perf(comm): gather MoE-finalize routes together and accumulate in FP32 by haosdent · Pull Request #6631 · flashinfer-ai/flashinfer

haosdent · 2026-10-17T10:45:55Z

📌 Description

Stacked on #6630 (poll the local Lamport buffer); please review that one first.

This makes trtllm_moe_finalize_allreduce_fusion faster and more accurate:

Gather the top-k rows together. The finalize loop handled one route at a time: load the permuted index, then
load that row, then move to the next route. Each row load had to wait for its own index load. Now the kernel
loads the indices and scales of 8 routes first, then all 8 rows, so the row loads are in flight together.
Accumulate in FP32. The routed sum, the routed_scaling_factor, the shared-expert add and the sum over ranks
were all done in the activation dtype, rounding after every step. They now stay in FP32: each rank's finalized
row is rounded once, and the rank sum is rounded once before the residual add and RMSNorm, which work on the
rounded sum as before. This matches allreduce_fusion with fp32_acc=True.

The sibling moereduction kernel in this file still accumulates in the activation dtype; that is left for a
follow-up.

Test changes:

test_trtllm_moe_allreduce_fusion_finalize.py:
- Its reference is now computed in FP32. The old BF16 reference does each multiply and add in BF16, and the
  BF16/TP4 case failed on B300 even before this change.
- It also runs top_k 6 and 10 (one partial gather chunk, and a full chunk plus a partial one) besides 8, with a
  quarter of the routes set to -1.
- scale_out is now sized for the SWIZZLED_128x4 layout the kernel writes, with
  compute_fp4_swizzled_layout_sf_size. The test allocated T * H / 16 elements, and the kernel wrote past the
  end. That only showed up as an illegal address once the FP32 reference allocated tensors next to it.
test_cake_moe_finalize_allreduce.py checked the TRT-LLM backend against a reference that rounds after every
route and rank, as the kernel did, so its BF16 cases failed with this PR. That reference now models FP32
accumulation for TRT-LLM and keeps per-step rounding for Cake. The kernel without this PR fails the updated
check, so it pins the new numerics.

🔍 Related Issues

#2269, #3671 (fused finalize slower than finalize + kARResidualRMSNorm)

🚀 Pull Request Checklist

✅ Pre-commit Checks

I have installed pre-commit by running pip install pre-commit (or used your preferred method).
I have installed the hooks with pre-commit install.
I have run the hooks manually with pre-commit run --all-files and fixed any reported issues.

🧪 Tests

Tests have been added or updated as needed.
All tests are passing (unittest, etc.).

B300 (SM103), CUDA 13.4, with this PR's test files (also with #6632 on top):

	before (#6630)	this PR
`test_trtllm_moe_allreduce_fusion_finalize.py` (FP16/BF16 x TP2/TP4 x top_k 6/8/10, back-to-back)	BF16/TP4 fails	14 passed
`test_trtllm_moe_allreduce_fusion.py`	passes	4 passed
`test_cake_moe_finalize_allreduce.py`	TRT-LLM BF16 fails the updated check	4 passed, 2 skipped

Latency and accuracy: one launch inside a CUDA graph, TP4, BF16, top-k 8, launch_with_pdl=True. Each rank has
its own GEMM2 rows, softmax routing weights and shared-expert output. The reference is FP64. "stock" is FlashInfer
main; "#6630" adds only the local-poll fix.

hidden	tokens	stock (us)	#6630 (us)	this PR (us)
6144	1	8.60	7.50	6.20
6144	4	8.75	7.63	6.49
6144	16	9.22	8.25	7.10
6144	32	15.06	11.14	9.14
6144	64	26.24	20.13	15.19
6144	128	hangs	38.85	29.02
7168	1	8.80	8.10	6.56
7168	16	9.66	8.76	7.40
7168	64	19.08	13.04	12.11
7168	128	hangs	23.47	21.85

From 18 tokens at hidden 6144 (37 at 7168) each cluster finalizes several tokens in turn, which is why 6144 is
slower than 7168 at 64 and 128 tokens; #6632 sizes the clusters to the token count.

mean abs error vs FP64	stock / #6630	this PR	BF16 rounding of the exact result
`residual_out`	7.6e-3 to 8.0e-3	6.0e-3	4.8e-3
`norm_out`	2.1e-3 to 2.2e-3	1.75e-3

End to end in vLLM (GLM-5.2-NVFP4, TP4, MTP 3, B300), with a vLLM change that finalizes each decode MoE layer in
this kernel (PR to follow, it will link here). Target verify forward latency (vllm bench forward, CUDA graph,
spread < 0.1%), deferring vs not deferring the finalize:

kernel	1 request (4 tokens)	3 requests (12 tokens)	7 requests (28 tokens)
#6630 only	+0.4% to +0.6%	+0.4%	+2.1% to +2.2%
this PR	-1.2% to -1.3%	-1.5%	-0.8%

That is, with only #6630 fusing the finalize does not pay off; with this PR it does.

On InferenceX AgentX, GLM-5.3 on 4x GB200 (vllm-project/vllm#62174 with this PR, #6630 and #6632): decode time
per turn drops 1.6%, 1.8%, 3.3% and 2.1% at concurrency 1, 4, 8 and 16 (median over the same turns; main vs
main is within 0.7%).

Benchmark script: python fi_finalize_bench.py 4 out.json

"""FlashInfer trtllm_moe_finalize_allreduce_fusion: error vs an FP64 reference and latency.

usage: python fi_finalize_bench.py WORLD_SIZE OUT_JSON [--tokens 1 4 16 64 128] [--hidden 6144 7168]
Each rank gets its own random GEMM2 rows, routing (top-k 8, softmax weights) and shared expert output,
as in a TP MoE layer. The reference finalizes, adds the shared output, all-reduces, adds the residual
and applies RMSNorm in FP64. Latency: one CUDA graph of 20 back-to-back launches, replayed 50 times,
max over ranks of the median per-launch time.
"""
import argparse
import json
import socket
import statistics

import torch
import torch.distributed as dist
import torch.multiprocessing as mp

import flashinfer.comm as comm

TOP_K, MAX_TOKENS, EPS, RSF = 8, 2048, 1e-5, 2.5


def worker(rank, world_size, port, tokens_list, hidden_list, out_path):
    torch.cuda.set_device(rank)
    dev = torch.device("cuda", rank)
    dist.init_process_group("nccl", init_method=f"tcp://127.0.0.1:{port}", rank=rank,
                            world_size=world_size, device_id=dev)
    results = []
    for hidden in hidden_list:
        _, workspace = comm.trtllm_create_ipc_workspace_for_all_reduce_fusion(
            rank, world_size, MAX_TOKENS, hidden, group=dist.group.WORLD)
        for tokens in tokens_list:
            g = torch.Generator(device=dev).manual_seed(1000 * rank + tokens)
            rows = 2 * tokens * TOP_K + 8
            gemm2 = (torch.randn(rows, hidden, device=dev, generator=g) * 0.5).bfloat16()
            idx = torch.randperm(rows, device=dev, generator=g)[: tokens * TOP_K]
            idx = idx.view(tokens, TOP_K).int()
            weights = torch.softmax(torch.randn(tokens, TOP_K, device=dev, generator=g), -1).bfloat16()
            shared = (torch.randn(tokens, hidden, device=dev, generator=g) * 0.5).bfloat16()
            gs = torch.Generator(device=dev).manual_seed(7 + tokens)  # same on every rank
            residual = (torch.randn(tokens, hidden, device=dev, generator=gs) * 4).bfloat16()
            norm_w = (1 + 0.1 * torch.randn(hidden, device=dev, generator=gs)).bfloat16()
            norm_out = torch.empty_like(residual)
            res_out = torch.empty_like(residual)

            def run():
                comm.trtllm_moe_finalize_allreduce_fusion(
                    allreduce_in=gemm2, residual_in=residual, norm_weight=norm_w,
                    expanded_idx_to_permuted_idx=idx, norm_out=norm_out, residual_out=res_out,
                    quant_out=None, scale_out=None, workspace_ptrs=workspace,
                    launch_with_pdl=True, world_rank=rank, world_size=world_size, eps=EPS,
                    shared_expert_output=shared, expert_scale_factor=weights,
                    routed_scaling_factor=RSF)

            # Every rank's workspace and inputs are ready before any rank writes to a peer.
            torch.cuda.synchronize()
            dist.barrier()
            run()
            torch.cuda.synchronize()
            # FP64 reference over all ranks' partial outputs.
            partial = (gemm2.double()[idx.long()] * weights.double()[..., None]).sum(1) * RSF
            partial += shared.double()
            dist.all_reduce(partial)
            ref_res = partial + residual.double()
            ref_norm = ref_res * torch.rsqrt(ref_res.pow(2).mean(-1, keepdim=True) + EPS)
            ref_norm *= norm_w.double()
            err_res = (res_out.double() - ref_res).abs()
            err_norm = (norm_out.double() - ref_norm).abs()
            # Error of just rounding the reference to BF16, the floor for a BF16 output.
            floor_res = (ref_res.bfloat16().double() - ref_res).abs()

            s = torch.cuda.Stream()
            s.wait_stream(torch.cuda.current_stream())
            with torch.cuda.stream(s):
                for _ in range(3):
                    run()
            torch.cuda.current_stream().wait_stream(s)
            graph = torch.cuda.CUDAGraph()
            with torch.cuda.graph(graph):
                for _ in range(20):
                    run()
            times = []
            for _ in range(50):
                dist.barrier()
                torch.cuda.synchronize()
                st, en = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)
                st.record()
                graph.replay()
                en.record()
                torch.cuda.synchronize()
                times.append(st.elapsed_time(en) * 1000 / 20)
            t = torch.tensor([statistics.median(times)], device=dev)
            dist.all_reduce(t, op=dist.ReduceOp.MAX)
            stats = torch.tensor([err_res.max(), err_res.mean(), err_norm.max(), err_norm.mean(),
                                  floor_res.mean()], device=dev, dtype=torch.float64)
            dist.all_reduce(stats, op=dist.ReduceOp.MAX)
            row = {"hidden": hidden, "tokens": tokens, "us": round(t.item(), 2),
                   "res_max_err": stats[0].item(), "res_mean_err": stats[1].item(),
                   "norm_max_err": stats[2].item(), "norm_mean_err": stats[3].item(),
                   "res_mean_err_bf16_floor": stats[4].item()}
            results.append(row)
            if rank == 0:
                print(json.dumps(row), flush=True)
            del graph
    if rank == 0:
        json.dump(results, open(out_path, "w"), indent=1)
    dist.barrier()
    dist.destroy_process_group()


if __name__ == "__main__":
    p = argparse.ArgumentParser()
    p.add_argument("world_size", type=int)
    p.add_argument("out")
    p.add_argument("--tokens", type=int, nargs="+", default=[1, 4, 16, 64, 128])
    p.add_argument("--hidden", type=int, nargs="+", default=[6144, 7168])
    a = p.parse_args()
    with socket.socket() as sock:
        sock.bind(("127.0.0.1", 0))
        port = sock.getsockname()[1]
    mp.spawn(worker, args=(a.world_size, port, a.tokens, a.hidden, a.out), nprocs=a.world_size)

Summary by CodeRabbit

Bug Fixes
- Improved numerical accuracy in MoE output calculations by accumulating routed expert contributions and rank reductions in FP32 before converting results to the output type.
- Corrected handling of missing expert scales and invalid routes, and combined shared-expert output before the final conversion.

moefinalize_allreduce_fusion_kernel_oneshot_lamport polled every peer's Lamport buffer (comm.data_bufs[r]) for that peer's own contribution, while each rank clears only its own buffer, at the start of its next launch. A rank that finishes a launch early and starts the next one clears the slot its slower peers are still polling, and they spin on -0.0 forever. Every rank already writes its contribution into all peers' buffers, so read them from the local buffer like the other Lamport kernels (moereduce, allreduce_fusion) do. Back-to-back launches (a CUDA graph replay of 20 launches, 128 tokens, hidden 7168, TP4, B300) hung 4/4 before and never after; the poll is also cheaper (one-shot 7168 x 64 tokens: 19.1 -> 13.0 us). Signed-off-by: haosdent <haosdent@gmail.com>

The MoE-finalize all-reduce gathered the top-k expert rows one at a time (load the permuted index, then the row, then the next index) and accumulated them, the routed scaling, the shared expert output and the rank sum in the activation dtype. Load all indices and scales of a chunk of 8 routes first and then all their rows, so the row loads are in flight together, and keep the routed sum, the shared output add and the rank sum in FP32, rounding once. One-shot, B300, TP4, BF16, top-k 8 (CUDA graph, per launch): hidden 6144: 1 token 7.50 -> 6.20 us, 64 tokens 20.13 -> 15.19 us; hidden 7168: 1 token 8.10 -> 6.56 us, 64 tokens 13.04 -> 12.11 us. Mean abs error of residual_out vs an FP64 reference 7.9e-3 -> 6.0e-3 (BF16 rounding alone: 4.85e-3). The finalize test now computes its reference in FP32 (its BF16 reference failed for BF16 at TP4 on B300 before this change too) and sizes scale_out for the SWIZZLED_128x4 layout the kernel writes; the undersized buffer was overrun. Signed-off-by: haosdent <haosdent@gmail.com>

coderabbitai · 2026-10-17T10:46:27Z

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Files selected for processing: 3

include/flashinfer/comm/trtllm_moe_allreduce_fusion.cuh
tests/comm/test_cake_moe_finalize_allreduce.py
tests/comm/test_trtllm_moe_allreduce_fusion_finalize.py

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Walkthrough

The MoE finalize kernel now accumulates routed and shared-expert contributions in FP32. The all-reduce sum also uses FP32 accumulation. Tests update backend-specific references, cover variable top_k values and invalid routes, and add repeated CUDA graph execution coverage.

Changes: FP32 MoE Finalize and All-Reduce

Layer / File(s)	Summary
FP32 kernel accumulation `include/flashinfer/comm/trtllm_moe_allreduce_fusion.cuh`	MoE finalization accumulates routed and optional shared-expert contributions in FP32 before casting to `T`. The all-reduce sums rank values in FP32 before casting to `T`.
Backend-specific reference validation `tests/comm/test_cake_moe_finalize_allreduce.py`, `tests/comm/test_trtllm_moe_allreduce_fusion_finalize.py`	The Cake and TRT-LLM references model their respective accumulation behavior. TRT-LLM correctness coverage includes `top_k` values 6, 8, and 10, routes marked `-1`, and an FP4 swizzled scale-output buffer.
Repeated CUDA graph execution `tests/comm/test_trtllm_moe_allreduce_fusion_finalize.py`	A new worker captures and replays 20 launches 50 times, checks for completion within 60 seconds, and validates the residual. The test covers world sizes 2 and 4 when enough GPUs are available.

Priority: Normal

Estimated code review effort: 3 (Moderate) | ~25 minutes

Change: Bug fix

Possibly related issues

flashinfer-ai/flashinfer#7234 — The PR changes the same finalize-allreduce interface and updates related tests, but the issue objective is not described in the available context.
flashinfer-ai/flashinfer#6593 — The PR changes the MoE Finalize + All-Reduce Fusion kernel family, but the issue objective is not described in the available context.

Suggested labels: op: moe

Merge Risk: Minimal · up to b0e62

No actionable merge-blocking issue is established; the change is mergeable after normal checks.

Pre-merge checks: 4 · 1

Failed checks: 1 warning

Check name	Status	Explanation	Resolution
Docstring Coverage		Docstring coverage is 20.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 10 functions across 2 files. (1 skipped: …	Write docstrings for the functions missing them to satisfy the coverage threshold.

Passed checks: 4 passed

Check name	Status	Explanation
Title check		The title "perf(comm): gather MoE-finalize routes together and accumulate in FP32" matches the main changes in the changeset. The PR batches index and scale loads for eight routes before row loads, an…
Description check		The description follows the repository template. It has the Description section, which explains the gather and FP32 accumulation changes with rationale, and it adds test changes, benchmark tables, and…
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.

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The title "perf(comm): gather MoE-finalize routes together and accumulate in FP32" matches the main changes in the changeset. The PR batches index and scale loads for eight routes before row loads, and moves routed, shared-expert, and rank-sum accumulation to FP32 in the finalize kernel. The title is specific and concise, and it names both the performance change and the numerical change. It does not mention the test updates, which is acceptable because the title does not need to cover every detail.

Full details: Description check

Explanation

The description follows the repository template. It has the Description section, which explains the gather and FP32 accumulation changes with rationale, and it adds test changes, benchmark tables, and end-to-end results. The Related Issues section lists #2269 and #3671. The Pre-commit Checks and Tests sections are checked, and the B300 test results are reported. The Experimental Track section is not present, which is expected for a normal PR. The Reviewer Notes section is omitted, which is optional. The description is detailed and clearly complete. It has a stacking note on #6630 that reviewers need to read first.

Full details: Docstring Coverage

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Docstring coverage is 20.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 10 functions across 2 files. (1 skipped: 1 unsupported.)

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

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🧹 Nitpick comments: 1

tests/comm/test_trtllm_moe_allreduce_fusion_finalize.py: 1
169-180: 🎯 Functional Correctness | 🔵 Trivial | ⚡ Quick win

Add a precision-sensitive accumulation case.

The FP32 reference is compared with rtol=0.2 and atol=0.2, while the graph path uses rtol=2e-2 and atol=0.5. These tolerances can accept the rounding introduced when routed/shared values and rank sums are accumulated in T. Add a deterministic cancellation or high-dynamic-range fixture with a tighter tolerance around the final T conversion. Keep the existing broad-tolerance test for general numerical coverage.
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Review comment at @tests/comm/test_trtllm_moe_allreduce_fusion_finalize.py
around lines 169 - 180:
Add a deterministic cancellation or high-dynamic-range fixture to the test that
exercises accumulation of routed/shared values and rank sums in T, and assert
its result with a tighter tolerance around the final T conversion. Keep the
existing broad-tolerance coverage unchanged.

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Nitpick comments:
Review comments at @tests/comm/test_trtllm_moe_allreduce_fusion_finalize.py:
- Around line 169-180: Add a deterministic cancellation or high-dynamic-range
fixture to the test that exercises accumulation of routed/shared values and rank
sums in T, and assert its result with a tighter tolerance around the final T
conversion. Keep the existing broad-tolerance coverage unchanged.

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include/flashinfer/comm/trtllm_moe_allreduce_fusion.cuh
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…-LLM model - test_cake_moe_finalize_allreduce compares the TRT-LLM backend with a reference that rounds after every route and rank, which the kernel no longer does; its BF16 cases failed. The reference now models FP32 accumulation for TRT-LLM and keeps per-step rounding for Cake. The kernel before the FP32 change fails it. - The finalize correctness test runs top_k 6, 8 and 10 (one partial chunk, one chunk, and a full plus a partial one) with a quarter of the routes set to -1. - Size scale_out with comm.compute_fp4_swizzled_layout_sf_size. - Simplify the finalize loop: no use_routed_scaling_factor flag (the factor is 1.0 when it is off), one bound check per route, a zero-initialized FP32 accumulator. Output is unchanged. Signed-off-by: haosdent <haosdent@gmail.com>

haosdent added 2 commits October 17, 2026 13:58

haosdent requested review from Anerudhan, Aneureka, Jingfan-Sun, YangXu1990uiuc, aleozlx, bkryu, feih-nv, jimmyzho, nv-yunzheq, yyihuang and yzh119 as code owners October 17, 2026 10:45

flashinfer-bot added the op: comm label Oct 17, 2026

haosdent mentioned this pull request Oct 17, 2026

[Perf] Fuse GLM-5.x MoE finalize into the next RMSNorm's all-reduce vllm-project/vllm#62174

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haosdent marked this pull request as draft October 17, 2026 10:56

haosdent commented Oct 25, 2026

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Comment thread include/flashinfer/comm/trtllm_moe_allreduce_fusion.cuh Resolved

haosdent marked this pull request as ready for review October 25, 2026 12:18

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perf(comm): gather MoE-finalize routes together and accumulate in FP32 - #6631

perf(comm): gather MoE-finalize routes together and accumulate in FP32#6631
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