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Improve native layer norm backward perf#68238

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Improve native layer norm backward perf#68238
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@ngimel ngimel commented Nov 12, 2021 •
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Benchmarks
At this PR

[------------------------------------------------------ ln ------------------------------------------------------]
                  |  fwd, torch.float32  |  fwdbwd, torch.float32  |  fwd, torch.float16  |  fwdbwd, torch.float16
1 threads: -------------------------------------------------------------------------------------------------------
      200, 256    |         17.5         |          106.6          |         18.1         |           94.7
      1000, 256   |         18.7         |          116.6          |         18.7         |          110.7
      6000, 256   |         28.1         |          111.8          |         19.4         |           92.3
      6272, 256   |         29.3         |          108.5          |         20.1         |           92.7
      200, 512    |         19.3         |           83.8          |         19.1         |          116.3
      1000, 512   |         17.9         |           88.0          |         17.9         |           93.0
      6000, 512   |         36.9         |          141.2          |         27.4         |          103.3
      6272, 512   |         38.2         |          146.5          |         28.1         |          107.9
      200, 1024   |         18.1         |           89.5          |         21.1         |          102.7
      1000, 1024  |         17.9         |           88.7          |         18.5         |           92.5
      6000, 1024  |         77.6         |          277.5          |         40.3         |          148.5
      6272, 1024  |         80.7         |          288.1          |         42.0         |          154.0
      200, 1536   |         17.9         |          117.3          |         18.1         |           88.1
      1000, 1536  |         22.9         |           92.0          |         19.4         |           89.0
      6000, 1536  |        123.4         |          436.3          |         61.7         |          228.5
      6272, 1536  |        129.1         |          457.3          |         64.3         |          238.5
      200, 2048   |         18.0         |           90.5          |         19.1         |          101.6
      1000, 2048  |         31.1         |          109.8          |         25.3         |          107.9
      6000, 2048  |        174.5         |          589.8          |         87.1         |          310.5
      6272, 2048  |        182.2         |          617.0          |         91.2         |          316.7
      200, 3072   |         19.8         |           96.4          |         19.4         |           89.3
      1000, 3072  |         48.1         |          168.7          |         23.5         |          100.9
      6000, 3072  |        267.1         |          930.0          |        134.8         |          519.2
      6272, 3072  |        278.2         |          971.2          |        140.7         |          540.2

Pre-#67977

[------------------------------------------------------- ln -------------------------------------------------------]
                    |  fwd, torch.float32  |  fwdbwd, torch.float32  |  fwd, torch.float16  |  fwdbwd, torch.float16
1 threads: ---------------------------------------------------------------------------------------------------------
        200,   256  |         20.9         |            92.6         |         21.3         |          110.1
       1000,   256  |         20.3         |            91.8         |         28.1         |          115.6
       6000,   256  |         93.0         |           310.7         |         86.3         |          299.8
       6272,   256  |         97.3         |           323.5         |         90.0         |          314.1
        200,   512  |         20.9         |           110.2         |         21.1         |           95.0
       1000,   512  |         24.0         |           102.8         |         22.2         |           95.9
       6000,   512  |        121.7         |           367.2         |        105.6         |          337.4
       6272,   512  |        127.0         |           382.3         |        111.3         |          352.0
        200,  1024  |         21.0         |           131.8         |         20.4         |           93.3
       1000,  1024  |         35.5         |           108.7         |         27.7         |           99.4
       6000,  1024  |        170.4         |           495.5         |        137.7         |          411.4
       6272,  1024  |        177.5         |           517.6         |        143.6         |          428.6
        200,  1536  |         21.9         |            97.6         |         20.8         |           92.7
       1000,  1536  |         44.3         |           129.7         |         33.9         |          100.1
       6000,  1536  |        215.8         |           619.2         |        167.2         |          480.9
       6272,  1536  |        225.0         |           646.9         |        174.8         |          505.9
        200,  2048  |         21.8         |           100.8         |         20.7         |           96.7
       1000,  2048  |         53.7         |           152.4         |         41.4         |          118.3
       6000,  2048  |        267.0         |           753.6         |        220.4         |          571.5
       6272,  2048  |        278.6         |           785.8         |        211.4         |          589.2
        200,  3072  |         20.9         |           103.7         |         21.9         |          104.6
       1000,  3072  |         71.4         |           201.1         |         53.1         |          148.3
       6000,  3072  |        365.7         |          1040.3         |        262.0         |          731.5
       6272,  3072  |        382.0         |          1084.4         |        273.3         |          766.3

Benchmarking script

import torch
from torch.utils.benchmark import Timer, Compare

results = []
for dtype in (torch.float, torch.half):
    for fs in (256, 512, 1024, 1536, 2048, 3072):
        for bs in (200, 1000, 6000, 196*32):
            ln = torch.nn.LayerNorm((fs,), device="cuda", dtype=dtype)
            X = torch.randn(bs, fs, device="cuda", dtype=dtype, requires_grad=True)
            gO = torch.rand_like(X)
            stmtfwd = "ln(X)"
            stmtfwdbwd = "X.grad=None; ln.zero_grad(set_to_none=True); out = ln(X); out.backward(gO)"
            tfwd = Timer(stmt=stmtfwd, label="ln", sub_label=f"{bs:5}, {fs:5}", description=f"fwd, {dtype}", globals=globals())
            tfwdbwd = Timer(stmt=stmtfwdbwd, label="ln", sub_label=f"{bs:5}, {fs:5}", description=f"fwdbwd, {dtype}", globals=globals())
            for t in (tfwd, tfwdbwd):
                results.append(t.blocked_autorange())
        print(fs, end='\r')
c = Compare(results)
c.print()

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@jxtps jxtps mentioned this pull request Dec 15, 2021
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@ngimel ngimel deleted the ngimel/lnbw branch December 26, 2021 06:43
@hubertlu-tw hubertlu-tw mentioned this pull request Oct 24, 2022
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@hubertlu-tw hubertlu-tw mentioned this pull request Oct 25, 2022
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pytorchmergebot pushed a commit that referenced this pull request Nov 22, 2022
@hubertlu-tw @pytorchmergebot
[ROCm] Optimize layer norm backward kernel for ROCm (#87635)
74e62a1 
We observed that the native PyTorch LayerNormBackwardKernelImplInternal has suboptimal performance for certain input sizes on AMD GPUs especially when `fs`  (=`config_m` in our benchmark script) is large and `bs`  (=`config_n` in our benchmark script) is small (commonly seen in [the CvT model](https://arxiv.org/abs/2103.15808)) in the benchmark script of [PR #68238](#68238 (comment)) on AMD GPUs.

This PR is to replace `GammaBetaBackwardCUDAKernel` with the Apex layernorm backward kernel with some ROCm-specific parameter tuning when `fs`  (=`config_m`) is larger than 512 on AMD GPUs.

There are a few PRs for LayerNorm kernel:
- #26201
- #27634
- #68238

Therefore, we have tested and compared the kernel before and at this PR with the input shapes in the last two PRs along with those commonly used in the CvT model on AMD MI100.

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M | N | fwd (half) | fwdbwd (half) | fwd (float) | fwdbwd (float)
-- | -- | -- | -- | -- | --
50432 | 384 | 0.387256 | 1.372758 | 0.378975 | 1.47892
50176 | 384 | 0.38231 | 1.362416 | 0.378084 | 1.473886
200704 | 192 | 0.997859 | 4.315875 | 0.989306 | 4.560827
802816 | 64 | 3.671828 | 16.68013 | 3.613515 | 16.827946
200 | 256 | 0.066503 | 0.332096 | 0.071422 | 0.325349
1000 | 256 | 0.071848 | 0.333355 | 0.073038 | 0.334753
6000 | 256 | 0.086334 | 0.345139 | 0.086834 | 0.347429
6272 | 256 | 0.088601 | 0.347906 | 0.087855 | 0.351245
200 | 512 | 0.071626 | 0.329726 | 0.073798 | 0.326878
1000 | 512 | 0.073975 | 0.330226 | 0.074166 | 0.332751
6000 | 512 | 0.099617 | 0.362367 | 0.100095 | 0.378313
6272 | 512 | 0.100378 | 0.358066 | 0.099857 | 0.395982
200 | 1024 | 0.072954 | 0.326382 | 0.073899 | 0.333007
1000 | 1024 | 0.0743 | 0.325532 | 0.071126 | 0.330991
6000 | 1024 | 0.127025 | 0.390084 | 0.128692 | 0.471504
6272 | 1024 | 0.130704 | 0.403536 | 0.135244 | 0.487133
200 | 1536 | 0.070331 | 0.339169 | 0.070086 | 0.331015
1000 | 1536 | 0.075085 | 0.330042 | 0.076295 | 0.328778
6000 | 1536 | 0.148889 | 0.44949 | 0.155781 | 0.659987
6272 | 1536 | 0.154939 | 0.478871 | 0.17673 | 0.716025
200 | 2048 | 0.070269 | 0.335585 | 0.072804 | 0.334655
1000 | 2048 | 0.080094 | 0.326991 | 0.080426 | 0.32685
6000 | 2048 | 0.187888 | 0.623023 | 0.245762 | 0.981635
6272 | 2048 | 0.195431 | 0.65244 | 0.262574 | 1.008141
200 | 3072 | 0.068205 | 0.339428 | 0.073068 | 0.344034
1000 | 3072 | 0.087554 | 0.328899 | 0.09218 | 0.346433
6000 | 3072 | 0.240352 | 0.905058 | 0.368135 | 1.280462
6272 | 3072 | 0.26179 | 0.959387 | 0.387782 | 1.476524
128 | 2097152 | 5.905976 | 22.724793 | 10.287974 | 30.242092
256 | 1048576 | 4.561596 | 19.554308 | 10.223171 | 29.42371
512 | 524288 | 4.146751 | 22.7247 | 11.404285 | 39.175902
1024 | 262144 | 5.193135 | 23.403325 | 11.334512 | 38.947192
2048 | 131072 | 4.992907 | 23.377801 | 11.400286 | 40.889191
4096 | 65536 | 5.429488 | 24.275701 | 11.196778 | 41.4751
8192 | 32768 | 5.35758 | 21.360312 | 10.535418 | 42.875646
16384 | 16384 | 5.44947 | 20.852605 | 10.357685 | 34.603408
32768 | 8192 | 4.688925 | 17.379392 | 9.635596 | 31.188271

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**At this PR**
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M | N | fwd (half) | fwdbwd (half) | fwd (float) | fwdbwd (float)
-- | -- | -- | -- | -- | --
50432 | 384 | 0.38797 | 0.93103 | 0.37966 | 1.15283
50176 | 384 | 0.3874 | 0.96417 | 0.38462 | 1.18595
200704 | 192 | 1.00002 | 2.40876 | 0.99224 | 2.55579
802816 | 64 | 3.67348 | 7.98658 | 3.61871 | 7.72404
200 | 256 | 0.07292 | 0.35119 | 0.07195 | 0.32602
1000 | 256 | 0.07354 | 0.33325 | 0.07237 | 0.33742
6000 | 256 | 0.08819 | 0.33283 | 0.08453 | 0.3279
6272 | 256 | 0.0886 | 0.33446 | 0.08774 | 0.33426
200 | 512 | 0.0701 | 0.33505 | 0.07072 | 0.33018
1000 | 512 | 0.07042 | 0.33442 | 0.074 | 0.33206
6000 | 512 | 0.09931 | 0.34956 | 0.09895 | 0.3572
6272 | 512 | 0.10103 | 0.32976 | 0.10041 | 0.36635
200 | 1024 | 0.07144 | 0.33579 | 0.07209 | 0.33216
1000 | 1024 | 0.0736 | 0.32803 | 0.07286 | 0.32936
6000 | 1024 | 0.12584 | 0.38916 | 0.12852 | 0.48273
6272 | 1024 | 0.13053 | 0.38804 | 0.13464 | 0.49545
200 | 1536 | 0.07159 | 0.3396 | 0.07062 | 0.33545
1000 | 1536 | 0.07443 | 0.33239 | 0.07366 | 0.33204
6000 | 1536 | 0.14959 | 0.45043 | 0.15826 | 0.69119
6272 | 1536 | 0.1542 | 0.47644 | 0.18249 | 0.72208
200 | 2048 | 0.07258 | 0.33982 | 0.07412 | 0.33859
1000 | 2048 | 0.0793 | 0.32816 | 0.07864 | 0.32583
6000 | 2048 | 0.18973 | 0.571 | 0.25506 | 0.91796
6272 | 2048 | 0.19719 | 0.64208 | 0.26445 | 0.95055
200 | 3072 | 0.07092 | 0.33867 | 0.07104 | 0.34695
1000 | 3072 | 0.08727 | 0.33144 | 0.09144 | 0.36633
6000 | 3072 | 0.24683 | 0.87275 | 0.37761 | 1.3289
6272 | 3072 | 0.26437 | 0.91178 | 0.38496 | 1.53694
128 | 2097152 | 6.27936 | 23.69425 | 10.40004 | 30.13699
256 | 1048576 | 4.5404 | 19.47675 | 10.28494 | 29.36936
512 | 524288 | 4.13951 | 18.78771 | 10.09557 | 32.67083
1024 | 262144 | 4.47576 | 18.00411 | 9.56488 | 31.47117
2048 | 131072 | 4.28026 | 16.95619 | 9.40297 | 30.82845
4096 | 65536 | 4.2653 | 16.5018 | 9.03315 | 30.08392
8192 | 32768 | 4.25613 | 16.13583 | 8.9258 | 30.75296
16384 | 16384 | 4.20256 | 16.38207 | 9.52587 | 31.31113
32768 | 8192 | 4.20231 | 16.19452 | 9.31478 | 31.03514

</body>

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---------

**Performance Improvement (%)**
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M | N | fwdbwd,   torch.float16 | fwdbwd,   torch.float32
-- | -- | -- | --
50432 | 384 | 32.178 | 22.049
50176 | 384 | 29.231 | 19.536
200704 | 192 | 44.188 | 43.962
802816 | 64 | 52.119 | 54.100
200 | 256 | -5.750 | -0.206
1000 | 256 | 0.031 | -0.797
6000 | 256 | 3.566 | 5.621
6272 | 256 | 3.865 | 4.836
200 | 512 | -1.615 | -1.010
1000 | 512 | -1.270 | 0.208
6000 | 512 | 3.534 | 5.581
6272 | 512 | 7.905 | 7.483
200 | 1024 | -2.883 | 0.254
1000 | 1024 | -0.767 | 0.493
6000 | 1024 | 0.237 | -2.381
6272 | 1024 | 3.840 | -1.707
200 | 1536 | -0.127 | -1.340
1000 | 1536 | -0.711 | -0.992
6000 | 1536 | -0.209 | -4.728
6272 | 1536 | 0.508 | -0.846
200 | 2048 | -1.262 | -1.176
1000 | 2048 | -0.358 | 0.312
6000 | 2048 | 8.350 | 6.487
6272 | 2048 | 1.588 | 5.713
200 | 3072 | 0.223 | -0.848
1000 | 3072 | -0.773 | -5.743
6000 | 3072 | 3.570 | -3.783
6272 | 3072 | 4.962 | -4.092
128 | 2097152 | -4.266 | 0.348
256 | 1048576 | 0.397 | 0.185
512 | 524288 | 17.325 | 16.605
1024 | 262144 | 23.070 | 19.195
2048 | 131072 | 27.469 | 24.605
4096 | 65536 | 32.023 | 27.465
8192 | 32768 | 24.459 | 28.274
16384 | 16384 | 21.439 | 9.514
32768 | 8192 | 6.818 | 0.491

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---------
**Benchmark script of this PR**
```
# Ref:
#       1. #26201
#       2. #68238

from distutils.command.config import config
import torch
from torch.nn import LayerNorm
import timeit

number_runs = 1000  # TODO: Modify this to save time!
def test_forward(layer_norm_cuda, input_cuda):
    layer_norm_cuda(input_cuda); torch.cuda.synchronize()

def test_backward(out_cuda, layer_norm_grad_cuda, create_graph):
    out_cuda.backward(layer_norm_grad_cuda, retain_graph=True, create_graph=create_graph); torch.cuda.synchronize()

def test_fwdbwd(input_cuda, layer_norm_cuda, gO):
    input_cuda.grad = None
    layer_norm_cuda.zero_grad(set_to_none=True)
    out = layer_norm_cuda(input_cuda)
    out.backward(gO)
    torch.cuda.synchronize()

def benchmark(config_m, config_n):

    print("M | N | fwd (half) | fwdbwd (half) | fwd (float) | fwdbwd (float)")
    if len(config_m) != len(config_n):
        print("Please make sure the lengths of config_m and config_m are the same.")

    for i in range(len(config_m)):
        normalized_shape = config_n[i]
        results = [config_m[i], config_n[i]]
        for dtype in (torch.half, torch.float):
            if dtype == torch.half:
                layer_norm_cuda = LayerNorm(normalized_shape).half().cuda()
            else:
                layer_norm_cuda = LayerNorm(normalized_shape).cuda()

            input_cuda = torch.randn(config_m[i], config_n[i], device='cuda', dtype=dtype, requires_grad=True)

            # print("cuda forward:")
            result_fwd = timeit.timeit(lambda: test_forward(layer_norm_cuda, input_cuda), number=number_runs)
            results.append(result_fwd / number_runs * 1000)

            gO = torch.rand_like(input_cuda)

            result_fwdbwd = timeit.timeit(lambda: test_fwdbwd(input_cuda, layer_norm_cuda, gO), number=number_runs)
            results.append(result_fwdbwd / number_runs * 1000)

        print('{:09d}|{:09d}|{:9.5f}|{:9.5f}|{:9.5f}|{:9.5f}'.format(results[0], results[1], results[2], results[3], results[4], results[5]))

    print("Times are in microseconds (us).")

# CVT
config_m_cvt = [50432, 50176, 200704, 802816]
config_n_cvt = [384, 384, 192, 64]

# #68238 (comment)
config_m_68238 = [200, 1000, 6000, 6272, 200, 1000, 6000, 6272, 200, 1000, 6000, 6272, 200, 1000, 6000, 6272, 200, 1000, 6000, 6272, 200, 1000, 6000, 6272]
config_n_68238 = [256,256,256,256,512,512,512,512,1024,1024,1024,1024,1536,1536,1536,1536,2048,2048,2048,2048,3072,3072,3072,3072]

# #27634
config_m_27634 = [128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768]
config_n_27634 = [2097152, 1048576, 524288, 262144, 131072, 65536, 32768, 16384, 8192]

config_m = config_m_cvt + config_m_68238 + config_m_27634
config_n = config_n_cvt + config_n_68238 + config_n_27634

benchmark(config_m, config_n)
```

CC: @jeffdaily

Pull Request resolved: #87635
Approved by: https://github.com/jataylo, https://github.com/jeffdaily, https://github.com/ezyang
pytorchmergebot pushed a commit that referenced this pull request Nov 28, 2022
@hubertlu-tw @pytorchmergebot
[ROCm] Replace layer_norm_grad_input_kernel with cuComputeGradInput f…
cf4969d 
…or ROCm (#87726)

We observed that the native PyTorch LayerNormBackwardKernelImplInternal has suboptimal performance for certain input sizes on AMD GPUs especially when fs (=config_m in our benchmark script) is large and bs (=config_n in our benchmark script) is small (commonly seen in [the CvT model](https://arxiv.org/abs/2103.15808)) in the benchmark script of #68238 (comment) on AMD GPUs.

This PR is to replace layer_norm_grad_input_kernel with the Apex cuComputeGradInput kernel with some ROCm-specific parameter tuning when fs (=config_m) is larger than or equal to `32768` on AMD GPUs. Some of the code changes in LayerNormBackwardKernelImplInternal are from another PR: #87635

We used the same benchmark script in the previous PR and tested the optimized kernel with various input shapes on AMD MI100 GPU.

**At [the previous PR](#87635
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M | N | fwd (half) | fwdbwd (half) | fwd (float) | fwdbwd (float)
-- | -- | -- | -- | -- | --
50432 | 384 | 0.38589 | 0.92603 | 0.38367 | 1.15148
50176 | 384 | 0.38719 | 0.91579 | 0.37815 | 1.13761
200704 | 192 | 0.99787 | 2.39954 | 0.98996 | 2.54284
802816 | 64 | 3.66525 | 7.96952 | 3.61293 | 7.69946
200 | 256 | 0.06578 | 0.34613 | 0.06966 | 0.35449
1000 | 256 | 0.07837 | 0.37631 | 0.07725 | 0.37758
6000 | 256 | 0.09318 | 0.3788 | 0.09202 | 0.37989
6272 | 256 | 0.08694 | 0.36267 | 0.08703 | 0.3615
200 | 512 | 0.06975 | 0.34506 | 0.06973 | 0.34208
1000 | 512 | 0.07012 | 0.36363 | 0.07307 | 0.36741
6000 | 512 | 0.09725 | 0.36251 | 0.09908 | 0.37078
6272 | 512 | 0.09899 | 0.36519 | 0.10068 | 0.37514
200 | 1024 | 0.07188 | 0.33896 | 0.0712 | 0.34683
1000 | 1024 | 0.07357 | 0.3625 | 0.0734 | 0.3598
6000 | 1024 | 0.12642 | 0.38949 | 0.12973 | 0.5035
6272 | 1024 | 0.12901 | 0.40759 | 0.13609 | 0.51871
200 | 1536 | 0.06998 | 0.34782 | 0.07419 | 0.3514
1000 | 1536 | 0.07987 | 0.37915 | 0.07888 | 0.37264
6000 | 1536 | 0.15401 | 0.47524 | 0.15416 | 0.68609
6272 | 1536 | 0.15286 | 0.48843 | 0.17681 | 0.72997
200 | 2048 | 0.07054 | 0.34791 | 0.07289 | 0.35138
1000 | 2048 | 0.07767 | 0.37954 | 0.08554 | 0.37464
6000 | 2048 | 0.18744 | 0.5811 | 0.25004 | 0.93338
6272 | 2048 | 0.20037 | 0.63398 | 0.26918 | 0.97018
200 | 3072 | 0.07687 | 0.36739 | 0.08917 | 0.37845
1000 | 3072 | 0.09323 | 0.38901 | 0.09739 | 0.39823
6000 | 3072 | 0.24314 | 0.89029 | 0.38093 | 1.30719
6272 | 3072 | 0.26079 | 0.92023 | 0.38352 | 1.51012
128 | 2097152 | 6.17775 | 23.876 | 10.27952 | 30.10848
256 | 1048576 | 4.51855 | 19.47637 | 10.07609 | 29.42678
512 | 524288 | 4.13615 | 18.80888 | 10.07853 | 32.29804
1024 | 262144 | 4.47397 | 17.88388 | 9.50367 | 31.15699
2048 | 131072 | 4.2458 | 16.70852 | 9.17979 | 30.51708
4096 | 65536 | 4.24412 | 16.43098 | 8.97651 | 30.1617
8192 | 32768 | 4.24556 | 16.09038 | 8.77001 | 30.3643
16384 | 16384 | 4.14642 | 15.80355 | 8.82402 | 30.35291
32768 | 8192 | 4.12599 | 15.68897 | 8.82605 | 30.43423

</body>

</html>

----

**At this PR:**

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M | N | fwd (half) | fwdbwd (half) | fwd (float) | fwdbwd (float)
-- | -- | -- | -- | -- | --
50432 | 384 | 0.38667 | 0.84133 | 0.37916 | 1.01222
50176 | 384 | 0.3814 | 0.87266 | 0.37858 | 1.04399
200704 | 192 | 0.99902 | 2.14386 | 0.98973 | 2.33265
802816 | 64 | 3.66578 | 6.85376 | 3.6092 | 7.00331
200 | 256 | 0.06607 | 0.34176 | 0.07009 | 0.34548
1000 | 256 | 0.06947 | 0.36461 | 0.07902 | 0.37851
6000 | 256 | 0.09319 | 0.37432 | 0.09342 | 0.36927
6272 | 256 | 0.09544 | 0.37565 | 0.09476 | 0.37377
200 | 512 | 0.07935 | 0.364 | 0.07891 | 0.36894
1000 | 512 | 0.07676 | 0.37552 | 0.07957 | 0.37564
6000 | 512 | 0.10472 | 0.37504 | 0.1051 | 0.38782
6272 | 512 | 0.1069 | 0.36662 | 0.10062 | 0.38506
200 | 1024 | 0.07793 | 0.36561 | 0.08023 | 0.35019
1000 | 1024 | 0.07426 | 0.36729 | 0.07345 | 0.35851
6000 | 1024 | 0.12729 | 0.39219 | 0.12974 | 0.51526
6272 | 1024 | 0.13622 | 0.41627 | 0.14252 | 0.52926
200 | 1536 | 0.07615 | 0.36621 | 0.0797 | 0.3695
1000 | 1536 | 0.08327 | 0.38174 | 0.07938 | 0.37573
6000 | 1536 | 0.14894 | 0.46197 | 0.15268 | 0.63814
6272 | 1536 | 0.15368 | 0.48818 | 0.16309 | 0.71441
200 | 2048 | 0.06935 | 0.36691 | 0.07258 | 0.35548
1000 | 2048 | 0.07738 | 0.36388 | 0.08036 | 0.36452
6000 | 2048 | 0.18757 | 0.58573 | 0.23701 | 0.92915
6272 | 2048 | 0.1938 | 0.61628 | 0.26475 | 0.96896
200 | 3072 | 0.07884 | 0.3673 | 0.07724 | 0.37869
1000 | 3072 | 0.09342 | 0.38193 | 0.09822 | 0.38646
6000 | 3072 | 0.24452 | 0.86776 | 0.38251 | 1.3036
6272 | 3072 | 0.25971 | 0.91053 | 0.38744 | 1.39039
128 | 2097152 | 6.06752 | 23.26379 | 9.87466 | 29.81851
256 | 1048576 | 4.50336 | 19.4614 | 10.11239 | 29.25554
512 | 524288 | 4.12649 | 18.72831 | 10.054 | 32.26784
1024 | 262144 | 4.40855 | 17.77993 | 9.38856 | 31.18679
2048 | 131072 | 4.18716 | 16.74615 | 9.14487 | 30.24603
4096 | 65536 | 4.17374 | 16.34444 | 8.94894 | 30.0326
8192 | 32768 | 4.19095 | 16.05751 | 8.70358 | 30.14669
16384 | 16384 | 4.15404 | 15.83771 | 8.80042 | 30.5022
32768 | 8192 | 4.12515 | 15.5657 | 8.66138 | 28.87386

</body>

</html>

---

**Performance Improvement (%)**

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M | N | fwdbwd, torch.float16 | fwdbwd, torch.float32
-- | -- | -- | --
50432 | 384 | 9.147 | 12.094
50176 | 384 | 4.710 | 8.230
200704 | 192 | 10.655 | 8.266
802816 | 64 | 14.000 | 9.042
200 | 256 | 1.263 | 2.542
1000 | 256 | 3.109 | -0.246
6000 | 256 | 1.183 | 2.796
6272 | 256 | -3.579 | -3.394
200 | 512 | -5.489 | -7.852
1000 | 512 | -3.270 | -2.240
6000 | 512 | -3.456 | -4.596
6272 | 512 | -0.392 | -2.644
200 | 1024 | -7.862 | -0.969
1000 | 1024 | -1.321 | 0.359
6000 | 1024 | -0.693 | -2.336
6272 | 1024 | -2.130 | -2.034
200 | 1536 | -5.287 | -5.151
1000 | 1536 | -0.683 | -0.829
6000 | 1536 | 2.792 | 6.989
6272 | 1536 | 0.051 | 2.132
200 | 2048 | -5.461 | -1.167
1000 | 2048 | 4.126 | 2.701
6000 | 2048 | -0.797 | 0.453
6272 | 2048 | 2.792 | 0.126
200 | 3072 | 0.024 | -0.063
1000 | 3072 | 1.820 | 2.956
6000 | 3072 | 2.531 | 0.275
6272 | 3072 | 1.054 | 7.929
128 | 2097152 | 2.564 | 0.963
256 | 1048576 | 0.077 | 0.582
512 | 524288 | 0.428 | 0.094
1024 | 262144 | 0.581 | -0.096
2048 | 131072 | -0.225 | 0.888
4096 | 65536 | 0.527 | 0.428
8192 | 32768 | 0.204 | 0.717
16384 | 16384 | -0.216 | -0.492
32768 | 8192 | 0.786 | 5.127

</body>

</html>

CC: @jeffdaily

Pull Request resolved: #87726
Approved by: https://github.com/ngimel
jithunnair-amd pushed a commit to ROCm/pytorch that referenced this pull request Dec 1, 2022
@hubertlu-tw @jithunnair-amd
[ROCm] Optimize layer norm backward kernel for ROCm (pytorch#87635)
3e7d422 
We observed that the native PyTorch LayerNormBackwardKernelImplInternal has suboptimal performance for certain input sizes on AMD GPUs especially when `fs`  (=`config_m` in our benchmark script) is large and `bs`  (=`config_n` in our benchmark script) is small (commonly seen in [the CvT model](https://arxiv.org/abs/2103.15808)) in the benchmark script of [PR pytorch#68238](pytorch#68238 (comment)) on AMD GPUs.

This PR is to replace `GammaBetaBackwardCUDAKernel` with the Apex layernorm backward kernel with some ROCm-specific parameter tuning when `fs`  (=`config_m`) is larger than 512 on AMD GPUs.

There are a few PRs for LayerNorm kernel:
- pytorch#26201
- pytorch#27634
- pytorch#68238

Therefore, we have tested and compared the kernel before and at this PR with the input shapes in the last two PRs along with those commonly used in the CvT model on AMD MI100.

---
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M | N | fwd (half) | fwdbwd (half) | fwd (float) | fwdbwd (float)
-- | -- | -- | -- | -- | --
50432 | 384 | 0.387256 | 1.372758 | 0.378975 | 1.47892
50176 | 384 | 0.38231 | 1.362416 | 0.378084 | 1.473886
200704 | 192 | 0.997859 | 4.315875 | 0.989306 | 4.560827
802816 | 64 | 3.671828 | 16.68013 | 3.613515 | 16.827946
200 | 256 | 0.066503 | 0.332096 | 0.071422 | 0.325349
1000 | 256 | 0.071848 | 0.333355 | 0.073038 | 0.334753
6000 | 256 | 0.086334 | 0.345139 | 0.086834 | 0.347429
6272 | 256 | 0.088601 | 0.347906 | 0.087855 | 0.351245
200 | 512 | 0.071626 | 0.329726 | 0.073798 | 0.326878
1000 | 512 | 0.073975 | 0.330226 | 0.074166 | 0.332751
6000 | 512 | 0.099617 | 0.362367 | 0.100095 | 0.378313
6272 | 512 | 0.100378 | 0.358066 | 0.099857 | 0.395982
200 | 1024 | 0.072954 | 0.326382 | 0.073899 | 0.333007
1000 | 1024 | 0.0743 | 0.325532 | 0.071126 | 0.330991
6000 | 1024 | 0.127025 | 0.390084 | 0.128692 | 0.471504
6272 | 1024 | 0.130704 | 0.403536 | 0.135244 | 0.487133
200 | 1536 | 0.070331 | 0.339169 | 0.070086 | 0.331015
1000 | 1536 | 0.075085 | 0.330042 | 0.076295 | 0.328778
6000 | 1536 | 0.148889 | 0.44949 | 0.155781 | 0.659987
6272 | 1536 | 0.154939 | 0.478871 | 0.17673 | 0.716025
200 | 2048 | 0.070269 | 0.335585 | 0.072804 | 0.334655
1000 | 2048 | 0.080094 | 0.326991 | 0.080426 | 0.32685
6000 | 2048 | 0.187888 | 0.623023 | 0.245762 | 0.981635
6272 | 2048 | 0.195431 | 0.65244 | 0.262574 | 1.008141
200 | 3072 | 0.068205 | 0.339428 | 0.073068 | 0.344034
1000 | 3072 | 0.087554 | 0.328899 | 0.09218 | 0.346433
6000 | 3072 | 0.240352 | 0.905058 | 0.368135 | 1.280462
6272 | 3072 | 0.26179 | 0.959387 | 0.387782 | 1.476524
128 | 2097152 | 5.905976 | 22.724793 | 10.287974 | 30.242092
256 | 1048576 | 4.561596 | 19.554308 | 10.223171 | 29.42371
512 | 524288 | 4.146751 | 22.7247 | 11.404285 | 39.175902
1024 | 262144 | 5.193135 | 23.403325 | 11.334512 | 38.947192
2048 | 131072 | 4.992907 | 23.377801 | 11.400286 | 40.889191
4096 | 65536 | 5.429488 | 24.275701 | 11.196778 | 41.4751
8192 | 32768 | 5.35758 | 21.360312 | 10.535418 | 42.875646
16384 | 16384 | 5.44947 | 20.852605 | 10.357685 | 34.603408
32768 | 8192 | 4.688925 | 17.379392 | 9.635596 | 31.188271

</body>

</html>

---------
**At this PR**
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M | N | fwd (half) | fwdbwd (half) | fwd (float) | fwdbwd (float)
-- | -- | -- | -- | -- | --
50432 | 384 | 0.38797 | 0.93103 | 0.37966 | 1.15283
50176 | 384 | 0.3874 | 0.96417 | 0.38462 | 1.18595
200704 | 192 | 1.00002 | 2.40876 | 0.99224 | 2.55579
802816 | 64 | 3.67348 | 7.98658 | 3.61871 | 7.72404
200 | 256 | 0.07292 | 0.35119 | 0.07195 | 0.32602
1000 | 256 | 0.07354 | 0.33325 | 0.07237 | 0.33742
6000 | 256 | 0.08819 | 0.33283 | 0.08453 | 0.3279
6272 | 256 | 0.0886 | 0.33446 | 0.08774 | 0.33426
200 | 512 | 0.0701 | 0.33505 | 0.07072 | 0.33018
1000 | 512 | 0.07042 | 0.33442 | 0.074 | 0.33206
6000 | 512 | 0.09931 | 0.34956 | 0.09895 | 0.3572
6272 | 512 | 0.10103 | 0.32976 | 0.10041 | 0.36635
200 | 1024 | 0.07144 | 0.33579 | 0.07209 | 0.33216
1000 | 1024 | 0.0736 | 0.32803 | 0.07286 | 0.32936
6000 | 1024 | 0.12584 | 0.38916 | 0.12852 | 0.48273
6272 | 1024 | 0.13053 | 0.38804 | 0.13464 | 0.49545
200 | 1536 | 0.07159 | 0.3396 | 0.07062 | 0.33545
1000 | 1536 | 0.07443 | 0.33239 | 0.07366 | 0.33204
6000 | 1536 | 0.14959 | 0.45043 | 0.15826 | 0.69119
6272 | 1536 | 0.1542 | 0.47644 | 0.18249 | 0.72208
200 | 2048 | 0.07258 | 0.33982 | 0.07412 | 0.33859
1000 | 2048 | 0.0793 | 0.32816 | 0.07864 | 0.32583
6000 | 2048 | 0.18973 | 0.571 | 0.25506 | 0.91796
6272 | 2048 | 0.19719 | 0.64208 | 0.26445 | 0.95055
200 | 3072 | 0.07092 | 0.33867 | 0.07104 | 0.34695
1000 | 3072 | 0.08727 | 0.33144 | 0.09144 | 0.36633
6000 | 3072 | 0.24683 | 0.87275 | 0.37761 | 1.3289
6272 | 3072 | 0.26437 | 0.91178 | 0.38496 | 1.53694
128 | 2097152 | 6.27936 | 23.69425 | 10.40004 | 30.13699
256 | 1048576 | 4.5404 | 19.47675 | 10.28494 | 29.36936
512 | 524288 | 4.13951 | 18.78771 | 10.09557 | 32.67083
1024 | 262144 | 4.47576 | 18.00411 | 9.56488 | 31.47117
2048 | 131072 | 4.28026 | 16.95619 | 9.40297 | 30.82845
4096 | 65536 | 4.2653 | 16.5018 | 9.03315 | 30.08392
8192 | 32768 | 4.25613 | 16.13583 | 8.9258 | 30.75296
16384 | 16384 | 4.20256 | 16.38207 | 9.52587 | 31.31113
32768 | 8192 | 4.20231 | 16.19452 | 9.31478 | 31.03514

</body>

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---------

**Performance Improvement (%)**
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M | N | fwdbwd,   torch.float16 | fwdbwd,   torch.float32
-- | -- | -- | --
50432 | 384 | 32.178 | 22.049
50176 | 384 | 29.231 | 19.536
200704 | 192 | 44.188 | 43.962
802816 | 64 | 52.119 | 54.100
200 | 256 | -5.750 | -0.206
1000 | 256 | 0.031 | -0.797
6000 | 256 | 3.566 | 5.621
6272 | 256 | 3.865 | 4.836
200 | 512 | -1.615 | -1.010
1000 | 512 | -1.270 | 0.208
6000 | 512 | 3.534 | 5.581
6272 | 512 | 7.905 | 7.483
200 | 1024 | -2.883 | 0.254
1000 | 1024 | -0.767 | 0.493
6000 | 1024 | 0.237 | -2.381
6272 | 1024 | 3.840 | -1.707
200 | 1536 | -0.127 | -1.340
1000 | 1536 | -0.711 | -0.992
6000 | 1536 | -0.209 | -4.728
6272 | 1536 | 0.508 | -0.846
200 | 2048 | -1.262 | -1.176
1000 | 2048 | -0.358 | 0.312
6000 | 2048 | 8.350 | 6.487
6272 | 2048 | 1.588 | 5.713
200 | 3072 | 0.223 | -0.848
1000 | 3072 | -0.773 | -5.743
6000 | 3072 | 3.570 | -3.783
6272 | 3072 | 4.962 | -4.092
128 | 2097152 | -4.266 | 0.348
256 | 1048576 | 0.397 | 0.185
512 | 524288 | 17.325 | 16.605
1024 | 262144 | 23.070 | 19.195
2048 | 131072 | 27.469 | 24.605
4096 | 65536 | 32.023 | 27.465
8192 | 32768 | 24.459 | 28.274
16384 | 16384 | 21.439 | 9.514
32768 | 8192 | 6.818 | 0.491

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---------
**Benchmark script of this PR**
```

from distutils.command.config import config
import torch
from torch.nn import LayerNorm
import timeit

number_runs = 1000  # TODO: Modify this to save time!
def test_forward(layer_norm_cuda, input_cuda):
    layer_norm_cuda(input_cuda); torch.cuda.synchronize()

def test_backward(out_cuda, layer_norm_grad_cuda, create_graph):
    out_cuda.backward(layer_norm_grad_cuda, retain_graph=True, create_graph=create_graph); torch.cuda.synchronize()

def test_fwdbwd(input_cuda, layer_norm_cuda, gO):
    input_cuda.grad = None
    layer_norm_cuda.zero_grad(set_to_none=True)
    out = layer_norm_cuda(input_cuda)
    out.backward(gO)
    torch.cuda.synchronize()

def benchmark(config_m, config_n):

    print("M | N | fwd (half) | fwdbwd (half) | fwd (float) | fwdbwd (float)")
    if len(config_m) != len(config_n):
        print("Please make sure the lengths of config_m and config_m are the same.")

    for i in range(len(config_m)):
        normalized_shape = config_n[i]
        results = [config_m[i], config_n[i]]
        for dtype in (torch.half, torch.float):
            if dtype == torch.half:
                layer_norm_cuda = LayerNorm(normalized_shape).half().cuda()
            else:
                layer_norm_cuda = LayerNorm(normalized_shape).cuda()

            input_cuda = torch.randn(config_m[i], config_n[i], device='cuda', dtype=dtype, requires_grad=True)

            # print("cuda forward:")
            result_fwd = timeit.timeit(lambda: test_forward(layer_norm_cuda, input_cuda), number=number_runs)
            results.append(result_fwd / number_runs * 1000)

            gO = torch.rand_like(input_cuda)

            result_fwdbwd = timeit.timeit(lambda: test_fwdbwd(input_cuda, layer_norm_cuda, gO), number=number_runs)
            results.append(result_fwdbwd / number_runs * 1000)

        print('{:09d}|{:09d}|{:9.5f}|{:9.5f}|{:9.5f}|{:9.5f}'.format(results[0], results[1], results[2], results[3], results[4], results[5]))

    print("Times are in microseconds (us).")

config_m_cvt = [50432, 50176, 200704, 802816]
config_n_cvt = [384, 384, 192, 64]

config_m_68238 = [200, 1000, 6000, 6272, 200, 1000, 6000, 6272, 200, 1000, 6000, 6272, 200, 1000, 6000, 6272, 200, 1000, 6000, 6272, 200, 1000, 6000, 6272]
config_n_68238 = [256,256,256,256,512,512,512,512,1024,1024,1024,1024,1536,1536,1536,1536,2048,2048,2048,2048,3072,3072,3072,3072]

config_m_27634 = [128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768]
config_n_27634 = [2097152, 1048576, 524288, 262144, 131072, 65536, 32768, 16384, 8192]

config_m = config_m_cvt + config_m_68238 + config_m_27634
config_n = config_n_cvt + config_n_68238 + config_n_27634

benchmark(config_m, config_n)
```

CC: @jeffdaily

Pull Request resolved: pytorch#87635
Approved by: https://github.com/jataylo, https://github.com/jeffdaily, https://github.com/ezyang
jithunnair-amd pushed a commit to ROCm/pytorch that referenced this pull request Dec 1, 2022
@hubertlu-tw @jithunnair-amd
[ROCm] Replace layer_norm_grad_input_kernel with cuComputeGradInput f…
27129e8 
…or ROCm (pytorch#87726)

We observed that the native PyTorch LayerNormBackwardKernelImplInternal has suboptimal performance for certain input sizes on AMD GPUs especially when fs (=config_m in our benchmark script) is large and bs (=config_n in our benchmark script) is small (commonly seen in [the CvT model](https://arxiv.org/abs/2103.15808)) in the benchmark script of pytorch#68238 (comment) on AMD GPUs.

This PR is to replace layer_norm_grad_input_kernel with the Apex cuComputeGradInput kernel with some ROCm-specific parameter tuning when fs (=config_m) is larger than or equal to `32768` on AMD GPUs. Some of the code changes in LayerNormBackwardKernelImplInternal are from another PR: pytorch#87635

We used the same benchmark script in the previous PR and tested the optimized kernel with various input shapes on AMD MI100 GPU.

**At [the previous PR](pytorch#87635
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M | N | fwd (half) | fwdbwd (half) | fwd (float) | fwdbwd (float)
-- | -- | -- | -- | -- | --
50432 | 384 | 0.38589 | 0.92603 | 0.38367 | 1.15148
50176 | 384 | 0.38719 | 0.91579 | 0.37815 | 1.13761
200704 | 192 | 0.99787 | 2.39954 | 0.98996 | 2.54284
802816 | 64 | 3.66525 | 7.96952 | 3.61293 | 7.69946
200 | 256 | 0.06578 | 0.34613 | 0.06966 | 0.35449
1000 | 256 | 0.07837 | 0.37631 | 0.07725 | 0.37758
6000 | 256 | 0.09318 | 0.3788 | 0.09202 | 0.37989
6272 | 256 | 0.08694 | 0.36267 | 0.08703 | 0.3615
200 | 512 | 0.06975 | 0.34506 | 0.06973 | 0.34208
1000 | 512 | 0.07012 | 0.36363 | 0.07307 | 0.36741
6000 | 512 | 0.09725 | 0.36251 | 0.09908 | 0.37078
6272 | 512 | 0.09899 | 0.36519 | 0.10068 | 0.37514
200 | 1024 | 0.07188 | 0.33896 | 0.0712 | 0.34683
1000 | 1024 | 0.07357 | 0.3625 | 0.0734 | 0.3598
6000 | 1024 | 0.12642 | 0.38949 | 0.12973 | 0.5035
6272 | 1024 | 0.12901 | 0.40759 | 0.13609 | 0.51871
200 | 1536 | 0.06998 | 0.34782 | 0.07419 | 0.3514
1000 | 1536 | 0.07987 | 0.37915 | 0.07888 | 0.37264
6000 | 1536 | 0.15401 | 0.47524 | 0.15416 | 0.68609
6272 | 1536 | 0.15286 | 0.48843 | 0.17681 | 0.72997
200 | 2048 | 0.07054 | 0.34791 | 0.07289 | 0.35138
1000 | 2048 | 0.07767 | 0.37954 | 0.08554 | 0.37464
6000 | 2048 | 0.18744 | 0.5811 | 0.25004 | 0.93338
6272 | 2048 | 0.20037 | 0.63398 | 0.26918 | 0.97018
200 | 3072 | 0.07687 | 0.36739 | 0.08917 | 0.37845
1000 | 3072 | 0.09323 | 0.38901 | 0.09739 | 0.39823
6000 | 3072 | 0.24314 | 0.89029 | 0.38093 | 1.30719
6272 | 3072 | 0.26079 | 0.92023 | 0.38352 | 1.51012
128 | 2097152 | 6.17775 | 23.876 | 10.27952 | 30.10848
256 | 1048576 | 4.51855 | 19.47637 | 10.07609 | 29.42678
512 | 524288 | 4.13615 | 18.80888 | 10.07853 | 32.29804
1024 | 262144 | 4.47397 | 17.88388 | 9.50367 | 31.15699
2048 | 131072 | 4.2458 | 16.70852 | 9.17979 | 30.51708
4096 | 65536 | 4.24412 | 16.43098 | 8.97651 | 30.1617
8192 | 32768 | 4.24556 | 16.09038 | 8.77001 | 30.3643
16384 | 16384 | 4.14642 | 15.80355 | 8.82402 | 30.35291
32768 | 8192 | 4.12599 | 15.68897 | 8.82605 | 30.43423

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----

**At this PR:**

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M | N | fwd (half) | fwdbwd (half) | fwd (float) | fwdbwd (float)
-- | -- | -- | -- | -- | --
50432 | 384 | 0.38667 | 0.84133 | 0.37916 | 1.01222
50176 | 384 | 0.3814 | 0.87266 | 0.37858 | 1.04399
200704 | 192 | 0.99902 | 2.14386 | 0.98973 | 2.33265
802816 | 64 | 3.66578 | 6.85376 | 3.6092 | 7.00331
200 | 256 | 0.06607 | 0.34176 | 0.07009 | 0.34548
1000 | 256 | 0.06947 | 0.36461 | 0.07902 | 0.37851
6000 | 256 | 0.09319 | 0.37432 | 0.09342 | 0.36927
6272 | 256 | 0.09544 | 0.37565 | 0.09476 | 0.37377
200 | 512 | 0.07935 | 0.364 | 0.07891 | 0.36894
1000 | 512 | 0.07676 | 0.37552 | 0.07957 | 0.37564
6000 | 512 | 0.10472 | 0.37504 | 0.1051 | 0.38782
6272 | 512 | 0.1069 | 0.36662 | 0.10062 | 0.38506
200 | 1024 | 0.07793 | 0.36561 | 0.08023 | 0.35019
1000 | 1024 | 0.07426 | 0.36729 | 0.07345 | 0.35851
6000 | 1024 | 0.12729 | 0.39219 | 0.12974 | 0.51526
6272 | 1024 | 0.13622 | 0.41627 | 0.14252 | 0.52926
200 | 1536 | 0.07615 | 0.36621 | 0.0797 | 0.3695
1000 | 1536 | 0.08327 | 0.38174 | 0.07938 | 0.37573
6000 | 1536 | 0.14894 | 0.46197 | 0.15268 | 0.63814
6272 | 1536 | 0.15368 | 0.48818 | 0.16309 | 0.71441
200 | 2048 | 0.06935 | 0.36691 | 0.07258 | 0.35548
1000 | 2048 | 0.07738 | 0.36388 | 0.08036 | 0.36452
6000 | 2048 | 0.18757 | 0.58573 | 0.23701 | 0.92915
6272 | 2048 | 0.1938 | 0.61628 | 0.26475 | 0.96896
200 | 3072 | 0.07884 | 0.3673 | 0.07724 | 0.37869
1000 | 3072 | 0.09342 | 0.38193 | 0.09822 | 0.38646
6000 | 3072 | 0.24452 | 0.86776 | 0.38251 | 1.3036
6272 | 3072 | 0.25971 | 0.91053 | 0.38744 | 1.39039
128 | 2097152 | 6.06752 | 23.26379 | 9.87466 | 29.81851
256 | 1048576 | 4.50336 | 19.4614 | 10.11239 | 29.25554
512 | 524288 | 4.12649 | 18.72831 | 10.054 | 32.26784
1024 | 262144 | 4.40855 | 17.77993 | 9.38856 | 31.18679
2048 | 131072 | 4.18716 | 16.74615 | 9.14487 | 30.24603
4096 | 65536 | 4.17374 | 16.34444 | 8.94894 | 30.0326
8192 | 32768 | 4.19095 | 16.05751 | 8.70358 | 30.14669
16384 | 16384 | 4.15404 | 15.83771 | 8.80042 | 30.5022
32768 | 8192 | 4.12515 | 15.5657 | 8.66138 | 28.87386

</body>

</html>

---

**Performance Improvement (%)**

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M | N | fwdbwd, torch.float16 | fwdbwd, torch.float32
-- | -- | -- | --
50432 | 384 | 9.147 | 12.094
50176 | 384 | 4.710 | 8.230
200704 | 192 | 10.655 | 8.266
802816 | 64 | 14.000 | 9.042
200 | 256 | 1.263 | 2.542
1000 | 256 | 3.109 | -0.246
6000 | 256 | 1.183 | 2.796
6272 | 256 | -3.579 | -3.394
200 | 512 | -5.489 | -7.852
1000 | 512 | -3.270 | -2.240
6000 | 512 | -3.456 | -4.596
6272 | 512 | -0.392 | -2.644
200 | 1024 | -7.862 | -0.969
1000 | 1024 | -1.321 | 0.359
6000 | 1024 | -0.693 | -2.336
6272 | 1024 | -2.130 | -2.034
200 | 1536 | -5.287 | -5.151
1000 | 1536 | -0.683 | -0.829
6000 | 1536 | 2.792 | 6.989
6272 | 1536 | 0.051 | 2.132
200 | 2048 | -5.461 | -1.167
1000 | 2048 | 4.126 | 2.701
6000 | 2048 | -0.797 | 0.453
6272 | 2048 | 2.792 | 0.126
200 | 3072 | 0.024 | -0.063
1000 | 3072 | 1.820 | 2.956
6000 | 3072 | 2.531 | 0.275
6272 | 3072 | 1.054 | 7.929
128 | 2097152 | 2.564 | 0.963
256 | 1048576 | 0.077 | 0.582
512 | 524288 | 0.428 | 0.094
1024 | 262144 | 0.581 | -0.096
2048 | 131072 | -0.225 | 0.888
4096 | 65536 | 0.527 | 0.428
8192 | 32768 | 0.204 | 0.717
16384 | 16384 | -0.216 | -0.492
32768 | 8192 | 0.786 | 5.127

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</html>

CC: @jeffdaily

Pull Request resolved: pytorch#87726
Approved by: https://github.com/ngimel
kulinseth pushed a commit to kulinseth/pytorch that referenced this pull request Dec 10, 2022
@hubertlu-tw @kulinseth
[ROCm] Optimize layer norm backward kernel for ROCm (pytorch#87635)
5eba9a3 
We observed that the native PyTorch LayerNormBackwardKernelImplInternal has suboptimal performance for certain input sizes on AMD GPUs especially when `fs`  (=`config_m` in our benchmark script) is large and `bs`  (=`config_n` in our benchmark script) is small (commonly seen in [the CvT model](https://arxiv.org/abs/2103.15808)) in the benchmark script of [PR pytorch#68238](pytorch#68238 (comment)) on AMD GPUs.

This PR is to replace `GammaBetaBackwardCUDAKernel` with the Apex layernorm backward kernel with some ROCm-specific parameter tuning when `fs`  (=`config_m`) is larger than 512 on AMD GPUs.

There are a few PRs for LayerNorm kernel:
- pytorch#26201
- pytorch#27634
- pytorch#68238

Therefore, we have tested and compared the kernel before and at this PR with the input shapes in the last two PRs along with those commonly used in the CvT model on AMD MI100.

---
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M | N | fwd (half) | fwdbwd (half) | fwd (float) | fwdbwd (float)
-- | -- | -- | -- | -- | --
50432 | 384 | 0.387256 | 1.372758 | 0.378975 | 1.47892
50176 | 384 | 0.38231 | 1.362416 | 0.378084 | 1.473886
200704 | 192 | 0.997859 | 4.315875 | 0.989306 | 4.560827
802816 | 64 | 3.671828 | 16.68013 | 3.613515 | 16.827946
200 | 256 | 0.066503 | 0.332096 | 0.071422 | 0.325349
1000 | 256 | 0.071848 | 0.333355 | 0.073038 | 0.334753
6000 | 256 | 0.086334 | 0.345139 | 0.086834 | 0.347429
6272 | 256 | 0.088601 | 0.347906 | 0.087855 | 0.351245
200 | 512 | 0.071626 | 0.329726 | 0.073798 | 0.326878
1000 | 512 | 0.073975 | 0.330226 | 0.074166 | 0.332751
6000 | 512 | 0.099617 | 0.362367 | 0.100095 | 0.378313
6272 | 512 | 0.100378 | 0.358066 | 0.099857 | 0.395982
200 | 1024 | 0.072954 | 0.326382 | 0.073899 | 0.333007
1000 | 1024 | 0.0743 | 0.325532 | 0.071126 | 0.330991
6000 | 1024 | 0.127025 | 0.390084 | 0.128692 | 0.471504
6272 | 1024 | 0.130704 | 0.403536 | 0.135244 | 0.487133
200 | 1536 | 0.070331 | 0.339169 | 0.070086 | 0.331015
1000 | 1536 | 0.075085 | 0.330042 | 0.076295 | 0.328778
6000 | 1536 | 0.148889 | 0.44949 | 0.155781 | 0.659987
6272 | 1536 | 0.154939 | 0.478871 | 0.17673 | 0.716025
200 | 2048 | 0.070269 | 0.335585 | 0.072804 | 0.334655
1000 | 2048 | 0.080094 | 0.326991 | 0.080426 | 0.32685
6000 | 2048 | 0.187888 | 0.623023 | 0.245762 | 0.981635
6272 | 2048 | 0.195431 | 0.65244 | 0.262574 | 1.008141
200 | 3072 | 0.068205 | 0.339428 | 0.073068 | 0.344034
1000 | 3072 | 0.087554 | 0.328899 | 0.09218 | 0.346433
6000 | 3072 | 0.240352 | 0.905058 | 0.368135 | 1.280462
6272 | 3072 | 0.26179 | 0.959387 | 0.387782 | 1.476524
128 | 2097152 | 5.905976 | 22.724793 | 10.287974 | 30.242092
256 | 1048576 | 4.561596 | 19.554308 | 10.223171 | 29.42371
512 | 524288 | 4.146751 | 22.7247 | 11.404285 | 39.175902
1024 | 262144 | 5.193135 | 23.403325 | 11.334512 | 38.947192
2048 | 131072 | 4.992907 | 23.377801 | 11.400286 | 40.889191
4096 | 65536 | 5.429488 | 24.275701 | 11.196778 | 41.4751
8192 | 32768 | 5.35758 | 21.360312 | 10.535418 | 42.875646
16384 | 16384 | 5.44947 | 20.852605 | 10.357685 | 34.603408
32768 | 8192 | 4.688925 | 17.379392 | 9.635596 | 31.188271

</body>

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---------
**At this PR**
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M | N | fwd (half) | fwdbwd (half) | fwd (float) | fwdbwd (float)
-- | -- | -- | -- | -- | --
50432 | 384 | 0.38797 | 0.93103 | 0.37966 | 1.15283
50176 | 384 | 0.3874 | 0.96417 | 0.38462 | 1.18595
200704 | 192 | 1.00002 | 2.40876 | 0.99224 | 2.55579
802816 | 64 | 3.67348 | 7.98658 | 3.61871 | 7.72404
200 | 256 | 0.07292 | 0.35119 | 0.07195 | 0.32602
1000 | 256 | 0.07354 | 0.33325 | 0.07237 | 0.33742
6000 | 256 | 0.08819 | 0.33283 | 0.08453 | 0.3279
6272 | 256 | 0.0886 | 0.33446 | 0.08774 | 0.33426
200 | 512 | 0.0701 | 0.33505 | 0.07072 | 0.33018
1000 | 512 | 0.07042 | 0.33442 | 0.074 | 0.33206
6000 | 512 | 0.09931 | 0.34956 | 0.09895 | 0.3572
6272 | 512 | 0.10103 | 0.32976 | 0.10041 | 0.36635
200 | 1024 | 0.07144 | 0.33579 | 0.07209 | 0.33216
1000 | 1024 | 0.0736 | 0.32803 | 0.07286 | 0.32936
6000 | 1024 | 0.12584 | 0.38916 | 0.12852 | 0.48273
6272 | 1024 | 0.13053 | 0.38804 | 0.13464 | 0.49545
200 | 1536 | 0.07159 | 0.3396 | 0.07062 | 0.33545
1000 | 1536 | 0.07443 | 0.33239 | 0.07366 | 0.33204
6000 | 1536 | 0.14959 | 0.45043 | 0.15826 | 0.69119
6272 | 1536 | 0.1542 | 0.47644 | 0.18249 | 0.72208
200 | 2048 | 0.07258 | 0.33982 | 0.07412 | 0.33859
1000 | 2048 | 0.0793 | 0.32816 | 0.07864 | 0.32583
6000 | 2048 | 0.18973 | 0.571 | 0.25506 | 0.91796
6272 | 2048 | 0.19719 | 0.64208 | 0.26445 | 0.95055
200 | 3072 | 0.07092 | 0.33867 | 0.07104 | 0.34695
1000 | 3072 | 0.08727 | 0.33144 | 0.09144 | 0.36633
6000 | 3072 | 0.24683 | 0.87275 | 0.37761 | 1.3289
6272 | 3072 | 0.26437 | 0.91178 | 0.38496 | 1.53694
128 | 2097152 | 6.27936 | 23.69425 | 10.40004 | 30.13699
256 | 1048576 | 4.5404 | 19.47675 | 10.28494 | 29.36936
512 | 524288 | 4.13951 | 18.78771 | 10.09557 | 32.67083
1024 | 262144 | 4.47576 | 18.00411 | 9.56488 | 31.47117
2048 | 131072 | 4.28026 | 16.95619 | 9.40297 | 30.82845
4096 | 65536 | 4.2653 | 16.5018 | 9.03315 | 30.08392
8192 | 32768 | 4.25613 | 16.13583 | 8.9258 | 30.75296
16384 | 16384 | 4.20256 | 16.38207 | 9.52587 | 31.31113
32768 | 8192 | 4.20231 | 16.19452 | 9.31478 | 31.03514

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---------

**Performance Improvement (%)**
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M | N | fwdbwd,   torch.float16 | fwdbwd,   torch.float32
-- | -- | -- | --
50432 | 384 | 32.178 | 22.049
50176 | 384 | 29.231 | 19.536
200704 | 192 | 44.188 | 43.962
802816 | 64 | 52.119 | 54.100
200 | 256 | -5.750 | -0.206
1000 | 256 | 0.031 | -0.797
6000 | 256 | 3.566 | 5.621
6272 | 256 | 3.865 | 4.836
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6000 | 512 | 3.534 | 5.581
6272 | 512 | 7.905 | 7.483
200 | 1024 | -2.883 | 0.254
1000 | 1024 | -0.767 | 0.493
6000 | 1024 | 0.237 | -2.381
6272 | 1024 | 3.840 | -1.707
200 | 1536 | -0.127 | -1.340
1000 | 1536 | -0.711 | -0.992
6000 | 1536 | -0.209 | -4.728
6272 | 1536 | 0.508 | -0.846
200 | 2048 | -1.262 | -1.176
1000 | 2048 | -0.358 | 0.312
6000 | 2048 | 8.350 | 6.487
6272 | 2048 | 1.588 | 5.713
200 | 3072 | 0.223 | -0.848
1000 | 3072 | -0.773 | -5.743
6000 | 3072 | 3.570 | -3.783
6272 | 3072 | 4.962 | -4.092
128 | 2097152 | -4.266 | 0.348
256 | 1048576 | 0.397 | 0.185
512 | 524288 | 17.325 | 16.605
1024 | 262144 | 23.070 | 19.195
2048 | 131072 | 27.469 | 24.605
4096 | 65536 | 32.023 | 27.465
8192 | 32768 | 24.459 | 28.274
16384 | 16384 | 21.439 | 9.514
32768 | 8192 | 6.818 | 0.491

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---------
**Benchmark script of this PR**
```
# Ref:
#       1. pytorch#26201
#       2. pytorch#68238

from distutils.command.config import config
import torch
from torch.nn import LayerNorm
import timeit

number_runs = 1000  # TODO: Modify this to save time!
def test_forward(layer_norm_cuda, input_cuda):
    layer_norm_cuda(input_cuda); torch.cuda.synchronize()

def test_backward(out_cuda, layer_norm_grad_cuda, create_graph):
    out_cuda.backward(layer_norm_grad_cuda, retain_graph=True, create_graph=create_graph); torch.cuda.synchronize()

def test_fwdbwd(input_cuda, layer_norm_cuda, gO):
    input_cuda.grad = None
    layer_norm_cuda.zero_grad(set_to_none=True)
    out = layer_norm_cuda(input_cuda)
    out.backward(gO)
    torch.cuda.synchronize()

def benchmark(config_m, config_n):

    print("M | N | fwd (half) | fwdbwd (half) | fwd (float) | fwdbwd (float)")
    if len(config_m) != len(config_n):
        print("Please make sure the lengths of config_m and config_m are the same.")

    for i in range(len(config_m)):
        normalized_shape = config_n[i]
        results = [config_m[i], config_n[i]]
        for dtype in (torch.half, torch.float):
            if dtype == torch.half:
                layer_norm_cuda = LayerNorm(normalized_shape).half().cuda()
            else:
                layer_norm_cuda = LayerNorm(normalized_shape).cuda()

            input_cuda = torch.randn(config_m[i], config_n[i], device='cuda', dtype=dtype, requires_grad=True)

            # print("cuda forward:")
            result_fwd = timeit.timeit(lambda: test_forward(layer_norm_cuda, input_cuda), number=number_runs)
            results.append(result_fwd / number_runs * 1000)

            gO = torch.rand_like(input_cuda)

            result_fwdbwd = timeit.timeit(lambda: test_fwdbwd(input_cuda, layer_norm_cuda, gO), number=number_runs)
            results.append(result_fwdbwd / number_runs * 1000)

        print('{:09d}|{:09d}|{:9.5f}|{:9.5f}|{:9.5f}|{:9.5f}'.format(results[0], results[1], results[2], results[3], results[4], results[5]))

    print("Times are in microseconds (us).")

# CVT
config_m_cvt = [50432, 50176, 200704, 802816]
config_n_cvt = [384, 384, 192, 64]

# pytorch#68238 (comment)
config_m_68238 = [200, 1000, 6000, 6272, 200, 1000, 6000, 6272, 200, 1000, 6000, 6272, 200, 1000, 6000, 6272, 200, 1000, 6000, 6272, 200, 1000, 6000, 6272]
config_n_68238 = [256,256,256,256,512,512,512,512,1024,1024,1024,1024,1536,1536,1536,1536,2048,2048,2048,2048,3072,3072,3072,3072]

# pytorch#27634
config_m_27634 = [128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768]
config_n_27634 = [2097152, 1048576, 524288, 262144, 131072, 65536, 32768, 16384, 8192]

config_m = config_m_cvt + config_m_68238 + config_m_27634
config_n = config_n_cvt + config_n_68238 + config_n_27634

benchmark(config_m, config_n)
```

CC: @jeffdaily

Pull Request resolved: pytorch#87635
Approved by: https://github.com/jataylo, https://github.com/jeffdaily, https://github.com/ezyang
kulinseth pushed a commit to kulinseth/pytorch that referenced this pull request Dec 10, 2022
@hubertlu-tw @kulinseth
[ROCm] Replace layer_norm_grad_input_kernel with cuComputeGradInput f…
e1f9bd6 
…or ROCm (pytorch#87726)

We observed that the native PyTorch LayerNormBackwardKernelImplInternal has suboptimal performance for certain input sizes on AMD GPUs especially when fs (=config_m in our benchmark script) is large and bs (=config_n in our benchmark script) is small (commonly seen in [the CvT model](https://arxiv.org/abs/2103.15808)) in the benchmark script of pytorch#68238 (comment) on AMD GPUs.

This PR is to replace layer_norm_grad_input_kernel with the Apex cuComputeGradInput kernel with some ROCm-specific parameter tuning when fs (=config_m) is larger than or equal to `32768` on AMD GPUs. Some of the code changes in LayerNormBackwardKernelImplInternal are from another PR: pytorch#87635

We used the same benchmark script in the previous PR and tested the optimized kernel with various input shapes on AMD MI100 GPU.

**At [the previous PR](pytorch#87635
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M | N | fwd (half) | fwdbwd (half) | fwd (float) | fwdbwd (float)
-- | -- | -- | -- | -- | --
50432 | 384 | 0.38589 | 0.92603 | 0.38367 | 1.15148
50176 | 384 | 0.38719 | 0.91579 | 0.37815 | 1.13761
200704 | 192 | 0.99787 | 2.39954 | 0.98996 | 2.54284
802816 | 64 | 3.66525 | 7.96952 | 3.61293 | 7.69946
200 | 256 | 0.06578 | 0.34613 | 0.06966 | 0.35449
1000 | 256 | 0.07837 | 0.37631 | 0.07725 | 0.37758
6000 | 256 | 0.09318 | 0.3788 | 0.09202 | 0.37989
6272 | 256 | 0.08694 | 0.36267 | 0.08703 | 0.3615
200 | 512 | 0.06975 | 0.34506 | 0.06973 | 0.34208
1000 | 512 | 0.07012 | 0.36363 | 0.07307 | 0.36741
6000 | 512 | 0.09725 | 0.36251 | 0.09908 | 0.37078
6272 | 512 | 0.09899 | 0.36519 | 0.10068 | 0.37514
200 | 1024 | 0.07188 | 0.33896 | 0.0712 | 0.34683
1000 | 1024 | 0.07357 | 0.3625 | 0.0734 | 0.3598
6000 | 1024 | 0.12642 | 0.38949 | 0.12973 | 0.5035
6272 | 1024 | 0.12901 | 0.40759 | 0.13609 | 0.51871
200 | 1536 | 0.06998 | 0.34782 | 0.07419 | 0.3514
1000 | 1536 | 0.07987 | 0.37915 | 0.07888 | 0.37264
6000 | 1536 | 0.15401 | 0.47524 | 0.15416 | 0.68609
6272 | 1536 | 0.15286 | 0.48843 | 0.17681 | 0.72997
200 | 2048 | 0.07054 | 0.34791 | 0.07289 | 0.35138
1000 | 2048 | 0.07767 | 0.37954 | 0.08554 | 0.37464
6000 | 2048 | 0.18744 | 0.5811 | 0.25004 | 0.93338
6272 | 2048 | 0.20037 | 0.63398 | 0.26918 | 0.97018
200 | 3072 | 0.07687 | 0.36739 | 0.08917 | 0.37845
1000 | 3072 | 0.09323 | 0.38901 | 0.09739 | 0.39823
6000 | 3072 | 0.24314 | 0.89029 | 0.38093 | 1.30719
6272 | 3072 | 0.26079 | 0.92023 | 0.38352 | 1.51012
128 | 2097152 | 6.17775 | 23.876 | 10.27952 | 30.10848
256 | 1048576 | 4.51855 | 19.47637 | 10.07609 | 29.42678
512 | 524288 | 4.13615 | 18.80888 | 10.07853 | 32.29804
1024 | 262144 | 4.47397 | 17.88388 | 9.50367 | 31.15699
2048 | 131072 | 4.2458 | 16.70852 | 9.17979 | 30.51708
4096 | 65536 | 4.24412 | 16.43098 | 8.97651 | 30.1617
8192 | 32768 | 4.24556 | 16.09038 | 8.77001 | 30.3643
16384 | 16384 | 4.14642 | 15.80355 | 8.82402 | 30.35291
32768 | 8192 | 4.12599 | 15.68897 | 8.82605 | 30.43423

</body>

</html>

----

**At this PR:**

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M | N | fwd (half) | fwdbwd (half) | fwd (float) | fwdbwd (float)
-- | -- | -- | -- | -- | --
50432 | 384 | 0.38667 | 0.84133 | 0.37916 | 1.01222
50176 | 384 | 0.3814 | 0.87266 | 0.37858 | 1.04399
200704 | 192 | 0.99902 | 2.14386 | 0.98973 | 2.33265
802816 | 64 | 3.66578 | 6.85376 | 3.6092 | 7.00331
200 | 256 | 0.06607 | 0.34176 | 0.07009 | 0.34548
1000 | 256 | 0.06947 | 0.36461 | 0.07902 | 0.37851
6000 | 256 | 0.09319 | 0.37432 | 0.09342 | 0.36927
6272 | 256 | 0.09544 | 0.37565 | 0.09476 | 0.37377
200 | 512 | 0.07935 | 0.364 | 0.07891 | 0.36894
1000 | 512 | 0.07676 | 0.37552 | 0.07957 | 0.37564
6000 | 512 | 0.10472 | 0.37504 | 0.1051 | 0.38782
6272 | 512 | 0.1069 | 0.36662 | 0.10062 | 0.38506
200 | 1024 | 0.07793 | 0.36561 | 0.08023 | 0.35019
1000 | 1024 | 0.07426 | 0.36729 | 0.07345 | 0.35851
6000 | 1024 | 0.12729 | 0.39219 | 0.12974 | 0.51526
6272 | 1024 | 0.13622 | 0.41627 | 0.14252 | 0.52926
200 | 1536 | 0.07615 | 0.36621 | 0.0797 | 0.3695
1000 | 1536 | 0.08327 | 0.38174 | 0.07938 | 0.37573
6000 | 1536 | 0.14894 | 0.46197 | 0.15268 | 0.63814
6272 | 1536 | 0.15368 | 0.48818 | 0.16309 | 0.71441
200 | 2048 | 0.06935 | 0.36691 | 0.07258 | 0.35548
1000 | 2048 | 0.07738 | 0.36388 | 0.08036 | 0.36452
6000 | 2048 | 0.18757 | 0.58573 | 0.23701 | 0.92915
6272 | 2048 | 0.1938 | 0.61628 | 0.26475 | 0.96896
200 | 3072 | 0.07884 | 0.3673 | 0.07724 | 0.37869
1000 | 3072 | 0.09342 | 0.38193 | 0.09822 | 0.38646
6000 | 3072 | 0.24452 | 0.86776 | 0.38251 | 1.3036
6272 | 3072 | 0.25971 | 0.91053 | 0.38744 | 1.39039
128 | 2097152 | 6.06752 | 23.26379 | 9.87466 | 29.81851
256 | 1048576 | 4.50336 | 19.4614 | 10.11239 | 29.25554
512 | 524288 | 4.12649 | 18.72831 | 10.054 | 32.26784
1024 | 262144 | 4.40855 | 17.77993 | 9.38856 | 31.18679
2048 | 131072 | 4.18716 | 16.74615 | 9.14487 | 30.24603
4096 | 65536 | 4.17374 | 16.34444 | 8.94894 | 30.0326
8192 | 32768 | 4.19095 | 16.05751 | 8.70358 | 30.14669
16384 | 16384 | 4.15404 | 15.83771 | 8.80042 | 30.5022
32768 | 8192 | 4.12515 | 15.5657 | 8.66138 | 28.87386

</body>

</html>

---

**Performance Improvement (%)**

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M | N | fwdbwd, torch.float16 | fwdbwd, torch.float32
-- | -- | -- | --
50432 | 384 | 9.147 | 12.094
50176 | 384 | 4.710 | 8.230
200704 | 192 | 10.655 | 8.266
802816 | 64 | 14.000 | 9.042
200 | 256 | 1.263 | 2.542
1000 | 256 | 3.109 | -0.246
6000 | 256 | 1.183 | 2.796
6272 | 256 | -3.579 | -3.394
200 | 512 | -5.489 | -7.852
1000 | 512 | -3.270 | -2.240
6000 | 512 | -3.456 | -4.596
6272 | 512 | -0.392 | -2.644
200 | 1024 | -7.862 | -0.969
1000 | 1024 | -1.321 | 0.359
6000 | 1024 | -0.693 | -2.336
6272 | 1024 | -2.130 | -2.034
200 | 1536 | -5.287 | -5.151
1000 | 1536 | -0.683 | -0.829
6000 | 1536 | 2.792 | 6.989
6272 | 1536 | 0.051 | 2.132
200 | 2048 | -5.461 | -1.167
1000 | 2048 | 4.126 | 2.701
6000 | 2048 | -0.797 | 0.453
6272 | 2048 | 2.792 | 0.126
200 | 3072 | 0.024 | -0.063
1000 | 3072 | 1.820 | 2.956
6000 | 3072 | 2.531 | 0.275
6272 | 3072 | 1.054 | 7.929
128 | 2097152 | 2.564 | 0.963
256 | 1048576 | 0.077 | 0.582
512 | 524288 | 0.428 | 0.094
1024 | 262144 | 0.581 | -0.096
2048 | 131072 | -0.225 | 0.888
4096 | 65536 | 0.527 | 0.428
8192 | 32768 | 0.204 | 0.717
16384 | 16384 | -0.216 | -0.492
32768 | 8192 | 0.786 | 5.127

</body>

</html>

CC: @jeffdaily

Pull Request resolved: pytorch#87726
Approved by: https://github.com/ngimel
laurentdupin pushed a commit to laurentdupin/pytorch that referenced this pull request Apr 25, 2026
@facebook-github-bot
Improve native layer norm backward perf (pytorch#68238)
81da2fd 
Summary:
Benchmarks
At this PR
```
[------------------------------------------------------ ln ------------------------------------------------------]
                  |  fwd, torch.float32  |  fwdbwd, torch.float32  |  fwd, torch.float16  |  fwdbwd, torch.float16
1 threads: -------------------------------------------------------------------------------------------------------
      200, 256    |         17.5         |          106.6          |         18.1         |           94.7
      1000, 256   |         18.7         |          116.6          |         18.7         |          110.7
      6000, 256   |         28.1         |          111.8          |         19.4         |           92.3
      6272, 256   |         29.3         |          108.5          |         20.1         |           92.7
      200, 512    |         19.3         |           83.8          |         19.1         |          116.3
      1000, 512   |         17.9         |           88.0          |         17.9         |           93.0
      6000, 512   |         36.9         |          141.2          |         27.4         |          103.3
      6272, 512   |         38.2         |          146.5          |         28.1         |          107.9
      200, 1024   |         18.1         |           89.5          |         21.1         |          102.7
      1000, 1024  |         17.9         |           88.7          |         18.5         |           92.5
      6000, 1024  |         77.6         |          277.5          |         40.3         |          148.5
      6272, 1024  |         80.7         |          288.1          |         42.0         |          154.0
      200, 1536   |         17.9         |          117.3          |         18.1         |           88.1
      1000, 1536  |         22.9         |           92.0          |         19.4         |           89.0
      6000, 1536  |        123.4         |          436.3          |         61.7         |          228.5
      6272, 1536  |        129.1         |          457.3          |         64.3         |          238.5
      200, 2048   |         18.0         |           90.5          |         19.1         |          101.6
      1000, 2048  |         31.1         |          109.8          |         25.3         |          107.9
      6000, 2048  |        174.5         |          589.8          |         87.1         |          310.5
      6272, 2048  |        182.2         |          617.0          |         91.2         |          316.7
      200, 3072   |         19.8         |           96.4          |         19.4         |           89.3
      1000, 3072  |         48.1         |          168.7          |         23.5         |          100.9
      6000, 3072  |        267.1         |          930.0          |        134.8         |          519.2
      6272, 3072  |        278.2         |          971.2          |        140.7         |          540.2
```
Pre-pytorch#67977
```
[------------------------------------------------------- ln -------------------------------------------------------]
                    |  fwd, torch.float32  |  fwdbwd, torch.float32  |  fwd, torch.float16  |  fwdbwd, torch.float16
1 threads: ---------------------------------------------------------------------------------------------------------
        200,   256  |         20.9         |            92.6         |         21.3         |          110.1
       1000,   256  |         20.3         |            91.8         |         28.1         |          115.6
       6000,   256  |         93.0         |           310.7         |         86.3         |          299.8
       6272,   256  |         97.3         |           323.5         |         90.0         |          314.1
        200,   512  |         20.9         |           110.2         |         21.1         |           95.0
       1000,   512  |         24.0         |           102.8         |         22.2         |           95.9
       6000,   512  |        121.7         |           367.2         |        105.6         |          337.4
       6272,   512  |        127.0         |           382.3         |        111.3         |          352.0
        200,  1024  |         21.0         |           131.8         |         20.4         |           93.3
       1000,  1024  |         35.5         |           108.7         |         27.7         |           99.4
       6000,  1024  |        170.4         |           495.5         |        137.7         |          411.4
       6272,  1024  |        177.5         |           517.6         |        143.6         |          428.6
        200,  1536  |         21.9         |            97.6         |         20.8         |           92.7
       1000,  1536  |         44.3         |           129.7         |         33.9         |          100.1
       6000,  1536  |        215.8         |           619.2         |        167.2         |          480.9
       6272,  1536  |        225.0         |           646.9         |        174.8         |          505.9
        200,  2048  |         21.8         |           100.8         |         20.7         |           96.7
       1000,  2048  |         53.7         |           152.4         |         41.4         |          118.3
       6000,  2048  |        267.0         |           753.6         |        220.4         |          571.5
       6272,  2048  |        278.6         |           785.8         |        211.4         |          589.2
        200,  3072  |         20.9         |           103.7         |         21.9         |          104.6
       1000,  3072  |         71.4         |           201.1         |         53.1         |          148.3
       6000,  3072  |        365.7         |          1040.3         |        262.0         |          731.5
       6272,  3072  |        382.0         |          1084.4         |        273.3         |          766.3
```
Benchmarking script
```
import torch
from torch.utils.benchmark import Timer, Compare

results = []
for dtype in (torch.float, torch.half):
    for fs in (256, 512, 1024, 1536, 2048, 3072):
        for bs in (200, 1000, 6000, 196*32):
            ln = torch.nn.LayerNorm((fs,), device="cuda", dtype=dtype)
            X = torch.randn(bs, fs, device="cuda", dtype=dtype, requires_grad=True)
            gO = torch.rand_like(X)
            stmtfwd = "ln(X)"
            stmtfwdbwd = "X.grad=None; ln.zero_grad(set_to_none=True); out = ln(X); out.backward(gO)"
            tfwd = Timer(stmt=stmtfwd, label="ln", sub_label=f"{bs:5}, {fs:5}", description=f"fwd, {dtype}", globals=globals())
            tfwdbwd = Timer(stmt=stmtfwdbwd, label="ln", sub_label=f"{bs:5}, {fs:5}", description=f"fwdbwd, {dtype}", globals=globals())
            for t in (tfwd, tfwdbwd):
                results.append(t.blocked_autorange())
        print(fs, end='\r')
c = Compare(results)
c.print()
```

Pull Request resolved: pytorch#68238

Reviewed By: mruberry

Differential Revision: D32469450

Pulled By: ngimel

fbshipit-source-id: 08fe755c156d3d5c366c966cb808bf0f3e74c050
laurentdupin pushed a commit to laurentdupin/pytorch that referenced this pull request Apr 25, 2026
@hubertlu-tw @pytorchmergebot
[ROCm] Optimize layer norm backward kernel for ROCm (pytorch#87635)
48196c7 
We observed that the native PyTorch LayerNormBackwardKernelImplInternal has suboptimal performance for certain input sizes on AMD GPUs especially when `fs`  (=`config_m` in our benchmark script) is large and `bs`  (=`config_n` in our benchmark script) is small (commonly seen in [the CvT model](https://arxiv.org/abs/2103.15808)) in the benchmark script of [PR pytorch#68238](pytorch#68238 (comment)) on AMD GPUs.

This PR is to replace `GammaBetaBackwardCUDAKernel` with the Apex layernorm backward kernel with some ROCm-specific parameter tuning when `fs`  (=`config_m`) is larger than 512 on AMD GPUs.

There are a few PRs for LayerNorm kernel:
- pytorch#26201
- pytorch#27634
- pytorch#68238

Therefore, we have tested and compared the kernel before and at this PR with the input shapes in the last two PRs along with those commonly used in the CvT model on AMD MI100.

---
**Current**
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M | N | fwd (half) | fwdbwd (half) | fwd (float) | fwdbwd (float)
-- | -- | -- | -- | -- | --
50432 | 384 | 0.387256 | 1.372758 | 0.378975 | 1.47892
50176 | 384 | 0.38231 | 1.362416 | 0.378084 | 1.473886
200704 | 192 | 0.997859 | 4.315875 | 0.989306 | 4.560827
802816 | 64 | 3.671828 | 16.68013 | 3.613515 | 16.827946
200 | 256 | 0.066503 | 0.332096 | 0.071422 | 0.325349
1000 | 256 | 0.071848 | 0.333355 | 0.073038 | 0.334753
6000 | 256 | 0.086334 | 0.345139 | 0.086834 | 0.347429
6272 | 256 | 0.088601 | 0.347906 | 0.087855 | 0.351245
200 | 512 | 0.071626 | 0.329726 | 0.073798 | 0.326878
1000 | 512 | 0.073975 | 0.330226 | 0.074166 | 0.332751
6000 | 512 | 0.099617 | 0.362367 | 0.100095 | 0.378313
6272 | 512 | 0.100378 | 0.358066 | 0.099857 | 0.395982
200 | 1024 | 0.072954 | 0.326382 | 0.073899 | 0.333007
1000 | 1024 | 0.0743 | 0.325532 | 0.071126 | 0.330991
6000 | 1024 | 0.127025 | 0.390084 | 0.128692 | 0.471504
6272 | 1024 | 0.130704 | 0.403536 | 0.135244 | 0.487133
200 | 1536 | 0.070331 | 0.339169 | 0.070086 | 0.331015
1000 | 1536 | 0.075085 | 0.330042 | 0.076295 | 0.328778
6000 | 1536 | 0.148889 | 0.44949 | 0.155781 | 0.659987
6272 | 1536 | 0.154939 | 0.478871 | 0.17673 | 0.716025
200 | 2048 | 0.070269 | 0.335585 | 0.072804 | 0.334655
1000 | 2048 | 0.080094 | 0.326991 | 0.080426 | 0.32685
6000 | 2048 | 0.187888 | 0.623023 | 0.245762 | 0.981635
6272 | 2048 | 0.195431 | 0.65244 | 0.262574 | 1.008141
200 | 3072 | 0.068205 | 0.339428 | 0.073068 | 0.344034
1000 | 3072 | 0.087554 | 0.328899 | 0.09218 | 0.346433
6000 | 3072 | 0.240352 | 0.905058 | 0.368135 | 1.280462
6272 | 3072 | 0.26179 | 0.959387 | 0.387782 | 1.476524
128 | 2097152 | 5.905976 | 22.724793 | 10.287974 | 30.242092
256 | 1048576 | 4.561596 | 19.554308 | 10.223171 | 29.42371
512 | 524288 | 4.146751 | 22.7247 | 11.404285 | 39.175902
1024 | 262144 | 5.193135 | 23.403325 | 11.334512 | 38.947192
2048 | 131072 | 4.992907 | 23.377801 | 11.400286 | 40.889191
4096 | 65536 | 5.429488 | 24.275701 | 11.196778 | 41.4751
8192 | 32768 | 5.35758 | 21.360312 | 10.535418 | 42.875646
16384 | 16384 | 5.44947 | 20.852605 | 10.357685 | 34.603408
32768 | 8192 | 4.688925 | 17.379392 | 9.635596 | 31.188271

</body>

</html>

---------
**At this PR**
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M | N | fwd (half) | fwdbwd (half) | fwd (float) | fwdbwd (float)
-- | -- | -- | -- | -- | --
50432 | 384 | 0.38797 | 0.93103 | 0.37966 | 1.15283
50176 | 384 | 0.3874 | 0.96417 | 0.38462 | 1.18595
200704 | 192 | 1.00002 | 2.40876 | 0.99224 | 2.55579
802816 | 64 | 3.67348 | 7.98658 | 3.61871 | 7.72404
200 | 256 | 0.07292 | 0.35119 | 0.07195 | 0.32602
1000 | 256 | 0.07354 | 0.33325 | 0.07237 | 0.33742
6000 | 256 | 0.08819 | 0.33283 | 0.08453 | 0.3279
6272 | 256 | 0.0886 | 0.33446 | 0.08774 | 0.33426
200 | 512 | 0.0701 | 0.33505 | 0.07072 | 0.33018
1000 | 512 | 0.07042 | 0.33442 | 0.074 | 0.33206
6000 | 512 | 0.09931 | 0.34956 | 0.09895 | 0.3572
6272 | 512 | 0.10103 | 0.32976 | 0.10041 | 0.36635
200 | 1024 | 0.07144 | 0.33579 | 0.07209 | 0.33216
1000 | 1024 | 0.0736 | 0.32803 | 0.07286 | 0.32936
6000 | 1024 | 0.12584 | 0.38916 | 0.12852 | 0.48273
6272 | 1024 | 0.13053 | 0.38804 | 0.13464 | 0.49545
200 | 1536 | 0.07159 | 0.3396 | 0.07062 | 0.33545
1000 | 1536 | 0.07443 | 0.33239 | 0.07366 | 0.33204
6000 | 1536 | 0.14959 | 0.45043 | 0.15826 | 0.69119
6272 | 1536 | 0.1542 | 0.47644 | 0.18249 | 0.72208
200 | 2048 | 0.07258 | 0.33982 | 0.07412 | 0.33859
1000 | 2048 | 0.0793 | 0.32816 | 0.07864 | 0.32583
6000 | 2048 | 0.18973 | 0.571 | 0.25506 | 0.91796
6272 | 2048 | 0.19719 | 0.64208 | 0.26445 | 0.95055
200 | 3072 | 0.07092 | 0.33867 | 0.07104 | 0.34695
1000 | 3072 | 0.08727 | 0.33144 | 0.09144 | 0.36633
6000 | 3072 | 0.24683 | 0.87275 | 0.37761 | 1.3289
6272 | 3072 | 0.26437 | 0.91178 | 0.38496 | 1.53694
128 | 2097152 | 6.27936 | 23.69425 | 10.40004 | 30.13699
256 | 1048576 | 4.5404 | 19.47675 | 10.28494 | 29.36936
512 | 524288 | 4.13951 | 18.78771 | 10.09557 | 32.67083
1024 | 262144 | 4.47576 | 18.00411 | 9.56488 | 31.47117
2048 | 131072 | 4.28026 | 16.95619 | 9.40297 | 30.82845
4096 | 65536 | 4.2653 | 16.5018 | 9.03315 | 30.08392
8192 | 32768 | 4.25613 | 16.13583 | 8.9258 | 30.75296
16384 | 16384 | 4.20256 | 16.38207 | 9.52587 | 31.31113
32768 | 8192 | 4.20231 | 16.19452 | 9.31478 | 31.03514

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---------

**Performance Improvement (%)**
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M | N | fwdbwd,   torch.float16 | fwdbwd,   torch.float32
-- | -- | -- | --
50432 | 384 | 32.178 | 22.049
50176 | 384 | 29.231 | 19.536
200704 | 192 | 44.188 | 43.962
802816 | 64 | 52.119 | 54.100
200 | 256 | -5.750 | -0.206
1000 | 256 | 0.031 | -0.797
6000 | 256 | 3.566 | 5.621
6272 | 256 | 3.865 | 4.836
200 | 512 | -1.615 | -1.010
1000 | 512 | -1.270 | 0.208
6000 | 512 | 3.534 | 5.581
6272 | 512 | 7.905 | 7.483
200 | 1024 | -2.883 | 0.254
1000 | 1024 | -0.767 | 0.493
6000 | 1024 | 0.237 | -2.381
6272 | 1024 | 3.840 | -1.707
200 | 1536 | -0.127 | -1.340
1000 | 1536 | -0.711 | -0.992
6000 | 1536 | -0.209 | -4.728
6272 | 1536 | 0.508 | -0.846
200 | 2048 | -1.262 | -1.176
1000 | 2048 | -0.358 | 0.312
6000 | 2048 | 8.350 | 6.487
6272 | 2048 | 1.588 | 5.713
200 | 3072 | 0.223 | -0.848
1000 | 3072 | -0.773 | -5.743
6000 | 3072 | 3.570 | -3.783
6272 | 3072 | 4.962 | -4.092
128 | 2097152 | -4.266 | 0.348
256 | 1048576 | 0.397 | 0.185
512 | 524288 | 17.325 | 16.605
1024 | 262144 | 23.070 | 19.195
2048 | 131072 | 27.469 | 24.605
4096 | 65536 | 32.023 | 27.465
8192 | 32768 | 24.459 | 28.274
16384 | 16384 | 21.439 | 9.514
32768 | 8192 | 6.818 | 0.491

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---------
**Benchmark script of this PR**
```
# Ref:
#       1. pytorch#26201
#       2. pytorch#68238

from distutils.command.config import config
import torch
from torch.nn import LayerNorm
import timeit

number_runs = 1000  # TODO: Modify this to save time!
def test_forward(layer_norm_cuda, input_cuda):
    layer_norm_cuda(input_cuda); torch.cuda.synchronize()

def test_backward(out_cuda, layer_norm_grad_cuda, create_graph):
    out_cuda.backward(layer_norm_grad_cuda, retain_graph=True, create_graph=create_graph); torch.cuda.synchronize()

def test_fwdbwd(input_cuda, layer_norm_cuda, gO):
    input_cuda.grad = None
    layer_norm_cuda.zero_grad(set_to_none=True)
    out = layer_norm_cuda(input_cuda)
    out.backward(gO)
    torch.cuda.synchronize()

def benchmark(config_m, config_n):

    print("M | N | fwd (half) | fwdbwd (half) | fwd (float) | fwdbwd (float)")
    if len(config_m) != len(config_n):
        print("Please make sure the lengths of config_m and config_m are the same.")

    for i in range(len(config_m)):
        normalized_shape = config_n[i]
        results = [config_m[i], config_n[i]]
        for dtype in (torch.half, torch.float):
            if dtype == torch.half:
                layer_norm_cuda = LayerNorm(normalized_shape).half().cuda()
            else:
                layer_norm_cuda = LayerNorm(normalized_shape).cuda()

            input_cuda = torch.randn(config_m[i], config_n[i], device='cuda', dtype=dtype, requires_grad=True)

            # print("cuda forward:")
            result_fwd = timeit.timeit(lambda: test_forward(layer_norm_cuda, input_cuda), number=number_runs)
            results.append(result_fwd / number_runs * 1000)

            gO = torch.rand_like(input_cuda)

            result_fwdbwd = timeit.timeit(lambda: test_fwdbwd(input_cuda, layer_norm_cuda, gO), number=number_runs)
            results.append(result_fwdbwd / number_runs * 1000)

        print('{:09d}|{:09d}|{:9.5f}|{:9.5f}|{:9.5f}|{:9.5f}'.format(results[0], results[1], results[2], results[3], results[4], results[5]))

    print("Times are in microseconds (us).")

# CVT
config_m_cvt = [50432, 50176, 200704, 802816]
config_n_cvt = [384, 384, 192, 64]

# pytorch#68238 (comment)
config_m_68238 = [200, 1000, 6000, 6272, 200, 1000, 6000, 6272, 200, 1000, 6000, 6272, 200, 1000, 6000, 6272, 200, 1000, 6000, 6272, 200, 1000, 6000, 6272]
config_n_68238 = [256,256,256,256,512,512,512,512,1024,1024,1024,1024,1536,1536,1536,1536,2048,2048,2048,2048,3072,3072,3072,3072]

# pytorch#27634
config_m_27634 = [128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768]
config_n_27634 = [2097152, 1048576, 524288, 262144, 131072, 65536, 32768, 16384, 8192]

config_m = config_m_cvt + config_m_68238 + config_m_27634
config_n = config_n_cvt + config_n_68238 + config_n_27634

benchmark(config_m, config_n)
```

CC: @jeffdaily

Pull Request resolved: pytorch#87635
Approved by: https://github.com/jataylo, https://github.com/jeffdaily, https://github.com/ezyang
laurentdupin pushed a commit to laurentdupin/pytorch that referenced this pull request Apr 25, 2026
@hubertlu-tw @pytorchmergebot
[ROCm] Replace layer_norm_grad_input_kernel with cuComputeGradInput f…
e03cd10 
…or ROCm (pytorch#87726)

We observed that the native PyTorch LayerNormBackwardKernelImplInternal has suboptimal performance for certain input sizes on AMD GPUs especially when fs (=config_m in our benchmark script) is large and bs (=config_n in our benchmark script) is small (commonly seen in [the CvT model](https://arxiv.org/abs/2103.15808)) in the benchmark script of pytorch#68238 (comment) on AMD GPUs.

This PR is to replace layer_norm_grad_input_kernel with the Apex cuComputeGradInput kernel with some ROCm-specific parameter tuning when fs (=config_m) is larger than or equal to `32768` on AMD GPUs. Some of the code changes in LayerNormBackwardKernelImplInternal are from another PR: pytorch#87635

We used the same benchmark script in the previous PR and tested the optimized kernel with various input shapes on AMD MI100 GPU.

**At [the previous PR](pytorch#87635
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M | N | fwd (half) | fwdbwd (half) | fwd (float) | fwdbwd (float)
-- | -- | -- | -- | -- | --
50432 | 384 | 0.38589 | 0.92603 | 0.38367 | 1.15148
50176 | 384 | 0.38719 | 0.91579 | 0.37815 | 1.13761
200704 | 192 | 0.99787 | 2.39954 | 0.98996 | 2.54284
802816 | 64 | 3.66525 | 7.96952 | 3.61293 | 7.69946
200 | 256 | 0.06578 | 0.34613 | 0.06966 | 0.35449
1000 | 256 | 0.07837 | 0.37631 | 0.07725 | 0.37758
6000 | 256 | 0.09318 | 0.3788 | 0.09202 | 0.37989
6272 | 256 | 0.08694 | 0.36267 | 0.08703 | 0.3615
200 | 512 | 0.06975 | 0.34506 | 0.06973 | 0.34208
1000 | 512 | 0.07012 | 0.36363 | 0.07307 | 0.36741
6000 | 512 | 0.09725 | 0.36251 | 0.09908 | 0.37078
6272 | 512 | 0.09899 | 0.36519 | 0.10068 | 0.37514
200 | 1024 | 0.07188 | 0.33896 | 0.0712 | 0.34683
1000 | 1024 | 0.07357 | 0.3625 | 0.0734 | 0.3598
6000 | 1024 | 0.12642 | 0.38949 | 0.12973 | 0.5035
6272 | 1024 | 0.12901 | 0.40759 | 0.13609 | 0.51871
200 | 1536 | 0.06998 | 0.34782 | 0.07419 | 0.3514
1000 | 1536 | 0.07987 | 0.37915 | 0.07888 | 0.37264
6000 | 1536 | 0.15401 | 0.47524 | 0.15416 | 0.68609
6272 | 1536 | 0.15286 | 0.48843 | 0.17681 | 0.72997
200 | 2048 | 0.07054 | 0.34791 | 0.07289 | 0.35138
1000 | 2048 | 0.07767 | 0.37954 | 0.08554 | 0.37464
6000 | 2048 | 0.18744 | 0.5811 | 0.25004 | 0.93338
6272 | 2048 | 0.20037 | 0.63398 | 0.26918 | 0.97018
200 | 3072 | 0.07687 | 0.36739 | 0.08917 | 0.37845
1000 | 3072 | 0.09323 | 0.38901 | 0.09739 | 0.39823
6000 | 3072 | 0.24314 | 0.89029 | 0.38093 | 1.30719
6272 | 3072 | 0.26079 | 0.92023 | 0.38352 | 1.51012
128 | 2097152 | 6.17775 | 23.876 | 10.27952 | 30.10848
256 | 1048576 | 4.51855 | 19.47637 | 10.07609 | 29.42678
512 | 524288 | 4.13615 | 18.80888 | 10.07853 | 32.29804
1024 | 262144 | 4.47397 | 17.88388 | 9.50367 | 31.15699
2048 | 131072 | 4.2458 | 16.70852 | 9.17979 | 30.51708
4096 | 65536 | 4.24412 | 16.43098 | 8.97651 | 30.1617
8192 | 32768 | 4.24556 | 16.09038 | 8.77001 | 30.3643
16384 | 16384 | 4.14642 | 15.80355 | 8.82402 | 30.35291
32768 | 8192 | 4.12599 | 15.68897 | 8.82605 | 30.43423

</body>

</html>

----

**At this PR:**

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M | N | fwd (half) | fwdbwd (half) | fwd (float) | fwdbwd (float)
-- | -- | -- | -- | -- | --
50432 | 384 | 0.38667 | 0.84133 | 0.37916 | 1.01222
50176 | 384 | 0.3814 | 0.87266 | 0.37858 | 1.04399
200704 | 192 | 0.99902 | 2.14386 | 0.98973 | 2.33265
802816 | 64 | 3.66578 | 6.85376 | 3.6092 | 7.00331
200 | 256 | 0.06607 | 0.34176 | 0.07009 | 0.34548
1000 | 256 | 0.06947 | 0.36461 | 0.07902 | 0.37851
6000 | 256 | 0.09319 | 0.37432 | 0.09342 | 0.36927
6272 | 256 | 0.09544 | 0.37565 | 0.09476 | 0.37377
200 | 512 | 0.07935 | 0.364 | 0.07891 | 0.36894
1000 | 512 | 0.07676 | 0.37552 | 0.07957 | 0.37564
6000 | 512 | 0.10472 | 0.37504 | 0.1051 | 0.38782
6272 | 512 | 0.1069 | 0.36662 | 0.10062 | 0.38506
200 | 1024 | 0.07793 | 0.36561 | 0.08023 | 0.35019
1000 | 1024 | 0.07426 | 0.36729 | 0.07345 | 0.35851
6000 | 1024 | 0.12729 | 0.39219 | 0.12974 | 0.51526
6272 | 1024 | 0.13622 | 0.41627 | 0.14252 | 0.52926
200 | 1536 | 0.07615 | 0.36621 | 0.0797 | 0.3695
1000 | 1536 | 0.08327 | 0.38174 | 0.07938 | 0.37573
6000 | 1536 | 0.14894 | 0.46197 | 0.15268 | 0.63814
6272 | 1536 | 0.15368 | 0.48818 | 0.16309 | 0.71441
200 | 2048 | 0.06935 | 0.36691 | 0.07258 | 0.35548
1000 | 2048 | 0.07738 | 0.36388 | 0.08036 | 0.36452
6000 | 2048 | 0.18757 | 0.58573 | 0.23701 | 0.92915
6272 | 2048 | 0.1938 | 0.61628 | 0.26475 | 0.96896
200 | 3072 | 0.07884 | 0.3673 | 0.07724 | 0.37869
1000 | 3072 | 0.09342 | 0.38193 | 0.09822 | 0.38646
6000 | 3072 | 0.24452 | 0.86776 | 0.38251 | 1.3036
6272 | 3072 | 0.25971 | 0.91053 | 0.38744 | 1.39039
128 | 2097152 | 6.06752 | 23.26379 | 9.87466 | 29.81851
256 | 1048576 | 4.50336 | 19.4614 | 10.11239 | 29.25554
512 | 524288 | 4.12649 | 18.72831 | 10.054 | 32.26784
1024 | 262144 | 4.40855 | 17.77993 | 9.38856 | 31.18679
2048 | 131072 | 4.18716 | 16.74615 | 9.14487 | 30.24603
4096 | 65536 | 4.17374 | 16.34444 | 8.94894 | 30.0326
8192 | 32768 | 4.19095 | 16.05751 | 8.70358 | 30.14669
16384 | 16384 | 4.15404 | 15.83771 | 8.80042 | 30.5022
32768 | 8192 | 4.12515 | 15.5657 | 8.66138 | 28.87386

</body>

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---

**Performance Improvement (%)**

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M | N | fwdbwd, torch.float16 | fwdbwd, torch.float32
-- | -- | -- | --
50432 | 384 | 9.147 | 12.094
50176 | 384 | 4.710 | 8.230
200704 | 192 | 10.655 | 8.266
802816 | 64 | 14.000 | 9.042
200 | 256 | 1.263 | 2.542
1000 | 256 | 3.109 | -0.246
6000 | 256 | 1.183 | 2.796
6272 | 256 | -3.579 | -3.394
200 | 512 | -5.489 | -7.852
1000 | 512 | -3.270 | -2.240
6000 | 512 | -3.456 | -4.596
6272 | 512 | -0.392 | -2.644
200 | 1024 | -7.862 | -0.969
1000 | 1024 | -1.321 | 0.359
6000 | 1024 | -0.693 | -2.336
6272 | 1024 | -2.130 | -2.034
200 | 1536 | -5.287 | -5.151
1000 | 1536 | -0.683 | -0.829
6000 | 1536 | 2.792 | 6.989
6272 | 1536 | 0.051 | 2.132
200 | 2048 | -5.461 | -1.167
1000 | 2048 | 4.126 | 2.701
6000 | 2048 | -0.797 | 0.453
6272 | 2048 | 2.792 | 0.126
200 | 3072 | 0.024 | -0.063
1000 | 3072 | 1.820 | 2.956
6000 | 3072 | 2.531 | 0.275
6272 | 3072 | 1.054 | 7.929
128 | 2097152 | 2.564 | 0.963
256 | 1048576 | 0.077 | 0.582
512 | 524288 | 0.428 | 0.094
1024 | 262144 | 0.581 | -0.096
2048 | 131072 | -0.225 | 0.888
4096 | 65536 | 0.527 | 0.428
8192 | 32768 | 0.204 | 0.717
16384 | 16384 | -0.216 | -0.492
32768 | 8192 | 0.786 | 5.127

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CC: @jeffdaily

Pull Request resolved: pytorch#87726
Approved by: https://github.com/ngimel
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