From 3683752ec693609f97fdd1595cc42082a6ed6367 Mon Sep 17 00:00:00 2001 From: Benjamin Lefaudeux Date: Tue, 4 Jan 2022 15:45:35 -0800 Subject: [PATCH] [docs][minor] Adding more fused layers data to the HTML docs (#174) --- BENCHMARKS.md | 6 +- .../fused_linear/FusedLinear_fp16_FW.png | Bin 161875 -> 0 bytes .../fused_linear/FusedLinear_fp16_FW_BW.png | Bin 160025 -> 0 bytes .../FusedLinear_fp16_FW_BW_gelu.png | Bin 73750 -> 70335 bytes .../FusedLinear_fp16_FW_BW_leaky_relu.png | Bin 77547 -> 73902 bytes .../FusedLinear_fp16_FW_BW_none.png | Bin 71112 -> 67875 bytes .../FusedLinear_fp16_FW_BW_relu.png | Bin 68737 -> 68806 bytes .../FusedLinear_fp16_FW_BW_squared_relu.png | Bin 78975 -> 74678 bytes .../fused_linear/FusedLinear_fp16_FW_gelu.png | Bin 71846 -> 63684 bytes .../FusedLinear_fp16_FW_leaky_relu.png | Bin 74558 -> 64939 bytes .../fused_linear/FusedLinear_fp16_FW_none.png | Bin 70256 -> 65089 bytes .../fused_linear/FusedLinear_fp16_FW_relu.png | Bin 69836 -> 59533 bytes .../FusedLinear_fp16_FW_squared_relu.png | Bin 76133 -> 65855 bytes .../LayerNorm_FW+BW_torch.float16.png | Bin 55364 -> 54200 bytes .../LayerNorm_FW+BW_torch.float32.png | Bin 59560 -> 53751 bytes .../layer_norm/LayerNorm_FW_torch.float16.png | Bin 55626 -> 52409 bytes .../layer_norm/LayerNorm_FW_torch.float32.png | Bin 55996 -> 52834 bytes docs/source/tutorials/triton.rst | 161 +++++++++++------- 18 files changed, 104 insertions(+), 63 deletions(-) delete mode 100644 docs/plots/fused_linear/FusedLinear_fp16_FW.png delete mode 100644 docs/plots/fused_linear/FusedLinear_fp16_FW_BW.png diff --git a/BENCHMARKS.md b/BENCHMARKS.md index a189334a1a..eb443ffe67 100644 --- a/BENCHMARKS.md +++ b/BENCHMARKS.md @@ -52,9 +52,8 @@ You can reproduce these numbers locally by running `python3 xformers/benchmarks/ ### Fused linear layer -You can reproduce these numbers locally by running `python3 xformers/benchmarks/benchmark_triton_fused_linear_layer.py`. The units are TFlops/s. These results are for a nVidia V100, Triton 1.1 and PyTorch 1.9. +You can reproduce these numbers locally by running `python3 xformers/benchmarks/benchmark_triton_fused_linear_layer.py`. The units are TFlops/s. These results are for a laptop nVidia 3080, Triton 1.1 and PyTorch 1.10. -**As of October 2021, these Triton kernels are only competitive with Pytorch in float16, this is a work in progress**. ![Fused linear layers throughput in fp16 - inference](docs/plots/fused_linear/FusedLinear_fp16_FW_gelu.png) @@ -78,8 +77,7 @@ You can reproduce these numbers locally by running `python3 xformers/benchmarks/ ### Fused layer norm -You can reproduce these numbers locally by running `python3 xformers/benchmarks/benchmark_triton_layernorm.py`. The units are GB/s. These results are for a nVidia V100, Triton 1.1 and PyTorch 1.9. -Note that in the Triton case the slowdowns at extreme sizes are because of register spilling, A100s get much better performance. +You can reproduce these numbers locally by running `python3 xformers/benchmarks/benchmark_triton_layernorm.py`. The units are GB/s. These results are for a laptop nVidia 3080, Triton 1.1 and PyTorch 1.10. ![Fused layer norm throughput in fp16 - inference](docs/plots/layer_norm/LayerNorm_FW_torch.float16.png) diff --git a/docs/plots/fused_linear/FusedLinear_fp16_FW.png b/docs/plots/fused_linear/FusedLinear_fp16_FW.png deleted file mode 100644 index 7ee2d46b4936164f3a5d91c4ab4b267115c17d1a..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 161875 zcmbSzbyQW|`zHn}AR&@cN(e|vNOvh6(kb2Dtw?uBmw%h&#Wc5E^ zz-VJ{!qlHGIs_krV*5(n0SO6H5Ak#3vp}91(hVde2~i;>*VK(P7f&4J8s$;ve> z_wAdR93s)V_fW}?LcS{%gfx=}(JX|1e2bc;Ce)dM?1igKAM^|B1BNeo^C#;X+0#qr z6bW`@?At;~J#vOF%Y$h=MlLNLgY_%-8cNpH{_BzI*g49Fg!b2~POm z?UkGaoh;Q-9X6}qAKu-%o4z#<$C^5iIG6=C#m735=PCSNXKSQ0cJ*5g>jcl=d|4yy zp6xDBE_D5ypJzIni~c%KcIjx+rgZq|^z@YXe4}#8bAP~bb@*L(IMM!A<97Un&M4cA z>mvFc4B}UP@eFIf$|=TmefTtMEc1uGJ&rcim%Z>%{nc7Lx!?$wD~I^$$~U|4b)^@( z!__N|WrCgf+z;ly(UlertI^XBvj6#a1POSY>0g-t2vQ&WtdKp*mK@v1P&?Hg^ngP4 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z)2`X?oeDcHCs4gEuV6SRa+~-usNT}E6Lp&tLrp(FKQT|KC2LYwvl|&RWlFEM4}+@t(9xYJ4Me%azv3t@Hng`k zr88n|~yA?Bp@OL-_kv{{xcCZ{Cy{k5-2}%UvyPuIF9sW2x_^ zwfIS?#`touyn|<+vtD?fkL9@~nCcxob3gI68mi&%S9nBpNwxOv62rggYxI@B9rrPK z*ouG4ix)WpO<|>nS%CJ|*luAAca;~VDD&Olr^$EcHLBq*sz;vN^85TPwnyj|v;XV= btz2=-vS`KcgD<-&_-};$D7(Y9K4Je4S~+y% diff --git a/docs/source/tutorials/triton.rst b/docs/source/tutorials/triton.rst index ed2feaba09..cdcc255a8c 100644 --- a/docs/source/tutorials/triton.rst +++ b/docs/source/tutorials/triton.rst @@ -26,52 +26,24 @@ Log-softmax is also available. The actual Triton kernel is very similar to `this The expected throughput, when compared to PyTorch and on a nVidia V100, is along these lines -+-----------------------------------+----------------------+--------------------+--------------------+----------------------+--------------------+---------------------+--------------------------+ -| torch.float16 | Unit: GB/s | | | | | | | -+===================================+======================+====================+====================+======================+====================+=====================+==========================+ -| | B=8, M=384, K=128 | B=8, M=784, K=512 | B=4, M=2048, K=384| B=4, M=3136, K=1024 | B=2, M=1024, K=2048| B=2, M=2048, K=4096 | B=2, M=4096, K=4096 | -+-----------------------------------+----------------------+--------------------+--------------------+----------------------+--------------------+---------------------+--------------------------+ -|pytorch - fw | 170.7 | 501.8 | 512.0 | 597.3 | 399.6 | 524.3 | 553.0 | -+-----------------------------------+----------------------+--------------------+--------------------+----------------------+--------------------+---------------------+--------------------------+ -|triton - fw | 153.6 | 522.7 | 512.0 | 716.8 | 606.8 | 736.4 | 775.6 | -+-----------------------------------+----------------------+--------------------+--------------------+----------------------+--------------------+---------------------+--------------------------+ -|pytorch - log - fw | 192.0 | 545.4 | 534.3 | 669.0 | 496.5 | 601.2 | 615.4 | -+-----------------------------------+----------------------+--------------------+--------------------+----------------------+--------------------+---------------------+--------------------------+ -|triton - log - fw | 153.6 | 570.2 | 558.5 | 748.9 | 682.7 | 780.2 | 799.2 | -+-----------------------------------+----------------------+--------------------+--------------------+----------------------+--------------------+---------------------+--------------------------+ -|pytorch - fw+bw | 71.4 | 170.7 | 168.3 | 205.6 | 164.7 | 196.5 | 203.5 | -+-----------------------------------+----------------------+--------------------+--------------------+----------------------+--------------------+---------------------+--------------------------+ -|triton - fw+bw | 69.8 | 218.2 | 211.9 | 264.8 | 224.4 | 271.4 | 284.3 | -+-----------------------------------+----------------------+--------------------+--------------------+----------------------+--------------------+---------------------+--------------------------+ -|pytorch - log - fw+bw | 78.8 | 207.3 | 204.8 | 255.3 | 206.1 | 247.3 | 255.5 | -+-----------------------------------+----------------------+--------------------+--------------------+----------------------+--------------------+---------------------+--------------------------+ -|triton - log - fw+bw | 71.4 | 220.1 | 213.7 | 266.9 | 229.1 | 273.6 | 285.6 | -+-----------------------------------+----------------------+--------------------+--------------------+----------------------+--------------------+---------------------+--------------------------+ - - -+-----------------------------------+----------------------+--------------------+--------------------+----------------------+--------------------+---------------------+--------------------------+ -| torch.float32 | Unit: GB/s | | | | | | | -+===================================+======================+====================+====================+======================+====================+=====================+==========================+ -| | B=8, M=384, K=128 | B=8, M=784, K=512 | B=4, M=2048, K=384 | B=4, M=3136, K=1024 | B=2, M=1024, K=2048| B=2, M=2048, K=4096 | B=2, M=4096, K=4096 | -+-----------------------------------+----------------------+--------------------+--------------------+----------------------+--------------------+---------------------+--------------------------+ -|pytorch - fw | 341.3 | 660.2 | 682.7 | 760.2 | 555.4 | 636.3 | 650.5 | -+-----------------------------------+----------------------+--------------------+--------------------+----------------------+--------------------+---------------------+--------------------------+ -|triton - fw | 307.2 | 678.1 | 682.7 | 784.0 | 712.3 | 789.6 | 809.1 | -+-----------------------------------+----------------------+--------------------+--------------------+----------------------+--------------------+---------------------+--------------------------+ -|pytorch - log - fw | 384.0 | 696.9 | 702.2 | 777.9 | 537.2 | 541.6 | 543.9 | -+-----------------------------------+----------------------+--------------------+--------------------+----------------------+--------------------+---------------------+--------------------------+ -|triton - log - fw | 307.2 | 696.9 | 702.2 | 796.4 | 744.7 | 799.2 | 814.1 | -+-----------------------------------+----------------------+--------------------+--------------------+----------------------+--------------------+---------------------+--------------------------+ -|pytorch - fw+bw | 133.6 | 203.1 | 204.0 | 229.9 | 193.9 | 211.1 | 215.3 | -+-----------------------------------+----------------------+--------------------+--------------------+----------------------+--------------------+---------------------+--------------------------+ -|triton - fw+bw | 136.5 | 254.7 | 257.3 | 290.9 | 263.2 | 294.5 | 301.0 | -+-----------------------------------+----------------------+--------------------+--------------------+----------------------+--------------------+---------------------+--------------------------+ -|pytorch - log - fw+bw | 149.9 | 252.1 | 252.1 | 289.6 | 234.1 | 251.6 | 254.5 | -+-----------------------------------+----------------------+--------------------+--------------------+----------------------+--------------------+---------------------+--------------------------+ -|triton - log - fw+bw | 136.5 | 257.3 | 258.7 | 291.7 | 265.3 | 295.2 | 301.3 | -+-----------------------------------+----------------------+--------------------+--------------------+----------------------+--------------------+---------------------+--------------------------+ +.. image:: ../../plots/fused_softmax/Softmax_Bandwidth_FW_fp16.png + :width: 600 + :alt: Softmax throughput in fp16 - inference +.. image:: ../../plots/fused_softmax/Softmax_Bandwidth_FW_BW_fp16.png + :width: 600 + :alt: Softmax throughput in fp16 - training + +.. image:: ../../plots/fused_softmax/Softmax_Bandwidth_FW_fp32.png + :width: 600 + :alt: Softmax throughput in fp32 - inference + + +.. image:: ../../plots/fused_softmax/Softmax_Bandwidth_FW_BW_fp32.png + :width: 600 + :alt: Softmax throughput in fp32 - training + Fused linear layer ------------------- This is a drop-in replacement to two PyTorch operands: a `torch.nn.Linear`, and an activation, like `torch.nn.ReLU`. It is Torch AMP and autograd aware, and can be used very simply: @@ -89,22 +61,93 @@ This is a drop-in replacement to two PyTorch operands: a `torch.nn.Linear`, and It is possible to skip either the bias or the activation (just use `None` in that case). As of September 2021, this layer is **faster than PyTorch for non-sigmoid activations and fp16**. In all other usecases, you will be better served using PyTorch. -The following is an example of the measured performance on a nVidia V100. - -+-----------------------------------------+----------------------+--------------------+--------------------+----------------------+--------------------+ -| torch.float16 | Unit: TFlops | | | | | -+=========================================+======================+====================+====================+======================+====================+ -| | B=8, M=256, K=512 | B=8, M=512, K=1024 | B=4, M=1024, K=1024| B=2, M=2048, K=2048 | B=2, M=4096, K=4096| -+-----------------------------------------+----------------------+--------------------+--------------------+----------------------+--------------------+ -| pytorch - squared_relu - bias - fw | 6.3 | 12.4 | 12.3 | 17.1 | 19.0 | -+-----------------------------------------+----------------------+--------------------+--------------------+----------------------+--------------------+ -| triton - squared_relu - bias - fw | 13.8 | 18.9 | 18.9 | 21.9 | 21.7 | -+-----------------------------------------+----------------------+--------------------+--------------------+----------------------+--------------------+ -| pytorch - squared_relu - bias - fw+bw | 4.0 | 7.6 | 7.7 | 10.7 | 12.6 | -+-----------------------------------------+----------------------+--------------------+--------------------+----------------------+--------------------+ -| triton - squared_relu - bias - fw+bw | 8.4 | 13.5 | 13.3 | 15.9 | 16.8 | -+-----------------------------------------+----------------------+--------------------+--------------------+----------------------+--------------------+ +The following is an example of the measured performance on a laptop nVidia 3080, using Triton 1.1 and PyTorch 1.10. + +.. image:: ../../plots/fused_linear/FusedLinear_fp16_FW_gelu.png + :width: 600 + :alt: Fused linear layers throughput in fp16 - inference - GeLU + +.. image:: ../../plots/fused_linear/FusedLinear_fp16_FW_BW_gelu.png + :width: 600 + :alt: Fused linear layers throughput in fp16 - training - GeLU + +-- + +.. image:: ../../plots/fused_linear/FusedLinear_fp16_FW_leaky_relu.png + :width: 600 + :alt: Fused linear layers throughput in fp16 - inference - LeakyReLU + +.. image:: ../../plots/fused_linear/FusedLinear_fp16_FW_BW_leaky_relu.png + :width: 600 + :alt: Fused linear layers throughput in fp16 - training - LeakyReLU + +-- + +.. image:: ../../plots/fused_linear/FusedLinear_fp16_FW_squared_relu.png + :width: 600 + :alt: Fused linear layers throughput in fp16 - inference - Squared ReLU + +.. image:: ../../plots/fused_linear/FusedLinear_fp16_FW_BW_squared_relu.png + :width: 600 + :alt: Fused linear layers throughput in fp16 - training - Squared ReLU + +-- + +.. image:: ../../plots/fused_linear/FusedLinear_fp16_FW_relu.png + :width: 600 + :alt: Fused linear layers throughput in fp16 - inference - ReLU + +.. image:: ../../plots/fused_linear/FusedLinear_fp16_FW_BW_relu.png + :width: 600 + :alt: Fused linear layers throughput in fp16 - training - ReLU + + + + +Fused layer norm +----------------- + +You can reproduce these numbers locally by running `python3 xformers/benchmarks/benchmark_triton_layernorm.py`. The units are GB/s. These results are for a laptop nVidia 3080, Triton 1.1 and PyTorch 1.10. + +.. image:: ../../plots/layer_norm/LayerNorm_FW_torch.float16.png + :width: 600 + :alt: Fused layer norm throughput in fp16 - inference + +.. image:: ../../plots/layer_norm/LayerNorm_FW+BW_torch.float16.png + :width: 600 + :alt: Fused layer norm throughput in fp16 - training + +.. image:: ../../plots/layer_norm/LayerNorm_FW_torch.float32.png + :width: 600 + :alt: Fused layer norm throughput in fp32 - inference + +.. image:: ../../plots/layer_norm/LayerNorm_FW+BW_torch.float32.png + :width: 600 + :alt: Fused layer norm throughput in fp32 - training + + +Fused dropout + bias + activation +--------------------------------- + +You can reproduce these numbers locally by running `python3 xformers/benchmarks/benchmark_triton_dropout.py`. The units are GB/s. +These results are for a laptop nVidia 3080, Triton 1.1 and PyTorch 1.10. + + +.. image:: ../../plots/fused_dropout/Dropout_Bias_True_FW_torch.float16_Act:_gelu.png + :width: 600 + :alt: Fused dropout+ bias throughput in fp16 - inference - GeLU + +.. image:: ../../plots/fused_dropout/Dropout_Bias_True_FW+BW_torch.float16_Act:_gelu.png + :width: 600 + :alt: Fused dropout+ bias throughput in fp16 - training - GeLU + +.. image:: ../../plots/fused_dropout/Dropout_Bias_True_FW_torch.float16_Act:_squared_relu.png + :width: 600 + :alt: Fused dropout+ bias throughput in fp16 - inference - Squared ReLU +.. image:: ../../plots/fused_dropout/Dropout_Bias_True_FW+BW_torch.float16_Act:_squared_relu.png + :width: 600 + :alt: Fused dropout+ bias throughput in fp16 - training - Squared ReLU .. _Triton: https://triton-lang.org/

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