Port `amax` to structured kernel #72124

ansley · 2022-02-01T18:12:48Z

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pytorch-bot · 2022-02-01T18:12:52Z

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💊 CI failures summary and remediations

As of commit efb3ecd (more details on the Dr. CI page):

1/2 failures introduced in this PR
1/2 broken upstream at merge base 8aa3620 on Feb 14 from 8:02am to 9:18am

🕵️ 1 new failure recognized by patterns

The following CI failures do not appear to be due to upstream breakages:

linux-bionic-py3.7-clang9 / test (xla, 1, 1, linux.2xlarge) (1/1)

Step: "Test" (full log | diagnosis details | 🔁 rerun)

2022-02-14T17:02:23.2655799Z /var/lib/jenkins/w... at::Tensor&, const at::Tensor&, c10::string_view)

2022-02-14T17:02:23.2649065Z  at::Tensor XLANativeFunctions::gelu(const at::Tensor& self) {
2022-02-14T17:02:23.2649423Z             ^~~~~~~~~~~~~~~~~~
2022-02-14T17:02:23.2649862Z In file included from /var/lib/jenkins/workspace/xla/torch_xla/csrc/aten_xla_type.cpp:13:0:
2022-02-14T17:02:23.2650669Z /var/lib/jenkins/workspace/xla/torch_xla/csrc/XLANativeFunctions.h:182:19: error: candidate is: static at::Tensor torch_xla::XLANativeFunctions::gelu(const at::Tensor&, c10::string_view)
2022-02-14T17:02:23.2651412Z  static at::Tensor gelu(const at::Tensor & self, c10::string_view approximate);
2022-02-14T17:02:23.2651979Z                    ^~~~
2022-02-14T17:02:23.2653188Z /var/lib/jenkins/workspace/xla/torch_xla/csrc/aten_xla_type.cpp:1514:12: error: prototype for ‘at::Tensor torch_xla::XLANativeFunctions::gelu_backward(const at::Tensor&, const at::Tensor&)’ does not match any in class ‘torch_xla::XLANativeFunctions’
2022-02-14T17:02:23.2654080Z  at::Tensor XLANativeFunctions::gelu_backward(const at::Tensor& grad,
2022-02-14T17:02:23.2654509Z             ^~~~~~~~~~~~~~~~~~
2022-02-14T17:02:23.2654947Z In file included from /var/lib/jenkins/workspace/xla/torch_xla/csrc/aten_xla_type.cpp:13:0:
2022-02-14T17:02:23.2655799Z /var/lib/jenkins/workspace/xla/torch_xla/csrc/XLANativeFunctions.h:183:19: error: candidate is: static at::Tensor torch_xla::XLANativeFunctions::gelu_backward(const at::Tensor&, const at::Tensor&, c10::string_view)
2022-02-14T17:02:23.2656663Z  static at::Tensor gelu_backward(const at::Tensor & grad_output, const at::Tensor & self, c10::string_view approximate);
2022-02-14T17:02:23.2657146Z                    ^~~~~~~~~~~~~
2022-02-14T17:02:24.9910381Z [9/179] c++ -MMD -MF /var/lib/jenkins/workspace/xla/build/temp.linux-x86_64-3.7/torch_xla/csrc/convolution.o.d -pthread -B /opt/conda/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/var/lib/jenkins/workspace/xla -I/var/lib/jenkins/workspace/xla/third_party/tensorflow/bazel-tensorflow -I/var/lib/jenkins/workspace/xla/third_party/tensorflow/bazel-bin -I/var/lib/jenkins/workspace/xla/third_party/tensorflow/bazel-tensorflow/external/protobuf_archive/src -I/var/lib/jenkins/workspace/xla/third_party/tensorflow/bazel-tensorflow/external/com_google_protobuf/src -I/var/lib/jenkins/workspace/xla/third_party/tensorflow/bazel-tensorflow/external/eigen_archive -I/var/lib/jenkins/workspace/xla/third_party/tensorflow/bazel-tensorflow/external/com_google_absl -I/var/lib/jenkins/workspace -I/var/lib/jenkins/workspace/torch/csrc -I/var/lib/jenkins/workspace/torch/lib/tmp_install/include -I/opt/conda/lib/python3.7/site-packages/torch/include -I/opt/conda/lib/python3.7/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/lib/python3.7/site-packages/torch/include/TH -I/opt/conda/lib/python3.7/site-packages/torch/include/THC -I/opt/conda/include/python3.7m -c -c /var/lib/jenkins/workspace/xla/torch_xla/csrc/convolution.cpp -o /var/lib/jenkins/workspace/xla/build/temp.linux-x86_64-3.7/torch_xla/csrc/convolution.o -std=c++14 -Wno-sign-compare -Wno-deprecated-declarations -Wno-return-type -DNDEBUG -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_clang"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1002"' -DTORCH_EXTENSION_NAME=_XLAC -D_GLIBCXX_USE_CXX11_ABI=1
2022-02-14T17:02:24.9914515Z cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++
2022-02-14T17:02:24.9915098Z In file included from /var/lib/jenkins/workspace/c10/util/Logging.h:28:0,
2022-02-14T17:02:24.9915590Z                  from /var/lib/jenkins/workspace/c10/core/TensorImpl.h:14,
2022-02-14T17:02:24.9916288Z                  from /opt/conda/lib/python3.7/site-packages/torch/include/ATen/core/TensorBody.h:21,
2022-02-14T17:02:24.9916988Z                  from /opt/conda/lib/python3.7/site-packages/torch/include/ATen/Tensor.h:3,
2022-02-14T17:02:24.9917562Z                  from /var/lib/jenkins/workspace/torch/csrc/autograd/function_hook.h:5,
2022-02-14T17:02:24.9918085Z                  from /var/lib/jenkins/workspace/torch/csrc/autograd/variable.h:7,

🚧 1 fixed upstream failure:

These were probably caused by upstream breakages that were already fixed.

Please rebase on the viable/strict branch (expand for instructions)

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linux-xenial-py3.7-clang7-onnx / test (default, 1, 2, linux.2xlarge) on Feb 14 from 8:02am to 9:18am (8aa3620 - 8aa3620)
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aten/src/ATen/native/ReduceOps.cpp

bdhirsh · 2022-02-08T14:53:22Z

I see this error from CI (link):

AssertionError: The supported dtypes for amax on cpu according to its OpInfo are
        {torch.bfloat16, torch.float16, torch.uint8, torch.bool, torch.float32, torch.int8, torch.float64, torch.int16, torch.int32, torch.int64}, but the detected supported dtypes are {torch.bfloat16, torch.complex128, torch.float16, torch.uint8, torch.bool, torch.float32, torch.int8, torch.float64, torch.int16, torch.int32, torch.complex64, torch.int64}.
        The following dtypes should be added to the OpInfo: {torch.complex128, torch.complex64}.

I'm not sure what's causing that just from staring at it though- I'd have to repro locally.

It looks like the kernel eventually calls max_values_stub, which calls the kernel here. That kernel doesn't have any explicit support for complex dtypes, so I'm not sure why the OpInfo tests are saying that there's support for it with your change.

You can repro that OpInfo test with `python test/test_ops.py TestCommonCPU.test_dtypes_amax_cpu

bdhirsh · 2022-02-08T23:39:18Z

ahh I think I found it. In your meta function, you use this to get the output dtype: get_result_or_self_value_dtype. That's implemented as (here):


ScalarType get_result_or_self_value_dtype(
    const Tensor& self,
    const Tensor& result,
    const c10::optional<ScalarType>& dtype) {
  if (result.defined()) {
    return result.scalar_type();
  } else {
    return dtype.value_or(toValueType(self.scalar_type()));
  }
}

And it looks like toValueType() is written to explicitly remove the "complex" part of the dtype (here). That ends up... forcing the output dtype to be a float even if the input is ComplexFloat. The existing amax() kernel knows to error out when it sees complex dtypes, but now it (incorrectly) sees an ordinary float dtype and stops failing, which is why you see the test failure.

My guess is that you used that helper function because it's used by some similar ops in that file, which seems totally reasonable. The easiest fix would probably be to just directly put the logic in get_result_or_self_value_dtype() in your meta function, but remove the toValueType() bit. That way you preserve the existing behavior.

(@ansley lmk if you have any questions about the diagnosis!)

cc @anjali411 - it looks like the other reduction ops in this file have the same problem - if the input tensor is complex, the output tensor is forced not to be complex:

>>> import torch
>>> a = torch.ones(2, dtype=torch.complex64) # make a complex tensor
>>> torch.std(a).dtype # torch.std output is not a complex tensor anymore
torch.float32
>>> torch.norm(a).dtype # same thing with norm (because it also uses toValueType())
torch.float32
>>> (a + a).dtype # but ordinary ops like (+) propagate complex dtypes
torch.complex64

Do you know if that's intended behavior for reduction ops? Otherwise I can file an issue for it.

anjali411 · 2022-02-09T19:51:29Z

@bdhirsh that's the intended behavior for norm and std. we should disable amax for complex dtypes and rename toValueType to toRealValueType

bdhirsh

LGTM, thanks for changing toValueType() in the PR too!

I'll approve, but I think you accidentally included changes to third_party/* folders - just back out those changes before landing!

facebook-github-bot · 2022-02-14T17:37:54Z

@ansley has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator.

Summary: Pull Request resolved: #72124 Reviewed By: bdhirsh Differential Revision: D34215708 Pulled By: ansley fbshipit-source-id: fee887e331cb8bd9fab3d9d958ff13ac8d07be27

github-actions · 2022-02-16T06:33:43Z

Hey @ansley.
You've committed this PR, but it does not have both a 'release notes: ...' and 'topics: ...' label. Please add one of each to the PR. The 'release notes: ...' label should represent the part of PyTorch that this PR changes (fx, autograd, distributed, etc) and the 'topics: ...' label should represent the kind of PR it is (not user facing, new feature, bug fix, perf improvement, etc). The list of valid labels can be found here for the 'release notes: ...' and here for the 'topics: ...'.
For changes that are 'topic: not user facing' there is no need for a release notes label.

Summary: Pull Request resolved: pytorch/pytorch#72124 Reviewed By: bdhirsh Differential Revision: D34215708 Pulled By: ansley fbshipit-source-id: fee887e331cb8bd9fab3d9d958ff13ac8d07be27 (cherry picked from commit 94dbb5b7e7e14a663dc02ecf5013fad10b8701b3)

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ansley force-pushed the structured_amax branch from a75c29e to 51c68ee Compare February 3, 2022 14:26

ansley marked this pull request as ready for review February 3, 2022 14:30

ansley changed the title ~~[DRAFT] Port amax to structured kernel~~ Port amax to structured kernel Feb 3, 2022

ansley requested a review from bdhirsh February 3, 2022 14:30

ansley mentioned this pull request Feb 3, 2022

Port kernels to be structured [tracker] #55070

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Port amax to structured kernel

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