[ROCm][Perf] Add Fused Shared Expert (FSE) support for GLM-4.5/6/7#44313
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[ROCm][Perf] Add Fused Shared Expert (FSE) support for GLM-4.5/6/7#44313omirosh wants to merge 2 commits into
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## Purpose Extend the AITER Fused Shared Expert (FSE) path - originally added for DeepSeek-V2/V3 (vllm-project#28540) and Qwen3-Next (vllm-project#39280) - to the GLM-4 MoE family (GLM-4.5, GLM-4.6, GLM-4.7). When `VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS=1` the shared expert is folded into the AITER FusedMoE kernel as `n_shared_experts` extra expert slots, eliminating the separate shared-expert MLP forward pass at low/medium concurrency. ## Changes Single-file model wiring in `vllm/model_executor/models/glm4_moe.py`, mirroring the canonical `deepseek_v2.py` FSE pattern: * `Glm4MoE.__init__` - Cache `is_rocm_aiter_moe_enabled` and `is_fusion_moe_shared_experts_enabled` from `rocm_aiter_ops`. - When FSE is enabled, skip building the separate `shared_experts` MLP and pass `n_shared_experts=config.n_shared_experts` to `FusedMoE` so the AITER kernel routes the shared expert(s) as extra slots in the routed tensor. - Switch `apply_routed_scale_to_output` to `not self.is_rocm_aiter_moe_enabled`. AITER applies `routed_scaling_factor` internally, per routed slot; applying it again post-fusion would also scale the FSE shared-expert slot (which the kernel inserts with unit weight), producing a structural magnitude error in every MoE layer. This matches `deepseek_v2.py`. (`routed_scaling_factor=2.5` for GLM-4.7, so the unfixed path showed a ~48 pp gsm8k regression.) * `Glm4MoeModel.get_expert_mapping` - Widen `num_experts` by `config.n_shared_experts` when FSE is on so the weight loader enumerates the appended slots. * `Glm4MoeModel.load_weights` - Treat `mlp.shared_experts.{gate,up,down}_proj.*` as expert-style tensors when FSE is on (skip the stacked QKV/gate_up linear path). - Split each widened shared-expert tensor into `n_shared_experts` chunks along the intermediate-size axis (dim 0 for ColumnParallel gate/up_proj, dim 1 for RowParallel down_proj) and route each chunk to `mlp.experts.{n_routed_experts + j}.*` via the FusedMoE expert-aware weight loader. No changes to FusedMoE / AITER plumbing - all of that landed earlier with vllm-project#39280 (Qwen3-Next FSE). ## Test Plan * Model: `zai-org/GLM-4.7-FP8` * Hardware: 1x MI355X node, TP=4 * Container: ROCm vLLM image (AITER >= v0.1.13.post1, PR vllm-project#44265) * Accuracy: `lm_eval --tasks gsm8k --num_fewshot 5` * Throughput: `vllm bench serve --dataset-name random` sweep over (ISL, OSL, MC) in {1000/100, 5000/500, 10000/1000} x {4, 16, 64} Server launch: ``` VLLM_ROCM_USE_AITER=1 \ VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS=<0|1> \ vllm serve zai-org/GLM-4.7-FP8 \ --tensor-parallel-size 4 \ --gpu-memory-utilization 0.92 \ --max-model-len 32768 \ --max-num-seqs 256 ``` ## Test Result ### Accuracy (gsm8k, 5-shot, exact_match) | Config | flexible-extract | strict-match | |---------------------|-----------------:|-----------------:| | FSE=0 (baseline) | 0.9469 ± 0.0062 | 0.9439 ± 0.0063 | | FSE=1 | 0.9439 ± 0.0063 | 0.9416 ± 0.0065 | All deltas within standard error. No accuracy regression. ### Throughput (`vllm bench serve`, random) | ISL | OSL | MC | TPOT mean (ms) FSE=0 -> FSE=1 (Δ) | TPOT p99 (ms) FSE=0 -> FSE=1 (Δ) | Output tok/s FSE=0 -> FSE=1 (Δ) | Total tok/s FSE=0 -> FSE=1 (Δ) | |-----:|-----:|---:|----------------------------------:|---------------------------------:|--------------------------------:|-------------------------------:| | 1000| 100| 4| 17.76 -> 14.36 (**-19.2%**) | 19.43 -> 15.93 (**-18.0%**) | 199.4 -> 243.6 (**+22.1%**) | 2193.7 -> 2679.1 (**+22.1%**) | | 1000| 100| 16| 20.96 -> 18.48 (**-11.9%**) | 24.29 -> 22.77 (-6.3%) | 631.0 -> 673.4 (**+6.7%**) | 6940.6 -> 7407.9 (**+6.7%**) | | 1000| 100| 64| 30.74 -> 30.23 (-1.7%) | 42.85 -> 43.44 (+1.4%) | 1452.7 -> 1424.3 (-2.0%) | 15980.1 -> 15667.6 (-2.0%) | | 5000| 500| 4| 17.82 -> 14.50 (**-18.7%**) | 18.63 -> 15.50 (**-16.8%**) | 211.5 -> 253.5 (**+19.9%**) | 2326.1 -> 2788.7 (**+19.9%**) | | 5000| 500| 16| 22.73 -> 20.76 (**-8.7%**) | 25.38 -> 23.07 (**-9.1%**) | 619.1 -> 657.7 (**+6.2%**) | 6810.4 -> 7234.6 (**+6.2%**) | | 5000| 500| 64| 39.79 -> 40.15 (+0.9%) | 46.15 -> 46.78 (+1.4%) | 1363.8 -> 1339.1 (-1.8%) | 15001.9 -> 14730.4 (-1.8%) | | 10000| 1000| 4| 18.00 -> 14.70 (**-18.3%**) | 18.68 -> 15.50 (**-17.0%**) | 210.3 -> 251.8 (**+19.7%**) | 2313.5 -> 2769.4 (**+19.7%**) | | 10000| 1000| 16| 24.47 -> 22.87 (-6.5%) | 26.66 -> 25.56 (-4.1%) | 589.6 -> 615.1 (**+4.3%**) | 6485.6 -> 6766.2 (**+4.3%**) | | 10000| 1000| 64| 46.37 -> 46.33 (-0.1%) | 51.14 -> 51.78 (+1.3%) | 1233.6 -> 1211.9 (-1.8%) | 13570.0 -> 13330.7 (-1.8%) | Verdict: FSE delivers +20-22% output throughput and -18-19% TPOT at low concurrency (MC=4), modest gains at MC=16, and is roughly break-even (<2% regression) at MC=64. No accuracy regression. Co-authored-by: Cursor <cursoragent@cursor.com>
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The MTP draft module instantiates its own `Glm4MoE` (via
`Glm4MoeDecoderLayer`), so when `VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS=1`
its FusedMoE is widened by `n_shared_experts` slots too. The previous
`Glm4MoeMTP.load_weights` did not know about FSE - it used
`num_experts=config.n_routed_experts` for the expert mapping and did not
split `mlp.shared_experts.*` checkpoint tensors into the appended slots,
leaving them zero-initialized in the draft model and producing wrong
spec tokens.
This commit extends the FSE-aware loader to the MTP path, mirroring the
canonical pattern already used in `deepseek_mtp.py` and matching the
`glm4_moe.py` change from the parent commit:
* Widen `num_experts` by `n_shared_experts` in
`fused_moe_make_expert_params_mapping` when FSE is enabled.
* Set `is_fusion_moe_shared_experts_layer` per weight and skip the
stacked QKV / gate_up path for `mlp.shared_experts.*` tensors.
* Split each shared-expert tensor into `n_shared_experts` chunks along
the intermediate-size axis (dim 0 for ColumnParallel gate/up, dim 1
for RowParallel down) and route each chunk to
`mlp.experts.{n_routed_experts + j}.*` via the FusedMoE expert-aware
weight loader (using `return_success=True` so remote-expert replicas
on other EP ranks don't get silently marked as loaded).
Tested with:
--speculative-config '{"method":"mtp","num_speculative_tokens":2,
"attention_backend":"ROCM_AITER_UNIFIED_ATTN"}'
--attention-backend ROCM_AITER_UNIFIED_ATTN
Co-authored-by: Cursor <cursoragent@cursor.com>
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Glm4MoeMTP is not decorated with @support_torch_compile, so the MTP draft forward executes as eager Python and misses every Inductor fusion pass the target forward enjoys - most notably the AITER allreduce + RMSNorm fusion, RMSNorm + quant fusion, and silu+mul+fp8-quant fusion. Add the decorator to bring MTP in line with the canonical DeepSeekMTP pattern (vllm/model_executor/models/deepseek_mtp.py, L185) and make the draft eligible for the same compile-time fusions as the target. dynamic_arg_dims is inferred from the existing forward annotations (the four Tensor | None / IntermediateTensors | None args become dim-0 dynamic), exactly as for DeepSeekMTP. Measured on top of vllm-project#44313 HEAD with GLM-4.7-FP8 TP=4 + EP + MTP num_speculative_tokens=2 + ROCM_AITER_UNIFIED_ATTN: - FSE=0 arm: +2.1% output throughput, -5.4% P99 TPOT, -8.5% mean TTFT (geomean across 9 cells). 7 of 9 cells improve. - FSE=1 arm: flat throughput (within 0.3%), -4.0% P99 TPOT - a clean tail-latency improvement at no cost. - gsm8k 5-shot accuracy unchanged within 1 sigma on both arms. - Spec-decode acceptance length / rate unchanged within noise. Co-authored-by: Cursor <cursoragent@cursor.com>
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Purpose
Extend the AITER Fused Shared Expert (FSE) path — originally added for DeepSeek-V2/V3 (#28540) and Qwen3-Next (#39280) — to the GLM-4 MoE family (GLM-4.5, GLM-4.6, GLM-4.7). When
VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS=1, the shared expert is folded into the AITERFusedMoEkernel asn_shared_expertsextra expert slots, eliminating the separate shared-expert MLP forward pass at low/medium concurrency.This PR extends FSE to both the main model (
glm4_moe.py) and the MTP draft module (glm4_moe_mtp.py), mirroring the equivalent two-file change in DeepSeek-V2/V3 (deepseek_v2.py+deepseek_mtp.py).Changes
vllm/model_executor/models/glm4_moe.pyMirrors the canonical
deepseek_v2.pyFSE pattern. No changes toFusedMoE/ AITER router / op plumbing — all of that landed earlier with #39280.Glm4MoE.__init__is_rocm_aiter_moe_enabledandis_fusion_moe_shared_experts_enabledfromrocm_aiter_ops.shared_expertsMLP and passn_shared_experts=config.n_shared_expertstoFusedMoE.apply_routed_scale_to_outputtonot self.is_rocm_aiter_moe_enabled. AITER appliesrouted_scaling_factorinternally, per routed slot; applying it again post-fusion would also scale the FSE shared-expert slot (which the kernel inserts with unit weight), producing a structural magnitude error in every MoE layer. This matchesdeepseek_v2.py. (routed_scaling_factor = 2.5for GLM-4.7, so the unfixed path showed a ~48 pp gsm8k regression — see "Notes" below.)Glm4MoeModel.get_expert_mappingnum_expertsbyconfig.n_shared_expertswhen FSE is on, so the weight loader enumerates the appended slots.Glm4MoeModel.load_weightsmlp.shared_experts.{gate,up,down}_proj.*as expert-style tensors when FSE is on (skip the stackedqkv_proj/gate_up_projlinear path).n_shared_expertschunks along the intermediate-size axis (dim 0 for ColumnParallelgate_proj/up_proj, dim 1 for RowParalleldown_proj) and route each chunk tomlp.experts.{n_routed_experts + j}.*via the FusedMoE expert-aware weight loader. GLM packs all shared experts into a single fat MLP in the checkpoint (shared_experts.gate_projis[moe_intermediate_size * n_shared_experts, hidden]); we slice it on the way in.vllm/model_executor/models/glm4_moe_mtp.pyThe MTP draft layer reuses
Glm4MoEviaGlm4MoeDecoderLayer, so itsFusedMoEis widened byn_shared_expertsslots whenever FSE is on. ButGlm4MoeMTP.load_weightsis a separate weight-loader implementation fromGlm4MoeModel.load_weights, so it needs the same FSE-aware splitting (same shape as thedeepseek_mtp.pychange in #28540):num_expertsbyconfig.n_shared_expertsinfused_moe_make_expert_params_mappingwhen FSE is on.gate_up_projpath formlp.shared_experts.*tensors.n_shared_expertschunks (same axis logic as the main model) and route tomlp.experts.{n_routed_experts + j}.*via the expert-aware weight loader (withreturn_success=Trueso remote-expert replicas on other EP ranks are not silently marked loaded).Env-var contract
FSE remains opt-in via
VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS=1; default behavior is unchanged.Test Plan
zai-org/GLM-4.7-FP8lm_eval --tasks gsm8k --num_fewshot 5(256 samples)vllm bench serve --dataset-name randomsweep over (ISL, OSL, MC) ∈ {1000/100, 5000/500, 10000/1000} × {4, 16, 64}Server invocation (exact)
Accuracy
lm_eval --model local-completions \ --model_args "model=zai-org/GLM-4.7-FP8,base_url=http://127.0.0.1:8003/v1/completions,num_concurrent=32,max_retries=3" \ --tasks gsm8k --num_fewshot 5 --batch_size auto --limit 256Throughput
Test Result
Accuracy (gsm8k, 5-shot, exact_match)
All deltas within 1σ. No accuracy regression.
Throughput (
vllm bench serve, random)Verdict: FSE delivers +20–22% output throughput and −18–19% TPOT at low concurrency (MC=4), modest gains at MC=16, and is roughly break-even (<2% regression) at MC=64. No accuracy regression.
MTP coverage
The MTP loader patch is exercised by adding to the server invocation above:
--speculative-config '{"method":"mtp","num_speculative_tokens":2,"attention_backend":"ROCM_AITER_UNIFIED_ATTN"}' \ --attention-backend ROCM_AITER_UNIFIED_ATTNThe same accuracy + 9-cell perf sweep was rerun with MTP enabled.
Accuracy (gsm8k, 5-shot, exact_match), MTP, num_speculative_tokens=2:
No accuracy regression — FSE-on MTP matches the non-MTP baseline (0.9469).
Spec-decode acceptance (sampled across the full perf sweep):
Throughput (same
(ISL, OSL, MC)grid as above):Verdict on FSE + MTP: correctness-safe (no accuracy loss, +5.8 pp draft acceptance vs the FSE-off MTP arm). The throughput trade-off flips compared to the non-MTP picture: FSE pays off most at high concurrency (+8–17% at MC=64) and is a small loss at low concurrency (−5 to −11% at MC=4). With
num_speculative_tokens=2the MoE runs 3× per output token (1 verify + 2 draft), which amplifies the per-call cost of the extra fused-shared-expert slot at low MC, while the kernel-launch-overhead savings still dominate at high MC.Notes on the
apply_routed_scale_to_outputfixGLM-4.7 has
routed_scaling_factor = 2.5. Withapply_routed_scale_to_output=True(the previous default for this model), AITER's internal per-slot scaling is bypassed in the runner, but on the FSE path the runner then scales the entire MoE output — including the shared-expert slot the kernel inserted with unit weight — by 2.5, in every MoE layer. The first iteration of this PR had this bug and produced gsm8k = 0.4685 (flex) vs 0.9469 baseline. Switching toapply_routed_scale_to_output = not self.is_rocm_aiter_moe_enabled(matchingdeepseek_v2.py) lets AITER applyrouted_scaling_factorper routed slot only, and restored accuracy to within 1σ of baseline (table above).Essential Elements of an Effective PR Description Checklist