[t239573445]

Purpose

Fuse the gated tanh-GELU MLP activation with the downstream linear's static FP8 per-tensor input quantization into a single kernel, mirroring what already exists for SiLU (silu_and_mul_quant).

GELU-family models (Gemma 2/3, Gemma3n, Rnj1 -- all gelu_pytorch_tanh) run gelu_tanh_and_mul and then scaled_fp8_quant whenever the checkpoint carries static FP8 quantization. That writes a bf16 activation of intermediate_size elements per token and reads it straight back, so the intermediate crosses DRAM twice.

What it adds

• torch.ops._C.gelu_tanh_and_mul_quant(out, input, scale) -- reuses the existing act_and_mul_quant_kernel template with a tanh-GELU functor, so only the activation differs from silu_and_mul_quant.
• Registry entry (GeluAndMul, kFp8StaticTensorSym) in fused_act_quant.py, narrowed by a predicate so GeluAndMul(approximate="none") keeps the unfused path.
• Wiring: Gemma2MLP and Gemma4MLP now call maybe_fused_act_quant (one line each, the same pattern llama.py already uses). maybe_fused_act_quant and its existing call sites are upstream code; what is new is the GELU registry row, so without this wiring the row would be inert on real models and the fusion would never fire. gemma3.py is intentionally left to [Kernel][Fusion] Fuse GELU-tanh+Mul with dynamic per-token FP8 quantization #64212, which already edits that file for the dynamic per-token variant.
• fix_functionalization.py: added the branch for the new op, mirroring silu_and_mul_quant. The generic fallback goes through kwargs=node.kwargs, which the existing comment documents as returning the wrong answer for this op shape, and the two ops share an identical schema.
• docs/design/fusions.md: fusion-table row plus a dedicated section. The row
  records that this is a manual fusion (n/a for the PassConfig flag, always
  active) rather than one of the fuse_act_quant pass fusions. Note: this row's
  anchor is written with a single - because mkdocs collapses runs of
  separators; the -- form used by the pre-existing rows does not resolve
  (that is unchanged by this PR).
• benchmarks/kernels/benchmark_gelu_tanh_fp8_quant.py.

Regression check

Wiring maybe_fused_act_quant into a model must not change numerics when no fusion applies. Verified by A/B on Gemma2MLP (same seed, same weights, same input): the forward output is byte-identical with and without the wiring -- the SHA-256 of the output buffer matches in both cases. With no static-FP8 consumer the helper returns the plain activation.

Also verified locally on SM120:

• _C_stable_libtorch and _vllm_fa2_C built from this branch; import vllm, gemma2, gemma4 succeed and FA2_AVAILABLE is True
• Gemma2MLP / Gemma4MLP forward on random weights: expected shapes, all finite
• MLP output equals the manual gelu(tanh) -> down_proj path, bit-exact
• tests/kernels/test_fused_quant_activation.py: 282 passed

Numerics

Bit-identical to F.gelu(x, approximate="tanh") * up -> scaled_fp8_quant, verified over 33.5M elements (up to 8192x4096).

An algebraically equivalent 0.5 * x * erfc(-x/sqrt(2)) formulation (erfc(-z) == 1 + erf(z)) was measured to differ in 8 of 12.5M elements at the fp8 level, so the tanh formulation already used by gelu_tanh_kernel is kept. That also keeps fused and unfused paths identical.

Performance

RTX 5090 D v2, SM120, bf16 in / fp8 out, median of 200 iters after 50 warmup iters:

shape	unfused	fused	speedup
1x4096	6.0 us	2.7 us	2.21x
64x4096	5.9 us	2.7 us	2.15x
512x4096	8.2 us	4.1 us	2.00x
2048x4096	16.3 us	12.3 us	1.33x
2048x14336	165.6 us	105.3 us	1.57x

geomean 1.82x. Reproduce with python benchmarks/kernels/benchmark_gelu_tanh_fp8_quant.py --check. 2048x14336 matches the down_proj input width of a Gemma-4-31B-style MLP.

Tests

tests/kernels/test_fused_quant_activation.py ... 282 passed

• test_gelu_tanh_and_mul_quant -- op-level bit-exactness vs PyTorch F.gelu (50 params)
• test_gelu_tanh_and_mul_quant_matches_unfused -- bit-exactness vs the actual unfused vLLM path (gelu_tanh_and_mul + scaled_fp8_quant), which also guards the kernel's float association (50 params)
• test_gelu_tanh_and_mul_quant_odd_hidden_sizes -- d in {1, 5, 7, 9, 1001, 4095}, i.e. the scalar cleanup path (12 params)
• test_gelu_tanh_and_mul_quant_ndim -- [..., d] inputs, not just 2D (12 params)
• test_gelu_tanh_and_mul_quant_non_contiguous -- like silu_and_mul_quant, the op does not guard contiguity, so this pins that the fused path does not drift from the unfused one on a non-contiguous view (4 params)
• test_maybe_fused_act_quant_gelu_tanh -- registry-level, through maybe_fused_act_quant (30 params)
• test_gelu_erf_does_not_fuse -- approximate="none" must fall back

pre-commit run --files <changed files> is clean (ruff check/format, clang-format, typos, mypy, SPDX, markdownlint, and the torch.cuda API restriction hook).

Relation to #64212

#64212 adds the dynamic per-token counterpart (kFp8DynamicTokenSym); this PR covers static per-tensor (kFp8StaticTensorSym). Different QuantKey, selected by different checkpoint formats, so they are complementary. Both touch the same files -- happy to rebase on top of #64212 once it lands, or to drop this if maintainers would rather cover both quant keys in one PR.

Model-level check

The open question for a registry-only change is whether the row is reachable
from a real forward pass at all. To settle it, a tiny Gemma2
(hidden_activation=gelu_pytorch_tanh) was statically quantized to FP8 with
modelopt (quant_algo: FP8, dynamic: false) and served through vLLM:

• the GELU producer runs 32 times across 3 prompts x 16 greedy steps, i.e.
  the row is actually reached at runtime
• it receives a real input_scale (7.19e-4) from the quantized down_proj
• top-20 logprobs are bit-identical with the fusion enabled and disabled:
  48 steps x 20 logprobs per run, max |dlogprob| = 0.0 exactly (unrounded
  float comparison). The fusion-off baseline was run twice and also differs by
  0.0, so the comparison is not hiding GEMM nondeterminism.

Without the Gemma2MLP/Gemma4MLP wiring this would have been dead code, so
this is the check that the feature does something. The model is deliberately
tiny (hidden size 128), so no latency number is claimed from it -- the
microbenchmark above is the performance evidence.

On SM120 specifically: flashinfer 0.7.2's FP8 GEMM JIT does not support
sm_120 yet (No supported CUDA architectures found for major versions [12]
from get_nvcc_flags_list). Running with
kernel_config={"linear_backend": "cutlass"} avoids that path.

Not covered / follow-ups

• Model-level numerics were verified on a synthetic static-FP8 Gemma2, not
  on a trained checkpoint: the Gemma FP8 checkpoints on the Hub are gated and
  were not reachable from the dev box. A GSM8K + vllm bench latency A/B on
  RedHatAI/gemma-3-12b-it-FP8 is still worth doing before merge.
• GeluAndMul(approximate="none") + FP8 static stays unfused. The registry keys on (activation class, QuantKey) -- one producer per key -- so erf support needs either a separate op or threading the activation module into the producer.

AI assistance

AI assistance was used to set up the build environment, write the boilerplate (op registration, registry entry, docs row, benchmark, test scaffolding) and run the benchmarks. The kernel and the correctness argument were reviewed line by line, and all numbers come from runs on the hardware above.

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[mergify]

Documentation preview: https://vllm--64854.org.readthedocs.build/en/64854/

[claude]

Claude Code Review

This pull request is from a fork — automated review is disabled. A repository maintainer can comment @claude review to run a one-time review.

[selimfedakar]

Thanks for this — the SiLU-mirroring approach makes it easy to follow. I verified the correctness-relevant pieces against current main (7b77ed5); no blocking issues, a few small notes plus one cosmetic docs fix.

Verified against main:

• The fused kernel's numerics match the existing unfused tanh-GELU exactly. The new kernel uses BETA = M_SQRT2 * M_2_SQRTPI * 0.5f, KAPPA = 0.044715f, 0.5*f*(1+tanhf(BETA*(f+KAPPA*f^3))) — byte-for-byte the upstream vllm::gelu_tanh_kernel (csrc/activation_kernels.cu:204-211). It's the tanh approximation, not erf-GELU, so the bit-identical claim is structurally sound.
• Gate order matches the silu_and_mul convention: x_ptr = input + token*2*d (activated), y_ptr = input + token*2*d + d (multiplier), ACT_FN(x)*y — consistent with GeluAndMul's GELU(x[:d]) * x[d:] contract.
• The C++ function, op schema, and binding mirror silu_and_mul_quant exactly: gelu_tanh_and_mul_quant(Tensor! result, Tensor input, Tensor scale) -> () (torch_bindings.cpp) matches the silu schema line-for-line, same CUDA ops.impl block, same three STD_TORCH_CHECK dtype guards (e4m3 out / half|bf16 in / even last dim).
• Static-per-tensor scoping is structurally enforced: the registry key is (GeluAndMul, kFp8StaticTensorSym), and maybe_fused_act_quant looks up (type(act_fn), get_input_quant_key(linear)), so a dynamic/per-token consumer advertises a different QuantKey and cannot match — no dynamic→static misrouting. The erf-GELU fallback (approximate != "tanh" → unfused) is covered by test_gelu_erf_does_not_fuse.

Non-blocking notes:

1. No register_fake / meta impl for the new op. This matches the silu precedent (silu_and_mul_quant also has none, and it's a mutating Tensor! result -> () op whose output is pre-allocated, so tracing needn't infer shape). But fix_functionalization.py special-cases torch.ops._C.silu_and_mul_quant.default — should gelu_tanh_and_mul_quant be added there too for parity under full-graph compile? opcheck in the test doesn't exercise the functionalization pass.
2. The support predicate checks only approximate == "tanh", narrower than _relu_squared_static_fp8_quant_supported (which also validates cuda/bf16/contiguity/scale numel+dtype). The kernel's own STD_TORCH_CHECKs plus the registry-key match cover dtype, so it's not unsafe — intentional parity with silu, or worth a scale.numel()==1 fast-fail? No contiguity guard anywhere (inherited from silu), and no test covers non-contiguous input.

Cosmetic (worth fixing before merge): the new row in docs/design/fusions.md has a mojibake sequence (鈫?) where other rows use →, and its anchor link points at the SiLU section (#silumul--quantization-fuse_act_quant) rather than a GELU anchor — which also reads as if the fuse_act_quant compile pass covers GELU, though that pass is SiLU-only and GELU is manual-path only (as your body states).

One validation note: the model-level eval (GSM8K / latency on a static-FP8 GELU checkpoint) is still pending per your body — the kernel is currently exercised only by synthetic tensors, so that's the main remaining gap; a /ci run after approval would also exercise the real path.

(Prepared with AI assistance; all file:line references were independently verified against current main before posting.)

[t239573445]

Thanks for the careful read -- this was useful, and two of your points were real defects rather than nits.

Docs arrow (fixed in 514e22b). You were right that this was a real defect and not a rendering artifact: the arrow character was corrupted on my side when the file was rewritten through a non-UTF-8 code path. Fixed, and the row is re-padded so markdownlint MD060 passes again.

fix_functionalization.py (fixed in 514e22b). Worth flagging, thanks. I checked the fallback: when args is None it goes through kwargs=node.kwargs, which is exactly the path the existing comment documents as returning the wrong answer for silu_and_mul_quant. With an identical schema the new op would have hit the same failure under full-graph compile. Branch added, mirroring the silu one.

Wiring (new in 514e22b). Your closing note that the kernel is currently only exercised by synthetic tensors made me check who actually calls maybe_fused_act_quant -- and none of the Gemma MLPs do; they call self.act_fn(gate_up) directly. So the registry row would have been inert on real models. Gemma2MLP and Gemma4MLP are now wired (one line each, the llama.py pattern). gemma3.py is deliberately left alone because #64212 already edits it for the dynamic-per-token variant; I would rather not create a conflict there and will pick it up once that lands.

register_fake / meta. Agreed on parity with silu_and_mul_quant: the op mutates a pre-allocated Tensor! output, so there is no shape to infer.

Predicate / contiguity. Also parity: same three STD_TORCH_CHECKs as silu, and the registry key already scopes to kFp8StaticTensorSym. You are right that nothing covers non-contiguous input. I left the predicate as-is to stay symmetric with the silu row, but a scale.numel() == 1 fast-fail is cheap -- happy to add it if a maintainer prefers.

Model-level eval. Still the open item, as you note. It needs a statically quantized GELU checkpoint, and the Gemma FP8 checkpoints on the Hub are gated and were not reachable from my dev box. That is the next thing before this is mergeable.

Thanks again -- the fix_functionalization catch in particular would have been unpleasant to discover in CI.

[selimfedakar]

Great — I went through 514e22b and all three fixes look right:

• fix_functionalization.py: extending the silu_and_mul_quant branch with the hasattr(...) guard + gelu_tanh_and_mul_quant.default is exactly the right shape. Since the two ops share an identical schema (Tensor! result, Tensor input, Tensor scale -> ()), they need identical functionalization handling, and the hasattr guard keeps it safe when the custom op isn't compiled in. Good.
• Gemma wiring: maybe_fused_act_quant(self.act_fn, gate_up, self.down_proj) in Gemma2MLP / Gemma4MLP matches the helper signature, and leaving gemma3.py to [Kernel][Fusion] Fuse GELU-tanh+Mul with dynamic per-token FP8 quantization #64212 to avoid a conflict is the sensible call. That the registry row was inert on real models until this is the substantive part — glad the synthetic-tensor note led there.
• Docs row re-padded with the real →; MD060 concern resolved.

On the scale.numel() == 1 fast-fail: I'd leave it as-is for silu parity unless a maintainer asks — the registry key already scopes to kFp8StaticTensorSym and the STD_TORCH_CHECKs cover dtype, so it's defensive-only. The model-level eval on a static-FP8 GELU checkpoint remains the one gating item, as you said.

LGTM on my end (non-binding — I don't have write access); nice turnaround.

(Verified statically against 514e22b and current main; prepared with AI assistance.)