[Operator] index_add optimized

[GwokHiujin]

PR Category

Operator

Type of Change

Bug Fix & Performance Optimization

Description

The method of using indexing to implement index_add resolves the bugs present in the previous version, and I accelerate it using code generation.

I want to point out that I use tl.atomic_add in the kernel, which currently doesn't support bfloat16, so I am temporarily skipping this data type during testing.

BTW this solution doesn't perform well enough in some shapes, thus any suggestion is appreciated :)

Issue

Progress

• Change is properly reviewed (1 reviewer required, 2 recommended).
• Change is responded to an issue.
• Change is fully covered by a UT.

Performance

Before: (Bug in shape (200, 40999, 3))

test_select_and_slice_perf.py 
Operator: index_add  Performance Test (dtype=torch.float16, mode=cuda,level=comprehensive)
Status       Torch Latency (ms)    Gems Latency (ms)         Gems Speedup          Torch GBPS            Gems GBPS           Size Detail
-----------------------------------------------------------------------------------------------------------------------------------------
SUCCESS               0.011264            0.272384               0.041               0.750               0.031          [torch.Size([64, 64]), 1, torch.Size([32]), torch.Size([64, 32])]
SUCCESS               0.012288            0.276480               0.044              10.750               0.478          [torch.Size([256, 256]), 1, torch.Size([128]), torch.Size([256, 128])]
SUCCESS               0.027648            0.279552               0.099              76.000               7.516          [torch.Size([1024, 1024]), 1, torch.Size([512]), torch.Size([1024, 512])]
SUCCESS               0.276480            0.552960               0.500             121.422              60.711          [torch.Size([4096, 4096]), 1, torch.Size([2048]), torch.Size([4096, 2048])]
SUCCESS               1.100800            4.300800               0.256             122.166              31.269          [torch.Size([1024, 65536]), 1, torch.Size([32768]), torch.Size([1024, 32768])]
SUCCESS              24.657921           25.936895               0.951              65.318              62.097          [torch.Size([268435456]), 0, torch.Size([134217728]), torch.Size([134217728])]
SUCCESS               0.011264            0.270336               0.042               3.552               0.148          [torch.Size([10000, 1]), 1, torch.Size([1]), torch.Size([10000, 1])]
SUCCESS               0.043008            0.278528               0.154             119.071              18.386          [torch.Size([10000, 256]), 1, torch.Size([128]), torch.Size([10000, 128])]
SUCCESS              26.708992           60.860416               0.439              49.084              21.541          [torch.Size([10000, 65536]), 1, torch.Size([32768]), torch.Size([10000, 32768])]
SUCCESS               0.011264            0.273408               0.041               3.552               0.146          [torch.Size([100, 1, 100]), 1, torch.Size([1]), torch.Size([100, 1, 100])]
SUCCESS               0.044032            0.278528               0.158             116.302              18.386          [torch.Size([100, 256, 100]), 1, torch.Size([128]), torch.Size([100, 128, 100])]
SUCCESS               9.287680           68.525055               0.136             141.153              19.131          [torch.Size([100, 65536, 100]), 1, torch.Size([32768]), torch.Size([100, 32768, 100])]


Operator: index_add  Performance Test (dtype=torch.float32, mode=cuda,level=comprehensive)
Status       Torch Latency (ms)    Gems Latency (ms)         Gems Speedup          Torch GBPS            Gems GBPS           Size Detail
-----------------------------------------------------------------------------------------------------------------------------------------
SUCCESS               0.011264            0.273408               0.041               1.477               0.061          [torch.Size([64, 64]), 1, torch.Size([32]), torch.Size([64, 32])]
SUCCESS               0.011264            0.276480               0.041              23.364               0.952          [torch.Size([256, 256]), 1, torch.Size([128]), torch.Size([256, 128])]
SUCCESS               0.028672            0.278528               0.103             146.429              15.074          [torch.Size([1024, 1024]), 1, torch.Size([512]), torch.Size([1024, 512])]
SUCCESS               0.296960            0.715776               0.415             226.041              93.780          [torch.Size([4096, 4096]), 1, torch.Size([2048]), torch.Size([4096, 2048])]
SUCCESS               1.985536            5.766144               0.344             135.327              46.599          [torch.Size([1024, 65536]), 1, torch.Size([32768]), torch.Size([1024, 32768])]
SUCCESS              26.319872           28.300287               0.930              81.592              75.882          [torch.Size([268435456]), 0, torch.Size([134217728]), torch.Size([134217728])]
SUCCESS               0.011264            0.273408               0.041               7.103               0.293          [torch.Size([10000, 1]), 1, torch.Size([1]), torch.Size([10000, 1])]
SUCCESS               0.040960            0.280576               0.146             250.025              36.500          [torch.Size([10000, 256]), 1, torch.Size([128]), torch.Size([10000, 128])]
SUCCESS              51.834881           65.326080               0.793              50.578              40.133          [torch.Size([10000, 65536]), 1, torch.Size([32768]), torch.Size([10000, 32768])]
SUCCESS               0.011264            0.273408               0.041               7.103               0.293          [torch.Size([100, 1, 100]), 1, torch.Size([1]), torch.Size([100, 1, 100])]
SUCCESS               0.043008            0.277504               0.155             238.119              36.904          [torch.Size([100, 256, 100]), 1, torch.Size([128]), torch.Size([100, 128, 100])]
SUCCESS               8.082432           69.867523               0.116             324.370              37.524          [torch.Size([100, 65536, 100]), 1, torch.Size([32768]), torch.Size([100, 32768, 100])]

After:

test_select_and_slice_perf.py 
Operator: index_add  Performance Test (dtype=torch.float16, mode=cuda,level=comprehensive)
Status       Torch Latency (ms)    Gems Latency (ms)         Gems Speedup          Torch GBPS            Gems GBPS           Size Detail
-----------------------------------------------------------------------------------------------------------------------------------------
SUCCESS               0.011264            0.011264               1.000               0.750               0.750          [torch.Size([64, 64]), 1, torch.Size([32]), torch.Size([64, 32])]
SUCCESS               0.012288            0.022528               0.545              10.750               5.864          [torch.Size([256, 256]), 1, torch.Size([128]), torch.Size([256, 128])]
SUCCESS               0.027648            0.062464               0.443              76.000              33.639          [torch.Size([1024, 1024]), 1, torch.Size([512]), torch.Size([1024, 512])]
SUCCESS               0.276480            0.507904               0.544             121.422              66.097          [torch.Size([4096, 4096]), 1, torch.Size([2048]), torch.Size([4096, 2048])]
SUCCESS               1.101824            4.257792               0.259             122.052              31.584          [torch.Size([1024, 65536]), 1, torch.Size([32768]), torch.Size([1024, 32768])]
SUCCESS              24.667135           23.766016               1.038              65.294              67.770          [torch.Size([268435456]), 0, torch.Size([134217728]), torch.Size([134217728])]
SUCCESS               0.012288            0.011264               1.091               3.256               3.552          [torch.Size([10000, 1]), 1, torch.Size([1]), torch.Size([10000, 1])]
SUCCESS               0.043008            0.101376               0.424             119.071              50.515          [torch.Size([10000, 256]), 1, torch.Size([128]), torch.Size([10000, 128])]
SUCCESS              25.495552           60.827648               0.419              51.420              21.552          [torch.Size([10000, 65536]), 1, torch.Size([32768]), torch.Size([10000, 32768])]
SUCCESS               0.012288            0.015360               0.800               3.256               2.605          [torch.Size([100, 1, 100]), 1, torch.Size([1]), torch.Size([100, 1, 100])]
SUCCESS               0.045056            0.131072               0.344             113.659              39.070          [torch.Size([100, 256, 100]), 1, torch.Size([128]), torch.Size([100, 128, 100])]
SUCCESS               9.285632           68.473854               0.136             141.184              19.146          [torch.Size([100, 65536, 100]), 1, torch.Size([32768]), torch.Size([100, 32768, 100])]


Operator: index_add  Performance Test (dtype=torch.float32, mode=cuda,level=comprehensive)
Status       Torch Latency (ms)    Gems Latency (ms)         Gems Speedup          Torch GBPS            Gems GBPS           Size Detail
-----------------------------------------------------------------------------------------------------------------------------------------
SUCCESS               0.011264            0.010240               1.100               1.477               1.625          [torch.Size([64, 64]), 1, torch.Size([32]), torch.Size([64, 32])]
SUCCESS               0.011264            0.012288               0.917              23.364              21.417          [torch.Size([256, 256]), 1, torch.Size([128]), torch.Size([256, 128])]
SUCCESS               0.028672            0.035840               0.800             146.429             117.143          [torch.Size([1024, 1024]), 1, torch.Size([512]), torch.Size([1024, 512])]
SUCCESS               0.296960            0.684032               0.434             226.041              98.132          [torch.Size([4096, 4096]), 1, torch.Size([2048]), torch.Size([4096, 2048])]
SUCCESS               1.982464            5.712896               0.347             135.537              47.034          [torch.Size([1024, 65536]), 1, torch.Size([32768]), torch.Size([1024, 32768])]
SUCCESS              26.341375           26.116096               1.009              81.525              82.228          [torch.Size([268435456]), 0, torch.Size([134217728]), torch.Size([134217728])]
SUCCESS               0.011264            0.010240               1.100               7.103               7.813          [torch.Size([10000, 1]), 1, torch.Size([1]), torch.Size([10000, 1])]
SUCCESS               0.040960            0.066560               0.615             250.025             153.862          [torch.Size([10000, 256]), 1, torch.Size([128]), torch.Size([10000, 128])]
SUCCESS              51.115009           65.212418               0.784              51.290              40.202          [torch.Size([10000, 65536]), 1, torch.Size([32768]), torch.Size([10000, 32768])]
SUCCESS               0.011264            0.011264               1.000               7.103               7.103          [torch.Size([100, 1, 100]), 1, torch.Size([1]), torch.Size([100, 1, 100])]
SUCCESS               0.043008            0.067584               0.636             238.119             151.530          [torch.Size([100, 256, 100]), 1, torch.Size([128]), torch.Size([100, 128, 100])]
SUCCESS               8.084480           69.749763               0.116             324.288              37.587          [torch.Size([100, 65536, 100]), 1, torch.Size([32768]), torch.Size([100, 32768, 100])]

[tongxin]

It's safe to set sem='relaxed'