[ROCm] Add gelu_tanh to the AITER fp8 fused MoE and zero-allocate the padded expert weights - #55251
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Signed-off-by: simondanielsson <simon.danielsson99@hotmail.com>
Signed-off-by: simondanielsson <simon.danielsson99@hotmail.com>
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Included review availability: Your plan provides up to 10 included reviews per hour; 9 remain after this review. WalkthroughThe PR adds AITER-specific FP8 intermediate-size rounding with zero-initialized padding. It also enables ROCm AITER GELU tanh execution and adds accuracy, wire-value, padding, and rounding tests. ChangesAITER FP8 size rounding
ROCm AITER GELU tanh support
Estimated code review effort: 3 (Moderate) | ~25 minutes Sequence Diagram(s)sequenceDiagram
participant Fp8MoEMethod
participant Fp8SizeOracle
participant ExpertWeights
Fp8MoEMethod->>Fp8SizeOracle: request backend-specific MoE sizes
Fp8SizeOracle-->>Fp8MoEMethod: return rounded intermediate size
Fp8MoEMethod->>ExpertWeights: allocate padded weights with zero initialization
sequenceDiagram
participant Test
participant AiterExperts
participant AITERFusedMoE
Test->>AiterExperts: request GELU tanh fused MoE
AiterExperts->>AITERFusedMoE: pass activation wire value 4
AITERFusedMoE-->>Test: return fused output
Test->>Test: compare output with tanh reference
Suggested reviewers: Merge Risk: ⚪ Minimal · up to This change enables ROCm AITER FP8 MoE GELU-tanh execution and safely handles padded expert dimensions so padded weight tails do not affect output. The added accuracy and padding coverage supports merge readiness with no remaining actionable risk. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
✨ Finishing Touches 💡 1🛠️ Fix failing CI checks 💡
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fp8_round_up_hidden_size_and_intermediate_size pads the AITER intermediate size (704 -> 768 for Gemma4-26B-A4B), but the fp8 MoE methods allocated w13/w2 with torch.empty and the loader only narrow-copies the checkpoint rows, so the padded gate/up rows and w2 columns held stale device memory that acted as live weights. Depending on the allocation order at load time this yields an occasional process that emits sporadically implausible tokens with no error; reproduced by filling one layer's padding with stale-weight-like bytes. Allocate zeroed and correct the comment. Signed-off-by: Yuanfan Chen <yuanfan0504@gmail.com>
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In `@tests/kernels/moe/test_rocm_aiter_moe.py`:
- Around line 896-897: Strengthen the accuracy coverage around the test using
atol=0.05 and rtol=0.0 so it can distinguish exact GELU from GELU_TANH when wire
value 4 is selected. Add a deterministic activation-sensitive input or a direct
activation-level assertion with a sufficiently tight expected result, while
preserving the existing matrix-operation coverage.
In `@vllm/model_executor/layers/fused_moe/experts/rocm_aiter_moe.py`:
- Line 497: Update _supports_activation() so MoEActivation.GELU_TANH is not
reported as supported with AITER v0.1.19; alternatively enforce a compatible
AITER version before enabling it. Ensure the FP8 apply_router_weight_on_input
path cannot pass activation value 4 to _rocm_aiter_asm_moe_tkw1_impl under the
unsupported default.
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tests/kernels/moe/test_moe_kernel_oracle.pytests/kernels/moe/test_rocm_aiter_moe.pyvllm/model_executor/layers/fused_moe/experts/rocm_aiter_moe.pyvllm/model_executor/layers/fused_moe/oracle/fp8.pyvllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/compressed_tensors_moe_w8a8_fp8.pyvllm/model_executor/layers/quantization/fp8.py
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yewentao256
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Thanks for the work! Will this affect ModelOpt、Quark、online FP8? Please take a look
Also CC @AndreasKaratzas
| # WEIGHTS | ||
| w13_weight = torch.nn.Parameter( | ||
| torch.empty( | ||
| torch.zeros( |
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Why zeros needed? This will affect all model
| out.float(), | ||
| ref_out, | ||
| label="gelu_tanh_accuracy", | ||
| atol=0.05, |
…ersion; sharpen the tanh-GELU test - allocate expert weights with torch.zeros only when maybe_roundup_sizes actually changed a dimension (no cost for models whose sizes are already aligned) - report GELU_TANH as supported only when aiter exposes ActivationType.GeluTanh (v0.1.21+), so older aiter falls back instead of failing at dispatch - the gelu_tanh accuracy test now also asserts the kernel is closer to the tanh-GELU reference than to exact GELU Signed-off-by: Yuanfan Chen <yuanfan0504@gmail.com>
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Thanks for the review — addressed in the latest push: "Why zeros needed? This will affect all models" — fair point; it now only applies when a size was actually rounded up. ModelOpt / Quark / online FP8 — not affected by this change: ModelOpt and Quark have their own MoE methods and Test ("same to AI") — AITER version — |
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Thanks for the work! It would be good if you also add lm_eval result to show we don't hurt acc.
@AndreasKaratzas CC as I don't know AMD that much and can't test
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Added
Stderr is ±0.013 on the ifeval prompt-level and plain-gsm8k rows and ±0.006 on the chat-template gsm8k rows. The AITER gelu_tanh path is within run-to-run noise of both the Triton path on the same FP8 checkpoint and the bf16 baseline; two independent bf16 runs are included to show the noise floor. The plain 5-shot completion numbers are low for an instruct model because that format has no chat template and the score is dominated by answer-format compliance; the chat-template rows ( Happy to attach the full lm_eval JSON outputs if useful. |
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| @staticmethod | ||
| @functools.lru_cache(maxsize=1) | ||
| def fused_moe_supports_gelu_tanh() -> bool: |
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Suggestion: I think we can drop this given we ship v0.1.21 now. Instead we can just check whether aiter is available. (similarly with the error in rocm_aiter_fused_experts)
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Done in 891ebe7: removed fused_moe_supports_gelu_tanh() and the error in rocm_aiter_fused_experts; GELU_TANH is now just listed in _supports_activation.
| # Padded (rounded-up) expert weights must start zeroed: the loader only writes | ||
| # the checkpoint's real rows/columns and an uninitialized tail is live weight. | ||
| alloc = ( | ||
| torch.zeros if getattr(self, "_pad_expert_weights", False) else torch.empty |
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Suggestion: can we check hidden_size != self.moe.hidden_dim_unpadded and intermediate_size_per_partition != self.moe.intermediate_size_per_partition_unpadded instead to avoid having this new mutable state?
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Done. create_weights now compares hidden_size / intermediate_size_per_partition with self.moe.hidden_dim_unpadded / intermediate_size_per_partition_unpadded (RoutedExperts writes the rounded sizes into moe_config and the *_unpadded fields keep the originals), same in the compressed-tensors method. maybe_roundup_sizes is back to just returning the rounded sizes, no state on the method.
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| def test_aiter_fused_moe_gelu_tanh_accuracy(): |
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Question: should we also add a regression test where we test the padding logic to ensure that padding+gelu_tanh gives the same results as a non-padded reference?
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Added test_aiter_fused_moe_gelu_tanh_padded_matches_unpadded_reference, parametrized over bf16 and fp8 per-tensor: intermediate 704 -> 768, the kernel on zero-tailed padded weights has to match the unpadded float32 reference, and in the bf16 case a garbage tail has to move the output outside the budget (so the check is known to be sensitive to the tail).
Checked the same shapes and tolerances on an MI325X (gfx942, aiter with GeluTanh) against aiter directly: padded zero tail vs unpadded reference max abs diff 0.0097 (bf16) / 0.0011 (fp8 per-tensor and per-token); garbage tail max diff ~1e4 (bf16) / ~1e2 (fp8); the unpadded 704 fp8 shape doesn't run at all there (CK device_gemm rejects it), which is the reason for the round-up.
| # activation-independent). Align to 128. NOTE: the loader only | ||
| # narrow-copies the checkpoint's real rows/columns into the padded | ||
| # parameters, so the expert weights must be allocated zeroed (see the | ||
| # fp8 MoE methods' create_weights); an uninitialized tail is live | ||
| # garbage weight for the kernel. |
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Nit: don't think this comment is needed here
| # activation-independent). Align to 128. NOTE: the loader only | |
| # narrow-copies the checkpoint's real rows/columns into the padded | |
| # parameters, so the expert weights must be allocated zeroed (see the | |
| # fp8 MoE methods' create_weights); an uninitialized tail is live | |
| # garbage weight for the kernel. | |
| # activation-independent). Align to 128. |
…om the MoE config, test padded gelu_tanh - vLLM ships aiter v0.1.21, so GELU_TANH no longer needs a capability probe: remove fused_moe_supports_gelu_tanh() and the dispatch-time error, and list GELU_TANH as a supported AiterExperts activation. - Fp8MoEMethod / CompressedTensorsW8A8Fp8MoEMethod: decide whether the expert weights are padded by comparing the sizes create_weights receives with the config's hidden_dim_unpadded / intermediate_size_per_partition_unpadded instead of keeping a flag on the method. - Trim the oracle comment. - Add test_aiter_fused_moe_gelu_tanh_padded_matches_unpadded_reference: with intermediate 704 rounded up to 768, the kernel run on zero-tailed padded weights (bf16 and fp8 per-tensor) matches the unpadded float32 reference, and a garbage tail does not. Signed-off-by: Yuanfan Chen <yuanfan0504@gmail.com>
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@simondanielsson thanks for the review. All four points are in 891ebe7 (replies inline). @yewentao256 PTAL. |
| SILU = 0 | ||
| GELU = 1 | ||
| GELU_TANH = 4 | ||
| GELU_TANH = 4 # aiter >= v0.1.21 (ActivationType.GeluTanh) |
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Very nit: don't think this comment is necessary
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| def _run_padded(fill: str) -> torch.Tensor: | ||
| w1_padded, w2_padded = _pad_intermediate_dim( |
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This is a good test to prove we need the zero padding to ensure correctness of this round-up, but I think we also need to test that the create_weights() impl actually does that padding properly here and that the padded values are 0 when needed. That way we can show that this works e2e. WDYT?
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Agreed, added test_fp8_moe_create_weights_zeroes_rounded_up_experts: it builds a small fp8-serialized RoutedExperts with the AITER backend (intermediate 704), checks that the layer rounds it up to 768 while keeping intermediate_size_per_partition_unpadded == 704, that w13_weight/w2_weight come out of create_weights with the rounded-up shape and all-zero, and then loads a 704-wide checkpoint through load_weights and checks the real rows sit at the kernel's N/2 split (gate at [0, 704), up at [768, 1472)) with the padding still zero.
While running these on an MI325X I found that the tanh-vs-exact check in test_aiter_fused_moe_gelu_tanh_accuracy was not sound: it compared two mean errors that differ by less than the bf16 kernel noise (1.10e-3 vs 1.10e-3), and its reference-gap guard (> 1e-4) fails at ~8e-5, so it would have failed in CI. It now uses the least-squares coefficient of the kernel's deviation from the exact-GELU reference on the (tanh - exact) direction: 0.94-1.10 for the tanh-GELU kernel vs -0.03-0.10 for the exact-GELU kernel over four seeds at 128 tokens, threshold 0.5.
…heck statistical - test_fp8_moe_create_weights_zeroes_rounded_up_experts: build a small fp8-serialized RoutedExperts with the AITER backend (704 -> 768), check that Fp8MoEMethod.create_weights hands the loader zeroed expert weights of the rounded-up shape, then load a 704-wide checkpoint and check the real rows sit at the kernel's N/2 split while the padding stays zero. - test_aiter_fused_moe_gelu_tanh_accuracy: the tanh-vs-exact check compared two mean errors that differ by less than the bf16 kernel noise, and its reference gap guard (> 1e-4) fails at ~8e-5. Replace both with the least-squares coefficient of the kernel's deviation from the exact-GELU reference on the (tanh - exact) direction: ~1 for the tanh-GELU kernel, ~0 for exact GELU (measured 0.94-1.10 vs -0.03-0.10 over four seeds at 128 tokens). - Remove the version comment on ActivationMethod.GELU_TANH. Signed-off-by: Yuanfan Chen <yuanfan0504@gmail.com>
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@simondanielsson , I noticed there are many models that are using CK 2 stage, however, iirc, in the aiter moe API, we are also dispatching to other MoE implementations. Will this bugfix will incur additional overhead to moe of other implementations? |
Good point, this will affect any non-128-aligned invocation even when it's not dispatching to 2-stage. So I think we need to at least narrow the padding condition here I'd suggest (but let me know you thoughts @tjtanaa):
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…ITER AITER has no 1-stage fp8 MoE kernel for gelu_tanh, so that activation always lands on the CK 2-stage GEMM, which needs a 128-aligned intermediate size. silu/gelu do have 1-stage kernels and AITER routes their non-128-aligned shapes there, so padding them would only add work. Gate the round-up on the activation, pass it from the fp8 MoE methods, and cover silu/gelu in the oracle test. Signed-off-by: Yuanfan Chen <yuanfan0504@gmail.com>
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@tjtanaa @simondanielsson agreed, thanks. 3d6b042 narrows the round-up to Why gelu_tanh is the right gate, from aiter main's On the medium-term aiter fix: happy to help with that. From reading aiter, Could one of you add the |
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Opened the AITER-side ticket for removing the manual padding requirement: ROCm/aiter#5413 (also linked from the PR description). The round-up here stays as the interim workaround until that lands. |
4 participants
Purpose
Supersedes #53259 (draft; its two commits are carried here unchanged, authored by @simondanielsson, co-authored with @elliotz-ai) and adds the one thing that PR's round-up needs to be safe: allocating the padded fp8 MoE expert weights zeroed.
gelu_tanhto the AITER fp8 fused-MoE backend (Gemma4-style models), viaActivationMethod.GELU_TANH = 4from aiter v0.1.21 ([HIP] [CK] [MoE] Added Gelu with tanh approx for CK XDL 2-stage MoE ROCm/aiter#4620). Depends on the AITER bump in [ROCm] Bump AITER to 0.1.21.post1 #52826.gelu_tanhactivation to AITER fp8 fmoe #53259 rounds the size up to 768 for the AITER backend. Its comment claims the padded tail is zeroed by the loader — it is not:create_weightsallocatesw13_weight/w2_weightwithtorch.emptyand the loader only narrow-copies the checkpoint's real 704 rows/columns. Rows 704–767 ofw13(gate/up) and columns 704–767 ofw2keep whatever the caching allocator hands out — device memory freed earlier in the same process — and that is live weight for the kernel: non-zero pad activations after GELU pollute the per-tokena2quant scale and leak through thew2tail into the layer output.How this shows up
Whether a given process gets harmful leftovers in the padding depends on the allocation order at load time, so most processes look fine and an occasional one produces sporadically implausible tokens (typo-like output) with no error or warning. A load-time probe of the padded region in fresh processes finds non-zero bytes there; deliberately filling one layer's padding with stale-weight-like bytes reproduces the symptom, and filling every layer collapses the model. Zero-allocating removes the effect.
The other users of
maybe_roundup_sizes(mxfp4, quark nvfp4) already allocate withtorch.zeros; this makes the fp8 methods do the same and corrects the comment. The memset is a one-time cost at load.Test Plan
gelu_tanhactivation to AITER fp8 fmoe #53259 (test_fp8_round_up_intermediate_size_for_aiter,test_activation_method_enum_values,test_aiter_fused_moe_gelu_tanh_accuracy).VLLM_ROCM_USE_AITER=1 VLLM_ROCM_USE_AITER_MOE=1: with zeroed allocation, sampled reply length and plausibility under the bf16 model match the non-AITER path; withtorch.emptyand garbage in the padding, the corruption reproduces.Test Result
ROCm-only; needs a runner with aiter ≥ v0.1.21 (after #52826).
Tracking the AITER-side fix (remove the manual padding requirement so the round-up here can go away): ROCm/aiter#5413