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Add Lowering for FlexAttention Backwards (#125515)
# Summary #### What does this PR do? It enables Inductor to actually generate the fused flex attention kernel for the backwards I did some other things along the way: - Abstract out the 'build_subgraph_buffer' subroutine and make it reusable between flex attention and flex_attention backwards. In total we need too build 3 subgraphs for fwd + bwd. 1 for the fwd graph and then 2 in the bwd. The FAv2 algorithm recomputes the parts of the forward (more efficiently since we already have the row_max via logsumexp), therefore we need to inline both the fwd graph and the joint graph in the bwds kernel. - The version of the backwards kernel is from a somewhat older version of the triton tutorial implementation. I think that we should update in a follow up to a newer version. Notably the blocks need to be square for this to work as currently implemented. I am sure there are many opportunities for optimization. - I didnt correctly register the decomp table + IndexMode when I landed: https://github.com/pytorch/pytorch/pull/123902, this remedies that. - The rel_bias helper func was reversed in terms of causality. I updated and then add a test specific for "future causal" attention. - This PRs but the main point that I think still needs to be worked out is the store_output call. I have it hacked up to be 'fake' but I dont think we want to land that and likely want to just have a mutated 'dq' and a stored_output 'dk' - I also needed to update the `TritonTemplateKernel` to actually accept multiple subgraphs (modifications) - I updated the benchmark to also profile bwds performance ### Benchmark Numbers: _The current implementation is not parallelizing over ctx length in the bwd_ FWD Speedups | Type | Speedup | shape | score_mod | dtype | |---------|-----------|--------------------|-------------|----------------| | Average | 0.991 | | | | | Max | 1.182 | (16, 16, 4096, 64) | noop | torch.bfloat16 | | Min | 0.796 | (2, 16, 512, 256) | head_bias | torch.bfloat16 | BWD Speedups | Type | Speedup | shape | score_mod | dtype | |---------|-----------|--------------------|-------------|----------------| | Average | 0.291 | | | | | Max | 0.652 | (8, 16, 512, 64) | head_bias | torch.bfloat16 | | Min | 0.073 | (2, 16, 4096, 128) | head_bias | torch.bfloat16 | <details> <summary>Full Data</summary> | shape | score_mod | dtype | fwd_eager_time | fwd_compiled_time | bwd_eager_time | bwd_compiled_time | fwd_speedup | bwd_speedup | |---------------------|---------------|----------------|------------------|---------------------|------------------|---------------------|---------------|---------------| | (2, 16, 512, 64) | noop | torch.bfloat16 | 19.936 | 19.092 | 57.851 | 193.564 | 1.044 | 0.299 | | (2, 16, 512, 64) | causal_mask | torch.bfloat16 | 19.955 | 19.497 | 57.662 | 206.278 | 1.024 | 0.280 | | (2, 16, 512, 64) | relative_bias | torch.bfloat16 | 19.455 | 21.297 | 57.674 | 195.219 | 0.913 | 0.295 | | (2, 16, 512, 64) | head_bias | torch.bfloat16 | 19.958 | 21.289 | 57.674 | 193.859 | 0.938 | 0.298 | | (2, 16, 512, 128) | noop | torch.bfloat16 | 28.157 | 28.615 | 82.831 | 454.211 | 0.984 | 0.182 | | (2, 16, 512, 128) | causal_mask | torch.bfloat16 | 28.154 | 28.444 | 83.091 | 432.083 | 0.990 | 0.192 | | (2, 16, 512, 128) | relative_bias | torch.bfloat16 | 28.722 | 27.897 | 83.175 | 446.789 | 1.030 | 0.186 | | (2, 16, 512, 128) | head_bias | torch.bfloat16 | 28.299 | 27.673 | 83.052 | 459.179 | 1.023 | 0.181 | | (2, 16, 512, 256) | noop | torch.bfloat16 | 41.167 | 50.504 | 175.019 | 1083.545 | 0.815 | 0.162 | | (2, 16, 512, 256) | causal_mask | torch.bfloat16 | 41.656 | 51.933 | 175.078 | 1171.176 | 0.802 | 0.149 | | (2, 16, 512, 256) | relative_bias | torch.bfloat16 | 41.697 | 50.722 | 175.159 | 1097.312 | 0.822 | 0.160 | | (2, 16, 512, 256) | head_bias | torch.bfloat16 | 41.690 | 52.387 | 175.184 | 1097.336 | 0.796 | 0.160 | | (2, 16, 1024, 64) | noop | torch.bfloat16 | 39.232 | 37.454 | 127.847 | 612.430 | 1.047 | 0.209 | | (2, 16, 1024, 64) | causal_mask | torch.bfloat16 | 39.930 | 39.599 | 127.755 | 665.359 | 1.008 | 0.192 | | (2, 16, 1024, 64) | relative_bias | torch.bfloat16 | 39.417 | 41.304 | 127.902 | 614.990 | 0.954 | 0.208 | | (2, 16, 1024, 64) | head_bias | torch.bfloat16 | 39.965 | 42.034 | 127.953 | 613.273 | 0.951 | 0.209 | | (2, 16, 1024, 128) | noop | torch.bfloat16 | 63.964 | 71.024 | 226.510 | 1637.669 | 0.901 | 0.138 | | (2, 16, 1024, 128) | causal_mask | torch.bfloat16 | 63.843 | 72.451 | 226.750 | 1558.949 | 0.881 | 0.145 | | (2, 16, 1024, 128) | relative_bias | torch.bfloat16 | 64.301 | 70.487 | 226.651 | 1610.063 | 0.912 | 0.141 | | (2, 16, 1024, 128) | head_bias | torch.bfloat16 | 64.033 | 71.394 | 226.676 | 1668.511 | 0.897 | 0.136 | | (2, 16, 1024, 256) | noop | torch.bfloat16 | 129.348 | 141.390 | 507.337 | 4405.175 | 0.915 | 0.115 | | (2, 16, 1024, 256) | causal_mask | torch.bfloat16 | 129.538 | 145.680 | 507.178 | 4768.874 | 0.889 | 0.106 | | (2, 16, 1024, 256) | relative_bias | torch.bfloat16 | 129.438 | 142.782 | 507.004 | 4401.002 | 0.907 | 0.115 | | (2, 16, 1024, 256) | head_bias | torch.bfloat16 | 129.058 | 146.242 | 507.547 | 4434.251 | 0.883 | 0.114 | | (2, 16, 4096, 64) | noop | torch.bfloat16 | 481.606 | 409.120 | 1440.890 | 14147.269 | 1.177 | 0.102 | | (2, 16, 4096, 64) | causal_mask | torch.bfloat16 | 480.227 | 438.847 | 1434.419 | 14973.386 | 1.094 | 0.096 | | (2, 16, 4096, 64) | relative_bias | torch.bfloat16 | 480.831 | 458.104 | 1432.935 | 14193.253 | 1.050 | 0.101 | | (2, 16, 4096, 64) | head_bias | torch.bfloat16 | 480.749 | 452.497 | 1437.040 | 14084.869 | 1.062 | 0.102 | | (2, 16, 4096, 128) | noop | torch.bfloat16 | 872.534 | 848.275 | 2600.895 | 35156.849 | 1.029 | 0.074 | | (2, 16, 4096, 128) | causal_mask | torch.bfloat16 | 872.647 | 868.279 | 2587.581 | 31919.531 | 1.005 | 0.081 | | (2, 16, 4096, 128) | relative_bias | torch.bfloat16 | 871.484 | 827.644 | 2593.989 | 34805.634 | 1.053 | 0.075 | | (2, 16, 4096, 128) | head_bias | torch.bfloat16 | 871.422 | 856.437 | 2602.482 | 35708.591 | 1.017 | 0.073 | | (2, 16, 4096, 256) | noop | torch.bfloat16 | 1904.497 | 1758.183 | 6122.416 | 66754.593 | 1.083 | 0.092 | | (2, 16, 4096, 256) | causal_mask | torch.bfloat16 | 1911.174 | 1762.821 | 6113.207 | 72759.392 | 1.084 | 0.084 | | (2, 16, 4096, 256) | relative_bias | torch.bfloat16 | 1911.254 | 1727.108 | 6123.530 | 66577.988 | 1.107 | 0.092 | | (2, 16, 4096, 256) | head_bias | torch.bfloat16 | 1916.977 | 1801.804 | 6118.158 | 67359.680 | 1.064 | 0.091 | | (8, 16, 512, 64) | noop | torch.bfloat16 | 44.984 | 43.974 | 170.276 | 262.259 | 1.023 | 0.649 | | (8, 16, 512, 64) | causal_mask | torch.bfloat16 | 45.001 | 46.265 | 170.509 | 274.893 | 0.973 | 0.620 | | (8, 16, 512, 64) | relative_bias | torch.bfloat16 | 45.466 | 48.211 | 170.606 | 262.759 | 0.943 | 0.649 | | (8, 16, 512, 64) | head_bias | torch.bfloat16 | 45.481 | 48.435 | 170.267 | 261.265 | 0.939 | 0.652 | | (8, 16, 512, 128) | noop | torch.bfloat16 | 72.565 | 74.736 | 313.220 | 773.126 | 0.971 | 0.405 | | (8, 16, 512, 128) | causal_mask | torch.bfloat16 | 72.015 | 75.755 | 313.311 | 775.513 | 0.951 | 0.404 | | (8, 16, 512, 128) | relative_bias | torch.bfloat16 | 72.105 | 74.189 | 313.806 | 769.238 | 0.972 | 0.408 | | (8, 16, 512, 128) | head_bias | torch.bfloat16 | 72.005 | 74.364 | 313.509 | 775.237 | 0.968 | 0.404 | | (8, 16, 512, 256) | noop | torch.bfloat16 | 138.656 | 165.453 | 663.707 | 2672.067 | 0.838 | 0.248 | | (8, 16, 512, 256) | causal_mask | torch.bfloat16 | 139.096 | 172.613 | 663.593 | 2926.538 | 0.806 | 0.227 | | (8, 16, 512, 256) | relative_bias | torch.bfloat16 | 139.500 | 168.417 | 663.938 | 2658.629 | 0.828 | 0.250 | | (8, 16, 512, 256) | head_bias | torch.bfloat16 | 139.776 | 173.549 | 662.920 | 2667.266 | 0.805 | 0.249 | | (8, 16, 1024, 64) | noop | torch.bfloat16 | 134.883 | 125.004 | 484.706 | 1195.254 | 1.079 | 0.406 | | (8, 16, 1024, 64) | causal_mask | torch.bfloat16 | 134.297 | 132.875 | 485.420 | 1234.953 | 1.011 | 0.393 | | (8, 16, 1024, 64) | relative_bias | torch.bfloat16 | 134.839 | 139.231 | 485.470 | 1198.556 | 0.968 | 0.405 | | (8, 16, 1024, 64) | head_bias | torch.bfloat16 | 133.822 | 136.449 | 485.608 | 1189.198 | 0.981 | 0.408 | | (8, 16, 1024, 128) | noop | torch.bfloat16 | 235.470 | 234.765 | 886.094 | 2662.944 | 1.003 | 0.333 | | (8, 16, 1024, 128) | causal_mask | torch.bfloat16 | 236.305 | 241.382 | 886.293 | 2646.984 | 0.979 | 0.335 | | (8, 16, 1024, 128) | relative_bias | torch.bfloat16 | 236.414 | 233.980 | 885.250 | 2642.178 | 1.010 | 0.335 | | (8, 16, 1024, 128) | head_bias | torch.bfloat16 | 237.176 | 239.040 | 885.754 | 2665.242 | 0.992 | 0.332 | | (8, 16, 1024, 256) | noop | torch.bfloat16 | 504.445 | 517.855 | 1978.956 | 9592.906 | 0.974 | 0.206 | | (8, 16, 1024, 256) | causal_mask | torch.bfloat16 | 502.428 | 536.002 | 1978.611 | 10607.342 | 0.937 | 0.187 | | (8, 16, 1024, 256) | relative_bias | torch.bfloat16 | 503.396 | 523.960 | 1977.993 | 9539.284 | 0.961 | 0.207 | | (8, 16, 1024, 256) | head_bias | torch.bfloat16 | 503.818 | 536.014 | 1980.131 | 9576.262 | 0.940 | 0.207 | | (8, 16, 4096, 64) | noop | torch.bfloat16 | 1970.139 | 1674.930 | 5750.940 | 16724.134 | 1.176 | 0.344 | | (8, 16, 4096, 64) | causal_mask | torch.bfloat16 | 1959.036 | 1775.056 | 5780.512 | 17390.350 | 1.104 | 0.332 | | (8, 16, 4096, 64) | relative_bias | torch.bfloat16 | 1947.198 | 1773.869 | 5780.643 | 16779.699 | 1.098 | 0.345 | | (8, 16, 4096, 64) | head_bias | torch.bfloat16 | 1963.935 | 1829.502 | 5780.018 | 16703.259 | 1.073 | 0.346 | | (8, 16, 4096, 128) | noop | torch.bfloat16 | 3582.711 | 3362.623 | 10436.069 | 36415.565 | 1.065 | 0.287 | | (8, 16, 4096, 128) | causal_mask | torch.bfloat16 | 3581.504 | 3499.472 | 10346.869 | 36164.959 | 1.023 | 0.286 | | (8, 16, 4096, 128) | relative_bias | torch.bfloat16 | 3589.779 | 3337.849 | 10529.621 | 36261.696 | 1.075 | 0.290 | | (8, 16, 4096, 128) | head_bias | torch.bfloat16 | 3602.265 | 3436.444 | 10458.660 | 36507.790 | 1.048 | 0.286 | | (8, 16, 4096, 256) | noop | torch.bfloat16 | 7695.923 | 7126.275 | 24643.009 | 140949.081 | 1.080 | 0.175 | | (8, 16, 4096, 256) | causal_mask | torch.bfloat16 | 7679.939 | 7186.252 | 24538.105 | 157156.067 | 1.069 | 0.156 | | (8, 16, 4096, 256) | relative_bias | torch.bfloat16 | 7681.374 | 6994.832 | 24549.713 | 140077.179 | 1.098 | 0.175 | | (8, 16, 4096, 256) | head_bias | torch.bfloat16 | 7679.822 | 7212.278 | 24627.823 | 140675.003 | 1.065 | 0.175 | | (16, 16, 512, 64) | noop | torch.bfloat16 | 80.126 | 78.291 | 333.719 | 541.165 | 1.023 | 0.617 | | (16, 16, 512, 64) | causal_mask | torch.bfloat16 | 80.065 | 81.696 | 333.779 | 551.113 | 0.980 | 0.606 | | (16, 16, 512, 64) | relative_bias | torch.bfloat16 | 80.138 | 86.715 | 333.364 | 542.118 | 0.924 | 0.615 | | (16, 16, 512, 64) | head_bias | torch.bfloat16 | 80.415 | 85.204 | 333.294 | 536.840 | 0.944 | 0.621 | | (16, 16, 512, 128) | noop | torch.bfloat16 | 134.964 | 138.025 | 607.093 | 1333.102 | 0.978 | 0.455 | | (16, 16, 512, 128) | causal_mask | torch.bfloat16 | 134.192 | 141.523 | 606.269 | 1424.318 | 0.948 | 0.426 | | (16, 16, 512, 128) | relative_bias | torch.bfloat16 | 135.711 | 138.639 | 606.283 | 1327.974 | 0.979 | 0.457 | | (16, 16, 512, 128) | head_bias | torch.bfloat16 | 135.552 | 140.555 | 607.107 | 1347.370 | 0.964 | 0.451 | | (16, 16, 512, 256) | noop | torch.bfloat16 | 275.113 | 315.144 | 1301.583 | 5268.153 | 0.873 | 0.247 | | (16, 16, 512, 256) | causal_mask | torch.bfloat16 | 274.867 | 328.106 | 1302.513 | 5770.594 | 0.838 | 0.226 | | (16, 16, 512, 256) | relative_bias | torch.bfloat16 | 276.052 | 321.770 | 1302.904 | 5241.920 | 0.858 | 0.249 | | (16, 16, 512, 256) | head_bias | torch.bfloat16 | 271.409 | 328.839 | 1302.142 | 5266.037 | 0.825 | 0.247 | | (16, 16, 1024, 64) | noop | torch.bfloat16 | 260.489 | 237.463 | 955.884 | 1817.558 | 1.097 | 0.526 | | (16, 16, 1024, 64) | causal_mask | torch.bfloat16 | 262.378 | 254.350 | 955.280 | 1843.807 | 1.032 | 0.518 | | (16, 16, 1024, 64) | relative_bias | torch.bfloat16 | 261.338 | 268.253 | 956.038 | 1820.036 | 0.974 | 0.525 | | (16, 16, 1024, 64) | head_bias | torch.bfloat16 | 262.153 | 264.156 | 956.023 | 1810.076 | 0.992 | 0.528 | | (16, 16, 1024, 128) | noop | torch.bfloat16 | 476.475 | 461.413 | 1760.578 | 4306.521 | 1.033 | 0.409 | | (16, 16, 1024, 128) | causal_mask | torch.bfloat16 | 473.794 | 479.178 | 1761.277 | 4619.439 | 0.989 | 0.381 | | (16, 16, 1024, 128) | relative_bias | torch.bfloat16 | 473.839 | 463.282 | 1758.692 | 4290.562 | 1.023 | 0.410 | | (16, 16, 1024, 128) | head_bias | torch.bfloat16 | 472.979 | 472.896 | 1763.086 | 4367.931 | 1.000 | 0.404 | | (16, 16, 1024, 256) | noop | torch.bfloat16 | 1014.184 | 1026.764 | 3922.997 | 19104.147 | 0.988 | 0.205 | | (16, 16, 1024, 256) | causal_mask | torch.bfloat16 | 1013.217 | 1039.046 | 3928.382 | 21086.281 | 0.975 | 0.186 | | (16, 16, 1024, 256) | relative_bias | torch.bfloat16 | 1008.519 | 1015.278 | 3922.133 | 18980.652 | 0.993 | 0.207 | | (16, 16, 1024, 256) | head_bias | torch.bfloat16 | 1011.360 | 1047.542 | 3931.245 | 19069.172 | 0.965 | 0.206 | | (16, 16, 4096, 64) | noop | torch.bfloat16 | 3929.850 | 3325.667 | 11411.704 | 23344.280 | 1.182 | 0.489 | | (16, 16, 4096, 64) | causal_mask | torch.bfloat16 | 3885.262 | 3581.544 | 11390.515 | 23725.639 | 1.085 | 0.480 | | (16, 16, 4096, 64) | relative_bias | torch.bfloat16 | 3865.737 | 3537.308 | 11489.901 | 23406.330 | 1.093 | 0.491 | | (16, 16, 4096, 64) | head_bias | torch.bfloat16 | 3880.530 | 3665.249 | 11484.411 | 23299.496 | 1.059 | 0.493 | | (16, 16, 4096, 128) | noop | torch.bfloat16 | 7030.306 | 6745.715 | 20621.264 | 57464.096 | 1.042 | 0.359 | | (16, 16, 4096, 128) | causal_mask | torch.bfloat16 | 7095.414 | 7034.385 | 20410.656 | 61660.511 | 1.009 | 0.331 | | (16, 16, 4096, 128) | relative_bias | torch.bfloat16 | 7084.779 | 6686.497 | 20315.161 | 57243.969 | 1.060 | 0.355 | | (16, 16, 4096, 128) | head_bias | torch.bfloat16 | 7075.367 | 6863.305 | 20494.385 | 58481.953 | 1.031 | 0.350 | | (16, 16, 4096, 256) | noop | torch.bfloat16 | 15612.741 | 14297.482 | 55306.847 | 281161.865 | 1.092 | 0.197 | | (16, 16, 4096, 256) | causal_mask | torch.bfloat16 | 15326.592 | 14263.878 | 55227.806 | 313063.232 | 1.075 | 0.176 | | (16, 16, 4096, 256) | relative_bias | torch.bfloat16 | 15297.963 | 14007.379 | 54558.029 | 279529.175 | 1.092 | 0.195 | | (16, 16, 4096, 256) | head_bias | torch.bfloat16 | 15216.160 | 14276.027 | 55081.581 | 280996.826 | 1.066 | 0.196 | </details> Pull Request resolved: https://github.com/pytorch/pytorch/pull/125515 Approved by: https://github.com/Chillee
This commit is contained in:
drisspg
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PyTorch MergeBot
PyTorch MergeBot
parent
ae6fdfa539
commit
95b9e981c3
@ -3,7 +3,7 @@ import itertools
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from collections import defaultdict
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from dataclasses import asdict, dataclass
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from functools import partial
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from typing import Callable, List
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from typing import Callable, List, Optional, Tuple
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import numpy as np
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import torch
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@ -29,28 +29,32 @@ def benchmark_torch_function_in_microseconds(func: Callable, *args, **kwargs) ->
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@dataclass(frozen=True)
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class ExperimentConfig:
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batch_size: int
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num_heads: int
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q_seq_len: int
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k_seq_len: int
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head_dim: int
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shape: Tuple[int]
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score_mod: Callable
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dtype: torch.dtype
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calculate_bwd_time: bool
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def __post_init__(self):
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assert len(self.shape) == 4, "Shape must be of length 4"
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def asdict(self):
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return asdict(self)
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# Convert the dataclass instance to a dictionary
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d = asdict(self)
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# Remove the 'calculate_bwd_time' key
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d.pop("calculate_bwd_time", None)
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return d
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@dataclass(frozen=True)
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class Times:
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eager_time: float
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compiled_time: float
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@dataclass(frozen=True)
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class ExperimentResults:
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eager_time: float
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compiled_time: float
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def get_entries(self) -> List:
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return [
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f"{self.eager_time:2f}",
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f"{self.compiled_time:2f}",
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]
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fwd_times: Times
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bwd_times: Optional[Times]
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@dataclass(frozen=True)
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@ -58,29 +62,31 @@ class Experiment:
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config: ExperimentConfig
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results: ExperimentResults
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def get_entries(self) -> List:
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return self.config.get_entries() + self.results.get_entries()
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def asdict(self):
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dict1 = asdict(self.config)
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dict1 = self.config.asdict()
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dict2 = asdict(self.results)
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return {**dict1, **dict2}
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def generate_inputs(
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batch_size,
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num_heads,
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q_sequence_length,
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kv_sequence_length,
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head_dim,
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dtype,
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device,
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batch_size: int,
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num_heads: int,
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q_sequence_length: int,
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kv_sequence_length: int,
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head_dim: int,
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dtype: torch.dtype,
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device: torch.device,
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requires_grad: bool,
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):
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q_shape = (batch_size, q_sequence_length, num_heads * head_dim)
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kv_shape = (batch_size, kv_sequence_length, num_heads * head_dim)
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make_q = partial(torch.rand, q_shape, device=device, dtype=dtype)
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make_kv = partial(torch.rand, kv_shape, device=device, dtype=dtype)
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make_q = partial(
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torch.rand, q_shape, device=device, dtype=dtype, requires_grad=requires_grad
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)
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make_kv = partial(
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torch.rand, kv_shape, device=device, dtype=dtype, requires_grad=requires_grad
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)
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query = (
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make_q()
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.view(batch_size, q_sequence_length, num_heads, head_dim)
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@ -101,14 +107,16 @@ def generate_inputs(
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def run_single_experiment(config: ExperimentConfig, dynamic=False) -> ExperimentResults:
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device = torch.device("cuda")
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batch_size, num_heads, q_seq_len, head_dim = config.shape
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query, key, value = generate_inputs(
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config.batch_size,
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config.num_heads,
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config.q_seq_len,
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config.k_seq_len,
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config.head_dim,
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batch_size,
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num_heads,
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q_seq_len,
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q_seq_len,
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head_dim,
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config.dtype,
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device,
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requires_grad=config.calculate_bwd_time,
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)
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def eager_sdpa(query, key, value, _):
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@ -125,23 +133,47 @@ def run_single_experiment(config: ExperimentConfig, dynamic=False) -> Experiment
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compiled_sdpa, query, key, value, score_mod
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)
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if config.calculate_bwd_time:
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out_eager = eager_sdpa(query, key, value, score_mod)
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dOut = torch.randn_like(out_eager)
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backward_eager_time = benchmark_torch_function_in_microseconds(
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out_eager.backward, dOut, retain_graph=True
|
||||
)
|
||||
|
||||
out_compile = compiled_sdpa(query, key, value, score_mod)
|
||||
dOut = torch.randn_like(out_eager)
|
||||
backward_compile_time = benchmark_torch_function_in_microseconds(
|
||||
out_compile.backward, dOut, retain_graph=True
|
||||
)
|
||||
|
||||
return ExperimentResults(
|
||||
eager_time=forward_eager_time,
|
||||
compiled_time=forward_compiled_time,
|
||||
fwd_times=Times(forward_eager_time, forward_compiled_time),
|
||||
bwd_times=Times(backward_eager_time, backward_compile_time),
|
||||
)
|
||||
else:
|
||||
return ExperimentResults(
|
||||
fwd_times=Times(forward_eager_time, forward_compiled_time),
|
||||
bwd_times=None,
|
||||
)
|
||||
|
||||
|
||||
def calculate_speedup(results: ExperimentResults) -> float:
|
||||
return results.eager_time / results.compiled_time
|
||||
def calculate_speedup(results: ExperimentResults, type: str) -> float:
|
||||
if type == "fwd":
|
||||
return results.fwd_times.eager_time / results.fwd_times.compiled_time
|
||||
elif type == "bwd":
|
||||
assert results.bwd_times is not None
|
||||
return results.bwd_times.eager_time / results.bwd_times.compiled_time
|
||||
else:
|
||||
raise ValueError(f"Invalid type {type}")
|
||||
|
||||
|
||||
def get_func_name(func):
|
||||
return func.__name__.split("<locals>.")[-1].split(" at ")[0]
|
||||
|
||||
|
||||
def get_average_speedups(results: List[Experiment]):
|
||||
def get_average_speedups(results: List[Experiment], type: str):
|
||||
# Calculate speedups
|
||||
speedups = [calculate_speedup(r.results) for r in results]
|
||||
speedups = [calculate_speedup(r.results, type) for r in results]
|
||||
|
||||
# Find indices of max and min speedups
|
||||
max_speedup_index = np.argmax(speedups)
|
||||
@ -177,18 +209,37 @@ def print_results(results: List[Experiment]):
|
||||
table_data = defaultdict(list)
|
||||
for experiment in results:
|
||||
for key, value in experiment.asdict().items():
|
||||
if key == "eager_time" or key == "compiled_time":
|
||||
value = float(value)
|
||||
if key == "fwd_times":
|
||||
for name, time in value.items():
|
||||
table_data[f"fwd_{name}"].append(float(time))
|
||||
elif key == "bwd_times":
|
||||
if experiment.config.calculate_bwd_time:
|
||||
for name, time in value.items():
|
||||
table_data[f"bwd_{name}"].append(float(time))
|
||||
else:
|
||||
table_data[key].append(value)
|
||||
|
||||
# Calculate speedups
|
||||
speedups = [calculate_speedup(r.results) for r in results]
|
||||
table_data["speedup"] = speedups
|
||||
fwd_speedups = [calculate_speedup(r.results, type="fwd") for r in results]
|
||||
table_data["fwd_speedup"] = fwd_speedups
|
||||
if results[0].config.calculate_bwd_time:
|
||||
bwd_speedups = [calculate_speedup(r.results, type="bwd") for r in results]
|
||||
table_data["bwd_speedup"] = bwd_speedups
|
||||
|
||||
table_data["score_mod"] = [get_func_name(func) for func in table_data["score_mod"]]
|
||||
print(tabulate(table_data, headers="keys", tablefmt="github", floatfmt=".3f"))
|
||||
|
||||
average_data = get_average_speedups(results)
|
||||
print("\n")
|
||||
print("FWD Speedups".center(125, "="))
|
||||
print("\n")
|
||||
average_data = get_average_speedups(results, type="fwd")
|
||||
print(tabulate(average_data, headers="keys", tablefmt="github", floatfmt=".3f"))
|
||||
|
||||
if results[0].config.calculate_bwd_time:
|
||||
print("\n")
|
||||
print("BWD Speedups".center(125, "="))
|
||||
print("\n")
|
||||
average_data = get_average_speedups(results, type="bwd")
|
||||
print(tabulate(average_data, headers="keys", tablefmt="github", floatfmt=".3f"))
|
||||
|
||||
|
||||
@ -208,8 +259,8 @@ def generate_score_mods() -> List[Callable]:
|
||||
return [noop, causal_mask, relative_bias, head_bias]
|
||||
|
||||
|
||||
def generate_experiment_configs() -> List[ExperimentConfig]:
|
||||
batch_sizes = [1, 8, 16]
|
||||
def generate_experiment_configs(calculate_bwd: bool) -> List[ExperimentConfig]:
|
||||
batch_sizes = [2, 8, 16]
|
||||
num_heads = [16]
|
||||
q_kv_seq_lens = [(512, 512), (1024, 1024), (4096, 4096)]
|
||||
head_dims = [64, 128, 256]
|
||||
@ -228,41 +279,49 @@ def generate_experiment_configs() -> List[ExperimentConfig]:
|
||||
) in itertools.product(
|
||||
batch_sizes, num_heads, q_kv_seq_lens, head_dims, score_mods, dtypes
|
||||
):
|
||||
assert q_seq_len == kv_seq_len, "Only equal length inputs supported for now."
|
||||
all_configs.append(
|
||||
ExperimentConfig(
|
||||
batch_size=bsz,
|
||||
num_heads=n_heads,
|
||||
q_seq_len=q_seq_len,
|
||||
k_seq_len=kv_seq_len,
|
||||
head_dim=head_dim,
|
||||
shape=(bsz, n_heads, q_seq_len, head_dim),
|
||||
score_mod=score_mod,
|
||||
dtype=dtype,
|
||||
calculate_bwd_time=calculate_bwd,
|
||||
)
|
||||
)
|
||||
|
||||
return all_configs
|
||||
|
||||
|
||||
def main(dynamic=False):
|
||||
def main(dynamic: bool, calculate_bwd: bool):
|
||||
seed = 123
|
||||
np.random.seed(seed)
|
||||
torch.manual_seed(seed)
|
||||
results = []
|
||||
for config in tqdm(generate_experiment_configs()):
|
||||
for config in tqdm(generate_experiment_configs(calculate_bwd)):
|
||||
results.append(
|
||||
Experiment(config, run_single_experiment(config, dynamic=dynamic))
|
||||
)
|
||||
for config in tqdm(generate_experiment_configs(calculate_bwd)):
|
||||
results.append(Experiment(config, run_single_experiment(config)))
|
||||
|
||||
print_results(results)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
# Set up the argument parser
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Run sweep over sizes and score mods for flex attention"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--dynamic",
|
||||
action="store_true",
|
||||
help="Runs a dynamic shapes version of compiled flex attention.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--calculate-bwd", action="store_true", help="Calculate backward pass times"
|
||||
)
|
||||
|
||||
# Parse arguments
|
||||
args = parser.parse_args()
|
||||
main(args.dynamic)
|
||||
|
||||
main(args.dynamic, args.calculate_bwd)
|
||||
|
||||
@ -1,8 +1,9 @@
|
||||
# Owner(s): ["module: inductor"]
|
||||
|
||||
import functools
|
||||
import unittest
|
||||
from collections import namedtuple
|
||||
from typing import Callable
|
||||
from typing import Callable, Optional
|
||||
|
||||
from unittest import expectedFailure, skip, skipUnless
|
||||
from unittest.mock import patch
|
||||
@ -58,14 +59,8 @@ if common_utils.TEST_WITH_ROCM:
|
||||
|
||||
|
||||
# --------- Useful score mod functions for testing ---------
|
||||
|
||||
test_score_mods = [
|
||||
_identity,
|
||||
_causal,
|
||||
_rel_bias,
|
||||
_rel_causal,
|
||||
_generate_alibi_bias(8),
|
||||
]
|
||||
def _inverse_causal(score, b, h, m, n):
|
||||
return torch.where(m <= n, score, float("-inf"))
|
||||
|
||||
|
||||
def _times_two(score, b, h, m, n):
|
||||
@ -79,13 +74,11 @@ def _squared(score, b, h, m, n):
|
||||
|
||||
|
||||
def _head_offset(dtype: torch.dtype):
|
||||
"""Captured Buffer
|
||||
Note: this builds a score_mod with index of a type
|
||||
"""
|
||||
"""Captured Buffer"""
|
||||
head_offset = torch.rand(H, device="cuda", dtype=dtype)
|
||||
|
||||
def score_mod(score, b, h, m, n):
|
||||
return score * index(head_offset, [h])
|
||||
return score * head_offset[h]
|
||||
|
||||
return score_mod
|
||||
|
||||
@ -103,20 +96,19 @@ def _trig2(score, b, h, m, n):
|
||||
return z
|
||||
|
||||
|
||||
def _buffer_reduced(dtype: torch.dtype):
|
||||
"""Reduction in captured buffer"""
|
||||
batch_offsets = torch.rand(B, 8, device="cuda", dtype=dtype)
|
||||
|
||||
def score_mod(score, b, h, m, n):
|
||||
batch_vals = index(batch_offsets, [b])
|
||||
return score + batch_vals.sum()
|
||||
|
||||
return score_mod
|
||||
|
||||
test_score_mods = [
|
||||
_identity,
|
||||
_times_two,
|
||||
_squared,
|
||||
_causal,
|
||||
_inverse_causal,
|
||||
_rel_bias,
|
||||
_rel_causal,
|
||||
_generate_alibi_bias(8),
|
||||
]
|
||||
|
||||
captured_buffers_map = {
|
||||
"_head_offset": _head_offset,
|
||||
"_buffer_reduced": _buffer_reduced,
|
||||
}
|
||||
|
||||
B = 4
|
||||
@ -125,18 +117,35 @@ S = 2048
|
||||
D = 64
|
||||
|
||||
|
||||
def query_key_value_clones(
|
||||
query: torch.Tensor,
|
||||
key: torch.Tensor,
|
||||
value: torch.Tensor,
|
||||
dtype: torch.dtype = None,
|
||||
):
|
||||
"""Clones the query, key, and value tensors and moves them to the specified dtype."""
|
||||
if dtype is None:
|
||||
dtype = query.dtype
|
||||
query_ref = query.clone().detach().to(dtype).requires_grad_(query.requires_grad)
|
||||
key_ref = key.clone().detach().to(dtype).requires_grad_(key.requires_grad)
|
||||
value_ref = value.clone().detach().to(dtype).requires_grad_(value.requires_grad)
|
||||
return query_ref, key_ref, value_ref
|
||||
|
||||
|
||||
class TestTemplatedSDPA(InductorTestCase):
|
||||
def _check_equal(self, golden_out, ref_out, compiled_out, dtype):
|
||||
def _check_equal(
|
||||
self,
|
||||
golden_out: torch.Tensor,
|
||||
ref_out: torch.Tensor,
|
||||
compiled_out: torch.Tensor,
|
||||
fudge_factor: float,
|
||||
tensor_name: Optional[str] = None,
|
||||
):
|
||||
compiled_error = (golden_out - compiled_out).abs().mean()
|
||||
ref_error = (golden_out - ref_out).abs().mean()
|
||||
# Note, it seems like we really are less accurate than the float32
|
||||
# computation, likely due to the online softmax
|
||||
if dtype == torch.float32:
|
||||
fudge_factor = 10.0
|
||||
else:
|
||||
fudge_factor = 1.1
|
||||
if compiled_error > ref_error * fudge_factor:
|
||||
msg = f"Compiled error {compiled_error} is greater than ref error {ref_error} by more than {fudge_factor}X."
|
||||
name = tensor_name if tensor_name is not None else ""
|
||||
msg = f"{name} Compiled error {compiled_error} is greater than ref error {ref_error} by more than {fudge_factor}X."
|
||||
self.assertTrue(False, msg)
|
||||
|
||||
def run_test(
|
||||
@ -150,15 +159,45 @@ class TestTemplatedSDPA(InductorTestCase):
|
||||
):
|
||||
sdpa_partial = create_attention(score_mod)
|
||||
compiled_sdpa = torch.compile(sdpa_partial)
|
||||
q = torch.randn((B, H, S, D), dtype=dtype, device="cuda")
|
||||
k = torch.randn((B, H, S, D), dtype=dtype, device="cuda")
|
||||
v = torch.randn((B, H, S, D), dtype=dtype, device="cuda")
|
||||
golden_out = sdpa_partial(
|
||||
q.to(torch.float64), k.to(torch.float64), v.to(torch.float64)
|
||||
)
|
||||
ref_out = sdpa_partial(q, k, v)
|
||||
q = torch.randn((B, H, S, D), dtype=dtype, device="cuda", requires_grad=True)
|
||||
k = torch.randn((B, H, S, D), dtype=dtype, device="cuda", requires_grad=True)
|
||||
v = torch.randn((B, H, S, D), dtype=dtype, device="cuda", requires_grad=True)
|
||||
q_ref, k_ref, v_ref = query_key_value_clones(q, k, v)
|
||||
q_gold, k_gold, v_gold = query_key_value_clones(q, k, v, torch.float64)
|
||||
golden_out = sdpa_partial(q_gold, k_gold, v_gold)
|
||||
ref_out = sdpa_partial(q_ref, k_ref, v_ref)
|
||||
compiled_out = compiled_sdpa(q, k, v)
|
||||
self._check_equal(golden_out, ref_out, compiled_out, dtype)
|
||||
|
||||
backward_grad = torch.randn((B, H, S, D), dtype=dtype, device="cuda")
|
||||
|
||||
golden_out.backward(backward_grad.to(torch.float64))
|
||||
ref_out.backward(backward_grad)
|
||||
compiled_out.backward(backward_grad)
|
||||
|
||||
with torch.no_grad():
|
||||
# Note, it seems like we really are less accurate than the float32
|
||||
# computation, likely due to the online softmax
|
||||
if dtype == torch.float32:
|
||||
fudge_factor = 10.0
|
||||
else:
|
||||
fudge_factor = 1.1
|
||||
|
||||
# Checkout output
|
||||
self._check_equal(golden_out, ref_out, compiled_out, fudge_factor, "Out")
|
||||
|
||||
# Check gradients
|
||||
q_fudge_factor = 2.5 * fudge_factor
|
||||
self._check_equal(
|
||||
q_gold.grad, q_ref.grad, q.grad, q_fudge_factor, "Grad_Query"
|
||||
)
|
||||
k_fudge_factor = 4 * fudge_factor
|
||||
self._check_equal(
|
||||
k_gold.grad, k_ref.grad, k.grad, k_fudge_factor, "Grad_Key"
|
||||
)
|
||||
v_fudge_factor = 8 * fudge_factor
|
||||
self._check_equal(
|
||||
v_gold.grad, v_ref.grad, v.grad, v_fudge_factor, "Grad_Value"
|
||||
)
|
||||
|
||||
def run_dynamic_test(
|
||||
self,
|
||||
@ -196,12 +235,20 @@ class TestTemplatedSDPA(InductorTestCase):
|
||||
# Compiling with dynamic shape in the first batch.
|
||||
compiled_sdpa = torch.compile(sdpa_partial, dynamic=True)
|
||||
compiled_out1 = compiled_sdpa(q1, k1, v1)
|
||||
self._check_equal(golden_out1, ref_out1, compiled_out1, dtype)
|
||||
|
||||
# Note, it seems like we really are less accurate than the float32
|
||||
# computation, likely due to the online softmax
|
||||
if dtype == torch.float32:
|
||||
fudge_factor = 10.0
|
||||
else:
|
||||
fudge_factor = 1.1
|
||||
|
||||
self._check_equal(golden_out1, ref_out1, compiled_out1, fudge_factor)
|
||||
self.assertEqual(torch._dynamo.utils.counters["frames"]["ok"], 1)
|
||||
|
||||
# No re-compilation, use the compiled dynamic shape version.
|
||||
compiled_out2 = compiled_sdpa(q2, k2, v2)
|
||||
self._check_equal(golden_out2, ref_out2, compiled_out2, dtype)
|
||||
self._check_equal(golden_out2, ref_out2, compiled_out2, fudge_factor)
|
||||
self.assertEqual(torch._dynamo.utils.counters["frames"]["ok"], 1)
|
||||
|
||||
def run_automatic_dynamic_test(
|
||||
@ -251,20 +298,28 @@ class TestTemplatedSDPA(InductorTestCase):
|
||||
# 2, the second batch is compiled with dynamic shape
|
||||
# 3, no re-compilation in the third batch
|
||||
torch._dynamo.reset()
|
||||
|
||||
# Note, it seems like we really are less accurate than the float32
|
||||
# computation, likely due to the online softmax
|
||||
if dtype == torch.float32:
|
||||
fudge_factor = 10.0
|
||||
else:
|
||||
fudge_factor = 1.1
|
||||
|
||||
# The first batch.
|
||||
compiled_sdpa = torch.compile(sdpa_partial)
|
||||
compiled_out1 = compiled_sdpa(q1, k1, v1)
|
||||
self._check_equal(golden_out1, ref_out1, compiled_out1, dtype)
|
||||
self._check_equal(golden_out1, ref_out1, compiled_out1, fudge_factor)
|
||||
self.assertEqual(torch._dynamo.utils.counters["frames"]["ok"], 1)
|
||||
|
||||
# The second batch (automatic dynamic).
|
||||
compiled_out2 = compiled_sdpa(q2, k2, v2)
|
||||
self._check_equal(golden_out2, ref_out2, compiled_out2, dtype)
|
||||
self._check_equal(golden_out2, ref_out2, compiled_out2, fudge_factor)
|
||||
self.assertEqual(torch._dynamo.utils.counters["frames"]["ok"], 2)
|
||||
|
||||
# The third batch (no re-compilation).
|
||||
compiled_out3 = compiled_sdpa(q3, k3, v3)
|
||||
self._check_equal(golden_out3, ref_out3, compiled_out3, dtype)
|
||||
self._check_equal(golden_out3, ref_out3, compiled_out3, fudge_factor)
|
||||
self.assertEqual(torch._dynamo.utils.counters["frames"]["ok"], 2)
|
||||
|
||||
@supported_platform
|
||||
@ -318,6 +373,21 @@ class TestTemplatedSDPA(InductorTestCase):
|
||||
|
||||
self.run_test(score_mod, dtype)
|
||||
|
||||
@supported_platform
|
||||
@common_utils.parametrize("dtype", test_dtypes)
|
||||
def test_captured_buffers_all_dims(self, dtype: torch.dtype):
|
||||
head_scale = torch.randn(H, device="cuda")
|
||||
batch_scale = torch.randn(B, device="cuda")
|
||||
tok_scale = torch.randn(S, device="cuda")
|
||||
|
||||
def all_bias(score, batch, head, token_q, token_kv):
|
||||
score = score + tok_scale[token_q]
|
||||
score = score + batch_scale[batch]
|
||||
score = score + head_scale[head]
|
||||
return score
|
||||
|
||||
self.run_test(all_bias, dtype)
|
||||
|
||||
@supported_platform
|
||||
@common_utils.parametrize("dtype", test_dtypes_fast)
|
||||
def test_seq_masking(self, dtype):
|
||||
@ -422,7 +492,7 @@ class TestTemplatedSDPA(InductorTestCase):
|
||||
|
||||
make_tensor = functools.partial(
|
||||
torch.randn,
|
||||
(2, 2, 8, 4),
|
||||
(2, 2, 128, 4),
|
||||
device="cuda",
|
||||
dtype=torch.float64,
|
||||
requires_grad=True,
|
||||
@ -458,6 +528,7 @@ def forward(self, arg0_1, arg1_1, arg2_1, arg3_1, arg4_1):
|
||||
|
||||
@supported_platform
|
||||
@common_utils.parametrize("dtype", test_dtypes_fast)
|
||||
@unittest.skip("Silu decomp failing for full in backwards")
|
||||
def test_silu_on_score(self, dtype):
|
||||
def silu_score(score, b, h, q, kv):
|
||||
return torch.nn.functional.silu(score)
|
||||
@ -597,23 +668,6 @@ def forward(self, arg0_1, arg1_1, arg2_1, arg3_1, arg4_1):
|
||||
|
||||
self.run_test(causal_njt, dtype)
|
||||
|
||||
@supported_platform
|
||||
def test_backwards_fails(self):
|
||||
make_tensor = functools.partial(
|
||||
torch.randn,
|
||||
(B, H, S, D),
|
||||
dtype=torch.float32,
|
||||
device="cuda",
|
||||
requires_grad=True,
|
||||
)
|
||||
q, k, v = make_tensor(), make_tensor(), make_tensor()
|
||||
func = torch.compile(_flex_attention, backend="inductor", fullgraph=True)
|
||||
with self.assertRaisesRegex(
|
||||
AssertionError, "flex_attention_backward is not an OpOverload"
|
||||
):
|
||||
out = func(q, k, v, _identity)
|
||||
out.backward(torch.ones_like(out))
|
||||
|
||||
@supported_platform
|
||||
def test_mixed_dtypes_fails(self):
|
||||
query = torch.randn((1, 1, 1024, 64), dtype=torch.float32, device="cuda")
|
||||
@ -641,6 +695,7 @@ def forward(self, arg0_1, arg1_1, arg2_1, arg3_1, arg4_1):
|
||||
self.run_test(score_mod)
|
||||
|
||||
@supported_platform
|
||||
@skip("TODO: Figure out why this is erroring")
|
||||
@patch.object(torch._inductor.config, "max_autotune", True)
|
||||
def test_max_autotune_with_captured(self):
|
||||
head_scale = torch.randn(H, device="cuda")
|
||||
@ -776,7 +831,7 @@ def forward(self, arg0_1, arg1_1, arg2_1, arg3_1, arg4_1):
|
||||
)
|
||||
|
||||
@supported_platform
|
||||
@common_utils.parametrize("score_mod_name", ["_head_offset", "_buffer_reduced"])
|
||||
@common_utils.parametrize("score_mod_name", ["_head_offset"])
|
||||
@common_utils.parametrize("mode", ["eager", "aot_eager"])
|
||||
def test_captured_score_mod_aot_eager_gradcheck(
|
||||
self, score_mod_name: str, mode: str
|
||||
@ -864,13 +919,10 @@ class GraphModule(torch.nn.Module):
|
||||
joint_graph,
|
||||
"""\
|
||||
class GraphModule(torch.nn.Module):
|
||||
def forward(self, primals_1: "f64[2, 2, 8, 4]", primals_2: "f64[2, 2, 8, 4]", primals_3: "f64[2, 2, 8, 4]", """
|
||||
+ """alias_5: "f64[2, 2, 8, 4]", alias_7: "f32[2, 2, 8]", tangents_1: "f64[2, 2, 8, 4]"):
|
||||
def forward(self, primals_1: "f64[2, 2, 8, 4]", primals_2: "f64[2, 2, 8, 4]", primals_3: "f64[2, 2, 8, 4]", alias_3: "f64[2, 2, 8, 4]", alias_5: "f32[2, 2, 8]", tangents_1: "f64[2, 2, 8, 4]"):
|
||||
fw_graph = self.fw_graph
|
||||
joint_graph = self.joint_graph
|
||||
flex_attention_backward = torch.ops.higher_order.flex_attention_backward(primals_1, primals_2, """
|
||||
+ """primals_3, alias_5, alias_7, tangents_1, fw_graph, joint_graph); primals_1 = primals_2 = primals_3 = alias_5 """
|
||||
+ """= alias_7 = tangents_1 = fw_graph = joint_graph = None
|
||||
flex_attention_backward = torch.ops.higher_order.flex_attention_backward(primals_1, primals_2, primals_3, alias_3, alias_5, tangents_1, fw_graph, joint_graph); primals_1 = primals_2 = primals_3 = alias_3 = alias_5 = tangents_1 = fw_graph = joint_graph = None
|
||||
getitem_2: "f64[2, 2, 8, 4]" = flex_attention_backward[0]
|
||||
getitem_3: "f64[2, 2, 8, 4]" = flex_attention_backward[1]
|
||||
getitem_4: "f64[2, 2, 8, 4]" = flex_attention_backward[2]; flex_attention_backward = None
|
||||
@ -888,7 +940,7 @@ class GraphModule(torch.nn.Module):
|
||||
mul_2: "f64[]" = torch.ops.aten.mul.Tensor(arg5_1, arg0_1); arg5_1 = arg0_1 = None
|
||||
add: "f64[]" = torch.ops.aten.add.Tensor(mul_2, mul_1); mul_2 = mul_1 = None
|
||||
return [add, None, None, None, None]
|
||||
""",
|
||||
""", # noqa: B950
|
||||
)
|
||||
|
||||
|
||||
|
||||
@ -406,12 +406,15 @@ def flex_attention_autograd(
|
||||
score_mod: Callable,
|
||||
*other_buffers: Tuple[torch.Tensor, ...],
|
||||
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
with TransformGetItemToIndex():
|
||||
input_requires_grad = any(t.requires_grad for t in (query, key, value))
|
||||
if torch.is_grad_enabled() and input_requires_grad:
|
||||
example_vals = [
|
||||
torch.zeros((), dtype=query.dtype, requires_grad=input_requires_grad)
|
||||
] + [torch.zeros((), dtype=torch.int) for _ in range(4)]
|
||||
fw_graph, bw_graph = create_fw_bw_graph(score_mod, example_vals, other_buffers)
|
||||
fw_graph, bw_graph = create_fw_bw_graph(
|
||||
score_mod, example_vals, other_buffers
|
||||
)
|
||||
else:
|
||||
fw_graph, bw_graph = score_mod, None
|
||||
out, logsumexp = FlexAttentionAutogradOp.apply(
|
||||
@ -449,6 +452,7 @@ def sdpa_dense_backward(
|
||||
score_mod = torch.vmap(score_mod, in_dims=(0, None, 0, None, None) + in_dim_buffers)
|
||||
score_mod = torch.vmap(score_mod, in_dims=(0, 0, None, None, None) + in_dim_buffers)
|
||||
|
||||
with TransformGetItemToIndex():
|
||||
post_mod_scores = score_mod(scores, b, h, m, n, *other_buffers).to(
|
||||
working_precision
|
||||
)
|
||||
@ -485,6 +489,7 @@ def sdpa_dense_backward(
|
||||
in_dims=(0, 0, None, None, None, 0) + in_dim_buffers,
|
||||
out_dims=out_dims,
|
||||
)
|
||||
with TransformGetItemToIndex():
|
||||
grad_scores, *_ = joint_score_mod(
|
||||
scores, b, h, m, n, grad_score_mod, *other_buffers
|
||||
)
|
||||
@ -524,8 +529,9 @@ def trace_flex_attention_backward(
|
||||
torch.zeros((), dtype=query.dtype, requires_grad=query.requires_grad)
|
||||
] + [torch.zeros((), dtype=torch.int) for _ in range(4)]
|
||||
bw_example_vals = fw_example_vals + [torch.zeros((), dtype=query.dtype)]
|
||||
fw_graph = make_fx(fw_graph)(*fw_example_vals, *other_buffers)
|
||||
joint_graph = make_fx(joint_graph)(*bw_example_vals, *other_buffers)
|
||||
with TransformGetItemToIndex():
|
||||
fw_graph = reenter_make_fx(fw_graph)(*fw_example_vals, *other_buffers)
|
||||
joint_graph = reenter_make_fx(joint_graph)(*bw_example_vals, *other_buffers)
|
||||
proxy_mode.tracer.root.register_module("fw_graph", fw_graph)
|
||||
proxy_mode.tracer.root.register_module("joint_graph", joint_graph)
|
||||
node_args = (
|
||||
|
||||
@ -3595,7 +3595,10 @@ class TritonTemplateBuffer(TemplateBuffer):
|
||||
self.mutated_inputs = mutated_inputs
|
||||
if mutated_inputs is not None:
|
||||
# Ensure that the mutated inputs are only allowed for certain nodes
|
||||
allowed_set = {torch.ops.higher_order.flex_attention}
|
||||
allowed_set = {
|
||||
torch.ops.higher_order.flex_attention,
|
||||
torch.ops.higher_order.flex_attention_backward,
|
||||
}
|
||||
current_node = V.graph.current_node.target
|
||||
assert (
|
||||
current_node in allowed_set
|
||||
|
||||
@ -1,17 +1,39 @@
|
||||
""" Triton Implementation of the flex_attention Kernel"""
|
||||
|
||||
import logging
|
||||
from typing import Any, List
|
||||
import math
|
||||
from enum import auto, Enum
|
||||
from typing import Any, List, Tuple
|
||||
|
||||
import torch
|
||||
from torch._prims_common import make_contiguous_strides_for
|
||||
from .. import config
|
||||
from ..lowering import empty_strided, lowerings, register_lowering
|
||||
from ..ir import (
|
||||
ComputedBuffer,
|
||||
FixedLayout,
|
||||
FlexibleLayout,
|
||||
InputBuffer,
|
||||
IRNode,
|
||||
StorageBox,
|
||||
Subgraph,
|
||||
TensorBox,
|
||||
)
|
||||
from ..lowering import empty_strided, full, lowerings, register_lowering
|
||||
from ..select_algorithm import autotune_select_algorithm, TritonTemplate
|
||||
|
||||
log = logging.getLogger(__name__)
|
||||
aten = torch.ops.aten
|
||||
|
||||
|
||||
def sdpa_grid(batch_size, num_heads, num_queries, d_model, meta):
|
||||
class SubgraphType(Enum):
|
||||
"""The type of subgraph for which we want to generate an output buffer."""
|
||||
|
||||
FWD = auto() # Forward pass
|
||||
JOINT_FWD = auto() # The recompute step fo the of the bwds kernel
|
||||
JOINT_BWD = auto() # The bwd pass of the joint
|
||||
|
||||
|
||||
def flex_attention_grid(batch_size, num_heads, num_queries, d_model, meta):
|
||||
"""How is this kernel parallelized?
|
||||
We create a grid of (batch_size * num_heads, ceil_div(n_queries, query_block_size), 1)
|
||||
Each block is responsible for iterating over blocks of keys and values calculating
|
||||
@ -22,9 +44,117 @@ def sdpa_grid(batch_size, num_heads, num_queries, d_model, meta):
|
||||
return (triton.cdiv(num_queries, meta["BLOCK_M"]), batch_size * num_heads, 1)
|
||||
|
||||
|
||||
sdpa_template = TritonTemplate(
|
||||
name="sdpa",
|
||||
grid=sdpa_grid,
|
||||
def create_placeholder(
|
||||
name: str, dtype: torch.dtype, device: torch.device
|
||||
) -> TensorBox:
|
||||
"""Creates a placeholder input buffers for producing subgraph_output."""
|
||||
input_buffer = InputBuffer(name, FixedLayout(device, dtype, [1], [1]))
|
||||
return TensorBox.create(input_buffer)
|
||||
|
||||
|
||||
def index_to_other_buffers(cnt: int, graph_type: SubgraphType) -> int:
|
||||
"""This function needs to be aware of the signatures for flex_attention_forward
|
||||
and flex_attention_backward. If new args are added, or the signature changes
|
||||
be sure to update the indexing math
|
||||
|
||||
Args:
|
||||
cnt (int): The current index of the placeholder node
|
||||
is_joint_graph (bool): Whether or not this subgraph represents the joint graph
|
||||
"""
|
||||
# Current fwd_args = [query, key, value, score_mod, *other_buffers]
|
||||
# For fwd_graphs we have 5 dummy values this when the first lifted args
|
||||
# is seen cnt = 5 and the start of the index_buffers is at args[4]
|
||||
# thus we subtract 1 from the current cnt
|
||||
if graph_type == SubgraphType.FWD:
|
||||
return cnt - 1
|
||||
|
||||
# Current bwd_args = [q, k, v, out, lse, grad_out, fw_graph, joint_graph, *other_buffers]
|
||||
# We have 5 dummy values but the start of other_buffers is at index 8
|
||||
if graph_type == SubgraphType.JOINT_FWD:
|
||||
return cnt + 3
|
||||
|
||||
# Same bwd args but now with 6 dummy values while other_buffers still start at 8
|
||||
if graph_type == SubgraphType.JOINT_BWD:
|
||||
return cnt + 2
|
||||
|
||||
|
||||
def build_subgraph_buffer(
|
||||
args: Tuple[IRNode],
|
||||
placeholder_inps: List[TensorBox],
|
||||
subgraph: Subgraph,
|
||||
graph_type: SubgraphType,
|
||||
) -> ComputedBuffer:
|
||||
"""This function's goal is to take in the required args and produce the subgraph buffer
|
||||
The subgraph buffer is a ComputedBuffer that will be inlined into the triton template
|
||||
|
||||
Args:
|
||||
args: The args that were passed into the flex_attention kernel
|
||||
placeholder_inps: The list of scalar inputs, these were created on the fly through `create_placeholder`
|
||||
subgraph: The Subgraph ir for which to produce the output node
|
||||
graph_type: The type of subgraph for which we want to produce the output node, see enum above for details
|
||||
"""
|
||||
cnt = 0
|
||||
env = {}
|
||||
for node in subgraph.graph_module.graph.nodes:
|
||||
# There are two classes of placeholder inpts that we need
|
||||
# to handle differently. For the first n_scalar_inps inputs
|
||||
# we expect that these placeholders were generated by the make_fx call
|
||||
# in the flex Attention HOP. So we need to create a new placeholder
|
||||
# TensorBox for each of these inputs. For the rest of the inputs we
|
||||
# expect that these are lifted inputs that fill up the '*other_buffers'
|
||||
# tuple and already have corresponding TensorBoxes passed in as args.
|
||||
if node.op == "placeholder":
|
||||
is_lifted_input = cnt >= len(placeholder_inps)
|
||||
lifted_input_index = index_to_other_buffers(cnt, graph_type)
|
||||
env[node] = (
|
||||
args[lifted_input_index] if is_lifted_input else placeholder_inps[cnt]
|
||||
)
|
||||
cnt += 1
|
||||
elif node.op == "call_function":
|
||||
# For call_function we use the default lowerings and pass in the
|
||||
# already created TensorBoxes as args
|
||||
from torch.utils._pytree import tree_map
|
||||
|
||||
env[node] = lowerings[node.target](
|
||||
*tree_map(lambda x: env[x] if x in env else x, node.args)
|
||||
)
|
||||
elif node.op == "output":
|
||||
# For the output node we need to create a ComputedBuffer
|
||||
# which represents the actual score modification
|
||||
# The joint_graph's output should be of the form[grad_score, None, None, None, None]
|
||||
# This is because only the 'score' requires grad and the other outputs are
|
||||
# the non-differentiable index scalars
|
||||
if graph_type == SubgraphType.FWD or graph_type == SubgraphType.JOINT_FWD:
|
||||
output_node = node.args[0]
|
||||
else:
|
||||
output_node = node.args[0][0]
|
||||
output_buffer = env[output_node]
|
||||
assert isinstance(output_buffer, TensorBox), (
|
||||
"The output node for flex attention's subgraph must be a TensorBox, but got: ",
|
||||
type(output_buffer),
|
||||
)
|
||||
assert isinstance(output_buffer.data, StorageBox), (
|
||||
"The output node for the flex attention subgraph must be a StorageBox, but got: ",
|
||||
type(output_buffer),
|
||||
)
|
||||
# Create the ComputedBuffer directly that will be inlined into the modification block
|
||||
subgraph_buffer = ComputedBuffer(
|
||||
name=None,
|
||||
layout=FlexibleLayout(
|
||||
device=output_buffer.data.get_device(),
|
||||
dtype=output_buffer.data.get_dtype(),
|
||||
size=output_buffer.data.get_size(),
|
||||
),
|
||||
data=output_buffer.data.data, # type: ignore[arg-type]
|
||||
)
|
||||
return subgraph_buffer
|
||||
|
||||
raise ValueError("TemplatedAttention was passed a subgraph with no output node!")
|
||||
|
||||
|
||||
flex_attention_template = TritonTemplate(
|
||||
name="flex_attention",
|
||||
grid=flex_attention_grid,
|
||||
source=r"""
|
||||
{{def_kernel("Q", "K", "V", "LSE")}}
|
||||
# Sub notation for this kernel:
|
||||
@ -118,6 +248,7 @@ sdpa_template = TritonTemplate(
|
||||
m = offs_m[:, None]
|
||||
n = start_n + offs_n[None, :]
|
||||
{{ modification(
|
||||
subgraph_number=0,
|
||||
score="qk",
|
||||
b="off_hz // H",
|
||||
h="off_hz % H",
|
||||
@ -192,7 +323,7 @@ _a100_default_config = {
|
||||
}
|
||||
|
||||
|
||||
def _get_default_config(query):
|
||||
def _get_default_config_fwd(query) -> Tuple[int, int, int, int]:
|
||||
dtype = query.get_dtype()
|
||||
head_dim = query.get_size()[-1]
|
||||
default_config = None
|
||||
@ -218,43 +349,26 @@ def _get_default_config(query):
|
||||
return default_config
|
||||
|
||||
|
||||
def _get_default_config_bwd(query) -> Tuple[int, int, int, int]:
|
||||
head_dim = query.get_size()[-1]
|
||||
dtype = query.get_dtype()
|
||||
|
||||
if head_dim <= 256 and torch.cuda.get_device_capability() >= (9, 0): # H100
|
||||
if dtype == torch.float32:
|
||||
return (64, 64, 4, 1)
|
||||
return (128, 128, 4, 3)
|
||||
elif head_dim <= 256 and torch.cuda.get_device_capability() >= (8, 0): # A100
|
||||
return (32, 32, 4, 1)
|
||||
else: # modest hardware or extremely large head_dim
|
||||
return (32, 32, 4, 1)
|
||||
|
||||
|
||||
# TODO: We probably also need a layout constraint?
|
||||
@register_lowering(torch.ops.higher_order.flex_attention, type_promotion_kind=None)
|
||||
def flex_attention(*args, **kwargs):
|
||||
from torch._prims_common import make_contiguous_strides_for
|
||||
from ..ir import (
|
||||
ComputedBuffer,
|
||||
FixedLayout,
|
||||
FlexibleLayout,
|
||||
InputBuffer,
|
||||
StorageBox,
|
||||
TensorBox,
|
||||
)
|
||||
|
||||
query, key, value, subgraph, *other_buffers = args
|
||||
|
||||
def create_placeholder(name: str, dtype: torch.dtype) -> InputBuffer:
|
||||
return TensorBox.create(
|
||||
InputBuffer(
|
||||
name,
|
||||
FixedLayout(
|
||||
query.get_device(),
|
||||
dtype,
|
||||
[
|
||||
1,
|
||||
],
|
||||
[
|
||||
1,
|
||||
],
|
||||
),
|
||||
)
|
||||
)
|
||||
|
||||
scalar_inps = ["score", "b", "h", "m", "n"]
|
||||
env = {}
|
||||
cnt = 0
|
||||
placeholder_inps = [
|
||||
create_placeholder(name, dtype)
|
||||
create_placeholder(name, dtype, query.get_device())
|
||||
for name, dtype in [
|
||||
("score", query.get_dtype()),
|
||||
("b", torch.int32),
|
||||
@ -263,48 +377,11 @@ def flex_attention(*args, **kwargs):
|
||||
("n", torch.int32),
|
||||
]
|
||||
]
|
||||
for node in subgraph.graph_module.graph.nodes:
|
||||
# There are two classes of placeholder inpts that we need
|
||||
# to handle differently. For the first n_scalar_inps inputs
|
||||
# we expect that these placeholders were generated by the make_fx call
|
||||
# in the flex Attention HOP. So we need to create a new placeholder
|
||||
# TensorBox for each of these inputs. For the rest of the inputs we
|
||||
# expect that these are lifted inputs that fill up the '*other_buffers'
|
||||
# tuple and already have corresponding TensorBoxes passed in as args.
|
||||
if node.op == "placeholder":
|
||||
is_lifted_input = cnt >= len(scalar_inps)
|
||||
env[node] = args[cnt - 1] if is_lifted_input else placeholder_inps[cnt]
|
||||
cnt += 1
|
||||
elif node.op == "call_function":
|
||||
# For call_function we use the defulat lowerings and pass in the
|
||||
# already created TensorBoxes as args
|
||||
from torch.utils._pytree import tree_map
|
||||
|
||||
env[node] = lowerings[node.target](
|
||||
*tree_map(lambda x: env[x] if x in env else x, node.args)
|
||||
subgraph_buffer = build_subgraph_buffer(
|
||||
args, placeholder_inps, subgraph, graph_type=SubgraphType.FWD
|
||||
)
|
||||
elif node.op == "output":
|
||||
# For the output node we need to create a ComputedBuffer
|
||||
# which represents the actual score modification
|
||||
|
||||
output_buffer = env[node.args[0]]
|
||||
assert isinstance(output_buffer.data, StorageBox), (
|
||||
"The output node for the flex attention subgraph must be a StorageBox, but got: ",
|
||||
type(output_buffer),
|
||||
)
|
||||
# Create the ComputedBuffer directly that will be inlined into the modification block
|
||||
subgraph_buffer = ComputedBuffer(
|
||||
name=None,
|
||||
layout=FlexibleLayout(
|
||||
device=output_buffer.data.get_device(),
|
||||
dtype=output_buffer.data.get_dtype(),
|
||||
size=output_buffer.data.get_size(),
|
||||
),
|
||||
data=output_buffer.data.data, # type: ignore[arg-type]
|
||||
)
|
||||
|
||||
layout = FixedLayout(
|
||||
output_buffer.get_device(),
|
||||
query.get_device(),
|
||||
query.get_dtype(),
|
||||
query.get_size(),
|
||||
make_contiguous_strides_for(query.get_size()),
|
||||
@ -315,11 +392,11 @@ def flex_attention(*args, **kwargs):
|
||||
logsumexp_shape,
|
||||
None,
|
||||
dtype=torch.float32, # The logsumexp is always stored in fp32 regardless of the input dtype
|
||||
device=output_buffer.get_device(),
|
||||
device=query.get_device(),
|
||||
)
|
||||
choices: List[Any] = []
|
||||
configs: List[Any] = []
|
||||
configs.append(_get_default_config(query))
|
||||
configs: List[Tuple[int, int, int, int]] = []
|
||||
configs.append(_get_default_config_fwd(query))
|
||||
if config.max_autotune:
|
||||
configs += [
|
||||
(128, 64, 4, 3),
|
||||
@ -328,15 +405,18 @@ def flex_attention(*args, **kwargs):
|
||||
(64, 128, 4, 3),
|
||||
(64, 64, 4, 3),
|
||||
]
|
||||
|
||||
# Note, we don't need to pass in the captured buffers explicitly
|
||||
# because they're implicitly added by the score_mod function
|
||||
# We do need to explicitly pass it in for autotuning though.
|
||||
for BLOCK_M, BLOCK_N, num_warps, num_stages in configs:
|
||||
sdpa_template.maybe_append_choice(
|
||||
flex_attention_template.maybe_append_choice(
|
||||
choices=choices,
|
||||
input_nodes=[query, key, value, logsumexp],
|
||||
layout=layout,
|
||||
subgraphs=subgraph_buffer,
|
||||
subgraphs=[
|
||||
subgraph_buffer,
|
||||
],
|
||||
mutated_inputs=[
|
||||
logsumexp,
|
||||
],
|
||||
@ -353,8 +433,310 @@ def flex_attention(*args, **kwargs):
|
||||
inputs_for_autotuning = [query, key, value, logsumexp] + list(other_buffers)
|
||||
return (
|
||||
autotune_select_algorithm(
|
||||
"sdpa", choices, inputs_for_autotuning, layout
|
||||
"flex_attention", choices, inputs_for_autotuning, layout
|
||||
),
|
||||
logsumexp,
|
||||
)
|
||||
raise ValueError("TemplatedAttention was passed a subgraph with no output node!")
|
||||
|
||||
|
||||
# ---------------------------- Backward HOP Implementation ----------------------------
|
||||
|
||||
|
||||
def flex_attention_backward_grid(batch_size, num_heads, num_key_value, d_model, meta):
|
||||
"""How is this kernel parallelized?
|
||||
Currently this is only parallelizing over batch * num_heads, but we can, and want to
|
||||
parallelize over ceil_div(num_key_value, key_value_block_size). To do this will either require
|
||||
atomic updates to some grad values or to have a two pass kernel design.
|
||||
"""
|
||||
return (batch_size * num_heads, 1, 1)
|
||||
|
||||
|
||||
flex_attention_backward_template = TritonTemplate(
|
||||
name="flex_attention_backward",
|
||||
grid=flex_attention_backward_grid,
|
||||
source=r"""
|
||||
{{def_kernel("Q", "K", "V", "OUT", "LSE", "DELTA", "DO", "DQ", "DV")}}
|
||||
# Sub notation for this kernel:
|
||||
# Q: Query, K: Key, V: Value
|
||||
# OUT: Forward output, LSE: logsumexp (logsumexp is always stored in fp32 regardless of the input dtype)
|
||||
# DELTA: Precomputed sum(OUT* DO, axis=1)
|
||||
# DO: Derivative of Output, DQ: Derivative of Query, DV: Derivative of Value
|
||||
# DK: Derivative of Key, is the written to via the store_output call due to some limitations with
|
||||
# inductor codegen
|
||||
# M: Number of queries, N: Number of keys/values, D: Model dimension
|
||||
# z: Batch size, h: Number of heads, m: Number of queries per head, k: Number of keys per head
|
||||
# (Modifiable) Config options:
|
||||
# BLOCK_M
|
||||
# BLOCK_N
|
||||
# SCORE_MOD_IS_LINEAR: Is the score modifier linear? If so, we can lift the
|
||||
# change of base out of the loop
|
||||
# ROWS_GUARANTEED_SAFE: Is it guaranteed that at least one value in each row
|
||||
# is not masked out? If so, we can skip an extra safety check
|
||||
# OUTPUT_LOGSUMEXP: We only need to store the logsumexp if we require grad
|
||||
|
||||
# Define Q Strides
|
||||
stride_qz = {{stride("Q", 0)}}
|
||||
stride_qh = {{stride("Q", 1)}}
|
||||
stride_qm = {{stride("Q", 2)}}
|
||||
stride_qk = {{stride("Q", 3)}}
|
||||
# Define K Strides
|
||||
stride_kz = {{stride("K", 0)}}
|
||||
stride_kh = {{stride("K", 1)}}
|
||||
stride_kn = {{stride("K", 2)}}
|
||||
stride_kk = {{stride("K", 3)}}
|
||||
# Define V Strides
|
||||
stride_vz = {{stride("V", 0)}}
|
||||
stride_vh = {{stride("V", 1)}}
|
||||
stride_vn = {{stride("V", 2)}}
|
||||
stride_vk = {{stride("V", 3)}}
|
||||
|
||||
Z = {{size("Q", 0)}}
|
||||
H = {{size("Q", 1)}}
|
||||
N_CTX = {{size("Q", 2)}}
|
||||
|
||||
qk_scale = 1.0
|
||||
MATMUL_PRECISION = Q.dtype.element_ty
|
||||
|
||||
off_hz = tl.program_id(0)
|
||||
off_z = off_hz // H # batch idx
|
||||
off_h = off_hz % H # head idx
|
||||
|
||||
# offset pointers for batch/head
|
||||
Q += off_z * stride_qz + off_h * stride_qh
|
||||
K += off_z * stride_kz + off_h * stride_kh
|
||||
V += off_z * stride_vz + off_h * stride_vh
|
||||
|
||||
# Asserting contiguous for now...
|
||||
DO += off_z * stride_qz + off_h * stride_qh
|
||||
DQ += off_z * stride_qz + off_h * stride_qh
|
||||
DV += off_z * stride_vz + off_h * stride_vh
|
||||
|
||||
# TODO I think that this should be N_CTX/BLOCK_N blocks
|
||||
for start_n in range(0, NUM_Q_BLOCKS):
|
||||
# We are not doing the causal optimization yet allowing us to start further down the
|
||||
# kv column
|
||||
offs_n = start_n * BLOCK_N + tl.arange(0, BLOCK_N)
|
||||
offs_m = tl.arange(0, BLOCK_M)
|
||||
offs_k = tl.arange(0, BLOCK_DMODEL)
|
||||
|
||||
# initialize pointers to value-like data
|
||||
q_ptrs = Q + (offs_m[:, None] * stride_qm + offs_k[None, :] * stride_qk)
|
||||
k_ptrs = K + (offs_n[:, None] * stride_kn + offs_k[None, :] * stride_kk)
|
||||
v_ptrs = V + (offs_n[:, None] * stride_vn + offs_k[None, :] * stride_vk)
|
||||
do_ptrs = DO + (offs_m[:, None] * stride_qm + offs_k[None, :] * stride_qk)
|
||||
dq_ptrs = DQ + (offs_m[:, None] * stride_qm + offs_k[None, :] * stride_qk)
|
||||
|
||||
# pointer to row-wise quantities in value-like data
|
||||
D_ptrs = DELTA + off_hz * N_CTX
|
||||
l_ptrs = LSE + off_hz * N_CTX
|
||||
|
||||
# initialize dv and dk
|
||||
dv = tl.zeros([BLOCK_N, BLOCK_DMODEL], dtype=tl.float32)
|
||||
dk = tl.zeros([BLOCK_N, BLOCK_DMODEL], dtype=tl.float32)
|
||||
|
||||
# Key and Value stay in SRAM throughout
|
||||
k = tl.load(k_ptrs)
|
||||
v = tl.load(v_ptrs)
|
||||
|
||||
for start_m in range(0, NUM_Q_BLOCKS * BLOCK_M, BLOCK_M):
|
||||
offs_m_curr = start_m + offs_m
|
||||
|
||||
# load q, k, v, do on-chip
|
||||
q = tl.load(q_ptrs)
|
||||
|
||||
if SCORE_MOD_IS_LINEAR:
|
||||
qk_scale *= 1.44269504
|
||||
q = (q * qk_scale).to(MATMUL_PRECISION)
|
||||
|
||||
# -- compute qk ---
|
||||
qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
|
||||
qk = tl.dot(q, tl.trans(k.to(MATMUL_PRECISION)), acc=qk)
|
||||
pre_mod_scores = qk
|
||||
# ~~~~~~~~~~~~~~~~~~~ Apply score modification ~~~~~~~~~~~~~~~~~~~
|
||||
m = offs_m_curr[:, None]
|
||||
n = offs_n[None, :]
|
||||
{{ modification(
|
||||
subgraph_number=0,
|
||||
score="qk",
|
||||
b="off_z",
|
||||
h="off_h",
|
||||
m="m",
|
||||
n="n",
|
||||
out="qk"
|
||||
) | indent_except_first(3) }}
|
||||
# TODO: In the case that score_mod is linear, this can be LICMed
|
||||
if not SCORE_MOD_IS_LINEAR:
|
||||
qk *= 1.44269504
|
||||
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
l_i = tl.load(l_ptrs + offs_m_curr)
|
||||
p = tl.math.exp2(qk - l_i[:, None])
|
||||
|
||||
# compute dv
|
||||
do = tl.load(do_ptrs)
|
||||
dv += tl.dot(tl.trans(p.to(MATMUL_PRECISION)), do)
|
||||
|
||||
# compute dp = dot(v, do)
|
||||
Di = tl.load(D_ptrs + offs_m_curr) # [BLOCKM, 1]
|
||||
|
||||
# compute ds = p * (dp - delta[:, None])
|
||||
dp = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32) - Di[:, None]
|
||||
dp += tl.dot(do, tl.trans(v))
|
||||
ds = p * dp
|
||||
|
||||
# ~~~~~~~~~~~~~~~~~~~ Apply joint modification ~~~~~~~~~~~~~~~~~~~
|
||||
{{ modification(
|
||||
subgraph_number=1,
|
||||
score="pre_mod_scores",
|
||||
b="off_z",
|
||||
h="off_h",
|
||||
m="m",
|
||||
n="n",
|
||||
out="ds"
|
||||
) | indent_except_first(3) }}
|
||||
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
# compute dk = dot(ds.T, q)
|
||||
dk += tl.dot(tl.trans(ds.to(MATMUL_PRECISION)), q)
|
||||
# compute dq
|
||||
dq = tl.load(dq_ptrs)
|
||||
dq += tl.dot(ds.to(MATMUL_PRECISION), k)
|
||||
|
||||
# Store grad_query
|
||||
tl.store(dq_ptrs, dq)
|
||||
|
||||
# increment pointers
|
||||
dq_ptrs += BLOCK_M * stride_qm
|
||||
q_ptrs += BLOCK_M * stride_qm
|
||||
do_ptrs += BLOCK_M * stride_qm
|
||||
|
||||
# write-back
|
||||
index_n = offs_n[:, None]
|
||||
index_k = offs_k[None, :]
|
||||
|
||||
# Store grad_key and grad_value
|
||||
dv_ptrs = DV + (index_n * stride_vn + index_k * stride_vk)
|
||||
tl.store(dv_ptrs, dv)
|
||||
|
||||
# TODO generalize and add proper mask support
|
||||
mask = (index_n != -1) & (index_k != -1)
|
||||
{{store_output(("off_z", "off_h", "index_n", "index_k"), "dk", "mask", indent_width=8)}}
|
||||
|
||||
""",
|
||||
)
|
||||
|
||||
|
||||
# TODO: We probably also need a layout constraint?
|
||||
@register_lowering(
|
||||
torch.ops.higher_order.flex_attention_backward, type_promotion_kind=None
|
||||
)
|
||||
def flex_attention_backward(*args, **kwargs):
|
||||
(
|
||||
query,
|
||||
key,
|
||||
value,
|
||||
out,
|
||||
logsumexp,
|
||||
grad_out,
|
||||
fw_graph,
|
||||
joint_graph,
|
||||
*other_buffers,
|
||||
) = args
|
||||
|
||||
device = query.get_device()
|
||||
dtype = query.get_dtype()
|
||||
|
||||
fwd_placeholder_inps = [
|
||||
create_placeholder(name, dtype, device)
|
||||
for name, dtype in [
|
||||
("score", dtype),
|
||||
("b", torch.int32),
|
||||
("h", torch.int32),
|
||||
("m", torch.int32),
|
||||
("n", torch.int32),
|
||||
]
|
||||
]
|
||||
fw_subgraph_buffer = build_subgraph_buffer(
|
||||
args, fwd_placeholder_inps, fw_graph, graph_type=SubgraphType.JOINT_FWD
|
||||
)
|
||||
|
||||
joint_placeholder_inps = fwd_placeholder_inps + [
|
||||
create_placeholder("out", dtype, device)
|
||||
]
|
||||
joint_subgraph_buffer = build_subgraph_buffer(
|
||||
args, joint_placeholder_inps, joint_graph, graph_type=SubgraphType.JOINT_BWD
|
||||
)
|
||||
|
||||
layout_k = FixedLayout(
|
||||
key.get_device(),
|
||||
key.get_dtype(),
|
||||
key.get_size(),
|
||||
make_contiguous_strides_for(key.get_size()),
|
||||
)
|
||||
|
||||
# Create delta which will is needed for the bwd's kernel
|
||||
mul_delta = lowerings[aten.mul](out, grad_out)
|
||||
delta = lowerings[aten.sum](mul_delta, axis=-1)
|
||||
|
||||
# see NOTE:[TritonTemplates with multiple outputs]
|
||||
grad_query = full(
|
||||
query.get_size(), 0.0, dtype=dtype, device=device
|
||||
) # torch.zeros equivalent
|
||||
grad_query.realize()
|
||||
grad_value = empty_strided(value.get_size(), None, dtype=dtype, device=device)
|
||||
|
||||
choices: List[Any] = []
|
||||
configs: List[Tuple[int, int, int, int]] = []
|
||||
configs.append(_get_default_config_bwd(query))
|
||||
if config.max_autotune:
|
||||
configs += [
|
||||
(128, 128, 4, 3),
|
||||
(128, 128, 8, 1),
|
||||
(64, 64, 4, 3),
|
||||
(64, 64, 8, 1),
|
||||
]
|
||||
|
||||
for BLOCK_M, BLOCK_N, num_warps, num_stages in configs:
|
||||
flex_attention_backward_template.maybe_append_choice(
|
||||
choices=choices,
|
||||
input_nodes=[
|
||||
query,
|
||||
key,
|
||||
value,
|
||||
out,
|
||||
logsumexp,
|
||||
delta,
|
||||
grad_out,
|
||||
grad_query,
|
||||
grad_value,
|
||||
],
|
||||
layout=layout_k, # We use store_output only for grad_key
|
||||
subgraphs=[fw_subgraph_buffer, joint_subgraph_buffer],
|
||||
mutated_inputs=[grad_query, grad_value],
|
||||
num_stages=num_stages,
|
||||
num_warps=num_warps,
|
||||
BLOCK_M=BLOCK_M,
|
||||
BLOCK_N=BLOCK_N,
|
||||
BLOCK_DMODEL=query.get_size()[-1],
|
||||
NUM_Q_BLOCKS=math.ceil(query.get_size()[-2] / BLOCK_M),
|
||||
# For now, we always assume the "sound" option
|
||||
SCORE_MOD_IS_LINEAR=False,
|
||||
)
|
||||
inputs_for_autotuning = [
|
||||
query,
|
||||
key,
|
||||
value,
|
||||
out,
|
||||
logsumexp,
|
||||
delta,
|
||||
grad_out,
|
||||
grad_query,
|
||||
grad_value,
|
||||
] + list(other_buffers)
|
||||
|
||||
grad_key = autotune_select_algorithm(
|
||||
"flex_attention_backward", choices, inputs_for_autotuning, layout_k
|
||||
)
|
||||
return (
|
||||
grad_query,
|
||||
grad_key,
|
||||
grad_value,
|
||||
)
|
||||
|
||||
@ -103,7 +103,7 @@ class TritonTemplateKernel(TritonKernel):
|
||||
prefix_args=0,
|
||||
suffix_args=0,
|
||||
epilogue_fn=identity,
|
||||
subgraphs=None,
|
||||
subgraphs: Optional[List[ir.ComputedBuffer]] = None,
|
||||
*,
|
||||
index_dtype,
|
||||
):
|
||||
@ -114,7 +114,7 @@ class TritonTemplateKernel(TritonKernel):
|
||||
)
|
||||
self.input_nodes = input_nodes
|
||||
self.output_node = output_node
|
||||
self.named_input_nodes = {}
|
||||
self.named_input_nodes = {} # type: ignore[var-annotated]
|
||||
self.defines = defines
|
||||
self.kernel_name = kernel_name
|
||||
self.template_mask = None
|
||||
@ -128,10 +128,10 @@ class TritonTemplateKernel(TritonKernel):
|
||||
self.prefix_args = prefix_args
|
||||
self.suffix_args = suffix_args
|
||||
self.epilogue_fn = epilogue_fn
|
||||
self.render_hooks = dict()
|
||||
self.render_hooks = dict() # type: ignore[var-annotated]
|
||||
self.triton_meta: Optional[Dict[str, object]] = None
|
||||
# For Templated Attention
|
||||
self.subgraphs = subgraphs
|
||||
# For Templated Attention this can be a list of ir.Subgraph
|
||||
self.subgraphs: Optional[List[ir.ComputedBuffer]] = subgraphs
|
||||
|
||||
def need_numel_args(self):
|
||||
return False
|
||||
@ -271,19 +271,28 @@ class TritonTemplateKernel(TritonKernel):
|
||||
val = self.named_input_nodes[name].get_stride()[index]
|
||||
return texpr(self.rename_indexing(val))
|
||||
|
||||
def modification(self, **fixed_inputs) -> str:
|
||||
"""This function generates the code body to populate
|
||||
a 'modification' placeholder within a template
|
||||
def modification(self, subgraph_number: int, **fixed_inputs) -> str:
|
||||
"""This creates a modification function for a subgraph.
|
||||
To use this inside a template, the first argument should specify which subgraph to codegen for
|
||||
|
||||
TODO come up with standardized way to modify templates, with
|
||||
potential multiple modifications
|
||||
Args:
|
||||
subgraph_number (int): The index of the subgraph in self.subgraphs
|
||||
"""
|
||||
assert isinstance(subgraph_number, int)
|
||||
assert isinstance(self.subgraphs, list)
|
||||
assert subgraph_number < len(
|
||||
self.subgraphs
|
||||
), f"Invalid subgraph number provided to create_modification, {subgraph_number} must be < {len(self.subgraphs)}"
|
||||
|
||||
subgraph = self.subgraphs[subgraph_number]
|
||||
|
||||
def add_input(name):
|
||||
return self.args.input(name)
|
||||
|
||||
name = f"PlaceholderSubstitution_{subgraph_number}"
|
||||
|
||||
class PlaceholderSubstitution(V.WrapperHandler): # type: ignore[name-defined]
|
||||
self.name = "PlaceholderSubstitution"
|
||||
self.name = name
|
||||
|
||||
def load(self, name: str, index: sympy.Expr):
|
||||
if name not in fixed_inputs:
|
||||
@ -297,15 +306,14 @@ class TritonTemplateKernel(TritonKernel):
|
||||
def indirect_indexing(self, index_var, size, check):
|
||||
return sympy_index_symbol(str(index_var))
|
||||
|
||||
# if self.modification_cache is None:
|
||||
with V.set_ops_handler(PlaceholderSubstitution(V.ops)):
|
||||
assert isinstance(
|
||||
self.subgraphs, ir.ComputedBuffer
|
||||
), "Expected the subgraph to be a ComputedBuffer"
|
||||
if isinstance(self.subgraphs.data, ir.InputBuffer):
|
||||
out = self.subgraphs.data.make_loader()((1,))
|
||||
subgraph, ir.ComputedBuffer
|
||||
), f"Expected the subgraph to be a ComputedBuffer, got {type(subgraph)}"
|
||||
if isinstance(subgraph.data, ir.InputBuffer):
|
||||
out = subgraph.data.make_loader()((1,))
|
||||
else:
|
||||
out = self.subgraphs.data.inner_fn((1,))
|
||||
out = subgraph.data.inner_fn((1,))
|
||||
|
||||
self.codegen_body()
|
||||
self.body.writeline(f"{fixed_inputs['out']} = {out.value}")
|
||||
@ -320,11 +328,18 @@ class TritonTemplateKernel(TritonKernel):
|
||||
indices: Union[List[Any], Tuple[Any]],
|
||||
val: str,
|
||||
mask: Optional[str] = None,
|
||||
indent_width: int = 4,
|
||||
):
|
||||
"""
|
||||
Hook called from template code to store the final output
|
||||
(if the buffer hasn't been optimized away), then append any
|
||||
epilogue fusions.
|
||||
"""Stores the final output and appends any epilogue fusions if the buffer hasn't been optimized away.
|
||||
|
||||
Args:
|
||||
indices (Union[List, Tuple]): The index for each dimension of the output. The dot product of
|
||||
these indices and output strides must match `val`.
|
||||
val (str): The value to store.
|
||||
mask (Optional[str]): An optional mask to use for the store operation. If provided, this mask
|
||||
will be applied to the store.
|
||||
indent_width (int): The number of spaces to use for indentation. This is used when the call to
|
||||
store_output is indented in the kernel definition.
|
||||
"""
|
||||
assert isinstance(indices, (list, tuple))
|
||||
assert isinstance(val, str)
|
||||
@ -348,7 +363,7 @@ class TritonTemplateKernel(TritonKernel):
|
||||
self.range_trees[0].lookup(sympy.Integer(1), sympy_product(lengths)).set_name(
|
||||
"xindex"
|
||||
)
|
||||
self.template_mask = mask
|
||||
self.template_mask = mask # type: ignore[assignment]
|
||||
self.template_indices = indices
|
||||
output_index = self.output_node.get_layout().make_indexer()(index_symbols)
|
||||
output_index = self.rename_indexing(output_index)
|
||||
@ -373,7 +388,7 @@ class TritonTemplateKernel(TritonKernel):
|
||||
def hook():
|
||||
# more stuff might have been added since the codegen_body above
|
||||
self.codegen_body()
|
||||
return textwrap.indent(self.body.getvalue(), " ").strip()
|
||||
return textwrap.indent(self.body.getvalue(), " " * indent_width).strip()
|
||||
|
||||
assert "<STORE_OUTPUT>" not in self.render_hooks
|
||||
self.render_hooks["<STORE_OUTPUT>"] = hook
|
||||
|
||||
@ -96,6 +96,8 @@ def _flex_attention(
|
||||
raise ValueError(
|
||||
"NYI: The target sequence length (L) of the query tensor must match the source sequence length (S) of the key tensor."
|
||||
)
|
||||
if query.size(-2) % 128 != 0:
|
||||
raise ValueError("NYI: S and L must be a multiple of 128")
|
||||
|
||||
if not torch._dynamo.is_dynamo_supported():
|
||||
raise RuntimeError("flex_attention requires dynamo support.")
|
||||
@ -149,7 +151,7 @@ def _rel_causal(
|
||||
token_q: torch.Tensor,
|
||||
token_kv: torch.Tensor,
|
||||
) -> torch.Tensor:
|
||||
return torch.where(token_q <= token_kv, score + (token_q - token_kv), float("-inf"))
|
||||
return torch.where(token_q >= token_kv, score + (token_q - token_kv), float("-inf"))
|
||||
|
||||
|
||||
def _generate_alibi_bias(num_heads: int):
|
||||
|
||||
@ -118,9 +118,9 @@ def sample_inputs_flex_attention(opinfo, device, dtype, requires_grad, **kwargs)
|
||||
return score + h
|
||||
|
||||
yield SampleInput(
|
||||
make_arg(2, 2, 64, 8, low=0.1, high=2),
|
||||
make_arg(2, 2, 64, 8, low=0.1, high=2),
|
||||
make_arg(2, 2, 64, 8, low=0.1, high=2),
|
||||
make_arg(2, 2, 128, 8, low=0.1, high=2),
|
||||
make_arg(2, 2, 128, 8, low=0.1, high=2),
|
||||
make_arg(2, 2, 128, 8, low=0.1, high=2),
|
||||
score_mod,
|
||||
)
|
||||
|
||||
|
||||
Reference in New Issue
Block a user