294044352+xguannv@users.noreply.github.com>
With the Blackwell act-fusion kernel carrying the SiTU epilogue and the op carrying the soft-caps, what remains is to stop refusing the activation and to hand the constants over. CuteDslFusedMoE declares the alpha/beta pair on the *class*. Resolution reads the class attribute -- it has to, since it judges candidates before any instance exists -- and moe_resolution._activation_rejection states the invariant outright: an instance may narrow a shape, never admit one its class refuses. An earlier revision of this branch had it backwards, declaring UNSUPPORTED on the class and widening per instance, and every K3 layer was turned down on all 16 ranks with "CuteDslFusedMoE kernels take no activation alpha, which this layer's SiTu supplies". Kimi K3 permitted degradation for this backend, so the run produced correct text and exited zero while running CUTLASS. Declaring the pair on the class is safe for the other two kinds this backend executes: SwigluActivation.constants fills only limit and Relu2 fills nothing. SwigluBias is the kind that fills alpha/beta, and it is not in kinds. run_moe_nvfp4 admits SiTu and forwards act_alpha / act_beta as the two betas -- that is where SiTuActivation.constants() lands, reduced to uniform scalars by the declared shape. They are forwarded only for SiTU so every other kind keeps hitting the op default. The other three activation gates in the file are deliberately untouched: the unquantized BF16 method interleaves FC1 weights for a kernel that fuses SwiGLU by name, the locality-domain half-GEMM has no SiTU parameters on its op, and the FP8 block-scale path evaluates SwiGLU in Python. SiTU is turned down on SM107. run_moe_nvfp4 dispatches to the Rubin act-fusion kernel there, whose SUPPORTED_ACTIVATION_TYPES is still (Swiglu, Relu2); reaching it would trip an assert inside the kernel instead of resolving to another backend. CUTEDSL also joins the list of backends whose K3 request must not degrade silently. That list already held both MegaMoE backends for the reason above; CuteDSL declines for more causes than they do (activation shape, SM version, the CuTe DSL dependency), and the failure described above is what an unnoticed decline looks like. Also correct the NVFP4 eval recipe's comment, which still told readers CUTLASS was required because trtllm-gen served MXFP4 only. That stopped being true in NVIDIA#17940 and the guard repeating it was removed in NVIDIA#18709. Signed-off-by: Xin Guan <294044352+xguannv@users.noreply.github.com>
Three additions, each aimed at a way this backend can fail quietly. test_nvfp4_kernel_actually_applies_situ gains CUTEDSL. The other two backends fail loudly if SiTU goes missing -- CUTLASS has no matching activation enum, TRTLLM-Gen no matching cubin. CuteDSL does not: its soft-caps are trace-time scalars folded into a JIT-compiled epilogue, so dropping them compiles a SwiGLU kernel and returns plausible numbers. Scoring the output against a SiTU reference and a SwiGLU reference, with no tolerance, is the only way to tell those apart. test_situ_survives_resolution_not_just_construction calls _activation_rejection directly. Every other SiTU test constructs a backend and so never consults activation_support; resolution does, and it reads the class attribute. A per-instance declaration passed the entire unit suite and then resolved away to CUTLASS on hardware. CUTLASS is parametrized alongside so the next backend to grow SiTU inherits it. test_kimi_k3_allow_list_matches_what_the_backends_declare asserts the model-layer allow-list against each backend's own activation_support rather than a literal list. A hand-maintained second copy of a capability set is what NVIDIA#18709 had to fix; this keeps a new one from forming. The CUTEDSL parameter probes the CuTe DSL wheel inside the test body rather than in a skipif: importing cute_dsl_utils at module scope pulls in a package that appends its own directory to sys.path, which this repository's magic_import hooks reject at session level. Signed-off-by: Xin Guan <294044352+xguannv@users.noreply.github.com>
The shipped example took --model and --image only, so there was no way to exercise a backend other than the resolver's default -- including the one this series opens. Add --moe-backend to both the Python entry point and the sbatch wrapper that forwards to it. Signed-off-by: Xin Guan <294044352+xguannv@users.noreply.github.com>
Docstring coverage over the diff was 72%, under the 80% gate. Seven functions were missing one; each now says the thing a reader could not recover from the signature. The two that carry real information: unique_id() lists trace-time constants, which is why the activation soft-caps belong in it -- they are folded into the compiled kernel as const_expr, so two runners differing in a beta are different kernels and must not share a tuning result. Omitting them would silently serve one layer's kernel to a layer with different soft-caps. _skip_if_backend_unavailable() probes at call time rather than through pytest.mark.skipif, because the marker is evaluated during collection and importing cute_dsl_utils that early puts the CuTe DSL wheel's package directory on sys.path for every other test file in the session. No behaviour change. Signed-off-by: Xin Guan <294044352+xguannv@users.noreply.github.com>
Five review findings, all verified against the code first. _canonicalize_situ_beta mapped the whole `<= 0` range to None, not just the sentinel. On a SwiGLU layer that turned situ_beta=0.0 into "no soft-caps supplied" and accepted it, when the kernel's own check would have refused the combination. Only SITU_BETA_DISABLED disables now; everything else is forwarded so the kernel sees it. The kernel's `situ_beta <= 0` test let NaN and positive infinity through -- every comparison against NaN is false, and an infinity is positive. The epilogue folds 2/beta and 2*beta at trace time, so either one is compiled in and returns quietly wrong activations rather than failing. Replaced with `0 < beta < inf`. Note this file's `math` is the MLIR dialect, not Python's, so isfinite() is not available here. Three new tests: the sentinel is the only disabling value; the kernel refuses NaN, infinity, zero and negative betas; and an ineligible explicit CUTEDSL request raises rather than degrading to CUTLASS, with the allow_degradation=True case as the control so the assertion cannot pass because substitution stopped working altogether. _skip_if_backend_unavailable() checked only for the CuTe DSL wheel, but nvfp4_moe_supported admits every SM >= 100 and only the Blackwell act-fusion kernel carries the SiTU epilogue, so on other architectures the CUTEDSL case failed in resolution instead of skipping. Also the launcher's usage() text, which omitted --moe-backend while the parser accepted it -- and printed that incomplete text on the error path -- and the kernel class docstring, which still said SwiGLU or Relu2 only. Signed-off-by: Xin Guan <294044352+xguannv@users.noreply.github.com>
Four findings from review, all verified against the code first. The op schema does take Optional[float]. My earlier commit message claimed it cannot, and that claim was the whole reason SITU_BETA_DISABLED existed; trtllm::kda_mtp_decode in this repo has carried `scale: Optional[float] = None` all along. The sentinel, its canonicalization helper and the test that covered the helper are gone -- None now means "not a SiTU layer" the way it does everywhere else. test_nvfp4_kernel_actually_applies_situ compared against SwigluBias, `gate*sigmoid(gate*alpha)*(up+beta)`. No epilogue on this path computes that. What a CuteDSL layer computes when it does not run SiTU is `up * silu(gate)`, so a genuine SwiGLU fallback landed far from both references and still satisfied `situ_cos > swiglu_cos` -- the test could not fail the way it claimed to. The control is now the realistic wrong answer. The absolute bounds the review also asked for are deliberately not in this commit: cosine is scale-invariant, so the assertion that catches a mis-scaled SiTU has to be on rel_l2, and its threshold should come from the measured spread rather than a guess. Both scores are printed; the bounds land once there is a number to set them from. test_kimi_k3_allow_list_matches_what_the_backends_declare said "exactly" and tested inclusion, walking a dict of backends written out by hand -- a second copy of the capability set, which is the defect this module exists to catch. Both sides are derived now, from BACKEND_FAMILY and from asking the allow-list, and compared as sets so an offered-but-incapable backend fails too. `any` rather than `all` over a family because resolution walks members in IMPL_PRIORITY order: CUTEDSL qualifies through CuteDslFusedMoE while CuteDslB12xFusedMoE does not declare SiTu. None of the five tests this PR adds were in any CI list, including test_nvfp4_kernel_actually_applies_situ[CUTEDSL] -- the one the PR is for. All are listed in l0_b300.yml now. The [CUTLASS] arm is replaced rather than joined, per review: K3 rarely runs CUTLASS, and the parametrization keeps the case for local use. Signed-off-by: Xin Guan <294044352+xguannv@users.noreply.github.com>
…budget
Review asked for two things: compare against plain SwiGLU, and assert an
error bound, because the cosine ordering alone accepts both plain-SwiGLU
output and incorrectly scaled SiTU. The first landed earlier; this is the
second, and getting there needed two corrections first.
input_scale is 1.0. All 247296 *.input_scale entries in nvidia/Kimi-K3-NVFP4,
across 92 shards, are exactly 1.0, so that is what inference runs. This test
derived amax/(448*6) instead, a value no checkpoint produces, and on that
path the kernel lands 36% short of the reference -- norms 263.02 against
412.49 -- which is the open activation-scale issue that
test_nvfp4_experts_match_situ_reference already carries as a strict xfail.
At input_scale=1.0 the norms agree to 1.004. So the test was running off the
inference path and on top of an unrelated defect, and the cosine assertion
could not see the 36% at all, cosine being scale-invariant. That is the
reviewed failure mode, live in the suite rather than hypothetical.
Both references now read the weights back out of the checkpoint tensors via
e2m1_and_ufp8sf_scale_to_float_v2, as tests/unittest/_torch/thop/serial/
test_moe.py does, so weight quantization cancels instead of landing on one
side only.
The bound is derived rather than fitted. One NVFP4 round trip of Gaussian
data -- the e2m1 grid under a per-16 e4m3 block scale -- costs eps = 0.0950
relative L2, which the checkpoint weights confirm at 0.09515/0.09510/0.09512.
Quantization error does not amplify through a dot product of random data, so
N independent stages compose as sqrt(N)*eps; with the weight stages cancelled
three remain -- the activation reaching the gate, the activation reaching the
up projection, independent because they cross different weight matrices and
SiTU multiplies them, and the FC1->FC2 intermediate -- giving a floor of
sqrt(3)*0.0950 = 0.1645. Measured across 6 seeds x {CUTLASS, TRTLLM, CUTEDSL}:
0.1613 to 0.1656, i.e. 0.980 to 1.006 of prediction, norm ratio in
0.9992..1.0035. The budget is confirmed, not calibrated.
0.20 is that floor plus ~21% headroom against a +-2% spread, and catches a
SiTU mis-scaled by >=11.4% since a scale error s appears as
sqrt(s**2 + 0.1645**2). Fitting the bound to measurements instead would have
frozen whatever the implementation does today into the baseline; anchored on
the budget, an extra quantization stage reports as a failure rather than as a
number to loosen.
The ordering assertion stays as the diagnosis. The norm ratio is printed next
to cosine because rel_l2**2 = r**2 - 2*r*cos + 1, so the two split the
asserted number into a magnitude half and a direction half.
Signed-off-by: Xin Guan <294044352+xguannv@users.noreply.github.com>
Measured from nvidia/Kimi-K3-NVFP4: with the post_attention_layernorm weight giving the MoE input its per-channel magnitude, real g and u have sigma 0.095..0.234 across layers 3/10/30/55, against beta=4 and linear_beta=25. At that scale SiTU and plain SwiGLU differ by 0.08%..0.4%, well under the sqrt(3)*eps = 0.1645 quantization floor, so no assertion could separate them. The test's randn*0.05 puts sigma at 1.497, 6-15x production, where they differ by 14%. That is the reason the discriminator works, so it belongs in the docstring rather than being left to look like an arbitrary constant. Keeping the scale, not changing it: an earlier plan to raise it further was based on assuming production activations were larger, which the measurement reversed. Also drops two claims that the previous commit made stale by adding a bound: the comparison is no longer tolerance-free. Signed-off-by: Xin Guan <294044352+xguannv@users.noreply.github.com>
Review asked for regression coverage on beta-specific cache identity. The betas are trace-time const_expr folded into the compiled epilogue, so unique_id() lists them; if they ever drop out, the autotuner returns the kernel built for the previous betas and nothing raises -- the second layer just silently applies the first layer's soft-caps. Two layers run in one process, in cache order, and each output is checked against the reference built for ITS OWN betas. Asserting only that the two differ would pass if the second were wrong some other way. What varies is the betas, not the input distribution. Measured from the checkpoint, production g and u sit at sigma 0.10..0.23, so inflating the activation to make the caps bite would push the test further from inference, not closer. The second pair is (2.0, 10.0) because the residual grows as the caps tighten and that is the last pair the sqrt(3)*eps budget still covers -- 0.1644, 0.1674, 0.1869, 0.2185 for (4.0,25.0), (2.0,10.0), (0.5,2.0), (0.25,1.0). The growth is not explained: sharper clipping does make the FC1->FC2 intermediate quantize slightly worse, but that is ~3% of it, not 33%. Picking a pair the budget covers keeps one bound over both arms instead of granting an unexplained exception, and (2.0, 10.0) still lands 27% from the production output, far outside allclose, which is all the cache question needs. _make_routed_moe and _make_nvfp4_moe take the soft-caps as parameters now, defaulting to the production 4.0 / 25.0, so two layers can differ in nothing else. Listed in l0_b300.yml. Signed-off-by: Xin Guan <294044352+xguannv@users.noreply.github.com>
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Dev Engineer Review
situ_betaandsitu_linear_betathrough the custom op, kernel cache identity, and fused MoE execution.CUTEDSLand related backend requests from silently falling back to CUTLASS.QA Engineer Review
test_kimi_k3_situ_moe.pywith SiTU beta validation, backend allow-list checks, backend-specific execution coverage, and a plain SwiGLU fallback reference.test_situ_survives_resolution_not_just_constructionto verify SiTU parameters during backend resolution.l0_b300.ymlwith CUTEDSL SiTU execution, beta validation, backend allow-list, explicit failure-path, and resolution-preservation tests.Per-File QA Perspective
examples/kimi_k3/eval_extra_llm_options_nvfp4_dep16.yaml: Updates NVFP4 SiTU backend guidance. Verify the documented backend combinations and sizing warning.examples/kimi_k3/quick_start_kimi_k3.py: Adds optional--moe-backendhandling. Verify default behavior andMoeConfigpropagation.examples/kimi_k3/quick_start_kimi_k3.sbatch: Forwards both supported argument forms and creates a per-rank DeepGEMM JIT cache. Verify missing-value handling and cache paths.tensorrt_llm/_torch/custom_ops/cute_dsl_custom_ops.py: Adds optional SiTU beta parameters and includes them in kernel identity. VerifyNonehandling and cache separation.tensorrt_llm/_torch/cute_dsl_kernels/blackwell/blockscaled_contiguous_gather_grouped_gemm_act_fusion.py: Adds SiTU dispatch and scalar/vectorized epilogues with beta validation. Verify numerical behavior and invalid-value errors.tensorrt_llm/_torch/models/modeling_kimi_linear.py: Allows CUTEDSL for Kimi K3 SiTU and rejects explicit unsupported-backend degradation. Verify backend resolution errors.tensorrt_llm/_torch/moe/fused_moe/fused_moe_cute_dsl.py: Declares SiTU support and forwards beta parameters. Verify NVFP4 execution and SM107 rejection.tests/unittest/_torch/moe/test_kimi_k3_situ_moe.py: Covers beta validation, backend capability agreement, fallback behavior, and SiTU execution. The updated tests are listed intests/integration/test_lists/test-db/l0_b300.yml.tests/unittest/_torch/moe/test_moe_backend.py: Verifies SiTU parameters survive backend resolution for CUTLASS and CUTEDSL. The test is listed intests/integration/test_lists/test-db/l0_b300.yml.tests/integration/test_lists/test-db/l0_b300.yml: Replaces the CUTLASS SiTU kernel entry with CUTEDSL coverage and adds backend, validation, failure-path, and resolution-preservation entries.Description
Kimi K3's NVFP4 routed experts run on CUTLASS, TRTLLM and MEGAMOE_CUTEDSL, but
not on CUTEDSL. Three things are missing, in three different layers:
cute_dsl_kernels/blackwell/blockscaled_contiguous_gather_grouped_gemm_act_fusion.pyhas no SiTU epilogue, and
SiTuis absent from itsSUPPORTED_ACTIVATION_TYPEScould compute it would not receive
beta/linear_betaCuteDslFusedMoE.activation_supportdoes not declareSiTu, andKimiK3MoERuntime's allow-list does not nameCUTEDSLSo
--moe-backend CUTEDSLon an NVFP4 K3 checkpoint fails to resolve, and thebackend that is otherwise the fastest FC1 path on SM100/103 is unavailable to
this model.
Fix
The epilogue. SiTU is
situ_gate = beta*tanh(g/beta)*sigmoid(g),situ_up = linear_beta*tanh(u/linear_beta),out = situ_gate * situ_up.There is no packed
tanh, so the vectorized path usestanh(z) = 2*sigmoid(2z) - 1— the identityutils.gelu_tanh_f32alreadyuses — to stay on the packed f32x2 path. A scalar
tanhwould drop the wholeepilogue loop back to scalar. Both betas are
const_expr, so the reciprocalsand the
2*betafactors fold at trace time.Because they fold at trace time they are part of the compiled kernel's
identity, so they are in both the runner's
unique_id()and the kernel cachekey. Omitting either would silently serve a kernel compiled for one layer's
soft-caps to a layer with different ones.
torch.libraryschemas cannotexpress
Optional[float], hence theSITU_BETA_DISABLED = -1.0sentinel atthe op boundary, canonicalized back to
Noneon the kernel side.Why
activation_supportis declared on the class.moe_resolutionreadsthe class attribute — it has to, since it judges candidates before any
instance exists — and
_reject_unsupported_activationstates the invariantoutright: an instance may narrow a shape, never admit one its class refuses.
An earlier revision of this branch had it backwards: it declared the
alpha/beta pair
UNSUPPORTEDon the class and widened it per instance. Theoverride was never consulted. Every K3 MoE layer was declined on all 16 ranks,
and because K3 permitted degradation for this backend the run produced correct
text and exited zero while executing CUTLASS throughout. That is why
CUTEDSLjoins
MEGAMOE_DEEPGEMMandMEGAMOE_CUTEDSLin the no-silent-degradationlist;
CUTLASSis deliberately absent, being the fallback target itself.Declaring the pair on the class is safe for the other two kinds this backend
executes:
SwigluActivation.constants()fills onlylimit, andRelu2fillsnothing.
SwigluBiasis the kind that fills alpha/beta, and it is not inkinds.Scope. No API change, no new configuration. Every new rejection this PR
adds is guarded by
activation == SiTuorsitu_beta is not None, neither ofwhich could be true before it — so for every pre-existing configuration the
resolver's answer is unchanged, and for SiTU it becomes permissive where it was
not. The only shared file is
fused_moe_cute_dsl.py, where the change is oneadded
ActivationTypein a class attribute plus a SiTU-only branch inrun_moe_nvfp4; the other three activation gates in that file are deliberatelyuntouched, since the unquantized BF16 method interleaves FC1 weights for a
kernel that fuses SwiGLU by name, the locality-domain half-GEMM has no SiTU
parameters on its op, and the FP8 block-scale path evaluates SwiGLU in Python.
Also corrects the NVFP4 eval recipe's comment, which still told readers CUTLASS
was required because trtllm-gen served MXFP4 only. That stopped being true in
#17940 and the guard repeating it was removed in #18709.
Test Coverage
Unit.
tests/unittest/_torch/moe/on GB300 (SM103): 470 passed, 0 failed.Two new tests:
test_nvfp4_kernel_actually_applies_situ[CUTEDSL]— compares the quantizedkernel's output against a SiTU reference and a SwiGLU reference and reports
which one it is closer to, because "which activation did the kernel actually
run" is the question, not "did it return finite numbers". CuteDSL is the case
this parametrization exists for: its betas travel as trace-time scalars keyed
into the kernel cache, so dropping them does not raise — it compiles a SwiGLU
kernel and returns plausible values. The comparison is tolerance-free, so it
also catches a degenerate all-zero FC1, which scores zero against both
references.
test_situ_survives_resolution_not_just_construction— parametrized overCutlassFusedMoEandCuteDslFusedMoE, and goes through_reject_unsupported_activationrather than constructing a backend directly.Every other SiTU test builds the backend by hand and therefore never consults
activation_support; that blind spot is exactly what let the class/instancebug described above reach hardware while the unit suite stayed green.
Both are CPU-only apart from the kernel comparison, which needs an NVFP4-capable
device and skips otherwise.
End to end, Kimi K3 NVFP4, 4 nodes × 4 GPU (DEP16), one NVLink segment:
CuteDslFusedMoE::run_moe_nvfp4..._gather_grouped_gemm_act_fusion_blackwelltrtllm::fused_moe::gemm1The two arms' AutoTuner dispatch counts are mutually exclusive at 16 = one per
rank. That is the positive evidence that the requested backend ran; the absence
of an error is not, as the silent degradation above showed — that run had
correct output, a zero exit code, and the right backend name in its config line.
Accuracy. GSM8K via lm-eval, 5-shot, all 1319 questions, CUTEDSL:
96.13 ± 0.53, with the CuteDSL op counted independently in that run rather
than assumed from the L1 result. The same recipe on TRTLLM and CUTLASS lands
within the measured noise floor, so this is an "accuracy is not broken" check
and not a ranking.
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