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Add range checking for several function.
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} | PR #13293: [ROCm] Add gpu_plugin dependency for AMD gpus.
Imported from GitHub PR https://github.com/openxla/xla/pull/13293
Without this patch amdgpu_compiler dependency was not included for gpu tests and as a result most unit tests failed on amd platform
Copybara import of the project:
--
c87773390404c8bcd1ad00f3b0bc874bc1ad1dca by Harsha HS <Harsha.HavanurShamsundara@amd.com>:
[ROCm] Add gpu_plugin dependency for AMD gpus.
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gpu tests and as a result most unit tests failed on amd platform
Merging this change closes #13293
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13293 from ROCm:ci_add_gpu_plugin_20240531 c87773390404c8bcd1ad00f3b0bc874bc1ad1dca
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} | PR #13244: [ROCm] Get rid of .bazelrc file in run_xla script
Imported from GitHub PR https://github.com/openxla/xla/pull/13244
Copybara import of the project:
--
e05087b657e6014431826c62ffc417137e7a66b0 by Harsha HS <Harsha.HavanurShamsundara@amd.com>:
[ROCm] Get rid of .bazelrc file in run_xla script
Merging this change closes #13244
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13244 from ROCm:ci_no_bazelrc_20240530 e05087b657e6014431826c62ffc417137e7a66b0
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} | [jax:cpu] Add a test config to run JAX with new XLA:CPU runtime
XLA:CPU is migrating from compiling monolithic LLVM function for the whole HLO module to a thin runtime with separate functions for each kernel (fusion, individual operation, library call, etc.). While new runtime is not enabled by default we will use explicit opt-in on tests that are already compatible.
This tag will be removed after XLA:CPU will switch to the new runtime by default.
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Reverts changelist 641225077
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Fix a crash when an exception is thrown during PyArray construction.
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} | Bumps [requests](https://github.com/psf/requests) from 2.31.0 to 2.32.2.
<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>v2.32.2</h2>
<h2>2.32.2 (2024-05-21)</h2>
<p><strong>Deprecations</strong></p>
<ul>
<li>
<p>To provide a more stable migration for custom HTTPAdapters impacted
by the CVE changes in 2.32.0, we've renamed <code>_get_connection</code> to
a new public API, <code>get_connection_with_tls_context</code>. Existing custom
HTTPAdapters will need to migrate their code to use this new API.
<code>get_connection</code> is considered deprecated in all versions of Requests>=2.32.0.</p>
<p>A minimal (2-line) example has been provided in the linked PR to ease
migration, but we strongly urge users to evaluate if their custom adapter
is subject to the same issue described in CVE-2024-35195. (<a href="https://redirect.github.com/psf/requests/issues/6710">#6710</a>)</p>
</li>
</ul>
<h2>v2.32.1</h2>
<h2>2.32.1 (2024-05-20)</h2>
<p><strong>Bugfixes</strong></p>
<ul>
<li>Add missing test certs to the sdist distributed on PyPI.</li>
</ul>
<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>
<!-- 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>2.32.2 (2024-05-21)</h2>
<p><strong>Deprecations</strong></p>
<ul>
<li>
<p>To provide a more stable migration for custom HTTPAdapters impacted
by the CVE changes in 2.32.0, we've renamed <code>_get_connection</code> to
a new public API, <code>get_connection_with_tls_context</code>. Existing custom
HTTPAdapters will need to migrate their code to use this new API.
<code>get_connection</code> is considered deprecated in all versions of Requests>=2.32.0.</p>
<p>A minimal (2-line) example has been provided in the linked PR to ease
migration, but we strongly urge users to evaluate if their custom adapter
is subject to the same issue described in CVE-2024-35195. (<a href="https://redirect.github.com/psf/requests/issues/6710">#6710</a>)</p>
</li>
</ul>
<h2>2.32.1 (2024-05-20)</h2>
<p><strong>Bugfixes</strong></p>
<ul>
<li>Add missing test certs to the sdist distributed on PyPI.</li>
</ul>
<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>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/psf/requests/commit/88dce9d854797c05d0ff296b70e0430535ef8aaf"><code>88dce9d</code></a> v2.32.2</li>
<li><a href="https://github.com/psf/requests/commit/c98e4d133ef29c46a9b68cd783087218a8075e05"><code>c98e4d1</code></a> Merge pull request <a href="https://redirect.github.com/psf/requests/issues/6710">#6710</a> from nateprewitt/api_rename</li>
<li><a href="https://github.com/psf/requests/commit/92075b330a30b9883f466a43d3f7566ab849f91b"><code>92075b3</code></a> Add deprecation warning</li>
<li><a href="https://github.com/psf/requests/commit/aa1461b68aa73e2f6ec0e78c8853b635c76fd099"><code>aa1461b</code></a> Move _get_connection to get_connection_with_tls_context</li>
<li><a href="https://github.com/psf/requests/commit/970e8cec988421bd43da57350723b05c8ce8dc7e"><code>970e8ce</code></a> v2.32.1</li>
<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>Additional commits viewable in <a href="https://github.com/psf/requests/compare/v2.31.0...v2.32.2">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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kParameter instruction shouldn't be a part of HloFusionAnalysis. Causes msan failure.
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} | Update Linux official environment to use tf package from artifact registry instead of gcr.io. I've moved the image from gcr.io over to artifact registry.
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} | Implement a batched version of `Array::GetReadyFuture()`
Some IFRT implementations may be able to provide a more efficient `GetReadyFuture` implementations if they can see multiple arrays at a time, e.g., IFRT Proxy. `Client::GetReadyFuture({array})` is expected to have the same semantics as `array->GetReadyFuture()`.
IFRT Proxy is updated such that the changes are forward and backward compatible. If a newer IFRT Proxy client connects to an older IFRT Proxy server that does not support `Client::GetReadyFuture()`, the client falls back to `Array::GetReadyFuture()`. Also, the proto explicitly uses non-packed fields to maintain version compatibility.
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} | Reimplementation of ICI weight distribution.
1. Less Fill op generated in XlaBroadcast pass in MLIR.
2. Propagate colocation attr for a specified pattern in a graphdef optimization post MLIR bridge but before placer.
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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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} | Revert bytecode changes from #2259
The bytecode changes are causing issues due to some StableHLO
consumers using StableHLO directly rather than VHLO.
Temporarily remove the relevant roundtrip tests as well since this
breaks round-tripping.
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"Hi @StephenSzwiec ,\r\n- Could you try to update tensorflow 2.16.1, Here you are using tf 2.12 version compatible with python 3.9 to 3.11 versions. But you are using 3.12. Could you please update all the versions? If you facing any issues still then let us know.\r\n\r\nThank you!",
"Good day. Thank you for responding. I have updated to 2.16.1 and seem to encounter the same issue with Jupyter. \r\n```\r\n2024-06-06 09:03:48.670640: F external/local_xla/xla/service/gpu/nvptx_compiler.cc:619] ptxas returned an error during compilation of ptx to sass: 'INTERNAL: ptxas 12.3.103 has a bug that we think can affect XLA. Please use a different version.' If the error message indicates that a file could not be written, please verify that sufficient filesystem space is provided.\r\n```"
] | "2024-05-31T18:31:05" | "2024-06-06T14:08:54" | null | NONE | null | null | null | ### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.12.1
### Custom code
Yes
### OS platform and distribution
Red Hat Enterprise Linux release 8.5 (Ootpa) x86_64
### Mobile device
_No response_
### Python version
3.12
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
System: 12.3; Python env: nvidia-cuda-runtime-cu12==12.3.101 nvidia-cudnn-cu12==8.9.7.29
### GPU model and memory
Nvidia a10 24GB
### Current behavior?
### Test case
- The first linked standalone code implements a CNN for the CIFAR-10 dataset as a minimal example and test case.
- The second linked code is a kernel.json which implements the environment variables for the production environment.
### Reproduction of environment
I set up a conda environment as follows:
`$ conda create -n tf-2.16 python==3.12`
We then source the environment:
`$ source /mmfs1/apps/pyenvs/anaconda3-2022.05/bin/activate tf-2.16`
I then install Tensorflow as per [documentation](https://www.tensorflow.org/install/pip) and add jupyter to this environment:
`$ pip install tensorflow[and-cuda] jupyterlab`
A kernel is produced:
`$ python -m ipykernel install --user --name=MyEnvName`
### Terminal behavior
Within the terminal, normal execution of the test code works with some normally expected warnings.
```
# export environment variables for system CUDA and cudnn (nvidia upstream)
$ module load cuda/12.3
$ module load cudnn/8.9
$ source /mmfs1/apps/pyenvs/anaconda3-2022.05/bin/activate tf-2.16
$ python tf_gpu.py
```
When the code calls `model.fit()` we get the following STDERR:
```
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1717178625.148066 121055 service.cc:146] XLA service 0x155468007cb0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1717178625.148196 121055 service.cc:154] StreamExecutor device (0): NVIDIA A10, Compute Capability 8.6
2024-05-31 13:03:45.227617: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
2024-05-31 13:03:45.621453: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:531] Loaded cuDNN version 8907
2024-05-31 13:03:48.511964: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_1290', 4 bytes spill stores, 4 bytes spill loads
I0000 00:00:1717178635.476383 121055 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.
```
Despite warnings, the training and test work begins and main exits normally.
### Unexpected Jupyter behavior
Within jupyter, the same code run within blocks fails, with **Kernel Restarting: The kernel for .ipynb appears to have died. It will restart automatically** which is unhelpful. The STDERR from Jupyter is logged, and this gives
```
2024-05-31 11:15:06.061841: F external/local_xla/xla/service/gpu/nvptx_compiler.cc:619] ptxas returned an error during compilation of ptx to sass: 'INTERNAL: ptxas 12.3.103 has a bug that we think can affect XLA. Please use a different version.' If the error message indicates that a file could not be written, please verify that sufficient filesystem space is provided.
```
So, the ptxas shipped with the pip distribution of `tensorflow[and-cuda]` as per current documentation pulls a bad version of `nvidia-cuda-nvcc-cu12`.
### Fix which confirms the bug
I fixed this on production by bumping the offending version:
`$ pip install nvidia-cuda-nvcc-cu12==12.5.40`
Now, `fit()` finishes successfully, and a new warning is logged:
```
2024-05-31 13:19:06.243730: W external/local_xla/xla/service/gpu/nvptx_compiler.cc:764] The NVIDIA driver's CUDA version is 12.3 which is older than the ptxas CUDA version (12.5.40). Because the driver is older than the ptxas version, XLA is disabling parallel compilation, which may slow down compilation. You should update your NVIDIA driver or use the NVIDIA-provided CUDA forward compatibility packages.
```
The version of `nvidia-cuda-nvcc-cu12` which current stable pip installed during `pip install tensorflow[and-cuda]` does not work inside of Jupyter if not linked against some other system CUDA distribution (which is not always possible inside of a heterogeneous compute environment).
A better fix would be to bump the nvidia pip packages so that the offending version of ptxas are avoided completely.
### nvidia-smi output
```
$ nvidia-smi
Fri May 31 13:25:06 2024
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 545.23.08 Driver Version: 545.23.08 CUDA Version: 12.3 |
|-----------------------------------------+----------------------+----------------------+
| 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 A10 Off | 00000000:03:00.0 Off | 0 |
| 0% 29C P8 8W / 150W | 4MiB / 23028MiB | 0% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
| 1 NVIDIA A10 Off | 00000000:27:00.0 Off | 0 |
| 0% 30C P8 9W / 150W | 4MiB / 23028MiB | 0% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
| 2 NVIDIA A10 Off | 00000000:83:00.0 Off | 0 |
| 0% 27C P8 8W / 150W | 7MiB / 23028MiB | 0% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
| 3 NVIDIA A10 Off | 00000000:A3:00.0 Off | 0 |
| 0% 29C P8 15W / 150W | 4MiB / 23028MiB | 0% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| No running processes found |
+---------------------------------------------------------------------------------------+
```
### Standalone code to reproduce the issue
```shell
https://gist.github.com/StephenSzwiec/d4f6f587441f54b9b6c5fed5efd8002d
https://gist.github.com/StephenSzwiec/8107202f0bbdc52deb3c9ebcd5c7f828
```
### Relevant log output
```shell
2024-05-31 11:15:06.061841: F external/local_xla/xla/service/gpu/nvptx_compiler.cc:619] ptxas returned an error during compilation of ptx to sass: 'INTERNAL: ptxas 12.3.103 has a bug that we think can affect XLA. Please use a different version.' If the error message indicates that a file could not be written, please verify that sufficient filesystem space is provided.
```
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} | Integrate LLVM at llvm/llvm-project@ae8627809076
Updates LLVM usage to match
[ae8627809076](https://github.com/llvm/llvm-project/commit/ae8627809076)
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The --hlo_file flag is still is use.
Reverts 209d568900cf33e81cf77076459647453ad410d4
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} | #tf-data-sevice Downgrade null model warning to vlog.
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} | [xla:cpu] Add support for multi-output elemental loops
+ enabled exhaustive tests for thunks runtime
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} | Rename StreamExecutorInterface to be StreamExecutor to save on all the renaming elsewhere.
A followon change will move it from stream_executor_interface.h to stream_executor.h
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"Can you help me out\r\n",
"Epoch 1/60\r\nTraceback (most recent call last):\r\n File \"MDLSTM7_CRF.py\", line 422, in <module>\r\n model_build(input_file)\r\n File \"MDLSTM7_CRF.py\", line 311, in model_build\r\n history = model.fit([X_word_train, X_char_train, pos_tag_train, max_len_array], y_train, batch_size=5, epochs=60, validation_split=0.2, verbose=1)\r\n File \"C:\\Users\\saivi\\anaconda3\\envs\\mention\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\", line 66, in _method_wrapper\r\n return method(self, *args, **kwargs)\r\n File \"C:\\Users\\saivi\\anaconda3\\envs\\mention\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\", line 848, in fit\r\n tmp_logs = train_function(iterator)\r\n File \"C:\\Users\\saivi\\anaconda3\\envs\\mention\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\", line 580, in __call__\r\n result = self._call(*args, **kwds)\r\n File \"C:\\Users\\saivi\\anaconda3\\envs\\mention\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\", line 627, in _call\r\n self._initialize(args, kwds, add_initializers_to=initializers)\r\n File \"C:\\Users\\saivi\\anaconda3\\envs\\mention\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\", line 506, in _initialize\r\n *args, **kwds))\r\n File \"C:\\Users\\saivi\\anaconda3\\envs\\mention\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\", line 2446, in _get_concrete_function_internal_garbage_collected\r\n graph_function, _, _ = self._maybe_define_function(args, kwargs)\r\n File \"C:\\Users\\saivi\\anaconda3\\envs\\mention\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\", line 2777, in _maybe_define_function\r\n graph_function = self._create_graph_function(args, kwargs)\r\n File \"C:\\Users\\saivi\\anaconda3\\envs\\mention\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\", line 2667, in _create_graph_function\r\n capture_by_value=self._capture_by_value),\r\n File \"C:\\Users\\saivi\\anaconda3\\envs\\mention\\lib\\site-packages\\tensorflow\\python\\framework\\func_graph.py\", line 981, in func_graph_from_py_func\r\n func_outputs = python_func(*func_args, **func_kwargs)\r\n File \"C:\\Users\\saivi\\anaconda3\\envs\\mention\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\", line 441, in wrapped_fn\r\n return weak_wrapped_fn().__wrapped__(*args, **kwds)\r\n File \"C:\\Users\\saivi\\anaconda3\\envs\\mention\\lib\\site-packages\\tensorflow\\python\\framework\\func_graph.py\", line 968, in wrapper\r\n raise e.ag_error_metadata.to_exception(e)\r\nTypeError: in user code:\r\n\r\n C:\\Users\\saivi\\anaconda3\\envs\\mention\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:571 train_function *\r\n outputs = self.distribute_strategy.run(\r\n C:\\Users\\saivi\\anaconda3\\envs\\mention\\lib\\site-packages\\tensorflow\\python\\distribute\\distribute_lib.py:951 run **\r\n return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)\r\n C:\\Users\\saivi\\anaconda3\\envs\\mention\\lib\\site-packages\\tensorflow\\python\\distribute\\distribute_lib.py:2290 call_for_each_replica\r\n return self._call_for_each_replica(fn, args, kwargs)\r\n C:\\Users\\saivi\\anaconda3\\envs\\mention\\lib\\site-packages\\tensorflow\\python\\distribute\\distribute_lib.py:2649 _call_for_each_replica\r\n return fn(*args, **kwargs)\r\n C:\\Users\\saivi\\anaconda3\\envs\\mention\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:533 train_step **\r\n y, y_pred, sample_weight, regularization_losses=self.losses)\r\n C:\\Users\\saivi\\anaconda3\\envs\\mention\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\compile_utils.py:205 __call__\r\n loss_value = loss_obj(y_t, y_p, sample_weight=sw)\r\n C:\\Users\\saivi\\anaconda3\\envs\\mention\\lib\\site-packages\\tensorflow\\python\\keras\\losses.py:143 __call__\r\n losses = self.call(y_true, y_pred)\r\n C:\\Users\\saivi\\anaconda3\\envs\\mention\\lib\\site-packages\\tensorflow\\python\\keras\\losses.py:246 call\r\n return self.fn(y_true, y_pred, **self._fn_kwargs)\r\n C:\\Users\\saivi\\anaconda3\\envs\\mention\\lib\\site-packages\\tensorflow\\python\\keras\\losses.py:1558 sparse_categorical_crossentropy\r\n y_true, y_pred, from_logits=from_logits, axis=axis)\r\n C:\\Users\\saivi\\anaconda3\\envs\\mention\\lib\\site-packages\\tensorflow\\python\\keras\\backend.py:4620 sparse_categorical_crossentropy\r\n epsilon_ = _constant_to_tensor(epsilon(), output.dtype.base_dtype)\r\n C:\\Users\\saivi\\anaconda3\\envs\\mention\\lib\\site-packages\\tensorflow\\python\\keras\\backend.py:709 _constant_to_tensor\r\n return constant_op.constant(x, dtype=dtype)\r\n C:\\Users\\saivi\\anaconda3\\envs\\mention\\lib\\site-packages\\tensorflow\\python\\framework\\constant_op.py:262 constant\r\n allow_broadcast=True)\r\n C:\\Users\\saivi\\anaconda3\\envs\\mention\\lib\\site-packages\\tensorflow\\python\\framework\\constant_op.py:300 _constant_impl\r\n allow_broadcast=allow_broadcast))\r\n C:\\Users\\saivi\\anaconda3\\envs\\mention\\lib\\site-packages\\tensorflow\\python\\framework\\tensor_util.py:451 make_tensor_proto\r\n _AssertCompatible(values, dtype)\r\n C:\\Users\\saivi\\anaconda3\\envs\\mention\\lib\\site-packages\\tensorflow\\python\\framework\\tensor_util.py:331 _AssertCompatible\r\n (dtype.name, repr(mismatch), type(mismatch).__name__))\r\n\r\n TypeError: Expected int32, got 1e-07 of type 'float' instead.",
"@kusumlata123,\r\nLooks like the provided code is not the complete code. Can you please, share colab link or complete code with supporting files to reproduce the issue in our environment. It helps us in localizing the issue faster.\r\n\r\nAlso the Tensorflow v2.2 is a pretty old version. Could you please test the code in the latest TensorFlow v2.15 or v2.16 where most of the bugs are resolved in the updated versions.\r\nThank you!",
"As my code supported this one , i have run this code in 2021 and now\r\nagain want to run for research purpose\r\n\r\n\r\nOn Mon, Jun 3, 2024 at 12:47 PM tilakrayal ***@***.***> wrote:\r\n\r\n> @kusumlata123 <https://github.com/kusumlata123>,\r\n> Looks like the provided code is not the complete code. Can you please,\r\n> share colab link or complete code with supporting files to reproduce the\r\n> issue in our environment. It helps us in localizing the issue faster.\r\n>\r\n> Also the Tensorflow v2.2 is a pretty old version. Could you please test\r\n> the code in the latest TensorFlow v2.15 or v2.16 where most of the bugs are\r\n> resolved in the updated versions.\r\n> Thank you!\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/tensorflow/tensorflow/issues/68959#issuecomment-2144449490>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AJKEARCIVD6RAYB7FXIK3NLZFQKABAVCNFSM6AAAAABITECF4SVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDCNBUGQ2DSNBZGA>\r\n> .\r\n> You are receiving this because you were mentioned.Message ID:\r\n> ***@***.***>\r\n>\r\n",
" from tensorflow.keras.preprocessing.sequence import pad_sequences\r\nModuleNotFoundError: No module named 'tensorflow.keras'\r\n\r\n if i have upgraded version from 2.2.0 to 2.11.0 with keras 2.11.0",
"@kusumlata123,\r\nI guess your code was written which was compatible with the tensorflow v2.2. There are more number of apis which were deprecated in the older versions and couldn't be used in the latest versions. \r\n\r\nEg:from tensorflow.keras.preprocessing.sequence import pad_sequences---tf.keras.preprocessing.sequence was deprecated\r\n\r\n\r\nhttps://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/sequence\r\n\r\nhttps://stackoverflow.com/questions/72326025/cannot-import-name-pad-sequences-from-keras-preprocessing-sequence\r\n\r\nPlease try to convert the complete code and try to execute the code. Thank you!"
] | "2024-05-31T16:44:08" | "2024-06-05T12:36:50" | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.2.0, tf-gpu-2.2.0
### Custom code
Yes
### OS platform and distribution
window
### Mobile device
Laptop
### Python version
3.7.6
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
model_build(input_file)
File "MDLSTM7_CRF.py", line 307, in model_build
verbose=int(1)
File "C:\Users\saivi\anaconda3\envs\mention\lib\site-packages\tensorflow\python\keras\engine\training.py", line 66, in _method_wrapper
return method(self, *args, **kwargs)
File "C:\Users\saivi\anaconda3\envs\mention\lib\site-packages\tensorflow\python\keras\engine\training.py", line 848, in fit
tmp_logs = train_function(iterator)
File "C:\Users\saivi\anaconda3\envs\mention\lib\site-packages\tensorflow\python\eager\def_function.py", line 580, in __call__
result = self._call(*args, **kwds)
File "C:\Users\saivi\anaconda3\envs\mention\lib\site-packages\tensorflow\python\eager\def_function.py", line 627, in _call
self._initialize(args, kwds, add_initializers_to=initializers)
File "C:\Users\saivi\anaconda3\envs\mention\lib\site-packages\tensorflow\python\eager\def_function.py", line 506, in _initialize
*args, **kwds))
File "C:\Users\saivi\anaconda3\envs\mention\lib\site-packages\tensorflow\python\eager\function.py", line 2446, in _get_concrete_function_internal_garbage_collected
graph_function, _, _ = self._maybe_define_function(args, kwargs)
File "C:\Users\saivi\anaconda3\envs\mention\lib\site-packages\tensorflow\python\eager\function.py", line 2777, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "C:\Users\saivi\anaconda3\envs\mention\lib\site-packages\tensorflow\python\eager\function.py", line 2667, in _create_graph_function
capture_by_value=self._capture_by_value),
File "C:\Users\saivi\anaconda3\envs\mention\lib\site-packages\tensorflow\python\framework\func_graph.py", line 981, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "C:\Users\saivi\anaconda3\envs\mention\lib\site-packages\tensorflow\python\eager\def_function.py", line 441, in wrapped_fn
return weak_wrapped_fn().__wrapped__(*args, **kwds)
File "C:\Users\saivi\anaconda3\envs\mention\lib\site-packages\tensorflow\python\framework\func_graph.py", line 968, in wrapper
raise e.ag_error_metadata.to_exception(e)
TypeError: in user code:
C:\Users\saivi\anaconda3\envs\mention\lib\site-packages\tensorflow\python\keras\engine\training.py:571 train_function *
outputs = self.distribute_strategy.run(
C:\Users\saivi\anaconda3\envs\mention\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:951 run **
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
C:\Users\saivi\anaconda3\envs\mention\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2290 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
C:\Users\saivi\anaconda3\envs\mention\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2649 _call_for_each_replica
return fn(*args, **kwargs)
C:\Users\saivi\anaconda3\envs\mention\lib\site-packages\tensorflow\python\keras\engine\training.py:533 train_step **
y, y_pred, sample_weight, regularization_losses=self.losses)
C:\Users\saivi\anaconda3\envs\mention\lib\site-packages\tensorflow\python\keras\engine\compile_utils.py:205 __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
C:\Users\saivi\anaconda3\envs\mention\lib\site-packages\tensorflow\python\keras\losses.py:143 __call__
losses = self.call(y_true, y_pred)
C:\Users\saivi\anaconda3\envs\mention\lib\site-packages\tensorflow\python\keras\losses.py:246 call
return self.fn(y_true, y_pred, **self._fn_kwargs)
C:\Users\saivi\anaconda3\envs\mention\lib\site-packages\tensorflow\python\keras\losses.py:1558 sparse_categorical_crossentropy
y_true, y_pred, from_logits=from_logits, axis=axis)
C:\Users\saivi\anaconda3\envs\mention\lib\site-packages\tensorflow\python\keras\backend.py:4620 sparse_categorical_crossentropy
epsilon_ = _constant_to_tensor(epsilon(), output.dtype.base_dtype)
C:\Users\saivi\anaconda3\envs\mention\lib\site-packages\tensorflow\python\keras\backend.py:709 _constant_to_tensor
return constant_op.constant(x, dtype=dtype)
C:\Users\saivi\anaconda3\envs\mention\lib\site-packages\tensorflow\python\framework\constant_op.py:262 constant
allow_broadcast=True)
C:\Users\saivi\anaconda3\envs\mention\lib\site-packages\tensorflow\python\framework\constant_op.py:300 _constant_impl
allow_broadcast=allow_broadcast))
C:\Users\saivi\anaconda3\envs\mention\lib\site-packages\tensorflow\python\framework\tensor_util.py:451 make_tensor_proto
_AssertCompatible(values, dtype)
C:\Users\saivi\anaconda3\envs\mention\lib\site-packages\tensorflow\python\framework\tensor_util.py:331 _AssertCompatible
(dtype.name, repr(mismatch), type(mismatch).__name__))
TypeError: Expected int32, got 1e-07 of type 'float' instead.
### Standalone code to reproduce the issue
```shell
X_word_train = np.array(X_word_train, dtype=np.int32)
X_char_train = np.array(X_char_train, dtype=np.int32).reshape((len(X_char_train), max_len, max_len_char))
pos_tag_train = np.array(pos_tag_train, dtype=np.int32)
y_train = np.array(y_train, dtype=np.int32).reshape(len(y_train), max_len, 1)
# Use np.full to create an array of integers
max_len_array = np.full(len(X_word_train), max_len, dtype=np.int32)
history = model.fit(
[X_word_train, X_char_train, pos_tag_train, max_len_array],
y_train,
batch_size=5,
epochs=60,
shuffle=False,
validation_split=0.2,
verbose=1
)
```
### Relevant log output
```shell
model should be train
```
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} | Use get_api_version to choose gather/scatter
Instead of using try/except - it's cleaner.
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(I just noticed that someone disabled this previously.)
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Updates LLVM usage to match
[ae8627809076](https://github.com/llvm/llvm-project/commit/ae8627809076)
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} | PR #13258: Give GPU compiler class access to PJRT key-value store.
Imported from GitHub PR https://github.com/openxla/xla/pull/13258
Copybara import of the project:
--
735291df0028b9d3a1a73a7db8fe5104910b4ca5 by Ilia Sergachev <isergachev@nvidia.com>:
Give GPU compiler class access to PJRT key-value store.
Merging this change closes #13258
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13258 from openxla:kv 735291df0028b9d3a1a73a7db8fe5104910b4ca5
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} | [XLA:GPU] Add initial support for constraints in `SymbolicTile`.
This will allow us to filter out invalid tile sizes when choosing how to
generate a tiled program.
Currently, support is minimal and only adds an overly restricted divisibility
constraint when a split reshape occurs.
In cases where we know how to derive a tile but do not yet know how to derive a
correct constraint, we annotate the symbolic tile as "unsatisfiable": this
allows us to produce a valid symbolic tile, but later allows us to avoid
deriving tiles for it.
This gives us
1. better debuggability (we can check that symbolic tile derivation logic works, and know that it fails only at adding constraints), and
2. insurance that any constructed symbolic tile can be instantiated safely or not at all, which makes attempts at codegen safe from invalid tiling choices.
As per offline discussion, we choose to have these constraints outside of
indexing maps since they will require support for more complex infrastructure
(disjunctions, and likely explicit conjunctions as well).
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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/68923\">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/68923\">No</a>\n"
] | "2024-05-30T23:46:45" | "2024-05-31T03:35:40" | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.16.1
### Custom code
Yes
### OS platform and distribution
Windows
### Mobile device
_No response_
### Python version
3.12
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
It's currently not possible for `add' to interpret a floating point values as an integer
### Standalone code to reproduce the issue
```shell
# New code
import numpy as np
class TnpArray:
def __init__(self, data):
self.data = np.array(data)
def __add__(self, other):
if isinstance(other, (int, float)):
return TnpArray(self.data + other)
else:
raise TypeError(f"Unsupported operand type(s) for +: 'TnpArray' and '{type(other).__name__}'")
def __repr__(self):
return repr(self.data)
# Example usage:
a = TnpArray([1, 2, 3])
result = a + 1.5
print(result) # Output: array([2.5, 3.5, 4.5])
```
### Relevant log output
_No response_ | {
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Reverts 8e437155b9927d505afe1c708616f0763aef3d9b
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We found via git bisect that this commit breaks convergence (loss goes to NaN) for a TF Keras Resnet50 model with XLA enabled. | {
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Reverts 8e437155b9927d505afe1c708616f0763aef3d9b
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[b62ba7f5b1ca](https://github.com/llvm/llvm-project/commit/b62ba7f5b1ca)
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The current invocation does not consider the case in which the value instruction is an async wrapped call instruction residing in a separate computation.
Added unit test in hlo_ordering_test.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12537 from Intel-tensorflow:sachin/softmax-axis de9d6bd36272962faf76077f594649d229bb727a
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Reverts 8e437155b9927d505afe1c708616f0763aef3d9b
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} | #tf-data-service Add logic for dynamic SplitProviders.
Split providers marked as "dynamic" are expected to have a non-deterministic output of splits upon reinitialization/recreation and no logic should rely on the determinism of their output. As such, dispatcher logic for skipping over previously seen splits will not apply to these SplitProviders.
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} | PR #12952: [ROCM] hipblas-lt: bias pointer workaround
Imported from GitHub PR https://github.com/openxla/xla/pull/12952
This is a workaround for ROCM-6.2 version where the behaviour of bias pointer was changed. New version shall work fine both for old and new rocm versions
@xla-rotation: could you have a look please ?
Copybara import of the project:
--
1bd40dfa043978873652b38532e6a7c073fa2468 by Pavel Emeliyanenko <pavel.emeliyanenko@amd.com>:
added bias pointer workaround
--
d351a84a3cfc61fa3672a12cda9df3265780a7d4 by Pavel Emeliyanenko <pavel.emeliyanenko@amd.com>:
added comment
--
cb07babd3702c1311657f6dc6e3146dd75748448 by Pavel Emeliyanenko <pavel.emeliyanenko@amd.com>:
fixing linter errors
Merging this change closes #12952
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12952 from ROCm:ci_hipblaslt-bias-epilogue-fix cb07babd3702c1311657f6dc6e3146dd75748448
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