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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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This would simplify the preprocess_xplane.h and would allow for each mutator to be in it's own header.
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] | null | [] | "2024-06-11T17:39:05" | "2024-06-11T17:39:09" | null | NONE | null | null | null | ### Issue type
Support
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
TF 2.11
### Custom code
No
### OS platform and distribution
Linux Rhel
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Is there a utility method to batch sparse tensors together? Tensorflow Serving Batching support utilizes the DenseTensor Concat op - https://github.com/tensorflow/serving/blob/67a2dcb2b9c057847616fb5ce54cb8545955d144/tensorflow_serving/batching/batching_session.cc#L577
Is there an equivalent for SparseTensor batching?
### Standalone code to reproduce the issue
```shell
None
```
### Relevant log output
```shell
None
```
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} | PR #13477: Add SetupDerivedInstruction for whileop in spmd partitioner
Imported from GitHub PR https://github.com/openxla/xla/pull/13477
This is a minor change. It calls the SetupDerivedInstruction for while operation to make sure attributes are propagated properly.
Copybara import of the project:
--
9563c00004bc2a988d01964f213e5dc4dc535489 by Shraiysh Vaishay <svaishay@nvidia.com>:
Add SetupDerivedInstruction for whileop in spmd partitioner
Merging this change closes #13477
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13477 from shraiysh:fix-while-loop-derived-instruction 9563c00004bc2a988d01964f213e5dc4dc535489
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This better utilizes recent optimizations to collective approximation in the XLA latency hiding scheduler.
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} | [XLA:GPU] Better approximation for costly AR in LHS.
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} | [XLA:GPU] Initial simplification of Triton Support test.
This is a first change that demonstrates my intention for simplifying the Triton support tests. Once we agree on the general direction, I will submit additional CLs to simplify all tests.
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Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
latest
### Custom code
No
### OS platform and distribution
AWS m5a.xlarge: CPU 4, Memory 16, Linux Ubuntu 24.04 LTS
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Hi there,
I'm trying to reduce TensorFlow Lite binary size, [selectively building TensorFlow Lite with Docker using the guide](https://www.tensorflow.org/lite/guide/reduce_binary_size#selectively_build_tensorflow_lite_with_docker).
I have the same error log as in [the issue](https://github.com/tensorflow/tensorflow/issues/61101#issuecomment-1613075866), which was closed due to inactivity, and I'm not sure how to proceed.
### Standalone code to reproduce the issue
```shell
1. I downloaded the file: `https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/lite/tools/tflite-android.Dockerfile `
2. run: ` docker build . -t tflite-builder -f tflite-android.Dockerfile`
3. Downloaded the script for building TensorFlow with Docker by running:
curl -o build_aar_with_docker.sh \
https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/lite/tools/build_aar_with_docker.sh &&
chmod +x build_aar_with_docker.sh
```
4. Downloaded the TensorFlow Git repository and stay on the master branch `https://github.com/tensorflow/tensorflow.git`
5. Run the build_aar_with_docker.sh script with params (check log output)
```
### Relevant log output
```shell
ubuntu:~/tflite$ ./build_aar_with_docker.sh \
--input_models=/home/ubuntu/tflite/models/model.tflite \
--target_archs=x86,x86_64,arm64-v8a,armeabi-v7a \
--src_dir=/home/ubuntu/tflite/tensorflow
+++ dirname ./build_aar_with_docker.sh
++ cd .
++ pwd
+ SCRIPT_DIR=/home/ubuntu/tflite
+ ARGUMENTS='--input_models=/home/ubuntu/tflite/models/model.tflite --target_archs=x86,x86_64,arm64-v8a,armeabi-v7a --src_dir=/home/ubuntu/tflite/tensorflow'
+ BUILD_FLAGS=
+ TARGET_ARCHS=x86,x86_64,arm64-v8a,armeabi-v7a
+ FLAG_SRC_DIR=
+ '[' 3 -gt 5 ']'
+ for i in "$@"
+ case $i in
+ FLAG_MODELS=/home/ubuntu/tflite/models/model.tflite
+ BUILD_FLAGS=' --input_models=/home/ubuntu/tflite/models/model.tflite'
+ shift
+ for i in "$@"
+ case $i in
+ TARGET_ARCHS=x86,x86_64,arm64-v8a,armeabi-v7a
+ BUILD_FLAGS=' --input_models=/home/ubuntu/tflite/models/model.tflite --target_archs=x86,x86_64,arm64-v8a,armeabi-v7a'
+ shift
+ for i in "$@"
+ case $i in
+ FLAG_SRC_DIR=/home/ubuntu/tflite/tensorflow
+ shift
+ '[' '!' -d /tensorflow_src ']'
++ echo /home/ubuntu/tflite/models/model.tflite
++ sed 's/,/ /g'
+ for model in $(echo ${FLAG_MODELS} | sed "s/,/ /g")
+ FLAG_DIR=' -v /home/ubuntu/tflite/models/model.tflite:/home/ubuntu/tflite/models/model.tflite'
+ '[' -z ']'
+ mkdir -p bazel-build-cache
+ BAZEL_CACHE_DIR=/home/ubuntu/tflite/bazel-build-cache
+ ARGUMENTS='--input_models=/home/ubuntu/tflite/models/model.tflite --target_archs=x86,x86_64,arm64-v8a,armeabi-v7a --src_dir=/home/ubuntu/tflite/tensorflow --cache_dir=/home/ubuntu/tflite/bazel-build-cache'
+ FLAG_DIR=' -v /home/ubuntu/tflite/models/model.tflite:/home/ubuntu/tflite/models/model.tflite -v /home/ubuntu/tflite/bazel-build-cache:/home/ubuntu/tflite/bazel-build-cache'
+ docker run --rm -it -v /home/ubuntu/tflite/tensorflow:/tensorflow_src -v /home/ubuntu/tflite:/host_dir -v /home/ubuntu/tflite:/script_dir -v /home/ubuntu/tflite/models/model.tflite:/home/ubuntu/tflite/models/model.tflite -v /home/ubuntu/tflite/bazel-build-cache:/home/ubuntu/tflite/bazel-build-cache --entrypoint /script_dir/build_aar_with_docker.sh tflite-builder --input_models=/home/ubuntu/tflite/models/model.tflite --target_archs=x86,x86_64,arm64-v8a,armeabi-v7a --src_dir=/home/ubuntu/tflite/tensorflow --cache_dir=/home/ubuntu/tflite/bazel-build-cache
+++ dirname /script_dir/build_aar_with_docker.sh
++ cd /script_dir
++ pwd
+ SCRIPT_DIR=/script_dir
+ ARGUMENTS='--input_models=/home/ubuntu/tflite/models/model.tflite --target_archs=x86,x86_64,arm64-v8a,armeabi-v7a --src_dir=/home/ubuntu/tflite/tensorflow --cache_dir=/home/ubuntu/tflite/bazel-build-cache'
+ BUILD_FLAGS=
+ TARGET_ARCHS=x86,x86_64,arm64-v8a,armeabi-v7a
+ FLAG_SRC_DIR=
+ '[' 4 -gt 5 ']'
+ for i in "$@"
+ case $i in
+ FLAG_MODELS=/home/ubuntu/tflite/models/model.tflite
+ BUILD_FLAGS=' --input_models=/home/ubuntu/tflite/models/model.tflite'
+ shift
+ for i in "$@"
+ case $i in
+ TARGET_ARCHS=x86,x86_64,arm64-v8a,armeabi-v7a
+ BUILD_FLAGS=' --input_models=/home/ubuntu/tflite/models/model.tflite --target_archs=x86,x86_64,arm64-v8a,armeabi-v7a'
+ shift
+ for i in "$@"
+ case $i in
+ FLAG_SRC_DIR=/home/ubuntu/tflite/tensorflow
+ shift
+ for i in "$@"
+ case $i in
+ BAZEL_CACHE_DIR=/home/ubuntu/tflite/bazel-build-cache
+ shift
+ '[' '!' -d /tensorflow_src ']'
+ sdkmanager --licenses
6 of 6 SDK package licenses not accepted. 100% Computing updates...
Review licenses that have not been accepted (y/N)? N
+ sdkmanager 'build-tools;31.0.0' platform-tools 'platforms;android-30'
License android-sdk-license: ] 10% Computing updates...
---------------------------------------
Terms and Conditions
This is the Android Software Development Kit License Agreement
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1.2 "Android" means the Android software stack for devices, as made available under the Android Open Source Project, which is located at the following URL: http://source.android.com/, as updated from time to time.
1.3 A "compatible implementation" means any Android device that (i) complies with the Android Compatibility Definition document, which can be found at the Android compatibility website (http://source.android.com/compatibility) and which may be updated from time to time; and (ii) successfully passes the Android Compatibility Test Suite (CTS).
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2.2 By clicking to accept, you hereby agree to the terms of the License Agreement.
2.3 You may not use the SDK and may not accept the License Agreement if you are a person barred from receiving the SDK under the laws of the United States or other countries, including the country in which you are resident or from which you use the SDK.
2.4 If you are agreeing to be bound by the License Agreement on behalf of your employer or other entity, you represent and warrant that you have full legal authority to bind your employer or such entity to the License Agreement. If you do not have the requisite authority, you may not accept the License Agreement or use the SDK on behalf of your employer or other entity.
4. SDK License from Google
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3.2 You may not use this SDK to develop applications for other platforms (including non-compatible implementations of Android) or to develop another SDK. You are of course free to develop applications for other platforms, including non-compatible implementations of Android, provided that this SDK is not used for that purpose.
3.3 You agree that Google or third parties own all legal right, title and interest in and to the SDK, including any Intellectual Property Rights that subsist in the SDK. "Intellectual Property Rights" means any and all rights under patent law, copyright law, trade secret law, trademark law, and any and all other proprietary rights. Google reserves all rights not expressly granted to you.
3.4 You may not use the SDK for any purpose not expressly permitted by the License Agreement. Except to the extent required by applicable third party licenses, you may not copy (except for backup purposes), modify, adapt, redistribute, decompile, reverse engineer, disassemble, or create derivative works of the SDK or any part of the SDK.
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4.3 You agree that if you use the SDK to develop applications for general public users, you will protect the privacy and legal rights of those users. If the users provide you with user names, passwords, or other login information or personal information, you must make the users aware that the information will be available to your application, and you must provide legally adequate privacy notice and protection for those users. If your application stores personal or sensitive information provided by users, it must do so securely. If the user provides your application with Google Account information, your application may only use that information to access the user's Google Account when, and for the limited purposes for which, the user has given you permission to do so.
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January 16, 2019
---------------------------------------
Accept? (y/N): y
[=======================================] 100% Unzipping... android-11/framework
+ cd /tensorflow_src
+ configs=('/usr/bin/python3' '/usr/lib/python3/dist-packages' 'N' 'N' 'Y' '/usr/lib/llvm-17/bin/clang' '-Wno-sign-compare -Wno-c++20-designator -Wno-gnu-inline-cpp-without-extern' 'y' '/android/sdk')
+ printf '%s\n' /usr/bin/python3 /usr/lib/python3/dist-packages N N Y /usr/lib/llvm-17/bin/clang '-Wno-sign-compare -Wno-c++20-designator -Wno-gnu-inline-cpp-without-extern' y /android/sdk
+ ./configure
You have bazel 6.5.0 installed.
Please specify the location of python. [Default is /usr/bin/python3]:
Found possible Python library paths:
/usr/lib/python3/dist-packages
/usr/local/lib/python3.11/dist-packages
Please input the desired Python library path to use. Default is [/usr/lib/python3/dist-packages]
Do you wish to build TensorFlow with ROCm support? [y/N]: No ROCm support will be enabled for TensorFlow.
Do you wish to build TensorFlow with CUDA support? [y/N]: No CUDA support will be enabled for TensorFlow.
Do you want to use Clang to build TensorFlow? [Y/n]: Clang will be used to compile TensorFlow.
Please specify the path to clang executable. [Default is /usr/lib/llvm-17/bin/clang]:
You have Clang 17.0.6 installed.
Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -Wno-sign-compare]:
Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: Searching for NDK and SDK installations.
Preconfigured Bazel build configs. You can use any of the below by adding "--config=<>" to your build command. See .bazelrc for more details.
--config=mkl # Build with MKL support.
--config=mkl_aarch64 # Build with oneDNN and Compute Library for the Arm Architecture (ACL).
--config=monolithic # Config for mostly static monolithic build.
--config=numa # Build with NUMA support.
--config=dynamic_kernels # (Experimental) Build kernels into separate shared objects.
--config=v1 # Build with TensorFlow 1 API instead of TF 2 API.
Preconfigured Bazel build configs to DISABLE default on features:
--config=nogcp # Disable GCP support.
--config=nonccl # Disable NVIDIA NCCL support.
Configuration finished
+ source tensorflow/tools/ci_build/release/common.sh
++ LATEST_BAZEL_VERSION=6.5.0
++ MAC_CPU_MAX_WHL_SIZE=240M
++ WIN_CPU_MAX_WHL_SIZE=170M
++ WIN_GPU_MAX_WHL_SIZE=360M
+ install_bazelisk
+ date
Tue Jun 11 14:14:34 UTC 2024
+ case "$(uname -s)" in
++ uname -s
+ case "$(uname -m)" in
++ uname -m
+ local name=bazelisk-linux-amd64
+ mkdir -p /root/bin
+ wget --no-verbose -O /root/bin/bazel https://github.com/bazelbuild/bazelisk/releases/download/v1.11.0/bazelisk-linux-amd64
2024-06-11 14:14:35 URL:https://objects.githubusercontent.com/github-production-release-asset-2e65be/149661467/ac07a1b6-24bb-4857-868a-4a1816401ade?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=releaseassetproduction%2F20240611%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240611T141434Z&X-Amz-Expires=300&X-Amz-Signature=bc61841b4193c4e31a6710e370097aef186bbef0875fb541c3dd7c71e3449069&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=149661467&response-content-disposition=attachment%3B%20filename%3Dbazelisk-linux-amd64&response-content-type=application%2Foctet-stream [5152768/5152768] -> "/root/bin/bazel" [1]
+ chmod u+x /root/bin/bazel
+ [[ ! :/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/android/sdk/cmdline-tools/latest/bin:/android/sdk/platform-tools:/android/ndk: =~ :/root/bin/?: ]]
+ PATH=/root/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/android/sdk/cmdline-tools/latest/bin:/android/sdk/platform-tools:/android/ndk
+ set_bazel_outdir
+ mkdir -p /tmpfs/bazel_output
+ export TEST_TMPDIR=/tmpfs/bazel_output
+ TEST_TMPDIR=/tmpfs/bazel_output
+ which bazel
/root/bin/bazel
+ bazel version
Bazelisk version: v1.11.0
$TEST_TMPDIR defined: output root default is '/tmpfs/bazel_output' and max_idle_secs default is '15'.
Extracting Bazel installation...
Starting local Bazel server and connecting to it...
Build label: 6.5.0
Build target: bazel-out/k8-opt/bin/src/main/java/com/google/devtools/build/lib/bazel/BazelServer_deploy.jar
Build time: Tue Jan 23 16:44:21 2024 (1706028261)
Build timestamp: 1706028261
Build timestamp as int: 1706028261
+ date
Tue Jun 11 14:14:40 UTC 2024
+ export BAZEL_CACHE_DIR=/home/ubuntu/tflite/bazel-build-cache
+ BAZEL_CACHE_DIR=/home/ubuntu/tflite/bazel-build-cache
+ export OMIT_PRINTING_OUTPUT_PATHS=YES
+ OMIT_PRINTING_OUTPUT_PATHS=YES
+ '[' '' = true ']'
+ bash /tensorflow_src/tensorflow/lite/tools/build_aar.sh --input_models=/home/ubuntu/tflite/models/model.tflite --target_archs=x86,x86_64,arm64-v8a,armeabi-v7a
+++ dirname /tensorflow_src/tensorflow/lite/tools/build_aar.sh
++ cd /tensorflow_src/tensorflow/lite/tools
++ pwd
+ SCRIPT_DIR=/tensorflow_src/tensorflow/lite/tools
++ cd /tensorflow_src/tensorflow/lite/tools/../../../
++ pwd
+ ROOT_DIR=/tensorflow_src
+ TARGET_ARCHS=x86,x86_64,arm64-v8a,armeabi-v7a
+ '[' '!' -z /home/ubuntu/tflite/bazel-build-cache ']'
+ CACHE_DIR_FLAG=--output_user_root=/home/ubuntu/tflite/bazel-build-cache/cache
+ '[' 2 -gt 4 ']'
+ for i in "$@"
+ case $i in
+ FLAG_MODELS=/home/ubuntu/tflite/models/model.tflite
+ shift
+ for i in "$@"
+ case $i in
+ TARGET_ARCHS=x86,x86_64,arm64-v8a,armeabi-v7a
+ shift
+ cd /tensorflow_src
+ '[' '!' -f /tensorflow_src/.tf_configure.bazelrc ']'
+ grep -q ANDROID_SDK_HOME /tensorflow_src/.tf_configure.bazelrc
+ '[' -z /home/ubuntu/tflite/models/model.tflite ']'
+ TMP_DIR=/tensorflow_src/tmp/
+ rm -rf /tensorflow_src/tmp/
+ mkdir -p /tensorflow_src/tmp/
+ MODEL_NAMES=
++ echo /home/ubuntu/tflite/models/model.tflite
++ sed 's/,/ /g'
+ for model in $(echo ${FLAG_MODELS} | sed "s/,/ /g")
+ cp /home/ubuntu/tflite/models/model.tflite /tensorflow_src/tmp/
++ basename /home/ubuntu/tflite/models/model.tflite
+ MODEL_NAMES=,model.tflite
+ TFLITE_OPS_SRCS=
++ echo
++ sed 's/,/ /g'
+ generate_tflite_aar
+ pushd /tensorflow_src/tmp/
+ message=('load("//tensorflow/lite:build_def.bzl", "tflite_custom_android_library")' 'load("//tensorflow/lite/java:aar_with_jni.bzl", "aar_with_jni")' '' 'tflite_custom_android_library(' ' name = "custom_tensorflowlite",')
+ message+=(' '$(generate_list_field "models" $MODEL_NAMES))
++ generate_list_field models ,model.tflite
++ local name=models
++ local list_string=,model.tflite
++ list=('model.tflite')
++ local list
++ message=('models=[')
++ local message
++ for item in "${list[@]}"
++ message+=("\"$item\",")
++ message+=('],')
++ printf %s 'models=[' '"model.tflite",' '],'
+ message+=(' '$(generate_list_field "srcs" $TFLITE_OPS_SRCS))
++ generate_list_field srcs
++ local name=srcs
++ local list_string=
++ list=()
++ local list
++ message=('srcs=[')
++ local message
++ message+=('],')
++ printf %s 'srcs=[' '],'
+ message+=(' '$(generate_list_field "deps" $FLAG_TFLITE_OPS_DEPS))
++ generate_list_field deps
++ local name=deps
++ local list_string=
++ list=()
++ local list
++ message=('deps=[')
++ local message
++ message+=('],')
++ printf %s 'deps=[' '],'
+ message+=(')' '' 'aar_with_jni(' ' name = "tensorflow-lite",' ' android_library = ":custom_tensorflowlite",' ')' '')
+ printf '%s\n' 'load("//tensorflow/lite:build_def.bzl", "tflite_custom_android_library")' 'load("//tensorflow/lite/java:aar_with_jni.bzl", "aar_with_jni")' '' 'tflite_custom_android_library(' ' name = "custom_tensorflowlite",' ' models=["model.tflite",],' ' srcs=[],' ' deps=[],' ')' '' 'aar_with_jni(' ' name = "tensorflow-lite",' ' android_library = ":custom_tensorflowlite",' ')' ''
+ popd
+ bazel --output_user_root=/home/ubuntu/tflite/bazel-build-cache/cache build -c opt --config=opt --cxxopt=--std=c++17 --fat_apk_cpu=x86,x86_64,arm64-v8a,armeabi-v7a --define=android_dexmerger_tool=d8_dexmerger --define=android_incremental_dexing_tool=d8_dexbuilder --host_crosstool_top=@bazel_tools//tools/cpp:toolchain //tmp:tensorflow-lite
$TEST_TMPDIR defined: output root default is '/tmpfs/bazel_output' and max_idle_secs default is '15'.
Starting local Bazel server and connecting to it...
INFO: Reading 'startup' options from /tensorflow_src/.bazelrc: --windows_enable_symlinks
INFO: Options provided by the client:
Inherited 'common' options: --isatty=1 --terminal_columns=127
INFO: Reading rc options for 'build' from /tensorflow_src/.bazelrc:
Inherited 'common' options: --experimental_repo_remote_exec
INFO: Reading rc options for 'build' from /etc/bazel.bazelrc:
'build' options: --action_env=DOCKER_CACHEBUSTER=1717892676285195056 --host_action_env=DOCKER_HOST_CACHEBUSTER=1717892676351672658
INFO: Reading rc options for 'build' from /tensorflow_src/.bazelrc:
'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --features=-force_no_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility
INFO: Reading rc options for 'build' from /tensorflow_src/.tf_configure.bazelrc:
'build' options: --action_env PYTHON_BIN_PATH=/usr/bin/python3 --action_env PYTHON_LIB_PATH=/usr/lib/python3/dist-packages --python_path=/usr/bin/python3 --action_env CLANG_COMPILER_PATH=/usr/lib/llvm-17/bin/clang --repo_env=CC=/usr/lib/llvm-17/bin/clang --repo_env=BAZEL_COMPILER=/usr/lib/llvm-17/bin/clang --copt=-Wno-gnu-offsetof-extensions --action_env ANDROID_NDK_HOME=/android/ndk --action_env ANDROID_NDK_VERSION=25 --action_env ANDROID_NDK_API_LEVEL=30 --action_env ANDROID_BUILD_TOOLS_VERSION=31.0.0 --action_env ANDROID_SDK_API_LEVEL=30 --action_env ANDROID_SDK_HOME=/android/sdk
INFO: Found applicable config definition build:short_logs in file /tensorflow_src/.bazelrc: --output_filter=DONT_MATCH_ANYTHING
INFO: Found applicable config definition build:v2 in file /tensorflow_src/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1
INFO: Found applicable config definition build:opt in file /tensorflow_src/.tf_configure.bazelrc: --copt=-Wno-sign-compare --host_copt=-Wno-sign-compare --copt=-Wno-c++20-designator --host_copt=-Wno-c++20-designator --copt=-Wno-gnu-inline-cpp-without-extern --host_copt=-Wno-gnu-inline-cpp-without-extern
INFO: Found applicable config definition build:linux in file /tensorflow_src/.bazelrc: --host_copt=-w --copt=-Wno-all --copt=-Wno-extra --copt=-Wno-deprecated --copt=-Wno-deprecated-declarations --copt=-Wno-ignored-attributes --copt=-Wno-array-bounds --copt=-Wunused-result --copt=-Werror=unused-result --copt=-Wswitch --copt=-Werror=switch --copt=-Wno-error=unused-but-set-variable --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --config=dynamic_kernels --experimental_guard_against_concurrent_changes
INFO: Found applicable config definition build:dynamic_kernels in file /tensorflow_src/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_KERNELS
INFO: Analyzed target //tmp:tensorflow-lite (160 packages loaded, 15854 targets configured).
INFO: Found 1 target...
ERROR: /tensorflow_src/tmp/BUILD:4:30: gen_selected_ops failed: (Segmentation fault): generate_op_registrations failed: error executing command (from target //tmp:custom_tensorflowlite_cc_registration) bazel-out/k8-opt-exec-50AE0418/bin/tensorflow/lite/tools/generate_op_registrations '--namespace=' ... (remaining 3 arguments skipped)
Target //tmp:tensorflow-lite failed to build
Use --verbose_failures to see the command lines of failed build steps.
INFO: Elapsed time: 20.698s, Critical Path: 0.75s
INFO: 2 processes: 2 internal.
FAILED: Build did NOT complete successfully
```
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} | [XLA:GPU] Allow Triton emitter to handle fp8 matmuls.
At this point, the fp8 types don't reach the emitter, so in this sense it's a noop for now.
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Updates LLVM usage to match
[8c5d9c79b96e](https://github.com/llvm/llvm-project/commit/8c5d9c79b96e)
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Trying to parse RunHloModuleLiterals may already add one iteration. So we need
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FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13639 from philipphack:u_layer_uaf_xla 91ebf7b4a2ac90ebadce27d1a73e88fb4513aed4
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FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13108 from openxla:parallel_autotuning 22201078fadf807dbe00b0f4c3db83f9f015999f
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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:
--
6c366cf77a749f10c9e23a52e4ee7b16359baaf8 by Ilia Sergachev <isergachev@nvidia.com>:
[GPU] Let constants be emitted into a separate LLVM module.
--
a164cd1214fecd70e6538c8995d0a7318264a439 by Ilia Sergachev <isergachev@nvidia.com>:
[GPU] Add on-disk per-kernel compilation cache.
--
7808613eb73dda37f15b69a82ca0617613cd98ce by Ilia Sergachev <isergachev@nvidia.com>:
Skip kernel cache tests when linking is not available.
--
89694a031c441605f45b36fc606a1b6e6a351492 by Ilia Sergachev <isergachev@nvidia.com>:
Address feedback
--
cf8ddb3f41413d4fb8c7440cf3b10d2100a89bf4 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 cf8ddb3f41413d4fb8c7440cf3b10d2100a89bf4
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} | PR #13108: [GPU] Shard GEMM fusion autotuning across multiple compilation processes.
Imported from GitHub PR https://github.com/openxla/xla/pull/13108
This solves the problem of compiler processes compiling different programs due to fluctuations of measurements during autotuning and in addition makes some cases faster (depending on the number of fusions and the availability of CPU cores).
Copybara import of the project:
--
73a35a3dc21435061f86e53c02418cc69097e57e by Ilia Sergachev <isergachev@nvidia.com>:
[GPU] Shard GEMM fusion autotuning across compiler processes.
--
22201078fadf807dbe00b0f4c3db83f9f015999f by Ilia Sergachev <isergachev@nvidia.com>:
[NFC] Refactor CreateGpuClient().
Merging this change closes #13108
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13108 from openxla:parallel_autotuning 22201078fadf807dbe00b0f4c3db83f9f015999f
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} | PR #13502: [ROCm] Fix collective ops test
Imported from GitHub PR https://github.com/openxla/xla/pull/13502
After https://github.com/openxla/xla/commit/2431013a04ffa3526906bdea49e3423ccc2596dd VerifyChannels would, in the event of multiple Send/Recv instructions in the same channel, check for frontend attributes of SendDone/RecvDone instructions as well. This caused multiple subtests to fail, so I added the needed attributes.
Copybara import of the project:
--
63fe1eddc25676707d5486f61a3bac2246fe1693 by mmakevic <Milica.Makevic@amd.com>:
Fix collective ops test
Merging this change closes #13502
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13502 from ROCm:ci_collective_ops_test_240607 63fe1eddc25676707d5486f61a3bac2246fe1693
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} | PR #13486: [ROCm] Add AMD_SERIALIZE_KERNEL environment for gpu_offloading_test
Imported from GitHub PR https://github.com/openxla/xla/pull/13486
This ensures kernel lauch are serialized.
Copybara import of the project:
--
7e286c1ff889f27bfffdeeac7ecb1e591bc6ebe8 by Harsha HS <Harsha.HavanurShamsundara@amd.com>:
[ROCm] Add AMD_SERIALIZE_KERNEL environment for gpu_offloading_test
This ensures kernel lauch are serialized.
Merging this change closes #13486
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13486 from ROCm:ci_amd_serialize_kernel_20240606 7e286c1ff889f27bfffdeeac7ecb1e591bc6ebe8
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} | PR #13491: [ROCm] Fix profiler deadlock due to 3d7a4720b02e643faccd13f9a450213b5…
Imported from GitHub PR https://github.com/openxla/xla/pull/13491
…dfe17b7
Copybara import of the project:
--
5e584a13c226de0707dccf763501fccbf37dcc32 by Dragan Mladjenovic <Dragan.Mladjenovic@amd.com>:
[ROCm] Fix profiler deadlock due to 3d7a4720b02e643faccd13f9a450213b5dfe17b7
Merging this change closes #13491
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13491 from ROCm:rocm_profiler_deadlock 5e584a13c226de0707dccf763501fccbf37dcc32
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} | PR #13568: [GPU] Let constants be emitted into a separate LLVM module.
Imported from GitHub PR https://github.com/openxla/xla/pull/13568
Will not change the behavior of the compiler for now - the use of separate constant modules will be added in the next PR with on-disk kernel compilation cache: https://github.com/openxla/xla/pull/13569.
Copybara import of the project:
--
6c366cf77a749f10c9e23a52e4ee7b16359baaf8 by Ilia Sergachev <isergachev@nvidia.com>:
[GPU] Let constants be emitted into a separate LLVM module.
Merging this change closes #13568
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13568 from openxla:consts_module 6c366cf77a749f10c9e23a52e4ee7b16359baaf8
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} | Actually support the flag xla_dump_large_constants.
It was missing a flag declaration in debug_options_flags.cc
Add a test for the dumping tool to make sure the flag works.
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"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/69510\">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/69510\">No</a>\n"
] | "2024-06-11T06:57:11" | "2024-06-12T04:04:10" | "2024-06-12T04:04:07" | NONE | null | null | null | **System information**
Linux with tensorflow v2.16.1
**Standalone code to reproduce the issue**
I exported YOLOv5n integer quantized model from ultralytics.
I tested with python tflite code, and it worked fine.
But when I ported it to C++ tflite code, it did not work.
All the output score value is 0.
The output x,y,w,h value is not 0.
**Any other info / logs**
I attatched the exported YOLOv5n model.
https://1drv.ms/u/s!AnqHHtrBqwyUg8QHviF7T1KqPkXeog?e=c3fasi
The code I used it for C++ is as bellows.
```c++
std::unique_ptr<tflite::FlatBufferModel> model = tflite::FlatBufferModel::BuildFromFile("yolov5nu_integer_quant.tflite");
tflite::ops::builtin::BuiltinOpResolver resolver;
tflite::InterpreterBuilder builder(*model, resolver);
std::unique_ptr<tflite::Interpreter> interpreter;
builder(&interpreter);
interpreter->AllocateTensors();
TfLiteTensor* input_tensor_0 = interpreter->input_tensor(0);
TfLiteIntArray* input_dims = input_tensor_0->dims;
int input_height = input_dims->data[1];
int input_width = input_dims->data[2];
int input_channel = input_dims->data[3];
float * input_img_ptr = new float[input_height * input_width * input_channel];
memset(input_img_ptr, 0, input_height * input_width * input_channel * sizeof(float));
... //image decode and resize it as resize_img_ptr
for(int i = 0; i < resize_height; i++){
for(int j = 0; j < resize_width; j++){
int input_idx = i * input_width + j;
int resize_idx = i * resize_width + j;
input_img_ptr[3 * input_idx] = resize_img_ptr[3 * resize_idx] / 255.0;
input_img_ptr[3 * input_idx + 1] = resize_img_ptr[3 * resize_idx + 1] / 255.0;
input_img_ptr[3 * input_idx + 2] = resize_img_ptr[3 * resize_idx + 2] / 255.0;
}
}
memcpy(interpreter->typed_input_tensor<float>(0), input_img_ptr, input_height * input_width * input_channel * sizeof(float));
delete input_img_ptr;
interpreter->Invoke();
float * output = interpreter->typed_output_tensor<float>(0);
float (*output_arr)[output_width] = (float(*)[output_width])output;
TfLiteTensor* output_tensor_0 = interpreter->output_tensor(0);
TfLiteIntArray* output_dims = output_tensor_0->dims;
int output_height = output_dims->data[1];
int output_width = output_dims->data[2];
for(int i = 0; i < output_width; i++){
float x = output_arr[0][i];
float y = output_arr[1][i];
float w = output_arr[2][i];
float h = output_arr[3][i];
float xmin = (x - 0.5 * w) * input_width / scale_width ;
float ymin = (y - 0.5 * h) * input_height / scale_height ;
float width = w * input_width / scale_width;
float height = h * input_height / scale_height;
float max_score = 0;
int max_class_id = 0;
for(int j = 4; j < output_height; j++){
float score = output_arr[j][i];
int class_id = j - 4;
if(score > max_score){
max_score = score;
max_class_id = class_id;
}
std::cout << i << ", "<< j << ": class_id: " << class_id << ", score: " << score << ", xn: " << x << ", y: " << y << ", w: " << w << ", h: "<< h << std::endl;
}
```
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} | Add support for Pathways topology in GpuTopology.
This CL adds a new field to GpuTopologyProto to represent Pathways topology. The new field is a oneof field that can be either DefaultTopologyProto or PathwaysTopologyProto. The DefaultTopologyProto is used to represent the default topology, which is the topology used by the SE GPU PJRT client. The PathwaysTopologyProto is used to represent the Pathways topology, which is the topology used by the Pathways PJRT client.
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This CL also updates the SE GPU PJRT client to use the new field in GpuTopologyProto. The SE GPU PJRT client now uses the default topology by default.
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} | Integrate LLVM at llvm/llvm-project@3254f31a6626
Updates LLVM usage to match
[3254f31a6626](https://github.com/llvm/llvm-project/commit/3254f31a6626)
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Remove BlockUntilReady from ThunkExecutor and rely on AsyncValue::AndThen to process thunk execute events.
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} | Fix gradient path for tf.sparse.segment_{sum/mean/sqrt) and bfloat16/float16
operands. Use float32 accumulation for the gradients of the operation ops when
using bfloat16/float16 operands. This matches the behavior on the forward
pass, improving model accuracy.
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} | Improve the perf of tf.sparse.segment_(mean/sum/sqrt) ops when using
bfloat16/float16 precision with float32 for aggregation.
Previously, the memory B/W requirements were as follows for bfloat16/float16
operands using fp32 accumulation:
Input
* Read: #IDs * width * sizeof(bfloat16)
Temporary:
* Read: #Segments * width * sizeof(float32)
* Write: #Segments * width * sizeof(float32)
Output:
* Write: #Segments * width * sizeof(bfloat16)
With the optimization, the memory B/W requirements are as follows:
Input
* Read: #IDs * width * sizeof(bfloat16)
Output:
* Write: #Segments * width * sizeof(bfloat16)
Assuming #IDs = #Segments, and the overhead of writes (exclusive reads + flush)
is 2x that of reads for memory B/W, we reduce memory B/W requirements by 3x.
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} | Integrate LLVM at llvm/llvm-project@bb2bf3a2635a
Updates LLVM usage to match
[bb2bf3a2635a](https://github.com/llvm/llvm-project/commit/bb2bf3a2635a)
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} | dump: Add a flag to explicitly disable dumping.
While xla_enable_dumping=false is equivalent to the new xla_disable_dumping,
proto3 can't tell the difference between set-to-false explicitly and unset,
and when propagated to the command-line we actually do need to know the
difference. The need is particularly acute when merging stored DebugOptions
with command-line derived DebugOptions, as that merge is done at the proto
level.
As a side effect of this behavior, explicit disabling takes precedence over
explicit enabling.
While here, add a bunch of missing includes/build deps and some basic unit
tests.
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} | [XLA] Add layout constraint custom call and simplify it away after layout
assignment in the algebraic simplifier
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