Comments (8)
I don't have an answer for you, but the person on this other issue might have found the answer: #2360
from xla.
@tpopp, thank you for the suggestion.
I am not sure bazel build --config=monolithic //xla/...
does anything. What is the expected library artifact from it?
from xla.
In other comments, beyond the initial issue, we discuss this a bit. XLA does not provide a single target to build everything, but some potential targets with the most interesting functionality are listed. config=monolithic
should statically link everything if I remember correctly. This is a result of XLA being used so far as a dependency in other bazel projects where creating a standalone library has not been a need before.
from xla.
Would you be willing to accept a patch that adds a Bazel target that makes one library? I will do something very similar to tensorflow_cc as is done in TensorFlow.
from xla.
@cheshire would understand any side effects better than me. It sounds reasonable to me.
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Maybe it would make sense to have a few targets for the major components.
from xla.
I'd be excited to accept a patch for something like this, I know others have expressed interest in easier usage with CMake in the past. Interested in @cheshire's thoughts on this
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Having a single library with an interface at the PJRT layer makes sense to me.
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Related Issues (20)
- Integer overflow in ZlibOutputBuffer causes crash HOT 3
- XLA CollectiveOpsUtil does not include kReduceScatter HOT 2
- [XLA:CPU] oneDNN Softmax gives inaccurate results HOT 1
- call ptxas become defunct, cause xla hung HOT 2
- 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
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