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176 Commits

Author SHA1 Message Date
Edward Z. Yang
5df59f957f Fix G001,G002,G003 in logs to % syntax (#97812) 
Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/97812
Approved by: https://github.com/Skylion007, https://github.com/kiukchung, https://github.com/malfet, https://github.com/mlazos
 2023-04-01 01:43:33 +00:00
Bin Bao
c699ac17df [CI] Bump up torchbench version to fix dynamo graph breaks in transformers (#98003) 
Summary: When we bump up the torchbench version pin last time, we found
there were new graph breaks introduced with the trasformers version
upgrade, see https://github.com/pytorch/pytorch/pull/96782. Turns out
they are already fixed upstream, see
https://github.com/huggingface/transformers/pull/21648 and https://github.com/pytorch/benchmark/pull/1511

Pull Request resolved: https://github.com/pytorch/pytorch/pull/98003
Approved by: https://github.com/ngimel
 2023-03-31 16:52:09 +00:00
Edward Z. Yang
97fc8ea5f4 Run the benchmark suite with dynamic batch only (#97912) 
Symbolic shapes compile time on full CI with inductor is horribly long (even though our aot_eager local runs seemed to suggest that the added latency was only 10s per model.) To patch over the problem for now, run the benchmark suite with dynamic batch only.  This should absolve a lot of sins.

Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/97912
Approved by: https://github.com/janeyx99, https://github.com/desertfire
 2023-03-30 18:04:48 +00:00
Aaron Gokaslan
47dca20d80 [BE] Enable flake8-comprehension rule C417 (#97880) 
Enables flake8-comprehension rule C417. Ruff autogenerated these fixes to the codebase.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/97880
Approved by: https://github.com/ezyang, https://github.com/kit1980, https://github.com/albanD
 2023-03-30 14:34:24 +00:00
William Wen
b93e1f377e [dynamo, benchmarks] Add inductor-mode (for max-autotune) and warm start options to dynamo benchmarks (#97719) 
Title.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/97719
Approved by: https://github.com/shunting314
 2023-03-29 21:09:00 +00:00
Edward Z. Yang
f754be897a Disable speedup_experiment_ds (#97806) 
It seems to be broken.

Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/97806
Approved by: https://github.com/jansel
 2023-03-29 01:27:31 +00:00
Bin Bao
a9a81ab7e3 [CI] Run benchmark test with dynamo_eager in periodic (#97543) 
Summary: The idea is to catch any dynamo_eager regression earlier, and also
we can take that off the dashboard run.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/97543
Approved by: https://github.com/huydhn
 2023-03-28 01:02:49 +00:00
Shunting Zhang
652592efa9 [inductor] use torch.prifiler in the triton wrapper (#97405) 
I think it's helpful to use torch.profiler to profile the triton wrapper.

E.g., I tried it for nvidia_deeprecommender's infernece graph.

Even with max-autotune, we see the majority of the time the GPU is running 2 mm/addmm op. That's why max autotune does not help for this model since tuning does not affect the external mm ops.

<img width="711" alt="Screenshot 2023-03-22 at 5 49 28 PM" src="https://user-images.githubusercontent.com/52589240/227072474-2f0d7205-4a10-4929-b1b7-551214788c61.png">

next step I'll check why the triton mm kernels are not picked.

EDIT: the above screenshot is captured without max-autotune due to a typo. below is the trace with max-autotune enabled:
<img width="712" alt="Screenshot 2023-03-22 at 6 43 26 PM" src="https://user-images.githubusercontent.com/52589240/227077624-fdccf928-be08-4211-871b-a9e3d7b76fbe.png">

Pull Request resolved: https://github.com/pytorch/pytorch/pull/97405
Approved by: https://github.com/ngimel
 2023-03-27 21:54:25 +00:00
Edward Z. Yang
cff4826f28 pytorch_unet is now passing (#97309) 
Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/97309
Approved by: https://github.com/janeyx99, https://github.com/zou3519
 2023-03-22 13:55:05 +00:00
Bin Bao
be49d3b170 [CI] Turn on debug logging for dla102 and gernet_l (#97307) 
Summary: Log the generated code for those two flaky tests to see if
there is any codegen difference when they fail.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/97307
Approved by: https://github.com/ezyang
 2023-03-22 13:42:13 +00:00
Natalia Gimelshein
e7d9331688 [inductor] hoist symbolic padding expressions (#97099) 
Towards fixing pnasnet5large, see #96709. The generated kernel looks much better
```
@pointwise(size_hints=[1048576], filename=__file__, meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32', 4: 'i32', 5: 'i32', 6: 'i32'}, 'device': 0, 'constants': {}, 'mutated_arg_names': [], 'configs': [instance_descriptor(divisible_by_16=(0, 1, 6), equal_to_1=())]})
@triton.jit
def triton_(in_ptr0, out_ptr0, ks0, ks1, ks2, ks3, xnumel, XBLOCK : tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // ks0) % ks0
    x0 = xindex % ks0
    x2 = (xindex // ks3)
    x4 = xindex
    tmp0 = x1 + ((-1)*ks1)
    tmp1 = 0
    tmp2 = tmp0 >= tmp1
    tmp3 = ks2
    tmp4 = tmp0 < tmp3
    tmp5 = x0 + ((-1)*ks1)
    tmp6 = tmp5 >= tmp1
    tmp7 = tmp5 < tmp3
    tmp8 = tmp2 & tmp4
    tmp9 = tmp8 & tmp6
    tmp10 = tmp9 & tmp7
    tmp11 = tl.load(in_ptr0 + (x0 + ((-1)*ks1) + (ks2*x1) + (x2*(ks2*ks2)) + ((-1)*ks1*ks2) + tl.zeros([XBLOCK], tl.int32)), tmp10 & xmask, other=0)
    tmp12 = tl.where(tmp10, tmp11, 0.0)
    tl.store(out_ptr0 + (x4 + tl.zeros([XBLOCK], tl.int32)), tmp12, xmask)
 ```
Interestingly, removing `expand` in in index `simplify` function makes `load` expression a little bit better, but `store` fails to simplify to flat store in this case, so I'm leaving `expand` in.
 Full pnasnet still chokes on `ceiling` in batch_norm kernels, additionally, it looks like shape propagation goofs in inductor and generates overly complicated expressions, we should switch to meta data from fx graph.
 I'm still not adding `ceil` print to triton, because we should be able to hoist all indexing expression (and just printing ceil without converting to int64 doesn't work)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/97099
Approved by: https://github.com/jansel
 2023-03-21 21:43:32 +00:00
Edward Z. Yang
e74c5e5637 rexnet_100 is disabled for static, does not need dynamic listing (#97100) 
Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/97100
Approved by: https://github.com/Skylion007
 2023-03-19 20:57:49 +00:00
Bin Bao
577d930c39 [CI] Revert https://github.com/pytorch/pytorch/pull/96195 (#96897) 
Summary: https://github.com/pytorch/pytorch/pull/96195 was an experiment
for debugging flaky failures on CI.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/96897
Approved by: https://github.com/ngimel
 2023-03-16 06:28:18 +00:00
Edward Z. Yang
3606f59366 Default specialize_int to False (#96624) 
Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/96624
Approved by: https://github.com/janeyx99
 2023-03-16 02:54:18 +00:00
Will Constable
54cd4a67d0 Output peak memory stats from dynamo torchbench perf CI (#95666) 
Adds absolute memory usage numbers (in addition to compression ratio) to performance jobs.

Example output:
<img width="1211" alt="image" src="https://user-images.githubusercontent.com/4984825/225419950-500908c5-00ce-4711-afa2-c995bf90d35d.png">

Pull Request resolved: https://github.com/pytorch/pytorch/pull/95666
Approved by: https://github.com/ezyang, https://github.com/williamwen42
 2023-03-15 19:24:47 +00:00
Bin Bao
33c7be360f [reland][CI] switch torchbench to a pinned version (#96782) 
Summary: This is reland of https://github.com/pytorch/pytorch/pull/96553

Pull Request resolved: https://github.com/pytorch/pytorch/pull/96782
Approved by: https://github.com/huydhn
 2023-03-15 12:46:36 +00:00
Edward Z. Yang
037acd5a22 Update CI skips (#96745) 
Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/96745
Approved by: https://github.com/wconstab
 2023-03-14 22:19:10 +00:00
PyTorch MergeBot
be4eaa69c2 Revert "[CI] switch torchbench to a pinned version (#96553)" 
This reverts commit 61d6ccd29a.

Reverted https://github.com/pytorch/pytorch/pull/96553 on behalf of https://github.com/desertfire due to land race
 2023-03-14 21:39:45 +00:00
PyTorch MergeBot
ba4fb9b6ad Revert "Default specialize_int to False (#96624)" 
This reverts commit 1ac8782db2.

Reverted https://github.com/pytorch/pytorch/pull/96624 on behalf of https://github.com/kit1980 due to Broke inductor/test_torchinductor_dynamic_shapes.py
 2023-03-14 19:43:47 +00:00
Bin Bao
61d6ccd29a [CI] switch torchbench to a pinned version (#96553) 
Summary: Previously we were using a branch on torchbench which skips
torchaudio. We should switch to make sure a good test coverage.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/96553
Approved by: https://github.com/huydhn, https://github.com/ezyang
 2023-03-14 18:42:22 +00:00
Edward Z. Yang
1ac8782db2 Default specialize_int to False (#96624) 
Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/96624
Approved by: https://github.com/janeyx99
 2023-03-14 18:37:47 +00:00
David Berard
6e3d51b08a [inductor][CI] also skip rexnet_100 on non-dynamic shapes (#96691) 
Recent failures show rexnet_100 accuracy is flaky also on non-dynamic shapes (was already disabled for dynamic shapes in #96474). The failure occurs for the same reason (stem.bn.weight.grad).
e.g. https://github.com/pytorch/pytorch/actions/runs/4402868441/jobs/7710977874

Pull Request resolved: https://github.com/pytorch/pytorch/pull/96691
Approved by: https://github.com/desertfire
 2023-03-14 18:11:59 +00:00
Edward Z. Yang
ff7e510d1e Correctly use PythonPrinter for generating wrapper code referencing sympy (#96710) 
Otherwise you get stuff like ceiling(s0) which is not valid Python code. Fixes volo_d1_224

Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/96710
Approved by: https://github.com/ngimel, https://github.com/jansel
 2023-03-14 14:35:52 +00:00
Wang, Eikan
3cad8d23d0 [Inductor] Skip the hf_T5_base due to intermittent failure on CI (#96649) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96649
Approved by: https://github.com/desertfire
 2023-03-14 07:40:20 +00:00
Edward Z. Yang
507feb805f Don't specialize torch.Size with specialize_int = False (#96419) 
Fixes https://github.com/pytorch/pytorch/issues/95868

Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/96419
Approved by: https://github.com/jansel, https://github.com/ngimel
 2023-03-14 01:32:58 +00:00
Edward Z. Yang
c7f39c0820 Update CI skips (#96554) 
Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/96554
Approved by: https://github.com/janeyx99
 2023-03-13 13:40:45 +00:00
David Berard
29cd60dfb7 [CI] handle more dynamo benchmark models that are not expected to be deterministic (#96324) 
Follow-up to #96245. alexnet, Background_Matting, vision_maskrcnn, and vgg16 all have the same problem; but on float32 they were also failing on the previous day so I missed this. Once the amp jobs became available I could see that these have the same issue (on both float32 and amp).

Pull Request resolved: https://github.com/pytorch/pytorch/pull/96324
Approved by: https://github.com/desertfire
 2023-03-10 18:15:34 +00:00
Bin Bao
a651e6253a [CI] Change compile_threads to 1 when running benchmark accuracy test on CI (#96195) 
Summary: This is not a pretty solution, but it a way to verify if the flakiness is coming from parallel compilation.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/96195
Approved by: https://github.com/ngimel
 2023-03-10 17:39:38 +00:00
Edward Z. Yang
ff2e14f200 Skip rexnet_100 in dynamic CI (#96474) 
Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/96474
Approved by: https://github.com/yanboliang, https://github.com/msaroufim
 2023-03-10 01:23:19 +00:00
Edward Z. Yang
c988de1040 [EASY] Update inductor training dynamic skips (#96298) 
Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/96298
Approved by: https://github.com/Chillee, https://github.com/janeyx99
 2023-03-08 19:31:46 +00:00
Bin Bao
b3a079810e [CI] Add a workflow for quick perf comparison (#96166) 
Summary: ciflow/inductor-perf-test-nightly now contains full dashboard
run which takes a very long time. Ed proposed a simplification of the
perf run there, but it is still worth to have a set of fast perf test
which only includes one configuration (--training --amp).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96166
Approved by: https://github.com/huydhn, https://github.com/weiwangmeta
 2023-03-08 19:09:04 +00:00
Bin Bao
664381b293 [CI] Avoid calling torch.use_deterministic_algorithms for some models (#96245) 
tests

Pull Request resolved: https://github.com/pytorch/pytorch/pull/96245
Approved by: https://github.com/davidberard98
 2023-03-08 03:35:32 +00:00
Edward Z. Yang
d0641ed247 [TEST] Turn on unspecialize int dynamic training inductor CI (#96058) 
Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/96058
Approved by: https://github.com/janeyx99, https://github.com/voznesenskym
 2023-03-07 16:08:45 +00:00
Edward Z. Yang
a6e3e7905e Turn on unspecialize int dynamic inductor CI (#96034) 
Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/96034
Approved by: https://github.com/voznesenskym
 2023-03-07 12:39:55 +00:00
Jason Ansel
95d17dc93d [inductor] Reland #95567 part 1 (#96023) 
This is the non-problematic part of #95567.  The errors were coming from
IR printing changes which will be next in the stack.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/96023
Approved by: https://github.com/ngimel, https://github.com/mlazos
 2023-03-06 22:57:22 +00:00
Edward Z. Yang
1fd7ea1ba8 Update skips for RecursionError (#96109) 
Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/96109
Approved by: https://github.com/huydhn
 2023-03-06 17:55:38 +00:00
Bin Bao
60cf95610d [CI] Skip xcit_large_24_p8_224 in TIMM (#96048) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96048
Approved by: https://github.com/jansel
 2023-03-05 14:54:46 +00:00
Bin Bao
1359d16fe8 [CI] Further tighten the checking of two eager runs (#95902) 
Summary: To catch nondeterminism in eager if there is any.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/95902
Approved by: https://github.com/jansel
 2023-03-05 14:53:02 +00:00
Edward Z. Yang
c7c4a20321 Update dynamic skips (#95966) 
Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/95966
Approved by: https://github.com/janeyx99, https://github.com/voznesenskym
 2023-03-04 23:01:58 +00:00
Jason Ansel
43dd043ea7 Revert "[inductor] Improve error messages (#95567)" (#96014) 
This reverts commit 62b775583f.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/96014
Approved by: https://github.com/Chillee
 2023-03-04 04:03:31 +00:00
Edward Z. Yang
d303665d33 Make int unspecialization actually work (#95621) 
OK, so this PR used to be about reducing the number of constants we specialize on, but it turns out that unspecialization was ~essentially never used (because we still constant specialized way too aggressively) and I ended up having to fix a bunch of issues to actually get tests to pass. So this PR is now "make int unspecialization actually work". As part of this, I have to turn off unspecialization by default, as there are still latent bugs in inductor.

The general strategy is that an unspecialized int is represented as a SymInt. Representing it as a 0d tensor (which is what the code used to do) is untenable: (1) we often need unspecialized ints to participate in size computations, but we have no way of propagating sympy expressions through tensor compute, and (2) a lot of APIs work when passed SymInt, but not when passed a Tensor. However, I continue to represent Numpy scalars as Tensors, as they are rarely used for size computation and they have an explicit dtype, so they are more accurately modeled as 0d tensors.

* I folded in the changes from https://github.com/pytorch/pytorch/pull/95099 as I cannot represent unspecialized ints as SymInts without also turning on dynamic shapes. This also eliminates the necessity for test_unspec.py, as toggling specialization without dynamic shapes doesn't do anything. As dynamic shapes defaults to unspecializing, I just deleted this entirely; for the specialization case, I rely on regular static shape tests to catch it. (Hypothetically, we could also rerun all the tests with dynamic shapes, but WITH int/float specialization, but this seems... not that useful? I mean, I guess export wants it, but I'd kind of like our Source heuristic to improve enough that export doesn't have to toggle this either.)
* Only 0/1 integers get specialized by default now
* A hodgepodge of fixes. I'll comment on the PR about them.

Fixes https://github.com/pytorch/pytorch/issues/95469

Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/95621
Approved by: https://github.com/jansel, https://github.com/Chillee
 2023-03-04 01:22:08 +00:00
Jason Ansel
62b775583f [inductor] Improve error messages (#95567) 
Example error message before/after (710 to 131 lines):
https://gist.github.com/jansel/6fecad057738089fa95bf08c3de9fc8a

Pull Request resolved: https://github.com/pytorch/pytorch/pull/95567
Approved by: https://github.com/mlazos
 2023-03-02 02:20:55 +00:00
Bin Bao
879f0c3fee [CI] Increate the timeout limit for benchmark test (#95787) 
Summary: xcit_large_24_p8_224 occasionally hits TIMEOUT on CI. Bump up
the limit to reduce flakiness.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/95787
Approved by: https://github.com/ezyang, https://github.com/ZainRizvi
 2023-03-01 19:54:25 +00:00
Bin Bao
e79b2b7792 [CI] Force clear triton cache between running each test (#95729) 
Summary: The idea is to see if this reduces some of the flakiness
we have seen on CI. If it does help, then we have a problem in our
caching implementation.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/95729
Approved by: https://github.com/ngimel
 2023-03-01 04:10:03 +00:00
Will Constable
1a72712645 Add dynamo graph break stats to CI (#95635) 
Adds columns to csv produced by accuracy job including dynamo graph break stats.

Example output from torchbench CI job:
<img width="771" alt="image" src="https://user-images.githubusercontent.com/4984825/221716236-9276684e-1be8-43e1-837e-f41671d4e0e3.png">

Pull Request resolved: https://github.com/pytorch/pytorch/pull/95635
Approved by: https://github.com/ezyang
 2023-02-28 16:17:46 +00:00
Edward Z. Yang
3762e801ba Update dynamic skips (#95587) 
Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/95587
Approved by: https://github.com/voznesenskym
 2023-02-28 03:26:55 +00:00
Bin Bao
fa5a4b0dfc [CI] Do not compare two eager run results against fp64 result (#95616) 
Summary: When running the benchmark test with --accuracy, two eager runs
should return the same result. If not, we want to detect it early, but
comparing against fp64_output may hide the non-deterministism in eager.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/95616
Approved by: https://github.com/ZainRizvi
 2023-02-27 20:11:21 +00:00
Bin Bao
ab1ab3ab19 [CI] Specify more torch.backends.cudnn options to reduce non-determinism (#95478) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95478
Approved by: https://github.com/ezyang
 2023-02-25 18:54:12 +00:00
Bin Bao
4c8ad93a7c [Inductor][CI] Remove hf_GPT2_large from CPU inference test (#95473) 
Summary: hf_GPT2_large shows random failure on CI for the CPU inference. Created https://github.com/pytorch/pytorch/issues/95474 for the Intel team to investigate.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/95473
Approved by: https://github.com/anijain2305
 2023-02-24 18:21:36 +00:00
Will Constable
8de4238a31 Add dynamo bench arg --per_process_memory_fraction (#95260) 
Simply pipes the arg to the existing torch.cuda API by the same name.

Useful for locally debugging OOMs that happened on a smaller GPU.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/95260
Approved by: https://github.com/davidberard98
 2023-02-22 05:11:18 +00:00