[ATen][CUDA] Implement 128 bit vectorization v2 #145746

Aidyn-A · 2025-01-27T18:40:41Z

This is a re-base PR to my previous one #141959.

Description from the original PR:

This PR implements 128-bit vectorization. It improves the performance of contiguous elementwise ops by 4-10% on Hopper H100.

The benchmark code used

import time
import torch
from torch.profiler import profile, ProfilerActivity


def benchmark(function, dtype=torch.float32, check_numerics=True, print_profile=False):
    device = torch.device("cuda")

    shapes = []
    for p in range(24, 30):
        shape = 1<<p
        shapes.append(shape)

    for shape in shapes:
        for _ in range(6):
            x = torch.randn(shape, device=device, dtype=dtype)
            y = function(x)

        if print_profile:
            x = torch.randn(shape, device=device, dtype=dtype)
            with profile(activities=[ProfilerActivity.CUDA], record_shapes=True) as prof:
                y = function(x)
            print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))

        x = torch.randn(shape, device=device, dtype=dtype)
        torch.cuda.synchronize()
        t1 = time.perf_counter()
        for _ in range(6):
            y = function(x)
        torch.cuda.synchronize()
        t2 = time.perf_counter()
        perf_time = (t2 - t1) / 6

        print(f"{function.__name__}, {dtype}, {shape}, {perf_time}")
        if check_numerics:
            x_cpu = x.cpu()
            y_cpu = function(x_cpu).cuda()
            try:
                torch.testing.assert_allclose(y_cpu, y)
            except AssertionError as error:
                print("An exception occurred:", error)


def main():
    ops = [
            torch.relu,
            torch.sigmoid,
            torch.tanh,
            torch.nn.functional.gelu,
            torch.sin,
            torch.exp,
    ]

    dtypes = [
            torch.float16,
            torch.bfloat16,
            torch.float32,
    ]

    for op in ops:
        for dtype in dtypes:
            benchmark(op, dtype=dtype)
            torch.cuda.empty_cache()

if __name__ == "__main__":
    main()

Results

op	dtype	size	time after	time before	% improvement
relu	torch.float16	33554432	4.84E-05	5.06E-05	4.66296539127052
relu	torch.float16	67108864	9.22E-05	9.64E-05	4.56491432752297
relu	torch.float16	134217728	0.000180343495837102	0.000187981834945579	4.23543919508829
relu	torch.float16	268435456	0.000355071155354381	0.000370856161074092	4.44558942107169
relu	torch.float16	536870912	0.000704489842367669	0.000736006341564159	4.47366268483987
relu	torch.bfloat16	16777216	3.03E-05	3.04E-05	0.166504085842689
relu	torch.bfloat16	33554432	4.89E-05	5.06E-05	3.45848238875716
relu	torch.bfloat16	67108864	9.32E-05	9.65E-05	3.56122651631445
relu	torch.bfloat16	134217728	0.000180805509444326	0.000187998676362137	3.97840029317567
relu	torch.bfloat16	268435456	0.000356242332297067	0.000371279485989362	4.22104627356745
relu	torch.bfloat16	536870912	0.000708114336399982	0.000736773828975856	4.04729732229083
relu	torch.float32	16777216	5.61E-05	5.61E-05	0.0442587268354941
relu	torch.float32	33554432	9.33E-05	9.30E-05	-0.259070913799022
relu	torch.float32	67108864	0.000181321326332788	0.000181289506144822	-0.0175490597877115
relu	torch.float32	134217728	0.000356896334172537	0.000356570177245885	-0.0913870206618981
relu	torch.float32	268435456	0.000709421835684528	0.000707465515006334	-0.275762681635911
relu	torch.float32	536870912	0.00141372415237129	0.00141036518228551	-0.237597276678471
sigmoid	torch.float16	16777216	3.10E-05	3.16E-05	2.10012593866895
sigmoid	torch.float16	33554432	4.91E-05	5.23E-05	6.37710600666122
sigmoid	torch.float16	67108864	9.30E-05	0.000100057009452333	7.61866144555331
sigmoid	torch.float16	134217728	0.000180928347011407	0.000194982004662355	7.76752669390248
sigmoid	torch.float16	268435456	0.000355658994521946	0.00038468533117945	8.16128288742412
sigmoid	torch.float16	536870912	0.000705982849467546	0.000764021339515845	8.22094900634937
sigmoid	torch.bfloat16	16777216	3.08E-05	3.17E-05	2.90965915673149
sigmoid	torch.bfloat16	33554432	4.87E-05	5.24E-05	7.63503884668234
sigmoid	torch.bfloat16	67108864	9.33E-05	0.000100019678939134	7.21238137428013
sigmoid	torch.bfloat16	134217728	0.000180786165098349	0.000194868014659733	7.78922964250206
sigmoid	torch.bfloat16	268435456	0.000355564659306159	0.000384909333661199	8.25297835063321
sigmoid	torch.bfloat16	536870912	0.000705831005082776	0.000764102345177283	8.2557070566308
sigmoid	torch.float32	16777216	4.93E-05	5.65E-05	14.5314136197766
sigmoid	torch.float32	33554432	9.32E-05	9.31E-05	-0.120169865610833
sigmoid	torch.float32	67108864	0.000181328505277634	0.000180455681402236	-0.481349512069855
sigmoid	torch.float32	134217728	0.000357362829769651	0.000356093340087682	-0.35523831137877
sigmoid	torch.float32	268435456	0.000708921831877281	0.000707052337626616	-0.263709504574663
sigmoid	torch.float32	536870912	0.00141358317341656	0.0014090768333214	-0.318788464654745
tanh	torch.float16	16777216	3.03E-05	3.03E-05	-0.0912564658661808
tanh	torch.float16	33554432	4.90E-05	5.07E-05	3.46644442974484
tanh	torch.float16	67108864	9.30E-05	9.68E-05	3.99871369815531
tanh	torch.float16	134217728	0.00018052199933057	0.000188717152923346	4.53969799978138
tanh	torch.float16	268435456	0.000355684508879979	0.000373026006855071	4.8755280430115
tanh	torch.float16	536870912	0.000706660988119741	0.000740105014604827	4.73268328765002
tanh	torch.bfloat16	16777216	2.99E-05	3.03E-05	1.21049563135981
tanh	torch.bfloat16	33554432	4.89E-05	5.06E-05	3.48836101041744
tanh	torch.bfloat16	67108864	9.28E-05	9.69E-05	4.39944918036626
tanh	torch.bfloat16	134217728	0.000180710999605556	0.000189167990659674	4.67984299382829
tanh	torch.bfloat16	268435456	0.000356062994493792	0.000372666652159144	4.66312363882606
tanh	torch.bfloat16	536870912	0.000707100164921333	0.000740134331863374	4.67178040408393
tanh	torch.float32	16777216	5.61E-05	5.64E-05	0.439595755746353
tanh	torch.float32	33554432	9.31E-05	9.31E-05	0.00287633090228212
tanh	torch.float32	67108864	0.000181465332085888	0.000180895323865116	-0.31411411437098
tanh	torch.float32	134217728	0.000356963835656643	0.000356073161431899	-0.249513854283251
tanh	torch.float32	268435456	0.000709201170442005	0.00070707315656667	-0.300057862849997
tanh	torch.float32	536870912	0.00141367283261692	0.00141030051357423	-0.238550176877922
gelu	torch.float16	16777216	2.73E-05	3.17E-05	15.921079070745
gelu	torch.float16	33554432	5.06E-05	5.55E-05	9.76345374333098
gelu	torch.float16	67108864	9.65E-05	0.000106600326641152	10.4308039074712
gelu	torch.float16	134217728	0.000187776672343413	0.000208565829476962	11.0712139447915
gelu	torch.float16	268435456	0.000370216167842348	0.000412251994324227	11.3544005187205
gelu	torch.float16	536870912	0.000737301345604161	0.000819394170927505	11.1342296895002
gelu	torch.bfloat16	16777216	3.02E-05	3.08E-05	1.78405479367653
gelu	torch.bfloat16	33554432	5.13E-05	5.69E-05	10.9929393318302
gelu	torch.bfloat16	67108864	9.76E-05	0.00010968199543034	12.3420807512356
gelu	torch.bfloat16	134217728	0.000189661824454864	0.000214487663470209	13.0895287371091
gelu	torch.bfloat16	268435456	0.000374197009174774	0.000423670164309442	13.2211519391275
gelu	torch.bfloat16	536870912	0.000743675006863972	0.000842577001700799	13.299088166737
gelu	torch.float32	16777216	5.06E-05	5.04E-05	-0.413385894716413
gelu	torch.float32	33554432	9.31E-05	9.32E-05	0.134157041722546
gelu	torch.float32	67108864	0.000181480175039421	0.000180836669945469	-0.354586992112075
gelu	torch.float32	134217728	0.000356874331676712	0.000356305002545317	-0.159532104402047
gelu	torch.float32	268435456	0.000708909006789327	0.000706991491218408	-0.270488250615287
gelu	torch.float32	536870912	0.00141321367118508	0.00140937082081412	-0.271922813181618
sin	torch.float16	16777216	3.04E-05	3.11E-05	2.21834939018859
sin	torch.float16	33554432	4.85E-05	5.23E-05	7.72165512511596
sin	torch.float16	67108864	9.31E-05	9.98E-05	7.24947099480072
sin	torch.float16	134217728	0.000180371008658161	0.000194791161144773	7.99471744039613
sin	torch.float16	268435456	0.000355454161763191	0.000384903668115536	8.28503630574026
sin	torch.float16	536870912	0.000705183832906187	0.000764360166310022	8.39161799270973
sin	torch.bfloat16	16777216	3.11E-05	3.10E-05	-0.257677954940036
sin	torch.bfloat16	33554432	4.89E-05	5.24E-05	7.34808420323539
sin	torch.bfloat16	67108864	9.26E-05	0.000100248667877167	8.22347488801205
sin	torch.bfloat16	134217728	0.000180674154156198	0.00019567032965521	8.30012215584937
sin	torch.bfloat16	268435456	0.000355360486234228	0.000386023331278314	8.62865913118873
sin	torch.bfloat16	536870912	0.00070483615854755	0.000766805159704139	8.79197248964745
sin	torch.float32	16777216	5.67E-05	5.64E-05	-0.441348534920039
sin	torch.float32	33554432	9.34E-05	9.30E-05	-0.496458540364117
sin	torch.float32	67108864	0.000181706990891447	0.000180556671693921	-0.633062708199702
sin	torch.float32	134217728	0.000356894995396336	0.000356046327700218	-0.237791985616354
sin	torch.float32	268435456	0.000708777321657787	0.000707602652255446	-0.165731798471427
sin	torch.float32	536870912	0.00141263716310884	0.00140912582476934	-0.248566187496451
exp	torch.float16	16777216	3.00E-05	3.04E-05	1.40099098901014
exp	torch.float16	33554432	4.86E-05	5.03E-05	3.44611943643906
exp	torch.float16	67108864	9.37E-05	9.55E-05	1.96412400380129
exp	torch.float16	134217728	0.000180913504057874	0.000187193179347863	3.47109262113439
exp	torch.float16	268435456	0.00035607748820136	0.000369079003576189	3.65131630210701
exp	torch.float16	536870912	0.000707551507124056	0.000732363162872692	3.50669251620789
exp	torch.bfloat16	16777216	2.98E-05	3.04E-05	1.74345594341654
exp	torch.bfloat16	33554432	4.88E-05	5.04E-05	3.40217856534821
exp	torch.bfloat16	67108864	9.32E-05	9.62E-05	3.29219958210226
exp	torch.bfloat16	134217728	0.000180999826019009	0.000187239318620414	3.44723679499521
exp	torch.bfloat16	268435456	0.000355944503098726	0.000369370992605885	3.77207384585864
exp	torch.bfloat16	536870912	0.000707135167128096	0.000733066000975668	3.66702648277075
exp	torch.float32	16777216	4.89E-05	5.63E-05	15.1245314346532
exp	torch.float32	33554432	9.34E-05	9.31E-05	-0.259945454477446
exp	torch.float32	67108864	0.000181152504713585	0.000180474346658836	-0.374357536939058
exp	torch.float32	134217728	0.000356771342922002	0.000355627329554409	-0.3206573034212
exp	torch.float32	268435456	0.000708404501589636	0.00070713268360123	-0.179532736671163
exp	torch.float32	536870912	0.00141283582585553	0.00140944866385932	-0.23974208002295

cc @msaroufim @ptrblck @eqy @manuelcandales @SherlockNoMad @angelayi

pytorch-bot · 2025-01-27T18:40:45Z

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/145746

📄 Preview Python docs built from this PR
📄 Preview C++ docs built from this PR
❓ Need help or want to give feedback on the CI? Visit the bot commands wiki or our office hours

Note: Links to docs will display an error until the docs builds have been completed.

❌ 2 New Failures

As of commit 575b250 with merge base 0674ab7 ():

NEW FAILURES - The following jobs have failed:

Lint / lintrunner-noclang / linux-job (gh)
RuntimeError: Command docker exec -t 00b9d4ef833f698c4b9a2dbe9bc83f025a414d35c7ab3224989bf2fd5ae93045 /exec failed with exit code 1
Lint / Test tools / linux-job (gh)
RuntimeError: Command docker exec -t 61d7f96d4d4d7153c54cca332430a781e7e41a1e1683e4ec5543fb4798dad2b8 /exec failed with exit code 5

This comment was automatically generated by Dr. CI and updates every 15 minutes.

Skylion007 · 2025-01-27T18:48:25Z

Did we confirm not regresion on A100 per comments on the prev PR?

Aidyn-A · 2025-01-27T18:55:20Z

Did we confirm not regresion on A100 per comments on the prev PR?

Yes, the regression is due to a compiler (NVCC) bug. Moreover, I discovered the bug is present on H100 as well. I have omitted vec8/vec16 for 1-byte data on all archs to workaround the bug.

Skylion007 · 2025-01-27T20:58:15Z

Did we confirm not regresion on A100 per comments on the prev PR?

Yes, the regression is due to a compiler (NVCC) bug. Moreover, I discovered the bug is present on H100 as well. I have omitted vec8/vec16 for 1-byte data on all archs to workaround the bug.

What versions of NVCC are affected? We are potentially planning dropping old NVCC support (11.8/12.4)

Aidyn-A · 2025-01-27T21:17:18Z

What versions of NVCC are affected? We are potentially planning dropping old NVCC support (11.8/12.4)

I have not checked 11.8, but 12.4-12.6 are affected. 12.8 is doing fine if nvvm-latest flag was enforced. Though, I am not certain if it is generally safe to force nvvm-latest on sm_80-90.

ngimel · 2025-01-28T17:59:13Z

aten/src/ATen/native/cuda/CUDALoops.cuh

+  if constexpr (io_sizes == 1) {
+    return 16;
+  } else {
+    return 8;


is elems_per_thread = 8 allaround better than 4 we mostly used previously?

I observed little to no difference. The biggest improvement come from vec8.

ngimel · 2025-01-28T18:02:50Z

aten/src/ATen/native/cuda/CUDALoops.cuh

+  uint16_t vec_size = 16 / static_cast<uint16_t>(sizeof(cpp_type));
+  vec_size = std::min<uint16_t>(vec_size, max_vec_size);
+  if (sizeof(cpp_type) < 2) {
+    vec_size = std::min<uint16_t>(vec_size, 4);


why are you setting max vec size to 4 here for 1 byte datatypes? Is it to workaround that bug? Can you leave a comment then?

Correct. This is a workaround that bug. I have left a comment that explains it.

Aidyn-A · 2025-01-29T13:25:37Z

@pytorchbot merge

pytorchmergebot · 2025-01-29T13:27:17Z

Merge started

Your change will be merged once all checks pass (ETA 0-4 Hours).

Learn more about merging in the wiki.

Questions? Feedback? Please reach out to the PyTorch DevX Team

Advanced Debugging

Check the merge workflow status
here

Aidyn-A · 2025-01-31T06:34:51Z

The lint failures are unrelated

@pytorchbot merge -i

pytorchmergebot · 2025-01-31T06:36:24Z

Merge started

Your change will be merged while ignoring the following 2 checks: Lint / lintrunner-noclang / linux-job, Lint / Test tools / linux-job

Learn more about merging in the wiki.

Questions? Feedback? Please reach out to the PyTorch DevX Team

Advanced Debugging

Check the merge workflow status
here

This is a re-base PR to my previous one pytorch#141959. Description from the original PR: This PR implements 128-bit vectorization. It improves the performance of contiguous elementwise ops by 4-10% on Hopper H100. <details> <summary>The benchmark code used </summary> ```Python import time import torch from torch.profiler import profile, ProfilerActivity def benchmark(function, dtype=torch.float32, check_numerics=True, print_profile=False): device = torch.device("cuda") shapes = [] for p in range(24, 30): shape = 1<<p shapes.append(shape) for shape in shapes: for _ in range(6): x = torch.randn(shape, device=device, dtype=dtype) y = function(x) if print_profile: x = torch.randn(shape, device=device, dtype=dtype) with profile(activities=[ProfilerActivity.CUDA], record_shapes=True) as prof: y = function(x) print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10)) x = torch.randn(shape, device=device, dtype=dtype) torch.cuda.synchronize() t1 = time.perf_counter() for _ in range(6): y = function(x) torch.cuda.synchronize() t2 = time.perf_counter() perf_time = (t2 - t1) / 6 print(f"{function.__name__}, {dtype}, {shape}, {perf_time}") if check_numerics: x_cpu = x.cpu() y_cpu = function(x_cpu).cuda() try: torch.testing.assert_allclose(y_cpu, y) except AssertionError as error: print("An exception occurred:", error) def main(): ops = [ torch.relu, torch.sigmoid, torch.tanh, torch.nn.functional.gelu, torch.sin, torch.exp, ] dtypes = [ torch.float16, torch.bfloat16, torch.float32, ] for op in ops: for dtype in dtypes: benchmark(op, dtype=dtype) torch.cuda.empty_cache() if __name__ == "__main__": main() ``` </details> <details> <summary> Results </summary> | op | dtype | size | time after | time before | % improvement | | ---- | ---- | ---- | ---- | ---- | ---- | | relu | torch.float16 | 33554432 | 4.84E-05 | 5.06E-05 | 4.66296539127052 | | relu | torch.float16 | 67108864 | 9.22E-05 | 9.64E-05 | 4.56491432752297 | | relu | torch.float16 | 134217728 | 0.000180343495837102 | 0.000187981834945579 | 4.23543919508829 | | relu | torch.float16 | 268435456 | 0.000355071155354381 | 0.000370856161074092 | 4.44558942107169 | | relu | torch.float16 | 536870912 | 0.000704489842367669 | 0.000736006341564159 | 4.47366268483987 | | relu | torch.bfloat16 | 16777216 | 3.03E-05 | 3.04E-05 | 0.166504085842689 | | relu | torch.bfloat16 | 33554432 | 4.89E-05 | 5.06E-05 | 3.45848238875716 | | relu | torch.bfloat16 | 67108864 | 9.32E-05 | 9.65E-05 | 3.56122651631445 | | relu | torch.bfloat16 | 134217728 | 0.000180805509444326 | 0.000187998676362137 | 3.97840029317567 | | relu | torch.bfloat16 | 268435456 | 0.000356242332297067 | 0.000371279485989362 | 4.22104627356745 | | relu | torch.bfloat16 | 536870912 | 0.000708114336399982 | 0.000736773828975856 | 4.04729732229083 | | relu | torch.float32 | 16777216 | 5.61E-05 | 5.61E-05 | 0.0442587268354941 | | relu | torch.float32 | 33554432 | 9.33E-05 | 9.30E-05 | -0.259070913799022 | | relu | torch.float32 | 67108864 | 0.000181321326332788 | 0.000181289506144822 | -0.0175490597877115 | | relu | torch.float32 | 134217728 | 0.000356896334172537 | 0.000356570177245885 | -0.0913870206618981 | | relu | torch.float32 | 268435456 | 0.000709421835684528 | 0.000707465515006334 | -0.275762681635911 | | relu | torch.float32 | 536870912 | 0.00141372415237129 | 0.00141036518228551 | -0.237597276678471 | | sigmoid | torch.float16 | 16777216 | 3.10E-05 | 3.16E-05 | 2.10012593866895 | | sigmoid | torch.float16 | 33554432 | 4.91E-05 | 5.23E-05 | 6.37710600666122 | | sigmoid | torch.float16 | 67108864 | 9.30E-05 | 0.000100057009452333 | 7.61866144555331 | | sigmoid | torch.float16 | 134217728 | 0.000180928347011407 | 0.000194982004662355 | 7.76752669390248 | | sigmoid | torch.float16 | 268435456 | 0.000355658994521946 | 0.00038468533117945 | 8.16128288742412 | | sigmoid | torch.float16 | 536870912 | 0.000705982849467546 | 0.000764021339515845 | 8.22094900634937 | | sigmoid | torch.bfloat16 | 16777216 | 3.08E-05 | 3.17E-05 | 2.90965915673149 | | sigmoid | torch.bfloat16 | 33554432 | 4.87E-05 | 5.24E-05 | 7.63503884668234 | | sigmoid | torch.bfloat16 | 67108864 | 9.33E-05 | 0.000100019678939134 | 7.21238137428013 | | sigmoid | torch.bfloat16 | 134217728 | 0.000180786165098349 | 0.000194868014659733 | 7.78922964250206 | | sigmoid | torch.bfloat16 | 268435456 | 0.000355564659306159 | 0.000384909333661199 | 8.25297835063321 | | sigmoid | torch.bfloat16 | 536870912 | 0.000705831005082776 | 0.000764102345177283 | 8.2557070566308 | | sigmoid | torch.float32 | 16777216 | 4.93E-05 | 5.65E-05 | 14.5314136197766 | | sigmoid | torch.float32 | 33554432 | 9.32E-05 | 9.31E-05 | -0.120169865610833 | | sigmoid | torch.float32 | 67108864 | 0.000181328505277634 | 0.000180455681402236 | -0.481349512069855 | | sigmoid | torch.float32 | 134217728 | 0.000357362829769651 | 0.000356093340087682 | -0.35523831137877 | | sigmoid | torch.float32 | 268435456 | 0.000708921831877281 | 0.000707052337626616 | -0.263709504574663 | | sigmoid | torch.float32 | 536870912 | 0.00141358317341656 | 0.0014090768333214 | -0.318788464654745 | | tanh | torch.float16 | 16777216 | 3.03E-05 | 3.03E-05 | -0.0912564658661808 | | tanh | torch.float16 | 33554432 | 4.90E-05 | 5.07E-05 | 3.46644442974484 | | tanh | torch.float16 | 67108864 | 9.30E-05 | 9.68E-05 | 3.99871369815531 | | tanh | torch.float16 | 134217728 | 0.00018052199933057 | 0.000188717152923346 | 4.53969799978138 | | tanh | torch.float16 | 268435456 | 0.000355684508879979 | 0.000373026006855071 | 4.8755280430115 | | tanh | torch.float16 | 536870912 | 0.000706660988119741 | 0.000740105014604827 | 4.73268328765002 | | tanh | torch.bfloat16 | 16777216 | 2.99E-05 | 3.03E-05 | 1.21049563135981 | | tanh | torch.bfloat16 | 33554432 | 4.89E-05 | 5.06E-05 | 3.48836101041744 | | tanh | torch.bfloat16 | 67108864 | 9.28E-05 | 9.69E-05 | 4.39944918036626 | | tanh | torch.bfloat16 | 134217728 | 0.000180710999605556 | 0.000189167990659674 | 4.67984299382829 | | tanh | torch.bfloat16 | 268435456 | 0.000356062994493792 | 0.000372666652159144 | 4.66312363882606 | | tanh | torch.bfloat16 | 536870912 | 0.000707100164921333 | 0.000740134331863374 | 4.67178040408393 | | tanh | torch.float32 | 16777216 | 5.61E-05 | 5.64E-05 | 0.439595755746353 | | tanh | torch.float32 | 33554432 | 9.31E-05 | 9.31E-05 | 0.00287633090228212 | | tanh | torch.float32 | 67108864 | 0.000181465332085888 | 0.000180895323865116 | -0.31411411437098 | | tanh | torch.float32 | 9E88 134217728 | 0.000356963835656643 | 0.000356073161431899 | -0.249513854283251 | | tanh | torch.float32 | 268435456 | 0.000709201170442005 | 0.00070707315656667 | -0.300057862849997 | | tanh | torch.float32 | 536870912 | 0.00141367283261692 | 0.00141030051357423 | -0.238550176877922 | | gelu | torch.float16 | 16777216 | 2.73E-05 | 3.17E-05 | 15.921079070745 | | gelu | torch.float16 | 33554432 | 5.06E-05 | 5.55E-05 | 9.76345374333098 | | gelu | torch.float16 | 67108864 | 9.65E-05 | 0.000106600326641152 | 10.4308039074712 | | gelu | torch.float16 | 134217728 | 0.000187776672343413 | 0.000208565829476962 | 11.0712139447915 | | gelu | torch.float16 | 268435456 | 0.000370216167842348 | 0.000412251994324227 | 11.3544005187205 | | gelu | torch.float16 | 536870912 | 0.000737301345604161 | 0.000819394170927505 | 11.1342296895002 | | gelu | torch.bfloat16 | 16777216 | 3.02E-05 | 3.08E-05 | 1.78405479367653 | | gelu | torch.bfloat16 | 33554432 | 5.13E-05 | 5.69E-05 | 10.9929393318302 | | gelu | torch.bfloat16 | 67108864 | 9.76E-05 | 0.00010968199543034 | 12.3420807512356 | | gelu | torch.bfloat16 | 134217728 | 0.000189661824454864 | 0.000214487663470209 | 13.0895287371091 | | gelu | torch.bfloat16 | 268435456 | 0.000374197009174774 | 0.000423670164309442 | 13.2211519391275 | | gelu | torch.bfloat16 | 536870912 | 0.000743675006863972 | 0.000842577001700799 | 13.299088166737 | | gelu | torch.float32 | 16777216 | 5.06E-05 | 5.04E-05 | -0.413385894716413 | | gelu | torch.float32 | 33554432 | 9.31E-05 | 9.32E-05 | 0.134157041722546 | | gelu | torch.float32 | 67108864 | 0.000181480175039421 | 0.000180836669945469 | -0.354586992112075 | | gelu | torch.float32 | 134217728 | 0.000356874331676712 | 0.000356305002545317 | -0.159532104402047 | | gelu | torch.float32 | 268435456 | 0.000708909006789327 | 0.000706991491218408 | -0.270488250615287 | | gelu | torch.float32 | 536870912 | 0.00141321367118508 | 0.00140937082081412 | -0.271922813181618 | | sin | torch.float16 | 16777216 | 3.04E-05 | 3.11E-05 | 2.21834939018859 | | sin | torch.float16 | 33554432 | 4.85E-05 | 5.23E-05 | 7.72165512511596 | | sin | torch.float16 | 67108864 | 9.31E-05 | 9.98E-05 | 7.24947099480072 | | sin | torch.float16 | 134217728 | 0.000180371008658161 | 0.000194791161144773 | 7.99471744039613 | | sin | torch.float16 | 268435456 | 0.000355454161763191 | 0.000384903668115536 | 8.28503630574026 | | sin | torch.float16 | 536870912 | 0.000705183832906187 | 0.000764360166310022 | 8.39161799270973 | | sin | torch.bfloat16 | 16777216 | 3.11E-05 | 3.10E-05 | -0.257677954940036 | | sin | torch.bfloat16 | 33554432 | 4.89E-05 | 5.24E-05 | 7.34808420323539 | | sin | torch.bfloat16 | 67108864 | 9.26E-05 | 0.000100248667877167 | 8.22347488801205 | | sin | torch.bfloat16 | 134217728 | 0.000180674154156198 | 0.00019567032965521 | 8.30012215584937 | | sin | torch.bfloat16 | 268435456 | 0.000355360486234228 | 0.000386023331278314 | 8.62865913118873 | | sin | torch.bfloat16 | 536870912 | 0.00070483615854755 | 0.000766805159704139 | 8.79197248964745 | | sin | torch.float32 | 16777216 | 5.67E-05 | 5.64E-05 | -0.441348534920039 | | sin | torch.float32 | 33554432 | 9.34E-05 | 9.30E-05 | -0.496458540364117 | | sin | torch.float32 | 67108864 | 0.000181706990891447 | 0.000180556671693921 | -0.633062708199702 | | sin | torch.float32 | 134217728 | 0.000356894995396336 | 0.000356046327700218 | -0.237791985616354 | | sin | torch.float32 | 268435456 | 0.000708777321657787 | 0.000707602652255446 | -0.165731798471427 | | sin | torch.float32 | 536870912 | 0.00141263716310884 | 0.00140912582476934 | -0.248566187496451 | | exp | torch.float16 | 16777216 | 3.00E-05 | 3.04E-05 | 1.40099098901014 | | exp | torch.float16 | 33554432 | 4.86E-05 | 5.03E-05 | 3.44611943643906 | | exp | torch.float16 | 67108864 | 9.37E-05 | 9.55E-05 | 1.96412400380129 | | exp | torch.float16 | 134217728 | 0.000180913504057874 | 0.000187193179347863 | 3.47109262113439 | | exp | torch.float16 | 268435456 | 0.00035607748820136 | 0.000369079003576189 | 3.65131630210701 | | exp | torch.float16 | 536870912 | 0.000707551507124056 | 0.000732363162872692 | 3.50669251620789 | | exp | torch.bfloat16 | 16777216 | 2.98E-05 | 3.04E-05 | 1.74345594341654 | | exp | torch.bfloat16 | 33554432 | 4.88E-05 | 5.04E-05 | 3.40217856534821 | | exp | torch.bfloat16 | 67108864 | 9.32E-05 | 9.62E-05 | 3.29219958210226 | | exp | torch.bfloat16 | 134217728 | 0.000180999826019009 | 0.000187239318620414 | 3.44723679499521 | | exp | torch.bfloat16 | 268435456 | 0.000355944503098726 | 0.000369370992605885 | 3.77207384585864 | | exp | torch.bfloat16 | 536870912 | 0.000707135167128096 | 0.000733066000975668 | 3.66702648277075 | | exp | torch.float32 | 16777216 | 4.89E-05 | 5.63E-05 | 15.1245314346532 | | exp | torch.float32 | 33554432 | 9.34E-05 | 9.31E-05 | -0.259945454477446 | | exp | torch.float32 | 67108864 | 0.000181152504713585 | 0.000180474346658836 | -0.374357536939058 | | exp | torch.float32 | 134217728 | 0.000356771342922002 | 0.000355627329554409 | -0.3206573034212 | | exp | torch.float32 | 268435456 | 0.000708404501589636 | 0.00070713268360123 | -0.179532736671163 | | exp | torch.float32 | 536870912 | 0.00141283582585553 | 0.00140944866385932 | -0.23974208002295 | </details> Pull Request resolved: pytorch#145746 Approved by: https://github.com/eqy, https://github.com/ngimel Co-authored-by: Aaron Gokaslan <aaronGokaslan@gmail.com>

…)" This reverts commit e84bf88.

malfet

IMO it should be guarded with __CUDA_ARCH__ >= 90 to avoid compile time increases for older architectures

ngimel · 2025-02-25T20:51:38Z

I think it helps perf even on H100

malfet · 2025-02-25T21:39:09Z

I think it helps perf even on H100

I've made off by one error in my calculations anyway, CUDA_ARCH > 10 are Fermi and above :)

Aidyn-A · 2025-02-28T18:43:36Z

IMO it should be guarded with __CUDA_ARCH__ >= 90 to avoid compile time increases for older architectures

I apologize for the delay, but it seems like I will need to do some refactoring to achieve it, because __CUDA_ARCH__ is available in kernels and device functions only.

…)" This reverts commit e84bf88.

By addressing a feedback requested at #145746 Pull Request resolved: #150705 Approved by: https://github.com/atalman

By addressing a feedback requested at #145746 Pull Request resolved: #150705 Approved by: https://github.com/atalman (cherry picked from commit 5228986)

[CUDA] Only use vec128 if CUDA version is newer than 12.8 (#150705) By addressing a feedback requested at #145746 Pull Request resolved: #150705 Approved by: https://github.com/atalman (cherry picked from commit 5228986) Co-authored-by: Nikita Shulga <nshulga@meta.com>

This reverts commit e84bf88.

…0705) By addressing a feedback requested at pytorch#145746 Pull Request resolved: pytorch#150705 Approved by: https://github.com/atalman

Fixes #147376. As per request: #145746 (review) This PR omits sm80 or older of using vec8 kernels due to long compilation and large binary size. Pull Request resolved: #148320 Approved by: https://github.com/eqy, https://github.com/malfet, https://github.com/atalman

Fixes #147376. As per request: #145746 (review) This PR omits sm80 or older of using vec8 kernels due to long compilation and large binary size. Pull Request resolved: #148320 Approved by: https://github.com/eqy, https://github.com/malfet, https://github.com/atalman (cherry picked from commit 72337bd)

[ATen][CUDA] Optimize 128 bit vectorization (#148320) Fixes #147376. As per request: #145746 (review) This PR omits sm80 or older of using vec8 kernels due to long compilation and large binary size. Pull Request resolved: #148320 Approved by: https://github.com/eqy, https://github.com/malfet, https://github.com/atalman (cherry picked from commit 72337bd) Co-authored-by: Aidyn-A <31858918+Aidyn-A@users.noreply.github.com>

implement 128-bit vectorization

002a567

Aidyn-A added module: cuda Related to torch.cuda, and CUDA support in general topic: not user facing topic category module: core aten Related to change to the Core ATen opset labels Jan 27, 2025

Aidyn-A requested review from eqy and ngimel January 27, 2025 18:40

Aidyn-A self-assigned this Jan 27, 2025

Aidyn-A requested a review from syed-ahmed as a code owner January 27, 2025 18:40

pytorchbot added the open source label Jan 27, 2025

update cuda_vectorized_test.cu

5132046

eqy approved these changes Jan 28, 2025

View reviewed changes

ngimel approved these changes Jan 28, 2025

View reviewed changes

add comment

02ca03e

Aidyn-A added the ciflow/trunk Trigger trunk jobs on your pull request label Jan 29, 2025

pytorch-bot bot temporarily deployed to upload-benchmark-results January 29, 2025 07:41 Inactive

pytorchmergebot added the merging label Jan 29, 2025

pytorchmergebot added the Merged label Jan 29, 2025

pytorchmergebot closed this in 81685d8 Jan 29, 2025

pytorchmergebot removed the merging label Jan 29, 2025

Aidyn-A mentioned this pull request Jan 29, 2025

[ATen][CUDA] Implement 128 bit vectorization #141959

Closed

pytorchmergebot added the merging label Jan 31, 2025

pytorchmergebot closed this in e84bf88 Jan 31, 2025

pytorchmergebot removed the merging label Jan 31, 2025

This was referenced Feb 18, 2025

[CD] Increase timeout for windows binary builds #147390

Closed

Nightly Windows builds started to time out around Jan 31, 2025 #147376

Closed

atalman added a commit to atalman/pytorch that referenced this pull request Feb 19, 2025

Revert "[ATen][CUDA] Implement 128 bit vectorization v2 (pytorch#145746…

123265b

…)" This reverts commit e84bf88.

malfet reviewed Feb 25, 2025

View reviewed changes

Aidyn-A mentioned this pull request Mar 3, 2025

[ATen][CUDA] Optimize 128 bit vectorization #148320

Closed

atalman added a commit to atalman/pytorch that referenced this pull request Apr 4, 2025

Revert "[ATen][CUDA] Implement 128 bit vectorization v2 (pytorch#145746…

dc69435

…)" This reverts commit e84bf88.

malfet mentioned this pull request Apr 4, 2025

[CUDA] Only use vec128 if CUDA version is newer than 12.8 #150705

Closed

pytorchmergebot pushed a commit that referenced this pull request Apr 8, 2025

[CUDA] Only use vec128 if CUDA version is newer than 12.8 (#150705)

5228986

By addressing a feedback requested at #145746 Pull Request resolved: #150705 Approved by: https://github.com/atalman

pytorchbot mentioned this pull request Apr 8, 2025

[CUDA] Only use vec128 if CUDA version is newer than 12.8 #150818

Merged

malfet added a commit that referenced this pull request Apr 9, 2025

Revert "[ATen][CUDA] Implement 128 bit vectorization v2 (#145746)"

c02aba6

This reverts commit e84bf88.

xytintel mentioned this pull request Apr 21, 2025

Keep track on the latest CUDA op impl intel/torch-xpu-ops#1587

Open

pytorchbot mentioned this pull request May 6, 2025

[ATen][CUDA] Optimize 128 bit vectorization #152967

Merged

wsqdhrINnine mentioned this pull request May 27, 2025

Revert "[ATen][CUDA] Implement 128 bit vectorization v2 (#145746)" #150895

Closed

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Uh oh!

[ATen][CUDA] Implement 128 bit vectorization v2 #145746

[ATen][CUDA] Implement 128 bit vectorization v2 #145746

Uh oh!

Uh oh!

Uh oh!

Uh oh!

Uh oh!

Uh oh!

Uh oh!

Uh oh!

Uh oh!

Uh oh!

Uh oh!

Uh oh!

Uh oh!

Uh oh!

Uh oh!

Uh oh!

Uh oh!

Uh oh!

Uh oh!

Uh oh!

[ATen][CUDA] Implement 128 bit vectorization v2 #145746

[ATen][CUDA] Implement 128 bit vectorization v2 #145746

Uh oh!

Conversation

Uh oh!

Uh oh!

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/145746

❌ 2 New Failures

Uh oh!

Uh oh!

Uh oh!

Uh oh!

Uh oh!

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Uh oh!

Merge started

Uh oh!

Uh oh!

Merge started

Uh oh!

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Uh oh!

Uh oh!

Uh oh!

Uh oh!

Uh oh!