Comments (7)
I think so. For now, you can download asmjit once and point ASMJIT_SRC_DIR env var to the downloaded source code to avoid downloading with build. Particular git tag we use is in https://github.com/pytorch/FBGEMM/blob/master/cmake/modules/DownloadASMJIT.cmake
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@t-vi I run into this problem all the time too. I created a pull request. Perhaps you could test it out and let me know if it works for you.
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@dhpollack Thanks a lot for the pull request. Let me test its impact on Caffe2/Pytorch build.
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Update: I had to revert the commit because adding submodules involves one more step so that internal tools work correctly. Will re-commit soon.
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@dskhudia I also noticed a few other things that I can update before you commit this pr.
- I may want to use
${FBGEMM_SOURCE_DIR}
instead of${CMAKE_SOURCE_DIR}
, because I'm not sure what the latter is when FBGEMM is a submodule itself. - You may want to consider renaming
${ASMJIT_SRC_DIR}
to${ASMJIT_SOURCE_DIR}
to make it consistent with the other variables.
Let me know if you want me to make those changes. I am not sure it matters as both work for me right now.
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- I will change it to FBGEMM_SOURCE_DIR
- I remember it being having an issue with ASMJIT's cmake file. So we should keep that.
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@t-vi and @dhpollack : It's fixed now. Once the pytorch's fbgemm submodule gets updated (should happen soon) to fbgemm's latest commit, pytorch build will not download asmjit at build time.
@dhpollack : Thanks for your PR 👍
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Related Issues (20)
- uvm cache HOT 1
- Better and earlier error message when libnvidia-ml.so cannot be found HOT 1
- Nightly packages should have same package name as regular packages HOT 3
- import fbgemm_gpu get a undefined symbol error: undefined symbol: _ZN3c104impl8GPUTrace13gpuTraceStateE HOT 3
- fbgemm_gpu_py.so: undefined symbol: _ZN6fbgemm8internal31compressed_indices_remap_avx512IiLb1EEEviPKT_PKiS4_PKfPS2_S9_Pf HOT 1
- Undefined symbol _ZN6asmjit8_abi_1_910JitRuntimeD1Ev HOT 2
- Any plan to publish fbgemm_gpu that supports cuda 12.1? HOT 9
- Support MacOS? HOT 1
- `.to("meta")` is leaked to the public main branch in the tests. HOT 2
- Can't compile FBGEMM with GCC 12.3.0 HOT 3
- compiling FBGEMM for ARM HOT 3
- How `partition_avx512` is auto-tuned? HOT 3
- Compiling on windows with mingw
- Error importing fbgemm_gpu HOT 15
- nvcc fatal : Unknown option '-mavx' HOT 5
- Build failure on MacOS HOT 6
- momentum for SGD/Adagrad HOT 2
- RuntimeError: No such operator fbgemm::jagged_2d_to_dense HOT 3
- Having issue installing FBGEMM-gpu on MacOS HOT 8
- Latest FBGEMM doesn't build with latest PyTorch HOT 1
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