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This is the first step in splitting TritonSupport into legacy and current infra.
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```
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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"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."
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The thread pool was missing for example during thunks runtime convolution tests.
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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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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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"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 | null | null | ### 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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} | 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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Updates LLVM usage to match
[c012e487b724](https://github.com/llvm/llvm-project/commit/c012e487b724)
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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
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
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
ParseTextProto is already used in two tests.
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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
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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] | null | [] | "2024-06-12T07:21:42" | "2024-06-12T07:21:47" | null | NONE | null | null | null | ### 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
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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Updates LLVM usage to match
[3af35251c8cd](https://github.com/llvm/llvm-project/commit/3af35251c8cd)
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Also add a test to verify that absl::StatusOr<absl::Status> and AsyncValueRef<absl::Status> have consistent implicit construction behavior.
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Reverts changelist 641306427
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Updates LLVM usage to match
[3af35251c8cd](https://github.com/llvm/llvm-project/commit/3af35251c8cd)
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