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CUDA: optimize MMQ int8 tensor core performance #8062

Merged
merged 3 commits into from
Jun 24, 2024
Merged

CUDA: optimize MMQ int8 tensor core performance #8062

merged 3 commits into from
Jun 24, 2024

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JohannesGaessler
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This PR adds the following optimizations for the CUDA MMQ kernels using int8 tensor cores:

  • After CUDA: stream-k decomposition for MMQ #8018 the restrictions on tile sizes have become much looser so I changed the tile sizes for >= 48 tokens in such a way that less data needs to be loaded from shared memory per tensor core operation. This necessitates that the tile size in terms of parallel tokens is a multiple of 16 (instead of 8). As a side effect that also means that fewer kernel versions need to be compiled so compile time and binary size go down.
  • I changed the shared memory layout for the quantized data in such a way that there are fewer shared memory bank conflicts when loading the data.
  • I exposed the ldmatrix PTX instruction to load data in blocks of 16 bytes instead of 4.
Performance vs. master MMQ
GPU Model Microbatch size Test t/s master t/s cuda-mmq-2xa-3 Speedup
RTX 4090 llama 8B Q2_K_M 16 pp2048 1959.31 2058.73 1.05
RTX 4090 llama 8B Q2_K_M 32 pp2048 3041.88 3093.97 1.02
RTX 4090 llama 8B Q2_K_M 63 pp2048 4327.05 4566.56 1.06
RTX 4090 llama 8B Q2_K_M 128 pp2048 5779.88 6088.58 1.05
RTX 4090 llama 8B Q2_K_M 256 pp2048 7145.78 7726.39 1.08
RTX 4090 llama 8B Q2_K_M 512 pp2048 7553.00 8194.04 1.08
RTX 4090 llama 8B Q2_K_M 1024 pp2048 7458.42 8046.76 1.08
RTX 4090 llama 8B Q2_K_M 2048 pp2048 6906.43 7424.69 1.08
RTX 4090 llama 8B Q3_K_S 16 pp2048 1777.22 1945.13 1.09
RTX 4090 llama 8B Q3_K_S 32 pp2048 3015.02 3090.91 1.03
RTX 4090 llama 8B Q3_K_S 63 pp2048 4620.00 4446.98 0.96
RTX 4090 llama 8B Q3_K_S 128 pp2048 6713.58 6592.28 0.98
RTX 4090 llama 8B Q3_K_S 256 pp2048 8489.05 8324.30 0.98
RTX 4090 llama 8B Q3_K_S 512 pp2048 8968.09 8768.22 0.98
RTX 4090 llama 8B Q3_K_S 1024 pp2048 8819.05 8589.54 0.97
RTX 4090 llama 8B Q3_K_S 2048 pp2048 8020.62 7882.24 0.98
RTX 4090 llama 8B Q4_0 16 pp2048 1970.65 1988.00 1.01
RTX 4090 llama 8B Q4_0 32 pp2048 3431.49 3445.99 1.00
RTX 4090 llama 8B Q4_0 63 pp2048 5357.19 5284.59 0.99
RTX 4090 llama 8B Q4_0 128 pp2048 7509.03 7502.92 1.00
RTX 4090 llama 8B Q4_0 256 pp2048 9609.65 9726.08 1.01
RTX 4090 llama 8B Q4_0 512 pp2048 10285.00 10683.60 1.04
RTX 4090 llama 8B Q4_0 1024 pp2048 10018.49 10364.80 1.03
RTX 4090 llama 8B Q4_0 2048 pp2048 9155.64 9417.97 1.03
RTX 4090 llama 8B Q4_1 16 pp2048 1862.71 1874.43 1.01
RTX 4090 llama 8B Q4_1 32 pp2048 2979.06 3018.03 1.01
RTX 4090 llama 8B Q4_1 63 pp2048 5145.03 5312.52 1.03
RTX 4090 llama 8B Q4_1 128 pp2048 7229.84 7462.47 1.03
RTX 4090 llama 8B Q4_1 256 pp2048 9130.95 9438.28 1.03
RTX 4090 llama 8B Q4_1 512 pp2048 9747.77 10059.40 1.03
RTX 4090 llama 8B Q4_1 1024 pp2048 9545.98 9830.56 1.03
RTX 4090 llama 8B Q4_1 2048 pp2048 8699.96 8906.72 1.02
RTX 4090 llama 8B Q4_K_S 16 pp2048 1982.75 1987.60 1.00
RTX 4090 llama 8B Q4_K_S 32 pp2048 3361.63 3500.66 1.04
RTX 4090 llama 8B Q4_K_S 63 pp2048 5149.95 5331.79 1.04
RTX 4090 llama 8B Q4_K_S 128 pp2048 6994.57 7288.84 1.04
RTX 4090 llama 8B Q4_K_S 256 pp2048 8788.43 9232.26 1.05
RTX 4090 llama 8B Q4_K_S 512 pp2048 9403.70 9877.78 1.05
RTX 4090 llama 8B Q4_K_S 1024 pp2048 9294.39 9741.86 1.05
RTX 4090 llama 8B Q4_K_S 2048 pp2048 8552.56 8897.37 1.04
RTX 4090 llama 8B Q5_0 16 pp2048 1660.02 1713.62 1.03
RTX 4090 llama 8B Q5_0 32 pp2048 3029.92 3073.33 1.01
RTX 4090 llama 8B Q5_0 63 pp2048 4691.46 5140.13 1.10
RTX 4090 llama 8B Q5_0 128 pp2048 6847.64 7470.47 1.09
RTX 4090 llama 8B Q5_0 256 pp2048 8927.18 9696.99 1.09
RTX 4090 llama 8B Q5_0 512 pp2048 9707.85 10536.44 1.09
RTX 4090 llama 8B Q5_0 1024 pp2048 9529.31 10299.92 1.08
RTX 4090 llama 8B Q5_0 2048 pp2048 8725.90 9363.21 1.07
RTX 4090 llama 8B Q5_1 16 pp2048 1572.10 1627.59 1.04
RTX 4090 llama 8B Q5_1 32 pp2048 2527.92 2727.62 1.08
RTX 4090 llama 8B Q5_1 63 pp2048 4273.50 4395.52 1.03
RTX 4090 llama 8B Q5_1 128 pp2048 6769.11 7198.66 1.06
RTX 4090 llama 8B Q5_1 256 pp2048 8493.96 9079.53 1.07
RTX 4090 llama 8B Q5_1 512 pp2048 9064.31 9827.46 1.08
RTX 4090 llama 8B Q5_1 1024 pp2048 9008.68 9696.96 1.08
RTX 4090 llama 8B Q5_1 2048 pp2048 8304.00 8897.12 1.07
RTX 4090 llama 8B Q5_K_S 16 pp2048 1690.31 1717.22 1.02
RTX 4090 llama 8B Q5_K_S 32 pp2048 2760.78 3021.09 1.09
RTX 4090 llama 8B Q5_K_S 63 pp2048 4415.75 4883.11 1.11
RTX 4090 llama 8B Q5_K_S 128 pp2048 6646.87 6905.05 1.04
RTX 4090 llama 8B Q5_K_S 256 pp2048 8316.00 8707.63 1.05
RTX 4090 llama 8B Q5_K_S 512 pp2048 8924.37 9348.09 1.05
RTX 4090 llama 8B Q5_K_S 1024 pp2048 8858.57 9265.80 1.05
RTX 4090 llama 8B Q5_K_S 2048 pp2048 8203.50 8525.00 1.04
RTX 4090 llama 8B Q6_K 16 pp2048 1445.48 1466.03 1.01
RTX 4090 llama 8B Q6_K 32 pp2048 2736.55 2774.79 1.01
RTX 4090 llama 8B Q6_K 63 pp2048 4473.27 4541.41 1.02
RTX 4090 llama 8B Q6_K 128 pp2048 6516.45 6720.65 1.03
RTX 4090 llama 8B Q6_K 256 pp2048 8328.57 8696.60 1.04
RTX 4090 llama 8B Q6_K 512 pp2048 9004.15 9461.48 1.05
RTX 4090 llama 8B Q6_K 1024 pp2048 8816.76 9285.52 1.05
RTX 4090 llama 8B Q6_K 2048 pp2048 8056.57 8451.71 1.05
RTX 4090 llama 8B Q8_0 16 pp2048 1271.09 1264.01 0.99
RTX 4090 llama 8B Q8_0 32 pp2048 2362.24 2369.64 1.00
RTX 4090 llama 8B Q8_0 63 pp2048 4111.23 4141.30 1.01
RTX 4090 llama 8B Q8_0 128 pp2048 6820.92 6870.90 1.01
RTX 4090 llama 8B Q8_0 256 pp2048 9500.61 9903.76 1.04
RTX 4090 llama 8B Q8_0 512 pp2048 10607.57 11209.17 1.06
RTX 4090 llama 8B Q8_0 1024 pp2048 10481.10 11106.60 1.06
RTX 4090 llama 8B Q8_0 2048 pp2048 9610.52 10098.05 1.05
RTX 3090 llama 8B Q2_K_M 16 pp2048 1048.92 1083.08 1.03
RTX 3090 llama 8B Q2_K_M 32 pp2048 1535.68 1531.48 1.00
RTX 3090 llama 8B Q2_K_M 63 pp2048 2053.18 2165.83 1.05
RTX 3090 llama 8B Q2_K_M 128 pp2048 2477.58 2625.68 1.06
RTX 3090 llama 8B Q2_K_M 256 pp2048 2753.83 2906.06 1.06
RTX 3090 llama 8B Q2_K_M 512 pp2048 2848.71 3033.38 1.06
RTX 3090 llama 8B Q2_K_M 1024 pp2048 2885.83 3051.31 1.06
RTX 3090 llama 8B Q2_K_M 2048 pp2048 2831.48 3008.83 1.06
RTX 3090 llama 8B Q3_K_S 16 pp2048 981.92 1109.08 1.13
RTX 3090 llama 8B Q3_K_S 32 pp2048 1583.54 1595.45 1.01
RTX 3090 llama 8B Q3_K_S 63 pp2048 2242.77 2119.10 0.94
RTX 3090 llama 8B Q3_K_S 128 pp2048 2957.15 2854.64 0.97
RTX 3090 llama 8B Q3_K_S 256 pp2048 3296.41 3161.27 0.96
RTX 3090 llama 8B Q3_K_S 512 pp2048 3400.63 3258.34 0.96
RTX 3090 llama 8B Q3_K_S 1024 pp2048 3455.97 3309.41 0.96
RTX 3090 llama 8B Q3_K_S 2048 pp2048 3374.90 3232.75 0.96
RTX 3090 llama 8B Q4_0 16 pp2048 1238.54 1301.00 1.05
RTX 3090 llama 8B Q4_0 32 pp2048 1929.33 1940.42 1.01
RTX 3090 llama 8B Q4_0 63 pp2048 2727.29 2722.48 1.00
RTX 3090 llama 8B Q4_0 128 pp2048 3469.76 3491.56 1.01
RTX 3090 llama 8B Q4_0 256 pp2048 3885.53 3924.80 1.01
RTX 3090 llama 8B Q4_0 512 pp2048 4030.46 4124.14 1.02
RTX 3090 llama 8B Q4_0 1024 pp2048 4043.94 4138.45 1.02
RTX 3090 llama 8B Q4_0 2048 pp2048 3911.72 4000.28 1.02
RTX 3090 llama 8B Q4_1 16 pp2048 1371.83 1409.82 1.03
RTX 3090 llama 8B Q4_1 32 pp2048 1794.42 1754.93 0.98
RTX 3090 llama 8B Q4_1 63 pp2048 2586.43 2676.11 1.03
RTX 3090 llama 8B Q4_1 128 pp2048 3271.37 3354.55 1.03
RTX 3090 llama 8B Q4_1 256 pp2048 3625.96 3734.81 1.03
RTX 3090 llama 8B Q4_1 512 pp2048 3763.37 3876.53 1.03
RTX 3090 llama 8B Q4_1 1024 pp2048 3791.94 3884.89 1.02
RTX 3090 llama 8B Q4_1 2048 pp2048 3650.00 3776.34 1.03
RTX 3090 llama 8B Q4_K_S 16 pp2048 1304.90 1343.82 1.03
RTX 3090 llama 8B Q4_K_S 32 pp2048 1820.00 1998.71 1.10
RTX 3090 llama 8B Q4_K_S 63 pp2048 2491.79 2656.05 1.07
RTX 3090 llama 8B Q4_K_S 128 pp2048 3096.96 3205.98 1.04
RTX 3090 llama 8B Q4_K_S 256 pp2048 3449.95 3588.17 1.04
RTX 3090 llama 8B Q4_K_S 512 pp2048 3600.86 3736.54 1.04
RTX 3090 llama 8B Q4_K_S 1024 pp2048 3645.23 3780.41 1.04
RTX 3090 llama 8B Q4_K_S 2048 pp2048 3568.97 3683.96 1.03
RTX 3090 llama 8B Q5_0 16 pp2048 1023.34 1094.85 1.07
RTX 3090 llama 8B Q5_0 32 pp2048 1747.94 1804.44 1.03
RTX 3090 llama 8B Q5_0 63 pp2048 2392.03 2744.89 1.15
RTX 3090 llama 8B Q5_0 128 pp2048 3233.48 3444.51 1.07
RTX 3090 llama 8B Q5_0 256 pp2048 3619.52 3870.99 1.07
RTX 3090 llama 8B Q5_0 512 pp2048 3765.05 4056.88 1.08
RTX 3090 llama 8B Q5_0 1024 pp2048 3769.53 4098.97 1.09
RTX 3090 llama 8B Q5_0 2048 pp2048 3664.35 3941.37 1.08
RTX 3090 llama 8B Q5_1 16 pp2048 1041.17 1125.51 1.08
RTX 3090 llama 8B Q5_1 32 pp2048 1508.22 1632.28 1.08
RTX 3090 llama 8B Q5_1 63 pp2048 2280.39 2443.51 1.07
RTX 3090 llama 8B Q5_1 128 pp2048 3048.77 3207.34 1.05
RTX 3090 llama 8B Q5_1 256 pp2048 3377.53 3569.53 1.06
RTX 3090 llama 8B Q5_1 512 pp2048 3517.54 3743.99 1.06
RTX 3090 llama 8B Q5_1 1024 pp2048 3547.11 3794.01 1.07
RTX 3090 llama 8B Q5_1 2048 pp2048 3431.20 3676.47 1.07
RTX 3090 llama 8B Q5_K_S 16 pp2048 1083.87 1143.45 1.05
RTX 3090 llama 8B Q5_K_S 32 pp2048 1562.63 1737.00 1.11
RTX 3090 llama 8B Q5_K_S 63 pp2048 2212.98 2400.75 1.08
RTX 3090 llama 8B Q5_K_S 128 pp2048 2934.14 3021.05 1.03
RTX 3090 llama 8B Q5_K_S 256 pp2048 3284.45 3375.86 1.03
RTX 3090 llama 8B Q5_K_S 512 pp2048 3412.32 3532.41 1.04
RTX 3090 llama 8B Q5_K_S 1024 pp2048 3453.93 3553.91 1.03
RTX 3090 llama 8B Q5_K_S 2048 pp2048 3387.06 3478.17 1.03
RTX 3090 llama 8B Q6_K 16 pp2048 991.17 1011.44 1.02
RTX 3090 llama 8B Q6_K 32 pp2048 1660.10 1715.38 1.03
RTX 3090 llama 8B Q6_K 63 pp2048 2385.54 2456.09 1.03
RTX 3090 llama 8B Q6_K 128 pp2048 2957.59 3075.06 1.04
RTX 3090 llama 8B Q6_K 256 pp2048 3289.25 3459.49 1.05
RTX 3090 llama 8B Q6_K 512 pp2048 3433.88 3596.13 1.05
RTX 3090 llama 8B Q6_K 1024 pp2048 3467.94 3646.15 1.05
RTX 3090 llama 8B Q6_K 2048 pp2048 3385.46 3539.88 1.05
RTX 3090 llama 8B Q8_0 16 pp2048 982.24 995.66 1.01
RTX 3090 llama 8B Q8_0 32 pp2048 1706.93 1750.15 1.03
RTX 3090 llama 8B Q8_0 63 pp2048 2597.08 2707.43 1.04
RTX 3090 llama 8B Q8_0 128 pp2048 3516.55 3651.09 1.04
RTX 3090 llama 8B Q8_0 256 pp2048 4043.19 4205.97 1.04
RTX 3090 llama 8B Q8_0 512 pp2048 4209.31 4425.70 1.05
RTX 3090 llama 8B Q8_0 1024 pp2048 4211.55 4466.05 1.06
RTX 3090 llama 8B Q8_0 2048 pp2048 4070.12 4294.37 1.06
Performance vs. master FP16 cuBLAS
GPU Model Microbatch size Test t/s master t/s cuda-mmq-2xa-3 Speedup
RTX 4090 llama 8B Q2_K_M 16 pp2048 1958.10 2056.25 1.05
RTX 4090 llama 8B Q2_K_M 32 pp2048 3042.70 3088.43 1.02
RTX 4090 llama 8B Q2_K_M 63 pp2048 4365.36 4559.46 1.04
RTX 4090 llama 8B Q2_K_M 128 pp2048 3645.77 6110.00 1.68
RTX 4090 llama 8B Q2_K_M 256 pp2048 5901.11 7720.55 1.31
RTX 4090 llama 8B Q2_K_M 512 pp2048 7772.35 8107.88 1.04
RTX 4090 llama 8B Q2_K_M 1024 pp2048 9004.92 8059.08 0.89
RTX 4090 llama 8B Q2_K_M 2048 pp2048 8893.63 7388.58 0.83
RTX 4090 llama 8B Q3_K_S 16 pp2048 1768.07 1938.69 1.10
RTX 4090 llama 8B Q3_K_S 32 pp2048 3001.33 3079.55 1.03
RTX 4090 llama 8B Q3_K_S 63 pp2048 4611.69 4442.86 0.96
RTX 4090 llama 8B Q3_K_S 128 pp2048 3543.05 6586.41 1.86
RTX 4090 llama 8B Q3_K_S 256 pp2048 5776.37 8308.35 1.44
RTX 4090 llama 8B Q3_K_S 512 pp2048 7675.56 8721.77 1.14
RTX 4090 llama 8B Q3_K_S 1024 pp2048 8966.03 8576.01 0.96
RTX 4090 llama 8B Q3_K_S 2048 pp2048 8880.83 7863.38 0.89
RTX 4090 llama 8B Q4_0 16 pp2048 1968.84 1982.97 1.01
RTX 4090 llama 8B Q4_0 32 pp2048 3408.90 3447.84 1.01
RTX 4090 llama 8B Q4_0 63 pp2048 5340.36 5289.94 0.99
RTX 4090 llama 8B Q4_0 128 pp2048 3476.02 7461.56 2.15
RTX 4090 llama 8B Q4_0 256 pp2048 5746.13 9681.05 1.68
RTX 4090 llama 8B Q4_0 512 pp2048 7762.86 10600.17 1.37
RTX 4090 llama 8B Q4_0 1024 pp2048 9046.57 10342.53 1.14
RTX 4090 llama 8B Q4_0 2048 pp2048 9023.52 9366.15 1.04
RTX 4090 llama 8B Q4_1 16 pp2048 1861.62 1878.00 1.01
RTX 4090 llama 8B Q4_1 32 pp2048 2968.46 3015.56 1.02
RTX 4090 llama 8B Q4_1 63 pp2048 5127.76 5287.13 1.03
RTX 4090 llama 8B Q4_1 128 pp2048 3467.76 7429.16 2.14
RTX 4090 llama 8B Q4_1 256 pp2048 5686.38 9363.62 1.65
RTX 4090 llama 8B Q4_1 512 pp2048 7623.50 10010.90 1.31
RTX 4090 llama 8B Q4_1 1024 pp2048 8924.97 9762.37 1.09
RTX 4090 llama 8B Q4_1 2048 pp2048 8823.83 8862.15 1.00
RTX 4090 llama 8B Q4_K_S 16 pp2048 1982.94 1999.60 1.01
RTX 4090 llama 8B Q4_K_S 32 pp2048 3368.79 3518.83 1.04
RTX 4090 llama 8B Q4_K_S 63 pp2048 5144.50 5334.99 1.04
RTX 4090 llama 8B Q4_K_S 128 pp2048 3475.47 7283.99 2.10
RTX 4090 llama 8B Q4_K_S 256 pp2048 5745.24 9216.08 1.60
RTX 4090 llama 8B Q4_K_S 512 pp2048 7637.48 9867.40 1.29
RTX 4090 llama 8B Q4_K_S 1024 pp2048 8954.20 9664.16 1.08
RTX 4090 llama 8B Q4_K_S 2048 pp2048 8975.33 8827.73 0.98
RTX 4090 llama 8B Q5_0 16 pp2048 1656.90 1715.82 1.04
RTX 4090 llama 8B Q5_0 32 pp2048 3017.03 3073.50 1.02
RTX 4090 llama 8B Q5_0 63 pp2048 4674.71 5137.43 1.10
RTX 4090 llama 8B Q5_0 128 pp2048 3386.65 7461.09 2.20
RTX 4090 llama 8B Q5_0 256 pp2048 5618.24 9655.87 1.72
RTX 4090 llama 8B Q5_0 512 pp2048 7578.65 10491.78 1.38
RTX 4090 llama 8B Q5_0 1024 pp2048 8898.76 10266.71 1.15
RTX 4090 llama 8B Q5_0 2048 pp2048 8957.79 9336.48 1.04
RTX 4090 llama 8B Q5_1 16 pp2048 1567.99 1627.41 1.04
RTX 4090 llama 8B Q5_1 32 pp2048 2529.22 2726.07 1.08
RTX 4090 llama 8B Q5_1 63 pp2048 4257.82 4379.58 1.03
RTX 4090 llama 8B Q5_1 128 pp2048 3425.06 7162.23 2.09
RTX 4090 llama 8B Q5_1 256 pp2048 5601.90 9040.43 1.61
RTX 4090 llama 8B Q5_1 512 pp2048 7515.86 9783.51 1.30
RTX 4090 llama 8B Q5_1 1024 pp2048 8842.66 9664.97 1.09
RTX 4090 llama 8B Q5_1 2048 pp2048 8889.69 8856.04 1.00
RTX 4090 llama 8B Q5_K_S 16 pp2048 1687.77 1710.00 1.01
RTX 4090 llama 8B Q5_K_S 32 pp2048 2760.63 3001.93 1.09
RTX 4090 llama 8B Q5_K_S 63 pp2048 4421.52 4867.15 1.10
RTX 4090 llama 8B Q5_K_S 128 pp2048 3455.52 6889.52 1.99
RTX 4090 llama 8B Q5_K_S 256 pp2048 5677.46 8695.14 1.53
RTX 4090 llama 8B Q5_K_S 512 pp2048 7601.29 9319.90 1.23
RTX 4090 llama 8B Q5_K_S 1024 pp2048 8894.83 9237.02 1.04
RTX 4090 llama 8B Q5_K_S 2048 pp2048 8911.79 8500.17 0.95
RTX 4090 llama 8B Q6_K 16 pp2048 1447.89 1468.62 1.01
RTX 4090 llama 8B Q6_K 32 pp2048 2741.57 2780.23 1.01
RTX 4090 llama 8B Q6_K 63 pp2048 4472.51 4543.34 1.02
RTX 4090 llama 8B Q6_K 128 pp2048 3385.99 6716.01 1.98
RTX 4090 llama 8B Q6_K 256 pp2048 5577.95 8680.28 1.56
RTX 4090 llama 8B Q6_K 512 pp2048 7491.83 9425.78 1.26
RTX 4090 llama 8B Q6_K 1024 pp2048 8701.13 9252.84 1.06
RTX 4090 llama 8B Q6_K 2048 pp2048 8602.28 8408.26 0.98
RTX 4090 llama 8B Q8_0 16 pp2048 1271.44 1264.01 0.99
RTX 4090 llama 8B Q8_0 32 pp2048 2362.88 2372.23 1.00
RTX 4090 llama 8B Q8_0 63 pp2048 4123.72 4128.31 1.00
RTX 4090 llama 8B Q8_0 128 pp2048 3304.49 6850.57 2.07
RTX 4090 llama 8B Q8_0 256 pp2048 5472.11 9855.14 1.80
RTX 4090 llama 8B Q8_0 512 pp2048 7388.37 11189.63 1.51
RTX 4090 llama 8B Q8_0 1024 pp2048 8714.98 11046.46 1.27
RTX 4090 llama 8B Q8_0 2048 pp2048 8850.18 10048.28 1.14
RTX 3090 llama 8B Q2_K_M 16 pp2048 1026.74 1078.88 1.05
RTX 3090 llama 8B Q2_K_M 32 pp2048 1489.03 1526.54 1.03
RTX 3090 llama 8B Q2_K_M 63 pp2048 1993.09 2160.55 1.08
RTX 3090 llama 8B Q2_K_M 128 pp2048 2181.14 2616.21 1.20
RTX 3090 llama 8B Q2_K_M 256 pp2048 3220.43 2912.93 0.90
RTX 3090 llama 8B Q2_K_M 512 pp2048 3802.51 3020.78 0.79
RTX 3090 llama 8B Q2_K_M 1024 pp2048 4440.76 3049.70 0.69
RTX 3090 llama 8B Q2_K_M 2048 pp2048 4489.62 2999.00 0.67
RTX 3090 llama 8B Q3_K_S 16 pp2048 967.25 1107.61 1.15
RTX 3090 llama 8B Q3_K_S 32 pp2048 1547.20 1592.01 1.03
RTX 3090 llama 8B Q3_K_S 63 pp2048 2200.63 2111.31 0.96
RTX 3090 llama 8B Q3_K_S 128 pp2048 2021.43 2855.68 1.41
RTX 3090 llama 8B Q3_K_S 256 pp2048 3036.72 3155.41 1.04
RTX 3090 llama 8B Q3_K_S 512 pp2048 3671.35 3266.99 0.89
RTX 3090 llama 8B Q3_K_S 1024 pp2048 4362.81 3304.01 0.76
RTX 3090 llama 8B Q3_K_S 2048 pp2048 4443.61 3233.17 0.73
RTX 3090 llama 8B Q4_0 16 pp2048 1209.55 1298.82 1.07
RTX 3090 llama 8B Q4_0 32 pp2048 1859.34 1943.08 1.05
RTX 3090 llama 8B Q4_0 63 pp2048 2656.01 2730.11 1.03
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I now consider the performance good enough that I think MMQ should be made the default again; the performance for small quants is still suboptimal but for those I think the memory savings outweigh the hit to speed. I would prefer to do the default change in a separate PR.

@github-actions github-actions bot added Nvidia GPU Issues specific to Nvidia GPUs ggml changes relating to the ggml tensor library for machine learning labels Jun 22, 2024
slaren
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@JohannesGaessler JohannesGaessler mentioned this pull request Jun 23, 2024
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I see similar improvements with 3080 and 3090 Ti.

@JohannesGaessler JohannesGaessler merged commit 9a590c8 into ggerganov:master Jun 24, 2024
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arthw pushed a commit to arthw/llama.cpp that referenced this pull request Jun 30, 2024
@JohannesGaessler @NeoZhangJianyu
CUDA: optimize MMQ int8 tensor core performance (ggerganov#8062)
42288fa 
* CUDA: optimize MMQ int8 tensor core performance

* only a single get_mma_tile_x_k function

* simplify code, make functions constexpr
MagnusS0 pushed a commit to MagnusS0/llama.cpp-normistral-tokenizer that referenced this pull request Jul 1, 2024
@JohannesGaessler @MagnusS0
CUDA: optimize MMQ int8 tensor core performance (ggerganov#8062)
502748f 
* CUDA: optimize MMQ int8 tensor core performance

* only a single get_mma_tile_x_k function

* simplify code, make functions constexpr
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