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} | Fix compute capability as given in `TF_CUDA_COMPUTE_CAPABILITIES`
It expects "7.5" instead of "75"
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Needed to move JAX/TF builds to this script
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This allows using RETURN_IF_ERROR and ASSIGN_OR_RETURN macro in functions retuning AsyncValueRef with a compatible payload type.
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Prepare for making thunk execution fully asynchronous
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`core/public/version.h`, `tools/pip_package/setup.py`, and
`tensorflow/tensorflow.bzl`. Also review the execution notes below:
```
Major: 2 -> 2
Minor: 16 -> 16
Patch: 1 -> 2
No lingering old version strings "2.16.1" found in source directory
"tensorflow/". Good.
No lingering old version strings "2.16.1" found in source directory
"tensorflow/". Good.
``` | {
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Documentation Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
latest
### Custom code
No
### OS platform and distribution
_No response_
### 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?
The python documentation notebook at https://github.com/tensorflow/docs/blob/master/site/en/tutorials/images/transfer_learning.ipynb outputs unexpected results.
As the tutorial explains how to fine-tune a model to differentiate cats and dogs, the resulting output fails and always detect dogs, no matter the pet.
Here's a screenshot directly taken at https://www.tensorflow.org/tutorials/images/transfer_learning :
One would expect that the result is a correct detection of cats and dogs.
### Standalone code to reproduce the issue
```shell
Just take a look at https://www.tensorflow.org/tutorials/images/transfer_learning.
Also ran the notebook on Google Colab, got the same output.
```
### Relevant log output
_No response_ | {
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} | Delay using tuple arguments in Shardonnay until after it's run.
Users of XLA can choose whether XLA should use tuple args for the params. This is a problem as when arguments become a tuple they lose their frontend attributes, which is where we keep our `xla.sdy.sharding` attributes. So we can't create tuple arguments until after Shardonnay is run.
Code that doesn't relay on Shardonnay behaves the same way with using tuple args all the way through.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13639 from philipphack:u_layer_uaf_xla 91ebf7b4a2ac90ebadce27d1a73e88fb4513aed4
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} | [XLA:GPU][NFC] Replace `ABSL_ATTRIBUTE_UNUSED` with `[[maybe_unused]]`.
This is available since C++17 and therefore available to us.
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It just causes problems and isn't really necessary.
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} | [xla:cpu] NFC: Add factory constructor to all CPU thunks
Factory construction method allows to add additional verification without having to rely on CHECK/DCHECK or pushing it higher up the stack to ThunkEmitter.
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These functions were anyway covered by `IsTritonSupportedInstruction`. This change also moves some functions inside an unnamed namespace to make the public API more explicit.
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"Hi **@x0w3n** ,\r\n- Hi @x0w3n ,\r\n- I was able to reproduce the issue on Colab using TF v2.16 and TF-nightly ,Please find the [gist](https://colab.research.google.com/gist/Venkat6871/62972741cd453df0c7ef901e8cf1f7d7/69471_2-16-1-nightly-v.ipynb) here for reference.\r\n\r\nThank you!"
] | "2024-06-10T13:24:51" | "2024-06-11T14:05:57" | 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
Linux Ubuntu 22.04.3 LTS (x86_64)
### Mobile device
_No response_
### Python version
3.9.13
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Under specific input, tf.experimental.numpy.diag/tf.compat.v1.linalg.diag/tf.experimental.numpy.diagflat/tf.keras.ops.diag encounters "Aborted (core dumped)".
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
input_dict = {
'diagonal': tf.constant([1,2], dtype=tf.int32),
'k': tf.constant([1, 2], dtype=tf.int32)
}
# crash
tf.experimental.numpy.diag(
v=tf.constant([1,2], dtype=tf.int32), k=tf.constant([1, 2], dtype=tf.int32)
)
# crash
# tf.compat.v1.linalg.diag(
# diagonal=tf.constant([1,2], dtype=tf.int32),
# name='diag',
# k= tf.constant([1, 2], dtype=tf.int32),
# num_rows=-1,
# num_cols=-1,
# padding_value=0,
# align='RIGHT_LEFT'
# )
# crash
# tf.experimental.numpy.diagflat(
# v=tf.constant([1,2], dtype=tf.int32), k=tf.constant([1, 2], dtype=tf.int32)
# )
# crash
# tf.keras.ops.diag(
# x=tf.constant([1,2], dtype=tf.int32), k=tf.constant([1, 2], dtype=tf.int32)
# )
```
### Relevant log output
```shell
2024-06-10 13:23:09.021306: 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.
2024-06-10 13:23:10.029041: F tensorflow/core/framework/tensor_shape.cc:356] Check failed: d >= 0 (0 vs. -1)
Aborted (core dumped)
```
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"@x0w3n,\r\nI tried to execute the mentioned code on tf-nightly and it was not aborted as above, and fails with the same error as the input is very long(Encountered overflow when multiplying). Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/af41845d49b59bc070b15e61faf5d0ad/untitled1948.ipynb). Thank you!"
] | "2024-06-10T12:55:29" | "2024-06-11T06:44:07" | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 2.16
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04.3 LTS (x86_64)
### Mobile device
_No response_
### Python version
3.9.13
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Under specific input, tf.raw_ops.SparseReduceSum\tf.raw_ops.SparseReduceMax encounters "Aborted (core dumped)".
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
input_dict = {
'input_indices': tf.constant([[-3, 1, -3], [-2, -4, 2]], dtype=tf.int64),
'input_values': tf.constant([1, 4], dtype=tf.float64),
'input_shape': tf.constant([0, 1700000000000, 6000000], dtype=tf.int64),
'reduction_axes': tf.constant([0], dtype=tf.int32),
'keep_dims': False
}
# crash
tf.raw_ops.SparseReduceSum(
**input_dict
)
# crash
# tf.raw_ops.SparseReduceMax(
# **input_dict
# )
```
### Relevant log output
```shell
2024-06-10 12:47:47.733403: 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.
2024-06-10 12:47:48.724820: F tensorflow/core/framework/tensor_shape.cc:201] Non-OK-status: InitDims(dim_sizes) status: INVALID_ARGUMENT: Encountered overflow when multiplying 1700000000000 with 6000000, result: -8246744073709551616
Aborted (core dumped)
```
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} | Remove the standalone python autotuner for Triton
Triton integration is now stable and up-to-date with upstream, and the built-in autotuner in XLA is in a better state. Thus, there is no need to maintain a separate python autotuner anymore, so we can remove this code (we also haven't use it for months now).
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} | PR #12599: [ROCM] rccl kernel traces included in gpu profiler
Imported from GitHub PR https://github.com/openxla/xla/pull/12599
Adding HIP_API_ID_hipExtLaunchKernel in xla gpu's rocm-tracer to captures rccl kernel traces.
Copybara import of the project:
--
0dc90376d09a91862f56aabecd785ee7bb9afd86 by zahiqbal <zahid.iqbal@amd.com>:
[ROCM] rccl kernel traces included in gpu profiler
Merging this change closes #12599
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/12599 from ROCm:rocm-jaxlib-v0.4.26-w-profiler-fixes 0dc90376d09a91862f56aabecd785ee7bb9afd86
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} | Replace Interval::NumElements with ::GetLoopTripCount.
The latter has some safety checks built in. Replace unsafe
uses of NumElements with `IsFeasible`.
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The API can't fail.
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Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04.3 LTS (x86_64)
### Mobile device
_No response_
### Python version
3.9.13
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
tf.raw_ops.FractionalMaxPoolGrad triggers a crash when the col_pooling_sequence parameter is a very small negative number. Additionally, the same situation exists with the row_pooling_sequence parameter.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
overlapping = True
orig_input = tf.constant(100, shape=[1,4,7,1], dtype=tf.float32)
orig_output = tf.constant(.453409232, shape=[1,7,13,1], dtype=tf.float32)
out_backprop = tf.constant(.453409232, shape=[1,7,13,1], dtype=tf.float32)
# row_pooling_sequence = tf.constant(-1250999896764, shape=[5], dtype=tf.int64) # same
row_pooling_sequence = tf.constant(0, shape=[5], dtype=tf.int64)
col_pooling_sequence = tf.constant(-1250999896764, shape=[5], dtype=tf.int64)
tf.raw_ops.FractionalMaxPoolGrad(orig_input=orig_input, orig_output=orig_output, out_backprop=out_backprop, row_pooling_sequence=row_pooling_sequence, col_pooling_sequence=col_pooling_sequence, overlapping=overlapping)
```
### Relevant log output
```shell
2024-06-10 08:49:22.910373: 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.
Segmentation fault (core dumped)
```
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} | Don't attempt to vectorize complex reductions.
vector<complex<...>> is not a thing. It's pointless anyway.
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"Due to our project plan, could you share arrival version and expected date for fixed so release ? \r\n\r\nOur project limits use not 16Kb aligned so files... we need to explain them.. please help us. \r\n"
] | "2024-06-10T06:46:27" | "2024-06-11T12:45:03" | null | NONE | null | null | null | ### 1. System information
TF lite 2.16.1
### 2. Code
/arm64-v8a/libtensorflowlite_jni.so does not support 16KB alignement.
google guide :https://developer.android.com/guide/practices/page-sizes?hl=en
when is ETA for supporting 16KB so alignment? | {
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} | PR #13542: [NVIDIA] Change DCE to replay control deps for GTE-fusion simplification.
Imported from GitHub PR https://github.com/openxla/xla/pull/13542
We have seen a case when some optimization pipelines(ie tuple simplifier) add control deps to a GTE output of a fusion kernel to preserve execution order, DCE's un-used GTE simplification logic will error out trying to remove the GTE. Instead of replacing and removing, we can use ReplaceInstruction with relay_control_dependency set to true to move the control deps to the fusion instruction.
Copybara import of the project:
--
1de482f5a80f4e9a9b2c8203129111d2aaa63f28 by TJ <tjx@nvidia.com>:
Change DCE to replay control deps for GTE-fusion simplification.
Merging this change closes #13542
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13542 from Tixxx:tixxx/dce_control_dep 1de482f5a80f4e9a9b2c8203129111d2aaa63f28
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Now only VLOG messages are captured, which matches what
TF_CPP_VLOG_FILENAME suggests.
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"I was able to reproduce the issue on TensorFlow v2.15, v2.16 and tf-nightly. Kindly find the [gist](https://colab.research.google.com/gist/tilakrayal/bbbed178ef32469355164182366d3a90/untitled1947.ipynb) and also the screenshot for the reference.\r\n\r\n\r\n"
] | "2024-06-10T03:32:42" | "2024-06-11T06:20:17" | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
TensorFlow Nightly
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04.3 LTS (x86_64)
### Mobile device
_No response_
### Python version
3.9.13
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
On specific input, tf.raw_ops.SparseCountSparseOutput will output "The session crashed because it took up all available RAM."
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
binary_output = True
indices = tf.constant(0, shape=[3456,2], dtype=tf.int64)
values = tf.constant(536870912, shape=[3456], dtype=tf.int32)
dense_shape = tf.constant([125099989676412,125099989676412], shape=[2], dtype=tf.int64)
weights = tf.constant(51, shape=[3456], dtype=tf.int32)
tf.raw_ops.SparseCountSparseOutput(
indices=indices, values=values, dense_shape=dense_shape, weights=weights, binary_output=binary_output,
minlength=0,
maxlength=0,
name=None
)
```
### Relevant log output
```shell
Jun 10, 2024, 11:31:57 AM WARNING WARNING:root:kernel 7c5d2f95-9ab7-4b7c-99c0-d8949477c9f1 restarted
Jun 10, 2024, 11:31:57 AM INFO KernelRestarter: restarting kernel (1/5), keep random ports
Jun 10, 2024, 11:31:29 AM WARNING 2024-06-10 03:31:29.145870: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
Jun 10, 2024, 11:31:26 AM WARNING To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
Jun 10, 2024, 11:31:26 AM WARNING 2024-06-10 03:31:26.481147: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
Jun 10, 2024, 11:31:26 AM WARNING 2024-06-10 03:31:26.469832: 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
Jun 10, 2024, 11:31:26 AM WARNING 2024-06-10 03:31:26.467591: 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
Jun 10, 2024, 11:31:26 AM WARNING 2024-06-10 03:31:26.467524: 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
Jun 10, 2024, 11:31:18 AM INFO Kernel started: 7c5d2f95-9ab7-4b7c-99c0-d8949477c9f1, name: python3
Jun 10, 2024, 11:30:19 AM INFO Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
```
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} | Batch `pxla.shard_args` calls triggered by `jax.device_put`
With this change, one `jax.device_put` call now corresponds to one `device_put_p.bind()` instead of one per array. Immediately, this improves the performance of `jax.device_put(...)` with a large pytree by amortizing the calls to `pxla.shard_args`. Also, backends that implement efficient batch transfers (https://github.com/tensorflow/tensorflow/pull/69096) will batch device-to-device transfers across arrays in a pytree.
The api_benchmark indicates that `device_put` with a single array is slower by 10%, but `device_put` with 10 to 1000 arrays is ~30% faster. This seems acceptable since a single-array `device_put` is fast anyway (~45us on my workstation). If this becomes a problem, this can be addressed by rewriting part of `device_put` in C++.
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} | [xla:cpu] Emit partitioned loops if operation marked for parallel execution
+ temporary rolled back thread pool support in HostKernel as I'll be reworking it to support thunks
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"@yuvch98 Could you try creating a new virtual environment to avoid conflicts with existing project dependencies and if you still face the issue then please post this issue on [models](https://github.com/tensorflow/models/issues?q=is%3Aissue+is%3Aopen) repository for further assistance.\r\nThank you!"
] | "2024-06-09T17:48:09" | "2024-06-10T08:53:30" | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 2.16.1
### Custom code
No
### OS platform and distribution
Google Colab
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I encountered an AttributeError while trying to fit the model using the MoViNet Streaming Model Training and Inference notebook provided in the TensorFlow Model Garden repository. The specific error message is as follows:
in addition, there is a minor adjustment needed to be done in this code:
### Standalone code to reproduce the issue
```shell
The issue can be reproduced using the notebook from the official TensorFlow Models GitHub repository:
https://github.com/tensorflow/models/blob/f9fdc4faef47af76351204b6d8df576f0e79baab/official/projects/movinet/movinet_streaming_model_training_and_inference.ipynb
```
### Relevant log output
```shell
AttributeError Traceback (most recent call last)
<ipython-input-18-49dc73e1e0f6> in <cell line: 1>()
----> 1 results = model.fit(train_ds,
2 validation_data=val_ds,
3 epochs=2,
4 validation_freq=1,
5 verbose=1,
1 frames
/usr/local/lib/python3.10/dist-packages/tf_keras/src/callbacks.py in <genexpr>(.0)
243 getattr(cb, "_supports_tf_logs", False)
244 for cb in self.callbacks
--> 245 if cb._implements_train_batch_hooks()
246 or cb._implements_test_batch_hooks()
247 or cb._implements_predict_batch_hooks()
AttributeError: 'ModelCheckpoint' object has no attribute '_implements_train_batch_hooks'
```
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} | Issue a warning where code relies on a bug where treedef.flatten_up_to(...) was overly permissive for None treedefs.
For example, tree_map(..., None, [2, 3]) previously did not raise an error, but None is a container and only leaves can be considered tree prefixes in this case.
In a future release of JAX, this behavior will become an error.
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"Hi **@ALAZZURRI** ,\r\n- I was able to reproduce same issue. here i am providing screenshots for reference.\r\n<img width=\"1495\" alt=\"image\" src=\"https://github.com/tensorflow/tensorflow/assets/147127861/3c23b9e9-799f-4de9-a4f7-4ef57b2adce0\">\r\n<img width=\"1495\" alt=\"image (2)\" src=\"https://github.com/tensorflow/tensorflow/assets/147127861/b32a72fd-2822-442a-9e33-b95b7a31e50c\">\r\nThank you!"
] | "2024-06-08T16:12:29" | "2024-06-11T08:45:27" | null | NONE | null | null | null | i want install tflite-model-maker but i face issue when install
i do this
1. install python 3.12.4
2. create Virtual Environments and Activate
3. inside env use " pip install tflite-model-maker"
env_name>pip install tflite-model-maker
Collecting tflite-model-maker
Using cached tflite_model_maker-0.4.3-py3-none-any.whl.metadata (5.4 kB)
Collecting tf-models-official==2.3.0 (from tflite-model-maker)
Using cached tf_models_official-2.3.0-py2.py3-none-any.whl.metadata (1.3 kB)
Collecting numpy<1.23.4,>=1.17.3 (from tflite-model-maker)
Using cached numpy-1.23.3.tar.gz (10.7 MB)
Installing build dependencies ... done
Getting requirements to build wheel ... error
error: subprocess-exited-with-error
× Getting requirements to build wheel did not run successfully.
│ exit code: 1
╰─> [33 lines of output]
Traceback (most recent call last):
File "C:\Users\aalrahahleh\AppData\Local\Programs\Python\Python312\Lib\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 353, in <module>
main()
File "C:\Users\aalrahahleh\AppData\Local\Programs\Python\Python312\Lib\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 335, in main
json_out['return_val'] = hook(**hook_input['kwargs'])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\aalrahahleh\AppData\Local\Programs\Python\Python312\Lib\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 112, in get_requires_for_build_wheel
backend = _build_backend()
^^^^^^^^^^^^^^^^
File "C:\Users\aalrahahleh\AppData\Local\Programs\Python\Python312\Lib\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 77, in _build_backend
obj = import_module(mod_path)
^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\aalrahahleh\AppData\Local\Programs\Python\Python312\Lib\importlib\__init__.py", line 90, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<frozen importlib._bootstrap>", line 1387, in _gcd_import
File "<frozen importlib._bootstrap>", line 1360, in _find_and_load
File "<frozen importlib._bootstrap>", line 1310, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 488, in _call_with_frames_removed
File "<frozen importlib._bootstrap>", line 1387, in _gcd_import
File "<frozen importlib._bootstrap>", line 1360, in _find_and_load
File "<frozen importlib._bootstrap>", line 1331, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 935, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 995, in exec_module
File "<frozen importlib._bootstrap>", line 488, in _call_with_frames_removed
File "C:\Users\aalrahahleh\AppData\Local\Temp\pip-build-env-e6sy9x22\overlay\Lib\site-packages\setuptools\__init__.py", line 16, in <module>
import setuptools.version
File "C:\Users\aalrahahleh\AppData\Local\Temp\pip-build-env-e6sy9x22\overlay\Lib\site-packages\setuptools\version.py", line 1, in <module>
import pkg_resources
File "C:\Users\aalrahahleh\AppData\Local\Temp\pip-build-env-e6sy9x22\overlay\Lib\site-packages\pkg_resources\__init__.py", line 2172, in <module>
register_finder(pkgutil.ImpImporter, find_on_path)
^^^^^^^^^^^^^^^^^^^
AttributeError: module 'pkgutil' has no attribute 'ImpImporter'. Did you mean: 'zipimporter'?
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error
× Getting requirements to build wheel did not run successfully.
│ exit code: 1
╰─> See above for output.
note: This error originates from a subprocess, and is likely not a problem with pip.
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"@VivekEedeeCreations,\r\nLooks like this issue is not related to tensorflow. AFAIK the reason might be that Some surface formats are not compatible with Direct2D operations regardless of how they are used. This message indicates that the application should choose another surface format.\r\n\r\nhttps://learn.microsoft.com/en-us/windows/win32/direct2d/d1128\r\nhttps://github.com/mpv-player/mpv/issues/11206\r\n\r\nThank you!"
] | "2024-06-08T12:27:15" | "2024-06-10T06:30:11" | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
4.17
### Custom code
Yes
### OS platform and distribution
Windows 10
### 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?
I'm using tensor flow's pose-detection model and a canvas is drawing in a webcam feed with p5.js but in my system i'm not able to see the green lines drawn on my p5 and getting my console filled with these
514:1 DXGI format does not support cross-API sharing.
at ValidateVideoTextureCanBeShared (..\..\third_party\dawn\src\dawn\native\d3d12\TextureD3D12.cpp:183)
[Invalid Texture] is invalid.
- While calling [Invalid Texture].CreateView([TextureViewDescriptor]).
### Standalone code to reproduce the issue
```shell
514:1 DXGI format does not support cross-API sharing.
at ValidateVideoTextureCanBeShared (..\..\third_party\dawn\src\dawn\native\d3d12\TextureD3D12.cpp:183)
[Invalid Texture] is invalid.
- While calling [Invalid Texture].CreateView([TextureViewDescriptor]).
This is console filled with when starting the video feed with tensor flow pose detection
```
### Relevant log output
```shell
514:1 DXGI format does not support cross-API sharing.
at ValidateVideoTextureCanBeShared (..\..\third_party\dawn\src\dawn\native\d3d12\TextureD3D12.cpp:183)
[Invalid Texture] is invalid.
- While calling [Invalid Texture].CreateView([TextureViewDescriptor]).
```
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"@wx0608,\r\nCould you please share a reproducible code/colab gist that supports your statement so that the issue can be easily understood? Thank you!"
] | "2024-06-08T06:18:41" | "2024-06-10T09:41:17" | null | NONE | null | null | null | ### Issue type
Feature Request
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.16
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 22.04
### Mobile device
_No response_
### Python version
3.9.19
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
12.4/8.9.7.29
### GPU model and memory
NVIDIA GeForce RTX 3090
### Current behavior?
When TF deterministic was set, runtime exception was thrown at MaxPooling2D().
### Standalone code to reproduce the issue
```shell
When TF deterministic was set, runtime exception was thrown at MaxPooling2D().
```
### Relevant log output
```shell
Traceback (most recent call last):
File "/home/ws/miniconda3/envs/tf216/lib/python3.9/site-packages/IPython/core/interactiveshell.py", line 3526, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-2-3dda39ff370e>", line 1, in <module>
runfile('/mnt/projects/Projects/Test_Classification/train_model.py', wdir='/mnt/projects/Projects/Test_Classification')
File "/opt/pycharm-community-2024.1/plugins/python-ce/helpers/pydev/_pydev_bundle/pydev_umd.py", line 197, in runfile
pydev_imports.execfile(filename, global_vars, local_vars) # execute the script
File "/opt/pycharm-community-2024.1/plugins/python-ce/helpers/pydev/_pydev_imps/_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "/mnt/projects/Projects/Test_Classification/train_model.py", line 956, in <module>
history = model.fit(x_train, y_train,
File "/home/ws/miniconda3/envs/tf216/lib/python3.9/site-packages/keras/src/utils/traceback_utils.py", line 122, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/home/ws/miniconda3/envs/tf216/lib/python3.9/site-packages/tensorflow/python/eager/execute.py", line 53, in quick_execute
tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.UnimplementedError: Graph execution error:
Detected at node gradient_tape/functional_1_1/max_pooling2d_4_1/MaxPool2d/MaxPoolGrad defined at (most recent call last):
<stack traces unavailable>
GPU MaxPool gradient ops do not yet have a deterministic XLA implementation.
[[{{node gradient_tape/functional_1_1/max_pooling2d_4_1/MaxPool2d/MaxPoolGrad}}]]
tf2xla conversion failed while converting __inference_one_step_on_data_13588[]. Run with TF_DUMP_GRAPH_PREFIX=/path/to/dump/dir and --vmodule=xla_compiler=2 to obtain a dump of the compiled functions.
[[StatefulPartitionedCall]] [Op:__inference_one_step_on_iterator_14045]
```
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We have substantial changes coming up to TSL logging; add tests
to make sure we do not introduce regressions.
Also remove vmodule_test from tensorflow/core/platform since
it is now redundant.
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This keeps outputs of entry computation when it's fused into another
compuatation.
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FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13523 from ROCm:ci_hotfit_20240607 931ddf1d5b87c85c3fe1d825665015882d50ef42
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Also added a verification to ensure there are no multiple pinned allocations.
Reverts changelist 641225077
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} | Refactor in preparation for moving JAX/XLA CI to build.py
* Move some `DockerImage` fields to arguments to `pull_and_run`
* Don't supply a default workdir
* Don't give a default for `target_patterns` in BUILD
* Rename `_DEFAULT_TARGET_PATTERNS` to `_XLA_DEFAULT_TARGET_PATTERNS`
* Move comment about cudnn9 container to a more appropriate location
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} | [xla:cpu] Use recursive work splitting to submit host tasks
This significantly improves wall time to submit tasks into a thread pool:
BM_HostKernelAsyncLaunch/1/process_time 15.4ns ± 0% 15.0ns ± 4% -2.84%
BM_HostKernelAsyncLaunch/4/process_time 7.99µs ± 3% 7.53µs ±34% -5.72%
BM_HostKernelAsyncLaunch/8/process_time 18.5µs ± 3% 17.6µs ±30% -4.84%
BM_HostKernelAsyncLaunch/16/process_time 39.4µs ± 2% 25.0µs ±12% -36.50%
BM_HostKernelAsyncLaunch/32/process_time 81.5µs ± 2% 31.3µs ± 9% -61.63%
BM_HostKernelAsyncLaunch/64/process_time 165µs ± 3% 33µs ± 7% -79.91%
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} | [xla:cpu] Optimize async kernel launch for a single task
----------------------------------------------------------------------------------
Benchmark Time CPU Iterations
----------------------------------------------------------------------------------
BM_HostKernelSyncLaunch/1/process_time 11.0 ns 11.0 ns 62432863
BM_HostKernelAsyncLaunch/1/process_time 13.7 ns 13.7 ns 50849533
~2-3 ns lost in loading ready async value from a static variable
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Reverts changelist 641225077
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13062 from philipphack:u_fp8_windowed_einsum_xla 3ea415d65e47087bb077fa2b13e34c82a4dae87b
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} | [xla:cpu] Keep frame pointer in host kernel functions
Reverts changelist 641225077
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/13062 from philipphack:u_fp8_windowed_einsum_xla 3ea415d65e47087bb077fa2b13e34c82a4dae87b
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