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} | PR #12912: Overload operator<< for XLA_FFI_DataType
Imported from GitHub PR https://github.com/openxla/xla/pull/12912
Copybara import of the project:
--
32908d2d90a89269c655dff6d999bfc974f405a9 by Andrey Portnoy <aportnoy@nvidia.com>:
Overload operator<< for XLA_FFI_DataType
Merging this change closes #12912
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12912 from openxla:aportnoy/improve-ffi-c-api-datatype-error-message 32908d2d90a89269c655dff6d999bfc974f405a9
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This helps eliminate some circular dependencies.
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`quantized_bias_type`: this will determine the default value for quantize bias. Not relevant for GPU, since all operations are in floating-point.
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} | PR #12901: [ROCM] fixing gemm_rewriter subtest BF16GemmCodeGen
Imported from GitHub PR https://github.com/openxla/xla/pull/12901
This is a subtest fix for ROCM platform
@xla-rotation: would you please take a look?
Copybara import of the project:
--
fe6147564252a291003f9dae1de504052a9296f1 by Pavel Emeliyanenko <pavel.emeliyanenko@amd.com>:
fixing gemm_rewriter subtest
Merging this change closes #12901
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12901 from ROCm:ci_gemm_rewrite_test_fix fe6147564252a291003f9dae1de504052a9296f1
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Updates LLVM usage to match
[446f66d685c2](https://github.com/llvm/llvm-project/commit/446f66d685c2)
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} | Remove GOOGLE_CUDA preprocssor guards from scoped_annotation.h
They are not needed anymore and only break the platform abstraction, so let's remove them.
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Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 2.16.1
### Custom code
Yes
### OS platform and distribution
Linux Arch
### Mobile device
_No response_
### Python version
3.12.3
### Bazel version
6.5.0
### GCC/compiler version
14.1.1
### CUDA/cuDNN version
12.4
### GPU model and memory
RTX 4090 - 24gb
### Current behavior?
While building from source, when I specify to use Tensorrt 10.0.1, the bazel configuration uses the header files from version 8 (https://github.com/tensorflow/tensorflow/blob/master/third_party/tensorrt/tensorrt_configure.bzl#L50-L65), which were changed in major 9 of Tensorrt (https://github.com/NVIDIA/TensorRT/tree/v9.1.0/include).
There is no longer `NvUtils.h` and a new header `NvOnnxConfig.h` (since 9.x.x and still like this on TensorRT 10).
I believe that either a new configuration must be created to support TensorRT 9.x.x and 10.x.x, or you should specify that Tensorflow only accepts TensortRT <= 8.6.
### Standalone code to reproduce the issue
```shell
USE_BAZEL_VERSION=6.5.0 bazel build //tensorflow/tools/pip_package/v2:wheel --repo_env=WHEEL_NAME=tensorflow --config=cuda --jobs=30 --verbose_failuresj
```
### Relevant log output
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} | [XLA] Minor cleanups
No functional change is intended.
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} | PR #12907: Fix link error in for_all_thunks_test
Imported from GitHub PR https://github.com/openxla/xla/pull/12907
Use xla_cc_test instead of cc_test to ensure that all shared objects are available for linking.
Copybara import of the project:
--
dc432e9d10e40f2bd0759fa30764d4eefcc5360c by Andrew Goodbody <andrew.goodbody@linaro.org>:
Fix link error in for_all_thunks_test
Use xla_cc_test instead of cc_test to ensure that all shared objects
are available for linking.
Merging this change closes #12907
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12907 from elfringham:fix_all_thunks_test dc432e9d10e40f2bd0759fa30764d4eefcc5360c
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} | PR #12896: [GPU][NFC] Remove deprecated runtime proto message.
Imported from GitHub PR https://github.com/openxla/xla/pull/12896
Copybara import of the project:
--
32ded36419a4a1b2177a6374846497a8bb458ac9 by Ilia Sergachev <isergachev@nvidia.com>:
[GPU][NFC] Remove deprecated runtime proto message.
Merging this change closes #12896
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12896 from openxla:cleanup 32ded36419a4a1b2177a6374846497a8bb458ac9
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This enables Event to be a completely virtual base class, and removes it from circular dependencies.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12942 from openxla:fix_test_cudnn c438b08ea7240c23ae98bc8dcf4ef45fa6d2e89c
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} | Check shape of inputs in XNNPack delegate.
- Add.
- Dequantize
- Div.
- Mul.
- Quantize
- Square.
- Squared difference.
- Sub.
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} | Support negative transpose permutation in TFLite XNNPack delegate.
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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/68345/checks?check_run_id=25234867785) 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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} | [XLA:FFI] Fix annotating buffer `dimensions` memory
The last dimension of FFI buffers `dimensions` member variable was not annotated as initialized memory, because of an off-by-one code error.
It went unnoticed because of usage absl::c_accumulate algorithm in tests. Modified one test to directly access dimensions in a for loop, cause that triggers sanitizer check.
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} | Split sum-modification from sum-visitation.
Currently, we're both computing values and modifying sums in one step, which is
not very nice. This splits the modification parts from the traversal parts
where feasible. There's some almost-duplication in the mod/div simplifiers
which could probably be cleaned up, but so far I failed to find a good name for
it.
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} | Introduce ScatterNdGpuCompatible.
This change is to enable the functionality of ScatterNd to ignore bad indices as what GPU doest.
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} | Tighten reduction loop bounds for MLIR emitter.
See the added comment for an example of a problematic bound
that is currently created.
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} | [Triton] Fix a use-after-free bug in LinearLayout::compose.
`output.apply(bases)` returns a temporary object and `llvm::make_second_range` doesn't copy or take ownership. As a result, `newBases` points to deallocated memory.
Also rename `newBases` to `outerBases`, because there is `newBases` variable in the outer scope.
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} | PR #12897: [GPU] Fix cuDNN GEMM scalar constants test condition.
Imported from GitHub PR https://github.com/openxla/xla/pull/12897
Copybara import of the project:
--
71b57fd71d603bc8c346283796f6859761eef014 by Ilia Sergachev <isergachev@nvidia.com>:
[GPU] Fix cuDNN GEMM scalar constants test condition.
Merging this change closes #12897
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12897 from openxla:fix_cudnn_tests 71b57fd71d603bc8c346283796f6859761eef014
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} | Rename `RewriteSumIf` to `RemoveSummands`.
It was pointed out to me that this code is terrible. Let this
be the first part of my formal apology.
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} | PR #12892: [spmd] Add collective-permute-valid-iteration-annotator
Imported from GitHub PR https://github.com/openxla/xla/pull/12892
This patch adds the `collective-permute-valid-iteration-annotator` pass.
If a while loop is known to be a pipelined while loop, has a known trip count and increments with step=1, then this pass annotates the `collective-permute` operations within the while loop with valid iterations for each GPU. This is only done when the source-target pairs of the `collective-permute` operation form a forward or backward cycle.
For example, if the trip count is 10 (iteration 0 to 9), with step=1, and the source-target pairs of a `collective-permute` operation are `{{0,1},{1,2},{2,3},{3,0}}`, then this pass would annotate such operation with `_xla_send_recv_validation="{{0,6},{1,7},{2,8},{3,9}}"`. This annotation means that
- for GPU index 0, the valid iterations are 0,1,2,3,4,5,6.
- for GPU index 1, the valid iterations are 1,2,3,4,5,6,7.
- for GPU index 2, the valid iterations are 2,3,4,5,6,7,8.
- for GPU index 3, the valid iterations are 3,4,5,6,7,8,9.
The index in the list denotes the device index and the bounds {start,end} are inclusive.
This patch also updates the while loop trip count annotator and while loop analysis to detect the step if the update is of the form i+=k where k > 0. This is useful because circular repeat expects loops with increment of one.
Copybara import of the project:
--
8c1a6dfc2574ec6d4eb228fee05d736dc01493ac by Shraiysh Vaishay <svaishay@nvidia.com>:
[spmd] Add collective-permute-valid-iteration-annotator
This patch adds the `collective-permute-valid-iteration-annotator` pass.
If a while loop is known to be a pipelined while loop, has a known trip count
and increments with step=1, then this pass annotates the `collective-permute`
operations within the while loop with valid iterations for each GPU. This is only done
when the source-target pairs of the `collective-permute` operation form a forward or
backward cycle.
For example, if the trip count is 10 (iteration 0 to 9), with step=1, and the source-target
pairs of a `collective-permute` operation are `{{0,1},{1,2},{2,3},{3,0}}`, then this pass
would annotate such operation with `_xla_send_recv_validation="{{0,6},{1,7},{2,8},{3,9}}"`.
This annotation means that
- for GPU index 0, the valid iterations are 0,1,2,3,4,5,6.
- for GPU index 1, the valid iterations are 1,2,3,4,5,6,7.
- for GPU index 2, the valid iterations are 2,3,4,5,6,7,8.
- for GPU index 3, the valid iterations are 3,4,5,6,7,8,9.
The index in the list denotes the device index and the bounds {start,end} are inclusive.
This patch also updates the while loop trip count annotator and while loop analysis to
detect the step if the update is of the form i+=k where k > 0. This is useful because
circular repeat expects loops with increment of one.
Merging this change closes #12892
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12892 from shraiysh:CR-valid-iteration-annotator 8c1a6dfc2574ec6d4eb228fee05d736dc01493ac
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"@MohanKrishnaGR The standard approach of installing TensorRT directly on your Windows host might not work for WSL 2. Instead, you'll need to install it within the WSL 2 environment itself. Please make sure to follow [these](https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html) instructions. Within your WSL 2 environment, please print the value of LD_LIBRARY_PATH to confirm it includes the TensorRT directory and let us know?\r\n\r\nThank you!",
"I still did not solve TensorRT problem (\"TF-TRT Warning: Could not find TensorRT\")\r\n\r\nWSL2, Ubuntu 22.04, RTX 3090, Python 3.11, TensorFlow 2.15.1\r\n(I installed TensorFlow as `pip install tensorflow[and-cuda]==2.15.1`)\r\n\r\nOnce I execute \r\n```\r\npython3 -c \"import tensorflow.compiler as tf_cc; \\\r\nprint(tf_cc.tf2tensorrt._pywrap_py_utils.get_linked_tensorrt_version())\"\r\n```\r\n\r\nI get the following error\r\n\r\n```\r\n2024-05-28 11:29:40.984287: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\r\n2024-05-28 11:29:40.984330: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\r\n2024-05-28 11:29:40.984879: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\r\n2024-05-28 11:29:40.988008: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2024-05-28 11:29:41.499909: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n(8, 6, 1)\r\n```\r\n\r\nSo, obviously, I need 8.6.1 version of TensorRT and I downloaded \"TensorRT-8.6.1.6.Linux.x86_64-gnu.cuda-12.0.tar.gz\"\r\nAfter extract it, moved folder to \"~/local/opt/TensorRT-8.6.1.6\"\r\n\r\nFor example, \r\n```\r\n(tf_215_py_311_wsl) enhorse@DESKTOP-5950X:~/local/opt/TensorRT-8.6.1.6$ ls\r\nbin data doc graphsurgeon include lib onnx_graphsurgeon python samples targets uff\r\n```\r\n\r\nFollowing Nvidia's installation instruction (https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html#installing-tar)\r\n\r\nI did\r\n\r\n```\r\npython3 -m pip install tensorrt-8.6.1-cp311-none-linux_x86_64.whl\r\npython3 -m pip install tensorrt_lean-8.6.1-cp311-none-linux_x86_64.whl\r\npython3 -m pip install tensorrt_dispatch-8.6.1-cp311-none-linux_x86_64.whl\r\npython3 -m pip install onnx_graphsurgeon-0.3.12-py2.py3-none-any.whl\r\n```\r\n\r\nI added followings after open `sudo nano ~/.bashrc`\r\n\r\nexport LD_LIBRARY_PATH=/home/enhorse/local/opt/TensorRT-8.6.1.6/lib:$LD_LIBRARY_PATH\r\nexport TensorRT_ROOT=/home/enhorse/local/opt/TensorRT-8.6.1.6\r\n\r\n\r\nWhen I execute the commend\r\n`strace -e open,openat python -c \"import tensorflow as tf\" 2>&1 | grep \"libnvinfer\\|TF-TRT\"`\r\n\r\nI got as follows:\r\n```\r\nopenat(AT_FDCWD, \"/home/enhorse/local/opt/TensorRT-8.6.1.6/lib/libnvinfer.so.8.6.1\", O_RDONLY|O_CLOEXEC) = 3\r\nopenat(AT_FDCWD, \"/home/enhorse/local/opt/TensorRT-8.6.1.6/lib/libnvinfer_plugin.so.8.6.1\", O_RDONLY|O_CLOEXEC) = 3\r\n2024-05-28 11:41:21.209619: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n\r\n```\r\n\r\n\r\nI still have no idea what I can do more. Is there something wrong in my installation?\r\n",
"@MohanKrishnaGR The library paths mentioned (/home/enhorse/local/opt/TensorRT-8.6.1.6/lib) might be incorrect or not set properly in the environment variables.\r\n\r\nKindly verify that and let us know?\r\nThank you!",
"Thanks, @sushreebarsa !\r\nfor \r\n```\r\necho $LD_LIBRARY_PATH\r\n```\r\nI got:\r\n```\r\n/usr/local/cuda/lib64:/home/mohan/TensorRT-10.0.1.6/lib:\r\n```\r\nBut still the TRT Warning: Could not find TensorRT exits:\r\n```\r\n(env) mohan@LAPTOP-INPO8147:~$ python3 -c \"import tensorflow.compiler as tf_cc; \\\r\nprint(tf_cc.tf2tensorrt._pywrap_py_utils.get_linked_tensorrt_version())\"\r\n\r\n2024-05-30 19:12:05.605386: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n2024-05-30 19:12:05.646956: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\r\n2024-05-30 19:12:05.647012: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\r\n2024-05-30 19:12:05.647739: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\r\n2024-05-30 19:12:05.654095: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2024-05-30 19:12:06.221625: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n(8, 6, 1)\r\n```",
"@MohanKrishnaGR,\r\nCould you please try to execute the below commands and let us know if it working in Tensorflow v2.16.1\r\n\r\n```python\r\ncd ~/miniconda3/envs/cuda12/lib/python3.11/site-packages/tensorrt_libs/\r\nln -s libnvinfer.so.10 libnvinfer.so.8.6.1\r\nln -s libnvinfer_plugin.so.10 libnvinfer_plugin.so.8.6.1\r\n```\r\nhttps://github.com/tensorflow/tensorflow/issues/64809#issuecomment-2147599600\r\n\r\nThank you!"
] | 2024-05-21T11:32:10 | 2024-06-10T10:00:26 | null | NONE | null | null | null | Couldn't resolve TF-TRT Warning: Could not find TensorRT
I'm using wsl2 in windows 11.
```
Distributor ID: Ubuntu
Description: Ubuntu 22.04.4 LTS
Release: 22.04
Codename: jammy
```
Previously, I wasn't able to have GPU as the backend, I had tried all the methods in installing tensorflow in wsl.
But, trying with python3.11 and tensorflow 2.15.1, got me access to the GPU backend.
```
conda create -n tmpenv python=3.11
conda activate tmpenv
pip install tensorflow[and-cuda]==2.15.1
```
versions:
```
tensorflow 2.15.1
tensorrt 10.0.1
```
nvidia-smi:
```
Tue May 21 15:45:23 2024
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.76.01 Driver Version: 552.44 CUDA Version: 12.4 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA GeForce RTX 4080 ... On | 00000000:01:00.0 Off | N/A |
| N/A 38C P4 17W / 55W | 0MiB / 12282MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| No running processes found |
+-----------------------------------------------------------------------------------------+
```
```
2024-05-21 15:46:26.487553: I external/local_xla/xla/service/service.cc:168] XLA service 0x7fcc3f7b2c00 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2024-05-21 15:46:26.487663: I external/local_xla/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 4080 Laptop GPU, Compute Capability 8.9
```
I've tried all ways of installing tensorrt - pip, .deb, .tar;
updated path variable, etc.
Its importing in python shell
```
>>> import tensorrt as trt
>>> trt.__version__
'10.0.1'
>>> trt.__file__
'/home/mohan/miniconda3/envs/tmpenv/lib/python3.11/site-packages/tensorrt/__init__.py'
```
All the files are available (.tar installation method)
```
(tmpenv) mohan@LAPTOP-INPO8147:~$ ls TensorRT-10.0.1.6/
bin data doc include lib onnx_graphsurgeon python samples targets
(tmpenv) mohan@LAPTOP-INPO8147:~$ ls TensorRT-10.0.1.6/lib/
libnvinfer.so libnvinfer_plugin.so.10.0.1
libnvinfer.so.10 libnvinfer_plugin_static.a
libnvinfer.so.10.0.1 libnvinfer_static.a
libnvinfer_builder_resource.so.10.0.1 libnvinfer_vc_plugin.so
libnvinfer_dispatch.so libnvinfer_vc_plugin.so.10
libnvinfer_dispatch.so.10 libnvinfer_vc_plugin.so.10.0.1
libnvinfer_dispatch.so.10.0.1 libnvinfer_vc_plugin_static.a
libnvinfer_dispatch_static.a libnvonnxparser.so
libnvinfer_lean.so libnvonnxparser.so.10
libnvinfer_lean.so.10 libnvonnxparser.so.10.0.1
libnvinfer_lean.so.10.0.1 libnvonnxparser_static.a
libnvinfer_lean_static.a libonnx_proto.a
libnvinfer_plugin.so stubs
libnvinfer_plugin.so.10
(tmpenv) mohan@LAPTOP-INPO8147:~/TensorRT-10.0.1.6/python$ ls
tensorrt-10.0.1-cp310-none-linux_x86_64.whl
tensorrt-10.0.1-cp311-none-linux_x86_64.whl
tensorrt-10.0.1-cp312-none-linux_x86_64.whl
tensorrt-10.0.1-cp38-none-linux_x86_64.whl
tensorrt-10.0.1-cp39-none-linux_x86_64.whl
tensorrt_dispatch-10.0.1-cp310-none-linux_x86_64.whl
tensorrt_dispatch-10.0.1-cp311-none-linux_x86_64.whl
tensorrt_dispatch-10.0.1-cp312-none-linux_x86_64.whl
tensorrt_dispatch-10.0.1-cp38-none-linux_x86_64.whl
tensorrt_dispatch-10.0.1-cp39-none-linux_x86_64.whl
tensorrt_lean-10.0.1-cp310-none-linux_x86_64.whl
tensorrt_lean-10.0.1-cp311-none-linux_x86_64.whl
tensorrt_lean-10.0.1-cp312-none-linux_x86_64.whl
tensorrt_lean-10.0.1-cp38-none-linux_x86_64.whl
tensorrt_lean-10.0.1-cp39-none-linux_x86_64.whl
```
I've also used the files from .tar method to install using pip:
```
(tmpenv) mohan@LAPTOP-INPO8147:~/TensorRT-10.0.1.6/python$ python3 -m pip install tensorrt-10.0.1-cp311-none-linux_x86_64.whl
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Processing ./tensorrt-10.0.1-cp311-none-linux_x86_64.whl
tensorrt is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.
(tmpenv) mohan@LAPTOP-INPO8147:~/TensorRT-10.0.1.6/python$ python3 -m pip install tensorrt_dispatch-10.0.1-cp311-none-linux_x86_64.whl
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Processing ./tensorrt_dispatch-10.0.1-cp311-none-linux_x86_64.whl
tensorrt-dispatch is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.
(tmpenv) mohan@LAPTOP-INPO8147:~/TensorRT-10.0.1.6/python$ python3 -m pip install tensorrt_lean-10.0.1-cp311-none-linux_x86_64.whl
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Processing ./tensorrt_lean-10.0.1-cp311-none-linux_x86_64.whl
tensorrt-lean is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.
```
But still I get this error
```
2024-05-21 15:46:19.659564: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
```
_Originally posted by @MohanKrishnaGR in https://github.com/tensorflow/tensorflow/issues/64809#issuecomment-2122328887_
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Updates LLVM usage to match
[9f449c342781](https://github.com/llvm/llvm-project/commit/9f449c342781)
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} | Replace all remaining uses of affine.apply with apply_indexing.
Also split indexing maps into one map per result, to not block
LICM.
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Imported from GitHub PR https://github.com/openxla/xla/pull/12864
Copybara import of the project:
--
2e6dfa841a495c1f0babf0ff6f877bc1866bf319 by Ruturaj4 <ruturaj.vaidya@amd.com>:
[ROCm] fix DeviceAllocate code
Merging this change closes #12864
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12864 from ROCm:ci_deviceallocate_fix 2e6dfa841a495c1f0babf0ff6f877bc1866bf319
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Fix header includes
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"Hi @RunnerZhong ,\r\n\r\nLet me verify the typo and I will get back to you."
] | 2024-05-21T07:27:49 | 2024-06-11T14:32:54 | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16
### Custom code
Yes
### OS platform and distribution
Ubuntu 22.04
### 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?
in tflite convtranspose3d optimized kernel, pad depth seems typo error
And also compared with same convtransposed3d with torch interface, tflite have mismatch
### Standalone code to reproduce the issue
```shell
in tflite convtranspose3d optimized kernel, pad depth seems typo error
here, seems
const int spatial_dim_1_padding_after =
params.padding_values.depth + params.padding_values.depth_offset;
```
### Relevant log output
_No response_ | {
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"Hi @Hawthorne001 Can you please resolve the conflicts? Thank you!",
"A PR needs to have a description of what is happening an why, these look to just be bot changes passed through into this PR based on commits. \r\n\r\nThe lock file updated does not change the SHA versions an update there should ideally be done by a global requirements lock update not a single dependency in a single python version.\r\n\r\nThe docker file is not used in active CI and likely does not need an update.\r\n",
"HBT"
] | 2024-05-21T06:56:49 | 2024-06-03T05:58:50 | null | NONE | null | false | {
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InterpreterValue::Buffer having the same name as Buffer is considered
invalid by GCC.
sed "s/\.Buffer/.GetBuffer/g" -i `find . -name '*.cc'`
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"Hi @MichaelHudgins Can you please review this PR ? Thank you!",
"Looks like requests is up-to-date now, so this is no longer needed."
] | 2024-05-21T05:56:07 | 2024-06-11T19:13:12 | 2024-06-11T19:13:08 | CONTRIBUTOR | null | false | {
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} | Bumps [requests](https://github.com/psf/requests) from 2.31.0 to 2.32.0.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/psf/requests/releases">requests's releases</a>.</em></p>
<blockquote>
<h2>v2.32.0</h2>
<h2>2.32.0 (2024-05-20)</h2>
<h2>🐍 PYCON US 2024 EDITION 🐍</h2>
<p><strong>Security</strong></p>
<ul>
<li>Fixed an issue where setting <code>verify=False</code> on the first request from a
Session will cause subsequent requests to the <em>same origin</em> to also ignore
cert verification, regardless of the value of <code>verify</code>.
(<a href="https://github.com/psf/requests/security/advisories/GHSA-9wx4-h78v-vm56">https://github.com/psf/requests/security/advisories/GHSA-9wx4-h78v-vm56</a>)</li>
</ul>
<p><strong>Improvements</strong></p>
<ul>
<li><code>verify=True</code> now reuses a global SSLContext which should improve
request time variance between first and subsequent requests. It should
also minimize certificate load time on Windows systems when using a Python
version built with OpenSSL 3.x. (<a href="https://redirect.github.com/psf/requests/issues/6667">#6667</a>)</li>
<li>Requests now supports optional use of character detection
(<code>chardet</code> or <code>charset_normalizer</code>) when repackaged or vendored.
This enables <code>pip</code> and other projects to minimize their vendoring
surface area. The <code>Response.text()</code> and <code>apparent_encoding</code> APIs
will default to <code>utf-8</code> if neither library is present. (<a href="https://redirect.github.com/psf/requests/issues/6702">#6702</a>)</li>
</ul>
<p><strong>Bugfixes</strong></p>
<ul>
<li>Fixed bug in length detection where emoji length was incorrectly
calculated in the request content-length. (<a href="https://redirect.github.com/psf/requests/issues/6589">#6589</a>)</li>
<li>Fixed deserialization bug in JSONDecodeError. (<a href="https://redirect.github.com/psf/requests/issues/6629">#6629</a>)</li>
<li>Fixed bug where an extra leading <code>/</code> (path separator) could lead
urllib3 to unnecessarily reparse the request URI. (<a href="https://redirect.github.com/psf/requests/issues/6644">#6644</a>)</li>
</ul>
<p><strong>Deprecations</strong></p>
<ul>
<li>Requests has officially added support for CPython 3.12 (<a href="https://redirect.github.com/psf/requests/issues/6503">#6503</a>)</li>
<li>Requests has officially added support for PyPy 3.9 and 3.10 (<a href="https://redirect.github.com/psf/requests/issues/6641">#6641</a>)</li>
<li>Requests has officially dropped support for CPython 3.7 (<a href="https://redirect.github.com/psf/requests/issues/6642">#6642</a>)</li>
<li>Requests has officially dropped support for PyPy 3.7 and 3.8 (<a href="https://redirect.github.com/psf/requests/issues/6641">#6641</a>)</li>
</ul>
<p><strong>Documentation</strong></p>
<ul>
<li>Various typo fixes and doc improvements.</li>
</ul>
<p><strong>Packaging</strong></p>
<ul>
<li>Requests has started adopting some modern packaging practices.
The source files for the projects (formerly <code>requests</code>) is now located
in <code>src/requests</code> in the Requests sdist. (<a href="https://redirect.github.com/psf/requests/issues/6506">#6506</a>)</li>
<li>Starting in Requests 2.33.0, Requests will migrate to a PEP 517 build system
using <code>hatchling</code>. This should not impact the average user, but extremely old
versions of packaging utilities may have issues with the new packaging format.</li>
</ul>
<h2>New Contributors</h2>
<ul>
<li><a href="https://github.com/matthewarmand"><code>@matthewarmand</code></a> made their first contribution in <a href="https://redirect.github.com/psf/requests/pull/6258">psf/requests#6258</a></li>
<li><a href="https://github.com/cpzt"><code>@cpzt</code></a> made their first contribution in <a href="https://redirect.github.com/psf/requests/pull/6456">psf/requests#6456</a></li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a href="https://github.com/psf/requests/blob/main/HISTORY.md">requests's changelog</a>.</em></p>
<blockquote>
<h2>2.32.0 (2024-05-20)</h2>
<p><strong>Security</strong></p>
<ul>
<li>Fixed an issue where setting <code>verify=False</code> on the first request from a
Session will cause subsequent requests to the <em>same origin</em> to also ignore
cert verification, regardless of the value of <code>verify</code>.
(<a href="https://github.com/psf/requests/security/advisories/GHSA-9wx4-h78v-vm56">https://github.com/psf/requests/security/advisories/GHSA-9wx4-h78v-vm56</a>)</li>
</ul>
<p><strong>Improvements</strong></p>
<ul>
<li><code>verify=True</code> now reuses a global SSLContext which should improve
request time variance between first and subsequent requests. It should
also minimize certificate load time on Windows systems when using a Python
version built with OpenSSL 3.x. (<a href="https://redirect.github.com/psf/requests/issues/6667">#6667</a>)</li>
<li>Requests now supports optional use of character detection
(<code>chardet</code> or <code>charset_normalizer</code>) when repackaged or vendored.
This enables <code>pip</code> and other projects to minimize their vendoring
surface area. The <code>Response.text()</code> and <code>apparent_encoding</code> APIs
will default to <code>utf-8</code> if neither library is present. (<a href="https://redirect.github.com/psf/requests/issues/6702">#6702</a>)</li>
</ul>
<p><strong>Bugfixes</strong></p>
<ul>
<li>Fixed bug in length detection where emoji length was incorrectly
calculated in the request content-length. (<a href="https://redirect.github.com/psf/requests/issues/6589">#6589</a>)</li>
<li>Fixed deserialization bug in JSONDecodeError. (<a href="https://redirect.github.com/psf/requests/issues/6629">#6629</a>)</li>
<li>Fixed bug where an extra leading <code>/</code> (path separator) could lead
urllib3 to unnecessarily reparse the request URI. (<a href="https://redirect.github.com/psf/requests/issues/6644">#6644</a>)</li>
</ul>
<p><strong>Deprecations</strong></p>
<ul>
<li>Requests has officially added support for CPython 3.12 (<a href="https://redirect.github.com/psf/requests/issues/6503">#6503</a>)</li>
<li>Requests has officially added support for PyPy 3.9 and 3.10 (<a href="https://redirect.github.com/psf/requests/issues/6641">#6641</a>)</li>
<li>Requests has officially dropped support for CPython 3.7 (<a href="https://redirect.github.com/psf/requests/issues/6642">#6642</a>)</li>
<li>Requests has officially dropped support for PyPy 3.7 and 3.8 (<a href="https://redirect.github.com/psf/requests/issues/6641">#6641</a>)</li>
</ul>
<p><strong>Documentation</strong></p>
<ul>
<li>Various typo fixes and doc improvements.</li>
</ul>
<p><strong>Packaging</strong></p>
<ul>
<li>Requests has started adopting some modern packaging practices.
The source files for the projects (formerly <code>requests</code>) is now located
in <code>src/requests</code> in the Requests sdist. (<a href="https://redirect.github.com/psf/requests/issues/6506">#6506</a>)</li>
<li>Starting in Requests 2.33.0, Requests will migrate to a PEP 517 build system
using <code>hatchling</code>. This should not impact the average user, but extremely old
versions of packaging utilities may have issues with the new packaging format.</li>
</ul>
</blockquote>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/psf/requests/commit/d6ebc4a2f1f68b7e355fb7e4dd5ffc0845547f9f"><code>d6ebc4a</code></a> v2.32.0</li>
<li><a href="https://github.com/psf/requests/commit/9a40d1277807f0a4f26c9a37eea8ec90faa8aadc"><code>9a40d12</code></a> Avoid reloading root certificates to improve concurrent performance (<a href="https://redirect.github.com/psf/requests/issues/6667">#6667</a>)</li>
<li><a href="https://github.com/psf/requests/commit/0c030f78d24f29a459dbf39b28b4cc765e2153d7"><code>0c030f7</code></a> Merge pull request <a href="https://redirect.github.com/psf/requests/issues/6702">#6702</a> from nateprewitt/no_char_detection</li>
<li><a href="https://github.com/psf/requests/commit/555b870eb19d497ddb67042645420083ec8efb02"><code>555b870</code></a> Allow character detection dependencies to be optional in post-packaging steps</li>
<li><a href="https://github.com/psf/requests/commit/d6dded3f00afcf56a7e866cb0732799045301eb0"><code>d6dded3</code></a> Merge pull request <a href="https://redirect.github.com/psf/requests/issues/6700">#6700</a> from franekmagiera/update-redirect-to-invalid-uri-test</li>
<li><a href="https://github.com/psf/requests/commit/bf24b7d8d17da34be720c19e5978b2d3bf94a53b"><code>bf24b7d</code></a> Use an invalid URI that will not cause httpbin to throw 500</li>
<li><a href="https://github.com/psf/requests/commit/2d5f54779ad174035c5437b3b3c1146b0eaf60fe"><code>2d5f547</code></a> Pin 3.8 and 3.9 runners back to macos-13 (<a href="https://redirect.github.com/psf/requests/issues/6688">#6688</a>)</li>
<li><a href="https://github.com/psf/requests/commit/f1bb07d39b74d6444e333879f8b8a3d9dd4d2311"><code>f1bb07d</code></a> Merge pull request <a href="https://redirect.github.com/psf/requests/issues/6687">#6687</a> from psf/dependabot/github_actions/github/codeql-act...</li>
<li><a href="https://github.com/psf/requests/commit/60047ade64b0b882cbc94e047198818ab580911e"><code>60047ad</code></a> Bump github/codeql-action from 3.24.0 to 3.25.0</li>
<li><a href="https://github.com/psf/requests/commit/31ebb8102c00f8cf8b396a6356743cca4362e07b"><code>31ebb81</code></a> Merge pull request <a href="https://redirect.github.com/psf/requests/issues/6682">#6682</a> from frenzymadness/pytest8</li>
<li>Additional commits viewable in <a href="https://github.com/psf/requests/compare/v2.31.0...v2.32.0">compare view</a></li>
</ul>
</details>
<br />
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You can trigger Dependabot actions by commenting on this PR:
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} | [IFRT] Add xla::ifrt::Sharding::IsFullyReplicated()
IFRT Sharding type gains `IsFullyReplicated()`, which quickly tells if the
sharding represents a fully-replicated sharding.
The main motivation is to make full replication information queriable at IFRT
shardings and prepare for enabling IFRT implementations to handle full
replication directly.
There are a preset of rules:
* `SingleDeviceSharding` is trivially fully replicated by its definition.
* `ConcreteSharding` and `OpaqueSharding` is not fully replicated. They have special cases where it may be fully replicated, but the user is advised to use a more specific sharding type to represent such cases.
* `ConcreteEvenSharding` may/may not fully replicated. This is controlled at creation time.
* `ShardingParamSharding` and (IFRT) `HloSharding` depend on whether their lower-level sharding represents full replication.
`ConcreteEvenSharding` is a noteworthy case where the full replication information
does not come from the existing source of the information. This is because the
creators of this sharding (e.g., JAX) typically has the information, but the
replication information is lost when coercing it into `ConcreteEvenSharding`.
This problem will be gradually less problematic once JAX uses a higher-level
IFRT sharding type (mainly (IFRT) `HloSharding`) at more places.
This change extends the `Sharding` type, but the new method is not used by any
existing code.
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} | Integrate LLVM at llvm/llvm-project@9f449c342781
Updates LLVM usage to match
[9f449c342781](https://github.com/llvm/llvm-project/commit/9f449c342781)
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"hi @bhbruce ,\r\n\r\nI will benchmark the model and will get back to you.",
"hi @bhbruce ,\r\n\r\nI replicated the issue and i got the same error. So its probably a bug in the model's metadata . I will raise a PR to get it fixed. Thank you ",
"Hi @sawantkumar \r\nAppreciate for confirming it. If you have a PR to fix it, please share it with me. \r\nThanks.\r\n"
] | 2024-05-21T03:13:13 | 2024-05-27T06:43:58 | null | CONTRIBUTOR | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
97a794b0199b6e507e3f577861856ff09357a3cd
### Current behavior?
There is a TFLite model called `person_detect.tflite` in tflite-micro https://github.com/tensorflow/tflite-micro/blob/main/tensorflow/lite/micro/models/person_detect.tflite.
I cannot run this model via tensorflow's benchmark_model.
It looks like all depthwise_convolutions' quantization_dim meta-data are wrong.
### Command to reproduce the issue
```shell
`tools/benchmark/benchmark_model --num_runs=3 --enable_op_profiling=true --num_threads=2 --graph=person_detect.tflite`
```
### Relevant log output
```shell
### Error Log
INFO: STARTING!
INFO: Log parameter values verbosely: [0]
INFO: Min num runs: [3]
INFO: Num threads: [2]
INFO: Graph: [person_detect.tflite]
INFO: Signature to run: []
INFO: Enable op profiling: [1]
INFO: #threads used for CPU inference: [2]
INFO: Loaded model person_detect.tflite
ERROR: quantized_dimension must be in range [0, 1). Was 3.
ERROR: Tensor 33 has invalid quantization parameters.
ERROR: quantized_dimension must be in range [0, 1). Was 3.
ERROR: Tensor 36 has invalid quantization parameters.
ERROR: quantized_dimension must be in range [0, 1). Was 3.
ERROR: Tensor 40 has invalid quantization parameters.
ERROR: quantized_dimension must be in range [0, 1). Was 3.
ERROR: Tensor 44 has invalid quantization parameters.
ERROR: quantized_dimension must be in range [0, 1). Was 3.
ERROR: Tensor 48 has invalid quantization parameters.
ERROR: quantized_dimension must be in range [0, 1). Was 3.
ERROR: Tensor 52 has invalid quantization parameters.
ERROR: quantized_dimension must be in range [0, 1). Was 3.
ERROR: Tensor 56 has invalid quantization parameters.
ERROR: quantized_dimension must be in range [0, 1). Was 3.
ERROR: Tensor 60 has invalid quantization parameters.
ERROR: quantized_dimension must be in range [0, 1). Was 3.
ERROR: Tensor 64 has invalid quantization parameters.
ERROR: quantized_dimension must be in range [0, 1). Was 3.
ERROR: Tensor 68 has invalid quantization parameters.
ERROR: quantized_dimension must be in range [0, 1). Was 3.
ERROR: Tensor 72 has invalid quantization parameters.
ERROR: quantized_dimension must be in range [0, 1). Was 3.
ERROR: Tensor 76 has invalid quantization parameters.
ERROR: quantized_dimension must be in range [0, 1). Was 3.
ERROR: Tensor 80 has invalid quantization parameters.
ERROR: quantized_dimension must be in range [0, 1). Was 3.
ERROR: Tensor 84 has invalid quantization parameters.
ERROR: Failed to initialize the interpreter
ERROR: Benchmarking failed.
```
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"Hi @sachinmuradi This PR is in draft, any update on this? Please. Thank you!"
] | 2024-05-21T01:41:14 | 2024-06-07T17:28:53 | null | CONTRIBUTOR | null | true | {
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} | PR #12845: [XLA:GPU] add workspace rewrite for FP8 gemm
Imported from GitHub PR https://github.com/openxla/xla/pull/12845
Add workspace rewrite for FP8 with amax fusion.
Copybara import of the project:
--
a2b7f41caff2924798021d130566379e65984750 by Shawn Wang <shawnw@nvidia.com>:
add workspace rewrite for FP8 gemm
Merging this change closes #12845
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12845 from shawnwang18:shawnw/fix_cublaslt a2b7f41caff2924798021d130566379e65984750
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This is needed for hermetic CUDA integration in Google ML projects since tensorRT is not distributed in the same free way as other CUDA/CUDNN distributives.
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} | PR #12848: [XLA:GPU] Add force_update function for conditional commands
Imported from GitHub PR https://github.com/openxla/xla/pull/12848
Copybara import of the project:
--
9f8daced1e91da4abca328406b8c42cfcc250d20 by Shawn Wang <shawnw@nvidia.com>:
update force_update for conditional commands
--
1ffd8e0f59158bb40ad35f0ed205c48c1287cc18 by Shawn Wang <shawnw@nvidia.com>:
fix
Merging this change closes #12848
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12848 from shawnwang18:shawnw/fix_nccl_error 1ffd8e0f59158bb40ad35f0ed205c48c1287cc18
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