From 30275b766ad637696ba8cea79b9a34fd491ef710 Mon Sep 17 00:00:00 2001
From: Surya Jasper <suryajasper2004@gmail.com>
Date: Thu, 8 Aug 2024 20:27:16 -0500
Subject: [PATCH] Triton harness & FP8 Attention

---
 .github/workflows/run_bench.yml               |   17 +-
 .gitignore                                    |    4 +-
 .vscode/settings.json                         |    7 +-
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 ...ention_B4_H32_SQ2048_SKV4096_DH128_f8.mlir |   11 +
 ...tion_B4_H32_SQ2048_SKV4096_DH128_fp16.mlir |   11 +
 ...ntion_B4_H32_SQ2048_SKV4096_DH128_fp8.mlir |   11 +
 ...ntion_B4_H32_SQ4096_SKV1024_DH128_f16.mlir |   11 +
 ...ention_B4_H32_SQ4096_SKV1024_DH128_f8.mlir |   11 +
 ...tion_B4_H32_SQ4096_SKV1024_DH128_fp16.mlir |   11 +
 ...ntion_B4_H32_SQ4096_SKV1024_DH128_fp8.mlir |   11 +
 ...ntion_B4_H32_SQ4096_SKV2048_DH128_f16.mlir |   11 +
 ...ention_B4_H32_SQ4096_SKV2048_DH128_f8.mlir |   11 +
 ...tion_B4_H32_SQ4096_SKV2048_DH128_fp16.mlir |   11 +
 ...ntion_B4_H32_SQ4096_SKV2048_DH128_fp8.mlir |   11 +
 ...ntion_B4_H32_SQ4096_SKV4096_DH128_f16.mlir |   11 +
 ...ention_B4_H32_SQ4096_SKV4096_DH128_f8.mlir |   11 +
 ...tion_B4_H32_SQ4096_SKV4096_DH128_fp16.mlir |   11 +
 ...ntion_B4_H32_SQ4096_SKV4096_DH128_fp8.mlir |   11 +
 ...ntion_B4_H40_SQ1024_SKV1024_DH128_f16.mlir |   11 +
 ...ention_B4_H40_SQ1024_SKV1024_DH128_f8.mlir |   11 +
 ...tion_B4_H40_SQ1024_SKV1024_DH128_fp16.mlir |   11 +
 ...ntion_B4_H40_SQ1024_SKV1024_DH128_fp8.mlir |   11 +
 ...ntion_B4_H40_SQ1024_SKV2048_DH128_f16.mlir |   11 +
 ...ention_B4_H40_SQ1024_SKV2048_DH128_f8.mlir |   11 +
 ...tion_B4_H40_SQ1024_SKV2048_DH128_fp16.mlir |   11 +
 ...ntion_B4_H40_SQ1024_SKV2048_DH128_fp8.mlir |   11 +
 ...ntion_B4_H40_SQ1024_SKV4096_DH128_f16.mlir |   11 +
 ...ention_B4_H40_SQ1024_SKV4096_DH128_f8.mlir |   11 +
 ...tion_B4_H40_SQ1024_SKV4096_DH128_fp16.mlir |   11 +
 ...ntion_B4_H40_SQ1024_SKV4096_DH128_fp8.mlir |   11 +
 ...ntion_B4_H40_SQ2048_SKV1024_DH128_f16.mlir |   11 +
 ...ention_B4_H40_SQ2048_SKV1024_DH128_f8.mlir |   11 +
 ...tion_B4_H40_SQ2048_SKV1024_DH128_fp16.mlir |   11 +
 ...ntion_B4_H40_SQ2048_SKV1024_DH128_fp8.mlir |   11 +
 ...ntion_B4_H40_SQ2048_SKV2048_DH128_f16.mlir |   11 +
 ...ention_B4_H40_SQ2048_SKV2048_DH128_f8.mlir |   11 +
 ...tion_B4_H40_SQ2048_SKV2048_DH128_fp16.mlir |   11 +
 ...ntion_B4_H40_SQ2048_SKV2048_DH128_fp8.mlir |   11 +
 ...ntion_B4_H40_SQ2048_SKV4096_DH128_f16.mlir |   11 +
 ...ention_B4_H40_SQ2048_SKV4096_DH128_f8.mlir |   11 +
 ...tion_B4_H40_SQ2048_SKV4096_DH128_fp16.mlir |   11 +
 ...ntion_B4_H40_SQ2048_SKV4096_DH128_fp8.mlir |   11 +
 ...ntion_B4_H40_SQ4096_SKV1024_DH128_f16.mlir |   11 +
 ...ention_B4_H40_SQ4096_SKV1024_DH128_f8.mlir |   11 +
 ...tion_B4_H40_SQ4096_SKV1024_DH128_fp16.mlir |   11 +
 ...ntion_B4_H40_SQ4096_SKV1024_DH128_fp8.mlir |   11 +
 ...ntion_B4_H40_SQ4096_SKV2048_DH128_f16.mlir |   11 +
 ...ention_B4_H40_SQ4096_SKV2048_DH128_f8.mlir |   11 +
 ...tion_B4_H40_SQ4096_SKV2048_DH128_fp16.mlir |   11 +
 ...ntion_B4_H40_SQ4096_SKV2048_DH128_fp8.mlir |   11 +
 ...ntion_B4_H40_SQ4096_SKV4096_DH128_f16.mlir |   11 +
 ...ention_B4_H40_SQ4096_SKV4096_DH128_f8.mlir |   11 +
 ...tion_B4_H40_SQ4096_SKV4096_DH128_fp16.mlir |   11 +
 ...ntion_B4_H40_SQ4096_SKV4096_DH128_fp8.mlir |   11 +
 ...ntion_B4_H64_SQ1024_SKV1024_DH128_f16.mlir |   11 +
 ...ention_B4_H64_SQ1024_SKV1024_DH128_f8.mlir |   11 +
 ...tion_B4_H64_SQ1024_SKV1024_DH128_fp16.mlir |   11 +
 ...ntion_B4_H64_SQ1024_SKV1024_DH128_fp8.mlir |   11 +
 ...ntion_B4_H64_SQ1024_SKV2048_DH128_f16.mlir |   11 +
 ...ention_B4_H64_SQ1024_SKV2048_DH128_f8.mlir |   11 +
 ...tion_B4_H64_SQ1024_SKV2048_DH128_fp16.mlir |   11 +
 ...ntion_B4_H64_SQ1024_SKV2048_DH128_fp8.mlir |   11 +
 ...ntion_B4_H64_SQ1024_SKV4096_DH128_f16.mlir |   11 +
 ...ention_B4_H64_SQ1024_SKV4096_DH128_f8.mlir |   11 +
 ...tion_B4_H64_SQ1024_SKV4096_DH128_fp16.mlir |   11 +
 ...ntion_B4_H64_SQ1024_SKV4096_DH128_fp8.mlir |   11 +
 ...ntion_B4_H64_SQ2048_SKV1024_DH128_f16.mlir |   11 +
 ...ention_B4_H64_SQ2048_SKV1024_DH128_f8.mlir |   11 +
 ...tion_B4_H64_SQ2048_SKV1024_DH128_fp16.mlir |   11 +
 ...ntion_B4_H64_SQ2048_SKV1024_DH128_fp8.mlir |   11 +
 ...ntion_B4_H64_SQ2048_SKV2048_DH128_f16.mlir |   11 +
 ...ention_B4_H64_SQ2048_SKV2048_DH128_f8.mlir |   11 +
 ...tion_B4_H64_SQ2048_SKV2048_DH128_fp16.mlir |   11 +
 ...ntion_B4_H64_SQ2048_SKV2048_DH128_fp8.mlir |   11 +
 ...ntion_B4_H64_SQ2048_SKV4096_DH128_f16.mlir |   11 +
 ...ention_B4_H64_SQ2048_SKV4096_DH128_f8.mlir |   11 +
 ...tion_B4_H64_SQ2048_SKV4096_DH128_fp16.mlir |   11 +
 ...ntion_B4_H64_SQ2048_SKV4096_DH128_fp8.mlir |   11 +
 ...ntion_B4_H64_SQ4096_SKV1024_DH128_f16.mlir |   11 +
 ...ention_B4_H64_SQ4096_SKV1024_DH128_f8.mlir |   11 +
 ...tion_B4_H64_SQ4096_SKV1024_DH128_fp16.mlir |   11 +
 ...ntion_B4_H64_SQ4096_SKV1024_DH128_fp8.mlir |   11 +
 ...ntion_B4_H64_SQ4096_SKV2048_DH128_f16.mlir |   11 +
 ...ention_B4_H64_SQ4096_SKV2048_DH128_f8.mlir |   11 +
 ...tion_B4_H64_SQ4096_SKV2048_DH128_fp16.mlir |   11 +
 ...ntion_B4_H64_SQ4096_SKV2048_DH128_fp8.mlir |   11 +
 ...ntion_B4_H64_SQ4096_SKV4096_DH128_f16.mlir |   11 +
 ...ention_B4_H64_SQ4096_SKV4096_DH128_f8.mlir |   11 +
 ...tion_B4_H64_SQ4096_SKV4096_DH128_fp16.mlir |   11 +
 ...ntion_B4_H64_SQ4096_SKV4096_DH128_fp8.mlir |   11 +
 ...ntion_B1_H32_SQ1024_SKV1024_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B1_H32_SQ1024_SKV1024_DH128_f8.vmfb |  Bin 0 -> 19412 bytes
 ...ntion_B1_H32_SQ1024_SKV2048_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B1_H32_SQ1024_SKV2048_DH128_f8.vmfb |  Bin 0 -> 19476 bytes
 ...ntion_B1_H32_SQ1024_SKV4096_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B1_H32_SQ1024_SKV4096_DH128_f8.vmfb |  Bin 0 -> 19476 bytes
 ...ntion_B1_H32_SQ2048_SKV1024_DH128_f16.vmfb |  Bin 0 -> 30164 bytes
 ...ention_B1_H32_SQ2048_SKV1024_DH128_f8.vmfb |  Bin 0 -> 19476 bytes
 ...ntion_B1_H32_SQ2048_SKV2048_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B1_H32_SQ2048_SKV2048_DH128_f8.vmfb |  Bin 0 -> 19412 bytes
 ...ntion_B1_H32_SQ2048_SKV4096_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B1_H32_SQ2048_SKV4096_DH128_f8.vmfb |  Bin 0 -> 19476 bytes
 ...ntion_B1_H32_SQ4096_SKV1024_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B1_H32_SQ4096_SKV1024_DH128_f8.vmfb |  Bin 0 -> 19476 bytes
 ...ntion_B1_H32_SQ4096_SKV2048_DH128_f16.vmfb |  Bin 0 -> 30164 bytes
 ...ention_B1_H32_SQ4096_SKV2048_DH128_f8.vmfb |  Bin 0 -> 19476 bytes
 ...ntion_B1_H32_SQ4096_SKV4096_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B1_H32_SQ4096_SKV4096_DH128_f8.vmfb |  Bin 0 -> 19412 bytes
 ...ntion_B1_H40_SQ1024_SKV1024_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B1_H40_SQ1024_SKV1024_DH128_f8.vmfb |  Bin 0 -> 19412 bytes
 ...ntion_B1_H40_SQ1024_SKV2048_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B1_H40_SQ1024_SKV2048_DH128_f8.vmfb |  Bin 0 -> 19476 bytes
 ...ntion_B1_H40_SQ1024_SKV4096_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B1_H40_SQ1024_SKV4096_DH128_f8.vmfb |  Bin 0 -> 19476 bytes
 ...ntion_B1_H40_SQ2048_SKV1024_DH128_f16.vmfb |  Bin 0 -> 30164 bytes
 ...ention_B1_H40_SQ2048_SKV1024_DH128_f8.vmfb |  Bin 0 -> 19476 bytes
 ...ntion_B1_H40_SQ2048_SKV2048_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B1_H40_SQ2048_SKV2048_DH128_f8.vmfb |  Bin 0 -> 19412 bytes
 ...ntion_B1_H40_SQ2048_SKV4096_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B1_H40_SQ2048_SKV4096_DH128_f8.vmfb |  Bin 0 -> 19476 bytes
 ...ntion_B1_H40_SQ4096_SKV1024_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B1_H40_SQ4096_SKV1024_DH128_f8.vmfb |  Bin 0 -> 19476 bytes
 ...ntion_B1_H40_SQ4096_SKV2048_DH128_f16.vmfb |  Bin 0 -> 30164 bytes
 ...ention_B1_H40_SQ4096_SKV2048_DH128_f8.vmfb |  Bin 0 -> 19476 bytes
 ...ntion_B1_H40_SQ4096_SKV4096_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B1_H40_SQ4096_SKV4096_DH128_f8.vmfb |  Bin 0 -> 19412 bytes
 ...ntion_B1_H64_SQ1024_SKV1024_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B1_H64_SQ1024_SKV1024_DH128_f8.vmfb |  Bin 0 -> 19412 bytes
 ...ntion_B1_H64_SQ1024_SKV2048_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B1_H64_SQ1024_SKV2048_DH128_f8.vmfb |  Bin 0 -> 19476 bytes
 ...ntion_B1_H64_SQ1024_SKV4096_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B1_H64_SQ1024_SKV4096_DH128_f8.vmfb |  Bin 0 -> 19476 bytes
 ...ntion_B1_H64_SQ2048_SKV1024_DH128_f16.vmfb |  Bin 0 -> 30164 bytes
 ...ention_B1_H64_SQ2048_SKV1024_DH128_f8.vmfb |  Bin 0 -> 19476 bytes
 ...ntion_B1_H64_SQ2048_SKV2048_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B1_H64_SQ2048_SKV2048_DH128_f8.vmfb |  Bin 0 -> 19412 bytes
 ...ntion_B1_H64_SQ2048_SKV4096_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B1_H64_SQ2048_SKV4096_DH128_f8.vmfb |  Bin 0 -> 19476 bytes
 ...ntion_B1_H64_SQ4096_SKV1024_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B1_H64_SQ4096_SKV1024_DH128_f8.vmfb |  Bin 0 -> 19476 bytes
 ...ntion_B1_H64_SQ4096_SKV2048_DH128_f16.vmfb |  Bin 0 -> 30164 bytes
 ...ention_B1_H64_SQ4096_SKV2048_DH128_f8.vmfb |  Bin 0 -> 19476 bytes
 ...ntion_B1_H64_SQ4096_SKV4096_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B1_H64_SQ4096_SKV4096_DH128_f8.vmfb |  Bin 0 -> 19412 bytes
 ...ention_B2_H10_SQ1024_SKV1024_DH64_f16.vmfb |  Bin 0 -> 25044 bytes
 ...tention_B2_H10_SQ1024_SKV1024_DH64_f8.vmfb |  Bin 0 -> 18324 bytes
 ...ention_B2_H10_SQ4096_SKV4096_DH64_f16.vmfb |  Bin 0 -> 25044 bytes
 ...tention_B2_H10_SQ4096_SKV4096_DH64_f8.vmfb |  Bin 0 -> 18324 bytes
 ...ttention_B2_H10_SQ4096_SKV64_DH64_f16.vmfb |  Bin 0 -> 20948 bytes
 ...attention_B2_H10_SQ4096_SKV64_DH64_f8.vmfb |  Bin 0 -> 18004 bytes
 ...ttention_B2_H20_SQ1024_SKV64_DH64_f16.vmfb |  Bin 0 -> 20948 bytes
 ...attention_B2_H20_SQ1024_SKV64_DH64_f8.vmfb |  Bin 0 -> 18004 bytes
 ...ntion_B2_H32_SQ1024_SKV1024_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B2_H32_SQ1024_SKV1024_DH128_f8.vmfb |  Bin 0 -> 19412 bytes
 ...ntion_B2_H32_SQ1024_SKV2048_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B2_H32_SQ1024_SKV2048_DH128_f8.vmfb |  Bin 0 -> 19476 bytes
 ...ntion_B2_H32_SQ1024_SKV4096_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B2_H32_SQ1024_SKV4096_DH128_f8.vmfb |  Bin 0 -> 19476 bytes
 ...ntion_B2_H32_SQ2048_SKV1024_DH128_f16.vmfb |  Bin 0 -> 30164 bytes
 ...ention_B2_H32_SQ2048_SKV1024_DH128_f8.vmfb |  Bin 0 -> 19476 bytes
 ...ntion_B2_H32_SQ2048_SKV2048_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B2_H32_SQ2048_SKV2048_DH128_f8.vmfb |  Bin 0 -> 19412 bytes
 ...ntion_B2_H32_SQ2048_SKV4096_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B2_H32_SQ2048_SKV4096_DH128_f8.vmfb |  Bin 0 -> 19476 bytes
 ...ntion_B2_H32_SQ4096_SKV1024_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B2_H32_SQ4096_SKV1024_DH128_f8.vmfb |  Bin 0 -> 19476 bytes
 ...ntion_B2_H32_SQ4096_SKV2048_DH128_f16.vmfb |  Bin 0 -> 30164 bytes
 ...ention_B2_H32_SQ4096_SKV2048_DH128_f8.vmfb |  Bin 0 -> 19476 bytes
 ...ntion_B2_H32_SQ4096_SKV4096_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B2_H32_SQ4096_SKV4096_DH128_f8.vmfb |  Bin 0 -> 19412 bytes
 ...ntion_B2_H40_SQ1024_SKV1024_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B2_H40_SQ1024_SKV1024_DH128_f8.vmfb |  Bin 0 -> 19412 bytes
 ...ntion_B2_H40_SQ1024_SKV2048_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B2_H40_SQ1024_SKV2048_DH128_f8.vmfb |  Bin 0 -> 19476 bytes
 ...ntion_B2_H40_SQ1024_SKV4096_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B2_H40_SQ1024_SKV4096_DH128_f8.vmfb |  Bin 0 -> 19476 bytes
 ...ntion_B2_H40_SQ2048_SKV1024_DH128_f16.vmfb |  Bin 0 -> 30164 bytes
 ...ention_B2_H40_SQ2048_SKV1024_DH128_f8.vmfb |  Bin 0 -> 19476 bytes
 ...ntion_B2_H40_SQ2048_SKV2048_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B2_H40_SQ2048_SKV2048_DH128_f8.vmfb |  Bin 0 -> 19412 bytes
 ...ntion_B2_H40_SQ2048_SKV4096_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B2_H40_SQ2048_SKV4096_DH128_f8.vmfb |  Bin 0 -> 19476 bytes
 ...ntion_B2_H40_SQ4096_SKV1024_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B2_H40_SQ4096_SKV1024_DH128_f8.vmfb |  Bin 0 -> 19476 bytes
 ...ntion_B2_H40_SQ4096_SKV2048_DH128_f16.vmfb |  Bin 0 -> 30164 bytes
 ...ention_B2_H40_SQ4096_SKV2048_DH128_f8.vmfb |  Bin 0 -> 19476 bytes
 ...ntion_B2_H40_SQ4096_SKV4096_DH128_f16.vmfb |  Bin 0 -> 30100 bytes
 ...ention_B2_H40_SQ4096_SKV4096_DH128_f8.vmfb |  Bin 0 -> 19412 bytes
 ...ntion_B2_H64_SQ1024_SKV1024_DH128_f16.vmfb |  Bin 0 -> 30104 bytes
 ...ention_B2_H64_SQ1024_SKV1024_DH128_f8.vmfb |  Bin 0 -> 19416 bytes
 ...ntion_B2_H64_SQ1024_SKV2048_DH128_f16.vmfb |  Bin 0 -> 30104 bytes
 ...ention_B2_H64_SQ1024_SKV2048_DH128_f8.vmfb |  Bin 0 -> 19480 bytes
 ...ntion_B2_H64_SQ1024_SKV4096_DH128_f16.vmfb |  Bin 0 -> 30104 bytes
 ...ention_B2_H64_SQ1024_SKV4096_DH128_f8.vmfb |  Bin 0 -> 19480 bytes
 ...ntion_B2_H64_SQ2048_SKV1024_DH128_f16.vmfb |  Bin 0 -> 30168 bytes
 ...ention_B2_H64_SQ2048_SKV1024_DH128_f8.vmfb |  Bin 0 -> 19480 bytes
 ...ntion_B2_H64_SQ2048_SKV2048_DH128_f16.vmfb |  Bin 0 -> 30104 bytes
 ...ention_B2_H64_SQ2048_SKV2048_DH128_f8.vmfb |  Bin 0 -> 19416 bytes
 ...ntion_B2_H64_SQ2048_SKV4096_DH128_f16.vmfb |  Bin 0 -> 30104 bytes
 ...ention_B2_H64_SQ2048_SKV4096_DH128_f8.vmfb |  Bin 0 -> 19480 bytes
 ...ntion_B2_H64_SQ4096_SKV1024_DH128_f16.vmfb |  Bin 0 -> 30104 bytes
 ...ention_B2_H64_SQ4096_SKV1024_DH128_f8.vmfb |  Bin 0 -> 19480 bytes
 ...ntion_B2_H64_SQ4096_SKV2048_DH128_f16.vmfb |  Bin 0 -> 30168 bytes
 ...ention_B2_H64_SQ4096_SKV2048_DH128_f8.vmfb |  Bin 0 -> 19480 bytes
 ...ntion_B2_H64_SQ4096_SKV4096_DH128_f16.vmfb |  Bin 0 -> 30104 bytes
 ...ention_B2_H64_SQ4096_SKV4096_DH128_f8.vmfb |  Bin 0 -> 19416 bytes
 ...ntion_B4_H32_SQ1024_SKV1024_DH128_f16.vmfb |  Bin 0 -> 30104 bytes
 ...ention_B4_H32_SQ1024_SKV1024_DH128_f8.vmfb |  Bin 0 -> 19416 bytes
 ...ntion_B4_H32_SQ1024_SKV2048_DH128_f16.vmfb |  Bin 0 -> 30104 bytes
 ...ention_B4_H32_SQ1024_SKV2048_DH128_f8.vmfb |  Bin 0 -> 19480 bytes
 ...ntion_B4_H32_SQ1024_SKV4096_DH128_f16.vmfb |  Bin 0 -> 30104 bytes
 ...ention_B4_H32_SQ1024_SKV4096_DH128_f8.vmfb |  Bin 0 -> 19480 bytes
 ...ntion_B4_H32_SQ2048_SKV1024_DH128_f16.vmfb |  Bin 0 -> 30168 bytes
 ...ention_B4_H32_SQ2048_SKV1024_DH128_f8.vmfb |  Bin 0 -> 19480 bytes
 ...ntion_B4_H32_SQ2048_SKV2048_DH128_f16.vmfb |  Bin 0 -> 30104 bytes
 ...ention_B4_H32_SQ2048_SKV2048_DH128_f8.vmfb |  Bin 0 -> 19416 bytes
 ...ntion_B4_H32_SQ2048_SKV4096_DH128_f16.vmfb |  Bin 0 -> 30104 bytes
 ...ention_B4_H32_SQ2048_SKV4096_DH128_f8.vmfb |  Bin 0 -> 19480 bytes
 ...ntion_B4_H32_SQ4096_SKV1024_DH128_f16.vmfb |  Bin 0 -> 30104 bytes
 ...ention_B4_H32_SQ4096_SKV1024_DH128_f8.vmfb |  Bin 0 -> 19480 bytes
 ...ntion_B4_H32_SQ4096_SKV2048_DH128_f16.vmfb |  Bin 0 -> 30168 bytes
 ...ention_B4_H32_SQ4096_SKV2048_DH128_f8.vmfb |  Bin 0 -> 19480 bytes
 ...ntion_B4_H32_SQ4096_SKV4096_DH128_f16.vmfb |  Bin 0 -> 30104 bytes
 ...ention_B4_H32_SQ4096_SKV4096_DH128_f8.vmfb |  Bin 0 -> 19416 bytes
 ...ntion_B4_H40_SQ1024_SKV1024_DH128_f16.vmfb |  Bin 0 -> 30104 bytes
 ...ention_B4_H40_SQ1024_SKV1024_DH128_f8.vmfb |  Bin 0 -> 19416 bytes
 ...ntion_B4_H40_SQ1024_SKV2048_DH128_f16.vmfb |  Bin 0 -> 30104 bytes
 ...ention_B4_H40_SQ1024_SKV2048_DH128_f8.vmfb |  Bin 0 -> 19480 bytes
 ...ntion_B4_H40_SQ1024_SKV4096_DH128_f16.vmfb |  Bin 0 -> 30104 bytes
 ...ention_B4_H40_SQ1024_SKV4096_DH128_f8.vmfb |  Bin 0 -> 19480 bytes
 ...ntion_B4_H40_SQ2048_SKV1024_DH128_f16.vmfb |  Bin 0 -> 30168 bytes
 ...ention_B4_H40_SQ2048_SKV1024_DH128_f8.vmfb |  Bin 0 -> 19480 bytes
 ...ntion_B4_H40_SQ2048_SKV2048_DH128_f16.vmfb |  Bin 0 -> 30104 bytes
 ...ention_B4_H40_SQ2048_SKV2048_DH128_f8.vmfb |  Bin 0 -> 19416 bytes
 ...ntion_B4_H40_SQ2048_SKV4096_DH128_f16.vmfb |  Bin 0 -> 30104 bytes
 ...ention_B4_H40_SQ2048_SKV4096_DH128_f8.vmfb |  Bin 0 -> 19480 bytes
 ...ntion_B4_H40_SQ4096_SKV1024_DH128_f16.vmfb |  Bin 0 -> 30104 bytes
 ...ention_B4_H40_SQ4096_SKV1024_DH128_f8.vmfb |  Bin 0 -> 19480 bytes
 ...ntion_B4_H40_SQ4096_SKV2048_DH128_f16.vmfb |  Bin 0 -> 30168 bytes
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 ...ntion_B4_H40_SQ4096_SKV4096_DH128_f16.vmfb |  Bin 0 -> 30104 bytes
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 ...ntion_B4_H64_SQ2048_SKV2048_DH128_f16.vmfb |  Bin 0 -> 30104 bytes
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 ...ntion_B4_H64_SQ4096_SKV2048_DH128_f16.vmfb |  Bin 0 -> 30168 bytes
 ...ention_B4_H64_SQ4096_SKV2048_DH128_f8.vmfb |  Bin 0 -> 19480 bytes
 ...ntion_B4_H64_SQ4096_SKV4096_DH128_f16.vmfb |  Bin 0 -> 30104 bytes
 ...ention_B4_H64_SQ4096_SKV4096_DH128_f8.vmfb |  Bin 0 -> 19416 bytes
 ...mlir => attention_and_matmul_spec_f16.mlir |    0
 attention_and_matmul_spec_f8.mlir             |  935 ++++++++++++
 attentionbench/attention_utils.py             |   48 +
 attentionbench/benchmark_torch_attention.py   |   77 +
 attentionbench/benchmark_triton_attention.py  | 1307 +++++++++++++++++
 {gemmbench => attentionbench}/shark_fa2.py    |   61 +-
 gemmbench/__init__.py                         |    1 +
 gemmbench/bench_utils.py                      |   36 +
 gemmbench/benchmark_torch_attention.py        |   89 --
 gemmbench/cli.py                              |  221 ++-
 gemmbench/csv_to_hdf.py                       |   42 +
 gemmbench/problems.py                         |   69 +-
 results/torch_llama_sdxl_attention.csv        |   49 +
 results/triton_llama_sdxl_attention.csv       |   49 +
 src/benchmark/driver.cpp                      |  151 --
 src/benchmark/gemm-bench.cpp                  |   18 +-
 src/benchmark/gemm-bench.hpp                  |    3 +-
 src/benchmark/main.cpp                        |   15 +-
 src/benchmark/run-hipblaslt.cpp               |    1 -
 src/benchmark/run_flashattention.cpp          |   15 +-
 src/benchmark/run_iree.cpp                    |    1 -
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 create mode 100644 attention/vmfb/attention_B2_H40_SQ2048_SKV1024_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B2_H40_SQ2048_SKV2048_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B2_H40_SQ2048_SKV2048_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B2_H40_SQ2048_SKV4096_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B2_H40_SQ2048_SKV4096_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B2_H40_SQ4096_SKV1024_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B2_H40_SQ4096_SKV1024_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B2_H40_SQ4096_SKV2048_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B2_H40_SQ4096_SKV2048_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B2_H40_SQ4096_SKV4096_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B2_H40_SQ4096_SKV4096_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B2_H64_SQ1024_SKV1024_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B2_H64_SQ1024_SKV1024_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B2_H64_SQ1024_SKV2048_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B2_H64_SQ1024_SKV2048_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B2_H64_SQ1024_SKV4096_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B2_H64_SQ1024_SKV4096_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B2_H64_SQ2048_SKV1024_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B2_H64_SQ2048_SKV1024_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B2_H64_SQ2048_SKV2048_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B2_H64_SQ2048_SKV2048_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B2_H64_SQ2048_SKV4096_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B2_H64_SQ2048_SKV4096_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B2_H64_SQ4096_SKV1024_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B2_H64_SQ4096_SKV1024_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B2_H64_SQ4096_SKV2048_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B2_H64_SQ4096_SKV2048_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B2_H64_SQ4096_SKV4096_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B2_H64_SQ4096_SKV4096_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B4_H32_SQ1024_SKV1024_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B4_H32_SQ1024_SKV1024_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B4_H32_SQ1024_SKV2048_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B4_H32_SQ1024_SKV2048_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B4_H32_SQ1024_SKV4096_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B4_H32_SQ1024_SKV4096_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B4_H32_SQ2048_SKV1024_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B4_H32_SQ2048_SKV1024_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B4_H32_SQ2048_SKV2048_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B4_H32_SQ2048_SKV2048_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B4_H32_SQ2048_SKV4096_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B4_H32_SQ2048_SKV4096_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B4_H32_SQ4096_SKV1024_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B4_H32_SQ4096_SKV1024_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B4_H32_SQ4096_SKV2048_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B4_H32_SQ4096_SKV2048_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B4_H32_SQ4096_SKV4096_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B4_H32_SQ4096_SKV4096_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B4_H40_SQ1024_SKV1024_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B4_H40_SQ1024_SKV1024_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B4_H40_SQ1024_SKV2048_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B4_H40_SQ1024_SKV2048_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B4_H40_SQ1024_SKV4096_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B4_H40_SQ1024_SKV4096_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B4_H40_SQ2048_SKV1024_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B4_H40_SQ2048_SKV1024_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B4_H40_SQ2048_SKV2048_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B4_H40_SQ2048_SKV2048_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B4_H40_SQ2048_SKV4096_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B4_H40_SQ2048_SKV4096_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B4_H40_SQ4096_SKV1024_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B4_H40_SQ4096_SKV1024_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B4_H40_SQ4096_SKV2048_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B4_H40_SQ4096_SKV2048_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B4_H40_SQ4096_SKV4096_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B4_H40_SQ4096_SKV4096_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B4_H64_SQ1024_SKV1024_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B4_H64_SQ1024_SKV1024_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B4_H64_SQ1024_SKV2048_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B4_H64_SQ1024_SKV2048_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B4_H64_SQ1024_SKV4096_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B4_H64_SQ1024_SKV4096_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B4_H64_SQ2048_SKV1024_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B4_H64_SQ2048_SKV1024_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B4_H64_SQ2048_SKV2048_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B4_H64_SQ2048_SKV2048_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B4_H64_SQ2048_SKV4096_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B4_H64_SQ2048_SKV4096_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B4_H64_SQ4096_SKV1024_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B4_H64_SQ4096_SKV1024_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B4_H64_SQ4096_SKV2048_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B4_H64_SQ4096_SKV2048_DH128_f8.vmfb
 create mode 100644 attention/vmfb/attention_B4_H64_SQ4096_SKV4096_DH128_f16.vmfb
 create mode 100644 attention/vmfb/attention_B4_H64_SQ4096_SKV4096_DH128_f8.vmfb
 rename attention_and_matmul_spec.mlir => attention_and_matmul_spec_f16.mlir (100%)
 create mode 100644 attention_and_matmul_spec_f8.mlir
 create mode 100644 attentionbench/attention_utils.py
 create mode 100644 attentionbench/benchmark_torch_attention.py
 create mode 100644 attentionbench/benchmark_triton_attention.py
 rename {gemmbench => attentionbench}/shark_fa2.py (68%)
 create mode 100644 gemmbench/bench_utils.py
 delete mode 100644 gemmbench/benchmark_torch_attention.py
 create mode 100644 gemmbench/csv_to_hdf.py
 create mode 100644 results/torch_llama_sdxl_attention.csv
 create mode 100644 results/triton_llama_sdxl_attention.csv
 delete mode 100644 src/benchmark/driver.cpp

diff --git a/.github/workflows/run_bench.yml b/.github/workflows/run_bench.yml
index f3df82a..2d72f36 100644
--- a/.github/workflows/run_bench.yml
+++ b/.github/workflows/run_bench.yml
@@ -62,7 +62,7 @@ jobs:
           cd $GITHUB_WORKSPACE
           source $GITHUB_WORKSPACE/venv/bin/activate
           for device in $(seq 3 7); do (sudo $GITHUB_WORKSPACE/build/gemm-bench --device=$device &); done
-          ./gb run --backends=rocblas --repeat=1 --output=results/rocblas.hdf
+          ./gb run --backends=rocblas --repeat=1 --output=results/rocblas.csv
           sudo pkill -f gemm-bench
           deactivate
 
@@ -72,16 +72,27 @@ jobs:
           cd $GITHUB_WORKSPACE
           source $GITHUB_WORKSPACE/venv/bin/activate
           for device in $(seq 3 7); do (sudo $GITHUB_WORKSPACE/build/gemm-bench --device=$device &); done
-          ./gb run --backends=hipblaslt --repeat=1 --output=results/hipblaslt.hdf
+          ./gb run --backends=hipblaslt --repeat=1 --output=results/hipblaslt.csv
           sudo pkill -f gemm-bench
           deactivate
 
       - name: Run IREE Benchmarks
         run: |
+          sudo pkill -f gemm-bench
+          cd $GITHUB_WORKSPACE
+          source $GITHUB_WORKSPACE/venv/bin/activate
+          for device in $(seq 3 7); do (sudo $GITHUB_WORKSPACE/build/gemm-bench --device=$device &); done
+          ./gb run --backends=iree --repeat=1 --output=results/iree.csv
+          sudo pkill -f gemm-bench
+          deactivate
+
+      - name: Run SHARK Attention Benchmarks
+        run: |
+          sudo pkill -f gemm-bench
           cd $GITHUB_WORKSPACE
           source $GITHUB_WORKSPACE/venv/bin/activate
           for device in $(seq 3 7); do (sudo $GITHUB_WORKSPACE/build/gemm-bench --device=$device &); done
-          ./gb run --backends=iree --repeat=1 --output=results/iree.hdf
+          ./gb run --backends=sharkfa --suite=flash_attention --repeat=1 --output=results/sharkfa_llama_sdxl_attention.csv
           sudo pkill -f gemm-bench
           deactivate
 
diff --git a/.gitignore b/.gitignore
index a128914..5faa7ac 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,6 +2,4 @@ build
 rocm_gemm_venv
 *venv
 __pycache__
-*.hdf
-attention/mlir
-attention/vmfb
\ No newline at end of file
+*.hdf
\ No newline at end of file
diff --git a/.vscode/settings.json b/.vscode/settings.json
index 19b0661..079441f 100644
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@@ -114,11 +114,14 @@
     "syncstream": "c",
     "runtime.hpp": "c",
     "strstream": "cpp",
-    "__functional_03": "cpp"
+    "__functional_03": "cpp",
+    "hash_map": "cpp",
+    "hash_set": "cpp"
   },
   "editor.formatOnSave": true,
   "editor.rulers": [80],
   "clang-format.executable": "usr/bin/clang-format",
   "C_Cpp.errorSquiggles": "enabled",
-  "dotnet.defaultSolution": "disable"
+  "dotnet.defaultSolution": "disable",
+  "git.ignoreLimitWarning": true
 }
diff --git a/attention/mlir/attention_B1_H32_SQ1024_SKV1024_DH128_f16.mlir b/attention/mlir/attention_B1_H32_SQ1024_SKV1024_DH128_f16.mlir
new file mode 100644
index 0000000..8d906fc
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ1024_SKV1024_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,1024,128],f16>, %298 : !torch.vtensor<[1,32,1024,128],f16>, %301 : !torch.vtensor<[1,32,1024,128],f16>) -> !torch.vtensor<[1,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ1024_SKV1024_DH128_f8.mlir b/attention/mlir/attention_B1_H32_SQ1024_SKV1024_DH128_f8.mlir
new file mode 100644
index 0000000..8d906fc
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ1024_SKV1024_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,1024,128],f16>, %298 : !torch.vtensor<[1,32,1024,128],f16>, %301 : !torch.vtensor<[1,32,1024,128],f16>) -> !torch.vtensor<[1,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ1024_SKV1024_DH128_fp16.mlir b/attention/mlir/attention_B1_H32_SQ1024_SKV1024_DH128_fp16.mlir
new file mode 100644
index 0000000..8d906fc
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ1024_SKV1024_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,1024,128],f16>, %298 : !torch.vtensor<[1,32,1024,128],f16>, %301 : !torch.vtensor<[1,32,1024,128],f16>) -> !torch.vtensor<[1,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ1024_SKV1024_DH128_fp8.mlir b/attention/mlir/attention_B1_H32_SQ1024_SKV1024_DH128_fp8.mlir
new file mode 100644
index 0000000..8d906fc
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ1024_SKV1024_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,1024,128],f16>, %298 : !torch.vtensor<[1,32,1024,128],f16>, %301 : !torch.vtensor<[1,32,1024,128],f16>) -> !torch.vtensor<[1,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ1024_SKV2048_DH128_f16.mlir b/attention/mlir/attention_B1_H32_SQ1024_SKV2048_DH128_f16.mlir
new file mode 100644
index 0000000..dc91ab6
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ1024_SKV2048_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,1024,128],f16>, %298 : !torch.vtensor<[1,32,2048,128],f16>, %301 : !torch.vtensor<[1,32,2048,128],f16>) -> !torch.vtensor<[1,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ1024_SKV2048_DH128_f8.mlir b/attention/mlir/attention_B1_H32_SQ1024_SKV2048_DH128_f8.mlir
new file mode 100644
index 0000000..dc91ab6
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ1024_SKV2048_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,1024,128],f16>, %298 : !torch.vtensor<[1,32,2048,128],f16>, %301 : !torch.vtensor<[1,32,2048,128],f16>) -> !torch.vtensor<[1,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ1024_SKV2048_DH128_fp16.mlir b/attention/mlir/attention_B1_H32_SQ1024_SKV2048_DH128_fp16.mlir
new file mode 100644
index 0000000..dc91ab6
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ1024_SKV2048_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,1024,128],f16>, %298 : !torch.vtensor<[1,32,2048,128],f16>, %301 : !torch.vtensor<[1,32,2048,128],f16>) -> !torch.vtensor<[1,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ1024_SKV2048_DH128_fp8.mlir b/attention/mlir/attention_B1_H32_SQ1024_SKV2048_DH128_fp8.mlir
new file mode 100644
index 0000000..dc91ab6
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ1024_SKV2048_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,1024,128],f16>, %298 : !torch.vtensor<[1,32,2048,128],f16>, %301 : !torch.vtensor<[1,32,2048,128],f16>) -> !torch.vtensor<[1,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ1024_SKV4096_DH128_f16.mlir b/attention/mlir/attention_B1_H32_SQ1024_SKV4096_DH128_f16.mlir
new file mode 100644
index 0000000..749431b
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ1024_SKV4096_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,1024,128],f16>, %298 : !torch.vtensor<[1,32,4096,128],f16>, %301 : !torch.vtensor<[1,32,4096,128],f16>) -> !torch.vtensor<[1,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ1024_SKV4096_DH128_f8.mlir b/attention/mlir/attention_B1_H32_SQ1024_SKV4096_DH128_f8.mlir
new file mode 100644
index 0000000..749431b
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ1024_SKV4096_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,1024,128],f16>, %298 : !torch.vtensor<[1,32,4096,128],f16>, %301 : !torch.vtensor<[1,32,4096,128],f16>) -> !torch.vtensor<[1,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ1024_SKV4096_DH128_fp16.mlir b/attention/mlir/attention_B1_H32_SQ1024_SKV4096_DH128_fp16.mlir
new file mode 100644
index 0000000..749431b
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ1024_SKV4096_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,1024,128],f16>, %298 : !torch.vtensor<[1,32,4096,128],f16>, %301 : !torch.vtensor<[1,32,4096,128],f16>) -> !torch.vtensor<[1,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ1024_SKV4096_DH128_fp8.mlir b/attention/mlir/attention_B1_H32_SQ1024_SKV4096_DH128_fp8.mlir
new file mode 100644
index 0000000..749431b
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ1024_SKV4096_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,1024,128],f16>, %298 : !torch.vtensor<[1,32,4096,128],f16>, %301 : !torch.vtensor<[1,32,4096,128],f16>) -> !torch.vtensor<[1,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ2048_SKV1024_DH128_f16.mlir b/attention/mlir/attention_B1_H32_SQ2048_SKV1024_DH128_f16.mlir
new file mode 100644
index 0000000..19bfa2b
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ2048_SKV1024_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,2048,128],f16>, %298 : !torch.vtensor<[1,32,1024,128],f16>, %301 : !torch.vtensor<[1,32,1024,128],f16>) -> !torch.vtensor<[1,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ2048_SKV1024_DH128_f8.mlir b/attention/mlir/attention_B1_H32_SQ2048_SKV1024_DH128_f8.mlir
new file mode 100644
index 0000000..19bfa2b
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ2048_SKV1024_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,2048,128],f16>, %298 : !torch.vtensor<[1,32,1024,128],f16>, %301 : !torch.vtensor<[1,32,1024,128],f16>) -> !torch.vtensor<[1,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ2048_SKV1024_DH128_fp16.mlir b/attention/mlir/attention_B1_H32_SQ2048_SKV1024_DH128_fp16.mlir
new file mode 100644
index 0000000..19bfa2b
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ2048_SKV1024_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,2048,128],f16>, %298 : !torch.vtensor<[1,32,1024,128],f16>, %301 : !torch.vtensor<[1,32,1024,128],f16>) -> !torch.vtensor<[1,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ2048_SKV1024_DH128_fp8.mlir b/attention/mlir/attention_B1_H32_SQ2048_SKV1024_DH128_fp8.mlir
new file mode 100644
index 0000000..19bfa2b
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ2048_SKV1024_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,2048,128],f16>, %298 : !torch.vtensor<[1,32,1024,128],f16>, %301 : !torch.vtensor<[1,32,1024,128],f16>) -> !torch.vtensor<[1,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ2048_SKV2048_DH128_f16.mlir b/attention/mlir/attention_B1_H32_SQ2048_SKV2048_DH128_f16.mlir
new file mode 100644
index 0000000..6036ee2
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ2048_SKV2048_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,2048,128],f16>, %298 : !torch.vtensor<[1,32,2048,128],f16>, %301 : !torch.vtensor<[1,32,2048,128],f16>) -> !torch.vtensor<[1,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ2048_SKV2048_DH128_f8.mlir b/attention/mlir/attention_B1_H32_SQ2048_SKV2048_DH128_f8.mlir
new file mode 100644
index 0000000..6036ee2
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ2048_SKV2048_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,2048,128],f16>, %298 : !torch.vtensor<[1,32,2048,128],f16>, %301 : !torch.vtensor<[1,32,2048,128],f16>) -> !torch.vtensor<[1,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ2048_SKV2048_DH128_fp16.mlir b/attention/mlir/attention_B1_H32_SQ2048_SKV2048_DH128_fp16.mlir
new file mode 100644
index 0000000..6036ee2
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ2048_SKV2048_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,2048,128],f16>, %298 : !torch.vtensor<[1,32,2048,128],f16>, %301 : !torch.vtensor<[1,32,2048,128],f16>) -> !torch.vtensor<[1,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ2048_SKV2048_DH128_fp8.mlir b/attention/mlir/attention_B1_H32_SQ2048_SKV2048_DH128_fp8.mlir
new file mode 100644
index 0000000..6036ee2
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ2048_SKV2048_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,2048,128],f16>, %298 : !torch.vtensor<[1,32,2048,128],f16>, %301 : !torch.vtensor<[1,32,2048,128],f16>) -> !torch.vtensor<[1,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ2048_SKV4096_DH128_f16.mlir b/attention/mlir/attention_B1_H32_SQ2048_SKV4096_DH128_f16.mlir
new file mode 100644
index 0000000..44ce76d
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ2048_SKV4096_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,2048,128],f16>, %298 : !torch.vtensor<[1,32,4096,128],f16>, %301 : !torch.vtensor<[1,32,4096,128],f16>) -> !torch.vtensor<[1,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ2048_SKV4096_DH128_f8.mlir b/attention/mlir/attention_B1_H32_SQ2048_SKV4096_DH128_f8.mlir
new file mode 100644
index 0000000..44ce76d
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ2048_SKV4096_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,2048,128],f16>, %298 : !torch.vtensor<[1,32,4096,128],f16>, %301 : !torch.vtensor<[1,32,4096,128],f16>) -> !torch.vtensor<[1,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ2048_SKV4096_DH128_fp16.mlir b/attention/mlir/attention_B1_H32_SQ2048_SKV4096_DH128_fp16.mlir
new file mode 100644
index 0000000..44ce76d
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ2048_SKV4096_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,2048,128],f16>, %298 : !torch.vtensor<[1,32,4096,128],f16>, %301 : !torch.vtensor<[1,32,4096,128],f16>) -> !torch.vtensor<[1,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ2048_SKV4096_DH128_fp8.mlir b/attention/mlir/attention_B1_H32_SQ2048_SKV4096_DH128_fp8.mlir
new file mode 100644
index 0000000..44ce76d
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ2048_SKV4096_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,2048,128],f16>, %298 : !torch.vtensor<[1,32,4096,128],f16>, %301 : !torch.vtensor<[1,32,4096,128],f16>) -> !torch.vtensor<[1,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ4096_SKV1024_DH128_f16.mlir b/attention/mlir/attention_B1_H32_SQ4096_SKV1024_DH128_f16.mlir
new file mode 100644
index 0000000..2690e4a
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ4096_SKV1024_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,4096,128],f16>, %298 : !torch.vtensor<[1,32,1024,128],f16>, %301 : !torch.vtensor<[1,32,1024,128],f16>) -> !torch.vtensor<[1,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ4096_SKV1024_DH128_f8.mlir b/attention/mlir/attention_B1_H32_SQ4096_SKV1024_DH128_f8.mlir
new file mode 100644
index 0000000..2690e4a
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ4096_SKV1024_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,4096,128],f16>, %298 : !torch.vtensor<[1,32,1024,128],f16>, %301 : !torch.vtensor<[1,32,1024,128],f16>) -> !torch.vtensor<[1,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ4096_SKV1024_DH128_fp16.mlir b/attention/mlir/attention_B1_H32_SQ4096_SKV1024_DH128_fp16.mlir
new file mode 100644
index 0000000..2690e4a
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ4096_SKV1024_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,4096,128],f16>, %298 : !torch.vtensor<[1,32,1024,128],f16>, %301 : !torch.vtensor<[1,32,1024,128],f16>) -> !torch.vtensor<[1,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ4096_SKV1024_DH128_fp8.mlir b/attention/mlir/attention_B1_H32_SQ4096_SKV1024_DH128_fp8.mlir
new file mode 100644
index 0000000..2690e4a
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ4096_SKV1024_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,4096,128],f16>, %298 : !torch.vtensor<[1,32,1024,128],f16>, %301 : !torch.vtensor<[1,32,1024,128],f16>) -> !torch.vtensor<[1,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,1024,128],f16>, !torch.vtensor<[1,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ4096_SKV2048_DH128_f16.mlir b/attention/mlir/attention_B1_H32_SQ4096_SKV2048_DH128_f16.mlir
new file mode 100644
index 0000000..87f4c6a
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ4096_SKV2048_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,4096,128],f16>, %298 : !torch.vtensor<[1,32,2048,128],f16>, %301 : !torch.vtensor<[1,32,2048,128],f16>) -> !torch.vtensor<[1,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ4096_SKV2048_DH128_f8.mlir b/attention/mlir/attention_B1_H32_SQ4096_SKV2048_DH128_f8.mlir
new file mode 100644
index 0000000..87f4c6a
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ4096_SKV2048_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,4096,128],f16>, %298 : !torch.vtensor<[1,32,2048,128],f16>, %301 : !torch.vtensor<[1,32,2048,128],f16>) -> !torch.vtensor<[1,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ4096_SKV2048_DH128_fp16.mlir b/attention/mlir/attention_B1_H32_SQ4096_SKV2048_DH128_fp16.mlir
new file mode 100644
index 0000000..87f4c6a
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ4096_SKV2048_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,4096,128],f16>, %298 : !torch.vtensor<[1,32,2048,128],f16>, %301 : !torch.vtensor<[1,32,2048,128],f16>) -> !torch.vtensor<[1,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ4096_SKV2048_DH128_fp8.mlir b/attention/mlir/attention_B1_H32_SQ4096_SKV2048_DH128_fp8.mlir
new file mode 100644
index 0000000..87f4c6a
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ4096_SKV2048_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,4096,128],f16>, %298 : !torch.vtensor<[1,32,2048,128],f16>, %301 : !torch.vtensor<[1,32,2048,128],f16>) -> !torch.vtensor<[1,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,2048,128],f16>, !torch.vtensor<[1,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ4096_SKV4096_DH128_f16.mlir b/attention/mlir/attention_B1_H32_SQ4096_SKV4096_DH128_f16.mlir
new file mode 100644
index 0000000..4a9a643
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ4096_SKV4096_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,4096,128],f16>, %298 : !torch.vtensor<[1,32,4096,128],f16>, %301 : !torch.vtensor<[1,32,4096,128],f16>) -> !torch.vtensor<[1,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ4096_SKV4096_DH128_f8.mlir b/attention/mlir/attention_B1_H32_SQ4096_SKV4096_DH128_f8.mlir
new file mode 100644
index 0000000..4a9a643
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ4096_SKV4096_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,4096,128],f16>, %298 : !torch.vtensor<[1,32,4096,128],f16>, %301 : !torch.vtensor<[1,32,4096,128],f16>) -> !torch.vtensor<[1,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ4096_SKV4096_DH128_fp16.mlir b/attention/mlir/attention_B1_H32_SQ4096_SKV4096_DH128_fp16.mlir
new file mode 100644
index 0000000..4a9a643
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ4096_SKV4096_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,4096,128],f16>, %298 : !torch.vtensor<[1,32,4096,128],f16>, %301 : !torch.vtensor<[1,32,4096,128],f16>) -> !torch.vtensor<[1,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H32_SQ4096_SKV4096_DH128_fp8.mlir b/attention/mlir/attention_B1_H32_SQ4096_SKV4096_DH128_fp8.mlir
new file mode 100644
index 0000000..4a9a643
--- /dev/null
+++ b/attention/mlir/attention_B1_H32_SQ4096_SKV4096_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,32,4096,128],f16>, %298 : !torch.vtensor<[1,32,4096,128],f16>, %301 : !torch.vtensor<[1,32,4096,128],f16>) -> !torch.vtensor<[1,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,32,4096,128],f16>, !torch.vtensor<[1,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ1024_SKV1024_DH128_f16.mlir b/attention/mlir/attention_B1_H40_SQ1024_SKV1024_DH128_f16.mlir
new file mode 100644
index 0000000..e55a29d
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ1024_SKV1024_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,1024,128],f16>, %298 : !torch.vtensor<[1,40,1024,128],f16>, %301 : !torch.vtensor<[1,40,1024,128],f16>) -> !torch.vtensor<[1,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ1024_SKV1024_DH128_f8.mlir b/attention/mlir/attention_B1_H40_SQ1024_SKV1024_DH128_f8.mlir
new file mode 100644
index 0000000..e55a29d
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ1024_SKV1024_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,1024,128],f16>, %298 : !torch.vtensor<[1,40,1024,128],f16>, %301 : !torch.vtensor<[1,40,1024,128],f16>) -> !torch.vtensor<[1,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ1024_SKV1024_DH128_fp16.mlir b/attention/mlir/attention_B1_H40_SQ1024_SKV1024_DH128_fp16.mlir
new file mode 100644
index 0000000..e55a29d
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ1024_SKV1024_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,1024,128],f16>, %298 : !torch.vtensor<[1,40,1024,128],f16>, %301 : !torch.vtensor<[1,40,1024,128],f16>) -> !torch.vtensor<[1,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ1024_SKV1024_DH128_fp8.mlir b/attention/mlir/attention_B1_H40_SQ1024_SKV1024_DH128_fp8.mlir
new file mode 100644
index 0000000..e55a29d
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ1024_SKV1024_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,1024,128],f16>, %298 : !torch.vtensor<[1,40,1024,128],f16>, %301 : !torch.vtensor<[1,40,1024,128],f16>) -> !torch.vtensor<[1,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ1024_SKV2048_DH128_f16.mlir b/attention/mlir/attention_B1_H40_SQ1024_SKV2048_DH128_f16.mlir
new file mode 100644
index 0000000..5a8c0d7
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ1024_SKV2048_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,1024,128],f16>, %298 : !torch.vtensor<[1,40,2048,128],f16>, %301 : !torch.vtensor<[1,40,2048,128],f16>) -> !torch.vtensor<[1,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ1024_SKV2048_DH128_f8.mlir b/attention/mlir/attention_B1_H40_SQ1024_SKV2048_DH128_f8.mlir
new file mode 100644
index 0000000..5a8c0d7
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ1024_SKV2048_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,1024,128],f16>, %298 : !torch.vtensor<[1,40,2048,128],f16>, %301 : !torch.vtensor<[1,40,2048,128],f16>) -> !torch.vtensor<[1,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ1024_SKV2048_DH128_fp16.mlir b/attention/mlir/attention_B1_H40_SQ1024_SKV2048_DH128_fp16.mlir
new file mode 100644
index 0000000..5a8c0d7
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ1024_SKV2048_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,1024,128],f16>, %298 : !torch.vtensor<[1,40,2048,128],f16>, %301 : !torch.vtensor<[1,40,2048,128],f16>) -> !torch.vtensor<[1,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ1024_SKV2048_DH128_fp8.mlir b/attention/mlir/attention_B1_H40_SQ1024_SKV2048_DH128_fp8.mlir
new file mode 100644
index 0000000..5a8c0d7
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ1024_SKV2048_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,1024,128],f16>, %298 : !torch.vtensor<[1,40,2048,128],f16>, %301 : !torch.vtensor<[1,40,2048,128],f16>) -> !torch.vtensor<[1,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ1024_SKV4096_DH128_f16.mlir b/attention/mlir/attention_B1_H40_SQ1024_SKV4096_DH128_f16.mlir
new file mode 100644
index 0000000..00f1de6
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ1024_SKV4096_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,1024,128],f16>, %298 : !torch.vtensor<[1,40,4096,128],f16>, %301 : !torch.vtensor<[1,40,4096,128],f16>) -> !torch.vtensor<[1,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ1024_SKV4096_DH128_f8.mlir b/attention/mlir/attention_B1_H40_SQ1024_SKV4096_DH128_f8.mlir
new file mode 100644
index 0000000..00f1de6
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ1024_SKV4096_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,1024,128],f16>, %298 : !torch.vtensor<[1,40,4096,128],f16>, %301 : !torch.vtensor<[1,40,4096,128],f16>) -> !torch.vtensor<[1,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ1024_SKV4096_DH128_fp16.mlir b/attention/mlir/attention_B1_H40_SQ1024_SKV4096_DH128_fp16.mlir
new file mode 100644
index 0000000..00f1de6
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ1024_SKV4096_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,1024,128],f16>, %298 : !torch.vtensor<[1,40,4096,128],f16>, %301 : !torch.vtensor<[1,40,4096,128],f16>) -> !torch.vtensor<[1,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ1024_SKV4096_DH128_fp8.mlir b/attention/mlir/attention_B1_H40_SQ1024_SKV4096_DH128_fp8.mlir
new file mode 100644
index 0000000..00f1de6
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ1024_SKV4096_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,1024,128],f16>, %298 : !torch.vtensor<[1,40,4096,128],f16>, %301 : !torch.vtensor<[1,40,4096,128],f16>) -> !torch.vtensor<[1,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ2048_SKV1024_DH128_f16.mlir b/attention/mlir/attention_B1_H40_SQ2048_SKV1024_DH128_f16.mlir
new file mode 100644
index 0000000..5204bd7
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ2048_SKV1024_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,2048,128],f16>, %298 : !torch.vtensor<[1,40,1024,128],f16>, %301 : !torch.vtensor<[1,40,1024,128],f16>) -> !torch.vtensor<[1,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ2048_SKV1024_DH128_f8.mlir b/attention/mlir/attention_B1_H40_SQ2048_SKV1024_DH128_f8.mlir
new file mode 100644
index 0000000..5204bd7
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ2048_SKV1024_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,2048,128],f16>, %298 : !torch.vtensor<[1,40,1024,128],f16>, %301 : !torch.vtensor<[1,40,1024,128],f16>) -> !torch.vtensor<[1,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ2048_SKV1024_DH128_fp16.mlir b/attention/mlir/attention_B1_H40_SQ2048_SKV1024_DH128_fp16.mlir
new file mode 100644
index 0000000..5204bd7
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ2048_SKV1024_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,2048,128],f16>, %298 : !torch.vtensor<[1,40,1024,128],f16>, %301 : !torch.vtensor<[1,40,1024,128],f16>) -> !torch.vtensor<[1,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ2048_SKV1024_DH128_fp8.mlir b/attention/mlir/attention_B1_H40_SQ2048_SKV1024_DH128_fp8.mlir
new file mode 100644
index 0000000..5204bd7
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ2048_SKV1024_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,2048,128],f16>, %298 : !torch.vtensor<[1,40,1024,128],f16>, %301 : !torch.vtensor<[1,40,1024,128],f16>) -> !torch.vtensor<[1,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ2048_SKV2048_DH128_f16.mlir b/attention/mlir/attention_B1_H40_SQ2048_SKV2048_DH128_f16.mlir
new file mode 100644
index 0000000..c9761e6
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ2048_SKV2048_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,2048,128],f16>, %298 : !torch.vtensor<[1,40,2048,128],f16>, %301 : !torch.vtensor<[1,40,2048,128],f16>) -> !torch.vtensor<[1,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ2048_SKV2048_DH128_f8.mlir b/attention/mlir/attention_B1_H40_SQ2048_SKV2048_DH128_f8.mlir
new file mode 100644
index 0000000..c9761e6
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ2048_SKV2048_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,2048,128],f16>, %298 : !torch.vtensor<[1,40,2048,128],f16>, %301 : !torch.vtensor<[1,40,2048,128],f16>) -> !torch.vtensor<[1,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ2048_SKV2048_DH128_fp16.mlir b/attention/mlir/attention_B1_H40_SQ2048_SKV2048_DH128_fp16.mlir
new file mode 100644
index 0000000..c9761e6
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ2048_SKV2048_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,2048,128],f16>, %298 : !torch.vtensor<[1,40,2048,128],f16>, %301 : !torch.vtensor<[1,40,2048,128],f16>) -> !torch.vtensor<[1,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ2048_SKV2048_DH128_fp8.mlir b/attention/mlir/attention_B1_H40_SQ2048_SKV2048_DH128_fp8.mlir
new file mode 100644
index 0000000..c9761e6
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ2048_SKV2048_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,2048,128],f16>, %298 : !torch.vtensor<[1,40,2048,128],f16>, %301 : !torch.vtensor<[1,40,2048,128],f16>) -> !torch.vtensor<[1,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ2048_SKV4096_DH128_f16.mlir b/attention/mlir/attention_B1_H40_SQ2048_SKV4096_DH128_f16.mlir
new file mode 100644
index 0000000..e99a61c
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ2048_SKV4096_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,2048,128],f16>, %298 : !torch.vtensor<[1,40,4096,128],f16>, %301 : !torch.vtensor<[1,40,4096,128],f16>) -> !torch.vtensor<[1,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ2048_SKV4096_DH128_f8.mlir b/attention/mlir/attention_B1_H40_SQ2048_SKV4096_DH128_f8.mlir
new file mode 100644
index 0000000..e99a61c
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ2048_SKV4096_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,2048,128],f16>, %298 : !torch.vtensor<[1,40,4096,128],f16>, %301 : !torch.vtensor<[1,40,4096,128],f16>) -> !torch.vtensor<[1,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ2048_SKV4096_DH128_fp16.mlir b/attention/mlir/attention_B1_H40_SQ2048_SKV4096_DH128_fp16.mlir
new file mode 100644
index 0000000..e99a61c
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ2048_SKV4096_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,2048,128],f16>, %298 : !torch.vtensor<[1,40,4096,128],f16>, %301 : !torch.vtensor<[1,40,4096,128],f16>) -> !torch.vtensor<[1,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ2048_SKV4096_DH128_fp8.mlir b/attention/mlir/attention_B1_H40_SQ2048_SKV4096_DH128_fp8.mlir
new file mode 100644
index 0000000..e99a61c
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ2048_SKV4096_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,2048,128],f16>, %298 : !torch.vtensor<[1,40,4096,128],f16>, %301 : !torch.vtensor<[1,40,4096,128],f16>) -> !torch.vtensor<[1,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ4096_SKV1024_DH128_f16.mlir b/attention/mlir/attention_B1_H40_SQ4096_SKV1024_DH128_f16.mlir
new file mode 100644
index 0000000..d05fa28
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ4096_SKV1024_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,4096,128],f16>, %298 : !torch.vtensor<[1,40,1024,128],f16>, %301 : !torch.vtensor<[1,40,1024,128],f16>) -> !torch.vtensor<[1,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ4096_SKV1024_DH128_f8.mlir b/attention/mlir/attention_B1_H40_SQ4096_SKV1024_DH128_f8.mlir
new file mode 100644
index 0000000..d05fa28
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ4096_SKV1024_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,4096,128],f16>, %298 : !torch.vtensor<[1,40,1024,128],f16>, %301 : !torch.vtensor<[1,40,1024,128],f16>) -> !torch.vtensor<[1,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ4096_SKV1024_DH128_fp16.mlir b/attention/mlir/attention_B1_H40_SQ4096_SKV1024_DH128_fp16.mlir
new file mode 100644
index 0000000..d05fa28
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ4096_SKV1024_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,4096,128],f16>, %298 : !torch.vtensor<[1,40,1024,128],f16>, %301 : !torch.vtensor<[1,40,1024,128],f16>) -> !torch.vtensor<[1,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ4096_SKV1024_DH128_fp8.mlir b/attention/mlir/attention_B1_H40_SQ4096_SKV1024_DH128_fp8.mlir
new file mode 100644
index 0000000..d05fa28
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ4096_SKV1024_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,4096,128],f16>, %298 : !torch.vtensor<[1,40,1024,128],f16>, %301 : !torch.vtensor<[1,40,1024,128],f16>) -> !torch.vtensor<[1,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,1024,128],f16>, !torch.vtensor<[1,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ4096_SKV2048_DH128_f16.mlir b/attention/mlir/attention_B1_H40_SQ4096_SKV2048_DH128_f16.mlir
new file mode 100644
index 0000000..8d1a244
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ4096_SKV2048_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,4096,128],f16>, %298 : !torch.vtensor<[1,40,2048,128],f16>, %301 : !torch.vtensor<[1,40,2048,128],f16>) -> !torch.vtensor<[1,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ4096_SKV2048_DH128_f8.mlir b/attention/mlir/attention_B1_H40_SQ4096_SKV2048_DH128_f8.mlir
new file mode 100644
index 0000000..8d1a244
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ4096_SKV2048_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,4096,128],f16>, %298 : !torch.vtensor<[1,40,2048,128],f16>, %301 : !torch.vtensor<[1,40,2048,128],f16>) -> !torch.vtensor<[1,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ4096_SKV2048_DH128_fp16.mlir b/attention/mlir/attention_B1_H40_SQ4096_SKV2048_DH128_fp16.mlir
new file mode 100644
index 0000000..8d1a244
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ4096_SKV2048_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,4096,128],f16>, %298 : !torch.vtensor<[1,40,2048,128],f16>, %301 : !torch.vtensor<[1,40,2048,128],f16>) -> !torch.vtensor<[1,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ4096_SKV2048_DH128_fp8.mlir b/attention/mlir/attention_B1_H40_SQ4096_SKV2048_DH128_fp8.mlir
new file mode 100644
index 0000000..8d1a244
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ4096_SKV2048_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,4096,128],f16>, %298 : !torch.vtensor<[1,40,2048,128],f16>, %301 : !torch.vtensor<[1,40,2048,128],f16>) -> !torch.vtensor<[1,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,2048,128],f16>, !torch.vtensor<[1,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ4096_SKV4096_DH128_f16.mlir b/attention/mlir/attention_B1_H40_SQ4096_SKV4096_DH128_f16.mlir
new file mode 100644
index 0000000..736628a
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ4096_SKV4096_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,4096,128],f16>, %298 : !torch.vtensor<[1,40,4096,128],f16>, %301 : !torch.vtensor<[1,40,4096,128],f16>) -> !torch.vtensor<[1,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ4096_SKV4096_DH128_f8.mlir b/attention/mlir/attention_B1_H40_SQ4096_SKV4096_DH128_f8.mlir
new file mode 100644
index 0000000..736628a
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ4096_SKV4096_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,4096,128],f16>, %298 : !torch.vtensor<[1,40,4096,128],f16>, %301 : !torch.vtensor<[1,40,4096,128],f16>) -> !torch.vtensor<[1,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ4096_SKV4096_DH128_fp16.mlir b/attention/mlir/attention_B1_H40_SQ4096_SKV4096_DH128_fp16.mlir
new file mode 100644
index 0000000..736628a
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ4096_SKV4096_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,4096,128],f16>, %298 : !torch.vtensor<[1,40,4096,128],f16>, %301 : !torch.vtensor<[1,40,4096,128],f16>) -> !torch.vtensor<[1,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H40_SQ4096_SKV4096_DH128_fp8.mlir b/attention/mlir/attention_B1_H40_SQ4096_SKV4096_DH128_fp8.mlir
new file mode 100644
index 0000000..736628a
--- /dev/null
+++ b/attention/mlir/attention_B1_H40_SQ4096_SKV4096_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,40,4096,128],f16>, %298 : !torch.vtensor<[1,40,4096,128],f16>, %301 : !torch.vtensor<[1,40,4096,128],f16>) -> !torch.vtensor<[1,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,40,4096,128],f16>, !torch.vtensor<[1,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ1024_SKV1024_DH128_f16.mlir b/attention/mlir/attention_B1_H64_SQ1024_SKV1024_DH128_f16.mlir
new file mode 100644
index 0000000..1e32ec9
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ1024_SKV1024_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,1024,128],f16>, %298 : !torch.vtensor<[1,64,1024,128],f16>, %301 : !torch.vtensor<[1,64,1024,128],f16>) -> !torch.vtensor<[1,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ1024_SKV1024_DH128_f8.mlir b/attention/mlir/attention_B1_H64_SQ1024_SKV1024_DH128_f8.mlir
new file mode 100644
index 0000000..1e32ec9
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ1024_SKV1024_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,1024,128],f16>, %298 : !torch.vtensor<[1,64,1024,128],f16>, %301 : !torch.vtensor<[1,64,1024,128],f16>) -> !torch.vtensor<[1,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ1024_SKV1024_DH128_fp16.mlir b/attention/mlir/attention_B1_H64_SQ1024_SKV1024_DH128_fp16.mlir
new file mode 100644
index 0000000..1e32ec9
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ1024_SKV1024_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,1024,128],f16>, %298 : !torch.vtensor<[1,64,1024,128],f16>, %301 : !torch.vtensor<[1,64,1024,128],f16>) -> !torch.vtensor<[1,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ1024_SKV1024_DH128_fp8.mlir b/attention/mlir/attention_B1_H64_SQ1024_SKV1024_DH128_fp8.mlir
new file mode 100644
index 0000000..1e32ec9
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ1024_SKV1024_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,1024,128],f16>, %298 : !torch.vtensor<[1,64,1024,128],f16>, %301 : !torch.vtensor<[1,64,1024,128],f16>) -> !torch.vtensor<[1,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ1024_SKV2048_DH128_f16.mlir b/attention/mlir/attention_B1_H64_SQ1024_SKV2048_DH128_f16.mlir
new file mode 100644
index 0000000..a0d8435
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ1024_SKV2048_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,1024,128],f16>, %298 : !torch.vtensor<[1,64,2048,128],f16>, %301 : !torch.vtensor<[1,64,2048,128],f16>) -> !torch.vtensor<[1,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ1024_SKV2048_DH128_f8.mlir b/attention/mlir/attention_B1_H64_SQ1024_SKV2048_DH128_f8.mlir
new file mode 100644
index 0000000..a0d8435
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ1024_SKV2048_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,1024,128],f16>, %298 : !torch.vtensor<[1,64,2048,128],f16>, %301 : !torch.vtensor<[1,64,2048,128],f16>) -> !torch.vtensor<[1,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ1024_SKV2048_DH128_fp16.mlir b/attention/mlir/attention_B1_H64_SQ1024_SKV2048_DH128_fp16.mlir
new file mode 100644
index 0000000..a0d8435
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ1024_SKV2048_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,1024,128],f16>, %298 : !torch.vtensor<[1,64,2048,128],f16>, %301 : !torch.vtensor<[1,64,2048,128],f16>) -> !torch.vtensor<[1,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ1024_SKV2048_DH128_fp8.mlir b/attention/mlir/attention_B1_H64_SQ1024_SKV2048_DH128_fp8.mlir
new file mode 100644
index 0000000..a0d8435
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ1024_SKV2048_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,1024,128],f16>, %298 : !torch.vtensor<[1,64,2048,128],f16>, %301 : !torch.vtensor<[1,64,2048,128],f16>) -> !torch.vtensor<[1,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ1024_SKV4096_DH128_f16.mlir b/attention/mlir/attention_B1_H64_SQ1024_SKV4096_DH128_f16.mlir
new file mode 100644
index 0000000..bc28d65
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ1024_SKV4096_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,1024,128],f16>, %298 : !torch.vtensor<[1,64,4096,128],f16>, %301 : !torch.vtensor<[1,64,4096,128],f16>) -> !torch.vtensor<[1,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ1024_SKV4096_DH128_f8.mlir b/attention/mlir/attention_B1_H64_SQ1024_SKV4096_DH128_f8.mlir
new file mode 100644
index 0000000..bc28d65
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ1024_SKV4096_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,1024,128],f16>, %298 : !torch.vtensor<[1,64,4096,128],f16>, %301 : !torch.vtensor<[1,64,4096,128],f16>) -> !torch.vtensor<[1,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ1024_SKV4096_DH128_fp16.mlir b/attention/mlir/attention_B1_H64_SQ1024_SKV4096_DH128_fp16.mlir
new file mode 100644
index 0000000..bc28d65
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ1024_SKV4096_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,1024,128],f16>, %298 : !torch.vtensor<[1,64,4096,128],f16>, %301 : !torch.vtensor<[1,64,4096,128],f16>) -> !torch.vtensor<[1,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ1024_SKV4096_DH128_fp8.mlir b/attention/mlir/attention_B1_H64_SQ1024_SKV4096_DH128_fp8.mlir
new file mode 100644
index 0000000..bc28d65
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ1024_SKV4096_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,1024,128],f16>, %298 : !torch.vtensor<[1,64,4096,128],f16>, %301 : !torch.vtensor<[1,64,4096,128],f16>) -> !torch.vtensor<[1,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[1,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ2048_SKV1024_DH128_f16.mlir b/attention/mlir/attention_B1_H64_SQ2048_SKV1024_DH128_f16.mlir
new file mode 100644
index 0000000..40c9a8a
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ2048_SKV1024_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,2048,128],f16>, %298 : !torch.vtensor<[1,64,1024,128],f16>, %301 : !torch.vtensor<[1,64,1024,128],f16>) -> !torch.vtensor<[1,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ2048_SKV1024_DH128_f8.mlir b/attention/mlir/attention_B1_H64_SQ2048_SKV1024_DH128_f8.mlir
new file mode 100644
index 0000000..40c9a8a
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ2048_SKV1024_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,2048,128],f16>, %298 : !torch.vtensor<[1,64,1024,128],f16>, %301 : !torch.vtensor<[1,64,1024,128],f16>) -> !torch.vtensor<[1,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ2048_SKV1024_DH128_fp16.mlir b/attention/mlir/attention_B1_H64_SQ2048_SKV1024_DH128_fp16.mlir
new file mode 100644
index 0000000..40c9a8a
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ2048_SKV1024_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,2048,128],f16>, %298 : !torch.vtensor<[1,64,1024,128],f16>, %301 : !torch.vtensor<[1,64,1024,128],f16>) -> !torch.vtensor<[1,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ2048_SKV1024_DH128_fp8.mlir b/attention/mlir/attention_B1_H64_SQ2048_SKV1024_DH128_fp8.mlir
new file mode 100644
index 0000000..40c9a8a
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ2048_SKV1024_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,2048,128],f16>, %298 : !torch.vtensor<[1,64,1024,128],f16>, %301 : !torch.vtensor<[1,64,1024,128],f16>) -> !torch.vtensor<[1,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ2048_SKV2048_DH128_f16.mlir b/attention/mlir/attention_B1_H64_SQ2048_SKV2048_DH128_f16.mlir
new file mode 100644
index 0000000..f674ae6
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ2048_SKV2048_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,2048,128],f16>, %298 : !torch.vtensor<[1,64,2048,128],f16>, %301 : !torch.vtensor<[1,64,2048,128],f16>) -> !torch.vtensor<[1,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ2048_SKV2048_DH128_f8.mlir b/attention/mlir/attention_B1_H64_SQ2048_SKV2048_DH128_f8.mlir
new file mode 100644
index 0000000..f674ae6
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ2048_SKV2048_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,2048,128],f16>, %298 : !torch.vtensor<[1,64,2048,128],f16>, %301 : !torch.vtensor<[1,64,2048,128],f16>) -> !torch.vtensor<[1,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ2048_SKV2048_DH128_fp16.mlir b/attention/mlir/attention_B1_H64_SQ2048_SKV2048_DH128_fp16.mlir
new file mode 100644
index 0000000..f674ae6
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ2048_SKV2048_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,2048,128],f16>, %298 : !torch.vtensor<[1,64,2048,128],f16>, %301 : !torch.vtensor<[1,64,2048,128],f16>) -> !torch.vtensor<[1,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ2048_SKV2048_DH128_fp8.mlir b/attention/mlir/attention_B1_H64_SQ2048_SKV2048_DH128_fp8.mlir
new file mode 100644
index 0000000..f674ae6
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ2048_SKV2048_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,2048,128],f16>, %298 : !torch.vtensor<[1,64,2048,128],f16>, %301 : !torch.vtensor<[1,64,2048,128],f16>) -> !torch.vtensor<[1,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ2048_SKV4096_DH128_f16.mlir b/attention/mlir/attention_B1_H64_SQ2048_SKV4096_DH128_f16.mlir
new file mode 100644
index 0000000..287734c
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ2048_SKV4096_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,2048,128],f16>, %298 : !torch.vtensor<[1,64,4096,128],f16>, %301 : !torch.vtensor<[1,64,4096,128],f16>) -> !torch.vtensor<[1,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ2048_SKV4096_DH128_f8.mlir b/attention/mlir/attention_B1_H64_SQ2048_SKV4096_DH128_f8.mlir
new file mode 100644
index 0000000..287734c
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ2048_SKV4096_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,2048,128],f16>, %298 : !torch.vtensor<[1,64,4096,128],f16>, %301 : !torch.vtensor<[1,64,4096,128],f16>) -> !torch.vtensor<[1,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ2048_SKV4096_DH128_fp16.mlir b/attention/mlir/attention_B1_H64_SQ2048_SKV4096_DH128_fp16.mlir
new file mode 100644
index 0000000..287734c
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ2048_SKV4096_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,2048,128],f16>, %298 : !torch.vtensor<[1,64,4096,128],f16>, %301 : !torch.vtensor<[1,64,4096,128],f16>) -> !torch.vtensor<[1,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ2048_SKV4096_DH128_fp8.mlir b/attention/mlir/attention_B1_H64_SQ2048_SKV4096_DH128_fp8.mlir
new file mode 100644
index 0000000..287734c
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ2048_SKV4096_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,2048,128],f16>, %298 : !torch.vtensor<[1,64,4096,128],f16>, %301 : !torch.vtensor<[1,64,4096,128],f16>) -> !torch.vtensor<[1,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[1,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ4096_SKV1024_DH128_f16.mlir b/attention/mlir/attention_B1_H64_SQ4096_SKV1024_DH128_f16.mlir
new file mode 100644
index 0000000..b820d89
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ4096_SKV1024_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,4096,128],f16>, %298 : !torch.vtensor<[1,64,1024,128],f16>, %301 : !torch.vtensor<[1,64,1024,128],f16>) -> !torch.vtensor<[1,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ4096_SKV1024_DH128_f8.mlir b/attention/mlir/attention_B1_H64_SQ4096_SKV1024_DH128_f8.mlir
new file mode 100644
index 0000000..b820d89
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ4096_SKV1024_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,4096,128],f16>, %298 : !torch.vtensor<[1,64,1024,128],f16>, %301 : !torch.vtensor<[1,64,1024,128],f16>) -> !torch.vtensor<[1,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ4096_SKV1024_DH128_fp16.mlir b/attention/mlir/attention_B1_H64_SQ4096_SKV1024_DH128_fp16.mlir
new file mode 100644
index 0000000..b820d89
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ4096_SKV1024_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,4096,128],f16>, %298 : !torch.vtensor<[1,64,1024,128],f16>, %301 : !torch.vtensor<[1,64,1024,128],f16>) -> !torch.vtensor<[1,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ4096_SKV1024_DH128_fp8.mlir b/attention/mlir/attention_B1_H64_SQ4096_SKV1024_DH128_fp8.mlir
new file mode 100644
index 0000000..b820d89
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ4096_SKV1024_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,4096,128],f16>, %298 : !torch.vtensor<[1,64,1024,128],f16>, %301 : !torch.vtensor<[1,64,1024,128],f16>) -> !torch.vtensor<[1,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,1024,128],f16>, !torch.vtensor<[1,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ4096_SKV2048_DH128_f16.mlir b/attention/mlir/attention_B1_H64_SQ4096_SKV2048_DH128_f16.mlir
new file mode 100644
index 0000000..ae01ffa
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ4096_SKV2048_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,4096,128],f16>, %298 : !torch.vtensor<[1,64,2048,128],f16>, %301 : !torch.vtensor<[1,64,2048,128],f16>) -> !torch.vtensor<[1,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ4096_SKV2048_DH128_f8.mlir b/attention/mlir/attention_B1_H64_SQ4096_SKV2048_DH128_f8.mlir
new file mode 100644
index 0000000..ae01ffa
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ4096_SKV2048_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,4096,128],f16>, %298 : !torch.vtensor<[1,64,2048,128],f16>, %301 : !torch.vtensor<[1,64,2048,128],f16>) -> !torch.vtensor<[1,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ4096_SKV2048_DH128_fp16.mlir b/attention/mlir/attention_B1_H64_SQ4096_SKV2048_DH128_fp16.mlir
new file mode 100644
index 0000000..ae01ffa
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ4096_SKV2048_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,4096,128],f16>, %298 : !torch.vtensor<[1,64,2048,128],f16>, %301 : !torch.vtensor<[1,64,2048,128],f16>) -> !torch.vtensor<[1,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ4096_SKV2048_DH128_fp8.mlir b/attention/mlir/attention_B1_H64_SQ4096_SKV2048_DH128_fp8.mlir
new file mode 100644
index 0000000..ae01ffa
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ4096_SKV2048_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,4096,128],f16>, %298 : !torch.vtensor<[1,64,2048,128],f16>, %301 : !torch.vtensor<[1,64,2048,128],f16>) -> !torch.vtensor<[1,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,2048,128],f16>, !torch.vtensor<[1,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ4096_SKV4096_DH128_f16.mlir b/attention/mlir/attention_B1_H64_SQ4096_SKV4096_DH128_f16.mlir
new file mode 100644
index 0000000..d85089e
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ4096_SKV4096_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,4096,128],f16>, %298 : !torch.vtensor<[1,64,4096,128],f16>, %301 : !torch.vtensor<[1,64,4096,128],f16>) -> !torch.vtensor<[1,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ4096_SKV4096_DH128_f8.mlir b/attention/mlir/attention_B1_H64_SQ4096_SKV4096_DH128_f8.mlir
new file mode 100644
index 0000000..d85089e
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ4096_SKV4096_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,4096,128],f16>, %298 : !torch.vtensor<[1,64,4096,128],f16>, %301 : !torch.vtensor<[1,64,4096,128],f16>) -> !torch.vtensor<[1,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ4096_SKV4096_DH128_fp16.mlir b/attention/mlir/attention_B1_H64_SQ4096_SKV4096_DH128_fp16.mlir
new file mode 100644
index 0000000..d85089e
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ4096_SKV4096_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,4096,128],f16>, %298 : !torch.vtensor<[1,64,4096,128],f16>, %301 : !torch.vtensor<[1,64,4096,128],f16>) -> !torch.vtensor<[1,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B1_H64_SQ4096_SKV4096_DH128_fp8.mlir b/attention/mlir/attention_B1_H64_SQ4096_SKV4096_DH128_fp8.mlir
new file mode 100644
index 0000000..d85089e
--- /dev/null
+++ b/attention/mlir/attention_B1_H64_SQ4096_SKV4096_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[1,64,4096,128],f16>, %298 : !torch.vtensor<[1,64,4096,128],f16>, %301 : !torch.vtensor<[1,64,4096,128],f16>) -> !torch.vtensor<[1,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,64,4096,128],f16>, !torch.vtensor<[1,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[1,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H10_SQ1024_SKV1024_DH64_f16.mlir b/attention/mlir/attention_B2_H10_SQ1024_SKV1024_DH64_f16.mlir
new file mode 100644
index 0000000..08d9d9f
--- /dev/null
+++ b/attention/mlir/attention_B2_H10_SQ1024_SKV1024_DH64_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,10,1024,64],f16>, %298 : !torch.vtensor<[2,10,1024,64],f16>, %301 : !torch.vtensor<[2,10,1024,64],f16>) -> !torch.vtensor<[2,10,1024,64],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,10,1024,64],f16>, !torch.vtensor<[2,10,1024,64],f16>, !torch.vtensor<[2,10,1024,64],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,10,1024,64],f16>, !torch.vtensor<[2,10,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,10,1024,64],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H10_SQ1024_SKV1024_DH64_f8.mlir b/attention/mlir/attention_B2_H10_SQ1024_SKV1024_DH64_f8.mlir
new file mode 100644
index 0000000..08d9d9f
--- /dev/null
+++ b/attention/mlir/attention_B2_H10_SQ1024_SKV1024_DH64_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,10,1024,64],f16>, %298 : !torch.vtensor<[2,10,1024,64],f16>, %301 : !torch.vtensor<[2,10,1024,64],f16>) -> !torch.vtensor<[2,10,1024,64],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,10,1024,64],f16>, !torch.vtensor<[2,10,1024,64],f16>, !torch.vtensor<[2,10,1024,64],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,10,1024,64],f16>, !torch.vtensor<[2,10,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,10,1024,64],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H10_SQ1024_SKV1024_DH64_fp16.mlir b/attention/mlir/attention_B2_H10_SQ1024_SKV1024_DH64_fp16.mlir
new file mode 100644
index 0000000..08d9d9f
--- /dev/null
+++ b/attention/mlir/attention_B2_H10_SQ1024_SKV1024_DH64_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,10,1024,64],f16>, %298 : !torch.vtensor<[2,10,1024,64],f16>, %301 : !torch.vtensor<[2,10,1024,64],f16>) -> !torch.vtensor<[2,10,1024,64],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,10,1024,64],f16>, !torch.vtensor<[2,10,1024,64],f16>, !torch.vtensor<[2,10,1024,64],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,10,1024,64],f16>, !torch.vtensor<[2,10,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,10,1024,64],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H10_SQ1024_SKV1024_DH64_fp8.mlir b/attention/mlir/attention_B2_H10_SQ1024_SKV1024_DH64_fp8.mlir
new file mode 100644
index 0000000..08d9d9f
--- /dev/null
+++ b/attention/mlir/attention_B2_H10_SQ1024_SKV1024_DH64_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,10,1024,64],f16>, %298 : !torch.vtensor<[2,10,1024,64],f16>, %301 : !torch.vtensor<[2,10,1024,64],f16>) -> !torch.vtensor<[2,10,1024,64],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,10,1024,64],f16>, !torch.vtensor<[2,10,1024,64],f16>, !torch.vtensor<[2,10,1024,64],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,10,1024,64],f16>, !torch.vtensor<[2,10,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,10,1024,64],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H10_SQ4096_SKV4096_DH64_f16.mlir b/attention/mlir/attention_B2_H10_SQ4096_SKV4096_DH64_f16.mlir
new file mode 100644
index 0000000..5bcd2d6
--- /dev/null
+++ b/attention/mlir/attention_B2_H10_SQ4096_SKV4096_DH64_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,10,4096,64],f16>, %298 : !torch.vtensor<[2,10,4096,64],f16>, %301 : !torch.vtensor<[2,10,4096,64],f16>) -> !torch.vtensor<[2,10,4096,64],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,10,4096,64],f16>, !torch.vtensor<[2,10,4096,64],f16>, !torch.vtensor<[2,10,4096,64],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,10,4096,64],f16>, !torch.vtensor<[2,10,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,10,4096,64],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H10_SQ4096_SKV4096_DH64_f8.mlir b/attention/mlir/attention_B2_H10_SQ4096_SKV4096_DH64_f8.mlir
new file mode 100644
index 0000000..5bcd2d6
--- /dev/null
+++ b/attention/mlir/attention_B2_H10_SQ4096_SKV4096_DH64_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,10,4096,64],f16>, %298 : !torch.vtensor<[2,10,4096,64],f16>, %301 : !torch.vtensor<[2,10,4096,64],f16>) -> !torch.vtensor<[2,10,4096,64],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,10,4096,64],f16>, !torch.vtensor<[2,10,4096,64],f16>, !torch.vtensor<[2,10,4096,64],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,10,4096,64],f16>, !torch.vtensor<[2,10,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,10,4096,64],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H10_SQ4096_SKV4096_DH64_fp16.mlir b/attention/mlir/attention_B2_H10_SQ4096_SKV4096_DH64_fp16.mlir
new file mode 100644
index 0000000..5bcd2d6
--- /dev/null
+++ b/attention/mlir/attention_B2_H10_SQ4096_SKV4096_DH64_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,10,4096,64],f16>, %298 : !torch.vtensor<[2,10,4096,64],f16>, %301 : !torch.vtensor<[2,10,4096,64],f16>) -> !torch.vtensor<[2,10,4096,64],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,10,4096,64],f16>, !torch.vtensor<[2,10,4096,64],f16>, !torch.vtensor<[2,10,4096,64],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,10,4096,64],f16>, !torch.vtensor<[2,10,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,10,4096,64],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H10_SQ4096_SKV4096_DH64_fp8.mlir b/attention/mlir/attention_B2_H10_SQ4096_SKV4096_DH64_fp8.mlir
new file mode 100644
index 0000000..5bcd2d6
--- /dev/null
+++ b/attention/mlir/attention_B2_H10_SQ4096_SKV4096_DH64_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,10,4096,64],f16>, %298 : !torch.vtensor<[2,10,4096,64],f16>, %301 : !torch.vtensor<[2,10,4096,64],f16>) -> !torch.vtensor<[2,10,4096,64],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,10,4096,64],f16>, !torch.vtensor<[2,10,4096,64],f16>, !torch.vtensor<[2,10,4096,64],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,10,4096,64],f16>, !torch.vtensor<[2,10,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,10,4096,64],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H10_SQ4096_SKV64_DH64_f16.mlir b/attention/mlir/attention_B2_H10_SQ4096_SKV64_DH64_f16.mlir
new file mode 100644
index 0000000..c1fca9f
--- /dev/null
+++ b/attention/mlir/attention_B2_H10_SQ4096_SKV64_DH64_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,10,4096,64],f16>, %298 : !torch.vtensor<[2,10,64,64],f16>, %301 : !torch.vtensor<[2,10,64,64],f16>) -> !torch.vtensor<[2,10,4096,64],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,10,4096,64],f16>, !torch.vtensor<[2,10,64,64],f16>, !torch.vtensor<[2,10,64,64],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,10,4096,64],f16>, !torch.vtensor<[2,10,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,10,4096,64],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H10_SQ4096_SKV64_DH64_f8.mlir b/attention/mlir/attention_B2_H10_SQ4096_SKV64_DH64_f8.mlir
new file mode 100644
index 0000000..c1fca9f
--- /dev/null
+++ b/attention/mlir/attention_B2_H10_SQ4096_SKV64_DH64_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,10,4096,64],f16>, %298 : !torch.vtensor<[2,10,64,64],f16>, %301 : !torch.vtensor<[2,10,64,64],f16>) -> !torch.vtensor<[2,10,4096,64],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,10,4096,64],f16>, !torch.vtensor<[2,10,64,64],f16>, !torch.vtensor<[2,10,64,64],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,10,4096,64],f16>, !torch.vtensor<[2,10,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,10,4096,64],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H10_SQ4096_SKV64_DH64_fp16.mlir b/attention/mlir/attention_B2_H10_SQ4096_SKV64_DH64_fp16.mlir
new file mode 100644
index 0000000..c1fca9f
--- /dev/null
+++ b/attention/mlir/attention_B2_H10_SQ4096_SKV64_DH64_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,10,4096,64],f16>, %298 : !torch.vtensor<[2,10,64,64],f16>, %301 : !torch.vtensor<[2,10,64,64],f16>) -> !torch.vtensor<[2,10,4096,64],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,10,4096,64],f16>, !torch.vtensor<[2,10,64,64],f16>, !torch.vtensor<[2,10,64,64],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,10,4096,64],f16>, !torch.vtensor<[2,10,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,10,4096,64],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H10_SQ4096_SKV64_DH64_fp8.mlir b/attention/mlir/attention_B2_H10_SQ4096_SKV64_DH64_fp8.mlir
new file mode 100644
index 0000000..c1fca9f
--- /dev/null
+++ b/attention/mlir/attention_B2_H10_SQ4096_SKV64_DH64_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,10,4096,64],f16>, %298 : !torch.vtensor<[2,10,64,64],f16>, %301 : !torch.vtensor<[2,10,64,64],f16>) -> !torch.vtensor<[2,10,4096,64],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,10,4096,64],f16>, !torch.vtensor<[2,10,64,64],f16>, !torch.vtensor<[2,10,64,64],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,10,4096,64],f16>, !torch.vtensor<[2,10,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,10,4096,64],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H20_SQ1024_SKV64_DH64_f16.mlir b/attention/mlir/attention_B2_H20_SQ1024_SKV64_DH64_f16.mlir
new file mode 100644
index 0000000..9b1a643
--- /dev/null
+++ b/attention/mlir/attention_B2_H20_SQ1024_SKV64_DH64_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,20,1024,64],f16>, %298 : !torch.vtensor<[2,20,64,64],f16>, %301 : !torch.vtensor<[2,20,64,64],f16>) -> !torch.vtensor<[2,20,1024,64],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,20,1024,64],f16>, !torch.vtensor<[2,20,64,64],f16>, !torch.vtensor<[2,20,64,64],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,20,1024,64],f16>, !torch.vtensor<[2,20,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,20,1024,64],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H20_SQ1024_SKV64_DH64_f8.mlir b/attention/mlir/attention_B2_H20_SQ1024_SKV64_DH64_f8.mlir
new file mode 100644
index 0000000..9b1a643
--- /dev/null
+++ b/attention/mlir/attention_B2_H20_SQ1024_SKV64_DH64_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,20,1024,64],f16>, %298 : !torch.vtensor<[2,20,64,64],f16>, %301 : !torch.vtensor<[2,20,64,64],f16>) -> !torch.vtensor<[2,20,1024,64],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,20,1024,64],f16>, !torch.vtensor<[2,20,64,64],f16>, !torch.vtensor<[2,20,64,64],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,20,1024,64],f16>, !torch.vtensor<[2,20,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,20,1024,64],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H20_SQ1024_SKV64_DH64_fp16.mlir b/attention/mlir/attention_B2_H20_SQ1024_SKV64_DH64_fp16.mlir
new file mode 100644
index 0000000..9b1a643
--- /dev/null
+++ b/attention/mlir/attention_B2_H20_SQ1024_SKV64_DH64_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,20,1024,64],f16>, %298 : !torch.vtensor<[2,20,64,64],f16>, %301 : !torch.vtensor<[2,20,64,64],f16>) -> !torch.vtensor<[2,20,1024,64],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,20,1024,64],f16>, !torch.vtensor<[2,20,64,64],f16>, !torch.vtensor<[2,20,64,64],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,20,1024,64],f16>, !torch.vtensor<[2,20,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,20,1024,64],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H20_SQ1024_SKV64_DH64_fp8.mlir b/attention/mlir/attention_B2_H20_SQ1024_SKV64_DH64_fp8.mlir
new file mode 100644
index 0000000..9b1a643
--- /dev/null
+++ b/attention/mlir/attention_B2_H20_SQ1024_SKV64_DH64_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,20,1024,64],f16>, %298 : !torch.vtensor<[2,20,64,64],f16>, %301 : !torch.vtensor<[2,20,64,64],f16>) -> !torch.vtensor<[2,20,1024,64],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,20,1024,64],f16>, !torch.vtensor<[2,20,64,64],f16>, !torch.vtensor<[2,20,64,64],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,20,1024,64],f16>, !torch.vtensor<[2,20,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,20,1024,64],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ1024_SKV1024_DH128_f16.mlir b/attention/mlir/attention_B2_H32_SQ1024_SKV1024_DH128_f16.mlir
new file mode 100644
index 0000000..a09d0bc
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ1024_SKV1024_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,1024,128],f16>, %298 : !torch.vtensor<[2,32,1024,128],f16>, %301 : !torch.vtensor<[2,32,1024,128],f16>) -> !torch.vtensor<[2,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ1024_SKV1024_DH128_f8.mlir b/attention/mlir/attention_B2_H32_SQ1024_SKV1024_DH128_f8.mlir
new file mode 100644
index 0000000..a09d0bc
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ1024_SKV1024_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,1024,128],f16>, %298 : !torch.vtensor<[2,32,1024,128],f16>, %301 : !torch.vtensor<[2,32,1024,128],f16>) -> !torch.vtensor<[2,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ1024_SKV1024_DH128_fp16.mlir b/attention/mlir/attention_B2_H32_SQ1024_SKV1024_DH128_fp16.mlir
new file mode 100644
index 0000000..a09d0bc
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ1024_SKV1024_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,1024,128],f16>, %298 : !torch.vtensor<[2,32,1024,128],f16>, %301 : !torch.vtensor<[2,32,1024,128],f16>) -> !torch.vtensor<[2,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ1024_SKV1024_DH128_fp8.mlir b/attention/mlir/attention_B2_H32_SQ1024_SKV1024_DH128_fp8.mlir
new file mode 100644
index 0000000..a09d0bc
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ1024_SKV1024_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,1024,128],f16>, %298 : !torch.vtensor<[2,32,1024,128],f16>, %301 : !torch.vtensor<[2,32,1024,128],f16>) -> !torch.vtensor<[2,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ1024_SKV2048_DH128_f16.mlir b/attention/mlir/attention_B2_H32_SQ1024_SKV2048_DH128_f16.mlir
new file mode 100644
index 0000000..b98493a
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ1024_SKV2048_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,1024,128],f16>, %298 : !torch.vtensor<[2,32,2048,128],f16>, %301 : !torch.vtensor<[2,32,2048,128],f16>) -> !torch.vtensor<[2,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ1024_SKV2048_DH128_f8.mlir b/attention/mlir/attention_B2_H32_SQ1024_SKV2048_DH128_f8.mlir
new file mode 100644
index 0000000..b98493a
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ1024_SKV2048_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,1024,128],f16>, %298 : !torch.vtensor<[2,32,2048,128],f16>, %301 : !torch.vtensor<[2,32,2048,128],f16>) -> !torch.vtensor<[2,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ1024_SKV2048_DH128_fp16.mlir b/attention/mlir/attention_B2_H32_SQ1024_SKV2048_DH128_fp16.mlir
new file mode 100644
index 0000000..b98493a
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ1024_SKV2048_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,1024,128],f16>, %298 : !torch.vtensor<[2,32,2048,128],f16>, %301 : !torch.vtensor<[2,32,2048,128],f16>) -> !torch.vtensor<[2,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ1024_SKV2048_DH128_fp8.mlir b/attention/mlir/attention_B2_H32_SQ1024_SKV2048_DH128_fp8.mlir
new file mode 100644
index 0000000..b98493a
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ1024_SKV2048_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,1024,128],f16>, %298 : !torch.vtensor<[2,32,2048,128],f16>, %301 : !torch.vtensor<[2,32,2048,128],f16>) -> !torch.vtensor<[2,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ1024_SKV4096_DH128_f16.mlir b/attention/mlir/attention_B2_H32_SQ1024_SKV4096_DH128_f16.mlir
new file mode 100644
index 0000000..23b3632
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ1024_SKV4096_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,1024,128],f16>, %298 : !torch.vtensor<[2,32,4096,128],f16>, %301 : !torch.vtensor<[2,32,4096,128],f16>) -> !torch.vtensor<[2,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ1024_SKV4096_DH128_f8.mlir b/attention/mlir/attention_B2_H32_SQ1024_SKV4096_DH128_f8.mlir
new file mode 100644
index 0000000..23b3632
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ1024_SKV4096_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,1024,128],f16>, %298 : !torch.vtensor<[2,32,4096,128],f16>, %301 : !torch.vtensor<[2,32,4096,128],f16>) -> !torch.vtensor<[2,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ1024_SKV4096_DH128_fp16.mlir b/attention/mlir/attention_B2_H32_SQ1024_SKV4096_DH128_fp16.mlir
new file mode 100644
index 0000000..23b3632
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ1024_SKV4096_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,1024,128],f16>, %298 : !torch.vtensor<[2,32,4096,128],f16>, %301 : !torch.vtensor<[2,32,4096,128],f16>) -> !torch.vtensor<[2,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ1024_SKV4096_DH128_fp8.mlir b/attention/mlir/attention_B2_H32_SQ1024_SKV4096_DH128_fp8.mlir
new file mode 100644
index 0000000..23b3632
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ1024_SKV4096_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,1024,128],f16>, %298 : !torch.vtensor<[2,32,4096,128],f16>, %301 : !torch.vtensor<[2,32,4096,128],f16>) -> !torch.vtensor<[2,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ2048_SKV1024_DH128_f16.mlir b/attention/mlir/attention_B2_H32_SQ2048_SKV1024_DH128_f16.mlir
new file mode 100644
index 0000000..6fdc255
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ2048_SKV1024_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,2048,128],f16>, %298 : !torch.vtensor<[2,32,1024,128],f16>, %301 : !torch.vtensor<[2,32,1024,128],f16>) -> !torch.vtensor<[2,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ2048_SKV1024_DH128_f8.mlir b/attention/mlir/attention_B2_H32_SQ2048_SKV1024_DH128_f8.mlir
new file mode 100644
index 0000000..6fdc255
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ2048_SKV1024_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,2048,128],f16>, %298 : !torch.vtensor<[2,32,1024,128],f16>, %301 : !torch.vtensor<[2,32,1024,128],f16>) -> !torch.vtensor<[2,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ2048_SKV1024_DH128_fp16.mlir b/attention/mlir/attention_B2_H32_SQ2048_SKV1024_DH128_fp16.mlir
new file mode 100644
index 0000000..6fdc255
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ2048_SKV1024_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,2048,128],f16>, %298 : !torch.vtensor<[2,32,1024,128],f16>, %301 : !torch.vtensor<[2,32,1024,128],f16>) -> !torch.vtensor<[2,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ2048_SKV1024_DH128_fp8.mlir b/attention/mlir/attention_B2_H32_SQ2048_SKV1024_DH128_fp8.mlir
new file mode 100644
index 0000000..6fdc255
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ2048_SKV1024_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,2048,128],f16>, %298 : !torch.vtensor<[2,32,1024,128],f16>, %301 : !torch.vtensor<[2,32,1024,128],f16>) -> !torch.vtensor<[2,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ2048_SKV2048_DH128_f16.mlir b/attention/mlir/attention_B2_H32_SQ2048_SKV2048_DH128_f16.mlir
new file mode 100644
index 0000000..b5b3839
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ2048_SKV2048_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,2048,128],f16>, %298 : !torch.vtensor<[2,32,2048,128],f16>, %301 : !torch.vtensor<[2,32,2048,128],f16>) -> !torch.vtensor<[2,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ2048_SKV2048_DH128_f8.mlir b/attention/mlir/attention_B2_H32_SQ2048_SKV2048_DH128_f8.mlir
new file mode 100644
index 0000000..b5b3839
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ2048_SKV2048_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,2048,128],f16>, %298 : !torch.vtensor<[2,32,2048,128],f16>, %301 : !torch.vtensor<[2,32,2048,128],f16>) -> !torch.vtensor<[2,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ2048_SKV2048_DH128_fp16.mlir b/attention/mlir/attention_B2_H32_SQ2048_SKV2048_DH128_fp16.mlir
new file mode 100644
index 0000000..b5b3839
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ2048_SKV2048_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,2048,128],f16>, %298 : !torch.vtensor<[2,32,2048,128],f16>, %301 : !torch.vtensor<[2,32,2048,128],f16>) -> !torch.vtensor<[2,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ2048_SKV2048_DH128_fp8.mlir b/attention/mlir/attention_B2_H32_SQ2048_SKV2048_DH128_fp8.mlir
new file mode 100644
index 0000000..b5b3839
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ2048_SKV2048_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,2048,128],f16>, %298 : !torch.vtensor<[2,32,2048,128],f16>, %301 : !torch.vtensor<[2,32,2048,128],f16>) -> !torch.vtensor<[2,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ2048_SKV4096_DH128_f16.mlir b/attention/mlir/attention_B2_H32_SQ2048_SKV4096_DH128_f16.mlir
new file mode 100644
index 0000000..30f3a51
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ2048_SKV4096_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,2048,128],f16>, %298 : !torch.vtensor<[2,32,4096,128],f16>, %301 : !torch.vtensor<[2,32,4096,128],f16>) -> !torch.vtensor<[2,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ2048_SKV4096_DH128_f8.mlir b/attention/mlir/attention_B2_H32_SQ2048_SKV4096_DH128_f8.mlir
new file mode 100644
index 0000000..30f3a51
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ2048_SKV4096_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,2048,128],f16>, %298 : !torch.vtensor<[2,32,4096,128],f16>, %301 : !torch.vtensor<[2,32,4096,128],f16>) -> !torch.vtensor<[2,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ2048_SKV4096_DH128_fp16.mlir b/attention/mlir/attention_B2_H32_SQ2048_SKV4096_DH128_fp16.mlir
new file mode 100644
index 0000000..30f3a51
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ2048_SKV4096_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,2048,128],f16>, %298 : !torch.vtensor<[2,32,4096,128],f16>, %301 : !torch.vtensor<[2,32,4096,128],f16>) -> !torch.vtensor<[2,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ2048_SKV4096_DH128_fp8.mlir b/attention/mlir/attention_B2_H32_SQ2048_SKV4096_DH128_fp8.mlir
new file mode 100644
index 0000000..30f3a51
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ2048_SKV4096_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,2048,128],f16>, %298 : !torch.vtensor<[2,32,4096,128],f16>, %301 : !torch.vtensor<[2,32,4096,128],f16>) -> !torch.vtensor<[2,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ4096_SKV1024_DH128_f16.mlir b/attention/mlir/attention_B2_H32_SQ4096_SKV1024_DH128_f16.mlir
new file mode 100644
index 0000000..08ac4eb
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ4096_SKV1024_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,4096,128],f16>, %298 : !torch.vtensor<[2,32,1024,128],f16>, %301 : !torch.vtensor<[2,32,1024,128],f16>) -> !torch.vtensor<[2,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ4096_SKV1024_DH128_f8.mlir b/attention/mlir/attention_B2_H32_SQ4096_SKV1024_DH128_f8.mlir
new file mode 100644
index 0000000..08ac4eb
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ4096_SKV1024_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,4096,128],f16>, %298 : !torch.vtensor<[2,32,1024,128],f16>, %301 : !torch.vtensor<[2,32,1024,128],f16>) -> !torch.vtensor<[2,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ4096_SKV1024_DH128_fp16.mlir b/attention/mlir/attention_B2_H32_SQ4096_SKV1024_DH128_fp16.mlir
new file mode 100644
index 0000000..08ac4eb
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ4096_SKV1024_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,4096,128],f16>, %298 : !torch.vtensor<[2,32,1024,128],f16>, %301 : !torch.vtensor<[2,32,1024,128],f16>) -> !torch.vtensor<[2,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ4096_SKV1024_DH128_fp8.mlir b/attention/mlir/attention_B2_H32_SQ4096_SKV1024_DH128_fp8.mlir
new file mode 100644
index 0000000..08ac4eb
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ4096_SKV1024_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,4096,128],f16>, %298 : !torch.vtensor<[2,32,1024,128],f16>, %301 : !torch.vtensor<[2,32,1024,128],f16>) -> !torch.vtensor<[2,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,1024,128],f16>, !torch.vtensor<[2,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ4096_SKV2048_DH128_f16.mlir b/attention/mlir/attention_B2_H32_SQ4096_SKV2048_DH128_f16.mlir
new file mode 100644
index 0000000..a480464
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ4096_SKV2048_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,4096,128],f16>, %298 : !torch.vtensor<[2,32,2048,128],f16>, %301 : !torch.vtensor<[2,32,2048,128],f16>) -> !torch.vtensor<[2,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ4096_SKV2048_DH128_f8.mlir b/attention/mlir/attention_B2_H32_SQ4096_SKV2048_DH128_f8.mlir
new file mode 100644
index 0000000..a480464
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ4096_SKV2048_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,4096,128],f16>, %298 : !torch.vtensor<[2,32,2048,128],f16>, %301 : !torch.vtensor<[2,32,2048,128],f16>) -> !torch.vtensor<[2,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ4096_SKV2048_DH128_fp16.mlir b/attention/mlir/attention_B2_H32_SQ4096_SKV2048_DH128_fp16.mlir
new file mode 100644
index 0000000..a480464
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ4096_SKV2048_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,4096,128],f16>, %298 : !torch.vtensor<[2,32,2048,128],f16>, %301 : !torch.vtensor<[2,32,2048,128],f16>) -> !torch.vtensor<[2,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ4096_SKV2048_DH128_fp8.mlir b/attention/mlir/attention_B2_H32_SQ4096_SKV2048_DH128_fp8.mlir
new file mode 100644
index 0000000..a480464
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ4096_SKV2048_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,4096,128],f16>, %298 : !torch.vtensor<[2,32,2048,128],f16>, %301 : !torch.vtensor<[2,32,2048,128],f16>) -> !torch.vtensor<[2,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,2048,128],f16>, !torch.vtensor<[2,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ4096_SKV4096_DH128_f16.mlir b/attention/mlir/attention_B2_H32_SQ4096_SKV4096_DH128_f16.mlir
new file mode 100644
index 0000000..ca78571
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ4096_SKV4096_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,4096,128],f16>, %298 : !torch.vtensor<[2,32,4096,128],f16>, %301 : !torch.vtensor<[2,32,4096,128],f16>) -> !torch.vtensor<[2,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ4096_SKV4096_DH128_f8.mlir b/attention/mlir/attention_B2_H32_SQ4096_SKV4096_DH128_f8.mlir
new file mode 100644
index 0000000..ca78571
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ4096_SKV4096_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,4096,128],f16>, %298 : !torch.vtensor<[2,32,4096,128],f16>, %301 : !torch.vtensor<[2,32,4096,128],f16>) -> !torch.vtensor<[2,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ4096_SKV4096_DH128_fp16.mlir b/attention/mlir/attention_B2_H32_SQ4096_SKV4096_DH128_fp16.mlir
new file mode 100644
index 0000000..ca78571
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ4096_SKV4096_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,4096,128],f16>, %298 : !torch.vtensor<[2,32,4096,128],f16>, %301 : !torch.vtensor<[2,32,4096,128],f16>) -> !torch.vtensor<[2,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H32_SQ4096_SKV4096_DH128_fp8.mlir b/attention/mlir/attention_B2_H32_SQ4096_SKV4096_DH128_fp8.mlir
new file mode 100644
index 0000000..ca78571
--- /dev/null
+++ b/attention/mlir/attention_B2_H32_SQ4096_SKV4096_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,32,4096,128],f16>, %298 : !torch.vtensor<[2,32,4096,128],f16>, %301 : !torch.vtensor<[2,32,4096,128],f16>) -> !torch.vtensor<[2,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,32,4096,128],f16>, !torch.vtensor<[2,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ1024_SKV1024_DH128_f16.mlir b/attention/mlir/attention_B2_H40_SQ1024_SKV1024_DH128_f16.mlir
new file mode 100644
index 0000000..bb29a69
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ1024_SKV1024_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,1024,128],f16>, %298 : !torch.vtensor<[2,40,1024,128],f16>, %301 : !torch.vtensor<[2,40,1024,128],f16>) -> !torch.vtensor<[2,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ1024_SKV1024_DH128_f8.mlir b/attention/mlir/attention_B2_H40_SQ1024_SKV1024_DH128_f8.mlir
new file mode 100644
index 0000000..bb29a69
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ1024_SKV1024_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,1024,128],f16>, %298 : !torch.vtensor<[2,40,1024,128],f16>, %301 : !torch.vtensor<[2,40,1024,128],f16>) -> !torch.vtensor<[2,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ1024_SKV1024_DH128_fp16.mlir b/attention/mlir/attention_B2_H40_SQ1024_SKV1024_DH128_fp16.mlir
new file mode 100644
index 0000000..bb29a69
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ1024_SKV1024_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,1024,128],f16>, %298 : !torch.vtensor<[2,40,1024,128],f16>, %301 : !torch.vtensor<[2,40,1024,128],f16>) -> !torch.vtensor<[2,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ1024_SKV1024_DH128_fp8.mlir b/attention/mlir/attention_B2_H40_SQ1024_SKV1024_DH128_fp8.mlir
new file mode 100644
index 0000000..bb29a69
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ1024_SKV1024_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,1024,128],f16>, %298 : !torch.vtensor<[2,40,1024,128],f16>, %301 : !torch.vtensor<[2,40,1024,128],f16>) -> !torch.vtensor<[2,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ1024_SKV2048_DH128_f16.mlir b/attention/mlir/attention_B2_H40_SQ1024_SKV2048_DH128_f16.mlir
new file mode 100644
index 0000000..218078d
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ1024_SKV2048_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,1024,128],f16>, %298 : !torch.vtensor<[2,40,2048,128],f16>, %301 : !torch.vtensor<[2,40,2048,128],f16>) -> !torch.vtensor<[2,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ1024_SKV2048_DH128_f8.mlir b/attention/mlir/attention_B2_H40_SQ1024_SKV2048_DH128_f8.mlir
new file mode 100644
index 0000000..218078d
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ1024_SKV2048_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,1024,128],f16>, %298 : !torch.vtensor<[2,40,2048,128],f16>, %301 : !torch.vtensor<[2,40,2048,128],f16>) -> !torch.vtensor<[2,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ1024_SKV2048_DH128_fp16.mlir b/attention/mlir/attention_B2_H40_SQ1024_SKV2048_DH128_fp16.mlir
new file mode 100644
index 0000000..218078d
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ1024_SKV2048_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,1024,128],f16>, %298 : !torch.vtensor<[2,40,2048,128],f16>, %301 : !torch.vtensor<[2,40,2048,128],f16>) -> !torch.vtensor<[2,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ1024_SKV2048_DH128_fp8.mlir b/attention/mlir/attention_B2_H40_SQ1024_SKV2048_DH128_fp8.mlir
new file mode 100644
index 0000000..218078d
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ1024_SKV2048_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,1024,128],f16>, %298 : !torch.vtensor<[2,40,2048,128],f16>, %301 : !torch.vtensor<[2,40,2048,128],f16>) -> !torch.vtensor<[2,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ1024_SKV4096_DH128_f16.mlir b/attention/mlir/attention_B2_H40_SQ1024_SKV4096_DH128_f16.mlir
new file mode 100644
index 0000000..bb601e0
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ1024_SKV4096_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,1024,128],f16>, %298 : !torch.vtensor<[2,40,4096,128],f16>, %301 : !torch.vtensor<[2,40,4096,128],f16>) -> !torch.vtensor<[2,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ1024_SKV4096_DH128_f8.mlir b/attention/mlir/attention_B2_H40_SQ1024_SKV4096_DH128_f8.mlir
new file mode 100644
index 0000000..bb601e0
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ1024_SKV4096_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,1024,128],f16>, %298 : !torch.vtensor<[2,40,4096,128],f16>, %301 : !torch.vtensor<[2,40,4096,128],f16>) -> !torch.vtensor<[2,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ1024_SKV4096_DH128_fp16.mlir b/attention/mlir/attention_B2_H40_SQ1024_SKV4096_DH128_fp16.mlir
new file mode 100644
index 0000000..bb601e0
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ1024_SKV4096_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,1024,128],f16>, %298 : !torch.vtensor<[2,40,4096,128],f16>, %301 : !torch.vtensor<[2,40,4096,128],f16>) -> !torch.vtensor<[2,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ1024_SKV4096_DH128_fp8.mlir b/attention/mlir/attention_B2_H40_SQ1024_SKV4096_DH128_fp8.mlir
new file mode 100644
index 0000000..bb601e0
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ1024_SKV4096_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,1024,128],f16>, %298 : !torch.vtensor<[2,40,4096,128],f16>, %301 : !torch.vtensor<[2,40,4096,128],f16>) -> !torch.vtensor<[2,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ2048_SKV1024_DH128_f16.mlir b/attention/mlir/attention_B2_H40_SQ2048_SKV1024_DH128_f16.mlir
new file mode 100644
index 0000000..31fffb1
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ2048_SKV1024_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,2048,128],f16>, %298 : !torch.vtensor<[2,40,1024,128],f16>, %301 : !torch.vtensor<[2,40,1024,128],f16>) -> !torch.vtensor<[2,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ2048_SKV1024_DH128_f8.mlir b/attention/mlir/attention_B2_H40_SQ2048_SKV1024_DH128_f8.mlir
new file mode 100644
index 0000000..31fffb1
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ2048_SKV1024_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,2048,128],f16>, %298 : !torch.vtensor<[2,40,1024,128],f16>, %301 : !torch.vtensor<[2,40,1024,128],f16>) -> !torch.vtensor<[2,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ2048_SKV1024_DH128_fp16.mlir b/attention/mlir/attention_B2_H40_SQ2048_SKV1024_DH128_fp16.mlir
new file mode 100644
index 0000000..31fffb1
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ2048_SKV1024_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,2048,128],f16>, %298 : !torch.vtensor<[2,40,1024,128],f16>, %301 : !torch.vtensor<[2,40,1024,128],f16>) -> !torch.vtensor<[2,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ2048_SKV1024_DH128_fp8.mlir b/attention/mlir/attention_B2_H40_SQ2048_SKV1024_DH128_fp8.mlir
new file mode 100644
index 0000000..31fffb1
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ2048_SKV1024_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,2048,128],f16>, %298 : !torch.vtensor<[2,40,1024,128],f16>, %301 : !torch.vtensor<[2,40,1024,128],f16>) -> !torch.vtensor<[2,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ2048_SKV2048_DH128_f16.mlir b/attention/mlir/attention_B2_H40_SQ2048_SKV2048_DH128_f16.mlir
new file mode 100644
index 0000000..b0cef8f
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ2048_SKV2048_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,2048,128],f16>, %298 : !torch.vtensor<[2,40,2048,128],f16>, %301 : !torch.vtensor<[2,40,2048,128],f16>) -> !torch.vtensor<[2,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ2048_SKV2048_DH128_f8.mlir b/attention/mlir/attention_B2_H40_SQ2048_SKV2048_DH128_f8.mlir
new file mode 100644
index 0000000..b0cef8f
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ2048_SKV2048_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,2048,128],f16>, %298 : !torch.vtensor<[2,40,2048,128],f16>, %301 : !torch.vtensor<[2,40,2048,128],f16>) -> !torch.vtensor<[2,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ2048_SKV2048_DH128_fp16.mlir b/attention/mlir/attention_B2_H40_SQ2048_SKV2048_DH128_fp16.mlir
new file mode 100644
index 0000000..b0cef8f
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ2048_SKV2048_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,2048,128],f16>, %298 : !torch.vtensor<[2,40,2048,128],f16>, %301 : !torch.vtensor<[2,40,2048,128],f16>) -> !torch.vtensor<[2,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ2048_SKV2048_DH128_fp8.mlir b/attention/mlir/attention_B2_H40_SQ2048_SKV2048_DH128_fp8.mlir
new file mode 100644
index 0000000..b0cef8f
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ2048_SKV2048_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,2048,128],f16>, %298 : !torch.vtensor<[2,40,2048,128],f16>, %301 : !torch.vtensor<[2,40,2048,128],f16>) -> !torch.vtensor<[2,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ2048_SKV4096_DH128_f16.mlir b/attention/mlir/attention_B2_H40_SQ2048_SKV4096_DH128_f16.mlir
new file mode 100644
index 0000000..7f1b976
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ2048_SKV4096_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,2048,128],f16>, %298 : !torch.vtensor<[2,40,4096,128],f16>, %301 : !torch.vtensor<[2,40,4096,128],f16>) -> !torch.vtensor<[2,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ2048_SKV4096_DH128_f8.mlir b/attention/mlir/attention_B2_H40_SQ2048_SKV4096_DH128_f8.mlir
new file mode 100644
index 0000000..7f1b976
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ2048_SKV4096_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,2048,128],f16>, %298 : !torch.vtensor<[2,40,4096,128],f16>, %301 : !torch.vtensor<[2,40,4096,128],f16>) -> !torch.vtensor<[2,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ2048_SKV4096_DH128_fp16.mlir b/attention/mlir/attention_B2_H40_SQ2048_SKV4096_DH128_fp16.mlir
new file mode 100644
index 0000000..7f1b976
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ2048_SKV4096_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,2048,128],f16>, %298 : !torch.vtensor<[2,40,4096,128],f16>, %301 : !torch.vtensor<[2,40,4096,128],f16>) -> !torch.vtensor<[2,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ2048_SKV4096_DH128_fp8.mlir b/attention/mlir/attention_B2_H40_SQ2048_SKV4096_DH128_fp8.mlir
new file mode 100644
index 0000000..7f1b976
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ2048_SKV4096_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,2048,128],f16>, %298 : !torch.vtensor<[2,40,4096,128],f16>, %301 : !torch.vtensor<[2,40,4096,128],f16>) -> !torch.vtensor<[2,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ4096_SKV1024_DH128_f16.mlir b/attention/mlir/attention_B2_H40_SQ4096_SKV1024_DH128_f16.mlir
new file mode 100644
index 0000000..2597a3e
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ4096_SKV1024_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,4096,128],f16>, %298 : !torch.vtensor<[2,40,1024,128],f16>, %301 : !torch.vtensor<[2,40,1024,128],f16>) -> !torch.vtensor<[2,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ4096_SKV1024_DH128_f8.mlir b/attention/mlir/attention_B2_H40_SQ4096_SKV1024_DH128_f8.mlir
new file mode 100644
index 0000000..2597a3e
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ4096_SKV1024_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,4096,128],f16>, %298 : !torch.vtensor<[2,40,1024,128],f16>, %301 : !torch.vtensor<[2,40,1024,128],f16>) -> !torch.vtensor<[2,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ4096_SKV1024_DH128_fp16.mlir b/attention/mlir/attention_B2_H40_SQ4096_SKV1024_DH128_fp16.mlir
new file mode 100644
index 0000000..2597a3e
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ4096_SKV1024_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,4096,128],f16>, %298 : !torch.vtensor<[2,40,1024,128],f16>, %301 : !torch.vtensor<[2,40,1024,128],f16>) -> !torch.vtensor<[2,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ4096_SKV1024_DH128_fp8.mlir b/attention/mlir/attention_B2_H40_SQ4096_SKV1024_DH128_fp8.mlir
new file mode 100644
index 0000000..2597a3e
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ4096_SKV1024_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,4096,128],f16>, %298 : !torch.vtensor<[2,40,1024,128],f16>, %301 : !torch.vtensor<[2,40,1024,128],f16>) -> !torch.vtensor<[2,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,1024,128],f16>, !torch.vtensor<[2,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ4096_SKV2048_DH128_f16.mlir b/attention/mlir/attention_B2_H40_SQ4096_SKV2048_DH128_f16.mlir
new file mode 100644
index 0000000..4159851
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ4096_SKV2048_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,4096,128],f16>, %298 : !torch.vtensor<[2,40,2048,128],f16>, %301 : !torch.vtensor<[2,40,2048,128],f16>) -> !torch.vtensor<[2,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ4096_SKV2048_DH128_f8.mlir b/attention/mlir/attention_B2_H40_SQ4096_SKV2048_DH128_f8.mlir
new file mode 100644
index 0000000..4159851
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ4096_SKV2048_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,4096,128],f16>, %298 : !torch.vtensor<[2,40,2048,128],f16>, %301 : !torch.vtensor<[2,40,2048,128],f16>) -> !torch.vtensor<[2,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ4096_SKV2048_DH128_fp16.mlir b/attention/mlir/attention_B2_H40_SQ4096_SKV2048_DH128_fp16.mlir
new file mode 100644
index 0000000..4159851
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ4096_SKV2048_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,4096,128],f16>, %298 : !torch.vtensor<[2,40,2048,128],f16>, %301 : !torch.vtensor<[2,40,2048,128],f16>) -> !torch.vtensor<[2,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ4096_SKV2048_DH128_fp8.mlir b/attention/mlir/attention_B2_H40_SQ4096_SKV2048_DH128_fp8.mlir
new file mode 100644
index 0000000..4159851
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ4096_SKV2048_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,4096,128],f16>, %298 : !torch.vtensor<[2,40,2048,128],f16>, %301 : !torch.vtensor<[2,40,2048,128],f16>) -> !torch.vtensor<[2,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,2048,128],f16>, !torch.vtensor<[2,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ4096_SKV4096_DH128_f16.mlir b/attention/mlir/attention_B2_H40_SQ4096_SKV4096_DH128_f16.mlir
new file mode 100644
index 0000000..07273f8
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ4096_SKV4096_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,4096,128],f16>, %298 : !torch.vtensor<[2,40,4096,128],f16>, %301 : !torch.vtensor<[2,40,4096,128],f16>) -> !torch.vtensor<[2,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ4096_SKV4096_DH128_f8.mlir b/attention/mlir/attention_B2_H40_SQ4096_SKV4096_DH128_f8.mlir
new file mode 100644
index 0000000..07273f8
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ4096_SKV4096_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,4096,128],f16>, %298 : !torch.vtensor<[2,40,4096,128],f16>, %301 : !torch.vtensor<[2,40,4096,128],f16>) -> !torch.vtensor<[2,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ4096_SKV4096_DH128_fp16.mlir b/attention/mlir/attention_B2_H40_SQ4096_SKV4096_DH128_fp16.mlir
new file mode 100644
index 0000000..07273f8
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ4096_SKV4096_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,4096,128],f16>, %298 : !torch.vtensor<[2,40,4096,128],f16>, %301 : !torch.vtensor<[2,40,4096,128],f16>) -> !torch.vtensor<[2,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H40_SQ4096_SKV4096_DH128_fp8.mlir b/attention/mlir/attention_B2_H40_SQ4096_SKV4096_DH128_fp8.mlir
new file mode 100644
index 0000000..07273f8
--- /dev/null
+++ b/attention/mlir/attention_B2_H40_SQ4096_SKV4096_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,40,4096,128],f16>, %298 : !torch.vtensor<[2,40,4096,128],f16>, %301 : !torch.vtensor<[2,40,4096,128],f16>) -> !torch.vtensor<[2,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,40,4096,128],f16>, !torch.vtensor<[2,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ1024_SKV1024_DH128_f16.mlir b/attention/mlir/attention_B2_H64_SQ1024_SKV1024_DH128_f16.mlir
new file mode 100644
index 0000000..7386143
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ1024_SKV1024_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,1024,128],f16>, %298 : !torch.vtensor<[2,64,1024,128],f16>, %301 : !torch.vtensor<[2,64,1024,128],f16>) -> !torch.vtensor<[2,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ1024_SKV1024_DH128_f8.mlir b/attention/mlir/attention_B2_H64_SQ1024_SKV1024_DH128_f8.mlir
new file mode 100644
index 0000000..7386143
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ1024_SKV1024_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,1024,128],f16>, %298 : !torch.vtensor<[2,64,1024,128],f16>, %301 : !torch.vtensor<[2,64,1024,128],f16>) -> !torch.vtensor<[2,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ1024_SKV1024_DH128_fp16.mlir b/attention/mlir/attention_B2_H64_SQ1024_SKV1024_DH128_fp16.mlir
new file mode 100644
index 0000000..7386143
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ1024_SKV1024_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,1024,128],f16>, %298 : !torch.vtensor<[2,64,1024,128],f16>, %301 : !torch.vtensor<[2,64,1024,128],f16>) -> !torch.vtensor<[2,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ1024_SKV1024_DH128_fp8.mlir b/attention/mlir/attention_B2_H64_SQ1024_SKV1024_DH128_fp8.mlir
new file mode 100644
index 0000000..7386143
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ1024_SKV1024_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,1024,128],f16>, %298 : !torch.vtensor<[2,64,1024,128],f16>, %301 : !torch.vtensor<[2,64,1024,128],f16>) -> !torch.vtensor<[2,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ1024_SKV2048_DH128_f16.mlir b/attention/mlir/attention_B2_H64_SQ1024_SKV2048_DH128_f16.mlir
new file mode 100644
index 0000000..85be1ae
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ1024_SKV2048_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,1024,128],f16>, %298 : !torch.vtensor<[2,64,2048,128],f16>, %301 : !torch.vtensor<[2,64,2048,128],f16>) -> !torch.vtensor<[2,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ1024_SKV2048_DH128_f8.mlir b/attention/mlir/attention_B2_H64_SQ1024_SKV2048_DH128_f8.mlir
new file mode 100644
index 0000000..85be1ae
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ1024_SKV2048_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,1024,128],f16>, %298 : !torch.vtensor<[2,64,2048,128],f16>, %301 : !torch.vtensor<[2,64,2048,128],f16>) -> !torch.vtensor<[2,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ1024_SKV2048_DH128_fp16.mlir b/attention/mlir/attention_B2_H64_SQ1024_SKV2048_DH128_fp16.mlir
new file mode 100644
index 0000000..85be1ae
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ1024_SKV2048_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,1024,128],f16>, %298 : !torch.vtensor<[2,64,2048,128],f16>, %301 : !torch.vtensor<[2,64,2048,128],f16>) -> !torch.vtensor<[2,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ1024_SKV2048_DH128_fp8.mlir b/attention/mlir/attention_B2_H64_SQ1024_SKV2048_DH128_fp8.mlir
new file mode 100644
index 0000000..85be1ae
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ1024_SKV2048_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,1024,128],f16>, %298 : !torch.vtensor<[2,64,2048,128],f16>, %301 : !torch.vtensor<[2,64,2048,128],f16>) -> !torch.vtensor<[2,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ1024_SKV4096_DH128_f16.mlir b/attention/mlir/attention_B2_H64_SQ1024_SKV4096_DH128_f16.mlir
new file mode 100644
index 0000000..0ba180e
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ1024_SKV4096_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,1024,128],f16>, %298 : !torch.vtensor<[2,64,4096,128],f16>, %301 : !torch.vtensor<[2,64,4096,128],f16>) -> !torch.vtensor<[2,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ1024_SKV4096_DH128_f8.mlir b/attention/mlir/attention_B2_H64_SQ1024_SKV4096_DH128_f8.mlir
new file mode 100644
index 0000000..0ba180e
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ1024_SKV4096_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,1024,128],f16>, %298 : !torch.vtensor<[2,64,4096,128],f16>, %301 : !torch.vtensor<[2,64,4096,128],f16>) -> !torch.vtensor<[2,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ1024_SKV4096_DH128_fp16.mlir b/attention/mlir/attention_B2_H64_SQ1024_SKV4096_DH128_fp16.mlir
new file mode 100644
index 0000000..0ba180e
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ1024_SKV4096_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,1024,128],f16>, %298 : !torch.vtensor<[2,64,4096,128],f16>, %301 : !torch.vtensor<[2,64,4096,128],f16>) -> !torch.vtensor<[2,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ1024_SKV4096_DH128_fp8.mlir b/attention/mlir/attention_B2_H64_SQ1024_SKV4096_DH128_fp8.mlir
new file mode 100644
index 0000000..0ba180e
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ1024_SKV4096_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,1024,128],f16>, %298 : !torch.vtensor<[2,64,4096,128],f16>, %301 : !torch.vtensor<[2,64,4096,128],f16>) -> !torch.vtensor<[2,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[2,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ2048_SKV1024_DH128_f16.mlir b/attention/mlir/attention_B2_H64_SQ2048_SKV1024_DH128_f16.mlir
new file mode 100644
index 0000000..03981e6
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ2048_SKV1024_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,2048,128],f16>, %298 : !torch.vtensor<[2,64,1024,128],f16>, %301 : !torch.vtensor<[2,64,1024,128],f16>) -> !torch.vtensor<[2,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ2048_SKV1024_DH128_f8.mlir b/attention/mlir/attention_B2_H64_SQ2048_SKV1024_DH128_f8.mlir
new file mode 100644
index 0000000..03981e6
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ2048_SKV1024_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,2048,128],f16>, %298 : !torch.vtensor<[2,64,1024,128],f16>, %301 : !torch.vtensor<[2,64,1024,128],f16>) -> !torch.vtensor<[2,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ2048_SKV1024_DH128_fp16.mlir b/attention/mlir/attention_B2_H64_SQ2048_SKV1024_DH128_fp16.mlir
new file mode 100644
index 0000000..03981e6
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ2048_SKV1024_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,2048,128],f16>, %298 : !torch.vtensor<[2,64,1024,128],f16>, %301 : !torch.vtensor<[2,64,1024,128],f16>) -> !torch.vtensor<[2,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ2048_SKV1024_DH128_fp8.mlir b/attention/mlir/attention_B2_H64_SQ2048_SKV1024_DH128_fp8.mlir
new file mode 100644
index 0000000..03981e6
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ2048_SKV1024_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,2048,128],f16>, %298 : !torch.vtensor<[2,64,1024,128],f16>, %301 : !torch.vtensor<[2,64,1024,128],f16>) -> !torch.vtensor<[2,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ2048_SKV2048_DH128_f16.mlir b/attention/mlir/attention_B2_H64_SQ2048_SKV2048_DH128_f16.mlir
new file mode 100644
index 0000000..fd0ad8f
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ2048_SKV2048_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,2048,128],f16>, %298 : !torch.vtensor<[2,64,2048,128],f16>, %301 : !torch.vtensor<[2,64,2048,128],f16>) -> !torch.vtensor<[2,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ2048_SKV2048_DH128_f8.mlir b/attention/mlir/attention_B2_H64_SQ2048_SKV2048_DH128_f8.mlir
new file mode 100644
index 0000000..fd0ad8f
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ2048_SKV2048_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,2048,128],f16>, %298 : !torch.vtensor<[2,64,2048,128],f16>, %301 : !torch.vtensor<[2,64,2048,128],f16>) -> !torch.vtensor<[2,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ2048_SKV2048_DH128_fp16.mlir b/attention/mlir/attention_B2_H64_SQ2048_SKV2048_DH128_fp16.mlir
new file mode 100644
index 0000000..fd0ad8f
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ2048_SKV2048_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,2048,128],f16>, %298 : !torch.vtensor<[2,64,2048,128],f16>, %301 : !torch.vtensor<[2,64,2048,128],f16>) -> !torch.vtensor<[2,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ2048_SKV2048_DH128_fp8.mlir b/attention/mlir/attention_B2_H64_SQ2048_SKV2048_DH128_fp8.mlir
new file mode 100644
index 0000000..fd0ad8f
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ2048_SKV2048_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,2048,128],f16>, %298 : !torch.vtensor<[2,64,2048,128],f16>, %301 : !torch.vtensor<[2,64,2048,128],f16>) -> !torch.vtensor<[2,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ2048_SKV4096_DH128_f16.mlir b/attention/mlir/attention_B2_H64_SQ2048_SKV4096_DH128_f16.mlir
new file mode 100644
index 0000000..2f3efa1
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ2048_SKV4096_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,2048,128],f16>, %298 : !torch.vtensor<[2,64,4096,128],f16>, %301 : !torch.vtensor<[2,64,4096,128],f16>) -> !torch.vtensor<[2,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ2048_SKV4096_DH128_f8.mlir b/attention/mlir/attention_B2_H64_SQ2048_SKV4096_DH128_f8.mlir
new file mode 100644
index 0000000..2f3efa1
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ2048_SKV4096_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,2048,128],f16>, %298 : !torch.vtensor<[2,64,4096,128],f16>, %301 : !torch.vtensor<[2,64,4096,128],f16>) -> !torch.vtensor<[2,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ2048_SKV4096_DH128_fp16.mlir b/attention/mlir/attention_B2_H64_SQ2048_SKV4096_DH128_fp16.mlir
new file mode 100644
index 0000000..2f3efa1
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ2048_SKV4096_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,2048,128],f16>, %298 : !torch.vtensor<[2,64,4096,128],f16>, %301 : !torch.vtensor<[2,64,4096,128],f16>) -> !torch.vtensor<[2,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ2048_SKV4096_DH128_fp8.mlir b/attention/mlir/attention_B2_H64_SQ2048_SKV4096_DH128_fp8.mlir
new file mode 100644
index 0000000..2f3efa1
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ2048_SKV4096_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,2048,128],f16>, %298 : !torch.vtensor<[2,64,4096,128],f16>, %301 : !torch.vtensor<[2,64,4096,128],f16>) -> !torch.vtensor<[2,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[2,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ4096_SKV1024_DH128_f16.mlir b/attention/mlir/attention_B2_H64_SQ4096_SKV1024_DH128_f16.mlir
new file mode 100644
index 0000000..3e56679
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ4096_SKV1024_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,4096,128],f16>, %298 : !torch.vtensor<[2,64,1024,128],f16>, %301 : !torch.vtensor<[2,64,1024,128],f16>) -> !torch.vtensor<[2,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ4096_SKV1024_DH128_f8.mlir b/attention/mlir/attention_B2_H64_SQ4096_SKV1024_DH128_f8.mlir
new file mode 100644
index 0000000..3e56679
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ4096_SKV1024_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,4096,128],f16>, %298 : !torch.vtensor<[2,64,1024,128],f16>, %301 : !torch.vtensor<[2,64,1024,128],f16>) -> !torch.vtensor<[2,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ4096_SKV1024_DH128_fp16.mlir b/attention/mlir/attention_B2_H64_SQ4096_SKV1024_DH128_fp16.mlir
new file mode 100644
index 0000000..3e56679
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ4096_SKV1024_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,4096,128],f16>, %298 : !torch.vtensor<[2,64,1024,128],f16>, %301 : !torch.vtensor<[2,64,1024,128],f16>) -> !torch.vtensor<[2,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ4096_SKV1024_DH128_fp8.mlir b/attention/mlir/attention_B2_H64_SQ4096_SKV1024_DH128_fp8.mlir
new file mode 100644
index 0000000..3e56679
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ4096_SKV1024_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,4096,128],f16>, %298 : !torch.vtensor<[2,64,1024,128],f16>, %301 : !torch.vtensor<[2,64,1024,128],f16>) -> !torch.vtensor<[2,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,1024,128],f16>, !torch.vtensor<[2,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ4096_SKV2048_DH128_f16.mlir b/attention/mlir/attention_B2_H64_SQ4096_SKV2048_DH128_f16.mlir
new file mode 100644
index 0000000..61acf25
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ4096_SKV2048_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,4096,128],f16>, %298 : !torch.vtensor<[2,64,2048,128],f16>, %301 : !torch.vtensor<[2,64,2048,128],f16>) -> !torch.vtensor<[2,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ4096_SKV2048_DH128_f8.mlir b/attention/mlir/attention_B2_H64_SQ4096_SKV2048_DH128_f8.mlir
new file mode 100644
index 0000000..61acf25
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ4096_SKV2048_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,4096,128],f16>, %298 : !torch.vtensor<[2,64,2048,128],f16>, %301 : !torch.vtensor<[2,64,2048,128],f16>) -> !torch.vtensor<[2,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ4096_SKV2048_DH128_fp16.mlir b/attention/mlir/attention_B2_H64_SQ4096_SKV2048_DH128_fp16.mlir
new file mode 100644
index 0000000..61acf25
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ4096_SKV2048_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,4096,128],f16>, %298 : !torch.vtensor<[2,64,2048,128],f16>, %301 : !torch.vtensor<[2,64,2048,128],f16>) -> !torch.vtensor<[2,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ4096_SKV2048_DH128_fp8.mlir b/attention/mlir/attention_B2_H64_SQ4096_SKV2048_DH128_fp8.mlir
new file mode 100644
index 0000000..61acf25
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ4096_SKV2048_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,4096,128],f16>, %298 : !torch.vtensor<[2,64,2048,128],f16>, %301 : !torch.vtensor<[2,64,2048,128],f16>) -> !torch.vtensor<[2,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,2048,128],f16>, !torch.vtensor<[2,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ4096_SKV4096_DH128_f16.mlir b/attention/mlir/attention_B2_H64_SQ4096_SKV4096_DH128_f16.mlir
new file mode 100644
index 0000000..e88c031
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ4096_SKV4096_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,4096,128],f16>, %298 : !torch.vtensor<[2,64,4096,128],f16>, %301 : !torch.vtensor<[2,64,4096,128],f16>) -> !torch.vtensor<[2,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ4096_SKV4096_DH128_f8.mlir b/attention/mlir/attention_B2_H64_SQ4096_SKV4096_DH128_f8.mlir
new file mode 100644
index 0000000..e88c031
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ4096_SKV4096_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,4096,128],f16>, %298 : !torch.vtensor<[2,64,4096,128],f16>, %301 : !torch.vtensor<[2,64,4096,128],f16>) -> !torch.vtensor<[2,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ4096_SKV4096_DH128_fp16.mlir b/attention/mlir/attention_B2_H64_SQ4096_SKV4096_DH128_fp16.mlir
new file mode 100644
index 0000000..e88c031
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ4096_SKV4096_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,4096,128],f16>, %298 : !torch.vtensor<[2,64,4096,128],f16>, %301 : !torch.vtensor<[2,64,4096,128],f16>) -> !torch.vtensor<[2,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B2_H64_SQ4096_SKV4096_DH128_fp8.mlir b/attention/mlir/attention_B2_H64_SQ4096_SKV4096_DH128_fp8.mlir
new file mode 100644
index 0000000..e88c031
--- /dev/null
+++ b/attention/mlir/attention_B2_H64_SQ4096_SKV4096_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[2,64,4096,128],f16>, %298 : !torch.vtensor<[2,64,4096,128],f16>, %301 : !torch.vtensor<[2,64,4096,128],f16>) -> !torch.vtensor<[2,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[2,64,4096,128],f16>, !torch.vtensor<[2,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[2,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ1024_SKV1024_DH128_f16.mlir b/attention/mlir/attention_B4_H32_SQ1024_SKV1024_DH128_f16.mlir
new file mode 100644
index 0000000..59bfdf8
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ1024_SKV1024_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,1024,128],f16>, %298 : !torch.vtensor<[4,32,1024,128],f16>, %301 : !torch.vtensor<[4,32,1024,128],f16>) -> !torch.vtensor<[4,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ1024_SKV1024_DH128_f8.mlir b/attention/mlir/attention_B4_H32_SQ1024_SKV1024_DH128_f8.mlir
new file mode 100644
index 0000000..59bfdf8
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ1024_SKV1024_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,1024,128],f16>, %298 : !torch.vtensor<[4,32,1024,128],f16>, %301 : !torch.vtensor<[4,32,1024,128],f16>) -> !torch.vtensor<[4,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ1024_SKV1024_DH128_fp16.mlir b/attention/mlir/attention_B4_H32_SQ1024_SKV1024_DH128_fp16.mlir
new file mode 100644
index 0000000..59bfdf8
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ1024_SKV1024_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,1024,128],f16>, %298 : !torch.vtensor<[4,32,1024,128],f16>, %301 : !torch.vtensor<[4,32,1024,128],f16>) -> !torch.vtensor<[4,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ1024_SKV1024_DH128_fp8.mlir b/attention/mlir/attention_B4_H32_SQ1024_SKV1024_DH128_fp8.mlir
new file mode 100644
index 0000000..59bfdf8
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ1024_SKV1024_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,1024,128],f16>, %298 : !torch.vtensor<[4,32,1024,128],f16>, %301 : !torch.vtensor<[4,32,1024,128],f16>) -> !torch.vtensor<[4,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ1024_SKV2048_DH128_f16.mlir b/attention/mlir/attention_B4_H32_SQ1024_SKV2048_DH128_f16.mlir
new file mode 100644
index 0000000..bd72204
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ1024_SKV2048_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,1024,128],f16>, %298 : !torch.vtensor<[4,32,2048,128],f16>, %301 : !torch.vtensor<[4,32,2048,128],f16>) -> !torch.vtensor<[4,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ1024_SKV2048_DH128_f8.mlir b/attention/mlir/attention_B4_H32_SQ1024_SKV2048_DH128_f8.mlir
new file mode 100644
index 0000000..bd72204
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ1024_SKV2048_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,1024,128],f16>, %298 : !torch.vtensor<[4,32,2048,128],f16>, %301 : !torch.vtensor<[4,32,2048,128],f16>) -> !torch.vtensor<[4,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ1024_SKV2048_DH128_fp16.mlir b/attention/mlir/attention_B4_H32_SQ1024_SKV2048_DH128_fp16.mlir
new file mode 100644
index 0000000..bd72204
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ1024_SKV2048_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,1024,128],f16>, %298 : !torch.vtensor<[4,32,2048,128],f16>, %301 : !torch.vtensor<[4,32,2048,128],f16>) -> !torch.vtensor<[4,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ1024_SKV2048_DH128_fp8.mlir b/attention/mlir/attention_B4_H32_SQ1024_SKV2048_DH128_fp8.mlir
new file mode 100644
index 0000000..bd72204
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ1024_SKV2048_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,1024,128],f16>, %298 : !torch.vtensor<[4,32,2048,128],f16>, %301 : !torch.vtensor<[4,32,2048,128],f16>) -> !torch.vtensor<[4,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ1024_SKV4096_DH128_f16.mlir b/attention/mlir/attention_B4_H32_SQ1024_SKV4096_DH128_f16.mlir
new file mode 100644
index 0000000..74bd33e
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ1024_SKV4096_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,1024,128],f16>, %298 : !torch.vtensor<[4,32,4096,128],f16>, %301 : !torch.vtensor<[4,32,4096,128],f16>) -> !torch.vtensor<[4,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ1024_SKV4096_DH128_f8.mlir b/attention/mlir/attention_B4_H32_SQ1024_SKV4096_DH128_f8.mlir
new file mode 100644
index 0000000..74bd33e
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ1024_SKV4096_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,1024,128],f16>, %298 : !torch.vtensor<[4,32,4096,128],f16>, %301 : !torch.vtensor<[4,32,4096,128],f16>) -> !torch.vtensor<[4,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ1024_SKV4096_DH128_fp16.mlir b/attention/mlir/attention_B4_H32_SQ1024_SKV4096_DH128_fp16.mlir
new file mode 100644
index 0000000..74bd33e
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ1024_SKV4096_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,1024,128],f16>, %298 : !torch.vtensor<[4,32,4096,128],f16>, %301 : !torch.vtensor<[4,32,4096,128],f16>) -> !torch.vtensor<[4,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ1024_SKV4096_DH128_fp8.mlir b/attention/mlir/attention_B4_H32_SQ1024_SKV4096_DH128_fp8.mlir
new file mode 100644
index 0000000..74bd33e
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ1024_SKV4096_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,1024,128],f16>, %298 : !torch.vtensor<[4,32,4096,128],f16>, %301 : !torch.vtensor<[4,32,4096,128],f16>) -> !torch.vtensor<[4,32,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,32,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ2048_SKV1024_DH128_f16.mlir b/attention/mlir/attention_B4_H32_SQ2048_SKV1024_DH128_f16.mlir
new file mode 100644
index 0000000..6f99074
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ2048_SKV1024_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,2048,128],f16>, %298 : !torch.vtensor<[4,32,1024,128],f16>, %301 : !torch.vtensor<[4,32,1024,128],f16>) -> !torch.vtensor<[4,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ2048_SKV1024_DH128_f8.mlir b/attention/mlir/attention_B4_H32_SQ2048_SKV1024_DH128_f8.mlir
new file mode 100644
index 0000000..6f99074
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ2048_SKV1024_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,2048,128],f16>, %298 : !torch.vtensor<[4,32,1024,128],f16>, %301 : !torch.vtensor<[4,32,1024,128],f16>) -> !torch.vtensor<[4,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ2048_SKV1024_DH128_fp16.mlir b/attention/mlir/attention_B4_H32_SQ2048_SKV1024_DH128_fp16.mlir
new file mode 100644
index 0000000..6f99074
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ2048_SKV1024_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,2048,128],f16>, %298 : !torch.vtensor<[4,32,1024,128],f16>, %301 : !torch.vtensor<[4,32,1024,128],f16>) -> !torch.vtensor<[4,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ2048_SKV1024_DH128_fp8.mlir b/attention/mlir/attention_B4_H32_SQ2048_SKV1024_DH128_fp8.mlir
new file mode 100644
index 0000000..6f99074
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ2048_SKV1024_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,2048,128],f16>, %298 : !torch.vtensor<[4,32,1024,128],f16>, %301 : !torch.vtensor<[4,32,1024,128],f16>) -> !torch.vtensor<[4,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ2048_SKV2048_DH128_f16.mlir b/attention/mlir/attention_B4_H32_SQ2048_SKV2048_DH128_f16.mlir
new file mode 100644
index 0000000..e769176
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ2048_SKV2048_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,2048,128],f16>, %298 : !torch.vtensor<[4,32,2048,128],f16>, %301 : !torch.vtensor<[4,32,2048,128],f16>) -> !torch.vtensor<[4,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ2048_SKV2048_DH128_f8.mlir b/attention/mlir/attention_B4_H32_SQ2048_SKV2048_DH128_f8.mlir
new file mode 100644
index 0000000..e769176
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ2048_SKV2048_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,2048,128],f16>, %298 : !torch.vtensor<[4,32,2048,128],f16>, %301 : !torch.vtensor<[4,32,2048,128],f16>) -> !torch.vtensor<[4,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ2048_SKV2048_DH128_fp16.mlir b/attention/mlir/attention_B4_H32_SQ2048_SKV2048_DH128_fp16.mlir
new file mode 100644
index 0000000..e769176
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ2048_SKV2048_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,2048,128],f16>, %298 : !torch.vtensor<[4,32,2048,128],f16>, %301 : !torch.vtensor<[4,32,2048,128],f16>) -> !torch.vtensor<[4,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ2048_SKV2048_DH128_fp8.mlir b/attention/mlir/attention_B4_H32_SQ2048_SKV2048_DH128_fp8.mlir
new file mode 100644
index 0000000..e769176
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ2048_SKV2048_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,2048,128],f16>, %298 : !torch.vtensor<[4,32,2048,128],f16>, %301 : !torch.vtensor<[4,32,2048,128],f16>) -> !torch.vtensor<[4,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ2048_SKV4096_DH128_f16.mlir b/attention/mlir/attention_B4_H32_SQ2048_SKV4096_DH128_f16.mlir
new file mode 100644
index 0000000..141949f
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ2048_SKV4096_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,2048,128],f16>, %298 : !torch.vtensor<[4,32,4096,128],f16>, %301 : !torch.vtensor<[4,32,4096,128],f16>) -> !torch.vtensor<[4,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ2048_SKV4096_DH128_f8.mlir b/attention/mlir/attention_B4_H32_SQ2048_SKV4096_DH128_f8.mlir
new file mode 100644
index 0000000..141949f
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ2048_SKV4096_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,2048,128],f16>, %298 : !torch.vtensor<[4,32,4096,128],f16>, %301 : !torch.vtensor<[4,32,4096,128],f16>) -> !torch.vtensor<[4,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ2048_SKV4096_DH128_fp16.mlir b/attention/mlir/attention_B4_H32_SQ2048_SKV4096_DH128_fp16.mlir
new file mode 100644
index 0000000..141949f
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ2048_SKV4096_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,2048,128],f16>, %298 : !torch.vtensor<[4,32,4096,128],f16>, %301 : !torch.vtensor<[4,32,4096,128],f16>) -> !torch.vtensor<[4,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ2048_SKV4096_DH128_fp8.mlir b/attention/mlir/attention_B4_H32_SQ2048_SKV4096_DH128_fp8.mlir
new file mode 100644
index 0000000..141949f
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ2048_SKV4096_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,2048,128],f16>, %298 : !torch.vtensor<[4,32,4096,128],f16>, %301 : !torch.vtensor<[4,32,4096,128],f16>) -> !torch.vtensor<[4,32,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,32,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ4096_SKV1024_DH128_f16.mlir b/attention/mlir/attention_B4_H32_SQ4096_SKV1024_DH128_f16.mlir
new file mode 100644
index 0000000..7b96aef
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ4096_SKV1024_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,4096,128],f16>, %298 : !torch.vtensor<[4,32,1024,128],f16>, %301 : !torch.vtensor<[4,32,1024,128],f16>) -> !torch.vtensor<[4,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ4096_SKV1024_DH128_f8.mlir b/attention/mlir/attention_B4_H32_SQ4096_SKV1024_DH128_f8.mlir
new file mode 100644
index 0000000..7b96aef
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ4096_SKV1024_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,4096,128],f16>, %298 : !torch.vtensor<[4,32,1024,128],f16>, %301 : !torch.vtensor<[4,32,1024,128],f16>) -> !torch.vtensor<[4,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ4096_SKV1024_DH128_fp16.mlir b/attention/mlir/attention_B4_H32_SQ4096_SKV1024_DH128_fp16.mlir
new file mode 100644
index 0000000..7b96aef
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ4096_SKV1024_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,4096,128],f16>, %298 : !torch.vtensor<[4,32,1024,128],f16>, %301 : !torch.vtensor<[4,32,1024,128],f16>) -> !torch.vtensor<[4,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ4096_SKV1024_DH128_fp8.mlir b/attention/mlir/attention_B4_H32_SQ4096_SKV1024_DH128_fp8.mlir
new file mode 100644
index 0000000..7b96aef
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ4096_SKV1024_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,4096,128],f16>, %298 : !torch.vtensor<[4,32,1024,128],f16>, %301 : !torch.vtensor<[4,32,1024,128],f16>) -> !torch.vtensor<[4,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,1024,128],f16>, !torch.vtensor<[4,32,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ4096_SKV2048_DH128_f16.mlir b/attention/mlir/attention_B4_H32_SQ4096_SKV2048_DH128_f16.mlir
new file mode 100644
index 0000000..3914188
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ4096_SKV2048_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,4096,128],f16>, %298 : !torch.vtensor<[4,32,2048,128],f16>, %301 : !torch.vtensor<[4,32,2048,128],f16>) -> !torch.vtensor<[4,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ4096_SKV2048_DH128_f8.mlir b/attention/mlir/attention_B4_H32_SQ4096_SKV2048_DH128_f8.mlir
new file mode 100644
index 0000000..3914188
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ4096_SKV2048_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,4096,128],f16>, %298 : !torch.vtensor<[4,32,2048,128],f16>, %301 : !torch.vtensor<[4,32,2048,128],f16>) -> !torch.vtensor<[4,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ4096_SKV2048_DH128_fp16.mlir b/attention/mlir/attention_B4_H32_SQ4096_SKV2048_DH128_fp16.mlir
new file mode 100644
index 0000000..3914188
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ4096_SKV2048_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,4096,128],f16>, %298 : !torch.vtensor<[4,32,2048,128],f16>, %301 : !torch.vtensor<[4,32,2048,128],f16>) -> !torch.vtensor<[4,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ4096_SKV2048_DH128_fp8.mlir b/attention/mlir/attention_B4_H32_SQ4096_SKV2048_DH128_fp8.mlir
new file mode 100644
index 0000000..3914188
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ4096_SKV2048_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,4096,128],f16>, %298 : !torch.vtensor<[4,32,2048,128],f16>, %301 : !torch.vtensor<[4,32,2048,128],f16>) -> !torch.vtensor<[4,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,2048,128],f16>, !torch.vtensor<[4,32,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ4096_SKV4096_DH128_f16.mlir b/attention/mlir/attention_B4_H32_SQ4096_SKV4096_DH128_f16.mlir
new file mode 100644
index 0000000..7030eb2
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ4096_SKV4096_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,4096,128],f16>, %298 : !torch.vtensor<[4,32,4096,128],f16>, %301 : !torch.vtensor<[4,32,4096,128],f16>) -> !torch.vtensor<[4,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ4096_SKV4096_DH128_f8.mlir b/attention/mlir/attention_B4_H32_SQ4096_SKV4096_DH128_f8.mlir
new file mode 100644
index 0000000..7030eb2
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ4096_SKV4096_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,4096,128],f16>, %298 : !torch.vtensor<[4,32,4096,128],f16>, %301 : !torch.vtensor<[4,32,4096,128],f16>) -> !torch.vtensor<[4,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ4096_SKV4096_DH128_fp16.mlir b/attention/mlir/attention_B4_H32_SQ4096_SKV4096_DH128_fp16.mlir
new file mode 100644
index 0000000..7030eb2
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ4096_SKV4096_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,4096,128],f16>, %298 : !torch.vtensor<[4,32,4096,128],f16>, %301 : !torch.vtensor<[4,32,4096,128],f16>) -> !torch.vtensor<[4,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H32_SQ4096_SKV4096_DH128_fp8.mlir b/attention/mlir/attention_B4_H32_SQ4096_SKV4096_DH128_fp8.mlir
new file mode 100644
index 0000000..7030eb2
--- /dev/null
+++ b/attention/mlir/attention_B4_H32_SQ4096_SKV4096_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,32,4096,128],f16>, %298 : !torch.vtensor<[4,32,4096,128],f16>, %301 : !torch.vtensor<[4,32,4096,128],f16>) -> !torch.vtensor<[4,32,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,32,4096,128],f16>, !torch.vtensor<[4,32,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,32,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ1024_SKV1024_DH128_f16.mlir b/attention/mlir/attention_B4_H40_SQ1024_SKV1024_DH128_f16.mlir
new file mode 100644
index 0000000..6d67b0a
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ1024_SKV1024_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,1024,128],f16>, %298 : !torch.vtensor<[4,40,1024,128],f16>, %301 : !torch.vtensor<[4,40,1024,128],f16>) -> !torch.vtensor<[4,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ1024_SKV1024_DH128_f8.mlir b/attention/mlir/attention_B4_H40_SQ1024_SKV1024_DH128_f8.mlir
new file mode 100644
index 0000000..6d67b0a
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ1024_SKV1024_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,1024,128],f16>, %298 : !torch.vtensor<[4,40,1024,128],f16>, %301 : !torch.vtensor<[4,40,1024,128],f16>) -> !torch.vtensor<[4,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ1024_SKV1024_DH128_fp16.mlir b/attention/mlir/attention_B4_H40_SQ1024_SKV1024_DH128_fp16.mlir
new file mode 100644
index 0000000..6d67b0a
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ1024_SKV1024_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,1024,128],f16>, %298 : !torch.vtensor<[4,40,1024,128],f16>, %301 : !torch.vtensor<[4,40,1024,128],f16>) -> !torch.vtensor<[4,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ1024_SKV1024_DH128_fp8.mlir b/attention/mlir/attention_B4_H40_SQ1024_SKV1024_DH128_fp8.mlir
new file mode 100644
index 0000000..6d67b0a
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ1024_SKV1024_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,1024,128],f16>, %298 : !torch.vtensor<[4,40,1024,128],f16>, %301 : !torch.vtensor<[4,40,1024,128],f16>) -> !torch.vtensor<[4,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ1024_SKV2048_DH128_f16.mlir b/attention/mlir/attention_B4_H40_SQ1024_SKV2048_DH128_f16.mlir
new file mode 100644
index 0000000..6a2bb64
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ1024_SKV2048_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,1024,128],f16>, %298 : !torch.vtensor<[4,40,2048,128],f16>, %301 : !torch.vtensor<[4,40,2048,128],f16>) -> !torch.vtensor<[4,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ1024_SKV2048_DH128_f8.mlir b/attention/mlir/attention_B4_H40_SQ1024_SKV2048_DH128_f8.mlir
new file mode 100644
index 0000000..6a2bb64
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ1024_SKV2048_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,1024,128],f16>, %298 : !torch.vtensor<[4,40,2048,128],f16>, %301 : !torch.vtensor<[4,40,2048,128],f16>) -> !torch.vtensor<[4,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ1024_SKV2048_DH128_fp16.mlir b/attention/mlir/attention_B4_H40_SQ1024_SKV2048_DH128_fp16.mlir
new file mode 100644
index 0000000..6a2bb64
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ1024_SKV2048_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,1024,128],f16>, %298 : !torch.vtensor<[4,40,2048,128],f16>, %301 : !torch.vtensor<[4,40,2048,128],f16>) -> !torch.vtensor<[4,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ1024_SKV2048_DH128_fp8.mlir b/attention/mlir/attention_B4_H40_SQ1024_SKV2048_DH128_fp8.mlir
new file mode 100644
index 0000000..6a2bb64
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ1024_SKV2048_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,1024,128],f16>, %298 : !torch.vtensor<[4,40,2048,128],f16>, %301 : !torch.vtensor<[4,40,2048,128],f16>) -> !torch.vtensor<[4,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ1024_SKV4096_DH128_f16.mlir b/attention/mlir/attention_B4_H40_SQ1024_SKV4096_DH128_f16.mlir
new file mode 100644
index 0000000..060dbc3
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ1024_SKV4096_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,1024,128],f16>, %298 : !torch.vtensor<[4,40,4096,128],f16>, %301 : !torch.vtensor<[4,40,4096,128],f16>) -> !torch.vtensor<[4,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ1024_SKV4096_DH128_f8.mlir b/attention/mlir/attention_B4_H40_SQ1024_SKV4096_DH128_f8.mlir
new file mode 100644
index 0000000..060dbc3
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ1024_SKV4096_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,1024,128],f16>, %298 : !torch.vtensor<[4,40,4096,128],f16>, %301 : !torch.vtensor<[4,40,4096,128],f16>) -> !torch.vtensor<[4,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ1024_SKV4096_DH128_fp16.mlir b/attention/mlir/attention_B4_H40_SQ1024_SKV4096_DH128_fp16.mlir
new file mode 100644
index 0000000..060dbc3
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ1024_SKV4096_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,1024,128],f16>, %298 : !torch.vtensor<[4,40,4096,128],f16>, %301 : !torch.vtensor<[4,40,4096,128],f16>) -> !torch.vtensor<[4,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ1024_SKV4096_DH128_fp8.mlir b/attention/mlir/attention_B4_H40_SQ1024_SKV4096_DH128_fp8.mlir
new file mode 100644
index 0000000..060dbc3
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ1024_SKV4096_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,1024,128],f16>, %298 : !torch.vtensor<[4,40,4096,128],f16>, %301 : !torch.vtensor<[4,40,4096,128],f16>) -> !torch.vtensor<[4,40,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,40,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ2048_SKV1024_DH128_f16.mlir b/attention/mlir/attention_B4_H40_SQ2048_SKV1024_DH128_f16.mlir
new file mode 100644
index 0000000..5ff4592
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ2048_SKV1024_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,2048,128],f16>, %298 : !torch.vtensor<[4,40,1024,128],f16>, %301 : !torch.vtensor<[4,40,1024,128],f16>) -> !torch.vtensor<[4,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ2048_SKV1024_DH128_f8.mlir b/attention/mlir/attention_B4_H40_SQ2048_SKV1024_DH128_f8.mlir
new file mode 100644
index 0000000..5ff4592
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ2048_SKV1024_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,2048,128],f16>, %298 : !torch.vtensor<[4,40,1024,128],f16>, %301 : !torch.vtensor<[4,40,1024,128],f16>) -> !torch.vtensor<[4,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ2048_SKV1024_DH128_fp16.mlir b/attention/mlir/attention_B4_H40_SQ2048_SKV1024_DH128_fp16.mlir
new file mode 100644
index 0000000..5ff4592
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ2048_SKV1024_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,2048,128],f16>, %298 : !torch.vtensor<[4,40,1024,128],f16>, %301 : !torch.vtensor<[4,40,1024,128],f16>) -> !torch.vtensor<[4,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ2048_SKV1024_DH128_fp8.mlir b/attention/mlir/attention_B4_H40_SQ2048_SKV1024_DH128_fp8.mlir
new file mode 100644
index 0000000..5ff4592
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ2048_SKV1024_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,2048,128],f16>, %298 : !torch.vtensor<[4,40,1024,128],f16>, %301 : !torch.vtensor<[4,40,1024,128],f16>) -> !torch.vtensor<[4,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ2048_SKV2048_DH128_f16.mlir b/attention/mlir/attention_B4_H40_SQ2048_SKV2048_DH128_f16.mlir
new file mode 100644
index 0000000..f73d8ee
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ2048_SKV2048_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,2048,128],f16>, %298 : !torch.vtensor<[4,40,2048,128],f16>, %301 : !torch.vtensor<[4,40,2048,128],f16>) -> !torch.vtensor<[4,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ2048_SKV2048_DH128_f8.mlir b/attention/mlir/attention_B4_H40_SQ2048_SKV2048_DH128_f8.mlir
new file mode 100644
index 0000000..f73d8ee
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ2048_SKV2048_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,2048,128],f16>, %298 : !torch.vtensor<[4,40,2048,128],f16>, %301 : !torch.vtensor<[4,40,2048,128],f16>) -> !torch.vtensor<[4,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ2048_SKV2048_DH128_fp16.mlir b/attention/mlir/attention_B4_H40_SQ2048_SKV2048_DH128_fp16.mlir
new file mode 100644
index 0000000..f73d8ee
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ2048_SKV2048_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,2048,128],f16>, %298 : !torch.vtensor<[4,40,2048,128],f16>, %301 : !torch.vtensor<[4,40,2048,128],f16>) -> !torch.vtensor<[4,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ2048_SKV2048_DH128_fp8.mlir b/attention/mlir/attention_B4_H40_SQ2048_SKV2048_DH128_fp8.mlir
new file mode 100644
index 0000000..f73d8ee
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ2048_SKV2048_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,2048,128],f16>, %298 : !torch.vtensor<[4,40,2048,128],f16>, %301 : !torch.vtensor<[4,40,2048,128],f16>) -> !torch.vtensor<[4,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ2048_SKV4096_DH128_f16.mlir b/attention/mlir/attention_B4_H40_SQ2048_SKV4096_DH128_f16.mlir
new file mode 100644
index 0000000..e98abf9
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ2048_SKV4096_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,2048,128],f16>, %298 : !torch.vtensor<[4,40,4096,128],f16>, %301 : !torch.vtensor<[4,40,4096,128],f16>) -> !torch.vtensor<[4,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ2048_SKV4096_DH128_f8.mlir b/attention/mlir/attention_B4_H40_SQ2048_SKV4096_DH128_f8.mlir
new file mode 100644
index 0000000..e98abf9
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ2048_SKV4096_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,2048,128],f16>, %298 : !torch.vtensor<[4,40,4096,128],f16>, %301 : !torch.vtensor<[4,40,4096,128],f16>) -> !torch.vtensor<[4,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ2048_SKV4096_DH128_fp16.mlir b/attention/mlir/attention_B4_H40_SQ2048_SKV4096_DH128_fp16.mlir
new file mode 100644
index 0000000..e98abf9
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ2048_SKV4096_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,2048,128],f16>, %298 : !torch.vtensor<[4,40,4096,128],f16>, %301 : !torch.vtensor<[4,40,4096,128],f16>) -> !torch.vtensor<[4,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ2048_SKV4096_DH128_fp8.mlir b/attention/mlir/attention_B4_H40_SQ2048_SKV4096_DH128_fp8.mlir
new file mode 100644
index 0000000..e98abf9
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ2048_SKV4096_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,2048,128],f16>, %298 : !torch.vtensor<[4,40,4096,128],f16>, %301 : !torch.vtensor<[4,40,4096,128],f16>) -> !torch.vtensor<[4,40,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,40,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ4096_SKV1024_DH128_f16.mlir b/attention/mlir/attention_B4_H40_SQ4096_SKV1024_DH128_f16.mlir
new file mode 100644
index 0000000..850e45e
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ4096_SKV1024_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,4096,128],f16>, %298 : !torch.vtensor<[4,40,1024,128],f16>, %301 : !torch.vtensor<[4,40,1024,128],f16>) -> !torch.vtensor<[4,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ4096_SKV1024_DH128_f8.mlir b/attention/mlir/attention_B4_H40_SQ4096_SKV1024_DH128_f8.mlir
new file mode 100644
index 0000000..850e45e
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ4096_SKV1024_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,4096,128],f16>, %298 : !torch.vtensor<[4,40,1024,128],f16>, %301 : !torch.vtensor<[4,40,1024,128],f16>) -> !torch.vtensor<[4,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ4096_SKV1024_DH128_fp16.mlir b/attention/mlir/attention_B4_H40_SQ4096_SKV1024_DH128_fp16.mlir
new file mode 100644
index 0000000..850e45e
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ4096_SKV1024_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,4096,128],f16>, %298 : !torch.vtensor<[4,40,1024,128],f16>, %301 : !torch.vtensor<[4,40,1024,128],f16>) -> !torch.vtensor<[4,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ4096_SKV1024_DH128_fp8.mlir b/attention/mlir/attention_B4_H40_SQ4096_SKV1024_DH128_fp8.mlir
new file mode 100644
index 0000000..850e45e
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ4096_SKV1024_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,4096,128],f16>, %298 : !torch.vtensor<[4,40,1024,128],f16>, %301 : !torch.vtensor<[4,40,1024,128],f16>) -> !torch.vtensor<[4,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,1024,128],f16>, !torch.vtensor<[4,40,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ4096_SKV2048_DH128_f16.mlir b/attention/mlir/attention_B4_H40_SQ4096_SKV2048_DH128_f16.mlir
new file mode 100644
index 0000000..b4eb836
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ4096_SKV2048_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,4096,128],f16>, %298 : !torch.vtensor<[4,40,2048,128],f16>, %301 : !torch.vtensor<[4,40,2048,128],f16>) -> !torch.vtensor<[4,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ4096_SKV2048_DH128_f8.mlir b/attention/mlir/attention_B4_H40_SQ4096_SKV2048_DH128_f8.mlir
new file mode 100644
index 0000000..b4eb836
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ4096_SKV2048_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,4096,128],f16>, %298 : !torch.vtensor<[4,40,2048,128],f16>, %301 : !torch.vtensor<[4,40,2048,128],f16>) -> !torch.vtensor<[4,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ4096_SKV2048_DH128_fp16.mlir b/attention/mlir/attention_B4_H40_SQ4096_SKV2048_DH128_fp16.mlir
new file mode 100644
index 0000000..b4eb836
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ4096_SKV2048_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,4096,128],f16>, %298 : !torch.vtensor<[4,40,2048,128],f16>, %301 : !torch.vtensor<[4,40,2048,128],f16>) -> !torch.vtensor<[4,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ4096_SKV2048_DH128_fp8.mlir b/attention/mlir/attention_B4_H40_SQ4096_SKV2048_DH128_fp8.mlir
new file mode 100644
index 0000000..b4eb836
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ4096_SKV2048_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,4096,128],f16>, %298 : !torch.vtensor<[4,40,2048,128],f16>, %301 : !torch.vtensor<[4,40,2048,128],f16>) -> !torch.vtensor<[4,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,2048,128],f16>, !torch.vtensor<[4,40,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ4096_SKV4096_DH128_f16.mlir b/attention/mlir/attention_B4_H40_SQ4096_SKV4096_DH128_f16.mlir
new file mode 100644
index 0000000..6b38a12
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ4096_SKV4096_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,4096,128],f16>, %298 : !torch.vtensor<[4,40,4096,128],f16>, %301 : !torch.vtensor<[4,40,4096,128],f16>) -> !torch.vtensor<[4,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ4096_SKV4096_DH128_f8.mlir b/attention/mlir/attention_B4_H40_SQ4096_SKV4096_DH128_f8.mlir
new file mode 100644
index 0000000..6b38a12
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ4096_SKV4096_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,4096,128],f16>, %298 : !torch.vtensor<[4,40,4096,128],f16>, %301 : !torch.vtensor<[4,40,4096,128],f16>) -> !torch.vtensor<[4,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ4096_SKV4096_DH128_fp16.mlir b/attention/mlir/attention_B4_H40_SQ4096_SKV4096_DH128_fp16.mlir
new file mode 100644
index 0000000..6b38a12
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ4096_SKV4096_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,4096,128],f16>, %298 : !torch.vtensor<[4,40,4096,128],f16>, %301 : !torch.vtensor<[4,40,4096,128],f16>) -> !torch.vtensor<[4,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H40_SQ4096_SKV4096_DH128_fp8.mlir b/attention/mlir/attention_B4_H40_SQ4096_SKV4096_DH128_fp8.mlir
new file mode 100644
index 0000000..6b38a12
--- /dev/null
+++ b/attention/mlir/attention_B4_H40_SQ4096_SKV4096_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,40,4096,128],f16>, %298 : !torch.vtensor<[4,40,4096,128],f16>, %301 : !torch.vtensor<[4,40,4096,128],f16>) -> !torch.vtensor<[4,40,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,40,4096,128],f16>, !torch.vtensor<[4,40,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,40,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ1024_SKV1024_DH128_f16.mlir b/attention/mlir/attention_B4_H64_SQ1024_SKV1024_DH128_f16.mlir
new file mode 100644
index 0000000..379b222
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ1024_SKV1024_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,1024,128],f16>, %298 : !torch.vtensor<[4,64,1024,128],f16>, %301 : !torch.vtensor<[4,64,1024,128],f16>) -> !torch.vtensor<[4,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ1024_SKV1024_DH128_f8.mlir b/attention/mlir/attention_B4_H64_SQ1024_SKV1024_DH128_f8.mlir
new file mode 100644
index 0000000..379b222
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ1024_SKV1024_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,1024,128],f16>, %298 : !torch.vtensor<[4,64,1024,128],f16>, %301 : !torch.vtensor<[4,64,1024,128],f16>) -> !torch.vtensor<[4,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ1024_SKV1024_DH128_fp16.mlir b/attention/mlir/attention_B4_H64_SQ1024_SKV1024_DH128_fp16.mlir
new file mode 100644
index 0000000..379b222
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ1024_SKV1024_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,1024,128],f16>, %298 : !torch.vtensor<[4,64,1024,128],f16>, %301 : !torch.vtensor<[4,64,1024,128],f16>) -> !torch.vtensor<[4,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ1024_SKV1024_DH128_fp8.mlir b/attention/mlir/attention_B4_H64_SQ1024_SKV1024_DH128_fp8.mlir
new file mode 100644
index 0000000..379b222
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ1024_SKV1024_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,1024,128],f16>, %298 : !torch.vtensor<[4,64,1024,128],f16>, %301 : !torch.vtensor<[4,64,1024,128],f16>) -> !torch.vtensor<[4,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ1024_SKV2048_DH128_f16.mlir b/attention/mlir/attention_B4_H64_SQ1024_SKV2048_DH128_f16.mlir
new file mode 100644
index 0000000..58513cc
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ1024_SKV2048_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,1024,128],f16>, %298 : !torch.vtensor<[4,64,2048,128],f16>, %301 : !torch.vtensor<[4,64,2048,128],f16>) -> !torch.vtensor<[4,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ1024_SKV2048_DH128_f8.mlir b/attention/mlir/attention_B4_H64_SQ1024_SKV2048_DH128_f8.mlir
new file mode 100644
index 0000000..58513cc
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ1024_SKV2048_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,1024,128],f16>, %298 : !torch.vtensor<[4,64,2048,128],f16>, %301 : !torch.vtensor<[4,64,2048,128],f16>) -> !torch.vtensor<[4,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ1024_SKV2048_DH128_fp16.mlir b/attention/mlir/attention_B4_H64_SQ1024_SKV2048_DH128_fp16.mlir
new file mode 100644
index 0000000..58513cc
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ1024_SKV2048_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,1024,128],f16>, %298 : !torch.vtensor<[4,64,2048,128],f16>, %301 : !torch.vtensor<[4,64,2048,128],f16>) -> !torch.vtensor<[4,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ1024_SKV2048_DH128_fp8.mlir b/attention/mlir/attention_B4_H64_SQ1024_SKV2048_DH128_fp8.mlir
new file mode 100644
index 0000000..58513cc
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ1024_SKV2048_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,1024,128],f16>, %298 : !torch.vtensor<[4,64,2048,128],f16>, %301 : !torch.vtensor<[4,64,2048,128],f16>) -> !torch.vtensor<[4,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ1024_SKV4096_DH128_f16.mlir b/attention/mlir/attention_B4_H64_SQ1024_SKV4096_DH128_f16.mlir
new file mode 100644
index 0000000..d8ee6c5
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ1024_SKV4096_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,1024,128],f16>, %298 : !torch.vtensor<[4,64,4096,128],f16>, %301 : !torch.vtensor<[4,64,4096,128],f16>) -> !torch.vtensor<[4,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ1024_SKV4096_DH128_f8.mlir b/attention/mlir/attention_B4_H64_SQ1024_SKV4096_DH128_f8.mlir
new file mode 100644
index 0000000..d8ee6c5
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ1024_SKV4096_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,1024,128],f16>, %298 : !torch.vtensor<[4,64,4096,128],f16>, %301 : !torch.vtensor<[4,64,4096,128],f16>) -> !torch.vtensor<[4,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ1024_SKV4096_DH128_fp16.mlir b/attention/mlir/attention_B4_H64_SQ1024_SKV4096_DH128_fp16.mlir
new file mode 100644
index 0000000..d8ee6c5
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ1024_SKV4096_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,1024,128],f16>, %298 : !torch.vtensor<[4,64,4096,128],f16>, %301 : !torch.vtensor<[4,64,4096,128],f16>) -> !torch.vtensor<[4,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ1024_SKV4096_DH128_fp8.mlir b/attention/mlir/attention_B4_H64_SQ1024_SKV4096_DH128_fp8.mlir
new file mode 100644
index 0000000..d8ee6c5
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ1024_SKV4096_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,1024,128],f16>, %298 : !torch.vtensor<[4,64,4096,128],f16>, %301 : !torch.vtensor<[4,64,4096,128],f16>) -> !torch.vtensor<[4,64,1024,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,1024], f32>)
+        return %282#0 : !torch.vtensor<[4,64,1024,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ2048_SKV1024_DH128_f16.mlir b/attention/mlir/attention_B4_H64_SQ2048_SKV1024_DH128_f16.mlir
new file mode 100644
index 0000000..57dfd93
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ2048_SKV1024_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,2048,128],f16>, %298 : !torch.vtensor<[4,64,1024,128],f16>, %301 : !torch.vtensor<[4,64,1024,128],f16>) -> !torch.vtensor<[4,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ2048_SKV1024_DH128_f8.mlir b/attention/mlir/attention_B4_H64_SQ2048_SKV1024_DH128_f8.mlir
new file mode 100644
index 0000000..57dfd93
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ2048_SKV1024_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,2048,128],f16>, %298 : !torch.vtensor<[4,64,1024,128],f16>, %301 : !torch.vtensor<[4,64,1024,128],f16>) -> !torch.vtensor<[4,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ2048_SKV1024_DH128_fp16.mlir b/attention/mlir/attention_B4_H64_SQ2048_SKV1024_DH128_fp16.mlir
new file mode 100644
index 0000000..57dfd93
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ2048_SKV1024_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,2048,128],f16>, %298 : !torch.vtensor<[4,64,1024,128],f16>, %301 : !torch.vtensor<[4,64,1024,128],f16>) -> !torch.vtensor<[4,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ2048_SKV1024_DH128_fp8.mlir b/attention/mlir/attention_B4_H64_SQ2048_SKV1024_DH128_fp8.mlir
new file mode 100644
index 0000000..57dfd93
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ2048_SKV1024_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,2048,128],f16>, %298 : !torch.vtensor<[4,64,1024,128],f16>, %301 : !torch.vtensor<[4,64,1024,128],f16>) -> !torch.vtensor<[4,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ2048_SKV2048_DH128_f16.mlir b/attention/mlir/attention_B4_H64_SQ2048_SKV2048_DH128_f16.mlir
new file mode 100644
index 0000000..8378b3d
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ2048_SKV2048_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,2048,128],f16>, %298 : !torch.vtensor<[4,64,2048,128],f16>, %301 : !torch.vtensor<[4,64,2048,128],f16>) -> !torch.vtensor<[4,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ2048_SKV2048_DH128_f8.mlir b/attention/mlir/attention_B4_H64_SQ2048_SKV2048_DH128_f8.mlir
new file mode 100644
index 0000000..8378b3d
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ2048_SKV2048_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,2048,128],f16>, %298 : !torch.vtensor<[4,64,2048,128],f16>, %301 : !torch.vtensor<[4,64,2048,128],f16>) -> !torch.vtensor<[4,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ2048_SKV2048_DH128_fp16.mlir b/attention/mlir/attention_B4_H64_SQ2048_SKV2048_DH128_fp16.mlir
new file mode 100644
index 0000000..8378b3d
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ2048_SKV2048_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,2048,128],f16>, %298 : !torch.vtensor<[4,64,2048,128],f16>, %301 : !torch.vtensor<[4,64,2048,128],f16>) -> !torch.vtensor<[4,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ2048_SKV2048_DH128_fp8.mlir b/attention/mlir/attention_B4_H64_SQ2048_SKV2048_DH128_fp8.mlir
new file mode 100644
index 0000000..8378b3d
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ2048_SKV2048_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,2048,128],f16>, %298 : !torch.vtensor<[4,64,2048,128],f16>, %301 : !torch.vtensor<[4,64,2048,128],f16>) -> !torch.vtensor<[4,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ2048_SKV4096_DH128_f16.mlir b/attention/mlir/attention_B4_H64_SQ2048_SKV4096_DH128_f16.mlir
new file mode 100644
index 0000000..863607d
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ2048_SKV4096_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,2048,128],f16>, %298 : !torch.vtensor<[4,64,4096,128],f16>, %301 : !torch.vtensor<[4,64,4096,128],f16>) -> !torch.vtensor<[4,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ2048_SKV4096_DH128_f8.mlir b/attention/mlir/attention_B4_H64_SQ2048_SKV4096_DH128_f8.mlir
new file mode 100644
index 0000000..863607d
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ2048_SKV4096_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,2048,128],f16>, %298 : !torch.vtensor<[4,64,4096,128],f16>, %301 : !torch.vtensor<[4,64,4096,128],f16>) -> !torch.vtensor<[4,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ2048_SKV4096_DH128_fp16.mlir b/attention/mlir/attention_B4_H64_SQ2048_SKV4096_DH128_fp16.mlir
new file mode 100644
index 0000000..863607d
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ2048_SKV4096_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,2048,128],f16>, %298 : !torch.vtensor<[4,64,4096,128],f16>, %301 : !torch.vtensor<[4,64,4096,128],f16>) -> !torch.vtensor<[4,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ2048_SKV4096_DH128_fp8.mlir b/attention/mlir/attention_B4_H64_SQ2048_SKV4096_DH128_fp8.mlir
new file mode 100644
index 0000000..863607d
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ2048_SKV4096_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,2048,128],f16>, %298 : !torch.vtensor<[4,64,4096,128],f16>, %301 : !torch.vtensor<[4,64,4096,128],f16>) -> !torch.vtensor<[4,64,2048,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,2048], f32>)
+        return %282#0 : !torch.vtensor<[4,64,2048,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ4096_SKV1024_DH128_f16.mlir b/attention/mlir/attention_B4_H64_SQ4096_SKV1024_DH128_f16.mlir
new file mode 100644
index 0000000..90756b2
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ4096_SKV1024_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,4096,128],f16>, %298 : !torch.vtensor<[4,64,1024,128],f16>, %301 : !torch.vtensor<[4,64,1024,128],f16>) -> !torch.vtensor<[4,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ4096_SKV1024_DH128_f8.mlir b/attention/mlir/attention_B4_H64_SQ4096_SKV1024_DH128_f8.mlir
new file mode 100644
index 0000000..90756b2
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ4096_SKV1024_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,4096,128],f16>, %298 : !torch.vtensor<[4,64,1024,128],f16>, %301 : !torch.vtensor<[4,64,1024,128],f16>) -> !torch.vtensor<[4,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ4096_SKV1024_DH128_fp16.mlir b/attention/mlir/attention_B4_H64_SQ4096_SKV1024_DH128_fp16.mlir
new file mode 100644
index 0000000..90756b2
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ4096_SKV1024_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,4096,128],f16>, %298 : !torch.vtensor<[4,64,1024,128],f16>, %301 : !torch.vtensor<[4,64,1024,128],f16>) -> !torch.vtensor<[4,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ4096_SKV1024_DH128_fp8.mlir b/attention/mlir/attention_B4_H64_SQ4096_SKV1024_DH128_fp8.mlir
new file mode 100644
index 0000000..90756b2
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ4096_SKV1024_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,4096,128],f16>, %298 : !torch.vtensor<[4,64,1024,128],f16>, %301 : !torch.vtensor<[4,64,1024,128],f16>) -> !torch.vtensor<[4,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,1024,128],f16>, !torch.vtensor<[4,64,1024,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ4096_SKV2048_DH128_f16.mlir b/attention/mlir/attention_B4_H64_SQ4096_SKV2048_DH128_f16.mlir
new file mode 100644
index 0000000..d51f954
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ4096_SKV2048_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,4096,128],f16>, %298 : !torch.vtensor<[4,64,2048,128],f16>, %301 : !torch.vtensor<[4,64,2048,128],f16>) -> !torch.vtensor<[4,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ4096_SKV2048_DH128_f8.mlir b/attention/mlir/attention_B4_H64_SQ4096_SKV2048_DH128_f8.mlir
new file mode 100644
index 0000000..d51f954
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ4096_SKV2048_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,4096,128],f16>, %298 : !torch.vtensor<[4,64,2048,128],f16>, %301 : !torch.vtensor<[4,64,2048,128],f16>) -> !torch.vtensor<[4,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ4096_SKV2048_DH128_fp16.mlir b/attention/mlir/attention_B4_H64_SQ4096_SKV2048_DH128_fp16.mlir
new file mode 100644
index 0000000..d51f954
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ4096_SKV2048_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,4096,128],f16>, %298 : !torch.vtensor<[4,64,2048,128],f16>, %301 : !torch.vtensor<[4,64,2048,128],f16>) -> !torch.vtensor<[4,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ4096_SKV2048_DH128_fp8.mlir b/attention/mlir/attention_B4_H64_SQ4096_SKV2048_DH128_fp8.mlir
new file mode 100644
index 0000000..d51f954
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ4096_SKV2048_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,4096,128],f16>, %298 : !torch.vtensor<[4,64,2048,128],f16>, %301 : !torch.vtensor<[4,64,2048,128],f16>) -> !torch.vtensor<[4,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,2048,128],f16>, !torch.vtensor<[4,64,2048,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ4096_SKV4096_DH128_f16.mlir b/attention/mlir/attention_B4_H64_SQ4096_SKV4096_DH128_f16.mlir
new file mode 100644
index 0000000..dedd980
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ4096_SKV4096_DH128_f16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,4096,128],f16>, %298 : !torch.vtensor<[4,64,4096,128],f16>, %301 : !torch.vtensor<[4,64,4096,128],f16>) -> !torch.vtensor<[4,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ4096_SKV4096_DH128_f8.mlir b/attention/mlir/attention_B4_H64_SQ4096_SKV4096_DH128_f8.mlir
new file mode 100644
index 0000000..dedd980
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ4096_SKV4096_DH128_f8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,4096,128],f16>, %298 : !torch.vtensor<[4,64,4096,128],f16>, %301 : !torch.vtensor<[4,64,4096,128],f16>) -> !torch.vtensor<[4,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ4096_SKV4096_DH128_fp16.mlir b/attention/mlir/attention_B4_H64_SQ4096_SKV4096_DH128_fp16.mlir
new file mode 100644
index 0000000..dedd980
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ4096_SKV4096_DH128_fp16.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,4096,128],f16>, %298 : !torch.vtensor<[4,64,4096,128],f16>, %301 : !torch.vtensor<[4,64,4096,128],f16>) -> !torch.vtensor<[4,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,64,4096,128],f16>
+    }
+} 
diff --git a/attention/mlir/attention_B4_H64_SQ4096_SKV4096_DH128_fp8.mlir b/attention/mlir/attention_B4_H64_SQ4096_SKV4096_DH128_fp8.mlir
new file mode 100644
index 0000000..dedd980
--- /dev/null
+++ b/attention/mlir/attention_B4_H64_SQ4096_SKV4096_DH128_fp8.mlir
@@ -0,0 +1,11 @@
+
+module {
+    func.func @main_0(%295 : !torch.vtensor<[4,64,4096,128],f16>, %298 : !torch.vtensor<[4,64,4096,128],f16>, %301 : !torch.vtensor<[4,64,4096,128],f16>) -> !torch.vtensor<[4,64,4096,128],f16> {
+        %false_371 = torch.constant.bool false
+        %float0.000000e00 = torch.constant.float 0.000000e+00
+        %none_372 = torch.constant.none
+        %none_373 = torch.constant.none
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,4096,128],f16>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[4,64,4096,128],f16>, !torch.vtensor<[4,64,4096], f32>)
+        return %282#0 : !torch.vtensor<[4,64,4096,128],f16>
+    }
+} 
diff --git a/attention/vmfb/attention_B1_H32_SQ1024_SKV1024_DH128_f16.vmfb b/attention/vmfb/attention_B1_H32_SQ1024_SKV1024_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B1_H32_SQ1024_SKV1024_DH128_f8.vmfb b/attention/vmfb/attention_B1_H32_SQ1024_SKV1024_DH128_f8.vmfb
new file mode 100644
index 0000000000000000000000000000000000000000..41d6c1894e0c788766d6f36169c82f7a19ec7e75
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diff --git a/attention/vmfb/attention_B1_H32_SQ1024_SKV2048_DH128_f16.vmfb b/attention/vmfb/attention_B1_H32_SQ1024_SKV2048_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B1_H32_SQ1024_SKV2048_DH128_f8.vmfb b/attention/vmfb/attention_B1_H32_SQ1024_SKV2048_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B1_H32_SQ1024_SKV4096_DH128_f16.vmfb b/attention/vmfb/attention_B1_H32_SQ1024_SKV4096_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B1_H32_SQ1024_SKV4096_DH128_f8.vmfb b/attention/vmfb/attention_B1_H32_SQ1024_SKV4096_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B1_H32_SQ2048_SKV1024_DH128_f16.vmfb b/attention/vmfb/attention_B1_H32_SQ2048_SKV1024_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B1_H32_SQ2048_SKV1024_DH128_f8.vmfb b/attention/vmfb/attention_B1_H32_SQ2048_SKV1024_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B1_H32_SQ2048_SKV2048_DH128_f16.vmfb b/attention/vmfb/attention_B1_H32_SQ2048_SKV2048_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B1_H32_SQ2048_SKV2048_DH128_f8.vmfb b/attention/vmfb/attention_B1_H32_SQ2048_SKV2048_DH128_f8.vmfb
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diff --git a/attention/vmfb/attention_B1_H32_SQ2048_SKV4096_DH128_f16.vmfb b/attention/vmfb/attention_B1_H32_SQ2048_SKV4096_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B1_H32_SQ2048_SKV4096_DH128_f8.vmfb b/attention/vmfb/attention_B1_H32_SQ2048_SKV4096_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B1_H32_SQ4096_SKV1024_DH128_f16.vmfb b/attention/vmfb/attention_B1_H32_SQ4096_SKV1024_DH128_f16.vmfb
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diff --git a/attention/vmfb/attention_B1_H32_SQ4096_SKV2048_DH128_f16.vmfb b/attention/vmfb/attention_B1_H32_SQ4096_SKV2048_DH128_f16.vmfb
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diff --git a/attention/vmfb/attention_B1_H32_SQ4096_SKV2048_DH128_f8.vmfb b/attention/vmfb/attention_B1_H32_SQ4096_SKV2048_DH128_f8.vmfb
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diff --git a/attention/vmfb/attention_B1_H32_SQ4096_SKV4096_DH128_f16.vmfb b/attention/vmfb/attention_B1_H32_SQ4096_SKV4096_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B1_H32_SQ4096_SKV4096_DH128_f8.vmfb b/attention/vmfb/attention_B1_H32_SQ4096_SKV4096_DH128_f8.vmfb
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diff --git a/attention/vmfb/attention_B1_H40_SQ1024_SKV1024_DH128_f16.vmfb b/attention/vmfb/attention_B1_H40_SQ1024_SKV1024_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B1_H40_SQ1024_SKV1024_DH128_f8.vmfb b/attention/vmfb/attention_B1_H40_SQ1024_SKV1024_DH128_f8.vmfb
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diff --git a/attention/vmfb/attention_B1_H40_SQ1024_SKV2048_DH128_f8.vmfb b/attention/vmfb/attention_B1_H40_SQ1024_SKV2048_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B1_H40_SQ1024_SKV4096_DH128_f16.vmfb b/attention/vmfb/attention_B1_H40_SQ1024_SKV4096_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B1_H40_SQ1024_SKV4096_DH128_f8.vmfb b/attention/vmfb/attention_B1_H40_SQ1024_SKV4096_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B1_H40_SQ2048_SKV1024_DH128_f16.vmfb b/attention/vmfb/attention_B1_H40_SQ2048_SKV1024_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B1_H40_SQ2048_SKV1024_DH128_f8.vmfb b/attention/vmfb/attention_B1_H40_SQ2048_SKV1024_DH128_f8.vmfb
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diff --git a/attention/vmfb/attention_B1_H40_SQ2048_SKV2048_DH128_f8.vmfb b/attention/vmfb/attention_B1_H40_SQ2048_SKV2048_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B1_H40_SQ2048_SKV4096_DH128_f8.vmfb b/attention/vmfb/attention_B1_H40_SQ2048_SKV4096_DH128_f8.vmfb
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diff --git a/attention/vmfb/attention_B1_H40_SQ4096_SKV1024_DH128_f16.vmfb b/attention/vmfb/attention_B1_H40_SQ4096_SKV1024_DH128_f16.vmfb
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diff --git a/attention/vmfb/attention_B1_H40_SQ4096_SKV1024_DH128_f8.vmfb b/attention/vmfb/attention_B1_H40_SQ4096_SKV1024_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B1_H40_SQ4096_SKV2048_DH128_f16.vmfb b/attention/vmfb/attention_B1_H40_SQ4096_SKV2048_DH128_f16.vmfb
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diff --git a/attention/vmfb/attention_B1_H40_SQ4096_SKV2048_DH128_f8.vmfb b/attention/vmfb/attention_B1_H40_SQ4096_SKV2048_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B1_H40_SQ4096_SKV4096_DH128_f8.vmfb b/attention/vmfb/attention_B1_H40_SQ4096_SKV4096_DH128_f8.vmfb
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diff --git a/attention/vmfb/attention_B1_H64_SQ1024_SKV1024_DH128_f16.vmfb b/attention/vmfb/attention_B1_H64_SQ1024_SKV1024_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B1_H64_SQ1024_SKV1024_DH128_f8.vmfb b/attention/vmfb/attention_B1_H64_SQ1024_SKV1024_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B1_H64_SQ1024_SKV2048_DH128_f16.vmfb b/attention/vmfb/attention_B1_H64_SQ1024_SKV2048_DH128_f16.vmfb
new file mode 100644
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HcmV?d00001

diff --git a/attention/vmfb/attention_B1_H64_SQ1024_SKV4096_DH128_f16.vmfb b/attention/vmfb/attention_B1_H64_SQ1024_SKV4096_DH128_f16.vmfb
new file mode 100644
index 0000000000000000000000000000000000000000..91a1bdf6a8be1048a3cce8cff51e104922c8a345
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diff --git a/attention/vmfb/attention_B1_H64_SQ2048_SKV1024_DH128_f16.vmfb b/attention/vmfb/attention_B1_H64_SQ2048_SKV1024_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B1_H64_SQ2048_SKV1024_DH128_f8.vmfb b/attention/vmfb/attention_B1_H64_SQ2048_SKV1024_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B1_H64_SQ2048_SKV2048_DH128_f16.vmfb b/attention/vmfb/attention_B1_H64_SQ2048_SKV2048_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B1_H64_SQ2048_SKV2048_DH128_f8.vmfb b/attention/vmfb/attention_B1_H64_SQ2048_SKV2048_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B1_H64_SQ2048_SKV4096_DH128_f16.vmfb b/attention/vmfb/attention_B1_H64_SQ2048_SKV4096_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B1_H64_SQ2048_SKV4096_DH128_f8.vmfb b/attention/vmfb/attention_B1_H64_SQ2048_SKV4096_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B1_H64_SQ4096_SKV1024_DH128_f16.vmfb b/attention/vmfb/attention_B1_H64_SQ4096_SKV1024_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B1_H64_SQ4096_SKV1024_DH128_f8.vmfb b/attention/vmfb/attention_B1_H64_SQ4096_SKV1024_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B1_H64_SQ4096_SKV2048_DH128_f16.vmfb b/attention/vmfb/attention_B1_H64_SQ4096_SKV2048_DH128_f16.vmfb
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diff --git a/attention/vmfb/attention_B1_H64_SQ4096_SKV2048_DH128_f8.vmfb b/attention/vmfb/attention_B1_H64_SQ4096_SKV2048_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B1_H64_SQ4096_SKV4096_DH128_f16.vmfb b/attention/vmfb/attention_B1_H64_SQ4096_SKV4096_DH128_f16.vmfb
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diff --git a/attention/vmfb/attention_B1_H64_SQ4096_SKV4096_DH128_f8.vmfb b/attention/vmfb/attention_B1_H64_SQ4096_SKV4096_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H10_SQ1024_SKV1024_DH64_f16.vmfb b/attention/vmfb/attention_B2_H10_SQ1024_SKV1024_DH64_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H10_SQ1024_SKV1024_DH64_f8.vmfb b/attention/vmfb/attention_B2_H10_SQ1024_SKV1024_DH64_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H10_SQ4096_SKV4096_DH64_f16.vmfb b/attention/vmfb/attention_B2_H10_SQ4096_SKV4096_DH64_f16.vmfb
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diff --git a/attention/vmfb/attention_B2_H10_SQ4096_SKV4096_DH64_f8.vmfb b/attention/vmfb/attention_B2_H10_SQ4096_SKV4096_DH64_f8.vmfb
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diff --git a/attention/vmfb/attention_B2_H10_SQ4096_SKV64_DH64_f16.vmfb b/attention/vmfb/attention_B2_H10_SQ4096_SKV64_DH64_f16.vmfb
new file mode 100644
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GIT binary patch
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diff --git a/attention/vmfb/attention_B2_H10_SQ4096_SKV64_DH64_f8.vmfb b/attention/vmfb/attention_B2_H10_SQ4096_SKV64_DH64_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H20_SQ1024_SKV64_DH64_f16.vmfb b/attention/vmfb/attention_B2_H20_SQ1024_SKV64_DH64_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H20_SQ1024_SKV64_DH64_f8.vmfb b/attention/vmfb/attention_B2_H20_SQ1024_SKV64_DH64_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H32_SQ1024_SKV1024_DH128_f16.vmfb b/attention/vmfb/attention_B2_H32_SQ1024_SKV1024_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H32_SQ1024_SKV1024_DH128_f8.vmfb b/attention/vmfb/attention_B2_H32_SQ1024_SKV1024_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H32_SQ1024_SKV2048_DH128_f8.vmfb b/attention/vmfb/attention_B2_H32_SQ1024_SKV2048_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H32_SQ1024_SKV4096_DH128_f16.vmfb b/attention/vmfb/attention_B2_H32_SQ1024_SKV4096_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H32_SQ2048_SKV1024_DH128_f16.vmfb b/attention/vmfb/attention_B2_H32_SQ2048_SKV1024_DH128_f16.vmfb
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diff --git a/attention/vmfb/attention_B2_H32_SQ2048_SKV1024_DH128_f8.vmfb b/attention/vmfb/attention_B2_H32_SQ2048_SKV1024_DH128_f8.vmfb
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diff --git a/attention/vmfb/attention_B2_H32_SQ2048_SKV2048_DH128_f16.vmfb b/attention/vmfb/attention_B2_H32_SQ2048_SKV2048_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H32_SQ2048_SKV2048_DH128_f8.vmfb b/attention/vmfb/attention_B2_H32_SQ2048_SKV2048_DH128_f8.vmfb
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diff --git a/attention/vmfb/attention_B2_H32_SQ2048_SKV4096_DH128_f16.vmfb b/attention/vmfb/attention_B2_H32_SQ2048_SKV4096_DH128_f16.vmfb
new file mode 100644
index 0000000000000000000000000000000000000000..6d1076d66a4f7ad36a4f5be9096da1512540ac29
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diff --git a/attention/vmfb/attention_B2_H32_SQ2048_SKV4096_DH128_f8.vmfb b/attention/vmfb/attention_B2_H32_SQ2048_SKV4096_DH128_f8.vmfb
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diff --git a/attention/vmfb/attention_B2_H32_SQ4096_SKV1024_DH128_f16.vmfb b/attention/vmfb/attention_B2_H32_SQ4096_SKV1024_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H32_SQ4096_SKV1024_DH128_f8.vmfb b/attention/vmfb/attention_B2_H32_SQ4096_SKV1024_DH128_f8.vmfb
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diff --git a/attention/vmfb/attention_B2_H32_SQ4096_SKV2048_DH128_f8.vmfb b/attention/vmfb/attention_B2_H32_SQ4096_SKV2048_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H32_SQ4096_SKV4096_DH128_f16.vmfb b/attention/vmfb/attention_B2_H32_SQ4096_SKV4096_DH128_f16.vmfb
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diff --git a/attention/vmfb/attention_B2_H32_SQ4096_SKV4096_DH128_f8.vmfb b/attention/vmfb/attention_B2_H32_SQ4096_SKV4096_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H40_SQ1024_SKV1024_DH128_f16.vmfb b/attention/vmfb/attention_B2_H40_SQ1024_SKV1024_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H40_SQ1024_SKV1024_DH128_f8.vmfb b/attention/vmfb/attention_B2_H40_SQ1024_SKV1024_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H40_SQ1024_SKV2048_DH128_f16.vmfb b/attention/vmfb/attention_B2_H40_SQ1024_SKV2048_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H40_SQ1024_SKV2048_DH128_f8.vmfb b/attention/vmfb/attention_B2_H40_SQ1024_SKV2048_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H40_SQ1024_SKV4096_DH128_f16.vmfb b/attention/vmfb/attention_B2_H40_SQ1024_SKV4096_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H40_SQ1024_SKV4096_DH128_f8.vmfb b/attention/vmfb/attention_B2_H40_SQ1024_SKV4096_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H40_SQ2048_SKV1024_DH128_f16.vmfb b/attention/vmfb/attention_B2_H40_SQ2048_SKV1024_DH128_f16.vmfb
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diff --git a/attention/vmfb/attention_B2_H40_SQ2048_SKV1024_DH128_f8.vmfb b/attention/vmfb/attention_B2_H40_SQ2048_SKV1024_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H40_SQ2048_SKV2048_DH128_f16.vmfb b/attention/vmfb/attention_B2_H40_SQ2048_SKV2048_DH128_f16.vmfb
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diff --git a/attention/vmfb/attention_B2_H40_SQ2048_SKV2048_DH128_f8.vmfb b/attention/vmfb/attention_B2_H40_SQ2048_SKV2048_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H40_SQ2048_SKV4096_DH128_f16.vmfb b/attention/vmfb/attention_B2_H40_SQ2048_SKV4096_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H40_SQ4096_SKV1024_DH128_f16.vmfb b/attention/vmfb/attention_B2_H40_SQ4096_SKV1024_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H40_SQ4096_SKV1024_DH128_f8.vmfb b/attention/vmfb/attention_B2_H40_SQ4096_SKV1024_DH128_f8.vmfb
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diff --git a/attention/vmfb/attention_B2_H40_SQ4096_SKV2048_DH128_f16.vmfb b/attention/vmfb/attention_B2_H40_SQ4096_SKV2048_DH128_f16.vmfb
new file mode 100644
index 0000000000000000000000000000000000000000..a39f27a23b968fad0bf1556b19a366999c8114b0
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diff --git a/attention/vmfb/attention_B2_H40_SQ4096_SKV2048_DH128_f8.vmfb b/attention/vmfb/attention_B2_H40_SQ4096_SKV2048_DH128_f8.vmfb
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diff --git a/attention/vmfb/attention_B2_H40_SQ4096_SKV4096_DH128_f16.vmfb b/attention/vmfb/attention_B2_H40_SQ4096_SKV4096_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H40_SQ4096_SKV4096_DH128_f8.vmfb b/attention/vmfb/attention_B2_H40_SQ4096_SKV4096_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H64_SQ1024_SKV1024_DH128_f16.vmfb b/attention/vmfb/attention_B2_H64_SQ1024_SKV1024_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H64_SQ1024_SKV1024_DH128_f8.vmfb b/attention/vmfb/attention_B2_H64_SQ1024_SKV1024_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H64_SQ1024_SKV2048_DH128_f16.vmfb b/attention/vmfb/attention_B2_H64_SQ1024_SKV2048_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H64_SQ1024_SKV2048_DH128_f8.vmfb b/attention/vmfb/attention_B2_H64_SQ1024_SKV2048_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H64_SQ1024_SKV4096_DH128_f16.vmfb b/attention/vmfb/attention_B2_H64_SQ1024_SKV4096_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H64_SQ1024_SKV4096_DH128_f8.vmfb b/attention/vmfb/attention_B2_H64_SQ1024_SKV4096_DH128_f8.vmfb
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diff --git a/attention/vmfb/attention_B2_H64_SQ2048_SKV1024_DH128_f16.vmfb b/attention/vmfb/attention_B2_H64_SQ2048_SKV1024_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H64_SQ2048_SKV1024_DH128_f8.vmfb b/attention/vmfb/attention_B2_H64_SQ2048_SKV1024_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H64_SQ2048_SKV2048_DH128_f16.vmfb b/attention/vmfb/attention_B2_H64_SQ2048_SKV2048_DH128_f16.vmfb
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diff --git a/attention/vmfb/attention_B2_H64_SQ2048_SKV2048_DH128_f8.vmfb b/attention/vmfb/attention_B2_H64_SQ2048_SKV2048_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H64_SQ2048_SKV4096_DH128_f16.vmfb b/attention/vmfb/attention_B2_H64_SQ2048_SKV4096_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H64_SQ2048_SKV4096_DH128_f8.vmfb b/attention/vmfb/attention_B2_H64_SQ2048_SKV4096_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H64_SQ4096_SKV1024_DH128_f16.vmfb b/attention/vmfb/attention_B2_H64_SQ4096_SKV1024_DH128_f16.vmfb
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diff --git a/attention/vmfb/attention_B2_H64_SQ4096_SKV2048_DH128_f16.vmfb b/attention/vmfb/attention_B2_H64_SQ4096_SKV2048_DH128_f16.vmfb
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diff --git a/attention/vmfb/attention_B2_H64_SQ4096_SKV2048_DH128_f8.vmfb b/attention/vmfb/attention_B2_H64_SQ4096_SKV2048_DH128_f8.vmfb
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diff --git a/attention/vmfb/attention_B2_H64_SQ4096_SKV4096_DH128_f16.vmfb b/attention/vmfb/attention_B2_H64_SQ4096_SKV4096_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B2_H64_SQ4096_SKV4096_DH128_f8.vmfb b/attention/vmfb/attention_B2_H64_SQ4096_SKV4096_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H32_SQ1024_SKV1024_DH128_f16.vmfb b/attention/vmfb/attention_B4_H32_SQ1024_SKV1024_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H32_SQ1024_SKV1024_DH128_f8.vmfb b/attention/vmfb/attention_B4_H32_SQ1024_SKV1024_DH128_f8.vmfb
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diff --git a/attention/vmfb/attention_B4_H32_SQ1024_SKV2048_DH128_f16.vmfb b/attention/vmfb/attention_B4_H32_SQ1024_SKV2048_DH128_f16.vmfb
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diff --git a/attention/vmfb/attention_B4_H32_SQ1024_SKV2048_DH128_f8.vmfb b/attention/vmfb/attention_B4_H32_SQ1024_SKV2048_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H32_SQ1024_SKV4096_DH128_f16.vmfb b/attention/vmfb/attention_B4_H32_SQ1024_SKV4096_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H32_SQ1024_SKV4096_DH128_f8.vmfb b/attention/vmfb/attention_B4_H32_SQ1024_SKV4096_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H32_SQ2048_SKV1024_DH128_f16.vmfb b/attention/vmfb/attention_B4_H32_SQ2048_SKV1024_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H32_SQ2048_SKV1024_DH128_f8.vmfb b/attention/vmfb/attention_B4_H32_SQ2048_SKV1024_DH128_f8.vmfb
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diff --git a/attention/vmfb/attention_B4_H32_SQ2048_SKV2048_DH128_f16.vmfb b/attention/vmfb/attention_B4_H32_SQ2048_SKV2048_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H32_SQ2048_SKV2048_DH128_f8.vmfb b/attention/vmfb/attention_B4_H32_SQ2048_SKV2048_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H32_SQ2048_SKV4096_DH128_f16.vmfb b/attention/vmfb/attention_B4_H32_SQ2048_SKV4096_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H32_SQ2048_SKV4096_DH128_f8.vmfb b/attention/vmfb/attention_B4_H32_SQ2048_SKV4096_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H32_SQ4096_SKV1024_DH128_f16.vmfb b/attention/vmfb/attention_B4_H32_SQ4096_SKV1024_DH128_f16.vmfb
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diff --git a/attention/vmfb/attention_B4_H32_SQ4096_SKV1024_DH128_f8.vmfb b/attention/vmfb/attention_B4_H32_SQ4096_SKV1024_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H32_SQ4096_SKV2048_DH128_f16.vmfb b/attention/vmfb/attention_B4_H32_SQ4096_SKV2048_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H32_SQ4096_SKV2048_DH128_f8.vmfb b/attention/vmfb/attention_B4_H32_SQ4096_SKV2048_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H32_SQ4096_SKV4096_DH128_f16.vmfb b/attention/vmfb/attention_B4_H32_SQ4096_SKV4096_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H32_SQ4096_SKV4096_DH128_f8.vmfb b/attention/vmfb/attention_B4_H32_SQ4096_SKV4096_DH128_f8.vmfb
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diff --git a/attention/vmfb/attention_B4_H40_SQ1024_SKV1024_DH128_f16.vmfb b/attention/vmfb/attention_B4_H40_SQ1024_SKV1024_DH128_f16.vmfb
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diff --git a/attention/vmfb/attention_B4_H40_SQ1024_SKV1024_DH128_f8.vmfb b/attention/vmfb/attention_B4_H40_SQ1024_SKV1024_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H40_SQ1024_SKV2048_DH128_f16.vmfb b/attention/vmfb/attention_B4_H40_SQ1024_SKV2048_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H40_SQ1024_SKV2048_DH128_f8.vmfb b/attention/vmfb/attention_B4_H40_SQ1024_SKV2048_DH128_f8.vmfb
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diff --git a/attention/vmfb/attention_B4_H40_SQ1024_SKV4096_DH128_f16.vmfb b/attention/vmfb/attention_B4_H40_SQ1024_SKV4096_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H40_SQ1024_SKV4096_DH128_f8.vmfb b/attention/vmfb/attention_B4_H40_SQ1024_SKV4096_DH128_f8.vmfb
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diff --git a/attention/vmfb/attention_B4_H40_SQ2048_SKV1024_DH128_f16.vmfb b/attention/vmfb/attention_B4_H40_SQ2048_SKV1024_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H40_SQ2048_SKV1024_DH128_f8.vmfb b/attention/vmfb/attention_B4_H40_SQ2048_SKV1024_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H40_SQ2048_SKV2048_DH128_f16.vmfb b/attention/vmfb/attention_B4_H40_SQ2048_SKV2048_DH128_f16.vmfb
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diff --git a/attention/vmfb/attention_B4_H40_SQ2048_SKV2048_DH128_f8.vmfb b/attention/vmfb/attention_B4_H40_SQ2048_SKV2048_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H40_SQ2048_SKV4096_DH128_f16.vmfb b/attention/vmfb/attention_B4_H40_SQ2048_SKV4096_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H40_SQ2048_SKV4096_DH128_f8.vmfb b/attention/vmfb/attention_B4_H40_SQ2048_SKV4096_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H40_SQ4096_SKV1024_DH128_f8.vmfb b/attention/vmfb/attention_B4_H40_SQ4096_SKV1024_DH128_f8.vmfb
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diff --git a/attention/vmfb/attention_B4_H40_SQ4096_SKV2048_DH128_f16.vmfb b/attention/vmfb/attention_B4_H40_SQ4096_SKV2048_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H40_SQ4096_SKV2048_DH128_f8.vmfb b/attention/vmfb/attention_B4_H40_SQ4096_SKV2048_DH128_f8.vmfb
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diff --git a/attention/vmfb/attention_B4_H40_SQ4096_SKV4096_DH128_f16.vmfb b/attention/vmfb/attention_B4_H40_SQ4096_SKV4096_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H40_SQ4096_SKV4096_DH128_f8.vmfb b/attention/vmfb/attention_B4_H40_SQ4096_SKV4096_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H64_SQ1024_SKV1024_DH128_f16.vmfb b/attention/vmfb/attention_B4_H64_SQ1024_SKV1024_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H64_SQ1024_SKV1024_DH128_f8.vmfb b/attention/vmfb/attention_B4_H64_SQ1024_SKV1024_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H64_SQ1024_SKV2048_DH128_f16.vmfb b/attention/vmfb/attention_B4_H64_SQ1024_SKV2048_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H64_SQ1024_SKV4096_DH128_f16.vmfb b/attention/vmfb/attention_B4_H64_SQ1024_SKV4096_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H64_SQ1024_SKV4096_DH128_f8.vmfb b/attention/vmfb/attention_B4_H64_SQ1024_SKV4096_DH128_f8.vmfb
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diff --git a/attention/vmfb/attention_B4_H64_SQ2048_SKV1024_DH128_f16.vmfb b/attention/vmfb/attention_B4_H64_SQ2048_SKV1024_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H64_SQ2048_SKV1024_DH128_f8.vmfb b/attention/vmfb/attention_B4_H64_SQ2048_SKV1024_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H64_SQ2048_SKV2048_DH128_f16.vmfb b/attention/vmfb/attention_B4_H64_SQ2048_SKV2048_DH128_f16.vmfb
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diff --git a/attention/vmfb/attention_B4_H64_SQ2048_SKV2048_DH128_f8.vmfb b/attention/vmfb/attention_B4_H64_SQ2048_SKV2048_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H64_SQ2048_SKV4096_DH128_f16.vmfb b/attention/vmfb/attention_B4_H64_SQ2048_SKV4096_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H64_SQ2048_SKV4096_DH128_f8.vmfb b/attention/vmfb/attention_B4_H64_SQ2048_SKV4096_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H64_SQ4096_SKV1024_DH128_f16.vmfb b/attention/vmfb/attention_B4_H64_SQ4096_SKV1024_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H64_SQ4096_SKV1024_DH128_f8.vmfb b/attention/vmfb/attention_B4_H64_SQ4096_SKV1024_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H64_SQ4096_SKV2048_DH128_f16.vmfb b/attention/vmfb/attention_B4_H64_SQ4096_SKV2048_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H64_SQ4096_SKV2048_DH128_f8.vmfb b/attention/vmfb/attention_B4_H64_SQ4096_SKV2048_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H64_SQ4096_SKV4096_DH128_f16.vmfb b/attention/vmfb/attention_B4_H64_SQ4096_SKV4096_DH128_f16.vmfb
new file mode 100644
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diff --git a/attention/vmfb/attention_B4_H64_SQ4096_SKV4096_DH128_f8.vmfb b/attention/vmfb/attention_B4_H64_SQ4096_SKV4096_DH128_f8.vmfb
new file mode 100644
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diff --git a/attention_and_matmul_spec.mlir b/attention_and_matmul_spec_f16.mlir
similarity index 100%
rename from attention_and_matmul_spec.mlir
rename to attention_and_matmul_spec_f16.mlir
diff --git a/attention_and_matmul_spec_f8.mlir b/attention_and_matmul_spec_f8.mlir
new file mode 100644
index 0000000..da48da6
--- /dev/null
+++ b/attention_and_matmul_spec_f8.mlir
@@ -0,0 +1,935 @@
+// Transform dialect specification for attention on MI300 with MFMA.
+// This script only supports variants of attention with a sequence
+// length that is a multiple of 64. There are two near duplicate
+// because we need different tile sizes when the head dimension is 512.
+// TODO: Figure out how to parameterize the tile sizes without duplicating
+// the attention function.
+
+#layout_f8 = #iree_gpu.mma_layout<MFMA_F8E4M3FNUZ_16x16x32_F32>
+#layout_16 = #iree_gpu.mma_layout<MFMA_F16_16x16x16_F32>
+#layout = #iree_gpu.mma_layout<MFMA_F16_32x32x8_F32>
+
+module attributes { transform.with_named_sequence } {
+//===----------------------------------------------------------------------===//
+// Attention
+//===----------------------------------------------------------------------===//
+
+  // Utility matching for finding all undistributed fills.
+  transform.named_sequence @matcher(%arg0: !transform.any_op {transform.readonly}) -> !transform.any_op {
+    transform.match.operation_name %arg0 ["linalg.fill"] : !transform.any_op
+    %0 = transform.get_parent_op %arg0 {allow_empty_results, nth_parent = 2 : i64, op_name = "scf.forall"} : (!transform.any_op) -> !transform.any_op
+    transform.match.operation_empty %0 : !transform.any_op
+    transform.yield %arg0 : !transform.any_op
+  }
+
+  transform.named_sequence @get_undistributed_fills(%arg0: !transform.any_op {transform.readonly}) -> !transform.any_op {
+    %0 = transform.collect_matching @matcher in %arg0 : (!transform.any_op) -> !transform.any_op
+    transform.yield %0 : !transform.any_op
+  }
+
+  // Script for FA2 transform pipeline when head_dim % 64 = 0.
+  transform.named_sequence @__attention_main(%variant_op: !transform.any_op {transform.readonly}) {
+    // Get attention op
+    // ==========================================
+    %attention = transform.structured.match ops{["iree_linalg_ext.attention"]} in %variant_op : (!transform.any_op) -> !transform.any_op
+    %elementwise = transform.structured.match ops{["linalg.generic"]} in %variant_op : (!transform.any_op) -> !transform.any_op
+
+    // Tile and distribute to workgroups
+    // ==========================================
+
+    %blocked_el, %wg_forall =
+    transform.structured.tile_using_forall %elementwise tile_sizes [1, 1, 128, 0]
+      ( mapping = [#gpu.block<x>, #gpu.block<y>, #gpu.block<z>] ) : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
+    %blocked_att, %wg_forall2 = transform.structured.fuse_into_containing_op %attention into %wg_forall : (!transform.any_op, !transform.any_op) -> (!transform.any_op, !transform.any_op)
+
+    transform.iree.populate_workgroup_count_region_using_num_threads_slice %wg_forall2 : (!transform.any_op) -> ()
+
+    // Cleanup
+    %attfunc = transform.structured.match ops{["func.func"]} in %variant_op : (!transform.any_op) -> !transform.any_op
+    transform.apply_patterns to %attfunc {
+      transform.apply_patterns.canonicalization
+    } : !transform.any_op
+    transform.apply_cse to %attfunc : !transform.any_op
+
+    // Convert to online attention
+    // ==========================================
+    transform.iree.convert_to_online_attention %blocked_att : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
+
+    transform.apply_patterns to %attfunc {
+      transform.apply_patterns.canonicalization
+      transform.apply_patterns.linalg.tiling_canonicalization
+    } : !transform.any_op
+    transform.apply_cse to %attfunc : !transform.any_op
+
+    %func = transform.apply_registered_pass "linalg-fuse-elementwise-ops" to %attfunc : (!transform.any_op) -> (!transform.any_op)
+
+    // Tile along K2
+    %online_att = transform.structured.match ops{["iree_linalg_ext.online_attention"]} in %variant_op : (!transform.any_op) -> !transform.any_op
+    %tiled_att, %k2_for = transform.structured.tile_using_for %online_att tile_sizes [0, 0, 0, 0, 64, 0]: (!transform.any_op) -> (!transform.any_op, !transform.any_op)
+
+    transform.apply_patterns to %func {
+      transform.apply_patterns.canonicalization
+    } : !transform.any_op
+    transform.apply_cse to %func : !transform.any_op
+
+    // Promote key and value operands
+    // ==========================================
+    %attt = transform.structured.match ops{["iree_linalg_ext.online_attention"]} in %variant_op : (!transform.any_op) -> !transform.any_op
+    %promoted_att, %alloc0, %alloc1 = transform.iree.promote_operands %attt [1, 2]
+      : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
+
+    // This is a hack... We distribute the loop and the merging seperately and just assume they are fused.
+    %warp_attention = transform.structured.match ops{["iree_linalg_ext.online_attention"]} in %variant_op : (!transform.any_op) -> !transform.any_op
+    %warp_merge = transform.structured.match ops{["linalg.generic"]} in %variant_op : (!transform.any_op) -> !transform.any_op
+    transform.structured.tile_using_forall %warp_attention tile_sizes[0, 0, 32, 0, 0, 0] (mapping = [#gpu.warp<linear_dim_0>]) : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
+    transform.structured.tile_using_forall %warp_merge tile_sizes[0, 0, 32, 0] (mapping = [#gpu.warp<linear_dim_0>]) : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
+
+    transform.apply_patterns to %func {
+      transform.apply_patterns.canonicalization
+    } : !transform.any_op
+    transform.apply_cse to %func : !transform.any_op
+
+    %fills = transform.include @get_undistributed_fills failures(propagate) (%variant_op)  : (!transform.any_op) -> !transform.any_op
+    %acc_fill, %max_fill, %sum_fill = transform.split_handle %fills : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
+    transform.structured.tile_using_forall %acc_fill tile_sizes[0, 0, 32] (mapping = [#gpu.warp<linear_dim_0>]) : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
+    transform.structured.tile_using_forall %max_fill tile_sizes[0, 0, 32] (mapping = [#gpu.warp<linear_dim_0>]) : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
+    transform.structured.tile_using_forall %sum_fill tile_sizes[0, 0, 32] (mapping = [#gpu.warp<linear_dim_0>]) : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
+
+    transform.apply_patterns to %func {
+      transform.apply_patterns.canonicalization
+    } : !transform.any_op
+    transform.apply_cse to %func : !transform.any_op
+
+    // Decompose attention
+    %func2 = transform.apply_registered_pass "iree-linalg-ext-decompose-attention" to %func : (!transform.any_op) -> (!transform.any_op)
+
+    transform.apply_patterns to %func2 {
+      transform.apply_patterns.canonicalization
+    } : !transform.any_op
+    transform.apply_cse to %func2 : !transform.any_op
+
+    // Vectorize function
+    // ==========================================
+    transform.apply_patterns to %func2 {
+      transform.apply_patterns.iree.fold_reshape_into_tensor_hal_interface
+      transform.apply_patterns.vector.cast_away_vector_leading_one_dim
+      transform.apply_patterns.linalg.fold_unit_extent_dims_via_reshapes
+      transform.apply_patterns.canonicalization
+    } : !transform.any_op
+    %func_3 = transform.structured.vectorize_children_and_apply_patterns %func2 : (!transform.any_op) -> (!transform.any_op)
+
+    transform.apply_patterns to %func_3 {
+      transform.apply_patterns.canonicalization
+    } : !transform.any_op
+    transform.apply_cse to %func_3 : !transform.any_op
+
+    // Bufferization
+    // ==========================================
+    transform.apply_patterns to %func_3 {
+      transform.apply_patterns.tensor.reassociative_reshape_folding
+      transform.apply_patterns.canonicalization
+      transform.apply_patterns.iree.fold_fill_into_pad
+      transform.apply_patterns.linalg.tiling_canonicalization
+      transform.apply_patterns.scf.for_loop_canonicalization
+    } : !transform.any_op
+    transform.apply_cse to %func_3 : !transform.any_op
+    transform.iree.eliminate_empty_tensors %func_3 : (!transform.any_op) -> ()
+    transform.apply_patterns to %func_3 { transform.apply_patterns.linalg.erase_unnecessary_inputs } : !transform.any_op
+    %memref_func = transform.iree.bufferize { target_gpu } %func_3 : (!transform.any_op) -> (!transform.any_op)
+
+    // Step 5. Pre-process the contract and transfer ops to put it in the right form.
+    // ===========================================================================
+    transform.apply_patterns to %memref_func {
+      transform.apply_patterns.vector.fold_arith_extension
+    } : !transform.any_op
+
+    // Step 6. Post-bufferization vector distribution
+    // ===========================================================================
+    transform.iree.forall_to_workgroup %memref_func : (!transform.any_op) -> ()
+    transform.iree.map_nested_forall_to_gpu_threads %memref_func workgroup_dims = [64, 4, 1] subgroup_size = 64 sync_after_distribution = false : (!transform.any_op) -> ()
+
+    transform.apply_patterns to %memref_func {
+      transform.apply_patterns.memref.fold_memref_alias_ops
+    } : !transform.any_op
+    transform.iree.apply_licm %memref_func : !transform.any_op
+    transform.apply_patterns to %memref_func {
+      transform.apply_patterns.canonicalization
+    } : !transform.any_op
+    transform.apply_cse to %memref_func : !transform.any_op
+    %func_8 = transform.structured.hoist_redundant_vector_transfers %memref_func
+    : (!transform.any_op) -> !transform.any_op
+    transform.apply_patterns to %func_8 {
+      transform.apply_patterns.canonicalization
+    } : !transform.any_op
+    transform.apply_cse to %func_8 : !transform.any_op
+    transform.memref.erase_dead_alloc_and_stores %func_8 : (!transform.any_op) -> ()
+
+    // Apply chained matmul optimization.
+    %func_9 = transform.apply_registered_pass "iree-amdgpu-prepare-chained-matmul" to %func_8 : (!transform.any_op) -> (!transform.any_op)
+
+    %intrinsic = transform.param.constant #layout -> !transform.any_param
+
+    %mma = transform.structured.match ops{["vector.contract"]} in %variant_op :  (!transform.any_op) -> !transform.any_op
+    %mma1, %mma2 = transform.split_handle %mma : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
+    transform.annotate %mma "iree.amdgpu.mma" = %intrinsic : !transform.any_op, !transform.any_param
+
+    transform.apply_registered_pass "iree-llvmgpu-cast-type-to-fit-mma" to %func_9 : (!transform.any_op) -> (!transform.any_op)
+
+    %contracts = transform.structured.match ops{["vector.contract"]} in %variant_op :  (!transform.any_op) -> !transform.any_op
+    %contract1, %contract2 = transform.split_handle %contracts : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
+    transform.iree.set_contraction_layout_attributes %contract1, %intrinsic { read_layout_indices = array<i64: 0, 1> } : !transform.any_op, !transform.any_param
+    transform.iree.set_contraction_layout_attributes %contract2, %intrinsic : !transform.any_op, !transform.any_param
+
+    %distribute_func = transform.structured.match ops{["func.func"]} in %variant_op : (!transform.any_op) -> !transform.any_op
+
+    %distribute_func_2 = transform.iree.amdgpu_distribute_vectors %distribute_func : (!transform.any_op) -> !transform.any_op
+
+    transform.apply_patterns to %distribute_func_2 {
+      transform.apply_patterns.canonicalization
+    } : !transform.any_op
+    transform.apply_cse to %distribute_func_2 : !transform.any_op
+    %distribute_func_3 = transform.apply_registered_pass "iree-codegen-optimize-tensor-insert-extract-slices" to %distribute_func_2 : (!transform.any_op) -> (!transform.any_op)
+    transform.print %distribute_func_3 : !transform.any_op
+
+    // Distribute shared memory copies
+    // ==========================================
+    transform.iree.gpu_distribute_shared_memory_copy %distribute_func_3 : (!transform.any_op) -> ()
+    transform.apply_patterns to %distribute_func_3 {
+        transform.apply_patterns.memref.fold_memref_alias_ops
+        transform.apply_patterns.canonicalization
+        transform.apply_patterns.linalg.tiling_canonicalization
+      } : !transform.any_op
+    transform.apply_cse to %distribute_func_3 : !transform.any_op
+
+    %forop = transform.structured.match ops{["scf.for"]} in %variant_op : (!transform.any_op) -> !transform.any_op
+    %prefetched_forop = transform.iree.prefetch_shared_memory_copies %forop : (!transform.any_op) -> (!transform.any_op)
+
+    transform.apply_patterns to %distribute_func_3 {
+        transform.apply_patterns.memref.fold_memref_alias_ops
+        transform.apply_patterns.canonicalization
+        transform.apply_patterns.linalg.tiling_canonicalization
+      } : !transform.any_op
+    transform.apply_cse to %distribute_func_3 : !transform.any_op
+
+    %func_11 = transform.structured.match ops{["func.func"]} in %variant_op : (!transform.any_op) -> !transform.any_op
+    transform.iree.reduce_shared_memory_bank_conflicts %func_11 : (!transform.any_op) -> ()
+    transform.yield
+  }
+
+  // Script for FA2 transform pipeline for FP8 attention.
+  transform.named_sequence @__attention_main_f8(%variant_op: !transform.any_op {transform.readonly}) {
+    // Get attention op
+    // ==========================================
+    %attention = transform.structured.match ops{["iree_linalg_ext.attention"]} in %variant_op : (!transform.any_op) -> !transform.any_op
+
+    // Tile and distribute to workgroups
+    // ==========================================
+    %blocked_att, %wg_forall =
+    transform.structured.tile_using_forall %attention tile_sizes [1, 128, 0, 0, 0]
+      ( mapping = [#gpu.block<x>, #gpu.block<y>] ) : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
+    transform.iree.populate_workgroup_count_region_using_num_threads_slice %wg_forall : (!transform.any_op) -> ()
+
+    // Cleanup
+    %func = transform.structured.match ops{["func.func"]} in %variant_op : (!transform.any_op) -> !transform.any_op
+    transform.apply_patterns to %func {
+      transform.apply_patterns.canonicalization
+    } : !transform.any_op
+    transform.apply_cse to %func : !transform.any_op
+
+    // Convert to online attention
+    // ==========================================
+    transform.iree.convert_to_online_attention %blocked_att : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
+
+    transform.apply_patterns to %func {
+      transform.apply_patterns.canonicalization
+    } : !transform.any_op
+    transform.apply_cse to %func : !transform.any_op
+
+    // Tile along K2
+    %online_att = transform.structured.match ops{["iree_linalg_ext.online_attention"]} in %variant_op : (!transform.any_op) -> !transform.any_op
+    %tiled_att, %k2_for = transform.structured.tile_using_for %online_att tile_sizes [0, 0, 0, 64, 0]: (!transform.any_op) -> (!transform.any_op, !transform.any_op)
+
+    transform.apply_patterns to %func {
+      transform.apply_patterns.canonicalization
+    } : !transform.any_op
+    transform.apply_cse to %func : !transform.any_op
+
+    // Promote key and value operands
+    // ==========================================
+    %attt = transform.structured.match ops{["iree_linalg_ext.online_attention"]} in %variant_op : (!transform.any_op) -> !transform.any_op
+    %promoted_att, %alloc0, %alloc1 = transform.iree.promote_operands %attt [1, 2]
+      : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
+
+    // This is a hack... We distribute the loop and the merging seperately and just assume they are fused.
+    %warp_attention = transform.structured.match ops{["iree_linalg_ext.online_attention"]} in %variant_op : (!transform.any_op) -> !transform.any_op
+    %warp_merge = transform.structured.match ops{["linalg.generic"]} in %variant_op : (!transform.any_op) -> !transform.any_op
+    transform.structured.tile_using_forall %warp_attention tile_sizes[0, 32, 0, 0, 0] (mapping = [#gpu.warp<linear_dim_0>]) : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
+    transform.structured.tile_using_forall %warp_merge tile_sizes[0, 32, 0] (mapping = [#gpu.warp<linear_dim_0>]) : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
+
+    transform.apply_patterns to %func {
+      transform.apply_patterns.canonicalization
+    } : !transform.any_op
+    transform.apply_cse to %func : !transform.any_op
+
+    %fills = transform.include @get_undistributed_fills failures(propagate) (%variant_op)  : (!transform.any_op) -> !transform.any_op
+    %acc_fill, %max_fill, %sum_fill = transform.split_handle %fills : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
+    transform.structured.tile_using_forall %acc_fill tile_sizes[0, 32, 0] (mapping = [#gpu.warp<linear_dim_0>]) : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
+    transform.structured.tile_using_forall %max_fill tile_sizes[0, 32] (mapping = [#gpu.warp<linear_dim_0>]) : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
+    transform.structured.tile_using_forall %sum_fill tile_sizes[0, 32] (mapping = [#gpu.warp<linear_dim_0>]) : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
+
+    transform.apply_patterns to %func {
+      transform.apply_patterns.canonicalization
+    } : !transform.any_op
+    transform.apply_cse to %func : !transform.any_op
+
+    // Decompose attention
+    // TODO: Fix erroring out here!
+    %func2 = transform.apply_registered_pass "iree-linalg-ext-decompose-attention" to %func : (!transform.any_op) -> (!transform.any_op)
+
+    transform.apply_patterns to %func2 {
+      transform.apply_patterns.canonicalization
+    } : !transform.any_op
+    transform.apply_cse to %func2 : !transform.any_op
+
+    // Vectorize function
+    // ==========================================
+    transform.apply_patterns to %func2 {
+      transform.apply_patterns.iree.fold_reshape_into_tensor_hal_interface
+      transform.apply_patterns.vector.cast_away_vector_leading_one_dim
+      transform.apply_patterns.linalg.fold_unit_extent_dims_via_reshapes
+      transform.apply_patterns.canonicalization
+    } : !transform.any_op
+    %func_3 = transform.structured.vectorize_children_and_apply_patterns %func2 : (!transform.any_op) -> (!transform.any_op)
+
+    transform.apply_patterns to %func_3 {
+      transform.apply_patterns.canonicalization
+    } : !transform.any_op
+    transform.apply_cse to %func_3 : !transform.any_op
+
+    // Bufferization
+    // ==========================================
+    transform.apply_patterns to %func_3 {
+      transform.apply_patterns.tensor.reassociative_reshape_folding
+      transform.apply_patterns.canonicalization
+      transform.apply_patterns.iree.fold_fill_into_pad
+      transform.apply_patterns.linalg.tiling_canonicalization
+      transform.apply_patterns.scf.for_loop_canonicalization
+    } : !transform.any_op
+    transform.apply_cse to %func_3 : !transform.any_op
+    transform.iree.eliminate_empty_tensors %func_3 : (!transform.any_op) -> ()
+    transform.apply_patterns to %func_3 { transform.apply_patterns.linalg.erase_unnecessary_inputs } : !transform.any_op
+    %memref_func = transform.iree.bufferize { target_gpu } %func_3 : (!transform.any_op) -> (!transform.any_op)
+
+    // Step 5. Pre-process the contract and transfer ops to put it in the right form.
+    // ===========================================================================
+    transform.apply_patterns to %memref_func {
+      transform.apply_patterns.vector.fold_arith_extension
+    } : !transform.any_op
+
+    // Step 6. Post-bufferization vector distribution
+    // ===========================================================================
+    transform.iree.forall_to_workgroup %memref_func : (!transform.any_op) -> ()
+    transform.iree.map_nested_forall_to_gpu_threads %memref_func workgroup_dims = [64, 4, 1] subgroup_size = 64 sync_after_distribution = false : (!transform.any_op) -> ()
+
+    transform.apply_patterns to %memref_func {
+      transform.apply_patterns.memref.fold_memref_alias_ops
+    } : !transform.any_op
+    transform.iree.apply_licm %memref_func : !transform.any_op
+    transform.apply_patterns to %memref_func {
+      transform.apply_patterns.canonicalization
+    } : !transform.any_op
+    transform.apply_cse to %memref_func : !transform.any_op
+    %func_8 = transform.structured.hoist_redundant_vector_transfers %memref_func
+    : (!transform.any_op) -> !transform.any_op
+    transform.apply_patterns to %func_8 {
+      transform.apply_patterns.canonicalization
+    } : !transform.any_op
+    transform.apply_cse to %func_8 : !transform.any_op
+    transform.memref.erase_dead_alloc_and_stores %func_8 : (!transform.any_op) -> ()
+
+    // Apply chained matmul optimization.
+    %func_9 = transform.apply_registered_pass "iree-amdgpu-prepare-chained-matmul" to %func_8 : (!transform.any_op) -> (!transform.any_op)
+
+    %intrinsic = transform.param.constant #layout_f8 -> !transform.any_param
+
+    %mma = transform.structured.match ops{["vector.contract"]} in %variant_op :  (!transform.any_op) -> !transform.any_op
+    %mma1, %mma2 = transform.split_handle %mma : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
+    transform.annotate %mma "iree.amdgpu.mma" = %intrinsic : !transform.any_op, !transform.any_param
+
+    transform.apply_registered_pass "iree-llvmgpu-cast-type-to-fit-mma" to %func_9 : (!transform.any_op) -> (!transform.any_op)
+
+    %contracts = transform.structured.match ops{["vector.contract"]} in %variant_op :  (!transform.any_op) -> !transform.any_op
+    %contract1, %contract2 = transform.split_handle %contracts : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
+    transform.iree.set_contraction_layout_attributes %contract1, %intrinsic { read_layout_indices = array<i64: 0, 1> } : !transform.any_op, !transform.any_param
+    transform.iree.set_contraction_layout_attributes %contract2, %intrinsic : !transform.any_op, !transform.any_param
+
+    %distribute_func = transform.structured.match ops{["func.func"]} in %variant_op : (!transform.any_op) -> !transform.any_op
+
+    %distribute_func_2 = transform.iree.amdgpu_distribute_vectors %distribute_func : (!transform.any_op) -> !transform.any_op
+
+    transform.apply_patterns to %distribute_func_2 {
+      transform.apply_patterns.canonicalization
+    } : !transform.any_op
+    transform.apply_cse to %distribute_func_2 : !transform.any_op
+
+    // Distribute shared memory copies
+    // ==========================================
+    transform.iree.gpu_distribute_shared_memory_copy %distribute_func_2 : (!transform.any_op) -> ()
+    transform.apply_patterns to %distribute_func_2 {
+        transform.apply_patterns.memref.fold_memref_alias_ops
+        transform.apply_patterns.canonicalization
+        transform.apply_patterns.linalg.tiling_canonicalization
+      } : !transform.any_op
+    transform.apply_cse to %distribute_func_2 : !transform.any_op
+
+    %forop = transform.structured.match ops{["scf.for"]} in %variant_op : (!transform.any_op) -> !transform.any_op
+    %prefetched_forop = transform.iree.prefetch_shared_memory_copies %forop : (!transform.any_op) -> (!transform.any_op)
+
+    transform.apply_patterns to %distribute_func_2 {
+        transform.apply_patterns.memref.fold_memref_alias_ops
+        transform.apply_patterns.canonicalization
+        transform.apply_patterns.linalg.tiling_canonicalization
+      } : !transform.any_op
+    transform.apply_cse to %distribute_func_2 : !transform.any_op
+
+    %func_11 = transform.structured.match ops{["func.func"]} in %variant_op : (!transform.any_op) -> !transform.any_op
+    transform.iree.reduce_shared_memory_bank_conflicts %func_11 : (!transform.any_op) -> ()
+    transform.yield
+  }
+
+  // Send it down a custom transform dialect pipeline.
+  transform.named_sequence @custom_attention(%attention: !transform.any_op {transform.readonly}) {
+    %func = transform.get_parent_op %attention {op_name = "func.func"} : (!transform.any_op) -> !transform.any_op
+    %attn = transform.param.constant #iree_codegen.translation_info<TransformDialectCodegen codegen_spec = @__attention_main, { llvm_func_attrs = { "amdgpu-waves-per-eu" = "2", "denormal-fp-math-f32" = "preserve-sign" } }> -> !transform.any_param
+    transform.annotate %func "translation_info" = %attn : !transform.any_op, !transform.any_param
+    transform.yield
+  }
+
+  transform.named_sequence @match_attention(%attention: !transform.any_op {transform.readonly}) -> (!transform.any_op) {
+    transform.match.operation_name %attention ["iree_linalg_ext.attention"] : !transform.any_op
+    %in0 = transform.get_operand %attention[0] : (!transform.any_op) -> !transform.any_value
+    transform.iree.match.cast_compatible_type %in0 = tensor<?x?x?x?xf16> : !transform.any_value
+    transform.iree.match.dim_is_multiple_of %in0[3], 64 : !transform.any_value
+    transform.yield %attention : !transform.any_op
+  }
+
+  // Send it down a custom transform dialect pipeline.
+  transform.named_sequence @custom_attention_f8(%attention: !transform.any_op {transform.readonly}) {
+    %func = transform.get_parent_op %attention {op_name = "func.func"} : (!transform.any_op) -> !transform.any_op
+    %attn = transform.param.constant #iree_codegen.translation_info<TransformDialectCodegen codegen_spec = @__attention_main_f8, { llvm_func_attrs = { "amdgpu-waves-per-eu" = "2", "denormal-fp-math-f32" = "preserve-sign" } }> -> !transform.any_param
+    transform.annotate %func "translation_info" = %attn : !transform.any_op, !transform.any_param
+    transform.yield
+  }
+
+  transform.named_sequence @match_attention_f8(%attention: !transform.any_op {transform.readonly}) -> (!transform.any_op) {
+    transform.match.operation_name %attention ["iree_linalg_ext.attention"] : !transform.any_op
+    // Ensures K2 is divisible by 64.
+    %in1 = transform.get_operand %attention[1] : (!transform.any_op) -> !transform.any_value
+    transform.iree.match.cast_compatible_type %in1 = tensor<?x?x?xf8E4M3FNUZ> : !transform.any_value
+    transform.iree.match.dim_is_multiple_of %in1[1], 64 : !transform.any_value
+    transform.yield %attention : !transform.any_op
+  }
+
+//===----------------------------------------------------------------------===//
+// Tuning infra
+//===----------------------------------------------------------------------===//
+
+  transform.named_sequence @apply_op_config(%op: !transform.any_op {transform.readonly},
+                                            %config: !transform.any_param {transform.readonly}) {
+    transform.annotate %op "compilation_info" = %config : !transform.any_op, !transform.any_param
+    // transform.print %op {name = "Applied"} : !transform.any_op
+    transform.yield
+  }
+
+  transform.named_sequence @match_broadcast_rhs_mmt_i8_i8_i32(
+    %root: !transform.any_op {transform.readonly}) -> (!transform.any_op) {
+    transform.match.operation_name %root ["linalg.generic"] : !transform.any_op
+    // transform.print %root {name = "Generic"} : !transform.any_op
+    %ins, %outs = transform.iree.match.cast_compatible_dag_from_root %root {
+      ^bb0(%lhs: tensor<?x?x?xi8>, %rhs: tensor<?x?xi8>, %out: tensor<?x?x?xi32>):
+      %20 = linalg.generic {indexing_maps = [affine_map<(d0, d1, d2, d3) -> (d0, d1, d3)>,
+                                             affine_map<(d0, d1, d2, d3) -> (d2, d3)>,
+                                             affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)>],
+                            iterator_types = ["parallel", "parallel", "parallel", "reduction"]}
+          ins(%lhs, %rhs : tensor<?x?x?xi8>, tensor<?x?xi8>) outs(%out : tensor<?x?x?xi32>) {
+        ^bb0(%in: i8, %in_0: i8, %acc: i32):
+          %22 = arith.extsi %in : i8 to i32
+          %23 = arith.extsi %in_0 : i8 to i32
+          %24 = arith.muli %22, %23 : i32
+          %25 = arith.addi %acc, %24 : i32
+          linalg.yield %25 : i32
+        } -> tensor<?x?x?xi32>
+    } : (!transform.any_op) -> (!transform.any_value, !transform.any_value)
+    transform.yield %root : !transform.any_op
+  }
+
+// TUNING_SPEC_BEGIN DO NOT REMOVE
+
+//===----------------------------------------------------------------------===//
+// Matmul tuning
+//===----------------------------------------------------------------------===//
+
+
+//===----------------------------------------------------------------------===//
+// Convolution tuning
+//===----------------------------------------------------------------------===//
+
+  transform.named_sequence @match_conv_2d_nhwc_hwcf_Bx32x32x1280x3x3x1280(%conv: !transform.any_op {transform.readonly})
+    -> (!transform.any_op, !transform.any_param) {
+    %ins, %outs = transform.iree.match.cast_compatible_dag_from_root %conv {
+    ^bb0(%lhs: tensor<?x34x34x1280xi8>, %rhs: tensor<3x3x1280x1280xi8>, %out: tensor<?x32x32x1280xi32>):
+      %13 = linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>}
+        ins(%lhs, %rhs : tensor<?x34x34x1280xi8>, tensor<3x3x1280x1280xi8>)
+        outs(%out : tensor<?x32x32x1280xi32>) -> tensor<?x32x32x1280xi32>
+    } : (!transform.any_op) -> (!transform.any_value, !transform.any_value)
+      %config = transform.param.constant #iree_codegen.compilation_info<
+      lowering_config = #iree_codegen.lowering_config<tile_sizes = [[1, 1, 32, 320, 1, 1, 160]]>,
+        translation_info = #iree_codegen.translation_info<LLVMGPUVectorDistribute
+         workgroup_size = [320, 1, 1] subgroup_size = 64,
+          {mma_schedule = #iree_gpu.mma_schedule<
+              intrinsic = #iree_gpu.mma_layout<MFMA_I8_32x32x16_I32>,
+              subgroup_m_count = 1, subgroup_n_count = 5>
+          , prefetch_shared_memory}>
+      > -> !transform.any_param
+    transform.yield %conv, %config : !transform.any_op, !transform.any_param
+  }
+
+  transform.named_sequence @match_conv_2d_nhwc_hwcf_Bx32x32x1280x3x3x2560(%conv: !transform.any_op {transform.readonly})
+    -> (!transform.any_op, !transform.any_param) {
+    %ins, %outs = transform.iree.match.cast_compatible_dag_from_root %conv {
+    ^bb0(%lhs: tensor<?x34x34x2560xi8>, %rhs: tensor<3x3x2560x1280xi8>, %out: tensor<?x32x32x1280xi32>):
+      %13 = linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>}
+        ins(%lhs, %rhs : tensor<?x34x34x2560xi8>, tensor<3x3x2560x1280xi8>)
+        outs(%out : tensor<?x32x32x1280xi32>) -> tensor<?x32x32x1280xi32>
+    } : (!transform.any_op) -> (!transform.any_value, !transform.any_value)
+      %config = transform.param.constant #iree_codegen.compilation_info<
+      lowering_config = #iree_codegen.lowering_config<tile_sizes = [[1, 1, 32, 320, 1, 1, 160]]>,
+        translation_info = #iree_codegen.translation_info<LLVMGPUVectorDistribute
+         workgroup_size = [320, 1, 1] subgroup_size = 64,
+          {mma_schedule = #iree_gpu.mma_schedule<
+              intrinsic = #iree_gpu.mma_layout<MFMA_I8_32x32x16_I32>,
+              subgroup_m_count = 1, subgroup_n_count = 5>
+          , prefetch_shared_memory}>
+      > -> !transform.any_param
+    transform.yield %conv, %config : !transform.any_op, !transform.any_param
+  }
+
+  transform.named_sequence @match_conv_2d_nhwc_hwcf_Bx64x64x1280x3x3x1280(%conv: !transform.any_op {transform.readonly})
+    -> (!transform.any_op, !transform.any_param) {
+    %ins, %outs = transform.iree.match.cast_compatible_dag_from_root %conv {
+    ^bb0(%lhs: tensor<?x66x66x1280xi8>, %rhs: tensor<3x3x1280x1280xi8>, %out: tensor<?x64x64x1280xi32>):
+      %13 = linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>}
+        ins(%lhs, %rhs : tensor<?x66x66x1280xi8>, tensor<3x3x1280x1280xi8>)
+        outs(%out : tensor<?x64x64x1280xi32>) -> tensor<?x64x64x1280xi32>
+    } : (!transform.any_op) -> (!transform.any_value, !transform.any_value)
+      %config = transform.param.constant #iree_codegen.compilation_info<
+      lowering_config = #iree_codegen.lowering_config<tile_sizes = [[1, 1, 64, 320, 1, 1, 160]]>,
+        translation_info = #iree_codegen.translation_info<LLVMGPUVectorDistribute
+         workgroup_size = [320, 1, 1] subgroup_size = 64,
+          {mma_schedule = #iree_gpu.mma_schedule<
+              intrinsic = #iree_gpu.mma_layout<MFMA_I8_32x32x16_I32>,
+              subgroup_m_count = 1, subgroup_n_count = 5>
+          , prefetch_shared_memory}>
+      > -> !transform.any_param
+    transform.yield %conv, %config : !transform.any_op, !transform.any_param
+  }
+
+  transform.named_sequence @match_conv_2d_nhwc_hwcf_Bx128x128x320x3x3x640(%conv: !transform.any_op {transform.readonly})
+    -> (!transform.any_op, !transform.any_param) {
+    %ins, %outs = transform.iree.match.cast_compatible_dag_from_root %conv {
+    ^bb0(%lhs: tensor<?x130x130x640xi8>, %rhs: tensor<3x3x640x320xi8>, %out: tensor<?x128x128x320xi32>):
+      %13 = linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>}
+        ins(%lhs, %rhs : tensor<?x130x130x640xi8>, tensor<3x3x640x320xi8>)
+        outs(%out : tensor<?x128x128x320xi32>) -> tensor<?x128x128x320xi32>
+    } : (!transform.any_op) -> (!transform.any_value, !transform.any_value)
+      %config = transform.param.constant #iree_codegen.compilation_info<
+      lowering_config = #iree_codegen.lowering_config<tile_sizes = [[1, 1, 128, 64, 1, 1, 128]]>,
+        translation_info = #iree_codegen.translation_info<LLVMGPUVectorDistribute
+         workgroup_size = [128, 2, 1] subgroup_size = 64,
+          {mma_schedule = #iree_gpu.mma_schedule<
+              intrinsic = #iree_gpu.mma_layout<MFMA_I8_16x16x32_I32>,
+              subgroup_m_count = 2, subgroup_n_count = 2>
+          , prefetch_shared_memory}>
+      > -> !transform.any_param
+    transform.yield %conv, %config : !transform.any_op, !transform.any_param
+  }
+
+  transform.named_sequence @match_conv_2d_nhwc_hwcf_Bx128x128x640x3x3x640(%conv: !transform.any_op {transform.readonly})
+    -> (!transform.any_op, !transform.any_param) {
+    %ins, %outs = transform.iree.match.cast_compatible_dag_from_root %conv {
+    ^bb0(%lhs: tensor<?x130x130x640xi8>, %rhs: tensor<3x3x640x640xi8>, %out: tensor<?x128x128x640xi32>):
+      %13 = linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>}
+        ins(%lhs, %rhs : tensor<?x130x130x640xi8>, tensor<3x3x640x640xi8>)
+        outs(%out : tensor<?x128x128x640xi32>) -> tensor<?x128x128x640xi32>
+    } : (!transform.any_op) -> (!transform.any_value, !transform.any_value)
+      %config = transform.param.constant #iree_codegen.compilation_info<
+      lowering_config = #iree_codegen.lowering_config<tile_sizes = [[1, 1, 128, 128, 1, 1, 32]]>,
+        translation_info = #iree_codegen.translation_info<LLVMGPUVectorDistribute
+         workgroup_size = [128, 2, 1] subgroup_size = 64,
+          {mma_schedule = #iree_gpu.mma_schedule<
+              intrinsic = #iree_gpu.mma_layout<MFMA_I8_16x16x32_I32>,
+              subgroup_m_count = 2, subgroup_n_count = 2>
+          , prefetch_shared_memory}>
+      > -> !transform.any_param
+    transform.yield %conv, %config : !transform.any_op, !transform.any_param
+  }
+
+  transform.named_sequence @match_conv_2d_nhwc_hwcf_Bx128x128x320x3x3x960(%conv: !transform.any_op {transform.readonly})
+    -> (!transform.any_op, !transform.any_param) {
+    %ins, %outs = transform.iree.match.cast_compatible_dag_from_root %conv {
+    ^bb0(%lhs: tensor<?x130x130x960xi8>, %rhs: tensor<3x3x960x320xi8>, %out: tensor<?x128x128x320xi32>):
+      %13 = linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>}
+        ins(%lhs, %rhs : tensor<?x130x130x960xi8>, tensor<3x3x960x320xi8>)
+        outs(%out : tensor<?x128x128x320xi32>) -> tensor<?x128x128x320xi32>
+    } : (!transform.any_op) -> (!transform.any_value, !transform.any_value)
+      %config = transform.param.constant #iree_codegen.compilation_info<
+      lowering_config = #iree_codegen.lowering_config<tile_sizes = [[1, 1, 64, 320, 1, 1, 160]]>,
+        translation_info = #iree_codegen.translation_info<LLVMGPUVectorDistribute
+         workgroup_size = [320, 1, 1] subgroup_size = 64,
+          {mma_schedule = #iree_gpu.mma_schedule<
+              intrinsic = #iree_gpu.mma_layout<MFMA_I8_32x32x16_I32>,
+              subgroup_m_count = 1, subgroup_n_count = 5>
+          , prefetch_shared_memory}>
+      > -> !transform.any_param
+    transform.yield %conv, %config : !transform.any_op, !transform.any_param
+  }
+
+  transform.named_sequence @match_conv_2d_nhwc_hwcf_Bx128x128x320x3x3x320(%conv: !transform.any_op {transform.readonly})
+    -> (!transform.any_op, !transform.any_param) {
+    %ins, %outs = transform.iree.match.cast_compatible_dag_from_root %conv {
+    ^bb0(%lhs: tensor<?x130x130x320xi8>, %rhs: tensor<3x3x320x320xi8>, %out: tensor<?x128x128x320xi32>):
+      %13 = linalg.conv_2d_nhwc_hwcf {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>}
+        ins(%lhs, %rhs : tensor<?x130x130x320xi8>, tensor<3x3x320x320xi8>)
+        outs(%out : tensor<?x128x128x320xi32>) -> tensor<?x128x128x320xi32>
+    } : (!transform.any_op) -> (!transform.any_value, !transform.any_value)
+      %config = transform.param.constant #iree_codegen.compilation_info<
+      lowering_config = #iree_codegen.lowering_config<tile_sizes = [[1, 1, 128, 64, 1, 1, 32]]>,
+        translation_info = #iree_codegen.translation_info<LLVMGPUVectorDistribute
+         workgroup_size = [128, 1, 1] subgroup_size = 64,
+          {mma_schedule = #iree_gpu.mma_schedule<
+              intrinsic = #iree_gpu.mma_layout<MFMA_I8_16x16x32_I32>,
+              subgroup_m_count = 1, subgroup_n_count = 2>
+          , prefetch_shared_memory}>
+      > -> !transform.any_param
+    transform.yield %conv, %config : !transform.any_op, !transform.any_param
+  }
+
+//===----------------------------------------------------------------------===//
+// Batch matmul tuning
+//===----------------------------------------------------------------------===//
+
+//===----------------------------------------------------------------------===//
+// Broadcast lhs mmt tuning
+//===----------------------------------------------------------------------===//
+
+  transform.named_sequence @match_broadcast_rhs_mmt_Bx1024x10240x1280(%generic: !transform.any_op {transform.readonly}) -> (!transform.any_op, !transform.any_param) {
+    %mmt = transform.include @match_broadcast_rhs_mmt_i8_i8_i32 failures(propagate) (%generic) : (!transform.any_op) -> !transform.any_op
+    %lhs = transform.get_operand %generic[0] : (!transform.any_op) -> !transform.any_value
+    %rhs = transform.get_operand %generic[1] : (!transform.any_op) -> !transform.any_value
+    transform.iree.match.cast_compatible_type %lhs = tensor<?x1024x1280xi8> : !transform.any_value
+    transform.iree.match.cast_compatible_type %rhs = tensor<10240x1280xi8> : !transform.any_value
+    %config = transform.param.constant #iree_codegen.compilation_info<
+      lowering_config = #iree_codegen.lowering_config<tile_sizes = [[1, 128, 320, 128]]>,
+      translation_info = #iree_codegen.translation_info<LLVMGPUVectorDistribute
+        workgroup_size = [128, 4, 1] subgroup_size = 64,
+        {mma_schedule = #iree_gpu.mma_schedule<
+           intrinsic = #iree_gpu.mma_layout<MFMA_I8_16x16x32_I32>,
+           subgroup_m_count = 4, subgroup_n_count = 2>
+         , prefetch_shared_memory}>
+      > -> !transform.any_param
+    transform.yield %generic, %config : !transform.any_op, !transform.any_param
+  }
+
+  transform.named_sequence @match_broadcast_rhs_mmt_Bx1024x1280x5120(%generic: !transform.any_op {transform.readonly}) -> (!transform.any_op, !transform.any_param) {
+    %mmt = transform.include @match_broadcast_rhs_mmt_i8_i8_i32 failures(propagate) (%generic) : (!transform.any_op) -> !transform.any_op
+    %lhs = transform.get_operand %generic[0] : (!transform.any_op) -> !transform.any_value
+    %rhs = transform.get_operand %generic[1] : (!transform.any_op) -> !transform.any_value
+    transform.iree.match.cast_compatible_type %lhs = tensor<?x1024x5120xi8> : !transform.any_value
+    transform.iree.match.cast_compatible_type %rhs = tensor<1280x5120xi8> : !transform.any_value
+    %config = transform.param.constant #iree_codegen.compilation_info<
+      lowering_config = #iree_codegen.lowering_config<tile_sizes = [[1, 128, 80, 256]]>,
+      translation_info = #iree_codegen.translation_info<LLVMGPUVectorDistribute
+        workgroup_size = [64, 4, 1] subgroup_size = 64,
+        {mma_schedule = #iree_gpu.mma_schedule<
+           intrinsic = #iree_gpu.mma_layout<MFMA_I8_16x16x32_I32>,
+           subgroup_m_count = 4, subgroup_n_count = 1>
+         , prefetch_shared_memory}>
+      > -> !transform.any_param
+    transform.yield %generic, %config : !transform.any_op, !transform.any_param
+  }
+
+  transform.named_sequence @match_broadcast_rhs_mmt_Bx1024x1280x1280(%generic: !transform.any_op {transform.readonly}) -> (!transform.any_op, !transform.any_param) {
+    %mmt = transform.include @match_broadcast_rhs_mmt_i8_i8_i32 failures(propagate) (%generic) : (!transform.any_op) -> !transform.any_op
+    %lhs = transform.get_operand %generic[0] : (!transform.any_op) -> !transform.any_value
+    %rhs = transform.get_operand %generic[1] : (!transform.any_op) -> !transform.any_value
+    transform.iree.match.cast_compatible_type %lhs = tensor<?x1024x1280xi8> : !transform.any_value
+    transform.iree.match.cast_compatible_type %rhs = tensor<1280x1280xi8> : !transform.any_value
+    %config = transform.param.constant #iree_codegen.compilation_info<
+      lowering_config = #iree_codegen.lowering_config<tile_sizes = [[1, 64, 160, 128]]>,
+      translation_info = #iree_codegen.translation_info<LLVMGPUVectorDistribute
+        workgroup_size = [128, 2, 1] subgroup_size = 64,
+        {mma_schedule = #iree_gpu.mma_schedule<
+           intrinsic = #iree_gpu.mma_layout<MFMA_I8_16x16x32_I32>,
+           subgroup_m_count = 2, subgroup_n_count = 2>
+         , prefetch_shared_memory}>
+      > -> !transform.any_param
+    transform.yield %generic, %config : !transform.any_op, !transform.any_param
+  }
+
+  transform.named_sequence @match_broadcast_rhs_mmt_Bx4096x5120x640(%generic: !transform.any_op {transform.readonly}) -> (!transform.any_op, !transform.any_param) {
+    %mmt = transform.include @match_broadcast_rhs_mmt_i8_i8_i32 failures(propagate) (%generic) : (!transform.any_op) -> !transform.any_op
+    %lhs = transform.get_operand %generic[0] : (!transform.any_op) -> !transform.any_value
+    %rhs = transform.get_operand %generic[1] : (!transform.any_op) -> !transform.any_value
+    transform.iree.match.cast_compatible_type %lhs = tensor<?x4096x640xi8> : !transform.any_value
+    transform.iree.match.cast_compatible_type %rhs = tensor<5120x640xi8> : !transform.any_value
+    %config = transform.param.constant #iree_codegen.compilation_info<
+      lowering_config = #iree_codegen.lowering_config<tile_sizes = [[1, 128, 320, 128]]>,
+      translation_info = #iree_codegen.translation_info<LLVMGPUVectorDistribute
+        workgroup_size = [128, 4, 1] subgroup_size = 64,
+        {mma_schedule = #iree_gpu.mma_schedule<
+           intrinsic = #iree_gpu.mma_layout<MFMA_I8_16x16x32_I32>,
+           subgroup_m_count = 4, subgroup_n_count = 2>
+         , prefetch_shared_memory, reorder_workgroups = "transpose"}>
+      > -> !transform.any_param
+    transform.yield %generic, %config : !transform.any_op, !transform.any_param
+  }
+
+  transform.named_sequence @match_broadcast_rhs_mmt_Bx4096x640x640(%generic: !transform.any_op {transform.readonly}) -> (!transform.any_op, !transform.any_param) {
+    %mmt = transform.include @match_broadcast_rhs_mmt_i8_i8_i32 failures(propagate) (%generic) : (!transform.any_op) -> !transform.any_op
+    %lhs = transform.get_operand %generic[0] : (!transform.any_op) -> !transform.any_value
+    %rhs = transform.get_operand %generic[1] : (!transform.any_op) -> !transform.any_value
+    transform.iree.match.cast_compatible_type %lhs = tensor<?x4096x640xi8> : !transform.any_value
+    transform.iree.match.cast_compatible_type %rhs = tensor<640x640xi8> : !transform.any_value
+    %config = transform.param.constant #iree_codegen.compilation_info<
+      lowering_config = #iree_codegen.lowering_config<tile_sizes = [[1, 64, 320, 128]]>,
+      translation_info = #iree_codegen.translation_info<LLVMGPUVectorDistribute
+        workgroup_size = [128, 4, 1] subgroup_size = 64,
+        {mma_schedule = #iree_gpu.mma_schedule<
+           intrinsic = #iree_gpu.mma_layout<MFMA_I8_16x16x32_I32>,
+           subgroup_m_count = 4, subgroup_n_count = 2>
+         , prefetch_shared_memory}>
+      > -> !transform.any_param
+    transform.yield %generic, %config : !transform.any_op, !transform.any_param
+  }
+
+
+  transform.named_sequence @match_broadcast_rhs_mmt_Bx4096x640x2560(%generic: !transform.any_op {transform.readonly}) -> (!transform.any_op, !transform.any_param) {
+    %mmt = transform.include @match_broadcast_rhs_mmt_i8_i8_i32 failures(propagate) (%generic) : (!transform.any_op) -> !transform.any_op
+    %lhs = transform.get_operand %generic[0] : (!transform.any_op) -> !transform.any_value
+    %rhs = transform.get_operand %generic[1] : (!transform.any_op) -> !transform.any_value
+    transform.iree.match.cast_compatible_type %lhs = tensor<?x4096x2560xi8> : !transform.any_value
+    transform.iree.match.cast_compatible_type %rhs = tensor<640x2560xi8> : !transform.any_value
+    %config = transform.param.constant #iree_codegen.compilation_info<
+      lowering_config = #iree_codegen.lowering_config<tile_sizes = [[1, 64, 160, 128]]>,
+      translation_info = #iree_codegen.translation_info<LLVMGPUVectorDistribute
+        workgroup_size = [64, 4, 1] subgroup_size = 64,
+        {mma_schedule = #iree_gpu.mma_schedule<
+           intrinsic = #iree_gpu.mma_layout<MFMA_I8_16x16x32_I32>,
+           subgroup_m_count = 4, subgroup_n_count = 1>
+         , prefetch_shared_memory}>
+      > -> !transform.any_param
+    transform.yield %generic, %config : !transform.any_op, !transform.any_param
+  }
+
+//===----------------------------------------------------------------------===//
+// Contraction tuning
+//===----------------------------------------------------------------------===//
+
+  transform.named_sequence @match_matmul_like_Bx20x1024x64x1280_i8xi8xi32(%cont: !transform.any_op {transform.readonly})
+    -> (!transform.any_op, !transform.any_param) {
+    %ins, %outs = transform.iree.match.cast_compatible_dag_from_root %cont {
+    ^bb0(%lhs: tensor<?x1024x1280xi8>, %rhs: tensor<20x64x1280xi8>, %out: tensor<?x20x1024x64xi32>):
+      %16 = linalg.generic {indexing_maps = [affine_map<(d0, d1, d2, d3, d4) -> (d0, d2, d4)>,
+                                             affine_map<(d0, d1, d2, d3, d4) -> (d1, d3, d4)>,
+                                             affine_map<(d0, d1, d2, d3, d4) -> (d0, d1, d2, d3)>],
+                            iterator_types = ["parallel", "parallel", "parallel", "parallel", "reduction"]}
+        ins(%lhs, %rhs : tensor<?x1024x1280xi8>, tensor<20x64x1280xi8>)
+        outs(%out : tensor<?x20x1024x64xi32>) {
+      ^bb0(%in: i8, %in_0: i8, %acc: i32):
+        %18 = arith.extsi %in : i8 to i32
+        %19 = arith.extsi %in_0 : i8 to i32
+        %20 = arith.muli %18, %19 : i32
+        %21 = arith.addi %acc, %20 : i32
+        linalg.yield %21 : i32
+      } -> tensor<?x20x1024x64xi32>
+    } : (!transform.any_op) -> (!transform.any_value, !transform.any_value)
+    %config = transform.param.constant #iree_codegen.compilation_info<
+    lowering_config = #iree_codegen.lowering_config<tile_sizes = [[1, 1, 64, 160, 128]]>,
+      translation_info = #iree_codegen.translation_info<LLVMGPUVectorDistribute
+        workgroup_size = [128, 2, 1] subgroup_size = 64,
+        {mma_schedule = #iree_gpu.mma_schedule<
+            intrinsic = #iree_gpu.mma_layout<MFMA_I8_16x16x32_I32>,
+            subgroup_m_count = 2, subgroup_n_count = 2>
+        , prefetch_shared_memory}>
+    > -> !transform.any_param
+    transform.yield %cont, %config : !transform.any_op, !transform.any_param
+  }
+
+  transform.named_sequence @match_matmul_like_Bx20x64x64x2048_i8xi8xi32(%cont: !transform.any_op {transform.readonly})
+    -> (!transform.any_op, !transform.any_param) {
+    %ins, %outs = transform.iree.match.cast_compatible_dag_from_root %cont {
+    ^bb0(%lhs: tensor<?x64x2048xi8>, %rhs: tensor<20x64x2048xi8>, %out: tensor<?x20x64x64xi32>):
+      %16 = linalg.generic {indexing_maps = [affine_map<(d0, d1, d2, d3, d4) -> (d0, d2, d4)>,
+                                             affine_map<(d0, d1, d2, d3, d4) -> (d1, d3, d4)>,
+                                             affine_map<(d0, d1, d2, d3, d4) -> (d0, d1, d2, d3)>],
+                            iterator_types = ["parallel", "parallel", "parallel", "parallel", "reduction"]}
+        ins(%lhs, %rhs : tensor<?x64x2048xi8>, tensor<20x64x2048xi8>)
+        outs(%out : tensor<?x20x64x64xi32>) {
+      ^bb0(%in: i8, %in_0: i8, %acc: i32):
+        %18 = arith.extsi %in : i8 to i32
+        %19 = arith.extsi %in_0 : i8 to i32
+        %20 = arith.muli %18, %19 : i32
+        %21 = arith.addi %acc, %20 : i32
+        linalg.yield %21 : i32
+      } -> tensor<?x20x64x64xi32>
+    } : (!transform.any_op) -> (!transform.any_value, !transform.any_value)
+    %config = transform.param.constant #iree_codegen.compilation_info<
+    lowering_config = #iree_codegen.lowering_config<tile_sizes = [[1, 1, 16, 256, 128]]>,
+      translation_info = #iree_codegen.translation_info<LLVMGPUVectorDistribute
+        workgroup_size = [256, 1, 1] subgroup_size = 64,
+        {mma_schedule = #iree_gpu.mma_schedule<
+            intrinsic = #iree_gpu.mma_layout<MFMA_I8_16x16x32_I32>,
+            subgroup_m_count = 1, subgroup_n_count = 4>
+        , prefetch_shared_memory}>
+    > -> !transform.any_param
+    transform.yield %cont, %config : !transform.any_op, !transform.any_param
+  }
+
+  transform.named_sequence @match_matmul_like_Bx10x64x64x2048_i8xi8xi32(%cont: !transform.any_op {transform.readonly})
+    -> (!transform.any_op, !transform.any_param) {
+    %ins, %outs = transform.iree.match.cast_compatible_dag_from_root %cont {
+    ^bb0(%lhs: tensor<?x64x2048xi8>, %rhs: tensor<10x64x2048xi8>, %out: tensor<?x10x64x64xi32>):
+      %16 = linalg.generic {indexing_maps = [affine_map<(d0, d1, d2, d3, d4) -> (d0, d2, d4)>,
+                                             affine_map<(d0, d1, d2, d3, d4) -> (d1, d3, d4)>,
+                                             affine_map<(d0, d1, d2, d3, d4) -> (d0, d1, d2, d3)>],
+                            iterator_types = ["parallel", "parallel", "parallel", "parallel", "reduction"]}
+        ins(%lhs, %rhs : tensor<?x64x2048xi8>, tensor<10x64x2048xi8>)
+        outs(%out : tensor<?x10x64x64xi32>) {
+      ^bb0(%in: i8, %in_0: i8, %acc: i32):
+        %18 = arith.extsi %in : i8 to i32
+        %19 = arith.extsi %in_0 : i8 to i32
+        %20 = arith.muli %18, %19 : i32
+        %21 = arith.addi %acc, %20 : i32
+        linalg.yield %21 : i32
+      } -> tensor<?x10x64x64xi32>
+    } : (!transform.any_op) -> (!transform.any_value, !transform.any_value)
+    %config = transform.param.constant #iree_codegen.compilation_info<
+    lowering_config = #iree_codegen.lowering_config<tile_sizes = [[1, 1, 16, 320, 128]]>,
+      translation_info = #iree_codegen.translation_info<LLVMGPUVectorDistribute
+        workgroup_size = [256, 1, 1] subgroup_size = 64,
+        {mma_schedule = #iree_gpu.mma_schedule<
+            intrinsic = #iree_gpu.mma_layout<MFMA_I8_16x16x32_I32>,
+            subgroup_m_count = 1, subgroup_n_count = 4>
+        , prefetch_shared_memory}>
+    > -> !transform.any_param
+    transform.yield %cont, %config : !transform.any_op, !transform.any_param
+  }
+
+  transform.named_sequence @match_matmul_like_BxBx20x1024x64_i8xi8xi32(%cont: !transform.any_op {transform.readonly})
+    -> (!transform.any_op, !transform.any_param) {
+    %ins, %outs = transform.iree.match.cast_compatible_dag_from_root %cont {
+    ^bb0(%lhs: tensor<?x1024x1280xi8>, %rhs: tensor<?x20x64x1280xi8>, %out: tensor<?x?x20x1024x64xi32>):
+      %22 = linalg.generic {indexing_maps = [affine_map<(d0, d1, d2, d3, d4, d5) -> (d1, d3, d5)>,
+                                             affine_map<(d0, d1, d2, d3, d4, d5) -> (d0, d2, d4, d5)>,
+                                             affine_map<(d0, d1, d2, d3, d4, d5) -> (d0, d1, d2, d3, d4)>],
+                            iterator_types = ["parallel", "parallel", "parallel", "parallel", "parallel", "reduction"]}
+        ins(%lhs, %rhs : tensor<?x1024x1280xi8>, tensor<?x20x64x1280xi8>)
+        outs(%out : tensor<?x?x20x1024x64xi32>) {
+      ^bb0(%in: i8, %in_0: i8, %acc: i32):
+        %24 = arith.extsi %in : i8 to i32
+        %25 = arith.extsi %in_0 : i8 to i32
+        %26 = arith.muli %24, %25 : i32
+        %27 = arith.addi %acc, %26 : i32
+        linalg.yield %27 : i32
+      } -> tensor<?x?x20x1024x64xi32>
+    } : (!transform.any_op) -> (!transform.any_value, !transform.any_value)
+    %config = transform.param.constant #iree_codegen.compilation_info<
+    lowering_config = #iree_codegen.lowering_config<tile_sizes = [[1, 1, 1, 256, 64, 64]]>,
+      translation_info = #iree_codegen.translation_info<LLVMGPUVectorDistribute
+        workgroup_size = [128, 2, 1] subgroup_size = 64,
+        {mma_schedule = #iree_gpu.mma_schedule<
+            intrinsic = #iree_gpu.mma_layout<MFMA_I8_32x32x16_I32>,
+            subgroup_m_count = 2, subgroup_n_count = 2>
+        , prefetch_shared_memory}>
+    > -> !transform.any_param
+    transform.yield %cont, %config : !transform.any_op, !transform.any_param
+  }
+
+  transform.named_sequence @match_matmul_like_BxBx20x64x64x2048_i8xi8xi32(%cont: !transform.any_op {transform.readonly})
+    -> (!transform.any_op, !transform.any_param) {
+    %ins, %outs = transform.iree.match.cast_compatible_dag_from_root %cont {
+    ^bb0(%lhs: tensor<?x64x2048xi8>, %rhs: tensor<?x20x64x2048xi8>, %out: tensor<?x?x20x64x64xi32>):
+      %22 = linalg.generic {indexing_maps = [affine_map<(d0, d1, d2, d3, d4, d5) -> (d1, d3, d5)>,
+                                             affine_map<(d0, d1, d2, d3, d4, d5) -> (d0, d2, d4, d5)>,
+                                             affine_map<(d0, d1, d2, d3, d4, d5) -> (d0, d1, d2, d3, d4)>],
+                            iterator_types = ["parallel", "parallel", "parallel", "parallel", "parallel", "reduction"]}
+        ins(%lhs, %rhs : tensor<?x64x2048xi8>, tensor<?x20x64x2048xi8>)
+        outs(%out : tensor<?x?x20x64x64xi32>) {
+      ^bb0(%in: i8, %in_0: i8, %acc: i32):
+        %24 = arith.extsi %in : i8 to i32
+        %25 = arith.extsi %in_0 : i8 to i32
+        %26 = arith.muli %24, %25 : i32
+        %27 = arith.addi %acc, %26 : i32
+        linalg.yield %27 : i32
+      } -> tensor<?x?x20x64x64xi32>
+    } : (!transform.any_op) -> (!transform.any_value, !transform.any_value)
+    %config = transform.param.constant #iree_codegen.compilation_info<
+    lowering_config = #iree_codegen.lowering_config<tile_sizes = [[1, 1, 1, 32, 160, 256]]>,
+      translation_info = #iree_codegen.translation_info<LLVMGPUVectorDistribute
+        workgroup_size = [128, 2, 1] subgroup_size = 64,
+        {mma_schedule = #iree_gpu.mma_schedule<
+            intrinsic = #iree_gpu.mma_layout<MFMA_I8_16x16x32_I32>,
+            subgroup_m_count = 2, subgroup_n_count = 2>
+        , prefetch_shared_memory}>
+    > -> !transform.any_param
+    transform.yield %cont, %config : !transform.any_op, !transform.any_param
+  }
+
+// TUNING_SPEC_END DO NOT REMOVE
+
+//===----------------------------------------------------------------------===//
+// Entry point
+//===----------------------------------------------------------------------===//
+
+  transform.named_sequence @__kernel_config(%variant_op: !transform.any_op {transform.consumed}) {
+    transform.foreach_match in %variant_op
+        // Attention.
+        @match_attention -> @custom_attention,
+        @match_attention_f8 -> @custom_attention_f8
+
+        // TUNING_MATCH_BEGIN DO NOT REMOVE
+
+        // Matmul.
+
+        // Convolution.
+
+        , @match_conv_2d_nhwc_hwcf_Bx32x32x1280x3x3x1280 -> @apply_op_config
+        , @match_conv_2d_nhwc_hwcf_Bx32x32x1280x3x3x2560 -> @apply_op_config
+        , @match_conv_2d_nhwc_hwcf_Bx64x64x1280x3x3x1280 -> @apply_op_config
+        , @match_conv_2d_nhwc_hwcf_Bx128x128x320x3x3x640 -> @apply_op_config
+        , @match_conv_2d_nhwc_hwcf_Bx128x128x640x3x3x640 -> @apply_op_config
+        , @match_conv_2d_nhwc_hwcf_Bx128x128x320x3x3x960 -> @apply_op_config
+        , @match_conv_2d_nhwc_hwcf_Bx128x128x320x3x3x320 -> @apply_op_config
+
+        // Batch matmul.
+
+        // Broadcast rhs mmt.
+        , @match_broadcast_rhs_mmt_Bx1024x10240x1280 -> @apply_op_config
+        , @match_broadcast_rhs_mmt_Bx1024x1280x5120 -> @apply_op_config
+        , @match_broadcast_rhs_mmt_Bx1024x1280x1280 -> @apply_op_config
+        , @match_broadcast_rhs_mmt_Bx4096x5120x640 -> @apply_op_config
+        , @match_broadcast_rhs_mmt_Bx4096x640x640 -> @apply_op_config
+        , @match_broadcast_rhs_mmt_Bx4096x640x2560 -> @apply_op_config
+
+        // Contration.
+        , @match_matmul_like_Bx20x1024x64x1280_i8xi8xi32 -> @apply_op_config
+        , @match_matmul_like_Bx20x64x64x2048_i8xi8xi32 -> @apply_op_config
+        , @match_matmul_like_Bx10x64x64x2048_i8xi8xi32 -> @apply_op_config
+        , @match_matmul_like_BxBx20x1024x64_i8xi8xi32 -> @apply_op_config
+        , @match_matmul_like_BxBx20x64x64x2048_i8xi8xi32 -> @apply_op_config
+
+        // TUNING_MATCH_END DO NOT REMOVE
+      : (!transform.any_op) -> (!transform.any_op)
+    transform.yield
+  }
+} ////  module
\ No newline at end of file
diff --git a/attentionbench/attention_utils.py b/attentionbench/attention_utils.py
new file mode 100644
index 0000000..5d4fb12
--- /dev/null
+++ b/attentionbench/attention_utils.py
@@ -0,0 +1,48 @@
+import csv
+
+def read_shapes_from_csv(filename: str) -> list[tuple[int, int, int, int, int, str]]:
+    shapes = []
+    with open(filename, 'r') as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            shapes.append((
+                int(row['BATCH']),
+                int(row['NH']),
+                int(row['SEQ_Q']),
+                int(row['SEQ_KV']),
+                int(row['D_HEAD']),
+                str(row['dtype'])
+            ))
+    return shapes
+
+def write_results_to_csv(results : list[tuple] | list[list] | list[dict], output_filename: str):
+    if len(results) == 0:
+        print('No valid results')
+        return
+    
+    fieldnames = [
+        'index', 
+        'BATCH', 
+        'NH', 
+        'SEQ_Q', 
+        'SEQ_KV', 
+        'D_HEAD', 
+        'dtype', 
+        'mean_microseconds', 
+        'arithmetic_intensity', 
+        'tflops'
+    ]
+
+    with open(output_filename, 'w', newline='') as f:
+        if isinstance(results[0], list) or isinstance(results[0], tuple):
+            writer = csv.writer(f)
+            writer.writerow(fieldnames)
+        elif isinstance(results[0], dict):
+            writer = csv.DictWriter(f, fieldnames)
+            writer.writeheader()
+        else:
+            print('Invalid result format')
+            return
+        
+        for result in results:
+            writer.writerow(result)
\ No newline at end of file
diff --git a/attentionbench/benchmark_torch_attention.py b/attentionbench/benchmark_torch_attention.py
new file mode 100644
index 0000000..95784a0
--- /dev/null
+++ b/attentionbench/benchmark_torch_attention.py
@@ -0,0 +1,77 @@
+import torch
+import time
+import csv
+from typing import List, Tuple
+from tqdm import tqdm
+from attention_utils import *
+
+device = "cuda:0"
+
+class TestModule(torch.nn.Module):
+    def forward(self, q, k, v):
+        return torch.nn.functional.scaled_dot_product_attention(q, k, v)
+
+arg_to_torch_dtype = {
+    'fp16': torch.float16,
+    'bf16': torch.bfloat16,
+    'fp32': torch.float32
+}
+
+def run_benchmark(shape: tuple[int, int, int, int, int, str]) -> tuple[float, float, float]:
+    B, H, S_Q, S_KV, DH, dtype = shape
+
+    torch_dtype = arg_to_torch_dtype[dtype]
+    
+    q = torch.rand([B, H, S_Q, DH], dtype=torch_dtype, device=device)
+    k = torch.rand([B, H, S_KV, DH], dtype=torch_dtype, device=device)
+    v = torch.rand([B, H, S_KV, DH], dtype=torch_dtype, device=device)
+    
+    test_module = torch.compile(TestModule(), dynamic=True)
+    test_module.to(device=device, dtype=torch_dtype)
+    
+    # Warmup
+    for _ in range(10):
+        test_module(q, k, v)
+    torch.cuda.synchronize()
+    
+    # Benchmark
+    start_time = time.time()
+    eval_steps = 100
+    for _ in range(eval_steps):
+        test_module(q, k, v)
+    torch.cuda.synchronize()
+    
+    mean_microseconds = (time.time() - start_time) * 1e6 / eval_steps
+    
+    flops = 4 * S_Q * S_KV * DH * B * H
+    bytes = B * H * 2 * (2 * S_KV * DH + 2 * S_Q * DH + S_Q * S_KV)
+    arithmetic_intensity = flops / bytes
+    tflops_per_second = (flops / 1e12) / (mean_microseconds / 1e6)
+    
+    return mean_microseconds, arithmetic_intensity, tflops_per_second
+
+def main():
+    input_csv = "results/sharkfa.csv"
+    output_csv = "torch_llama_sdxl_attention.csv"
+    
+    shapes = read_shapes_from_csv(input_csv)
+    results = []
+    
+    for index, shape in enumerate(tqdm(shapes)):
+        B, H, S_Q, S_KV, DH, dtype = shape
+        if dtype not in arg_to_torch_dtype:
+            continue
+        mean_microseconds, arithmetic_intensity, tflops = run_benchmark(shape)
+        
+        results.append((
+            index, B, H, S_Q, S_KV, DH, dtype,
+            round(mean_microseconds, 4),
+            round(arithmetic_intensity, 4),
+            round(tflops, 4)
+        ))
+    
+    write_results_to_csv(results, output_csv)
+    print(f"Results written to {output_csv}")
+
+if __name__ == "__main__":
+    main()
diff --git a/attentionbench/benchmark_triton_attention.py b/attentionbench/benchmark_triton_attention.py
new file mode 100644
index 0000000..d134ff6
--- /dev/null
+++ b/attentionbench/benchmark_triton_attention.py
@@ -0,0 +1,1307 @@
+#!/usr/bin/env python
+"""
+Fused Attention
+===============
+
+This is a Triton implementation of the Flash Attention v2 algorithm from Tri Dao (https://tridao.me/publications/flash2/flash2.pdf)
+Credits: OpenAI kernel team, AMD ML Frameworks Triton team
+
+Features supported:
+
+1) Fwd with causal masking
+2) Any sequence lengths without padding (currently fwd kernel only)
+3) Support for different sequence lengths for q and k
+4) Nested tensor API currently does not support dropout or bias.
+
+Not currently supported:
+
+1) Non power of two head dims
+
+"""
+
+import argparse
+import pytest
+import random
+import sys
+import csv
+from tqdm import tqdm
+import torch
+
+import triton
+import triton.language as tl
+
+from attention_utils import *
+
+torch_dtype:tl.constexpr = torch.float16
+
+TORCH_HAS_FP8E5 = hasattr(torch, 'float8_e5m2fnuz')
+if TORCH_HAS_FP8E5:
+    torch_dtype:tl.constexpr = torch.float8_e5m2fnuz
+
+class MetaData():
+    cu_seqlens_q = None
+    cu_seqlens_k = None
+    max_seqlens_q = 0
+    max_seqlens_k = 0
+    bias = None
+    alibi_slopes = None
+    causal = False
+    num_contexts = 0
+    varlen = False
+    dropout_p, return_encoded_softmax = 0.0, False
+
+    def __init__(self, sm_scale=1.0):
+        self.sm_scale = sm_scale
+
+    def set_varlen_params(self, cu_seqlens_q, cu_seqlens_k):
+        self.varlen = True
+        self.cu_seqlens_q = cu_seqlens_q
+        self.cu_seqlens_k = cu_seqlens_k
+        # Without "varlen", there should still be one sequence.
+        assert len(cu_seqlens_q) >= 2
+        assert len(cu_seqlens_q) == len(cu_seqlens_k)
+        self.num_contexts = len(cu_seqlens_q) - 1
+        for i in range (0, self.num_contexts):
+            self.max_seqlens_q = max(cu_seqlens_q[i+1].item() - cu_seqlens_q[i].item(), self.max_seqlens_q)
+            self.max_seqlens_k = max(cu_seqlens_k[i+1].item() - cu_seqlens_k[i].item(), self.max_seqlens_k)
+
+    def need_bias(self, bias, batch, nheads, seqlen_q, seqlen_k):
+        assert bias.is_cuda
+        assert bias.dim() == 4
+        assert bias.shape[0] == 1
+        assert bias.shape[2:] == (seqlen_q, seqlen_k)
+        self.bias = bias
+
+    def need_alibi(self, alibi_slopes, batch, nheads):
+        assert alibi_slopes.is_cuda
+        assert alibi_slopes.dim() == 2
+        assert alibi_slopes.shape[0] == batch
+        assert alibi_slopes.shape[1] == nheads
+        self.alibi_slopes = alibi_slopes
+
+    def need_causal(self):
+        self.causal = True
+
+    def need_dropout(self, dropout_p, return_encoded_softmax):
+        self.dropout_p = dropout_p
+        self.return_encoded_softmax = return_encoded_softmax
+
+    def check_args(self, q, k, v, o):
+        assert q.dim() == k.dim() and q.dim() == v.dim()
+        if self.varlen:
+            assert q.dim() == 3
+            total_q, nheads_q, head_size = q.shape
+            total_k, nheads_k, _ = k.shape
+            assert self.cu_seqlens_q is not None
+            assert self.cu_seqlens_k is not None
+            assert len(self.cu_seqlens_q) == len(self.cu_seqlens_k)
+            # TODO: Remove once bias is supported with varlen
+            assert self.bias == None
+            # TODO:Remove once dropout is supported with varlen
+            assert self.dropout_p == 0.0
+            assert not self.return_encoded_softmax
+        else:
+            assert q.dim() == 4
+            batch, nheads_q, seqlen_q, head_size = q.shape
+            _, nheads_k, seqlen_k, _ = k.shape
+            assert self.max_seqlens_q > 0 and self.max_seqlens_k > 0
+            assert self.cu_seqlens_q is None and self.cu_seqlens_k is None
+        assert k.shape == v.shape
+        assert q.shape[-1] == k.shape[-1] and q.shape[-1] == v.shape[-1]
+        # TODO: Change assert if we support qkl f8 and v f16
+        assert q.dtype == k.dtype and q.dtype == v.dtype
+        assert head_size <= 256
+        assert o.shape == q.shape
+        assert (nheads_q % nheads_k) == 0
+
+@triton.jit
+def cdiv_fn(x,y):
+    return (x + y - 1) // y
+
+@triton.jit
+def max_fn(x, y):
+    return tl.math.max(x, y)
+
+@triton.jit
+def dropout_offsets(philox_seed, philox_offset, dropout_p, m, n, stride):
+    ms = tl.arange(0, m)
+    ns = tl.arange(0, n)
+    return philox_offset + ms[:, None] * stride + ns[None, :]
+
+@triton.jit
+def dropout_rng(philox_seed, philox_offset, dropout_p, m, n, stride):
+    rng_offsets = dropout_offsets(philox_seed, philox_offset, dropout_p, m, n, stride).to(tl.uint32)
+    # TODO: use tl.randint for better performance
+    return tl.rand(philox_seed, rng_offsets)
+
+@triton.jit
+def dropout_mask(philox_seed, philox_offset, dropout_p, m, n, stride):
+    rng_output = dropout_rng(philox_seed, philox_offset, dropout_p, m, n, stride)
+    rng_keep = rng_output > dropout_p
+    return rng_keep
+
+@triton.jit
+def load_fn(block_ptr, first, second, pad):
+    if first and second:
+        tensor = tl.load(block_ptr, boundary_check=(0,1), padding_option=pad)
+    elif first:
+        tensor = tl.load(block_ptr, boundary_check=(0,), padding_option=pad)
+    elif second:
+        tensor = tl.load(block_ptr, boundary_check=(1,), padding_option=pad)
+    else:
+        tensor = tl.load(block_ptr)
+    return tensor
+
+@triton.jit
+def print_gpu(prefix, val=None):
+    if (tl.program_id(0) == 0) and ((tl.program_id(1) == 0) and (tl.program_id(2) == 0)):
+        if val is not None:
+            tl.device_print(prefix, val)
+        else:
+            tl.device_print(prefix)
+
+@triton.jit
+def compute_alibi_block(alibi_slope, seqlen_q, seqlen_k, offs_m, offs_n, transpose = False):
+    # when seqlen_k and seqlen_q are different we want the diagonal to stick to the bottom right of the attention matrix
+    # for casual mask we want something like this where (1 is kept and 0 is masked)
+    # seqlen_q = 2 and seqlen_k = 5
+    #   1 1 1 1 0
+    #   1 1 1 1 1
+    # seqlen_q = 5 and seqlen_k = 2
+    #        0 0
+    #        0 0
+    #        0 0
+    #        1 0
+    #        1 1
+    # for alibi the diagonal is 0 indicating no penalty for attending to that spot and increasing penalty for attending further from the diagonal
+    # e.g. alibi_slope = 1, seqlen_q = 2, seqlen_k = 5, offs_m = [0, 1, 2, 3], offs_n = [0, 1, 2, 3, 4], transpose = False
+    # 1. offs_m[:,None] = [[0],
+    #                       [1],
+    # 2. offs_m[:,None] + seqlen_k = [[5],
+    #                                  [6],
+    # 3. offs_m[:,None] + seqlen_k - seqlen_q = [[3],
+    #                                             [4],
+    # 4. offs_m[:,None] + seqlen_k - seqlen_q - offs_n[None,:] = [[3], - [[0, 1, 2, 3, 4]] =  [[ 3, 2, 1, 0,-1],
+    #                                                            [4],                           [ 4, 3, 2, 1, 0]]
+    # 5. -1 * alibi_slope * tl.abs(relative_pos_block) = [[ -3, -2, -1, 0,-1],
+    #                                                     [ -4, -3, -2, -1, 0]],
+    relative_pos_block = offs_m[:,None] + seqlen_k - seqlen_q - offs_n[None,:]
+    alibi_block = -1 * alibi_slope * tl.abs(relative_pos_block)
+    if transpose:
+        return alibi_block.T
+    else:
+        return alibi_block
+
+def compute_alibi_tensor(alibi_slopes, seqlen_q, seqlen_k):
+    q_idx = torch.arange(seqlen_q, dtype=torch.int32, device="cuda").unsqueeze(-1) # (N_CTX_Q, 1)
+    k_idx = torch.arange(seqlen_k, dtype=torch.int32, device="cuda").unsqueeze(0) # (1, N_CTX_K)
+    relative_pos = torch.abs(q_idx + seqlen_k - seqlen_q - k_idx) # (N_CTX_Q, N_CTX_K)
+    return -1 * alibi_slopes.unsqueeze(-1).unsqueeze(-1) * relative_pos # (Z, H, N_CTX_Q, N_CTX_K)
+
+@triton.jit
+def _attn_fwd_inner(
+    acc, l_i, m_i, q,
+    K_block_ptr, V_block_ptr,
+    start_m,
+    actual_seqlen_k,
+    actual_seqlen_q,
+    dropout_p,
+    philox_seed,
+    batch_philox_offset,
+    encoded_softmax_block_ptr,
+    block_min, block_max,
+    offs_n_causal,
+    masked_blocks,
+    n_extra_tokens,
+    bias_ptr,
+    alibi_slope,
+    IS_CAUSAL: tl.constexpr,
+    BLOCK_M: tl.constexpr,
+    BLOCK_DMODEL: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+    OFFS_M: tl.constexpr,
+    OFFS_N: tl.constexpr,
+    PRE_LOAD_V: tl.constexpr,
+    MASK_STEPS: tl.constexpr,
+    ENABLE_DROPOUT: tl.constexpr,
+    RETURN_ENCODED_SOFTMAX: tl.constexpr,
+    PADDED_HEAD: tl.constexpr
+):
+    # loop over k, v, and update accumulator
+    for start_n in range (block_min, block_max, BLOCK_N):
+        # For padded blocks, we will overrun the tensor size if
+        # we load all BLOCK_N. For others, the blocks are all within range.
+        k = load_fn(K_block_ptr, PADDED_HEAD, MASK_STEPS and (n_extra_tokens != 0), "zero")
+        if PRE_LOAD_V:
+            v = load_fn(V_block_ptr, MASK_STEPS and (n_extra_tokens != 0), PADDED_HEAD, "zero")
+        qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
+        # We start from end of seqlen_k so only the first iteration would need
+        # to be checked for padding if it is not a multiple of block_n
+        # TODO: This can be optimized to only be true for the padded block.
+        if MASK_STEPS:
+            # If this is the last block / iteration, we want to
+            # mask if the sequence length is not a multiple of block size
+            # a solution is to always do BLOCK_M // BLOCK_N + 1 steps if not is_modulo_mn.
+            # last step might get wasted but that is okay. check if this masking works For
+            # that case.
+            if (start_n + BLOCK_N == block_max) and (n_extra_tokens != 0):
+                boundary_m = tl.full([BLOCK_M], actual_seqlen_k, dtype=tl.int32)
+                size_n = start_n + OFFS_N[None,:]
+                mask = size_n < boundary_m[:,None]
+                qk = tl.where(mask, qk, float("-inf"))
+        if IS_CAUSAL:
+            causal_boundary = start_n + offs_n_causal
+            causal_mask = OFFS_M[:, None] >= causal_boundary[None, :]
+            qk = tl.where(causal_mask, qk, float("-inf"))
+        # -- compute qk ----
+        qk += tl.dot(q, k)
+        if bias_ptr is not None:
+            bias = load_fn(bias_ptr, False, MASK_STEPS and (n_extra_tokens != 0), "zero")
+            # While bias is added after multiplying qk with sm_scale,
+            # our optimization to use 2^x instead of e^x results in an additional
+            # scale factor of log2(e) which we must also multiply the bias with.
+            qk += (bias * 1.44269504089)
+           
+        if alibi_slope is not None:
+            # Compute the global position of each token within the sequence
+            global_m_positions = start_m*BLOCK_M + tl.arange(0, BLOCK_M)
+            global_n_positions = start_n + tl.arange(0, BLOCK_N)
+
+            alibi_block = compute_alibi_block(alibi_slope, actual_seqlen_q, actual_seqlen_k, global_m_positions, global_n_positions)
+
+            qk += (alibi_block * 1.44269504089) # scale factor of log2(e)
+
+        # softmax
+        m_ij = tl.maximum(m_i, tl.max(qk,1))
+        qk = qk - m_ij[:, None]
+        p = tl.math.exp2(qk)
+
+        # CAVEAT: Must update l_ij before applying dropout
+        l_ij = tl.sum(p, 1)
+        if ENABLE_DROPOUT:
+            philox_offset = batch_philox_offset + start_m * BLOCK_M * actual_seqlen_k + start_n - BLOCK_N
+            keep = dropout_mask(philox_seed, philox_offset, dropout_p, BLOCK_M, BLOCK_N, actual_seqlen_k)
+            if RETURN_ENCODED_SOFTMAX:
+                tl.store(encoded_softmax_block_ptr, tl.where(keep, p, -p).to(encoded_softmax_block_ptr.type.element_ty))
+            p = tl.where(keep, p, 0.0)
+        elif RETURN_ENCODED_SOFTMAX:
+            tl.store(encoded_softmax_block_ptr, p.to(encoded_softmax_block_ptr.type.element_ty))
+        # -- update output accumulator --
+        alpha = tl.math.exp2(m_i - m_ij)
+        acc = acc * alpha[:, None]
+        if not PRE_LOAD_V:
+            v = load_fn(V_block_ptr, MASK_STEPS and (n_extra_tokens != 0), PADDED_HEAD, "zero")
+        # -- update m_i and l_i
+        l_i = l_i * alpha + l_ij
+        # update m_i and l_i
+        m_i = m_ij
+        acc += tl.dot(p.to(V_block_ptr.type.element_ty), v)
+        V_block_ptr = tl.advance(V_block_ptr, (BLOCK_N, 0))
+        K_block_ptr = tl.advance(K_block_ptr, (0, BLOCK_N))
+        if bias_ptr is not None:
+            bias_ptr = tl.advance(bias_ptr, (0, BLOCK_N))
+        if RETURN_ENCODED_SOFTMAX:
+            encoded_softmax_block_ptr = tl.advance(encoded_softmax_block_ptr, (0, BLOCK_N))
+    return acc, l_i, m_i
+
+@triton.autotune(
+   configs=[
+       triton.Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'waves_per_eu': 2, 'PRE_LOAD_V': False}, num_stages=1, num_warps=8),
+       triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'waves_per_eu': 2, 'PRE_LOAD_V': False}, num_stages=1, num_warps=4),
+       triton.Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'waves_per_eu': 2, 'PRE_LOAD_V': False}, num_stages=1, num_warps=8),
+       triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'waves_per_eu': 3, 'PRE_LOAD_V': True}, num_stages=1, num_warps=4),
+       triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'waves_per_eu': 3, 'PRE_LOAD_V': False}, num_stages=1, num_warps=4),
+       triton.Config({'BLOCK_M': 64, 'BLOCK_N': 64, 'waves_per_eu': 4, 'PRE_LOAD_V': False}, num_stages=1, num_warps=8),
+       triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'waves_per_eu': 1, 'PRE_LOAD_V': False}, num_stages=1, num_warps=4),
+       triton.Config({'BLOCK_M': 32, 'BLOCK_N': 32, 'waves_per_eu': 4, 'PRE_LOAD_V': False}, num_stages=1, num_warps=8),
+       # TODO: This config fails with head_size not pow2 with data mismatches. Check why.
+    #    triton.Config({'BLOCK_M': 32, 'BLOCK_N': 16, 'waves_per_eu': 1, 'PRE_LOAD_V': False}, num_stages=1, num_warps=4),
+       triton.Config({'BLOCK_M': 16, 'BLOCK_N': 16, 'waves_per_eu': 1, 'PRE_LOAD_V': False}, num_stages=1, num_warps=4),
+   ],
+   key=['IS_CAUSAL', 'dropout_p', 'BLOCK_DMODEL'],
+   use_cuda_graph=True,
+)
+@triton.jit
+def attn_fwd(
+    Q, K, V, bias, sm_scale, L, Out,
+    stride_qz, stride_qh, stride_qm, stride_qk,
+    stride_kz, stride_kh, stride_kn, stride_kk,
+    stride_vz, stride_vh, stride_vk, stride_vn,
+    stride_oz, stride_oh, stride_om, stride_on,
+    stride_bz, stride_bh, stride_bm, stride_bn,
+    stride_az, stride_ah,
+    cu_seqlens_q, cu_seqlens_k,
+    dropout_p, philox_seed, philox_offset_base, encoded_softmax,
+    alibi_slopes,
+    HQ: tl.constexpr, HK:tl.constexpr,
+    ACTUAL_BLOCK_DMODEL:tl.constexpr,
+    MAX_SEQLENS_Q:tl.constexpr, MAX_SEQLENS_K:tl.constexpr,
+    VARLEN: tl.constexpr,
+    IS_CAUSAL: tl.constexpr,
+    BLOCK_M: tl.constexpr, BLOCK_DMODEL: tl.constexpr, BLOCK_N: tl.constexpr,
+    PRE_LOAD_V: tl.constexpr,
+    BIAS_TYPE: tl.constexpr,
+    ENABLE_DROPOUT: tl.constexpr,
+    RETURN_ENCODED_SOFTMAX: tl.constexpr,
+    USE_ALIBI: tl.constexpr,
+    BATCH_SIZE: tl.constexpr,
+):
+    start_m = tl.program_id(0)
+    off_h_q = tl.program_id(1)
+    off_z = tl.program_id(2)
+    offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
+    offs_n = tl.arange(0, BLOCK_N)
+    if VARLEN:
+        cu_seqlens_q_start = tl.load(cu_seqlens_q + off_z)
+        cu_seqlens_q_end = tl.load(cu_seqlens_q + off_z + 1)
+        seqlen_q = cu_seqlens_q_end - cu_seqlens_q_start
+        # We have a one-size-fits-all grid in id(0). Some seqlens might be too
+        # small for all start_m so for those we return early.
+        if start_m * BLOCK_M > seqlen_q:
+            return
+        cu_seqlens_k_start = tl.load(cu_seqlens_k + off_z)
+        cu_seqlens_k_end = tl.load(cu_seqlens_k + off_z + 1)
+        seqlen_k = cu_seqlens_k_end - cu_seqlens_k_start
+    else:
+        cu_seqlens_q_start = 0
+        cu_seqlens_k_start = 0
+        seqlen_q = MAX_SEQLENS_Q
+        seqlen_k = MAX_SEQLENS_K
+
+    # Now we compute whether we need to exit early due to causal masking.
+    # This is because for seqlen_q > seqlen_k, M rows of the attn scores
+    # are completely masked, resulting in 0s written to the output, and
+    # inf written to LSE. We don't need to do any GEMMs in this case.
+    # This block of code determines what N is, and if this WG is operating
+    # on those M rows.
+    n_blocks = cdiv_fn(seqlen_k, BLOCK_N)
+    if (IS_CAUSAL):
+        # If seqlen_q == seqlen_k, the attn scores are a square matrix.
+        # If seqlen_q != seqlen_k, attn scores are rectangular which means
+        # the causal mask boundary is bottom right aligned, and ends at either
+        # the top edge (seqlen_q < seqlen_k) or left edge.
+        # This captures the decrease in n_blocks if we have a rectangular attn matrix
+        n_blocks_seqlen = cdiv_fn(
+            (start_m + 1) * BLOCK_M + seqlen_k - seqlen_q,
+            BLOCK_N
+        )
+        # This is what adjusts the block_max for the current WG, only
+        # if IS_CAUSAL. Otherwise we want to always iterate through all n_blocks
+        n_blocks = min(n_blocks, n_blocks_seqlen)
+        # If we have no blocks after adjusting for seqlen deltas, this WG is part of
+        # the blocks that are all 0. We exit early.
+        if n_blocks <= 0:
+            o_offset = off_z * stride_oz + cu_seqlens_q_start * stride_om + off_h_q * stride_oh
+            O_block_ptr = tl.make_block_ptr(
+                base=Out + o_offset,
+                shape=(seqlen_q, BLOCK_DMODEL),
+                strides=(stride_om, stride_on),
+                offsets=(start_m * BLOCK_M, 0),
+                block_shape=(BLOCK_M, BLOCK_DMODEL),
+                order=(1, 0)
+            )
+            acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=Out.type.element_ty)
+            # We still need to write 0s to the result
+            tl.store(O_block_ptr, acc.to(Out.type.element_ty), boundary_check=(0,1))
+            l_ptrs = L + off_z * HQ * MAX_SEQLENS_Q + off_h_q * MAX_SEQLENS_Q + offs_m
+            # We store inf to LSE, not -inf because in the bwd pass, we subtract this
+            # from qk which makes it -inf, such that exp(qk - inf) = 0 for these masked blocks.
+            l = tl.full([BLOCK_M], value=float("inf"), dtype=tl.float32)
+            tl.store(l_ptrs, l)
+            # TODO: Should dropout and return encoded softmax be handled here too?
+            return
+
+    # If MQA / GQA, set the K and V head offsets appropriately.
+    GROUP_SIZE: tl.constexpr = HQ // HK
+    if GROUP_SIZE != 1:
+        off_h_k = off_h_q // GROUP_SIZE
+    else:
+        off_h_k = off_h_q
+
+    need_padding = False
+    n_extra_tokens = 0
+    if seqlen_k < BLOCK_N:
+        need_padding = True
+        n_extra_tokens = BLOCK_N - seqlen_k
+    elif seqlen_k % BLOCK_N:
+        need_padding = True
+        n_extra_tokens = seqlen_k % BLOCK_N
+    PADDED_HEAD:tl.constexpr = (ACTUAL_BLOCK_DMODEL != BLOCK_DMODEL)
+
+    # Compute pointers for all the tensors used in this kernel.
+    q_offset = off_z * stride_qz +  off_h_q * stride_qh + cu_seqlens_q_start * stride_qm
+    Q_block_ptr = tl.make_block_ptr(
+        base=Q + q_offset,
+        shape=(seqlen_q, ACTUAL_BLOCK_DMODEL),
+        strides=(stride_qm, stride_qk),
+        offsets=(start_m * BLOCK_M, 0),
+        block_shape=(BLOCK_M, BLOCK_DMODEL),
+        order=(1, 0)
+    )
+    k_offset = off_z * stride_kz + off_h_k * stride_kh + cu_seqlens_k_start * stride_kn
+    K_block_ptr = tl.make_block_ptr(
+        base=K + k_offset,
+        shape=(ACTUAL_BLOCK_DMODEL, seqlen_k),
+        strides=(stride_kk, stride_kn),
+        offsets=(0, 0),
+        block_shape=(BLOCK_DMODEL, BLOCK_N),
+        order=(0, 1)
+    )
+    v_offset = off_z * stride_vz + off_h_k * stride_vh + cu_seqlens_k_start * stride_vk
+    V_block_ptr = tl.make_block_ptr(
+        base=V + v_offset,
+        shape=(seqlen_k, ACTUAL_BLOCK_DMODEL),
+        strides=(stride_vk, stride_vn),
+        offsets=(0, 0),
+        block_shape=(BLOCK_N, BLOCK_DMODEL),
+        order=(1, 0)
+    )
+    if BIAS_TYPE != 0:
+        b_offset = off_h_q * stride_bh # Note: this might get large enough to overflow on some configs
+        bias_ptr = tl.make_block_ptr(
+            base=bias + b_offset,
+            shape=(seqlen_q, seqlen_k),
+            strides=(stride_bm, stride_bn),
+            offsets=(start_m * BLOCK_M, 0),
+            block_shape=(BLOCK_M, BLOCK_N),
+            order=(1, 0),
+        )
+    else:
+        bias_ptr = None
+
+    if USE_ALIBI:
+        a_offset = off_z * stride_az +  off_h_q * stride_ah 
+        alibi_slope = tl.load(alibi_slopes + a_offset)
+    else:
+        alibi_slope = None
+
+    if ENABLE_DROPOUT:
+        batch_philox_offset = philox_offset_base + off_z * seqlen_q * seqlen_k
+    else:
+        batch_philox_offset = 0
+    # We can ask to return the dropout mask without actually doing any dropout. In
+    # this case, we return an invalid pointer so indicate the mask is not valid.
+    # TODO: Fix encoded softmax. It currently uses just h_q in the base offset.
+    if RETURN_ENCODED_SOFTMAX:
+        encoded_softmax_block_ptr = tl.make_block_ptr(
+                base=encoded_softmax + off_h_q * seqlen_q * seqlen_k,
+                shape=(seqlen_q, seqlen_k),
+                strides=(seqlen_k, 1),
+                offsets=(start_m * BLOCK_M, 0),
+                block_shape=(BLOCK_M, BLOCK_N),
+                order=(1, 0)
+                )
+    else:
+        encoded_softmax_block_ptr = 0
+    # initialize pointer to m and l
+    m_i = tl.full([BLOCK_M], float("-inf"), dtype=tl.float32)
+    l_i = tl.full([BLOCK_M], 1.0, dtype=tl.float32)
+    acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32)
+    # scale sm_scale by log_2(e) and use 2^x in the loop as we do not
+    # have native e^x support in HW.
+    qk_scale = sm_scale * 1.44269504089
+    # Q is loaded once at the beginning and shared by all N blocks.
+    q = load_fn(Q_block_ptr, True, PADDED_HEAD, "zero")
+    q = (q * qk_scale).to(Q_block_ptr.type.element_ty)
+
+    # Here we compute how many full and masked blocks we have.
+    padded_block_k = n_extra_tokens != 0
+    is_modulo_mn = not padded_block_k and (seqlen_q % BLOCK_M == 0)
+    if IS_CAUSAL:
+        # There are always at least BLOCK_M // BLOCK_N masked blocks.
+        # Additionally there might be one more due to dissimilar seqlens.
+        masked_blocks = BLOCK_M // BLOCK_N + (not is_modulo_mn)
+    else:
+        # Padding on Q does not need to be masked in the FA loop.
+        masked_blocks = padded_block_k
+    # if IS_CAUSAL, not is_modulo_mn does not always result in an additional block.
+    # In this case we might exceed n_blocks so pick the min.
+    masked_blocks = min(masked_blocks, n_blocks)
+    n_full_blocks = n_blocks - masked_blocks
+    block_min = 0
+    block_max = n_blocks * BLOCK_N
+    # Compute for full blocks. Here we set causal to false regardless of its actual
+    # value because there is no masking. Similarly we do not need padding.
+    if n_full_blocks > 0:
+        block_max = (n_blocks - masked_blocks) * BLOCK_N
+        acc, l_i, m_i = _attn_fwd_inner(
+            acc, l_i, m_i, q, K_block_ptr, V_block_ptr,
+            start_m, seqlen_k, seqlen_q,
+            dropout_p, philox_seed, batch_philox_offset, encoded_softmax_block_ptr,
+            # _, _, offs_n_causal, masked_blocks, n_extra_tokens, _
+            block_min, block_max, 0, 0, 0, bias_ptr, alibi_slope,
+            # IS_CAUSAL, ....
+            False, BLOCK_M, BLOCK_DMODEL, BLOCK_N, offs_m, offs_n,
+            # _, MASK_STEPS, ...
+            PRE_LOAD_V, False, ENABLE_DROPOUT, RETURN_ENCODED_SOFTMAX, PADDED_HEAD
+        )
+        block_min = block_max
+        block_max = n_blocks * BLOCK_N
+
+    tl.debug_barrier()
+    # Remaining blocks, if any, are full / not masked.
+    if (masked_blocks > 0):
+        if IS_CAUSAL:
+            offs_n_causal = offs_n + (seqlen_q - seqlen_k)
+        else:
+            offs_n_causal = 0
+        K_block_ptr = tl.advance(K_block_ptr, (0, n_full_blocks*BLOCK_N))
+        V_block_ptr = tl.advance(V_block_ptr, (n_full_blocks*BLOCK_N, 0))
+        if bias_ptr is not None:
+            bias_ptr = tl.advance(bias_ptr, (0, n_full_blocks*BLOCK_N))
+        if RETURN_ENCODED_SOFTMAX:
+            encoded_softmax_block_ptr = tl.advance(encoded_softmax_block_ptr,
+                                                   (0, n_full_blocks))
+        acc, l_i, m_i = _attn_fwd_inner(
+            acc, l_i, m_i, q, K_block_ptr, V_block_ptr,
+            start_m, seqlen_k, seqlen_q,
+            dropout_p, philox_seed, batch_philox_offset, encoded_softmax_block_ptr,
+            block_min, block_max, offs_n_causal, masked_blocks, n_extra_tokens, bias_ptr, alibi_slope,
+            IS_CAUSAL, BLOCK_M, BLOCK_DMODEL, BLOCK_N, offs_m, offs_n,
+            # _, MASK_STEPS, ...
+            PRE_LOAD_V, True, ENABLE_DROPOUT, RETURN_ENCODED_SOFTMAX, PADDED_HEAD
+        )
+    # epilogue
+    acc = acc / l_i[:, None]
+    if ENABLE_DROPOUT:
+        acc = acc / (1 - dropout_p)
+    # If seqlen_q > seqlen_k but the delta is not a multiple of BLOCK_M,
+    # then we have one block with a row of all NaNs which come from computing
+    # softmax over a row of all -infs (-inf - inf = NaN). We check for that here
+    # and store 0s where there are NaNs as these rows should've been zeroed out.
+    end_m_idx = (start_m + 1) * BLOCK_M
+    start_m_idx = start_m * BLOCK_M
+    causal_start_idx = seqlen_q - seqlen_k
+    acc = acc.to(Out.type.element_ty)
+    if IS_CAUSAL:
+        if causal_start_idx > start_m_idx and causal_start_idx < end_m_idx:
+            out_mask_boundary = tl.full((BLOCK_DMODEL,), causal_start_idx, dtype=tl.int32)
+            mask_m_offsets = start_m_idx + tl.arange(0, BLOCK_M)
+            out_ptrs_mask = mask_m_offsets[:, None] >= out_mask_boundary[None, :]
+            z = 0.0
+            acc = tl.where(out_ptrs_mask, acc, z.to(acc.type.element_ty))
+    # write back LSE
+    l_ptrs = L + off_z * HQ * MAX_SEQLENS_Q + off_h_q * MAX_SEQLENS_Q + offs_m
+    # If seqlen_q not multiple of BLOCK_M, we need to mask out the last few rows.
+    # This is only true for the last M block. For others, overflow_size will be -ve
+    overflow_size = end_m_idx - seqlen_q
+    if overflow_size > 0:
+        boundary = tl.full((BLOCK_M,), BLOCK_M - overflow_size, dtype=tl.int32)
+        # This is a > check because mask being 0 blocks the store.
+        l_ptrs_mask = boundary > tl.arange(0, BLOCK_M)
+        tl.store(l_ptrs, m_i + tl.math.log2(l_i), mask=l_ptrs_mask)
+    else:
+        tl.store(l_ptrs, m_i + tl.math.log2(l_i))
+
+    # write back O
+    o_offset = off_z * stride_oz + cu_seqlens_q_start * stride_om + off_h_q * stride_oh
+    O_block_ptr = tl.make_block_ptr(
+        base=Out + o_offset,
+        shape=(seqlen_q, ACTUAL_BLOCK_DMODEL),
+        strides=(stride_om, stride_on),
+        offsets=(start_m * BLOCK_M, 0),
+        block_shape=(BLOCK_M, BLOCK_DMODEL),
+        order=(1, 0)
+    )
+    # Need boundary check on this to make sure the padding from the
+    # Q and KV tensors in both dims are not part of what we store back.
+    # TODO: Do the boundary check optionally.
+    tl.store(O_block_ptr, acc, boundary_check=(0,1))
+
+@triton.jit
+def _attn_bwd_preprocess(
+    Out, DO,
+    Delta,
+    stride_oz, stride_oh, stride_om, stride_on,
+    stride_doz, stride_doh, stride_dom, stride_don,
+    seqlen_q,
+    head_dim,
+    BLOCK_M: tl.constexpr,
+    D_HEAD: tl.constexpr,
+):
+    # off_m = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M)
+    # off_n = tl.arange(0, D_HEAD)
+    off_m = tl.program_id(0) * BLOCK_M
+    off_h = tl.program_id(1) # head index
+    off_z = tl.program_id(2) # batch index
+    num_h = tl.num_programs(1)
+    o_offset = off_h * stride_oh + off_z * stride_oz
+    O_block_ptr = tl.make_block_ptr(
+        base=Out + o_offset,
+        shape=(seqlen_q, head_dim),
+        strides=(stride_om, stride_on),
+        offsets=(off_m, 0),
+        block_shape=(BLOCK_M, D_HEAD),
+        order=(1, 0)
+    )
+    do_offset = off_h * stride_doh + off_z * stride_doz
+    DO_block_ptr = tl.make_block_ptr(
+        base=DO + do_offset,
+        shape=(seqlen_q, head_dim),
+        strides=(stride_dom, stride_don),
+        offsets=(off_m, 0),
+        block_shape=(BLOCK_M, D_HEAD),
+        order=(1, 0)
+    )
+    # load
+    # o = tl.load(Out + off_m[:, None] * D_HEAD + off_n[None, :]).to(tl.float32)
+    # do = tl.load(DO + off_m[:, None] * D_HEAD + off_n[None, :]).to(tl.float32)
+    o = tl.load(O_block_ptr, boundary_check=(0,1), padding_option="zero").to(tl.float32)
+    do = tl.load(DO_block_ptr, boundary_check=(0,1), padding_option="zero").to(tl.float32)
+    # compute
+    delta = tl.sum(o * do, axis=1)
+    # write-back, shape (q.shape[0] * q.shape[1], q.shape[2])
+    off_zh = off_z * num_h + off_h * 1
+    # Check for OOB accesses
+    delta_ptrs = Delta + off_zh * seqlen_q + off_m + tl.arange(0, BLOCK_M)
+    overflow = off_m + BLOCK_M - seqlen_q
+    if overflow > 0:
+        boundary = tl.full((BLOCK_M, ), BLOCK_M - overflow, dtype=tl.int32)
+        mask = boundary > tl.arange(0, BLOCK_M)
+        tl.store(delta_ptrs, delta, mask=mask)
+    else:
+        tl.store(delta_ptrs, delta)
+
+@triton.jit
+def _bwd_kernel_dk_dv(
+                   dk, dv,
+                   Q, k, v, sm_scale, alibi_slope,
+                   DO,
+                   M, D,
+                   # shared by Q/K/V/DO.
+                   stride_tok, stride_d,
+                   H, N_CTX, BLOCK_M1: tl.constexpr,
+                   BLOCK_N1: tl.constexpr,
+                   BLOCK_DMODEL: tl.constexpr,
+                   # Filled in by the wrapper.
+                   start_n, start_m, num_steps,
+                   MASK: tl.constexpr):
+    offs_m = start_m + tl.arange(0, BLOCK_M1)
+    offs_n = start_n + tl.arange(0, BLOCK_N1)
+    offs_k = tl.arange(0, BLOCK_DMODEL)
+    QT_block_ptr = tl.make_block_ptr(
+        base=Q,
+        shape=(BLOCK_DMODEL, N_CTX),
+        strides=(stride_d, stride_tok),
+        offsets=(0, start_m),
+        block_shape=(BLOCK_DMODEL, BLOCK_M1),
+        order=(0,1)
+    )
+    DO_block_ptr = tl.make_block_ptr(
+        base=DO,
+        shape=(N_CTX, BLOCK_DMODEL),
+        strides=(stride_tok, stride_d),
+        offsets=(start_m, 0),
+        block_shape=(BLOCK_M1, BLOCK_DMODEL),
+        order=(1,0)
+    )
+    # BLOCK_N1 must be a multiple of BLOCK_M1, otherwise the code wouldn't work.
+    tl.static_assert(BLOCK_N1 % BLOCK_M1 == 0)
+    curr_m = start_m
+    step_m = BLOCK_M1
+    for blk_idx in range(num_steps):
+        qT = tl.load(QT_block_ptr)
+        # Load m before computing qk to reduce pipeline stall.
+        offs_m = curr_m + tl.arange(0, BLOCK_M1)
+        m = tl.load(M + offs_m)
+        kqT = tl.dot(k, qT)
+        if alibi_slope is not None:
+            alibi_block = compute_alibi_block(alibi_slope, N_CTX, N_CTX, offs_m, offs_n, True)
+            kqT += alibi_block * 1.44269504089
+
+        pT = tl.math.exp2(kqT - m[None, :])
+        # Autoregressive masking.
+        if MASK:
+            mask = (offs_m[None, :] >= offs_n[:, None])
+            pT = tl.where(mask, pT, 0.0)
+        do = tl.load(DO_block_ptr)
+        # Compute dV.
+        ppT = pT
+        ppT = ppT.to(tl.float16)
+        dv += tl.dot(ppT, do)
+        # D (= delta) is pre-divided by ds_scale.
+        Di = tl.load(D + offs_m)
+        # Compute dP and dS.
+        dpT = tl.dot(v, tl.trans(do))
+        dsT = pT * (dpT - Di[None, :])
+        dsT = dsT.to(tl.float16)
+        dk += tl.dot(dsT, tl.trans(qT))
+        # Increment pointers.
+        curr_m += step_m
+        QT_block_ptr = tl.advance(QT_block_ptr, (0, step_m))
+        DO_block_ptr = tl.advance(DO_block_ptr, (step_m, 0))
+    return dk, dv
+
+@triton.jit
+def _bwd_kernel_dq(dq, q, K, V,
+                 do, m, D, alibi_slope,
+                 # shared by Q/K/V/DO.
+                 stride_tok, stride_d,
+                 H, N_CTX,
+                 BLOCK_M2: tl.constexpr,
+                 BLOCK_N2: tl.constexpr,
+                 BLOCK_DMODEL: tl.constexpr,
+                 # Filled in by the wrapper.
+                 start_m, start_n, num_steps,
+                 MASK: tl.constexpr):
+    offs_m = start_m + tl.arange(0, BLOCK_M2)
+    offs_n = start_n + tl.arange(0, BLOCK_N2)
+    offs_k = tl.arange(0, BLOCK_DMODEL)
+    KT_block_ptr = tl.make_block_ptr(
+        base=K,
+        shape=(BLOCK_DMODEL, N_CTX),
+        strides=(stride_d, stride_tok),
+        offsets=(0, start_n),
+        block_shape=(BLOCK_DMODEL, BLOCK_N2),
+        order=(0, 1)
+    )
+    VT_block_ptr = tl.make_block_ptr(
+        base=V,
+        shape=(BLOCK_DMODEL, N_CTX),
+        strides=(stride_d, stride_tok),
+        offsets=(0, start_n),
+        block_shape=(BLOCK_DMODEL, BLOCK_N2),
+        order=(0, 1)
+    )
+    # D (= delta) is pre-divided by ds_scale.
+    Di = tl.load(D + offs_m)
+    # BLOCK_M2 must be a multiple of BLOCK_N2, otherwise the code wouldn't work.
+    tl.static_assert(BLOCK_M2 % BLOCK_N2 == 0)
+    curr_n = start_n
+    step_n = BLOCK_N2
+    for blk_idx in range(num_steps):
+        kT = tl.load(KT_block_ptr)
+        qk = tl.dot(q, kT)
+        if alibi_slope is not None:  
+            alibi_block = compute_alibi_block(alibi_slope, N_CTX, N_CTX, offs_m, offs_n)
+            qk += alibi_block * 1.44269504089
+
+        p = tl.math.exp2(qk - m)
+        # Autoregressive masking.
+        if MASK:
+            offs_n = curr_n + tl.arange(0, BLOCK_N2)
+            mask = (offs_m[:, None] >= offs_n[None, :])
+            p = tl.where(mask, p, 0.0)
+        # Compute dP and dS.
+        vT = tl.load(VT_block_ptr)
+        dp = tl.dot(do, vT).to(tl.float32)
+        ds = p * (dp - Di[:, None])
+        ds = ds.to(tl.float16)
+        # Compute dQ.0.
+        # NOTE: We need to de-scale dq in the end, because kT was pre-scaled.
+        dq += tl.dot(ds, tl.trans(kT))
+        # Increment pointers.
+        curr_n += step_n
+        KT_block_ptr = tl.advance(KT_block_ptr, (0, step_n))
+        VT_block_ptr = tl.advance(VT_block_ptr, (0, step_n))
+    return dq
+
+@triton.jit
+def _attn_bwd(Q, K, V, sm_scale, alibi_slopes,
+              DO,
+              DQ, DK, DV,
+              M, D,
+              # shared by Q/K/V/DO.
+              stride_z, stride_h, stride_tok, stride_d,
+              # H = 16, N_CTX = 1024
+              H, N_CTX,
+              BLOCK_DMODEL: tl.constexpr,
+              BLOCK_M1: tl.constexpr,
+              BLOCK_N1: tl.constexpr,
+              BLOCK_M2: tl.constexpr,
+              BLOCK_N2: tl.constexpr,
+              BLK_SLICE_FACTOR: tl.constexpr,
+              USE_ALIBI: tl.constexpr):
+    LN2: tl.constexpr = 0.6931471824645996  # = ln(2)
+
+    bhid = tl.program_id(2)
+    off_chz = (bhid * N_CTX).to(tl.int64)
+    adj = (stride_h * (bhid % H) + stride_z * (bhid // H)).to(tl.int64)
+    pid = tl.program_id(0)
+
+    # offset pointers for batch/head
+    Q += adj
+    K += adj
+    V += adj
+    DO += adj
+    DQ += adj
+    DK += adj
+    DV += adj
+    M += off_chz
+    D += off_chz
+
+    offs_k = tl.arange(0, BLOCK_DMODEL)
+
+    start_n = pid * BLOCK_N1
+    # This assignment is important. It is what allows us to pick the diagonal
+    # blocks. Later, when we want to do the lower triangular, we update start_m
+    # after the first dkdv call.
+    start_m = start_n
+
+    MASK_BLOCK_M1: tl.constexpr = BLOCK_M1 // BLK_SLICE_FACTOR
+    offs_n = start_n + tl.arange(0, BLOCK_N1)
+
+    dv = tl.zeros([BLOCK_N1, BLOCK_DMODEL], dtype=tl.float32)
+    dk = tl.zeros([BLOCK_N1, BLOCK_DMODEL], dtype=tl.float32)
+
+    K_block_ptr = tl.make_block_ptr(
+        base=K,
+        shape=(N_CTX, BLOCK_DMODEL),
+        strides=(stride_tok, stride_d),
+        offsets=(start_n, 0),
+        block_shape=(BLOCK_N1, BLOCK_DMODEL),
+        order=(1, 0),
+    )
+    V_block_ptr = tl.make_block_ptr(
+        base=V,
+        shape=(N_CTX, BLOCK_DMODEL),
+        strides=(stride_tok, stride_d),
+        offsets=(start_n, 0),
+        block_shape=(BLOCK_N1, BLOCK_DMODEL),
+        order=(1, 0),
+    )
+
+    # load K and V: they stay in SRAM throughout the inner loop for dkdv.
+    k = tl.load(K_block_ptr)
+    v = tl.load(V_block_ptr)
+
+    if USE_ALIBI:
+        a_offset = bhid
+        alibi_slope = tl.load(alibi_slopes + a_offset)
+    else:
+        alibi_slope = None
+
+    # compute dK and dV for blocks close to the diagonal that need to be masked
+    num_steps = BLOCK_N1 // MASK_BLOCK_M1
+    dk, dv = _bwd_kernel_dk_dv(
+                            dk, dv,
+                            Q, k, v, sm_scale, alibi_slope,
+                            DO,
+                            M, D,
+                            stride_tok, stride_d,
+                            H, N_CTX,
+                            MASK_BLOCK_M1, BLOCK_N1, BLOCK_DMODEL,
+                            start_n, start_m, num_steps,
+                            MASK=True
+                            )
+
+    # compute dK and dV for blocks that don't need masking further from the diagonal
+    start_m += num_steps * MASK_BLOCK_M1
+    num_steps = (N_CTX - start_m) // BLOCK_M1
+
+    dk, dv = _bwd_kernel_dk_dv(
+        dk, dv,
+        Q, k, v, sm_scale, alibi_slope,
+        DO,
+        M, D,
+        stride_tok, stride_d,
+        H, N_CTX,
+        BLOCK_M1, BLOCK_N1, BLOCK_DMODEL,
+        start_n, start_m, num_steps,
+        MASK=False
+    )
+
+    DV_block_ptrs = tl.make_block_ptr(
+        base=DV,
+        shape=(N_CTX, BLOCK_DMODEL),
+        strides=(stride_tok, stride_d),
+        offsets=(start_n, 0),
+        block_shape=(BLOCK_N1, BLOCK_DMODEL),
+        order=(1,0)
+    )
+    tl.store(DV_block_ptrs, dv.to(v.dtype))
+
+    # Write back dK.
+    dk *= sm_scale
+    DK_block_ptrs = tl.make_block_ptr(
+        base=DK,
+        shape=(N_CTX, BLOCK_DMODEL),
+        strides=(stride_tok, stride_d),
+        offsets=(start_n, 0),
+        block_shape=(BLOCK_N1, BLOCK_DMODEL),
+        order=(1,0)
+    )
+    tl.store(DK_block_ptrs, dk.to(k.dtype))
+
+    # THIS BLOCK DOES DQ:
+    start_m = pid * BLOCK_M2
+    end_n = start_m + BLOCK_M2
+
+    MASK_BLOCK_N2: tl.constexpr = BLOCK_N2 // BLK_SLICE_FACTOR
+    offs_m = start_m + tl.arange(0, BLOCK_M2)
+
+    Q_block_ptr = tl.make_block_ptr(
+        base=Q,
+        shape=(N_CTX, BLOCK_DMODEL),
+        strides=(stride_tok, stride_d),
+        offsets=(start_m, 0),
+        block_shape=(BLOCK_M2, BLOCK_DMODEL),
+        order=(1, 0)
+    )
+
+    DO_block_ptr = tl.make_block_ptr(
+        base=DO,
+        shape=(N_CTX, BLOCK_DMODEL),
+        strides=(stride_tok, stride_d),
+        offsets=(start_m, 0),
+        block_shape=(BLOCK_M2, BLOCK_DMODEL),
+        order=(1, 0)
+    )
+    q = tl.load(Q_block_ptr)
+    do = tl.load(DO_block_ptr)
+    dq = tl.zeros([BLOCK_M2, BLOCK_DMODEL], dtype=tl.float32)
+
+    m = tl.load(M + offs_m)
+    m = m[:, None]
+
+    # Compute dQ for masked (diagonal) blocks.
+    # NOTE: This code scans each row of QK^T backward (from right to left,
+    # but inside each call to _attn_bwd_dq, from left to right), but that's
+    # not due to anything important.  I just wanted to reuse the loop
+    # structure for dK & dV above as much as possible.
+    num_steps = BLOCK_M2 // MASK_BLOCK_N2
+    dq = _bwd_kernel_dq(dq, q, K, V,
+                      do, m, D, alibi_slope,
+                      stride_tok, stride_d,
+                      H, N_CTX,
+                      BLOCK_M2, MASK_BLOCK_N2, BLOCK_DMODEL,
+                      start_m, end_n - num_steps * MASK_BLOCK_N2, num_steps,
+                      MASK=True
+                      )
+    end_n -= num_steps * MASK_BLOCK_N2
+    # stage 2
+    num_steps = end_n // BLOCK_N2
+    dq = _bwd_kernel_dq(dq, q, K, V,
+                      do, m, D, alibi_slope,
+                      stride_tok, stride_d,
+                      H, N_CTX,
+                      BLOCK_M2, BLOCK_N2, BLOCK_DMODEL,
+                      start_m, end_n - num_steps * BLOCK_N2, num_steps,
+                      MASK=False
+                      )
+    # Write back dQ.
+    DQ_block_ptr = tl.make_block_ptr(
+        base=DQ,
+        shape=(N_CTX, BLOCK_DMODEL),
+        strides=(stride_tok, stride_d),
+        offsets=(start_m, 0),
+        block_shape=(BLOCK_M2, BLOCK_DMODEL),
+        order=(1, 0)
+    )
+    dq *= LN2
+    tl.store(DQ_block_ptr, dq.to(q.dtype))
+        
+empty = torch.empty(128, device="cuda")
+
+
+class _attention(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, q, k, v, o, metadata):
+        # NOTE: a large bias tensor leads to overflow during pointer arithmetic
+        if (metadata.bias is not None):
+            assert(metadata.bias.numel() < 2 ** 31)    
+
+        if o is None:
+            o = torch.empty_like(q, dtype=v.dtype)
+        metadata.check_args(q, k, v, o)
+        if metadata.varlen:
+            total_q, nheads_q, head_size = q.shape
+            total_k, nheads_k, _ = k.shape
+            batch = metadata.num_contexts
+            q_strides = (0, q.stride(1), q.stride(0), q.stride(2))
+            k_strides = (0, k.stride(1), k.stride(0), k.stride(2))
+            v_strides = (0, v.stride(1), v.stride(0), v.stride(2))
+            o_strides = (0, o.stride(1), o.stride(0), o.stride(2))
+        else:
+            batch, nheads_q, seqlen_q, head_size = q.shape
+            _, nheads_k, seqlen_k, _ = k.shape
+            q_strides = (q.stride(0), q.stride(1), q.stride(2), q.stride(3))
+            k_strides = (k.stride(0), k.stride(1), k.stride(2), k.stride(3))
+            v_strides = (v.stride(0), v.stride(1), v.stride(2), v.stride(3))
+            o_strides = (o.stride(0), o.stride(1), o.stride(2), o.stride(3))
+
+        # Get closest power of 2 over or equal to 32.
+        padded_d_model = 1 << (head_size - 1).bit_length()
+        padded_d_model = max(padded_d_model, 16)
+
+        grid = lambda META: (
+            triton.cdiv(metadata.max_seqlens_q, META['BLOCK_M']),
+            nheads_q,
+            batch
+        )
+
+        # encoded_softmax is used to validate dropout behavior vs the PyTorch SDPA math backend reference.  We zero this out
+        # to give a consistent starting point and then populate it with the output of softmax with the sign bit set according
+        # to the dropout mask. The resulting return allows this mask to be fed into the reference implementation for testing
+        # only.  This return holds no useful output aside from debugging.
+        if metadata.return_encoded_softmax:
+            encoded_softmax = torch.zeros((q.shape[0], q.shape[1], q.shape[2], k.shape[2]), device=q.device, dtype=torch.float32)
+        else:
+            encoded_softmax = None
+
+        M = torch.empty((batch, nheads_q, metadata.max_seqlens_q), device=q.device, dtype=torch.float32)
+
+        # Seed the RNG so we get reproducible results for testing.
+        philox_seed = 0x1BF52
+        philox_offset = 0x1D4B42
+
+        if metadata.bias is not None:
+            bias_strides = (metadata.bias.stride(0), metadata.bias.stride(1),
+                            metadata.bias.stride(2), metadata.bias.stride(3))
+        else:
+            bias_strides = (0,0,0,0)
+        
+        if metadata.alibi_slopes is not None:
+            alibi_strides = (metadata.alibi_slopes.stride(0), metadata.alibi_slopes.stride(1))
+        else:
+            alibi_strides = (0, 0)
+
+        attn_fwd[grid](
+            q, k, v, metadata.bias, metadata.sm_scale, M, o,
+            *q_strides, *k_strides, *v_strides, *o_strides, *bias_strides, *alibi_strides,
+            metadata.cu_seqlens_q, metadata.cu_seqlens_k,
+            dropout_p=metadata.dropout_p,
+            philox_seed=philox_seed,
+            philox_offset_base=philox_offset,
+            encoded_softmax=encoded_softmax,
+            alibi_slopes = metadata.alibi_slopes,
+            HQ=nheads_q, HK=nheads_k,
+            ACTUAL_BLOCK_DMODEL=head_size,
+            MAX_SEQLENS_Q=metadata.max_seqlens_q,
+            MAX_SEQLENS_K=metadata.max_seqlens_k,
+            IS_CAUSAL=metadata.causal,
+            VARLEN=metadata.varlen,
+            BLOCK_DMODEL=padded_d_model,
+            BIAS_TYPE=0 if metadata.bias is None else 1,
+            USE_ALIBI=False if metadata.alibi_slopes is None else True,
+            ENABLE_DROPOUT=metadata.dropout_p > 0.0,
+            RETURN_ENCODED_SOFTMAX=metadata.return_encoded_softmax,
+            BATCH_SIZE= q.shape[0]
+        )
+
+        ctx.save_for_backward(q, k, v, o, M)
+        ctx.grid = grid
+        ctx.sm_scale = metadata.sm_scale
+        ctx.BLOCK_DMODEL = head_size
+        ctx.causal = metadata.causal
+        ctx.alibi_slopes = metadata.alibi_slopes
+        ctx.dropout_p = metadata.dropout_p
+        ctx.philox_seed = philox_seed
+        ctx.philox_offset = philox_offset
+        ctx.encoded_softmax = encoded_softmax
+        ctx.return_encoded_softmax = metadata.return_encoded_softmax
+        return o, encoded_softmax
+
+    @staticmethod
+    def backward(ctx, do, _):
+        if torch.version.hip is not None:
+            BLOCK = 64
+        else:
+            BLOCK = 128
+        q, k, v, o, M = ctx.saved_tensors
+        assert do.is_contiguous()
+        assert q.stride() == k.stride() == v.stride() == o.stride() == do.stride()
+        seqlen_q = q.shape[2]
+        dq = torch.empty_like(q)
+        dk = torch.empty_like(k)
+        dv = torch.empty_like(v)
+        BATCH, N_HEAD, N_CTX = q.shape[:3]
+        PRE_BLOCK = 128
+        NUM_WARPS, NUM_STAGES = 4, 1
+        BLOCK_M1, BLOCK_N1, BLOCK_M2, BLOCK_N2 = 32, 64, 64, 32
+        BLK_SLICE_FACTOR = 2
+        RCP_LN2 = 1.4426950408889634  # = 1.0 / ln(2)
+        arg_k = k
+        arg_k = arg_k * (ctx.sm_scale * RCP_LN2)
+        assert N_CTX % PRE_BLOCK == 0
+        delta = torch.empty_like(M)
+        Lq, Lk, Lv = q.shape[-1], k.shape[-1], v.shape[-1]
+        padded_head = (Lk != ctx.BLOCK_DMODEL)
+        grid_preprocess = (triton.cdiv(do.shape[2], BLOCK), do.shape[1], do.shape[0])
+        _attn_bwd_preprocess[grid_preprocess](
+            o, do, delta,
+            o.stride(0), o.stride(1), o.stride(2), o.stride(3),
+            do.stride(0), do.stride(1), do.stride(2), do.stride(3),
+            seqlen_q,
+            head_dim=Lk,
+            BLOCK_M=BLOCK, D_HEAD=ctx.BLOCK_DMODEL,
+        )
+        grid = lambda META: (
+            triton.cdiv(N_CTX, META['BLOCK_N1']),
+            1,
+            BATCH * N_HEAD
+        )
+        _attn_bwd[grid](
+            q, arg_k, v, ctx.sm_scale, ctx.alibi_slopes, do, dq, dk, dv,
+            M, delta,
+            q.stride(0), q.stride(1), q.stride(2), q.stride(3),
+            N_HEAD, N_CTX,
+            BLOCK_DMODEL=ctx.BLOCK_DMODEL,
+            BLOCK_M1=BLOCK_M1, BLOCK_N1=BLOCK_N1, BLOCK_M2=BLOCK_M2, BLOCK_N2=BLOCK_N2,
+            BLK_SLICE_FACTOR=BLK_SLICE_FACTOR,
+            USE_ALIBI= False if ctx.alibi_slopes is None else True,
+        )
+
+        return dq, dk, dv, None, None
+
+attention = _attention.apply
+
+def input_helper(Z, HQ, HK, N_CTX_Q, N_CTX_K, D_HEAD, dtype):
+    torch.manual_seed(20)
+
+    # Initialize q, k, v
+    q = torch.randn((Z, HQ, N_CTX_Q, D_HEAD), dtype=dtype, device="cuda", requires_grad=True)
+    k = torch.randn((Z, HK, N_CTX_K, D_HEAD), dtype=dtype, device="cuda", requires_grad=True)
+    v = torch.randn((Z, HK, N_CTX_K, D_HEAD), dtype=dtype, device="cuda", requires_grad=True)
+    sm_scale = D_HEAD**-0.5
+    input_metadata = MetaData(sm_scale=sm_scale)
+    input_metadata.max_seqlens_q = N_CTX_Q
+    input_metadata.max_seqlens_k = N_CTX_K
+    return q, k, v, input_metadata
+
+def varlen_input_helper(Z, HQ, HK, N_CTX_Q, N_CTX_K, D_HEAD, dtype):
+    torch.manual_seed(20)
+
+    # Random sequence lengths. Using N_CTX as kind of max of sum of individual seqs
+    max_seqlens_q = N_CTX_Q // Z
+    max_seqlens_k = N_CTX_K // Z
+    seqlens_q = torch.randint(1, max_seqlens_q + 1, (Z,), dtype=torch.int32)
+    seqlens_k = torch.randint(1, max_seqlens_k + 1, (Z,), dtype=torch.int32)
+    max_seqlens_q = torch.max(seqlens_q).item()
+    max_seqlens_k = torch.max(seqlens_k).item()
+
+    # Calculate cumulative sequence lengths
+    cu_seqlens_q = torch.cat([torch.tensor([0], dtype=torch.int32), seqlens_q.cumsum(dim=0, dtype=torch.int32)])
+    cu_seqlens_k = torch.cat([torch.tensor([0], dtype=torch.int32), seqlens_k.cumsum(dim=0, dtype=torch.int32)])
+    cu_seqlens_q = cu_seqlens_q.to(device="cuda")
+    cu_seqlens_k = cu_seqlens_k.to(device="cuda")
+    # -1 because the last entry of cu_seqlens_q specifies the end of the last seq
+    num_ctxs = len(cu_seqlens_q) - 1
+
+    # Initialize q, k, v with variable lengths
+    total_q = cu_seqlens_q[-1].item()
+    total_k = cu_seqlens_k[-1].item()
+    q = torch.randn((total_q, HQ, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0., std=0.5).requires_grad_()
+    k = torch.randn((total_k, HK, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0., std=0.5).requires_grad_()
+    v = torch.randn((total_k, HK, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0., std=0.5).requires_grad_()
+    sm_scale = D_HEAD**-0.5
+    input_metadata = MetaData(sm_scale=sm_scale)
+    input_metadata.set_varlen_params(cu_seqlens_q, cu_seqlens_k)
+    return q, k, v, input_metadata
+
+def nonvarlen_benchmark_configs():
+    configs=[(16, 16, 16, 1024, 1024),
+            (8, 16, 16, 2048, 2048),
+            (4, 16, 16, 4096, 4096),
+            (2, 16, 16, 8192, 8192),
+            (1, 16, 16, 16384, 16384),
+            (2, 48, 48, 1024, 1024),
+            (2, 48, 48, 2048, 1024),
+            (2, 48, 48, 4096, 8192),
+            (2, 48, 48, 8192, 4096),
+            (2, 48, 48, 16384, 8192),
+            (8, 16, 16, 1989, 15344),
+            (4, 16, 16, 4097, 163),
+            (2, 16, 16, 8122, 2159),
+            (1, 16, 16, 16281, 7),
+            (2, 48, 48, 1021, 1020),
+            (2, 48, 48, 2001, 2048),
+            (2, 48, 48, 3996, 9639),
+            (2, 48, 48, 8181, 1021),
+            ]
+    return configs
+
+def varlen_benchmark_configs():
+    configs=[(2, 16, 4, 1024, 1024),
+            (8, 16, 2, 2048, 2048),
+            (4, 16, 8, 4096, 4096),
+            (2, 16, 4, 8192, 8192),
+            (2, 16, 8, 16384, 16384),
+            (2, 48, 12, 1024, 1024),
+            (2, 48, 24, 2048, 2048),
+            (2, 48, 8, 4096, 4096),
+            (2, 48, 4, 8192, 8192),
+            (2, 48, 2, 16384, 16384),
+            (2, 64, 32, 1024, 1024),
+            (4, 64, 16, 2048, 2048),
+            (4, 64, 8, 4096, 4096),
+            (4, 64, 32, 8192, 8192),
+            (4, 128, 16, 16384, 16384),
+            ]
+    return configs
+
+def run_benchmark(configs: list[tuple[int, int, int, int, int, str]]):
+    def bench_flash_attention(
+        BATCH, NH, N_CTX_Q, N_CTX_K, D_HEAD, dtype
+    ):
+        warmup = 50
+        rep = 100
+
+        dtype_torch = arg_to_torch_dtype[dtype]
+
+        q, k, v, input_metadata = input_helper(BATCH, NH, NH, N_CTX_Q, N_CTX_K, D_HEAD, dtype_torch)
+        o = torch.empty_like(q)
+        fn = lambda: attention(q, k, v, o, input_metadata)
+
+        for _ in range(warmup):
+            fn()
+        
+        cache = torch.empty(int(256e6 // 4), dtype=torch.int, device='cuda')
+        start_event = [torch.cuda.Event(enable_timing=True) for _ in range(rep)]
+        end_event = [torch.cuda.Event(enable_timing=True) for _ in range(rep)]
+        for i in range(rep):
+            # Clear L2 cache
+            cache.zero_()
+            # Benchmark attention
+            start_event[i].record()
+            fn()
+            end_event[i].record()
+        torch.cuda.synchronize()
+        
+        flops_per_matmul = 2.0 * BATCH * NH * N_CTX_Q * N_CTX_K * D_HEAD
+        total_flops = 2 * flops_per_matmul
+        total_bytes = BATCH * NH * 2 * (2 * N_CTX_K * D_HEAD + 2 * N_CTX_Q * D_HEAD + N_CTX_Q * N_CTX_K)
+        
+        times = torch.tensor([s.elapsed_time(e) for s, e in zip(start_event, end_event)], dtype=torch.float)
+        mean_microseconds = times.mean().item() * 1000
+
+        return {
+            "mean_microseconds": mean_microseconds,
+            "arithmetic_intensity": total_flops / total_bytes,
+            "tflops": total_flops / mean_microseconds * 1e-6,
+        }
+
+    results = []
+    for i, (BATCH, NH, N_CTX_Q, N_CTX_K, D_HEAD, dtype) in enumerate(tqdm(configs)):
+        if dtype not in arg_to_torch_dtype:
+            continue
+        result = bench_flash_attention(BATCH, NH, N_CTX_Q, N_CTX_K, D_HEAD, dtype)
+        result.update({
+            "index": i,
+            "BATCH": BATCH,
+            "NH": NH,
+            "SEQ_Q": N_CTX_Q,
+            "SEQ_KV": N_CTX_K,
+            "D_HEAD": D_HEAD,
+            "dtype": dtype,
+        })
+        results.append(result)
+    
+    return results
+
+arg_to_torch_dtype = {
+    'fp16': torch.float16,
+    'bf16': torch.bfloat16,
+    'fp32': torch.float32
+}
+
+def main():
+    input_csv = "results/sharkfa.csv"
+    output_csv = "results/triton_llama_sdxl_attention.csv"
+    
+    shapes = read_shapes_from_csv(input_csv)
+    results = run_benchmark(shapes)
+    write_results_to_csv(results, output_csv)
+
+if __name__ == '__main__':
+    sys.exit(main())
\ No newline at end of file
diff --git a/gemmbench/shark_fa2.py b/attentionbench/shark_fa2.py
similarity index 68%
rename from gemmbench/shark_fa2.py
rename to attentionbench/shark_fa2.py
index efee9eb..310ae57 100644
--- a/gemmbench/shark_fa2.py
+++ b/attentionbench/shark_fa2.py
@@ -4,12 +4,18 @@
 from multiprocessing import Pool, cpu_count
 import itertools
 
-def generate_attention_shapes():
-    batch_sizes = [1, 2, 4, 8, 16]
-    head_counts = [12, 24, 36, 42, 48]
-    head_dims = [32, 64, 128]
-    seq_lengths = [64, 128, 256, 384, 512, 1024, 2048, 4096, 8192, 16384, 32768, 64320]
-    datatypes = ["f16"]
+def generate_attention_shapes(
+    batch_sizes : list[int], 
+    head_counts : list[int], 
+    head_dims : list[int], 
+    seq_lengths : list[int], 
+    datatypes : list[int]):
+
+    # batch_sizes = [1, 2, 4, 8, 16]
+    # head_counts = [12, 24, 32, 36, 40, 42, 48, 64]
+    # head_dims = [32, 64, 128]
+    # seq_lengths = [64, 128, 256, 384, 512, 1024, 2048, 4096, 8192, 16384, 32768, 64320]
+    # datatypes = ["f8", "f16"]
 
     shapes = []
     for B, H, S_Q, S_KV, DH, datatype in itertools.product(batch_sizes, head_counts, seq_lengths, seq_lengths, head_dims, datatypes):
@@ -22,16 +28,16 @@ def generate_mlir_content(B, H, S_Q, S_KV, DH, datatype):
     query_shape = f"[{B},{H},{S_Q},{DH}]"
     value_shape = f"[{B},{H},{S_KV},{DH}]"
     output_shape = f"[{B},{H},{S_Q},{DH}]"
-
+    mlir_dtype = 'f16'
     mlir_template = f"""
 module {{
-    func.func @main_0(%295 : !torch.vtensor<{query_shape},{datatype}>, %298 : !torch.vtensor<{key_shape},{datatype}>, %301 : !torch.vtensor<{value_shape},{datatype}>) -> !torch.vtensor<{output_shape},{datatype}> {{
+    func.func @main_0(%295 : !torch.vtensor<{query_shape},{mlir_dtype}>, %298 : !torch.vtensor<{key_shape},{mlir_dtype}>, %301 : !torch.vtensor<{value_shape},{mlir_dtype}>) -> !torch.vtensor<{output_shape},{mlir_dtype}> {{
         %false_371 = torch.constant.bool false
         %float0.000000e00 = torch.constant.float 0.000000e+00
         %none_372 = torch.constant.none
         %none_373 = torch.constant.none
-        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<{query_shape},{datatype}>, !torch.vtensor<{key_shape},{datatype}>, !torch.vtensor<{value_shape},{datatype}>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<{output_shape},{datatype}>, !torch.vtensor<[{B},{H},{S_Q}], f32>)
-        return %282#0 : !torch.vtensor<{output_shape},{datatype}>
+        %282:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%295, %298, %301, %float0.000000e00, %false_371, %none_372, %none_373) : (!torch.vtensor<{query_shape},{mlir_dtype}>, !torch.vtensor<{key_shape},{mlir_dtype}>, !torch.vtensor<{value_shape},{mlir_dtype}>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<{output_shape},{mlir_dtype}>, !torch.vtensor<[{B},{H},{S_Q}], f32>)
+        return %282#0 : !torch.vtensor<{output_shape},{mlir_dtype}>
     }}
 }} 
 """
@@ -65,7 +71,7 @@ def compile_shape(shape):
             extra_args=[
                 f"--iree-rocm-target-chip={compile_options.rocm_target_chip}",
                 "--iree-global-opt-propagate-transposes=true",
-                "--iree-codegen-transform-dialect-library=attention_and_matmul_spec.mlir",
+                f"--iree-codegen-transform-dialect-library=attention_and_matmul_spec_{datatype}.mlir",
                 "--iree-opt-outer-dim-concat=true",
                 "--iree-opt-const-eval=false",
                 "--iree-opt-data-tiling=false",
@@ -86,17 +92,30 @@ def compile_shape(shape):
     except Exception as e:
         return f"Error compiling {mlir_filename}: {str(e)}"
 
+def llama70battention():
+    return generate_attention_shapes(
+        batch_sizes=[1, 2, 4],
+        head_counts=[32, 40, 64],
+        head_dims=[128],
+        seq_lengths=[1024, 2048, 4096],
+        datatypes=["f8", "f16"],
+    )
+
+SDXL_ATTN = [
+    (2, 10, 4096, 4096, 64, "f16"),
+    (2, 10, 4096, 64, 64, "f16"),
+    (2, 10, 1024, 1024, 64, "f16"),
+    (2, 20, 1024, 64, 64, "f16"),
+    (2, 10, 4096, 4096, 64, "f8"),
+    (2, 10, 4096, 64, 64, "f8"),
+    (2, 10, 1024, 1024, 64, "f8"),
+    (2, 20, 1024, 64, 64, "f8"),
+]
+
 if __name__ == "__main__":
-    shapes = generate_attention_shapes()
-    # shapes = [
-    #     (1, 42, 384, 64320, 64, "f16"),
-    #     (1, 42, 4096, 4096, 64, "f16"),
-    #     (1, 42, 384, 4096, 64, "f16"),
-    #     (1, 42, 8192, 8192, 64, "f16"),
-    #     (1, 42, 384, 8192, 64, "f16"),
-    #     (1, 42, 16384, 16384, 64, "f16"),
-    #     (1, 42, 384, 16384, 64, "f16"),
-    # ]
+    shapes = []
+    shapes.extend(llama70battention())
+    shapes.extend(SDXL_ATTN)
     print(f"Generated {len(shapes)} attention shapes.")
     
     num_cpus = max(1, cpu_count() - 20)
diff --git a/gemmbench/__init__.py b/gemmbench/__init__.py
index a3c4abb..bd8279d 100644
--- a/gemmbench/__init__.py
+++ b/gemmbench/__init__.py
@@ -1,4 +1,5 @@
 import gemmbench.problems
+import gemmbench.bench_utils
 import gemmbench.definitions
 import gemmbench.git
 import gemmbench.server
diff --git a/gemmbench/bench_utils.py b/gemmbench/bench_utils.py
new file mode 100644
index 0000000..65d10b6
--- /dev/null
+++ b/gemmbench/bench_utils.py
@@ -0,0 +1,36 @@
+from enum import Enum
+
+class DeviceDtype(Enum):
+    FP8 = 1
+    FP16 = 2
+    BF16 = 3
+
+def device_sizeof(dtype : DeviceDtype | str) -> int:
+    if isinstance(dtype, DeviceDtype):
+        if dtype == DeviceDtype.FP8:
+            return 1
+        if dtype == DeviceDtype.FP16 or dtype == DeviceDtype.BF16:
+            return 2
+    elif isinstance(dtype, str):
+        if dtype == 'fp8':
+            return 1
+        if dtype == 'fp16' or dtype == 'bf16':
+            return 2
+    return 0
+
+def get_problem_compute(algorithm : str, *args: float) -> tuple[float, float]:
+    flops = 0
+    bytes = 0
+    
+    if algorithm == 'gemm':
+        M, N, K, dtype = args
+        bpe = device_sizeof(dtype)
+        flops = 2 * M * N * K
+        bytes = bpe * M * K + N * K + M * N
+    elif algorithm == 'attention':
+        B, H, S_Q, S_KV, DH, dtype = args
+        bpe = device_sizeof(dtype)
+        flops = 4 * S_Q * S_KV * DH * B * H
+        bytes = bpe * B * H * (2 * S_KV * DH + 2 * S_Q * DH + S_Q * S_KV)
+    
+    return flops, bytes
\ No newline at end of file
diff --git a/gemmbench/benchmark_torch_attention.py b/gemmbench/benchmark_torch_attention.py
deleted file mode 100644
index 41d548b..0000000
--- a/gemmbench/benchmark_torch_attention.py
+++ /dev/null
@@ -1,89 +0,0 @@
-import torch
-import time
-import csv
-from typing import List, Tuple
-from tqdm import tqdm
-
-device = "cuda:0"
-dtype = torch.float16  # Using fp16 as specified
-
-class TestModule(torch.nn.Module):
-    def forward(self, q, k, v):
-        return torch.nn.functional.scaled_dot_product_attention(q, k, v)
-
-def read_shapes_from_csv(filename: str) -> List[Tuple[int, int, int, int, int, int]]:
-    shapes = []
-    with open(filename, 'r') as f:
-        reader = csv.DictReader(f)
-        for row in reader:
-            shapes.append((
-                int(row['batch_size']),
-                int(row['num_heads']),
-                int(row['seqlen_query']),
-                int(row['seqlen_key_value']),
-                int(row['head_dim']),
-                int(row['serial'])
-            ))
-    return shapes
-
-def run_benchmark(shape: Tuple[int, int, int, int, int, int]) -> Tuple[float, float, float]:
-    B, H, S_Q, S_KV, DH, serial = shape
-    
-    q = torch.rand([B, H, S_Q, DH], dtype=dtype, device=device)
-    k = torch.rand([B, H, S_KV, DH], dtype=dtype, device=device)
-    v = torch.rand([B, H, S_KV, DH], dtype=dtype, device=device)
-    
-    test_module = torch.compile(TestModule(), dynamic=True)
-    test_module.to(device=device, dtype=dtype)
-    
-    # Warmup
-    for _ in range(10):
-        test_module(q, k, v)
-    torch.cuda.synchronize()
-    
-    # Benchmark
-    start_time = time.time()
-    eval_steps = 100
-    for _ in range(eval_steps):
-        test_module(q, k, v)
-    torch.cuda.synchronize()
-    
-    mean_microseconds = (time.time() - start_time) * 1e6 / eval_steps
-    
-    flops = 4 * S_Q * S_KV * DH * B * H
-    bytes = B * H * 2 * (2 * S_KV * DH + 2 * S_Q * DH + S_Q * S_KV)
-    arithmetic_intensity = flops / bytes
-    tflops_per_second = (flops / 1e12) / (mean_microseconds / 1e6)
-    
-    return mean_microseconds, arithmetic_intensity, tflops_per_second
-
-def write_results_to_csv(results: List[Tuple], output_filename: str):
-    with open(output_filename, 'w', newline='') as f:
-        writer = csv.writer(f)
-        writer.writerow(['serial', 'batch_size', 'num_heads', 'seqlen_query', 'seqlen_key_value', 'head_dim', 'dtype', 'mean_microseconds', 'arithmetic_intensity', 'tflops'])
-        for result in results:
-            writer.writerow(result)
-
-def main():
-    input_csv = "sharkfa_large_mul_raw_data.csv"
-    output_csv = "torch_benchmark_results.csv"
-    
-    shapes = read_shapes_from_csv(input_csv)
-    results = []
-    
-    for shape in tqdm(shapes):
-        B, H, S_Q, S_KV, DH, serial = shape
-        mean_microseconds, arithmetic_intensity, tflops = run_benchmark(shape)
-        
-        results.append((
-            serial, B, H, S_Q, S_KV, DH, 'f16',
-            round(mean_microseconds, 4),
-            round(arithmetic_intensity, 4),
-            round(tflops, 4)
-        ))
-    
-    write_results_to_csv(results, output_csv)
-    print(f"Results written to {output_csv}")
-
-if __name__ == "__main__":
-    main()
diff --git a/gemmbench/cli.py b/gemmbench/cli.py
index 9a27014..374aa9f 100644
--- a/gemmbench/cli.py
+++ b/gemmbench/cli.py
@@ -8,6 +8,7 @@
 import sys
 
 import gemmbench
+from gemmbench.bench_utils import *
 import pandas
 
 import numpy as np
@@ -38,13 +39,48 @@ def run(top=None, suite=None, output=None, no_shuffle=False, repeat=10, backends
             datetime.datetime.now().strftime("%Y-%m-%d-%H%M")
             + "-"
             + gemmbench.git.short_hash()
-            + ".hdf"
+            + ".csv"
         )
     output = pathlib.Path(output)
 
     print(f"Writing results to: {output}")
 
-    with h5py.File(output, "w") as h5:
+    with open(output, mode='w', newline='') as csvfile:
+        if 'sharkfa' in backends:
+            fieldnames = [
+                "index", 
+                "tag",
+                "name",
+                "BATCH",
+                "NH",
+                "SEQ_Q",
+                "SEQ_KV",
+                "D_HEAD",
+                "dtype",
+                "mean_microseconds",
+                "arithmetic_intensity",
+                "tflops",
+                "ok",
+            ]
+        else:
+            fieldnames = [
+                "index", 
+                "tag",
+                "name",
+                "M",
+                "N",
+                "K",
+                "dtype",
+                "A",
+                "B",
+                "mean_microseconds",
+                "arithmetic_intensity",
+                "tflops",
+                "ok",
+            ]
+        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
+        writer.writeheader()
+
         for i, (problem, solution, configuration, result) in enumerate(
             gemmbench.server.run_problems(
                 problems,
@@ -54,25 +90,30 @@ def run(top=None, suite=None, output=None, no_shuffle=False, repeat=10, backends
                 repeat=repeat,
             )
         ):
-            params = {}
-            params.update(problem)
-            params.update(solution)
-            params.update(configuration)
-
-            experiment_group = h5.create_group(str(i))
-            for k in params.keys():
-                experiment_group.attrs[k] = params[k]
-            for k in result.keys():
-                if not isinstance(result[k], np.ndarray):
-                    experiment_group.attrs[k] = result[k]
-
-            hwmon_group = experiment_group.create_group("hwmon")
-            hwmon_group.create_dataset("system", data=result["sclk"])
-            hwmon_group.create_dataset("memory", data=result["mclk"])
-            hwmon_group.create_dataset("temperature", data=result["temperature"])
-            hwmon_group.create_dataset("power", data=result["power"])
-
-            h5.flush()
+            row = {"index": i}
+            row.update(problem)
+            row.update(solution)
+            row["ok"] = result["ok"]
+            row["mean_microseconds"] = result["mean_microseconds"]
+
+            if 'sharkfa' in backends:
+                row["BATCH"] = ord(str(row['A'])[0])
+                row["NH"] = ord(str(row['B'])[0])
+                row["SEQ_Q"] = row['M']
+                row["SEQ_KV"] = row['N']
+                row["D_HEAD"] = row['K']
+                flops, bytes = get_problem_compute('attention', row["BATCH"], row["NH"], row["SEQ_Q"], row["SEQ_KV"], row["D_HEAD"], row["dtype"])
+            else:
+                flops, bytes = get_problem_compute('gemm', row['M'], row['N'], row['K'], row['dtype'])
+            
+            if row['ok']:
+                row['arithmetic_intensity'] = flops / bytes
+                row['tflops'] = (flops / 1e12) / (row['mean_microseconds'] / 1e6)
+            else:
+                row['arithmetic_intensity'] = 0
+                row['tflops'] = 0
+
+            writer.writerow({k: row[k] for k in row.keys() if k in fieldnames})
 
 def roofline(results=None, **kwargs):
     """Generate a roofline plot of GEMM performance from multiple result files and save raw data as CSV."""
@@ -84,60 +125,19 @@ def roofline(results=None, **kwargs):
     
     plt.figure(figsize=(12, 8))
 
-    
     for idx, result_file in enumerate(files):
         data = []
-        if result_file.split('.')[-1] == 'hdf':
-            with h5py.File(result_file.strip(), "r") as h5:
-                for serial in h5.keys():
-                    experiment_group = h5[serial]
-                    data.append(dict(serial=int(serial), **experiment_group.attrs))
-        elif result_file.split('.')[-1] == 'csv':
-            df = pandas.read_csv(result_file)
-            data = df.to_dict(orient='records')
-
-        # data = [item for item in data if item["ok"]]
-
-        print(data[0])
-
-        for item in data:
-            flops = 0
-            bytes = 1
-
-            if 'sharkfa' in result_file or 'torch' in result_file:
-                B, H, S_Q, S_KV, DH = item['A'], item['B'], item['M'], item['N'], item['K']
-                if result_file.split('.')[-1] == 'hdf':
-                    item['A'], item['B'] = B, H = ord(item['A'][0]), ord(item['B'][0])
-                flops = 4 * S_Q * S_KV * DH * B * H
-                bytes = B * H * 2 * (2 * S_KV * DH + 2 * S_Q * DH + S_Q * S_KV)
-            else:
-                M, N, K = item['M'], item['N'], item['K']
-                flops = 2 * M * N * K
-                bytes = M * K + N * K + M * N
-            
-            if item['ok']:
-                item['arithmetic_intensity'] = flops / bytes
-                item['tflops'] = (flops / 1e12) / (item['mean_microseconds'] / 1e6)
-            else:
-                item['arithmetic_intensity'] = 0
-                item['tflops'] = 0
+        with open(result_file.strip(), mode='r') as csvfile:
+            reader = csv.DictReader(csvfile)
+            for row in reader:
+                row = {k: float(v) if k not in ['ok', 'tag', 'name', 'A', 'B', 'dtype'] else v for k, v in row.items()}
+                row['ok'] = True if 'ok' not in row else row['ok'] == 'True'
+                data.append(row)
         
-        # data = [item for item in data if item['dtype'] == 'fp16']
         x = [item['arithmetic_intensity'] for item in data]
         y = [item['tflops'] for item in data]
         
         plt.scatter(x, y, alpha=0.6, color=next(colors), label=result_file.strip())
-
-        # Save raw data as CSV
-        csv_filename = f"{os.path.splitext(result_file.strip())[0]}_raw_data.csv"
-        with open(csv_filename, 'w', newline='') as csvfile:
-            fieldnames = ['serial', 'A', 'B', 'M', 'N', 'K', 'dtype', 'mean_microseconds', 'arithmetic_intensity', 'tflops']
-            writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
-            writer.writeheader()
-            for item in data:
-                if item['ok']:
-                    writer.writerow({field: item[field] for field in fieldnames})
-        print(f"Raw data saved as '{csv_filename}'")
     
     plt.xscale('log')
     plt.yscale('log')
@@ -167,52 +167,6 @@ def roofline(results=None, **kwargs):
     
     print("Roofline plot saved as 'roofline_plot.png'")
 
-
-def summary(results=None, **kwargs):
-    """Print a summary of the results in an HDF file."""
-
-    sorting = [
-        "tag",
-        "dtype",
-        "M",
-        "N",
-        "K",
-        "A",
-        "B",
-        "fclk",
-        "mclk",
-        "mean_microseconds",
-    ]
-    columns = [
-        "{tag:24s}",
-        "{ok:1}",
-        "{device:1d}",
-        "{dtype:4s}",
-        "{M:7d}",
-        "{N:7d}",
-        "{K:7d}",
-        "{AB:2s}",
-        "{fclk:1d}/{mclk:1d}",
-        "{mean_microseconds:10.2f}us",
-        "{serial:6d}",
-        "{min_sclk:6.1f}/{mean_sclk:6.1f}/{max_sclk:6.1f}",
-        "{min_mclk:6.1f}/{mean_mclk:6.1f}/{max_mclk:6.1f}",
-        "{min_fclk:6.1f}/{mean_fclk:6.1f}/{max_fclk:6.1f}",
-    ]
-    columns = " ".join(columns)
-
-    summary = []
-    with h5py.File(results, "r") as h5:
-        for serial in h5.keys():
-            experiment_group = h5[serial]
-            summary.append(dict(serial=int(serial), **experiment_group.attrs))
-
-    summary.sort(key=itemgetter(*sorting))
-    for result in summary:
-        result["AB"] = result["A"] + result["B"]
-        result["ok"] = {True: " ", False: "X"}[result["ok"]]
-        print(columns.format(**result))
-
 def compare(results=None, **kwargs):
     """Compare performance based on GEMM problem size (M * N * K) across different result files."""
 
@@ -228,14 +182,15 @@ def compare(results=None, **kwargs):
     
     # Extract data from each result file
     for file in result_files:
-        with h5py.File(file, 'r') as h5:
-            backend_data = []
-            for serial in h5.keys():
-                experiment_group = h5[serial]
-                attrs = dict(serial=int(serial), **experiment_group.attrs)
-                attrs["AB"] = attrs["A"] + attrs["B"]
-                backend_data.append(attrs)
-                datatypes.add(attrs["dtype"])
+        backend_data = []
+        with open(file.strip(), mode='r') as csvfile:
+            reader = csv.DictReader(csvfile)
+            for row in reader:
+                row = {k: float(v) if k not in ['ok', 'tag', 'name', 'A', 'B', 'dtype'] else v for k, v in row.items()}
+                row['ok'] = row['ok'] == 'True'
+                row['AB'] = row['A'] + row['B']
+                backend_data.append(row)
+                datatypes.add(row["dtype"])
             data.append(backend_data)
     
     # Sort data for consistent plotting
@@ -255,19 +210,7 @@ def compare(results=None, **kwargs):
             dtype_data = [(mnk, mean) for mnk, mean, dt, algorithm in zip(M_N_K, mean_milliseconds, dtypes, algorithms) if dt == dtype]
             if dtype_data:
                 x, y = zip(*dtype_data)
-                plt.scatter(x, y, c=color, marker=symbol_map[dtype], label=f'{file} - {dtype}', alpha=0.6, edgecolors='w', linewidth=0.5)
-        # if 'iree' in file:
-        #     for algorithm in ["NN", "NT", "TN"]:
-        #         dtype_data = [(mnk, mean) for mnk, mean, dt, algo in zip(M_N_K, mean_milliseconds, dtypes, algorithms) if algo == algorithm]
-        #         if dtype_data:
-        #             x, y = zip(*dtype_data)
-        #             plt.scatter(x, y, c=next(colors), marker=symbol_map["fp16"], label=f'{file} - {algorithm}', alpha=0.6, edgecolors='w', linewidth=0.5)
-        # else:
-        #     dtype_data = [(mnk, mean) for mnk, mean, dt, algo in zip(M_N_K, mean_milliseconds, dtypes, algorithms)]
-        #     if dtype_data:
-        #         x, y = zip(*dtype_data)
-        #         plt.scatter(x, y, c=next(colors), marker=symbol_map["bf16"], label=f'{file}', alpha=0.6, edgecolors='w', linewidth=0.5)
-
+                plt.scatter(x, y, c=color, marker=symbol_map[dtype], label=f'{file.strip()} - {dtype}', alpha=0.6, edgecolors='w', linewidth=0.5)
     
     plt.xlabel('GEMM Problem Size (M * N * K)')
     plt.ylabel('Mean Milliseconds')
@@ -277,6 +220,9 @@ def compare(results=None, **kwargs):
     plt.xscale('log')
     plt.yscale('log')
     plt.savefig('comparison.png')
+    plt.close()
+    
+    print("Comparison plot saved as 'comparison.png'")
 
 
 def ls(top=None, suite=None, format=None, output=None, **kwargs):
@@ -323,10 +269,7 @@ def main(top: pathlib.Path):
     ls_parser.add_argument("--output")
 
     specs_parser = subparsers.add_parser("specs", help=specs.__doc__)
-
-    summary_parser = subparsers.add_parser("summary", help=summary.__doc__)
-    summary_parser.add_argument("--results")
-
+    
     summary_parser = subparsers.add_parser("compare", help=compare.__doc__)
     summary_parser.add_argument("--results")
 
diff --git a/gemmbench/csv_to_hdf.py b/gemmbench/csv_to_hdf.py
new file mode 100644
index 0000000..2dc9c9b
--- /dev/null
+++ b/gemmbench/csv_to_hdf.py
@@ -0,0 +1,42 @@
+import pandas as pd
+import h5py
+import numpy as np
+import sys
+
+def csv_to_hdf5(csv_file: str, hdf5_file: str):
+    """
+    Convert a CSV file to an HDF5 file.
+    
+    :param csv_file: Path to the input CSV file
+    :param hdf5_file: Path to the output HDF5 file
+    """
+    # Read the CSV file
+    df = pd.read_csv(csv_file)
+    
+    # Create an HDF5 file
+    with h5py.File(hdf5_file, 'w') as hf:
+        # Create a group for the dataset
+        group = hf.create_group('data')
+        
+        # Store each column as a dataset
+        for column in df.columns:
+            # Convert to appropriate numpy dtype
+            if df[column].dtype == 'object':
+                # For string columns, use variable-length string dtype
+                dt = h5py.special_dtype(vlen=str)
+                data = df[column].astype(str).to_numpy()
+            else:
+                # For numeric columns, use the corresponding numpy dtype
+                dt = df[column].dtype
+                data = df[column].to_numpy()
+            
+            # Create dataset
+            group.create_dataset(column, data=data, dtype=dt)
+        
+        # Store column names as an attribute
+        group.attrs['columns'] = df.columns.tolist()
+
+    print(f"CSV file '{csv_file}' has been converted to HDF5 file '{hdf5_file}'")
+
+if __name__ == '__main__':
+    csv_to_hdf5(sys.argv[1], sys.argv[2])
diff --git a/gemmbench/problems.py b/gemmbench/problems.py
index 93a862d..94c686a 100644
--- a/gemmbench/problems.py
+++ b/gemmbench/problems.py
@@ -636,6 +636,13 @@ def is_compute_bound(M, N, K, bpe):
     (8192, 5120, 640),
 ]
 
+SDXL_ATTN = [
+    (2, 10, 4096, 4096, 64, "fp16"),
+    (2, 10, 4096, 64, 64, "fp16"),
+    (2, 10, 1024, 1024, 64, "fp16"),
+    (2, 20, 1024, 64, 64, "fp16"),
+]
+
 def llama13bmatvec():
     """LLAMA 13b, single batch, FP16."""
     for m, n, k, model, gcount in LLAMA:
@@ -816,32 +823,60 @@ def unet():
                 for m, n, k in UNET:
                     yield GEMM("unet", m, n, k, tA, tB, dtype)
 
+def surya():
+    yield GEMM("surya", 50, 50, 50, "N", "T", "bf16")
+
+def generate_attention_shapes(
+    name : str,
+    batch_sizes : list[int], 
+    head_counts : list[int], 
+    head_dims : list[int], 
+    seq_lengths : list[int], 
+    datatypes : list[int]):
+
+    for B, H, S_Q, S_KV, DH, datatype in itertools.product(batch_sizes, head_counts, seq_lengths, seq_lengths, head_dims, datatypes):
+        bytes = B * H * 2 * (2 * S_KV * DH + 2 * S_Q * DH + S_Q * S_KV)
+        if bytes < 1e9:
+            yield GEMM(name, S_Q, S_KV, DH, chr(B), chr(H), datatype)
+
+def llama70battention():
+    yield from generate_attention_shapes(
+        "llama70battention",
+        batch_sizes=[1, 2, 4],
+        head_counts=[32, 40, 64],
+        head_dims=[128],
+        seq_lengths=[1024, 2048, 4096],
+        datatypes=["fp8", "fp16"],
+    )
+
+def sdxlattention():
+    for B, H, S_Q, S_KV, DH, _ in SDXL_ATTN:
+        bytes = B * H * 2 * (2 * S_KV * DH + 2 * S_Q * DH + S_Q * S_KV)
+        if bytes < 1e9:
+            for datatype in ["fp8", "fp16"]:
+                yield GEMM("sdxlattention", S_Q, S_KV, DH, chr(B), chr(H), datatype)
+
 def flash_attention():
-    batch_sizes = [1, 2, 4, 8, 16]
+    batch_sizes = [1, 2, 4, 8, 16, 32]
     head_counts = [12, 24, 36, 42, 48]
     head_dims = [32, 64, 128]
     seq_lengths = [64, 128, 256, 384, 512, 1024, 2048, 4096, 8192, 16384, 32768, 64320]
-    datatypes = ["f16"]
+    datatypes = ["fp16"]
 
     # shapes = []
     shapes = [
-        (1, 42, 384, 64320, 64, "f16"),
-        (1, 42, 4096, 4096, 64, "f16"),
-        (1, 42, 384, 4096, 64, "f16"),
-        (1, 42, 8192, 8192, 64, "f16"),
-        (1, 42, 384, 8192, 64, "f16"),
-        (1, 42, 16384, 16384, 64, "f16"),
-        (1, 42, 384, 16384, 64, "f16"),
+        (1, 42, 384, 64320, 64, "fp16"),
+        (1, 42, 4096, 4096, 64, "fp16"),
+        (1, 42, 384, 4096, 64, "fp16"),
+        (1, 42, 8192, 8192, 64, "fp16"),
+        (1, 42, 384, 8192, 64, "fp16"),
+        (1, 42, 16384, 16384, 64, "fp16"),
+        (1, 42, 384, 16384, 64, "fp16"),
     ]
-    for B, H, S_Q, S_KV, DH, datatype in itertools.product(batch_sizes, head_counts, seq_lengths, seq_lengths, head_dims, datatypes):
-        bytes = B * H * 2 * (2 * S_KV * DH + 2 * S_Q * DH + S_Q * S_KV)
-        if bytes < 1e9:
-            shapes.append((B, H, S_Q, S_KV, DH, datatype))
-
     
-    for B, H, S_Q, S_KV, DH, datatype in shapes:
-        yield GEMM("flash_attention", S_Q, S_KV, DH, chr(B), chr(H), datatype)
-        
+    # yield from generate_attention_shapes("generalattention", batch_sizes, head_counts, head_dims, seq_lengths, datatypes)
+    yield from llama70battention()
+    yield from sdxlattention()
 
 
 def all():
diff --git a/results/torch_llama_sdxl_attention.csv b/results/torch_llama_sdxl_attention.csv
new file mode 100644
index 0000000..29802d5
--- /dev/null
+++ b/results/torch_llama_sdxl_attention.csv
@@ -0,0 +1,49 @@
+index,BATCH,NH,SEQ_Q,SEQ_KV,D_HEAD,dtype,mean_microseconds,arithmetic_intensity,tflops
+1,1,32,1024,1024,128,fp16,93.7152,170.6667,183.32
+4,1,32,1024,2048,128,fp16,160.8467,186.1818,213.6179
+7,1,32,1024,4096,128,fp16,286.1047,195.0476,240.19
+9,1,32,2048,1024,128,fp16,158.4148,186.1818,216.8972
+12,1,32,2048,2048,128,fp16,282.1517,204.8,243.5551
+16,1,32,2048,4096,128,fp16,526.3758,215.5789,261.1043
+18,1,32,4096,1024,128,fp16,283.7729,195.0476,242.1636
+22,1,32,4096,2048,128,fp16,526.4759,215.5789,261.0546
+25,1,40,1024,1024,128,fp16,148.4346,170.6667,144.6754
+27,1,40,1024,2048,128,fp16,273.6926,186.1818,156.9267
+30,1,40,1024,4096,128,fp16,529.5491,195.0476,162.2122
+33,1,40,2048,1024,128,fp16,210.9456,186.1818,203.6054
+35,1,40,2048,2048,128,fp16,387.4922,204.8,221.6802
+38,1,40,2048,4096,128,fp16,736.8255,215.5789,233.1606
+42,1,40,4096,1024,128,fp16,351.2883,195.0476,244.5266
+45,1,40,4096,2048,128,fp16,656.1613,215.5789,261.8239
+48,1,64,1024,1024,128,fp16,165.4267,170.6667,207.7037
+51,1,64,1024,2048,128,fp16,292.7852,186.1818,234.7096
+54,1,64,1024,4096,128,fp16,549.8743,195.0476,249.9461
+57,1,64,2048,1024,128,fp16,292.0222,186.1818,235.3228
+60,1,64,2048,2048,128,fp16,542.0876,204.8,253.5364
+63,1,64,4096,1024,128,fp16,514.7648,195.0476,266.9937
+64,2,32,1024,1024,128,fp16,170.0473,170.6667,202.0599
+65,2,32,1024,2048,128,fp16,289.4998,186.1818,237.3732
+66,2,32,1024,4096,128,fp16,549.4213,195.0476,250.1522
+67,2,32,2048,1024,128,fp16,294.2371,186.1818,233.5513
+68,2,32,2048,2048,128,fp16,540.8454,204.8,254.1187
+70,2,32,4096,1024,128,fp16,516.7532,195.0476,265.9663
+71,2,40,1024,1024,128,fp16,218.4176,170.6667,196.6401
+72,2,40,1024,2048,128,fp16,397.4247,186.1818,216.1399
+73,2,40,1024,4096,128,fp16,760.5314,195.0476,225.893
+74,2,40,2048,1024,128,fp16,362.1292,186.1818,237.2063
+76,2,40,2048,2048,128,fp16,672.729,204.8,255.3758
+77,2,40,4096,1024,128,fp16,651.5932,195.0476,263.6594
+80,2,64,1024,1024,128,fp16,303.843,170.6667,226.1677
+83,2,64,1024,2048,128,fp16,561.7857,186.1818,244.6466
+84,2,64,2048,1024,128,fp16,529.6421,186.1818,259.494
+85,4,32,1024,1024,128,fp16,307.4455,170.6667,223.5176
+86,4,32,1024,2048,128,fp16,563.8218,186.1818,243.7631
+87,4,32,2048,1024,128,fp16,534.277,186.1818,257.2429
+88,4,40,1024,1024,128,fp16,375.4926,170.6667,228.7644
+89,4,40,1024,2048,128,fp16,701.1199,186.1818,245.0347
+90,4,40,2048,1024,128,fp16,669.6558,186.1818,256.5478
+91,4,64,1024,1024,128,fp16,558.8508,170.6667,245.9314
+92,2,10,4096,4096,64,fp16,465.9629,120.4706,184.348
+93,2,10,4096,64,64,fp16,42.0022,42.2268,31.9549
+94,2,10,1024,1024,64,fp16,60.6203,102.4,88.5629
+95,2,20,1024,64,64,fp16,37.0193,40.96,18.1281
diff --git a/results/triton_llama_sdxl_attention.csv b/results/triton_llama_sdxl_attention.csv
new file mode 100644
index 0000000..75bc523
--- /dev/null
+++ b/results/triton_llama_sdxl_attention.csv
@@ -0,0 +1,49 @@
+index,BATCH,NH,SEQ_Q,SEQ_KV,D_HEAD,dtype,mean_microseconds,arithmetic_intensity,tflops
+1,1,32,1024,1024,128,fp16,79.27373051643372,170.66666666666666,216.7157906166476
+4,1,32,1024,2048,128,fp16,141.49005711078644,186.1818181818182,242.84207010458968
+7,1,32,1024,4096,128,fp16,267.37093925476074,195.04761904761904,257.0192442287888
+9,1,32,2048,1024,128,fp16,122.34213203191757,186.1818181818182,280.84959610672763
+12,1,32,2048,2048,128,fp16,203.00737023353577,204.8,338.5072997938273
+16,1,32,2048,4096,128,fp16,387.8546953201294,215.57894736842104,354.356812307145
+18,1,32,4096,1024,128,fp16,239.3108606338501,195.04761904761904,287.155695959583
+22,1,32,4096,2048,128,fp16,407.92450308799744,215.57894736842104,336.92252471127404
+25,1,40,1024,1024,128,fp16,115.12292176485062,170.66666666666666,186.5383205254673
+27,1,40,1024,2048,128,fp16,200.27276873588562,186.1818181818182,214.45588050285997
+30,1,40,1024,4096,128,fp16,382.644921541214,195.04761904761904,224.4883992554124
+33,1,40,2048,1024,128,fp16,201.7291635274887,186.1818181818182,212.90760447805772
+35,1,40,2048,2048,128,fp16,350.2872586250305,204.8,245.225436566484
+38,1,40,2048,4096,128,fp16,676.7682433128357,215.57894736842104,253.85158588268177
+42,1,40,4096,1024,128,fp16,308.5607886314392,195.04761904761904,278.38710907173163
+45,1,40,4096,2048,128,fp16,548.4535098075867,215.57894736842104,313.24203194591996
+48,1,64,1024,1024,128,fp16,124.22925978899002,170.66666666666666,276.58329789907657
+51,1,64,1024,2048,128,fp16,228.56125235557556,186.1818181818182,300.6610964359447
+54,1,64,1024,4096,128,fp16,461.28371357917786,195.04761904761904,297.94885322438125
+57,1,64,2048,1024,128,fp16,242.20401048660278,186.1818181818182,283.725593964932
+60,1,64,2048,2048,128,fp16,423.3395457267761,204.8,324.65418092715413
+63,1,64,4096,1024,128,fp16,401.9632637500763,195.04761904761904,341.91918980301074
+64,2,32,1024,1024,128,fp16,125.2487301826477,170.66666666666666,274.3320296971784
+65,2,32,1024,2048,128,fp16,217.68762171268463,186.1818181818182,315.6793031929924
+66,2,32,1024,4096,128,fp16,437.68471479415894,195.04761904761904,314.01360117553315
+67,2,32,2048,1024,128,fp16,237.77064681053162,186.1818181818182,289.015812749836
+68,2,32,2048,2048,128,fp16,435.19556522369385,204.8,315.809636987811
+70,2,32,4096,1024,128,fp16,403.25263142585754,195.04761904761904,340.8259308464542
+71,2,40,1024,1024,128,fp16,206.66056871414185,170.66666666666666,207.8271303869732
+72,2,40,1024,2048,128,fp16,382.23332166671753,186.1818181818182,224.73013484391768
+73,2,40,1024,4096,128,fp16,714.5038843154907,195.04761904761904,240.44472760926504
+74,2,40,2048,1024,128,fp16,315.9957230091095,186.1818181818182,271.8370524196104
+76,2,40,2048,2048,128,fp16,573.7792253494263,204.8,299.41601969882436
+77,2,40,4096,1024,128,fp16,506.406307220459,195.04761904761904,339.25069532202554
+80,2,64,1024,1024,128,fp16,260.7502341270447,170.66666666666666,263.54521585019165
+83,2,64,1024,2048,128,fp16,479.6232283115387,186.1818181818182,286.55608268982064
+84,2,64,2048,1024,128,fp16,414.5455062389374,186.1818181818182,331.5412937868934
+85,4,32,1024,1024,128,fp16,260.3498697280884,170.66666666666666,263.95049403240034
+86,4,32,1024,2048,128,fp16,452.8902471065521,186.1818181818182,303.47077321730126
+87,4,32,2048,1024,128,fp16,415.0417447090149,186.1818181818182,331.14489138522254
+88,4,40,1024,1024,128,fp16,344.4783687591553,170.66666666666666,249.36063831647203
+89,4,40,1024,2048,128,fp16,571.794331073761,186.1818181818182,300.4553954170597
+90,4,40,2048,1024,128,fp16,520.0849175453186,186.1818181818182,330.3281561227547
+91,4,64,1024,1024,128,fp16,462.24674582481384,170.66666666666666,297.3281147209802
+92,2,10,4096,4096,64,fp16,452.58697867393494,120.47058823529412,189.79632638058274
+93,2,10,4096,64,64,fp16,54.765649139881134,42.22680412371134,24.507648518359424
+94,2,10,1024,1024,64,fp16,56.25920742750168,102.4,95.42809729266764
+95,2,20,1024,64,64,fp16,54.88397553563118,40.96,12.227405785579856
diff --git a/src/benchmark/driver.cpp b/src/benchmark/driver.cpp
deleted file mode 100644
index a12989a..0000000
--- a/src/benchmark/driver.cpp
+++ /dev/null
@@ -1,151 +0,0 @@
-#include <sys/stat.h>
-
-#include <csignal>
-#include <fstream>
-#include <iostream>
-#include <vector>
-
-#include "IREEGemm/Benchmark.hpp"
-
-#define RETURN_ON_ERROR(func_call, error_msg)       \
-  {                                                 \
-    int status = (func_call);                       \
-    if (status != 0) {                              \
-      fprintf(stderr, " - Error: %s\n", error_msg); \
-      return (double)-1;                            \
-    }                                               \
-  }
-
-inline bool fileExists(const std::string& name) {
-  struct stat buffer;
-  return (stat(name.c_str(), &buffer) == 0);
-}
-
-std::string IREEGemmBenchmarkPipeline::generateGemmString(const Problem& p) {
-  std::string result = "gemm_" + std::to_string(p.M) + "_" +
-                       std::to_string(p.K) + "_" + std::to_string(p.N) + "_" +
-                       p.dtype;
-
-  if (p.transposeA) result += "_tA";
-  if (p.transposeB) result += "_tB";
-
-  return result;
-}
-
-IREEGemmBenchmarkPipeline::IREEGemmBenchmarkPipeline() {
-  currOp = 0;
-  totalOps = 0;
-
-  char** compileArgs = new char*[2];
-  compileArgs[0] = (char*)"--iree-hal-target-backends=rocm";
-  compileArgs[1] = (char*)"--iree-rocm-target-chip=gfx942";
-  compile_state = ireeGemmCompilerInitialize(2, compileArgs);
-}
-
-IREEGemmBenchmarkPipeline::~IREEGemmBenchmarkPipeline() {
-  ireeGemmCompilerShutdown(compile_state);
-}
-
-double IREEGemmBenchmarkPipeline::benchmarkProblem(const Problem& p) {
-  bool showProgress = totalOps > 0 && ++currOp <= totalOps;
-
-  std::string gemmName = generateGemmString(p);
-  std::string mlirPath = "kernels/mlir/" + gemmName + ".mlir";
-  std::string vmfbPath = "kernels/vmfb/" + gemmName + ".vmfb";
-
-  runtime_state = NULL;
-  ireeGemmRuntimeInitialize("rocm://4", /*rotate_buffer*/ true, &runtime_state);
-
-  if (!fileExists(mlirPath)) {
-    if (showProgress)
-      print_progress(currOp, totalOps,
-                     ("Generating " + gemmName + ".mlir").c_str());
-    RETURN_ON_ERROR(ireeGemmMLIRGenerate(p.M, p.K, p.N, p.transposeA,
-                                         p.transposeB, p.dtype, mlirPath),
-                    "Failed to generate MLIR");
-  }
-
-  if (!fileExists(vmfbPath)) {
-    if (showProgress)
-      print_progress(currOp, totalOps,
-                     ("Compiling to " + gemmName + ".vmfb").c_str());
-    RETURN_ON_ERROR(ireeGemmCompilerCompile(compile_state, mlirPath.c_str(),
-                                            vmfbPath.c_str()),
-                    "Failed to compile");
-    ireeGemmCompilerCleanup(compile_state);
-  }
-
-  int numIterations = 50;
-
-  // const float* A = new float[(size_t)1e8];
-  // const float* B = new float[(size_t)1e8];
-
-  if (showProgress)
-    print_progress(currOp, totalOps, ("Setting up " + gemmName).c_str());
-  double executionTime = 0.0;
-  ireeGemmRuntimeSetupProblem(runtime_state, vmfbPath.c_str(), p.M, p.K, p.N,
-                              p.transposeA, p.transposeB, p.dtype.c_str(),
-                              nullptr, nullptr, 1e9);
-  ireeGemmRuntimeExecute(runtime_state, numIterations);
-
-  ireeGemmRuntimeCleanup(runtime_state);
-
-  // delete[] A;
-  // delete[] B;
-
-  return executionTime;
-}
-
-int main(void) {
-  using Problem = IREEGemmBenchmarkPipeline::Problem;
-
-  IREEGemmBenchmarkPipeline gemmBench;
-  std::vector<Problem> problems;
-
-  std::cout << "Generating problems" << std::endl;
-  for (int m = 1024; m <= 4096; m *= 2) {
-    for (int k = 1024; k <= 4096; k *= 2) {
-      for (int n = 1024; n <= 4096; n *= 2) {
-        for (bool transposeA : {true, false}) {
-          for (bool transposeB : {true, false}) {
-            if (!(transposeA && transposeB)) {
-              for (const std::string& dtype : {"fp16", "fp32", "bf16"}) {
-                problems.push_back(
-                    Problem{m, k, n, transposeA, transposeB, dtype});
-              }
-            }
-          }
-        }
-      }
-    }
-  }
-
-  std::cout << "Successfully generated " << problems.size() << " GEMM problems"
-            << std::endl
-            << "Running Benchmarks" << std::endl;
-
-  std::ofstream outFile("benchmark_results.csv");
-  if (!outFile.is_open()) {
-    std::cerr << "Failed to open the file." << std::endl;
-    return 1;
-  }
-
-  outFile << "M,K,N,Transpose A,Transpose B,Data Type,Execution Time (ms)\n";
-
-  gemmBench.initializeProgressBar(problems.size());
-
-  for (const Problem& p : problems) {
-    outFile << p.M << "," << p.K << "," << p.N << "," << p.transposeA << ","
-            << p.transposeB << "," << p.dtype << ",";
-    double executionTime = gemmBench.benchmarkProblem(p) * 1000;
-    if (executionTime < 0)
-      outFile << "ERROR\n";
-    else
-      outFile << executionTime << "\n";
-  }
-
-  outFile.close();
-  std::cout << "Benchmark results saved to benchmark_results.csv" << std::endl;
-
-  return 0;
-}
\ No newline at end of file
diff --git a/src/benchmark/gemm-bench.cpp b/src/benchmark/gemm-bench.cpp
index d348269..4868111 100644
--- a/src/benchmark/gemm-bench.cpp
+++ b/src/benchmark/gemm-bench.cpp
@@ -85,20 +85,20 @@ namespace GEMMBench
     /**
      * Connect to ZMQ server and dispatch GEMM runs via `run_problem`.
      */
-    int run(int device)
+    int run(int device, std::string backend)
     {
-        std::cout << "Initializing tensors with trig..." << std::endl;
-        GEMMNullInitializer initializer;
+        GEMMTrigInitializer initializer;
         GEMMData            data("fp32", 1e9, &initializer);
 
-        std::cout << "Running on " << benches.size() << " benches" << std::endl;
-
         for(const auto& [name, pipeline] : benches)
         {
-            std::cout << "Initializing " << name << std::endl;
-            pipeline->initialize();
-            pipeline->setDevice(device);
-            pipeline->linkData(&data);
+            if(name == backend || backend == "all")
+            {
+                std::cout << "Initializing " << name << std::endl;
+                pipeline->initialize();
+                pipeline->setDevice(device);
+                pipeline->linkData(&data);
+            }
         }
 
         Frequency::getFrequencyMonitor()->setDevice(device);
diff --git a/src/benchmark/gemm-bench.hpp b/src/benchmark/gemm-bench.hpp
index f5b6d8d..ab7f883 100644
--- a/src/benchmark/gemm-bench.hpp
+++ b/src/benchmark/gemm-bench.hpp
@@ -126,6 +126,7 @@ namespace GEMMBench
     private:
         IREEGemmRuntimeState*  runtime_state;
         IREEGemmDeviceStorage* storage_fp16;
+        IREEGemmDeviceStorage* storage_fp32;
         int                    device_id = 0;
 
     public:
@@ -184,5 +185,5 @@ namespace GEMMBench
     };
 
     int testDPM(int device);
-    int run(int device);
+    int run(int device, std::string backend);
 }
diff --git a/src/benchmark/main.cpp b/src/benchmark/main.cpp
index d5e0d73..c50ce60 100644
--- a/src/benchmark/main.cpp
+++ b/src/benchmark/main.cpp
@@ -26,6 +26,15 @@ std::optional<T> option(std::string const& prefix, int argc, char* argv[])
         }
         return false;
     }
+    else if constexpr(std::is_same_v<T, std::string>)
+    {
+        for(int i = 1; i < argc; ++i)
+        {
+            std::string arg(argv[i]);
+            if(!arg.compare(0, prefix.size(), prefix))
+                return arg.substr(prefix.size());
+        }
+    }
     else
     {
         std::cout << "Option type not implemented yet." << std::endl;
@@ -38,16 +47,18 @@ int main(int argc, char* argv[])
     // Parse arguments
     auto help    = option<bool>("--help", argc, argv).value_or(false);
     auto device  = option<int>("--device=", argc, argv).value_or(0);
+    auto backend = option<std::string>("--backend=", argc, argv).value_or("all");
     auto testDPM = option<bool>("--test-dpm", argc, argv).value_or(false);
 
     if(help)
     {
-        std::cout << "Usage: gemm-bench [--help] [--test-dpm] [--device=<int>]" << std::endl;
+        std::cout << "Usage: gemm-bench [--help] [--test-dpm] [--device=<int>] [--backend=<string>]"
+                  << std::endl;
         return 1;
     }
 
     if(testDPM)
         return GEMMBench::testDPM(device);
 
-    return GEMMBench::run(device);
+    return GEMMBench::run(device, backend);
 }
diff --git a/src/benchmark/run-hipblaslt.cpp b/src/benchmark/run-hipblaslt.cpp
index 67a74f0..f109df3 100644
--- a/src/benchmark/run-hipblaslt.cpp
+++ b/src/benchmark/run-hipblaslt.cpp
@@ -56,7 +56,6 @@ void HipBLASLtGEMMBench::initialize()
 
 void HipBLASLtGEMMBench::destroy()
 {
-    std::cout << "Destroying hipblaslt" << std::endl;
     if(handle)
     {
         CHECK_HIPBLASLT_ERROR(hipblasLtDestroy(handle));
diff --git a/src/benchmark/run_flashattention.cpp b/src/benchmark/run_flashattention.cpp
index 90e81f4..e318b94 100644
--- a/src/benchmark/run_flashattention.cpp
+++ b/src/benchmark/run_flashattention.cpp
@@ -26,9 +26,10 @@ inline bool fileExists(const std::string& name)
 std::string
     generateFAString(size_t B, size_t H, size_t S_Q, size_t S_KV, size_t DH, std::string dtype)
 {
-    std::string result = "attention_B" + std::to_string(B) + "_H" + std::to_string(H) + "_SQ"
+    std::string iree_dtype = dtype == "fp16" ? "f16" : "f8";
+    std::string result     = "attention_B" + std::to_string(B) + "_H" + std::to_string(H) + "_SQ"
                          + std::to_string(S_Q) + "_SKV" + std::to_string(S_KV) + "_DH"
-                         + std::to_string(DH) + "_" + "f16";
+                         + std::to_string(DH) + "_" + iree_dtype;
 
     return result;
 }
@@ -37,26 +38,23 @@ void SHARKFABench::initialize()
 {
     device_id    = 7;
     storage_fp16 = new IREEGemmDeviceStorage("fp16");
+    storage_fp32 = new IREEGemmDeviceStorage("fp32");
 }
 
 void SHARKFABench::linkData(GEMMData* data)
 {
     storage_fp16->allocate(runtime_state->device, data->getCapacity(), data->getBufferA());
+    storage_fp32->allocate(runtime_state->device, data->getCapacity(), data->getBufferB());
 }
 
 void SHARKFABench::setDevice(int device_id)
 {
     this->device_id = device_id;
     runtime_state   = new IREEGemmRuntimeState("rocm://" + std::to_string(device_id));
-    std::cout << "Running on " << device_id << std::endl;
 }
 
 Result SHARKFABench::run(Problem problem)
 {
-    // std::cout << "Parameters before = " << problem.A << " " << problem.B << " " << problem.M << " "
-    //           << problem.K << " " << problem.N << " " << problem.dtype << std::endl;
-    IREEGemmRunner runner(runtime_state, storage_fp16, false);
-
     size_t      S_Q   = (size_t)problem.M;
     size_t      S_KV  = (size_t)problem.N;
     size_t      DH    = (size_t)problem.K;
@@ -64,8 +62,7 @@ Result SHARKFABench::run(Problem problem)
     size_t      H     = (size_t)problem.B;
     std::string dtype = problem.dtype;
 
-    // std::cout << "Parameters afters = " << B << " " << H << " " << S_KV << " " << S_Q << " " << DH
-    //           << " " << dtype << std::endl;
+    IREEGemmRunner runner(runtime_state, dtype == "fp16" ? storage_fp16 : storage_fp32, false);
 
     int num_cold_iterations = 50;
     int num_warm_iterations = 100;
diff --git a/src/benchmark/run_iree.cpp b/src/benchmark/run_iree.cpp
index 9796cb3..832d487 100644
--- a/src/benchmark/run_iree.cpp
+++ b/src/benchmark/run_iree.cpp
@@ -59,7 +59,6 @@ void IREEGEMMBench::setDevice(int device_id)
 {
     this->device_id = device_id;
     runtime_state   = new IREEGemmRuntimeState("rocm://" + std::to_string(device_id));
-    std::cout << "Running on " << device_id << std::endl;
 }
 
 Result IREEGEMMBench::run(Problem problem)