Comments (5)
I think the fundamental issue here is that visibility is almost useless outside of a monorepo. We also have to build our integration with TF with --nocheck_visibility
(related, bazelbuild/bazel#16767)
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I think it's still useful to have visibility within each repository, e.g., for internal structuring purposes. The key is that we should open visibility to any public targets that cross repository boundaries.
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Right, it's nice intra-project but totally breaks across projects. Which is fine in google3 because it's all one "project". I think the concept of visibility groups is useful, but the implementation is totally not set up for a moderately complex constellation of projects
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Any updates? It's unfortunate to break visibility for all the downstream users and their own internals.
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As of, ccceb5f, this is resolved.
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Related Issues (20)
- gpu_hlo_cost_analysis NumOfDevices always return 0 HOT 2
- [Feature Request]Add more comm op support on gpu_hlo_cost_analysis HOT 4
- gpu f16 cast to fp32 calculation, and then converted back? HOT 1
- "Could not find executable `nvidia-smi`" for `./configure.py --backend=CUDA` HOT 6
- Build from source fails HOT 7
- XLA documentation for Windows HOT 1
- Implement GitHub Presubmit Checks for Windows Environment HOT 1
- Support builds with cuDNN 9
- gpu_fused_mha_test fails at HEAD on H100 HOT 1
- Workable example on normal DNN model
- [xla:auto_sharding] Question about resharding costs of Reshape strategies
- OpenCL Support. HOT 2
- BF16 matmul slower than F32 matmul on T4 GPU HOT 3
- PJRT `CopyCpuBufferToLiteral` of JAX buffer taking too long HOT 9
- Porting XLA to different backends. HOT 4
- Compiling xla/mlir/tools/mlir_interpreter/dialects/util.cc failed HOT 3
- MHLO Extraction from XLA Compiler HOT 4
- Controlling a single compiler pass in XLA for CPU target HOT 2
- Compiling scatter results in very slow while-loop on TPU
- Wrong output from JAX test after onednn change HOT 3
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