-
Notifications
You must be signed in to change notification settings - Fork 541
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
std::numeric_limits<_T>::infinity() compilation problem #374
Comments
This is a problem in hcc actually: #include <limits>
void f(int *data) [[hc]] [[cpu]] {
*data = std::numeric_limits<int>::infinity();
} And there is only an error when using both |
Do I have to open an issue for hcc? AFAIK, under ROCm v1.6 it was compiling fine. |
I would, so that they can see it. I dont know if they are all subscribed to HIP issues. |
Is defect opened againt HCC? Can someone please post the issue#. |
@ekondis We do not officially support this type of use of anything that is not P.S.: using |
Thank you @AlexVlx. I'll replace this call with a simple macro/constant. |
Summary: **Summary**: This PR is a followup of mruberry's #9318. It tries to achieve the following: - Specializing std common math functions for `at::Half` type. - Create `CUDANumerics.cuh` to contain necessary parts from `THCNumerics.cuh`. - Update `THCNumerics.cuh` with new usage and comments to demonstrate the best practice for developers and hence, making way for its deprecation. - Remove legacy/redundant code path. - Remove unused CUDA HALF macros (see separate PR #10147) **Comments**: `CUDANumerics.cuh` contains mathematical functions that are either not in the std namespace or are specialized for compilation with CUDA NVCC or CUDA NVRTC. This header is derived from the legacy `THCNumerics.cuh`. Following are some rationale behind why some functions were kept while others were removed: - All arithmetic can now be done in ATen using binary cuda kernel or CUDA tensor pointwise apply (check #8919 and `CUDAApplyUtils`). `at::Half` comparisons rely on implicit conversion to float. - Functions that are c/c++ standard compliant, have been specialized for user defined types, for instance, the std namespace has been opened up for `at::Half`, that defines math function definitions for `at::Half`. Check `Half-inl.h` - Some standard compliant functions are specialized here for performance reasons. For instance, `powi` is used for `pow` calculation on integral types. Moreover, `abs`, `isinf`, `isnan` are specialized to save one API call vs when used with std. Although this is subject to change, depending on if we really care about saving one API call. - Numeric limits such as `max/min` is removed since they call standard defines. Moreover, numeric limits for `at::Half` is present in `Half-inl.h`. I understood that HIP has some issue with `std::numeric_limits` and this the related github issue I found: ROCm/HIP#374. AlexVlx mentions that the issue can be avoided by launching `std::numeric_limits` in `__device__`. Since, we are launching lambdas with device contexts, I don't see an issue why `std::numeric_limits` won't compile on HIP if launched with device context within a kernel, unless I am not aware of the real reason why max/min was there in THCNumerics in the first place. (Haven't ever tried a build with HIP). Here are some reference PRs that was handy in refactoring TH into ATen: - #6786 - #5475 - #9401 - #8689 - #8919 Pull Request resolved: #10301 Differential Revision: D9204758 Pulled By: soumith fbshipit-source-id: 09f489c1656458c02367b6cd31c3eeeca5acdc8a
Summary: **Summary**: This PR is a followup of mruberry's pytorch/pytorch#9318. It tries to achieve the following: - Specializing std common math functions for `at::Half` type. - Create `CUDANumerics.cuh` to contain necessary parts from `THCNumerics.cuh`. - Update `THCNumerics.cuh` with new usage and comments to demonstrate the best practice for developers and hence, making way for its deprecation. - Remove legacy/redundant code path. - Remove unused CUDA HALF macros (see separate PR pytorch/pytorch#10147) **Comments**: `CUDANumerics.cuh` contains mathematical functions that are either not in the std namespace or are specialized for compilation with CUDA NVCC or CUDA NVRTC. This header is derived from the legacy `THCNumerics.cuh`. Following are some rationale behind why some functions were kept while others were removed: - All arithmetic can now be done in ATen using binary cuda kernel or CUDA tensor pointwise apply (check pytorch/pytorch#8919 and `CUDAApplyUtils`). `at::Half` comparisons rely on implicit conversion to float. - Functions that are c/c++ standard compliant, have been specialized for user defined types, for instance, the std namespace has been opened up for `at::Half`, that defines math function definitions for `at::Half`. Check `Half-inl.h` - Some standard compliant functions are specialized here for performance reasons. For instance, `powi` is used for `pow` calculation on integral types. Moreover, `abs`, `isinf`, `isnan` are specialized to save one API call vs when used with std. Although this is subject to change, depending on if we really care about saving one API call. - Numeric limits such as `max/min` is removed since they call standard defines. Moreover, numeric limits for `at::Half` is present in `Half-inl.h`. I understood that HIP has some issue with `std::numeric_limits` and this the related github issue I found: ROCm/HIP#374. AlexVlx mentions that the issue can be avoided by launching `std::numeric_limits` in `__device__`. Since, we are launching lambdas with device contexts, I don't see an issue why `std::numeric_limits` won't compile on HIP if launched with device context within a kernel, unless I am not aware of the real reason why max/min was there in THCNumerics in the first place. (Haven't ever tried a build with HIP). Here are some reference PRs that was handy in refactoring TH into ATen: - pytorch/pytorch#6786 - pytorch/pytorch#5475 - pytorch/pytorch#9401 - pytorch/pytorch#8689 - pytorch/pytorch#8919 Pull Request resolved: pytorch/pytorch#10301 Differential Revision: D9204758 Pulled By: soumith fbshipit-source-id: 09f489c1656458c02367b6cd31c3eeeca5acdc8a
…rch#10301) Summary: **Summary**: This PR is a followup of mruberry's pytorch#9318. It tries to achieve the following: - Specializing std common math functions for `at::Half` type. - Create `CUDANumerics.cuh` to contain necessary parts from `THCNumerics.cuh`. - Update `THCNumerics.cuh` with new usage and comments to demonstrate the best practice for developers and hence, making way for its deprecation. - Remove legacy/redundant code path. - Remove unused CUDA HALF macros (see separate PR pytorch#10147) **Comments**: `CUDANumerics.cuh` contains mathematical functions that are either not in the std namespace or are specialized for compilation with CUDA NVCC or CUDA NVRTC. This header is derived from the legacy `THCNumerics.cuh`. Following are some rationale behind why some functions were kept while others were removed: - All arithmetic can now be done in ATen using binary cuda kernel or CUDA tensor pointwise apply (check pytorch#8919 and `CUDAApplyUtils`). `at::Half` comparisons rely on implicit conversion to float. - Functions that are c/c++ standard compliant, have been specialized for user defined types, for instance, the std namespace has been opened up for `at::Half`, that defines math function definitions for `at::Half`. Check `Half-inl.h` - Some standard compliant functions are specialized here for performance reasons. For instance, `powi` is used for `pow` calculation on integral types. Moreover, `abs`, `isinf`, `isnan` are specialized to save one API call vs when used with std. Although this is subject to change, depending on if we really care about saving one API call. - Numeric limits such as `max/min` is removed since they call standard defines. Moreover, numeric limits for `at::Half` is present in `Half-inl.h`. I understood that HIP has some issue with `std::numeric_limits` and this the related github issue I found: ROCm/HIP#374. AlexVlx mentions that the issue can be avoided by launching `std::numeric_limits` in `__device__`. Since, we are launching lambdas with device contexts, I don't see an issue why `std::numeric_limits` won't compile on HIP if launched with device context within a kernel, unless I am not aware of the real reason why max/min was there in THCNumerics in the first place. (Haven't ever tried a build with HIP). Here are some reference PRs that was handy in refactoring TH into ATen: - pytorch#6786 - pytorch#5475 - pytorch#9401 - pytorch#8689 - pytorch#8919 Pull Request resolved: pytorch#10301 Differential Revision: D9204758 Pulled By: soumith fbshipit-source-id: 09f489c1656458c02367b6cd31c3eeeca5acdc8a
Using the
std::numeric_limits<_T>::infinity()
function within a kernel returns compilation error:To reproduce use the following HIP code :
The text was updated successfully, but these errors were encountered: