Upgrade cudnn_frontend to 1.24 and enable cuDNN SDPA for MHA/GQA

[tianleiwu]

Description

Upgrades the cudnn_frontend dependency from 1.12.0 → 1.24.0 and wires the updated cuDNN SDPA (scaled dot-product attention) kernels into the CUDA MultiHeadAttention and GroupQueryAttention operators. On SM≥90 (Hopper/Blackwell), cuDNN SDPA is auto-preferred for FP16/BF16 ahead of Flash Attention / cutlass FMHA, which significantly improves GQA prefill throughput.

Key Changes

Area	Change
Dependency	cmake/deps.txt: cudnn_frontend 1.12.0 → 1.24.0.
Build	cmake/external/cudnn_frontend.cmake: mark cudnn_frontend headers as SYSTEM includes so v1.24's unused static helper does not trip -Werror=unused-function.
SDPA wrapper	cudnn_fmha/cudnn_flash_attention.cc: migrate to the v1.24 API — set_generate_stats(false) (replaces deprecated set_is_inference), diagonal-band causal masking (set_diagonal_alignment + set_diagonal_band_right_bound/set_diagonal_band_left_bound), and synthesize the missing seq_len_q/seq_len_kv side that v1.24 now requires when a padding mask is used.
MHA	multihead_attention.{cc,h}: enable cuDNN SDPA for FP16 and BF16; compute cuDNN eligibility before Flash and prefer it on SM≥90 unless the user pinned a kernel.
GQA	group_query_attention.{cc,h}, group_query_attention_impl.cu, attention_data.h: add a cuDNN SDPA path (non-quantized FP16/BF16, no softcap/smooth-softmax/head-sink/local-window, BNSH KV cache), dispatched after XQA and before Flash/MEA/unfused.
Kernel selection	attention_kernel_options.{cc,h}: track explicit sdpa_kernel selection and honor an explicit ORT_ENABLE_CUDNN_FLASH_ATTENTION=0 so it disables the SM≥90 auto path.

Kernel Priority

• SM≥90, FP16/BF16: cuDNN SDPA is auto-preferred unless the user explicitly selects a kernel via the sdpa_kernel provider option or sets ORT_ENABLE_CUDNN_FLASH_ATTENTION=0.
• GQA decode: XQA remains highest priority where eligible; cuDNN SDPA outranks Flash/MEA/unfused for the remaining eligible cases.
• ORT_ENABLE_CUDNN_FLASH_ATTENTION=0 disables cuDNN entirely (including the auto path); =1 force-enables it; the sdpa_kernel provider option overrides env vars.

Benchmark Results

Measured with onnxruntime/test/python/transformers/benchmark_gqa.py on NVIDIA H200 (SM 9.0), CUDA 13.0 / cuDNN 9.19, Llama3-8B-shaped GQA (b1, 32 query heads, 8 KV heads, head size 128, FP16).

• Baseline = Flash Attention (ORT_ENABLE_CUDNN_FLASH_ATTENTION=0)
• This PR = cuDNN SDPA (default on SM≥90)

The prefill (prompt) phase shows the largest gains for the dense variants:

ORT-GQA-Dense — prompt latency (ms, lower is better)

seq_len	Baseline (Flash)	This PR (cuDNN)	Speedup
64	0.082	0.054	1.52×
128	0.186	0.056	3.33×
256	0.184	0.065	2.83×
512	0.247	0.074	3.32×
1024	0.295	0.122	2.42×
2048	0.681	0.250	2.72×
4096	1.294	0.699	1.85×
8192	3.864	1.256	3.08×

ORT-GQA-Dense-PackedQKV — prompt latency (ms, lower is better)

seq_len	Baseline (Flash)	This PR (cuDNN)	Speedup
64	0.197	0.053	3.69×
128	0.160	0.055	2.93×
256	0.213	0.060	3.53×
512	0.226	0.073	3.09×
1024	0.333	0.291	1.15×
2048	0.595	0.252	2.36×
4096	1.312	0.697	1.88×
8192	5.014	1.259	3.98×

Prefill is ~1.5×–4× faster across sequence lengths for both dense variants. Decode (token) latency is unchanged within run-to-run noise.

Testing

• onnxruntime_provider_test --gtest_filter='GroupQueryAttentionTest.*:MultiHeadAttentionTest.*' — GQA 44/44, MHA 18/18 pass on H200.
• Broader attention regression (AttentionTest.*:PackedMultiHeadAttentionTest.*:DecoderMaskedMultiHeadAttentionTest.*) — 143/143 pass.
• Verified the new path on cuDNN 9.8 and 9.19 (decode s_q==1 causal-mask edge case handled for cuDNN ≤ 9.9).
• Verified ORT_ENABLE_CUDNN_FLASH_ATTENTION=0 disables the SM≥90 auto path (0 cuDNN selections) while the default run selects cuDNN.