b9911

CUDA: Fuse MMVQ post-scale for NVFP4 (#24481)

• CUDA: Fuse MMVQ for NVFP4 and BS 1

TODO:

1. Add tests to test-backend-ops (did verify correctness manually for
   one model)
2. Reorder bias/scale once PRs for NVFP4 are merged/landed

• Add dense MMVQ fusion as well

Perf numbers on B4500. Note qwen35 is FP8->Q8

• ./scripts/compare-llama-bench.py -b master -c osimons/nvfp4_fuse_mmvq --tool llama-bench -i llama-bench.sqlite
  | Model | Test | t/s master | t/s osimons/nvfp4_fuse_mmvq | Speedup |
  |:-------------------------|:-------------|-------------:|------------------------------:|----------:|
  | qwen35moe 35B.A3B NVFP4 | tg128@d32768 | 150.15 | 156.29 | 1.04 |
  | qwen35moe 35B.A3B Q4_K_M | tg128@d32768 | 157.91 | 157.64 | 1.00 |

Perf numbers on DGX Spark

• ./scripts/compare-llama-bench.py -b master -c osimons/nvfp4_fuse_mmvq --tool llama-bench -i llama-bench.sqlite
  | Model | Test | t/s master | t/s osimons/nvfp4_fuse_mmvq | Speedup |
  |:-------------------------|:-------------|-------------:|------------------------------:|----------:|
  | qwen35moe 35B.A3B NVFP4 | tg128@d32768 | 58.31 | 59.69 | 1.02 |
  | qwen35moe 35B.A3B Q4_K_M | tg128@d32768 | 54.94 | 54.79 | 1.00 |

• Add tests for the added fusion ops

• Cleanup test-backend-ops

• Cleanup ggml-cuda/mmvq

1. Unrestrict post-scale fusion
2. Rename names accordingly
3. Remove env variable to disable fusion

• Merge old mul_mat patterns into the lane-based approach

• Enable fusion for MoE in shared MMVQ

• Restrict scale_view_nodes, enroll MM + ADD into lane-matcher

• Refactor mmvq loads, still does not help non-nvfp4 kernels

• Restrict scale-fusion to NVFP4

This is necessary, as the prolog is quite heavy in GEMV for some
quants/model configs, leading to net perf regression.
We should really be looking to refactor this such that ratio of
prologue/hot-loop/epilogue is better on the hot-loop
front:

• ./scripts/compare-llama-bench.py -b master -c c1b9381 --tool llama-bench -i llama-bench.sqlite
  | CPU | Model | Test | t/s master | t/s c1b9381 | Speedup |
  |:----------------------------|:-------------------------|:-------------|-------------:|----------------:|----------:|
  | INTEL(R) XEON(R) GOLD 6542Y | gemma4 26B.A4B NVFP4 | tg128@d32768 | 151.70 | 154.32 | 1.02 |
  | INTEL(R) XEON(R) GOLD 6542Y | gemma4 26B.A4B Q4_K_M | tg128@d32768 | 187.95 | 185.73 | 0.99 |
  | INTEL(R) XEON(R) GOLD 6542Y | gpt-oss 20B MXFP4 MoE | tg128@d32768 | 304.62 | 300.69 | 0.99 |
  | INTEL(R) XEON(R) GOLD 6542Y | qwen35moe 35B.A3B NVFP4 | tg128@d32768 | 193.72 | 211.99 | 1.09 |
  | INTEL(R) XEON(R) GOLD 6542Y | qwen35moe 35B.A3B Q4_K_M | tg128@d32768 | 217.76 | 218.15 | 1.00

• Reorder scale & bias-add to adhere to #24331

• Restrict lane scale to NVFP4

Don't need to test unfused combinations

• Cleanup

• Merge single-lane mm-fusion helpers

• Refactor and clean-up host-side fusion logic

• Move gate_bias and scale into the same active-thread guard

Latest perf numbers:
B6000

build: 5b7d9f272 (9578)

• ./scripts/compare-llama-bench.py -b master -c osimons/nvfp4_fuse_mmvq --tool llama-bench -i llama-bench.sqlite
  | CPU | Model | Test | t/s master | t/s osimons/nvfp4_fuse_mmvq | Speedup |
  |:----------------------------|:-------------------------|:-------------|-------------:|------------------------------:|----------:|
  | INTEL(R) XEON(R) GOLD 6542Y | gemma4 26B.A4B NVFP4 | tg128@d32768 | 151.79 | 154.10 | 1.02 |
  | INTEL(R) XEON(R) GOLD 6542Y | gemma4 26B.A4B Q4_K_M | tg128@d32768 | 187.90 | 187.27 | 1.00 |
  | INTEL(R) XEON(R) GOLD 6542Y | gpt-oss 20B MXFP4 MoE | tg128@d32768 | 303.77 | 306.56 | 1.01 |
  | INTEL(R) XEON(R) GOLD 6542Y | qwen35moe 35B.A3B NVFP4 | tg128@d32768 | 193.41 | 207.99 | 1.08 |
  | INTEL(R) XEON(R) GOLD 6542Y | qwen35moe 35B.A3B Q4_K_M | tg128@d32768 | 217.60 | 218.58 | 1.00 |

DGX Spark

build: 5b7d9f272 (9578)

• ./scripts/compare-llama-bench.py -b master -c osimons/nvfp4_fuse_mmvq --tool llama-bench -i llama-bench.sqlite
  | CPU | Model | Test | t/s master | t/s osimons/nvfp4_fuse_mmvq | Speedup |
  |:------|:-------------------------|:-------------|-------------:|------------------------------:|----------:|
  | CPU | gemma4 26B.A4B NVFP4 | tg128@d32768 | 34.61 | 34.84 | 1.01 |
  | CPU | gemma4 26B.A4B Q4_K_M | tg128@d32768 | 46.95 | 46.90 | 1.00 |
  | CPU | gpt-oss 20B MXFP4 MoE | tg128@d32768 | 64.84 | 64.62 | 1.00 |
  | CPU | qwen35moe 35B.A3B NVFP4 | tg128@d32768 | 59.63 | 60.72 | 1.02 |
  | CPU | qwen35moe 35B.A3B Q4_K_M | tg128@d32768 | 56.53 | 56.55 | 1.00 |

PPL values for 5 chunks:
this PR

model mode ppl uncertainty log
/mnt/share/gguf/unsloth/Qwen3.6-35B-A3B-GGUF/Qwen3.6-35B-A3B-UD-Q4_K_M.gguf fusion_enabled 5.2892 0.35389 ppl-value-checks/Qwen3.6-35B-A3B-UD-Q4_K_M.fusion_enabled.log
/mnt/share/gguf/unsloth/Qwen3.6-35B-A3B-GGUF/Qwen3.6-35B-A3B-UD-Q4_K_M.gguf fusion_disabled 5.2742 0.35215 ppl-value-checks/Qwen3.6-35B-A3B-UD-Q4_K_M.fusion_disabled.log
/mnt/share/gguf/nvidia/Qwen3.6-35B-A3B-2.06GB-per-token-CT/Qwen3.6-35B-A3B-2.06GB-per-token-CT_fp8_q8.gguf fusion_enabled 5.4487 0.36866 ppl-value-checks/Qwen3.6-35B-A3B-2.06GB-per-token-CT_fp8_q8.fusion_enabled.log
/mnt/share/gguf/nvidia/Qwen3.6-35B-A3B-2.06GB-per-token-CT/Qwen3.6-35B-A3B-2.06GB-per-token-CT_fp8_q8.gguf fusion_disabled 5.4403 0.36782 ppl-value-checks/Qwen3.6-35B-A3B-2.06GB-per-token-CT_fp8_q8.fusion_disabled.log
/mnt/share/gguf/nvidia/Gemma-4-26B-A4B-NVFP4/Gemma-4-26B-A4B-NVFP4_fp8_q8.gguf fusion_enabled 17342.4348 3703.13932 ppl-value-checks/Gemma-4-26B-A4B-NVFP4_fp8_q8.fusion_enabled.log
/mnt/share/gguf/nvidia/Gemma-4-26B-A4B-NVFP4/Gemma-4-26B-A4B-NVFP4_fp8_q8.gguf fusion_disabled 18627.0624 3998.42475 ppl-value-checks/Gemma-4-26B-A4B-NVFP4_fp8_q8.fusion_disabled.log
/mnt/share/gguf/ggml-org/gpt-oss-20b-GGUF/gpt-oss-20b-mxfp4.gguf fusion_enabled 363.8913 33.14007 ppl-value-checks/gpt-oss-20b-mxfp4.fusion_enabled.log
/mnt/share/gguf/ggml-org/gpt-oss-20b-GGUF/gpt-oss-20b-mxfp4.gguf fusion_disabled 363.8913 33.14007 ppl-value-checks/gpt-oss-20b-mxfp4.fusion_disabled.log
/mnt/share/gguf/unsloth/gemma-4-26B-A4B-it-GGUF/gemma-4-26B-A4B-it-UD-Q4_K_XL.gguf fusion_enabled 17330.3926 3716.70472 ppl-value-checks/gemma-4-26B-A4B-it-UD-Q4_K_XL.fusion_enabled.log
/mnt/share/gguf/unsloth/gemma-4-26B-A4B-it-GGUF/gemma-4-26B-A4B-it-UD-Q4_K_XL.gguf fusion_disabled 17933.9524 3883.17066 ppl-value-checks/gemma-4-26B-A4B-it-UD-Q4_K_XL.fusion_disabled.log

master:
summary: ppl-value-checks/summary.tsv
model mode ppl uncertainty log
/mnt/share/gguf/unsloth/Qwen3.6-35B-A3B-GGUF/Qwen3.6-35B-A3B-UD-Q4_K_M.gguf fusion_enabled 5.2892 0.35389 ppl-value-checks/Qwen3.6-35B-A3B-UD-Q4_K_M.fusion_enabled.log
/mnt/share/gguf/unsloth/Qwen3.6-35B-A3B-GGUF/Qwen3.6-35B-A3B-UD-Q4_K_M.gguf fusion_disabled 5.2742 0.35215 ppl-value-checks/Qwen3.6-35B-A3B-UD-Q4_K_M.fusion_disabled.log
/mnt/share/gguf/nvidia/Qwen3.6-35B-A3B-2.06GB-per-token-CT/Qwen3.6-35B-A3B-2.06GB-per-token-CT_fp8_q8.gguf fusion_enabled 5.4487 0.36866 ppl-value-checks/Qwen3.6-35B-A3B-2.06GB-per-token-CT_fp8_q8.fusion_enabled.log
/mnt/share/gguf/nvidia/Qwen3.6-35B-A3B-2.06GB-per-token-CT/Qwen3.6-35B-A3B-2.06GB-per-token-CT_fp8_q8.gguf fusion_disabled 5.4403 0.36782 ppl-value-checks/Qwen3.6-35B-A3B-2.06GB-per-token-CT_fp8_q8.fusion_disabled.log
/mnt/share/gguf/nvidia/Gemma-4-26B-A4B-NVFP4/Gemma-4-26B-A4B-NVFP4_fp8_q8.gguf fusion_enabled 17342.4348 3703.13932 ppl-value-checks/Gemma-4-26B-A4B-NVFP4_fp8_q8.fusion_enabled.log
/mnt/share/gguf/nvidia/Gemma-4-26B-A4B-NVFP4/Gemma-4-26B-A4B-NVFP4_fp8_q8.gguf fusion_disabled 18627.0624 3998.42475 ppl-value-checks/Gemma-4-26B-A4B-NVFP4_fp8_q8.fusion_disabled.log
/mnt/share/gguf/ggml-org/gpt-oss-20b-GGUF/gpt-oss-20b-mxfp4.gguf fusion_enabled 363.8913 33.14007 ppl-value-checks/gpt-oss-20b-mxfp4.fusion_enabled.log
/mnt/share/gguf/ggml-org/gpt-oss-20b-GGUF/gpt-oss-20b-mxfp4.gguf fusion_disabled 363.8913 33.14007 ppl-value-checks/gpt-oss-20b-mxfp4.fusion_disabled.log
/mnt/share/gguf/unsloth/gemma-4-26B-A4B-it-GGUF/gemma-4-26B-A4B-it-UD-Q4_K_XL.gguf fusion_enabled 17330.3926 3716.70472 ppl-value-checks/gemma-4-26B-A4B-it-UD-Q4_K_XL.fusion_enabled.log
/mnt/share/gguf/unsloth/gemma-4-26B-A4B-it-GGUF/gemma-4-26B-A4B-it-UD-Q4_K_XL.gguf fusion_disabled 17933.9524 3883.17066 ppl-value-checks/gemma-4-26B-A4B-it-UD-Q4_K_XL.fusion_disabled.log

• Allow views to weights in ggml_can_fuse_subgraph

• Remove gate_first from test_mul_mat_vec_fusion

• Ditch lane-parsing approach in favor of hard-coded patterns

• Apply suggestions from code review

Co-authored-by: Georgi Gerganov ggerganov@gmail.com

• Rename ggml_is_constant_view_src to ggml_is_constant

• Finish renaming of 0905129

• Readd descriptive prints for fusion debugging

• Add weight-buffer pre-allocation to test_case

This is required so we correctly test fusion of NVFP4.

• Update ggml/src/ggml.c

Co-authored-by: Johannes Gäßler johannesg@5d6.de

• Add 2nd context for weights as suggested by @JohannesGaessler

This reflects more natural use of ggml compared to artifically
pre-allocating weights into the same context

• Exclude fused tests from gradient mode

I'm unsure of the current state, but naively every fusion pattern
should require its own backpropagation implementation. I don't see these
implemented for the CUDA backend, so we can disable tests to avoid
triggering GGML_ASSERT for

ggml_tensor * build_graph(ggml_context * ctx) override {
    GGML_ASSERT(!use_weight_context());
    return build_graph(ctx, nullptr);
}

• Apply suggestions from code review

Co-authored-by: Johannes Gäßler johannesg@5d6.de

---

Co-authored-by: Georgi Gerganov ggerganov@gmail.com
Co-authored-by: Johannes Gäßler johannesg@5d6.de

macOS/iOS:

• macOS Apple Silicon (arm64)
• macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED
• macOS Intel (x64)
• iOS XCFramework

Linux:

• Ubuntu x64 (CPU)
• Ubuntu arm64 (CPU)
• Ubuntu s390x (CPU)
• Ubuntu x64 (Vulkan)
• Ubuntu arm64 (Vulkan)
• Ubuntu x64 (ROCm 7.2)
• Ubuntu x64 (OpenVINO)
• Ubuntu x64 (SYCL FP32)
• Ubuntu x64 (SYCL FP16)

Android:

• Android arm64 (CPU)

Windows:

• Windows x64 (CPU)
• Windows arm64 (CPU)
• Windows arm64 (OpenCL Adreno)
• Windows x64 (CUDA 12) - CUDA 12.4 DLLs
• Windows x64 (CUDA 13) - CUDA 13.3 DLLs
• Windows x64 (Vulkan)
• Windows x64 (OpenVINO)
• Windows x64 (SYCL)
• Windows x64 (HIP)

openEuler:

• DISABLED
• openEuler x86 (310p)
• openEuler x86 (910b, ACL Graph)
• openEuler aarch64 (310p)
• openEuler aarch64 (910b, ACL Graph)

UI:

• UI