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Generate XNNPack cache flatbuffers header in the CMake build.
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2024-06-12T12:33:06
2024-06-12T12:33:08
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Generate XNNPack cache flatbuffers header in the CMake build.
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[XLA:GPU] Extract legacy dot and dynamic_slice tests out of the triton_support_test.
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2024-06-12T12:11:16
2024-06-12T12:11:16
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[XLA:GPU] Extract legacy dot and dynamic_slice tests out of the triton_support_test. This is the first step in splitting TritonSupport into legacy and current infra.
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Inline callees that call the same function as the caller.
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2024-06-12T12:02:20
2024-06-12T12:02:20
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Inline callees that call the same function as the caller. ``` A -> B -> C `---------^ ``` In this case, inline `B` into `A`. This is a workaround for limitations of our partitioner. A proper solution needs to compute the actual indexing. Fixes RNG slowness.
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[XLA:GPU] Add initial SymbolicTileAnalysis::GetGoodTilings implementation
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2024-06-12T12:01:34
2024-06-12T12:01:35
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[XLA:GPU] Add initial SymbolicTileAnalysis::GetGoodTilings implementation
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[XLA] If a WhileOp with multiple results has a single sharding, we should broadcast that sharding when passing arg_shardings and res_shardings.
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2024-06-12T11:46:54
2024-06-12T11:46:54
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[XLA] If a WhileOp with multiple results has a single sharding, we should broadcast that sharding when passing arg_shardings and res_shardings.
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[xla:cpu] Pass thread pool to thunk execution.
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[ "Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/tensorflow/tensorflow/pull/69589/checks?check_run_id=26125439649) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request." ]
2024-06-12T11:34:54
2024-06-12T11:35:00
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[xla:cpu] Pass thread pool to thunk execution. The thread pool was missing for example during thunks runtime convolution tests.
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69,588
Enable hlo_module_config_test for OSS testing
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2024-06-12T11:06:43
2024-06-12T11:58:04
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Enable hlo_module_config_test for OSS testing We can avoid testing::EqualsProto by comparing the proto string serialization.
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[XLA:GPU] Simplify triton support tests - remove unnecessary SoftMax
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2024-06-12T10:57:31
2024-06-12T12:32:31
2024-06-12T12:32:30
NONE
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[XLA:GPU] Simplify triton support tests - remove unnecessary SoftMax This simplifies tests to not use the SoftMax pattern if they don't need to when using the Generic emitter. Also simplifies and unifies some names.
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tensorflow is so buggy, you guys should just gave up and should migrate to TORCH this is so bad , i cant anymore.
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[ "Canonical issue #63362. It also contains some work-arounds. TF 2.17 branch cut just happened, maybe this will get fixed now?\r\n\r\nSee also: https://github.com/tensorflow/tensorflow/issues/63362#issuecomment-1990133275", "Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/69586\">Yes</a>\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/69586\">No</a>\n" ]
2024-06-12T10:25:09
2024-06-12T10:25:15
null
NONE
null
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### Issue type Build/Install ### Have you reproduced the bug with TensorFlow Nightly? Yes ### Source source ### TensorFlow version tf 2.16 ### Custom code Yes ### OS platform and distribution ubuntu 24.04 ### Mobile device wsl ### Python version 3.10 ### Bazel version _No response_ ### GCC/compiler version _No response_ ### CUDA/cuDNN version 12.3 ### GPU model and memory _No response_ ### Current behavior? I dont wanna talk about it anymore , the fact that it's too high a tower (keras > tensorflow > graph > compiled code), it's just too complicated to troubleshoot and extend. JUST STOP IT you people are torturing your self, tell your boss to STOP developing tensorflow, and CLOSES IT already ### Standalone code to reproduce the issue ```shell Cuddn: 8.9 Cuda: 12.3 Installing wsl tensorflow wsl --install wsl --list --online wsl --install Ubuntu-24.04 wsl -d Ubuntu-24.04 mkdir -p ~/miniconda3 wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3 rm -rf ~/miniconda3/miniconda.sh ~/miniconda3/bin/conda init bash Second opt --------------------------------------------------------------------------------------- conda create -n variety_linux python=3.10 conda activate variety_linux pip install tensorflow[and-cuda] cd $CONDA_PREFIX mkdir -p ./etc/conda/activate.d mkdir -p ./etc/conda/deactivate.d touch ./etc/conda/activate.d/env_vars.sh touch ./etc/conda/deactivate.d/env_vars.sh #!/bin/sh # Store original LD_LIBRARY_PATH export ORIGINAL_LD_LIBRARY_PATH="${LD_LIBRARY_PATH}" # Get the CUDNN directory CUDNN_DIR=$(dirname $(dirname $(python -c "import nvidia.cudnn; print(nvidia.cudnn.__file__)"))) # Set LD_LIBRARY_PATH to include CUDNN directory export LD_LIBRARY_PATH=$(find ${CUDNN_DIR}/*/lib/ -type d -printf "%p:")${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} # Get the ptxas directory PTXAS_DIR=$(dirname $(dirname $(python -c "import nvidia.cuda_nvcc; print(nvidia.cuda_nvcc.__file__)"))) # Set PATH to include the directory containing ptxas export PATH=$(find ${PTXAS_DIR}/*/bin/ -type d -printf "%p:")${PATH:+:${PATH}} #!/bin/sh # Unset environment variables unset CUDNN_DIR unset PTXAS_DIR # Restore original LD_LIBRARY_PATH export LD_LIBRARY_PATH="${ORIGINAL_LD_LIBRARY_PATH}" python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))" ``` ### Relevant log output ```shell GPU is availabel , but it s not ```
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[XLA:GPU] Simplify triton support tests - remove unnecessary `dot`
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2024-06-12T10:16:13
2024-06-12T12:25:01
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[XLA:GPU] Simplify triton support tests - remove unnecessary `dot` This simplifies tests to not use `dot` if they don't need to when using the Generic emitter. Also simplifies and unifies some names.
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[XLA] [NFC] Remove unused Client APIs
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2024-06-12T09:58:49
2024-06-12T12:14:16
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[XLA] [NFC] Remove unused Client APIs
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Integrate LLVM at llvm/llvm-project@c012e487b724
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2024-06-12T09:35:52
2024-06-12T11:45:58
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Integrate LLVM at llvm/llvm-project@c012e487b724 Updates LLVM usage to match [c012e487b724](https://github.com/llvm/llvm-project/commit/c012e487b724)
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2024-06-12T09:21:43
2024-06-12T09:21:43
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Integrate LLVM at llvm/llvm-project@c012e487b724 Updates LLVM usage to match [c012e487b724](https://github.com/llvm/llvm-project/commit/c012e487b724)
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[XLA] Remove ServiceInterface
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2024-06-12T09:17:33
2024-06-12T09:17:34
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[XLA] Remove ServiceInterface The abstraction does not add much value over just using Service class. FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13477 from shraiysh:fix-while-loop-derived-instruction 9563c00004bc2a988d01964f213e5dc4dc535489
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PR #13462: [ROCM][NFC] gpublas-lt refactoring after adding workspace and scratch allocator
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2024-06-12T08:30:47
2024-06-12T09:49:36
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PR #13462: [ROCM][NFC] gpublas-lt refactoring after adding workspace and scratch allocator Imported from GitHub PR https://github.com/openxla/xla/pull/13462 This PR https://github.com/openxla/xla/pull/11514 added workspace allocation to cublas-lt. Basically, it doubled the implementation of a number of functions in gpu/cu/hipblas-lt, i.e. now we have: ``` DoMatmul(..., std::optional<DeviceMemoryBase> workspace) DoMatmul(..., std::optional<ScratchAllocator*> scratch_allocator) DoMatmul(..., std::optional<DeviceMemoryBase> workspace, std::optional<ScratchAllocator*> scratch_allocator) ``` and the same holds for ```ExecuteOnStream```. This makes gpublas_lt interface barely readable. The first two functions outlined above are just forwarding calls to the 3rd most generic one. Therefore, I do not see any need to implement these inside the derived classes, i.e. hip_blas_lt.h and cuda_blas_lt.h. Instead, forwarding can be handled in gpu_blas_lt.h interface. @xla-rotation: could you please have a look? Copybara import of the project: -- 6d3700a7b4141dee82a3b3f4d6be492a0a67d92b by Pavel Emeliyanenko <pavel.emeliyanenko@amd.com>: refactoring -- 495b2cc7b5a4e944804acddc9abc9442d9cce32a by Pavel Emeliyanenko <pavel.emeliyanenko@amd.com>: cuda side adaptions -- 4078221daebb8cb88faebe9423e87a1a781a765b by Pavel Emeliyanenko <pavel.emeliyanenko@amd.com>: parameter fix Merging this change closes #13462 FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13462 from ROCm:ci_gpublas_lt_workspace_refactor 4078221daebb8cb88faebe9423e87a1a781a765b
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PR #13639: Fix UAF in Norm Rewriter
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2024-06-12T08:30:19
2024-06-12T10:17:11
2024-06-12T10:17:09
NONE
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PR #13639: Fix UAF in Norm Rewriter Imported from GitHub PR https://github.com/openxla/xla/pull/13639 Resolves a use-after-free when matching and rewriting layer norm patterns. See #13606. Copybara import of the project: -- 91ebf7b4a2ac90ebadce27d1a73e88fb4513aed4 by Philipp Hack <phack@nvidia.com>: Resolves a use-after-free in the norm rewriter. Merging this change closes #13639 FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13639 from philipphack:u_layer_uaf_xla 91ebf7b4a2ac90ebadce27d1a73e88fb4513aed4
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Move common test helper to test_utils.h
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2024-06-12T08:30:00
2024-06-12T12:04:57
2024-06-12T12:04:56
NONE
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Move common test helper to test_utils.h ParseTextProto is already used in two tests.
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PR #13569: [GPU] Add on-disk per-kernel compilation cache.
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2024-06-12T08:05:55
2024-06-12T08:05:55
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PR #13569: [GPU] Add on-disk per-kernel compilation cache. Imported from GitHub PR https://github.com/openxla/xla/pull/13569 Copybara import of the project: -- 6ec3e04a2982ceecadc17d51adb31f61cea70067 by Ilia Sergachev <isergachev@nvidia.com>: [GPU] Let constants be emitted into a separate LLVM module. -- e4d5869895751dbea1f4c8e3af9c20e6963ba8ee by Ilia Sergachev <isergachev@nvidia.com>: [GPU] Add on-disk per-kernel compilation cache. -- 1dbf7fe8bf11cd15f8138eae7dc2d71c4d945fdd by Ilia Sergachev <isergachev@nvidia.com>: Skip kernel cache tests when linking is not available. -- 7d07531680954257a4c8a968969c9bd72c19e4ca by Ilia Sergachev <isergachev@nvidia.com>: Address feedback -- 363caaf6c299c4a0568f590e7118fcfcb675f110 by Ilia Sergachev <isergachev@nvidia.com>: Remove static keyword Merging this change closes #13569 FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13569 from openxla:disk_kernel_cache 363caaf6c299c4a0568f590e7118fcfcb675f110
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PR #11583: Weight offloading of Jax memories: support memory_kind for GPUs
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2024-06-12T07:55:30
2024-06-12T09:00:22
2024-06-12T09:00:21
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PR #11583: Weight offloading of Jax memories: support memory_kind for GPUs Imported from GitHub PR https://github.com/openxla/xla/pull/11583 The first step to implement weight offloading is to support the existing host memory kind for GPUs: pinned_host. Below are examples of the supported Jax APIs: ``` NamedSharding(mesh, P('x', 'y'), memory_kind='pinned_host') s_host.with_memory_kind('pinned_host') ``` The next step will be supporting Jax API of device_put() via PjRtStreamExecutorClient::BufferFromHostBuffer() in XLA. Each tensor can only have one memory space or memory kind. This will be implemented in PjRtBuffer to get the mapped memory space from NamedSharding() or with_memory_kind(). Copybara import of the project: -- 7ac05a767e23755f8505bef9d1f077d1a293ab0c by Jane Liu <janeliu@nvidia.com>: Support NamedSharding with memory_kind by adding host memory spaces. Merging this change closes #11583 FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11583 from zhenying-liu:weight-offloading 7ac05a767e23755f8505bef9d1f077d1a293ab0c
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tf.data.Dataset.save and load custom dataset throw "DataLossError: Unable to parse tensor from stored proto"
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2024-06-12T07:21:42
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### Issue type Bug ### Have you reproduced the bug with TensorFlow Nightly? No ### Source binary ### TensorFlow version 2.16.1 ### Custom code No ### OS platform and distribution Linux Ubuntu 22.04 ### Mobile device _No response_ ### Python version 3.10.14 ### Bazel version _No response_ ### GCC/compiler version _No response_ ### CUDA/cuDNN version _No response_ ### GPU model and memory _No response_ ### Current behavior? I am processing a custom dataset from [link](https://colab.research.google.com/drive/1b4PPH4XGht4Jve2xPKMCh-AXXAQziNQa?usp=sharing#scrollTo=HuiDoQnHcNsE). The dataset is in google drive. A sample can be downloaded with `gsutil -m cp -r gs://gresearch/robotics/droid_100 <your_local_path>` and then loaded with `ds = tfds.load("droid_100", data_dir=<your_local_path>, split="train")` The full data is too large for a single compute to handle. Thus, I need split the dataset into smaller batches, save to local drive and reload each batch for processing. I found no save API in tfds, but I find tf.data.Dataset.save() and load() promising. Unfortunately, after saving and reloading the same dataset, I am getting errors (see relevant log output) ### Standalone code to reproduce the issue ```shell import tensorflow as tf import tensorflow_datasets as tfds data_dir = <your_local_path> ds = tfds.load("droid_100", data_dir=data_dir, split="train").take(1) ds.save("/tmp/copycat") ds2 = tf.data.Dataset.load('/tmp/copycat') for x in ds2: pass ``` ### Relevant log output ```shell 2024-06-12 07:09:42.851470: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. 2024-06-12 07:09:43.816148: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT 2024-06-12 07:09:44.657802: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform. Skipping registering GPU devices... 2024-06-12 07:09:45.022935: E tensorflow/core/framework/dataset.cc:109] The Encode() method is not implemented for DatasetVariantWrapper objects. 2024-06-12 07:09:45.150111: E tensorflow/core/framework/variant.cc:100] Could not decode variant with type_name: "tensorflow::DatasetVariantWrapper". Perhaps you forgot to register a decoder via REGISTER_UNARY_VARIANT_DECODE_FUNCTION? 2024-06-12 07:09:45.150452: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: DATA_LOSS: Unable to parse tensor from stored proto in file: /tmp/copycat/8099738303123461199/00000000.shard/00000000.snapshot, record 307. TensorProto: dtype: DT_VARIANT tensor_shape { } tensor_content: "#\n!tensorflow::DatasetVariantWrapper" Traceback (most recent call last): File "/home/meiyang/src/ceprojects/magma/error.py", line 7, in <module> for x in ds2: File "/home/meiyang/miniconda3/envs/magma/lib/python3.10/site-packages/tensorflow/python/data/ops/iterator_ops.py", line 809, in __next__ return self._next_internal() File "/home/meiyang/miniconda3/envs/magma/lib/python3.10/site-packages/tensorflow/python/data/ops/iterator_ops.py", line 772, in _next_internal ret = gen_dataset_ops.iterator_get_next( File "/home/meiyang/miniconda3/envs/magma/lib/python3.10/site-packages/tensorflow/python/ops/gen_dataset_ops.py", line 3086, in iterator_get_next _ops.raise_from_not_ok_status(e, name) File "/home/meiyang/miniconda3/envs/magma/lib/python3.10/site-packages/tensorflow/python/framework/ops.py", line 5983, in raise_from_not_ok_status raise core._status_to_exception(e) from None # pylint: disable=protected-access tensorflow.python.framework.errors_impl.DataLossError: {{function_node __wrapped__IteratorGetNext_output_types_3_device_/job:localhost/replica:0/task:0/device:CPU:0}} Unable to parse tensor from stored proto in file: /tmp/copycat/8099738303123461199/00000000.shard/00000000.snapshot, record 307. TensorProto: dtype: DT_VARIANT tensor_shape { } tensor_content: "#\n!tensorflow::DatasetVariantWrapper" [Op:IteratorGetNext] name: ```
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PR #13411: [XLA:GPU][Allow cuda async allocator to use non-default pool
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2024-06-12T07:08:35
2024-06-12T10:22:52
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PR #13411: [XLA:GPU][Allow cuda async allocator to use non-default pool Imported from GitHub PR https://github.com/openxla/xla/pull/13411 This patch allows user to construct the GpuCudaMallocAsyncAllocator to use the non-default memory pool, this is required if we want the allocator to just allocate for a particular memory space allocations, without the interference allocation requests from other memory space. E.g, using a separate memory pool for XLA temp buffers will make the allocated temp buffer address stabled. Copybara import of the project: -- 6a0fe3dd36790fc11eeb23cb954ac53435ed8897 by Shawn Wang <shawnw@nvidia.com>: Allow cuda async allocator to use non-default pool Merging this change closes #13411 FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13411 from shawnwang18:shawnw/cuda_async_allocator_with_new_pool 6a0fe3dd36790fc11eeb23cb954ac53435ed8897
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Integrate LLVM at llvm/llvm-project@3af35251c8cd
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Integrate LLVM at llvm/llvm-project@3af35251c8cd Updates LLVM usage to match [3af35251c8cd](https://github.com/llvm/llvm-project/commit/3af35251c8cd)
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Remove the deprecated PjRtClient::LookupAddressableDevice().
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2024-06-12T07:02:44
2024-06-12T07:02:45
null
NONE
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Remove the deprecated PjRtClient::LookupAddressableDevice().
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Reverts c2e7e9f6c3f4d4937d8145f988ea74818e000ecc
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2024-06-12T03:35:32
2024-06-12T04:17:30
2024-06-12T04:17:30
NONE
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Reverts c2e7e9f6c3f4d4937d8145f988ea74818e000ecc
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[tsl:concurrency] Update AsyncValueRef documentation and add more implicit constructors
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2024-06-12T01:49:00
2024-06-12T07:03:06
2024-06-12T07:03:05
NONE
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[tsl:concurrency] Update AsyncValueRef documentation and add more implicit constructors Explicitly state in AsyncValueRef<T> documentation that it's an async version of absl::StatusOr<T> and that's how users should think about it. Also add a test to verify that absl::StatusOr<absl::Status> and AsyncValueRef<absl::Status> have consistent implicit construction behavior.
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Update the delegates to support int32 tensor upload and update parser to map the inputs axis with the kernel inputs.
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2024-06-12T01:25:41
2024-06-12T01:25:41
null
NONE
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Update the delegates to support int32 tensor upload and update parser to map the inputs axis with the kernel inputs. Reverts changelist 641306427
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Migrate ConvertMlirToGraph translates version without control ret nodes to tf2xla's version of ConvertMlirToGraph with control ret nodes. This is to consolidate & simplify API's. Translates version without control ret nodes is marked deprecated.
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2024-06-12T01:03:05
2024-06-12T01:03:06
null
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Migrate ConvertMlirToGraph translates version without control ret nodes to tf2xla's version of ConvertMlirToGraph with control ret nodes. This is to consolidate & simplify API's. Translates version without control ret nodes is marked deprecated.
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Reverts changelist 641306427
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2024-06-12T00:52:59
2024-06-12T01:24:57
2024-06-12T01:24:56
NONE
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Reverts changelist 641306427
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69,566
Add XLA:CPU module lines to the host trace.
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2024-06-12T00:35:58
2024-06-12T04:25:25
null
NONE
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Add XLA:CPU module lines to the host trace.
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1. Simplify a redundant code snippet
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2024-06-12T00:10:00
2024-06-12T02:32:11
2024-06-12T02:32:10
NONE
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1. Simplify a redundant code snippet 2. Print out a log message when we encounter a custom call target we have not handled.
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Reverts 49f4d9052259ae562ad0b0b84b5ba759494e6f83
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2024-06-11T22:41:13
2024-06-11T22:41:13
null
NONE
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Reverts 49f4d9052259ae562ad0b0b84b5ba759494e6f83
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69,563
Add 'decompose_optionals' pass.
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2024-06-11T21:54:33
2024-06-11T22:50:13
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Add 'decompose_optionals' pass.
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Use tensorboard nightly 2.18.0a
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2024-06-11T21:50:09
2024-06-11T21:50:10
null
NONE
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Use tensorboard nightly 2.18.0a
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Fix accounting for multiple "trivial" users of nested fusion in FusionParameterReadBytes.
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2024-06-11T21:45:44
2024-06-12T01:58:56
2024-06-12T01:58:54
NONE
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Fix accounting for multiple "trivial" users of nested fusion in FusionParameterReadBytes.
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69,560
Bump minSdkVersion of TFL Android libs to 21.
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2024-06-11T21:37:31
2024-06-11T22:09:26
2024-06-11T22:09:26
NONE
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Bump minSdkVersion of TFL Android libs to 21.
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PR_kwDOArmXAs5yKSVZ
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Internal BUILD file changes.
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2024-06-11T21:18:40
2024-06-12T06:43:42
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Internal BUILD file changes.
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[xla:gpu] Move the code that add SPMD pipeline to a utility function.
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2024-06-11T21:16:04
2024-06-11T21:16:05
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[xla:gpu] Move the code that add SPMD pipeline to a utility function. Also add a test for the GPU SPMD pipeline.
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2024-06-11T21:15:51
2024-06-12T00:18:31
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Integrate LLVM at llvm/llvm-project@3af35251c8cd Updates LLVM usage to match [3af35251c8cd](https://github.com/llvm/llvm-project/commit/3af35251c8cd)
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2024-06-11T20:31:32
2024-06-12T04:25:14
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[xla:cpu] Add proper error handling to ThunkExecutor
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2024-06-11T20:29:42
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[xla:cpu] Add error handling to async host kernel launch
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[tsl:concurrency] NFC: Remove KeepAsyncValuePayloadOnError alias
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2024-06-11T20:25:33
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[tsl:concurrency] NFC: Remove KeepAsyncValuePayloadOnError alias
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PR_kwDOArmXAs5yJu0F
69,548
#tf-data Turn up `map_fusion` experiment to 50% task-level.
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2024-06-11T19:50:37
2024-06-11T21:15:39
2024-06-11T21:15:39
NONE
null
true
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#tf-data Turn up `map_fusion` experiment to 50% task-level.
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https://github.com/tensorflow/tensorflow/pull/69547
2,347,119,502
PR_kwDOArmXAs5yJj0S
69,547
Move XplaneEventMutator and XplaneEventMutatorFactory to a separate file.
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2024-06-11T19:18:12
2024-06-11T20:33:22
2024-06-11T20:33:21
NONE
null
true
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Move XplaneEventMutator and XplaneEventMutatorFactory to a separate file. This would simplify the preprocess_xplane.h and would allow for each mutator to be in it's own header.
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