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Develop upstream sync 231030 #2287
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In the compact version of printing, any custom attributes a user of StableHLO sets will not appear. This makes sure they appear if they exist. PiperOrigin-RevId: 575778539
Imported from GitHub PR openxla/xla#6475 `bitcast_dtypes_expander_test` failed when executed in Docker container ``` //xla/service:bitcast_dtypes_expander_test FAILED in 3 out of 3 in 0.7s ``` Reason - swapped variable names in `S64toS32` test ``` %shift-right-logical.11 -> %shift-right-logical.12 %constant.12 -> %constant.11 ``` To resolve the issue we can use pattern based var names instead. ``` %[[VAL_10:.*]] %[[VAL_11:.*]] ``` Copybara import of the project: -- b99ed813b39c6b53f1aa0981dbaae060d8cb5aa8 by Alexander Pivovarov <pivovaa@amazon.com>: Fix flaky test bitcast_dtypes_expander_test Merging this change closes tensorflow#6475 PiperOrigin-RevId: 575793343
Updates LLVM usage to match [e558be51bab0](llvm/llvm-project@e558be51bab0) PiperOrigin-RevId: 575794929
… cuda 12.3 dependencies. Otherwise, when pulled, the 12.3 will change the alias of /usr/local/cuda to 12.3, causing failures. PiperOrigin-RevId: 575799576
Log the op attributes on `--vmodule=xla_call_module_op=3`. PiperOrigin-RevId: 575812286
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Fixed that IrEmitterTriton "swallowed" NaNs in minimum and maximum. Fixed that ElementalIrEmitter "swallowed" NaNs in the case of minimum(x, NaN) on GPU. That was likely caused by an llvm error. PiperOrigin-RevId: 575817645
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…mand buffers Before this CL the libraries supported by GPU graphs are determined a integer level. This CL allows the user to have fine-grained control on whether a library should be enabled in graphs. Example usage: XLA_FLAGS=--xla_gpu_command_buffer_command_types=FUSION,CUBLAS PiperOrigin-RevId: 575856977
Consolidate implementations of requantize and use the same one as TF quantizer. PiperOrigin-RevId: 575864178
PiperOrigin-RevId: 575866461
…dependencies, as part of a thunk clean up, and updated the necessary directories pointing to this thunk. tensorflow#5758 PiperOrigin-RevId: 575867727
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…en instruction, and reports the total # of such nodes. PiperOrigin-RevId: 575887322
This will guarantee that the iterators created inside of parallel interleave iterator get destroyed before itself. Otherwise, `CancelThreads(true)` might finish before `element`'s destructor is called because of `outstanding_threads_finished_cond_var_.wait(l);` PiperOrigin-RevId: 575888988
PiperOrigin-RevId: 575890152
…lag. The flag could have a different value in different processes, which would invalidate the inferred shape. The dependency also inflates the size of the op library, by adding a dependency on a kernel library (and transitively on MLIR), and pulls in unrelated ops which complicates wrapper generation. PiperOrigin-RevId: 575900618
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In preparation to create `xla_protos_all` to simplify further PiperOrigin-RevId: 575911998
…ow/core/tpu/ops:sparse_core_ops. These dependencies are unnecessary, and they can introduce duplicate op wrappers when we generate Python bindings. PiperOrigin-RevId: 575921758
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PiperOrigin-RevId: 575941719
Trying to get these in sync with the current jobs, which are failing, probably because of the wrong .so being used. PiperOrigin-RevId: 575978433
…ach fusion This is needed for xla_gpu_deterministic_ops to function reliably - as tested by determinism_test.cc. PiperOrigin-RevId: 577906200
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… SetPjRtCBufferToTensor). PiperOrigin-RevId: 577915709
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CreateTensorProto() that allows data to be loaded from non-std::vector sources, which is often more efficient. vector<bool> makes the implementation of internal::CreateTensorProto a bit messier than it would otherwise be due to it not being representable as an absl::Span. PiperOrigin-RevId: 577926897
In StreamExecutor we rely on a thread local hack to pass workspace to cuBLAS, long term we need to make workspace explicit in all BLAS APIs. Without user-managed workspace cuBLAS adds memory allocation nodes when captured into CUDA graphs, and that triggers graph update bugs in CUDA. PiperOrigin-RevId: 577928686
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… the dot 1. Reduce block sizes if they are bigger than NextPowerOf2(tensor size). 2. Reduce split_k to NextPowerOf2(k)/block_k. 3. Remove duplicates created by the previous steps. I intend this cl not to make any performance degradation. PiperOrigin-RevId: 577941843
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Could you use test.is_built_with_rocm()
instead of just commented out in the code? Also, in your tracking ticket https://github.com/ROCmSoftwarePlatform/frameworks-internal/issues/6264 could you also add the new added commit from upstream for tracking? Thanks
//tensorflow/compiler/tests:xla_call_module_no_platform_check_test_gpu //tensorflow/compiler/tests:xla_call_module_no_shape_assertions_check_test_gpu //tensorflow/compiler/tests:xla_call_module_test_gpu //tensorflow/python/eager:context_test_gpu //tensorflow/python/ops/memory_tests:custom_gradient_memory_test_gpu
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retest Ubuntu-CPU please! |
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retest Ubuntu-CPU please! |
retest Ubuntu-CPU please |
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