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PR #10759: [XLA:CPU][oneDNN] Enable matrix-vector and vector-vector product
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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/64688/checks?check_run_id=23210724686) 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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PR #10759: [XLA:CPU][oneDNN] Enable matrix-vector and vector-vector product
Imported from GitHub PR https://github.com/openxla/xla/pull/10759
This PR relaxes conditions to rewrite dot operations of the form vector-matrix, matrix-vector, or vector-vector to oneDNN custom calls, provided the original problem meets the empirically determined multiply-accumulate threshold. In particular this PR:
1. Relaxes some constraints on Dot to oneDNN matmul custom call conversion
2. Reconfigures the dimensions of the operands and outputs of convertible dot operations.
3. Adds tests to verify rewrite and execution result
Copybara import of the project:
--
d24e5cd0b77d0734a0f33011ae03127f00d80e7d by Akhil Goel <[email protected]>:
Relax constraints for matmul rewrite
--
9582288fc5d06eb8f4641be32d6c41746f13dbce by Akhil Goel <[email protected]>:
Fix gemv test after merge
--
604d4fbd29a78a7d305092356176e31081ea4ff0 by Akhil Goel <[email protected]>:
Address review comments
--
4534fbd7e96a6e45ccc5a501b38b940aa8bd9d38 by Akhil Goel <[email protected]>:
Optional commit
Merging this change closes #10759
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10759 from Intel-tensorflow:akhil/gemv 4534fbd7e96a6e45ccc5a501b38b940aa8bd9d38
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PR_kwDOArmXAs5rFP6m
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[xla][gpu] Extracting triton codegen requirements for hlo instructions
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[xla][gpu] Extracting triton codegen requirements for hlo instructions
This CL extracts current triton codegen requirements for each hlo instruction into a single function to clean the codes in the triton fusion passes.
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[xla] remove BorrowingLiteral constructor for LiteralProto
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[xla] remove BorrowingLiteral constructor for LiteralProto
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"i had the same error! \r\n\r\nI found a soultion using the keras legacy optimizer like this : \r\n\r\ntf.keras.optimizers.legacy.SGD\r\n\r\npython: 3.8.18\r\ntensorflow: 2.13.0",
"Hi @snehajhaveri01,\r\n\r\nPlease fill the [template](https://github.com/tensorflow/tensorflow/issues/new?assignees=&labels=TFLiteConverter&projects=&template=tflite-converter-issue.md) and share the reproducible code.\r\n\r\nThank You",
"> i had the same error!\r\n> \r\n> I found a soultion using the keras legacy optimizer like this :\r\n> \r\n> tf.keras.optimizers.legacy.SGD\r\n> \r\n> python: 3.8.18 tensorflow: 2.13.0\r\n\r\ni solved it the same way , thank you !",
"> 3.8.18\r\n\r\n\r\n\r\n> > i had the same error!\r\n> > I found a soultion using the keras legacy optimizer like this :\r\n> > tf.keras.optimizers.legacy.SGD\r\n> > python: 3.8.18 tensorflow: 2.13.0\r\n> \r\n> i solved it the same way , thank you !\r\n \r\nhow'd you solve it? i could not maintain python version in colab. I tried it on my machine. I have .h5 model but it is not able to load and showing\r\n\r\n```\r\nTypeError: Error when deserializing class 'InputLayer' using config={'batch_shape': [None, 50], 'dtype': 'float32', 'sparse': False, 'name': 'input_layer_10'}.\r\n\r\nException encountered: Unrecognized keyword arguments: ['batch_shape']\r\n```\r\n\r\nI tried making python version of 3.8.18 and when installed tensorflow==2.13.0. It caused some problems with numpy and when I resolved it. now it is still not resolving the issue.\r\n\r\n@maximilianofried @snehajhaveri01 ",
"I get this identical problem when loading a kaggle model into colab. \r\nHow can it be that code that works in Kaggle fails to run in Google Colab?\r\nOn kaggle I saved a model and verified that it can load correctly.\r\nmodel2 = load_model('/kaggle/working/model2.keras')\r\nThis works correctly on Kaggle and I can print the model summary. \r\nI then uploaded the .keras file to Google Colab, ran the same line of code to load the model and I get errors. \r\n\r\nValueError: Unrecognized keyword arguments: ['batch_shape']\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTypeError Traceback (most recent call last)\r\n[/usr/local/lib/python3.10/dist-packages/keras/src/engine/base_layer.py](https://localhost:8080/#) in from_config(cls, config)\r\n 868 return cls(**config)\r\n 869 except Exception as e:\r\n--> 870 raise TypeError(\r\n 871 f\"Error when deserializing class '{cls.__name__}' using \"\r\n 872 f\"config={config}.\\n\\nException encountered: {e}\"\r\n\r\nTypeError: Error when deserializing class 'InputLayer' using config={'batch_shape': [None, 224, 224, 3], 'dtype': 'float32', 'sparse': False, 'name': 'input_layer_7'}.\r\n\r\nException encountered: Unrecognized keyword arguments: ['batch_shape']",
"> I get this identical problem when loading a kaggle model into colab. How can it be that code that works in Kaggle fails to run in Google Colab? On kaggle I saved a model and verified that it can load correctly. model2 = load_model('/kaggle/working/model2.keras') This works correctly on Kaggle and I can print the model summary. I then uploaded the .keras file to Google Colab, ran the same line of code to load the model and I get errors.\r\n> \r\n> ValueError: Unrecognized keyword arguments: ['batch_shape']\r\n> \r\n> During handling of the above exception, another exception occurred:\r\n> \r\n> TypeError Traceback (most recent call last) [/usr/local/lib/python3.10/dist-packages/keras/src/engine/base_layer.py](https://localhost:8080/#) in from_config(cls, config) 868 return cls(**config) 869 except Exception as e: --> 870 raise TypeError( 871 f\"Error when deserializing class '{cls.**name**}' using \" 872 f\"config={config}.\\n\\nException encountered: {e}\"\r\n> \r\n> TypeError: Error when deserializing class 'InputLayer' using config={'batch_shape': [None, 224, 224, 3], 'dtype': 'float32', 'sparse': False, 'name': 'input_layer_7'}.\r\n> \r\n> Exception encountered: Unrecognized keyword arguments: ['batch_shape']\r\n\r\nThis thread is dead. Try opening a new issue.\r\nAlso, I don't really know but just to check, have you used `model.save()` to save your model or `model.export()`?\r\nI used `model.export()` and it worked for me, when you save your model using `model.export()` it saves the model's assets, variables and other stuff which works with conversion (at least in my case it did).\r\n\r\nAfter using `model.export('mymodel')` I used this code to convert:\r\n\r\n```\r\nimport tensorflow as tf\r\n\r\nconverter = tf.lite.TFLiteConverter.from_saved_model(\"mymodel\")\r\ntflite_model = converter.convert()\r\n\r\nwith open(\"mymodel.tflite\", \"wb\") as f:\r\n f.write(tflite_model)\r\n```",
"I will try model.export later, maybe that will work. \r\nIt is also possible the problem was connected to my loss function \r\nloss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\r\nI added a softmax activation to the model and changed the loss function to loss='categorical_crossentropy' \r\nAfter training in kaggle I saved only the weights, then created the model in code in colab and imported the weights. That model could then be converted to .tflite ",
"> I will try model.export later, maybe that will work. It is also possible the problem was connected to my loss function loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) I added a softmax activation to the model and changed the loss function to loss='categorical_crossentropy' After training in kaggle I saved only the weights, then created the model in code in colab and imported the weights. That model could then be converted to .tflite\r\n\r\nYou can use `softmax` with `SparseCategoricalCrossEntropy` it's how it is usually used.\r\nAlso one more thing `model.save()` is now considered **lagacy** so now we use `model.export()`. \r\nI hope your issue is resolved.",
"Your tip about export is good. \r\nI tried this : \r\nsave_name = '/kaggle/working/mymodelpath'\r\nmodel.export(save_name) # this will create a folder /mymodelpath with some subfolders and files\r\nconverter = tf.lite.TFLiteConverter.from_saved_model(save_name) # this will load from the subfolder and print some info\r\n\r\nThat seemed to work well. "
] | 2024-03-28T17:09:48 | 2024-06-12T03:31:51 | 2024-04-05T08:19:33 |
NONE
| null | null | null |
Error :
Traceback (most recent call last):
File "C:\Users\jhave\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\engine\base_layer.py", line 868, in from_config
return cls(**config)
File "C:\Users\jhave\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\utils\traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\jhave\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\engine\input_layer.py", line 153, in __init__
raise ValueError(
ValueError: Unrecognized keyword arguments: ['batch_shape']
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "c:\Users\jhave\OneDrive\Documents\mood-shift\model.py", line 6, in <module>
keras_model = tf.keras.models.load_model('mood-shift.keras')
File "C:\Users\jhave\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\saving\saving_api.py", line 254, in load_model
return saving_lib.load_model(
File "C:\Users\jhave\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\saving\saving_lib.py", line 281, in load_model
raise e
File "C:\Users\jhave\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\saving\saving_lib.py", line 246, in load_model
model = deserialize_keras_object(
File "C:\Users\jhave\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\saving\serialization_lib.py", line 728, in deserialize_keras_object
instance = cls.from_config(inner_config)
File "C:\Users\jhave\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\engine\sequential.py", line 466, in from_config
layer = layer_module.deserialize(
File "C:\Users\jhave\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\layers\serialization.py", line 276, in deserialize
return serialization_lib.deserialize_keras_object(
File "C:\Users\jhave\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\saving\serialization_lib.py", line 609, in deserialize_keras_object
return deserialize_keras_object(
File "C:\Users\jhave\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\saving\serialization_lib.py", line 728, in deserialize_keras_object
instance = cls.from_config(inner_config)
File "C:\Users\jhave\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\engine\base_layer.py", line 870, in from_config
raise TypeError(
TypeError: Error when deserializing class 'InputLayer' using config={'batch_shape': [None, 30], 'dtype': 'float32', 'sparse': False, 'name': 'input_layer'}.
Exception encountered: Unrecognized keyword arguments: ['batch_shape']
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PR_kwDOArmXAs5rFKbL
| 64,684 |
Pass the correct operands for branch computations when invoking EinsumHeightAnalysis::HandleCalledComputation.
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Pass the correct operands for branch computations when invoking EinsumHeightAnalysis::HandleCalledComputation.
Reverts e5c11f3d1e1bef39cb1ea0937ac5addf04942f32
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PR_kwDOArmXAs5rFEXP
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#shlo_ref: Fix adb typo
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#shlo_ref: Fix adb typo
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10344 from Intel-tensorflow:kanvi/bmm+mul+add 9c1fee304a6cfed55c0c869d1930641d942cf93f
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PR_kwDOArmXAs5rFApT
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Restore GOOGLE_CUDA guard in scoped_annotation.h
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Restore GOOGLE_CUDA guard in scoped_annotation.h
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I_kwDOArmXAs6D8SWS
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Check failed: ret == 0 (11 vs. 0)Thread tf_data_private_threadpool creation via pthread_create() failed. Aborted (core dumped)
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[
"@Wasim04,\r\nI am trying to execute the mentioned code with the GPU and CPU environments and the time taken to execute the code is more than expected. Could you please allow some duration to analyse the issue and provide the update on the same. Thank you!",
"@tilakrayal \r\nThanks for your response. Please take your time. \r\n",
"@tilakrayal \r\nHi, just wondering if you had a chance to look into this issue?"
] | 2024-03-28T16:15:23 | 2024-05-07T10:27:16 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.15.0.post1
### Custom code
Yes
### OS platform and distribution
Rocky Linux 8.9
### Mobile device
_No response_
### Python version
3.10.12
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
8.9.2.26
### GPU model and memory
NVIDIA A100-SXM4-80GB
### Current behavior?
I am trying to run a simple denoising autoencoder. my training data and label data are 900 samples of healpy maps with nside 64 resolution, loaded as numpy array. After normalising the maps, I used tf.data.Dataset.from_tensor_slices, to create dataset. when I used random noise to create these maps and ran on jupyter notebook, although took ages to initiate training after doing model.fit(), but it did run and produced some result. knowing that the model works, I tried to run on GPU with real data. this is where the issue started. it shows the following error: Check failed: ret == 0 (11 vs. 0)Thread tf_data_private_threadpool creation via pthread_create() failed. Aborted (core dumped), and the process stops.
### Standalone code to reproduce the issue
```shell
Here is a colab link:
https://colab.research.google.com/drive/1odbf3gQT9h-DI6zdh5e1llmG9zhjY45l?usp=sharing
It runs on colab. but it doesn't run in the terminal.
```
### Relevant log output
```shell
2024-03-27 12:14:07.288975: F external/local_tsl/tsl/platform/default/env.cc:74] Check failed: ret == 0 (11 vs. 0)Thread tf_data_private_threadpool creation via pthread_create() failed.
Aborted (core dumped)
```
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PR_kwDOArmXAs5rErK_
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#shlo_ref Add `compare` op.
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#shlo_ref Add `compare` op.
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Removed LOG to fix unwanted end of sequence warning when iterating over a dataset
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Fix #62963
By analising the stack traces in both the core and python parts, I believe the program flow and execution is correct, and we cannot stop the raise of the status OUT_OF_RANGE, with message "End of sequence" (that happens in IteratorGetNextOp::DoCompute), since it will cause an internal behaviour clearly different than when we return OK as a status, which is intended in the last iteration of the iterator, when the bug happens. It also does not make sense to stop the call to the StartAbort of the Rendezvous interface (that happens in ProcessFunctionLibraryRuntime::RunMultiDeviceSync) that originates the bug since it prints the LOG introduced in the LocalRendezvous::DoAbort function, since the documentation states that the only requirement for the StartAbort to be initiated is a status different from OK, which is the case. Therefore, to fix the bug, I think the solution is effectively removing the log introduced in the commit referenced in the issue.
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PR #10759: [XLA:CPU][oneDNN] Enable matrix-vector and vector-vector product
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PR #10759: [XLA:CPU][oneDNN] Enable matrix-vector and vector-vector product
Imported from GitHub PR https://github.com/openxla/xla/pull/10759
This PR relaxes conditions to rewrite dot operations of the form vector-matrix, matrix-vector, or vector-vector to oneDNN custom calls, provided the original problem meets the empirically determined multiply-accumulate threshold. In particular this PR:
1. Relaxes some constraints on Dot to oneDNN matmul custom call conversion
2. Reconfigures the dimensions of the operands and outputs of convertible dot operations.
3. Adds tests to verify rewrite and execution result
Copybara import of the project:
--
d24e5cd0b77d0734a0f33011ae03127f00d80e7d by Akhil Goel <[email protected]>:
Relax constraints for matmul rewrite
--
9582288fc5d06eb8f4641be32d6c41746f13dbce by Akhil Goel <[email protected]>:
Fix gemv test after merge
--
604d4fbd29a78a7d305092356176e31081ea4ff0 by Akhil Goel <[email protected]>:
Address review comments
--
4534fbd7e96a6e45ccc5a501b38b940aa8bd9d38 by Akhil Goel <[email protected]>:
Optional commit
Merging this change closes #10759
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10759 from Intel-tensorflow:akhil/gemv 4534fbd7e96a6e45ccc5a501b38b940aa8bd9d38
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#shlo_ref Print tensor types in error messages.
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#shlo_ref Print tensor types in error messages.
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PR_kwDOArmXAs5rEWgo
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PR #10344: [XLA:CPU] Enable BMM+Mul+Add fusion
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PR #10344: [XLA:CPU] Enable BMM+Mul+Add fusion
Imported from GitHub PR https://github.com/openxla/xla/pull/10344
This PR enables the BatchMatMul + Mul + Add fusion and adds a simple test
Copybara import of the project:
--
2a202c9d171064bb3005e0753af41e26f2e1baf3 by Kanvi Khanna <[email protected]>:
Enable BMM+Mul+Add fusion
--
584e59861f5f91fae8222b9281e501dbeb270b94 by Kanvi Khanna <[email protected]>:
Address review comments
Merging this change closes #10344
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10344 from Intel-tensorflow:kanvi/bmm+mul+add 9c1fee304a6cfed55c0c869d1930641d942cf93f
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PR_kwDOArmXAs5rESn8
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[PJRT:GPU] Fix implementation of .compute_capability on devices to remove the need for an ifdef.
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[PJRT:GPU] Fix implementation of .compute_capability on devices to remove the need for an ifdef.
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[xla:gpu] Added a test checking that Triton kernels compiled via XLA do not dedup arguments
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[xla:gpu] Added a test checking that Triton kernels compiled via XLA do not dedup arguments
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PR_kwDOArmXAs5rDxY_
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Update configure.py to support pre-release versions of clang
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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/64673/checks?check_run_id=23201411801) 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.",
"Fixed the CLA",
"Hi @kanglant Can you please review this PR ? Thank you!",
"For context, this is required for OSS-Fuzz. See https://github.com/google/oss-fuzz/pull/11714/files#r1584382892",
"Hi @kanglant Can you please review this PR ? Thank you!"
] | 2024-03-28T13:57:29 | 2024-06-07T17:23:33 | null |
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Since commit 055e421c4004b60588de2d728cc51cdd749dd350, pre-release versions of clang may no longer be supported, if they are built from the llvm git and include the "git" suffix in their version message.
In this case, `retrieve_clang_version` will return `None`, which will then abort the program:
```
Traceback (most recent call last):
File "configure.py", line 1466, in <module>
main()
File "configure.py", line 1417, in main
disable_clang_offsetof_extension(clang_version)
File "configure.py", line 886, in disable_clang_offsetof_extension
if int(clang_version.split('.')[0]) in (16, 17):
AttributeError: 'NoneType' object has no attribute 'split'
```
Fix this by falling back to an assumed version. That is, treat 18.0.0git as 18.0.0. The warning will still be printed.
For context, an example `clang --version` excerpt:
```
clang version 18.0.0git (https://github.com/llvm/llvm-project.git ea3a3b25b93664c8be750ac3cd550855dcd1848b)
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[xla:gpu][NFC] Refactor and rename in address_computation_fusion_rewriter for clarity
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[xla:gpu][NFC] Refactor and rename in address_computation_fusion_rewriter for clarity
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Building TFLite fails on nnapi_delegate requiring C++20 extensions
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[
"I'm also having this issue",
"Hi @kubaraczkowski, can you please share your model.tflite file used in this step of reproducing:\r\n\r\n```sh\r\nbash tensorflow/lite/tools/build_aar.sh \\\r\n --input_models=model.tflite \\\r\n --target_archs=arm64-v8a\r\n```\r\n\r\nThanks!",
"Hi @pkgoogle ! \r\n\r\nThe TF model that I am using that also produces the same error is located here:\r\n\r\nhttps://github.com/naedx/tflite_android_build/blob/dev/models/whisper.tflite \r\n\r\n\r\n\r\n\r\n### Setup/config details:\r\n\r\n<details>\r\n\r\n```bash\r\n\r\ntf-docker /host_dir > ./tensorflow_src/configure\r\nYou have bazel 7.1.1 installed.\r\nPlease specify the location of python. [Default is /usr/bin/python3]: \r\n\r\n\r\nFound possible Python library paths:\r\n /usr/lib/python3/dist-packages\r\n /usr/local/lib/python3.11/dist-packages\r\nPlease input the desired Python library path to use. Default is [/usr/lib/python3/dist-packages]\r\n\r\nDo you wish to build TensorFlow with ROCm support? [y/N]: \r\nNo ROCm support will be enabled for TensorFlow.\r\n\r\nDo you wish to build TensorFlow with CUDA support? [y/N]: \r\nNo CUDA support will be enabled for TensorFlow.\r\n\r\nDo you want to use Clang to build TensorFlow? [Y/n]: \r\nClang will be used to compile TensorFlow.\r\n\r\nPlease specify the path to clang executable. [Default is /usr/lib/llvm-17/bin/clang]: \r\n\r\n\r\nYou have Clang 17.0.6 installed.\r\n\r\nPlease specify optimization flags to use during compilation when bazel option \"--config=opt\" is specified [Default is -Wno-sign-compare]: \r\n\r\n\r\nWould you like to interactively configure ./WORKSPACE for Android builds? [y/N]: y\r\nSearching for NDK and SDK installations.\r\n\r\nPreconfigured Bazel build configs. You can use any of the below by adding \"--config=<>\" to your build command. See .bazelrc for more details.\r\n\t--config=mkl \t# Build with MKL support.\r\n\t--config=mkl_aarch64 \t# Build with oneDNN and Compute Library for the Arm Architecture (ACL).\r\n\t--config=monolithic \t# Config for mostly static monolithic build.\r\n\t--config=numa \t# Build with NUMA support.\r\n\t--config=dynamic_kernels\t# (Experimental) Build kernels into separate shared objects.\r\n\t--config=v1 \t# Build with TensorFlow 1 API instead of TF 2 API.\r\nPreconfigured Bazel build configs to DISABLE default on features:\r\n\t--config=nogcp \t# Disable GCP support.\r\n\t--config=nonccl \t# Disable NVIDIA NCCL support.\r\nConfiguration finished\r\n\r\n```\r\n\r\n</details>\r\n\r\n\r\n\r\n### Build:\r\n\r\n```bash\r\n\r\ntf-docker /host_dir > tensorflow_src/tensorflow/lite/tools/build_aar.sh \\\r\n --input_models=/host_dir/models/whisper.tflite \\\r\n --target_archs=arm64-v8a\r\n\r\n```\r\n\r\n<details>\r\n\r\n```bash\r\n\r\n+++ dirname tensorflow_src/tensorflow/lite/tools/build_aar.sh\r\n++ cd tensorflow_src/tensorflow/lite/tools\r\n++ pwd\r\n+ SCRIPT_DIR=/host_dir/tensorflow_src/tensorflow/lite/tools\r\n++ cd /host_dir/tensorflow_src/tensorflow/lite/tools/../../../\r\n++ pwd\r\n+ ROOT_DIR=/host_dir/tensorflow_src\r\n+ TARGET_ARCHS=x86,x86_64,arm64-v8a,armeabi-v7a\r\n+ '[' '!' -z ']'\r\n+ '[' 2 -gt 4 ']'\r\n+ for i in \"$@\"\r\n+ case $i in\r\n+ FLAG_MODELS=/host_dir/models/whisper.tflite\r\n+ shift\r\n+ for i in \"$@\"\r\n+ case $i in\r\n+ TARGET_ARCHS=arm64-v8a\r\n+ shift\r\n+ cd /host_dir/tensorflow_src\r\n+ '[' '!' -f /host_dir/tensorflow_src/.tf_configure.bazelrc ']'\r\n+ grep -q ANDROID_SDK_HOME /host_dir/tensorflow_src/.tf_configure.bazelrc\r\n+ '[' -z /host_dir/models/whisper.tflite ']'\r\n+ TMP_DIR=/host_dir/tensorflow_src/tmp/\r\n+ rm -rf /host_dir/tensorflow_src/tmp/\r\n+ mkdir -p /host_dir/tensorflow_src/tmp/\r\n+ MODEL_NAMES=\r\n++ echo /host_dir/models/whisper.tflite\r\n++ sed 's/,/ /g'\r\n+ for model in $(echo ${FLAG_MODELS} | sed \"s/,/ /g\")\r\n+ cp /host_dir/models/whisper.tflite /host_dir/tensorflow_src/tmp/\r\n++ basename /host_dir/models/whisper.tflite\r\n+ MODEL_NAMES=,whisper.tflite\r\n+ TFLITE_OPS_SRCS=\r\n++ echo\r\n++ sed 's/,/ /g'\r\n+ generate_tflite_aar\r\n+ pushd /host_dir/tensorflow_src/tmp/\r\n+ message=('load(\"//tensorflow/lite:build_def.bzl\", \"tflite_custom_android_library\")' 'load(\"//tensorflow/lite/java:aar_with_jni.bzl\", \"aar_with_jni\")' '' 'tflite_custom_android_library(' ' name = \"custom_tensorflowlite\",')\r\n+ message+=(' '$(generate_list_field \"models\" $MODEL_NAMES))\r\n++ generate_list_field models ,whisper.tflite\r\n++ local name=models\r\n++ local list_string=,whisper.tflite\r\n++ list=(${list_string//,/ })\r\n++ local list\r\n++ message=(\"$name=[\")\r\n++ local message\r\n++ for item in \"${list[@]}\"\r\n++ message+=(\"\\\"$item\\\",\")\r\n++ message+=('],')\r\n++ printf %s 'models=[' '\"whisper.tflite\",' '],'\r\n+ message+=(' '$(generate_list_field \"srcs\" $TFLITE_OPS_SRCS))\r\n++ generate_list_field srcs\r\n++ local name=srcs\r\n++ local list_string=\r\n++ list=(${list_string//,/ })\r\n++ local list\r\n++ message=(\"$name=[\")\r\n++ local message\r\n++ message+=('],')\r\n++ printf %s 'srcs=[' '],'\r\n+ message+=(' '$(generate_list_field \"deps\" $FLAG_TFLITE_OPS_DEPS))\r\n++ generate_list_field deps\r\n++ local name=deps\r\n++ local list_string=\r\n++ list=(${list_string//,/ })\r\n++ local list\r\n++ message=(\"$name=[\")\r\n++ local message\r\n++ message+=('],')\r\n++ printf %s 'deps=[' '],'\r\n+ message+=(')' '' 'aar_with_jni(' ' name = \"tensorflow-lite\",' ' android_library = \":custom_tensorflowlite\",' ')' '')\r\n+ printf '%s\\n' 'load(\"//tensorflow/lite:build_def.bzl\", \"tflite_custom_android_library\")' 'load(\"//tensorflow/lite/java:aar_with_jni.bzl\", \"aar_with_jni\")' '' 'tflite_custom_android_library(' ' name = \"custom_tensorflowlite\",' ' models=[\"whisper.tflite\",],' ' srcs=[],' ' deps=[],' ')' '' 'aar_with_jni(' ' name = \"tensorflow-lite\",' ' android_library = \":custom_tensorflowlite\",' ')' ''\r\n+ popd\r\n+ bazel build -c opt --config=opt --cxxopt=--std=c++17 --fat_apk_cpu=arm64-v8a --define=android_dexmerger_tool=d8_dexmerger --define=android_incremental_dexing_tool=d8_dexbuilder --host_crosstool_top=@bazel_tools//tools/cpp:toolchain //tmp:tensorflow-lite\r\nINFO: Reading 'startup' options from /host_dir/tensorflow_src/.bazelrc: --windows_enable_symlinks\r\nINFO: Options provided by the client:\r\n Inherited 'common' options: --isatty=1 --terminal_columns=265\r\nINFO: Reading rc options for 'build' from /host_dir/tensorflow_src/.bazelrc:\r\n Inherited 'common' options: --experimental_repo_remote_exec\r\nINFO: Reading rc options for 'build' from /etc/bazel.bazelrc:\r\n 'build' options: --action_env=DOCKER_CACHEBUSTER=1712448870699421512 --host_action_env=DOCKER_HOST_CACHEBUSTER=1712448870763599131\r\nINFO: Reading rc options for 'build' from /host_dir/tensorflow_src/.bazelrc:\r\n 'build' options: --define framework_shared_object=true --define tsl_protobuf_header_only=true --define=use_fast_cpp_protos=true --define=allow_oversize_protos=true --spawn_strategy=standalone -c opt --announce_rc --define=grpc_no_ares=true --noincompatible_remove_legacy_whole_archive --features=-force_no_whole_archive --enable_platform_specific_config --define=with_xla_support=true --config=short_logs --config=v2 --define=no_aws_support=true --define=no_hdfs_support=true --experimental_cc_shared_library --experimental_link_static_libraries_once=false --incompatible_enforce_config_setting_visibility\r\nINFO: Reading rc options for 'build' from /host_dir/tensorflow_src/.tf_configure.bazelrc:\r\n 'build' options: --action_env PYTHON_BIN_PATH=/usr/bin/python3 --action_env PYTHON_LIB_PATH=/usr/lib/python3/dist-packages --python_path=/usr/bin/python3 --action_env CLANG_COMPILER_PATH=/usr/lib/llvm-17/bin/clang --repo_env=CC=/usr/lib/llvm-17/bin/clang --repo_env=BAZEL_COMPILER=/usr/lib/llvm-17/bin/clang --copt=-Wno-gnu-offsetof-extensions --action_env ANDROID_NDK_HOME=/android/ndk --action_env ANDROID_NDK_VERSION=25 --action_env ANDROID_NDK_API_LEVEL=30 --action_env ANDROID_BUILD_TOOLS_VERSION=34.0.0 --action_env ANDROID_SDK_API_LEVEL=30 --action_env ANDROID_SDK_HOME=/android/sdk\r\nINFO: Found applicable config definition build:short_logs in file /host_dir/tensorflow_src/.bazelrc: --output_filter=DONT_MATCH_ANYTHING\r\nINFO: Found applicable config definition build:v2 in file /host_dir/tensorflow_src/.bazelrc: --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1\r\nINFO: Found applicable config definition build:opt in file /host_dir/tensorflow_src/.tf_configure.bazelrc: --copt=-Wno-sign-compare --host_copt=-Wno-sign-compare\r\nINFO: Found applicable config definition build:linux in file /host_dir/tensorflow_src/.bazelrc: --host_copt=-w --copt=-Wno-all --copt=-Wno-extra --copt=-Wno-deprecated --copt=-Wno-deprecated-declarations --copt=-Wno-ignored-attributes --copt=-Wno-array-bounds --copt=-Wunused-result --copt=-Werror=unused-result --copt=-Wswitch --copt=-Werror=switch --copt=-Wno-error=unused-but-set-variable --define=PREFIX=/usr --define=LIBDIR=$(PREFIX)/lib --define=INCLUDEDIR=$(PREFIX)/include --define=PROTOBUF_INCLUDE_PATH=$(PREFIX)/include --cxxopt=-std=c++17 --host_cxxopt=-std=c++17 --config=dynamic_kernels --experimental_guard_against_concurrent_changes\r\nINFO: Found applicable config definition build:dynamic_kernels in file /host_dir/tensorflow_src/.bazelrc: --define=dynamic_loaded_kernels=true --copt=-DAUTOLOAD_DYNAMIC_KERNELS\r\nINFO: Build option --define has changed, discarding analysis cache.\r\nINFO: Analyzed target //tmp:tensorflow-lite (1 packages loaded, 12614 targets configured).\r\nINFO: Found 1 target...\r\nERROR: /host_dir/tensorflow_src/tensorflow/lite/delegates/nnapi/BUILD:14:11: Compiling tensorflow/lite/delegates/nnapi/nnapi_delegate.cc failed: (Exit 1): clang failed: error executing command (from target //tensorflow/lite/delegates/nnapi:nnapi_delegate_no_nnapi_implementation) external/androidndk/toolchains/llvm/prebuilt/linux-x86_64/bin/clang -no-canonical-prefixes '--target=aarch64-linux-android30' -fdiagnostics-color -Wa,--noexecstack -fno-exceptions '-std=gnu++17' ... (remaining 158 arguments skipped)\r\ntensorflow/lite/delegates/nnapi/nnapi_delegate.cc:522:7: error: designated initializers are a C++20 extension [-Werror,-Wc++20-designator]\r\n .type = nn_type,\r\n ^\r\ntensorflow/lite/delegates/nnapi/nnapi_delegate.cc:1529:45: error: designated initializers are a C++20 extension [-Werror,-Wc++20-designator]\r\n ANeuralNetworksOperandType operand_type{.type = nn_type};\r\n ^\r\ntensorflow/lite/delegates/nnapi/nnapi_delegate.cc:1682:45: error: designated initializers are a C++20 extension [-Werror,-Wc++20-designator]\r\n ANeuralNetworksOperandType operand_type{.type = nn_type};\r\n ^\r\ntensorflow/lite/delegates/nnapi/nnapi_delegate.cc:1701:45: error: designated initializers are a C++20 extension [-Werror,-Wc++20-designator]\r\n ANeuralNetworksOperandType operand_type{.type = nn_type,\r\n ^\r\ntensorflow/lite/delegates/nnapi/nnapi_delegate.cc:1743:9: error: designated initializers are a C++20 extension [-Werror,-Wc++20-designator]\r\n .type = nn_type,\r\n ^\r\ntensorflow/lite/delegates/nnapi/nnapi_delegate.cc:1844:17: error: designated initializers are a C++20 extension [-Werror,-Wc++20-designator]\r\n .channelDim = static_cast<uint32_t>(\r\n ^\r\ntensorflow/lite/delegates/nnapi/nnapi_delegate.cc:6915:7: error: designated initializers are a C++20 extension [-Werror,-Wc++20-designator]\r\n .init = [](TfLiteContext* context, const char* buffer,\r\n ^\r\n\r\n\r\n```\r\n\r\n</details>\r\n\r\n```bash\r\n\r\n7 errors generated.\r\nTarget //tmp:tensorflow-lite failed to build\r\nUse --verbose_failures to see the command lines of failed build steps.\r\nINFO: Elapsed time: 11.868s, Critical Path: 8.84s\r\nINFO: 42 processes: 11 internal, 31 local.\r\nFAILED: Build did NOT complete successfully\r\n\r\n```\r\n\r\nThe same errors are produced even without specifying a model `tensorflow_src/tensorflow/lite/tools/build_aar.sh --target_archs=arm64-v8a`",
"I was able to reproduce with the original steps and the uploaded whisper model.\r\n\r\nHi @terryheo, can you please take a look? Thanks.",
"Hey @terryheo, any progress on this?",
"Hi @terryheo, this is still breaking the builds - did you have any chance to look at this?"
] | 2024-03-28T13:40:58 | 2024-06-04T06:30:55 | null |
NONE
| null | null | null |
### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.16.1
### Custom code
No
### OS platform and distribution
Docker
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Hi,
I'm following the steps to build TF Lite using docker (https://www.tensorflow.org/lite/android/lite_build#set_up_build_environment_using_docker) with TF release 2.16.1 (also tried 2.15). No modifications to the sources, Dockerfile or commands, only change is limiting the target architectures to arm64_v8a
The step of building aar for a given model fails by throwing C++20 extensions needed by the nnapi delegate. It seems that the rest of tooling is using gnu++17?
### Standalone code to reproduce the issue
```shell
docker build . -t tflite-builder -f tflite-android.Dockerfile
docker run -it -v $PWD:/host_dir tflite-builder bash
sdkmanager \
"build-tools;${ANDROID_BUILD_TOOLS_VERSION}" \
"platform-tools" \
"platforms;android-${ANDROID_API_LEVEL}"
./configure
bazel build -c opt --cxxopt=--std=c++17 --config=android_arm64 \
--fat_apk_cpu=arm64-v8a \
--define=android_dexmerger_tool=d8_dexmerger \
--define=android_incremental_dexing_tool=d8_dexbuilder \
//tensorflow/lite/java:tensorflow-lite
bash tensorflow/lite/tools/build_aar.sh \
--input_models=model.tflite \
--target_archs=arm64-v8a
```
### Relevant log output
```shell
ERROR: /host_dir/tensorflow-2.16.1/tensorflow/lite/delegates/nnapi/BUILD:14:11: Compiling tensorflow/lite/delegates/nnapi/nnapi_delegate.cc failed: (Exit 1): clang failed: error executing command (from target //tensorflow/lite/delegates/nnapi:nnapi_delegate_no_nnapi_implementation) external/androidndk/toolchains/llvm/prebuilt/linux-x86_64/bin/clang -no-canonical-prefixes '--target=aarch64-linux-android30' -fdiagnostics-color -Wa,--noexecstack -fno-exceptions '-std=gnu++17' ... (remaining 158 arguments skipped)
tensorflow/lite/delegates/nnapi/nnapi_delegate.cc:522:7: error: designated initializers are a C++20 extension [-Werror,-Wc++20-designator]
.type = nn_type,
^
tensorflow/lite/delegates/nnapi/nnapi_delegate.cc:1529:45: error: designated initializers are a C++20 extension [-Werror,-Wc++20-designator]
ANeuralNetworksOperandType operand_type{.type = nn_type};
^
tensorflow/lite/delegates/nnapi/nnapi_delegate.cc:1682:45: error: designated initializers are a C++20 extension [-Werror,-Wc++20-designator]
ANeuralNetworksOperandType operand_type{.type = nn_type};
^
tensorflow/lite/delegates/nnapi/nnapi_delegate.cc:1701:45: error: designated initializers are a C++20 extension [-Werror,-Wc++20-designator]
ANeuralNetworksOperandType operand_type{.type = nn_type,
^
tensorflow/lite/delegates/nnapi/nnapi_delegate.cc:1743:9: error: designated initializers are a C++20 extension [-Werror,-Wc++20-designator]
.type = nn_type,
^
tensorflow/lite/delegates/nnapi/nnapi_delegate.cc:1844:17: error: designated initializers are a C++20 extension [-Werror,-Wc++20-designator]
.channelDim = static_cast<uint32_t>(
^
tensorflow/lite/delegates/nnapi/nnapi_delegate.cc:6915:7: error: designated initializers are a C++20 extension [-Werror,-Wc++20-designator]
.init = [](TfLiteContext* context, const char* buffer,
^
7 errors generated.
Target //tmp:tensorflow-lite failed to build
Use --verbose_failures to see the command lines of failed build steps.
INFO: Elapsed time: 11.395s, Critical Path: 8.54s
INFO: 62 processes: 21 internal, 41 local.
FAILED: Build did NOT complete successfully
```
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[XLA:Python] Add pytype_srcs and pytype_deps attributes to pytype_strict_library.
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[] | 2024-03-28T13:31:35 | 2024-03-28T20:41:33 | 2024-03-28T20:41:31 |
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[XLA:Python] Add pytype_srcs and pytype_deps attributes to pytype_strict_library.
These attributes are ignored, because we don't use pytype in OSS builds at the moment.
While we're here, remove a copybara transformation that strips off a pytype_srcs attribute: we can just leave the attribute alone and it won't do any harm.
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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/64669/checks?check_run_id=23200281577) 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."
] | 2024-03-28T13:30:57 | 2024-03-28T17:51:23 | 2024-03-28T17:51:22 |
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[stream_executor:host] Rename host_gpu_executor
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[xla:gpu] Address computation thunk should not be inside command buffer
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[xla:gpu] Address computation thunk should not be inside command buffer
Address computation is not compatible with command buffer since it requires a host sync.
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Add Linux ppc64le support to CUDA stubs.
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Add Linux ppc64le support to CUDA stubs.
Update implib.so to include a commit with ppc64le support.
May fix https://github.com/google/jax/issues/19992, although I don't have such a machine so it's untested.
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#tf-data-service Reduce severity of missing default transfer server log line.
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#tf-data-service Reduce severity of missing default transfer server log line.
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#sdy save HloInputOutputAliasConfig and HloBufferDonorConfig in SDY XLA pipeline
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#sdy save HloInputOutputAliasConfig and HloBufferDonorConfig in SDY XLA pipeline
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[JAX] Automatically share PGO data for GPU latency-hiding scheduler.
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[JAX] Automatically share PGO data for GPU latency-hiding scheduler.
Overall the idea is to collect profile data for each module given amount of times (which can be configured) then recompile the module with the aggregated profile data.
1. We need to track how many times each module were profiled and collect profiling results. For this i added a ProfileSessionRunner class at profile.py. The class can track how many times an instance of it was called to profile a session and also can aggregate profile results.
2. We need associate profiling session to the module at the interpreter. To do this i added a dictionary to pjit.py which associates Jaxpr with profile session runner.
3. The profile session runner should be passed to pxla.py and then called.
4. We need to correctly deal with fast path at the interpreter level, so JAX won't use HLO directly if PGLE need to be collected, but also JAX will not recompiled the module only for PGLE. See changes in pjit.py and in lru_cache.h
5. Once FDO is collected we need to share it between hosts to keep deterministic compilation.
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Use indexing map symbol rescaling in the ReduceWindow emitter
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Use indexing map symbol rescaling in the ReduceWindow emitter
We current codegen the reduce window op as a loop nest with the inner loop
iterating over the window and accumulating input values.
If the op had a base dilation set we would still generate the same inner
loop but the loop body now woudl have an additional condition that checks
whether we are in bounds of the dilation.
This change makes use of IndexingMap's symbol rescaling which results
in the generation of an inner loop with fewer iterations. It also avoids
the in-bounds check by only iterating over the tensor elements that
actually need to be accumulated.
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Automated Code Change
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Fixed typos in TFLite metadata_writer_tutorial.ipynb
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Several typos are fixed in TFLite metadata_writer_tutorial.ipynb
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[XLA] add a setter for HloInputOutputAliasConfig and HloBufferDonorConfig
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[XLA] add a setter for HloInputOutputAliasConfig and HloBufferDonorConfig
This is needed to move those configs from an old HloModule to a new one.
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Installing tensorflow for CUDA and ROCM within the same Python installation
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"@samfux84 You're right about the challenges. Currently ROCm and CUDA versions of TensorFlow cannot coexist side-by-side within the same Python package. Could you create separate containers for each TensorFlow version (CUDA and ROCM), then select the desired container based on their specific GPU needs. You can also refer to this [doc](https://www.tensorflow.org/guide/keras/distributed_training) on multiple GPU architectures.\r\nThank you!",
"@sushreebarsa : Thank you for your reply. I somehow expected that currently it is not possible to have both packages available within one Python installation, but hoped that there is a workaround available.\r\n\r\nA very simple solution would be if both packages (Tensorflow for CUDA and for ROCM) would have different package names. Then both packages could be installed within the same Python version.\r\n\r\nI think that our current solution will be to have separate Python installations for CUDA and ROCM. This is not really desirable, as it duplicates the work of installing Python (and we plan to provide multiple versions of Python on our cluster).\r\n\r\nLong term, it would be desirable to have a single tensorflow package that checks for the GPU type and then uses the appropriate code (Nvidia GPU/ AMD GPU/ Intel or AMD CPU), so that a single installation is sufficient.\r\n\r\nPlease feel free to close this ticket.",
"@samfux84 Thank you for the response!\r\nClosing this ticket for now. Thank you!",
"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/64658\">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/64658\">No</a>\n"
] | 2024-03-28T08:58:09 | 2024-04-03T09:44:50 | 2024-04-03T09:44:47 |
NONE
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Hi,
The HPC cluster of our university has both, nodes with NVIDIA GPUs and nodes with AMD GPUs (and pure CPU nodes without any GPU). Since tensorflow packages provided for CUDA and ROCM are different but have the same name, we can only install one version for each Python installation.
Is there any solution to have both versions installed for the same Python installation?
Or is there any option to have a single package that can run on CPU, NVIDIA GPUs and AMD GPUs (by detecting the GPU)? The CUDA version can for instance already detect if an NVIDIA GPU is available and will otherwise fallback to the CPU version. So the package can run either on pure CPU nodes without any GPU and also on nodes with NVIDIA GPUs.
Using venvs is not an option, as a user can only have one venv activated at a time. So if we would provide tensorflow through a venv, users could not create their own venvs any more if they would like to use tensorflow.
I guess we cannot be the only institution with this problem, so I hope there is already a solution for having both, the ROCM and CUDA version side by side or within a single Python package?
Any help is appreciated.
Best regards
Sam
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Disable Batch Mat Mul delegate test until flaky behaviour is fixed.
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Disable Batch Mat Mul delegate test until flaky behaviour is fixed.
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NFC: Add `@llvm-project//mlir:TransformUtils` dependency as preparation for https://github.com/llvm/llvm-project/pull/86819.
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NFC: Add `@llvm-project//mlir:TransformUtils` dependency as preparation for https://github.com/llvm/llvm-project/pull/86819.
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Aborted (core dumped) with `tf.raw_ops.LoadAndRemapMatrix`
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"Hi **@LongZE666** ,\r\nI tried to run your code on Colab using TF v2.16.1, nightly and faced the same issue. Please find the [gist](https://colab.sandbox.google.com/gist/Venkat6871/c35da15e8fe8a8692af9c2ad8bd2d9db/64655-2-16-1-nightly.ipynb) here for reference.\r\nThank you!"
] | 2024-03-28T07:38:24 | 2024-06-12T06:16:04 | null |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16.1
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.11
### 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.tf.raw_ops.LoadAndRemapMatrix` encounters "Aborted (core dumped)".
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
num_rows = 7
num_cols = 16
max_rows_in_memory = -1
ckpt_path = tf.constant("/tmp", shape=[1], dtype=tf.string)
old_tensor_name = tf.constant("/tmp", shape=[1], dtype=tf.string)
row_remapping = tf.constant(-1, shape=[7], dtype=tf.int64)
col_remapping = tf.constant(0, shape=[], dtype=tf.int64)
initializing_values = tf.constant(42, shape=[112], dtype=tf.float32)
tf.raw_ops.LoadAndRemapMatrix(ckpt_path=ckpt_path, old_tensor_name=old_tensor_name, row_remapping=row_remapping, col_remapping=col_remapping, initializing_values=initializing_values, num_rows=num_rows, num_cols=num_cols, max_rows_in_memory=max_rows_in_memory)
```
### Relevant log output
```shell
2024-03-28 07:29:28.849010: F tensorflow/core/framework/tensor_shape.cc:45] Check failed: NDIMS == dims() (1 vs. 0)Asking for tensor of 1 dimensions from a tensor of 0 dimensions
Aborted (core dumped)
```
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Add support for binary ops in RTVar optimization
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Add support for binary ops in RTVar optimization
So far we were able to fold HLO graphs composed of
constants and unary ops without compute.
This change adds support for basic binary ops like:
- Add
- Multiply
- Subtract
- Divide
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Add support for folding constant unary non-compute ops in RTVar optimization
So far we can only optimize away RTVars that refer to a `constant` or `iota`
HLO op. This change is extending that to unary ops without compute (given
the operand is also constant).
That includes:
- Bitcast
- Broadcast
- Reshape
- Reverse
- Slice
- Transpose
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Update multinomial_op logits invalid arguments check description
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Updates `InvalidArgument` descriptions to reflect overflow checks exist on the logits dimensions independently (batch_size and num_classes), rather than simply a check on the overall logits.shape.
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Replace usage of `PermuteShape` to `Permute<T>`.
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Replace usage of `PermuteShape` to `Permute<T>`.
Replaces the duplicate implementation of array permutation.
As a corollary, replaces permutation values to existing definitions in `attrs_and_constraints.h`.
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Add Batch Norm to the integration test. Remove all the rng_seed parameters.
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Add Batch Norm to the integration test. Remove all the rng_seed parameters.
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[HloValueSemanticsAnalysis] Fix handling for async start and done.
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[HloValueSemanticsAnalysis] Fix handling for async start and done.
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[HloValueSemanticsAnalysis] Deduplicate some functions.
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[HloValueSemanticsAnalysis] Deduplicate some functions.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/11122 from ROCm:5.7-rccl-hang 2d196b645ccac046309d44f747d42777f00f1c9b
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Make usages of Eigen::array compatible with std::array.
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Make usages of Eigen::array compatible with std::array.
Eigen::array is no longer necessary, so will be deprecated/removed and replaced
with `std::array`. The main difference is the constructor - currently Eigen::array
allows `array(a, b, c, ...)` construction, whereas `std::array` requires an initializer
list. We also need to remove any direct access to the `Eigen::array::values`
internal parameter, in favor of regular index access.
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Async dispatch expensive computations on the JAX CPU backend.
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Async dispatch expensive computations on the JAX CPU backend.
Before the change, on CPU backend we always run computations inline unless there are multiple CPU devices with potential collectives. Now, we will use `HloCostAnalysis` to estimate the cost of the computation and do async dispatch if it is expensive.
Add a JAX flag for users to opt-out by adding `jax.config.update('jax_cpu_enable_async_dispatch', False)` in their programs.
Reverts 15c15ee35a11361b42aeb49dc53e9034dc54c400
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Do not wrap lifted function in `TF.CustomAggregatorOp` with improper `quantization_method`.
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Do not wrap lifted function in `TF.CustomAggregatorOp` with improper `quantization_method`.
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Use hermetic Python in TSL and XLA
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I_kwDOArmXAs6D3IO1
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`tf.raw_ops.AvgPoolGrad`: Heap buffer overflow
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[
"@LongZE666,\r\nI request you to take a look at this [issue](https://github.com/tensorflow/tensorflow/issues/63353) where a similar issue has been proposed and it is still open.Also I request to follow the similar issue which has been proposed to have the updates on the similar issue. Thank you!\r\n",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.",
"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/64642\">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/64642\">No</a>\n"
] | 2024-03-28T03:17:39 | 2024-04-17T01:47:48 | 2024-04-17T01:47:41 |
NONE
| null | null | null |
### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.16.1
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
gcc version 11.4.0
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
tf.raw_ops.AvgPoolGrad can lead to heap buffer overflow.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
ksize = [1, 1, 1, 1]
strides = [1, 1, 1, 1]
padding = "VALID"
data_format = "NHWC"
orig_input_shape = tf.constant(2, shape=[4], dtype=tf.int32)
grad = tf.constant(1, shape=[2,3,3,3], dtype=tf.float32)
tf.raw_ops.AvgPoolGrad(orig_input_shape=orig_input_shape, grad=grad, ksize=ksize, strides=strides, padding=padding, data_format=data_format)
```
### Relevant log output
```shell
ASAN Report:
2024-03-28 02:51:02.627721: 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: SSE3 SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
=================================================================
==113982==ERROR: AddressSanitizer: heap-buffer-overflow on address 0x60a000000080 at pc 0x7f385bd85135 bp 0x7ffc20705540 sp 0x7ffc20705530
READ of size 4 at 0x60a000000080 thread T0
#0 0x7f385bd85134 in tensorflow::AvgPoolingGradOp<Eigen::ThreadPoolDevice, float>::Compute(tensorflow::OpKernelContext*)::{lambda(long, long)#1}::operator()(long, long) const (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x4132d134)
#1 0x7f3886fd773a in std::_Function_handler<void (long, long), tsl::thread::ThreadPool::ParallelFor(long, long, std::function<void (long, long)> const&)::{lambda(long, long)#1}>::_M_invoke(std::_Any_data const&, long&&, long&&) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x41f373a)
#2 0x7f3886fdc4e9 in Eigen::ThreadPoolDevice::parallelFor(long, Eigen::TensorOpCost const&, std::function<long (long)>, std::function<void (long, long)>) const (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x41f84e9)
#3 0x7f3886fe5d41 in tsl::thread::ThreadPool::ParallelFor(long, long, std::function<void (long, long)> const&) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x4201d41)
#4 0x7f38854bcd71 in tensorflow::Shard(int, tsl::thread::ThreadPool*, long, long, std::function<void (long, long)>) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x26d8d71)
#5 0x7f385bda0e2b in tensorflow::AvgPoolingGradOp<Eigen::ThreadPoolDevice, float>::Compute(tensorflow::OpKernelContext*) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x41348e2b)
#6 0x7f3884ac014a in tensorflow::ThreadPoolDevice::Compute(tensorflow::OpKernel*, tensorflow::OpKernelContext*) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x1cdc14a)
#7 0x7f3884646fe6 in tensorflow::(anonymous namespace)::SingleThreadedExecutorImpl::Run(tensorflow::Executor::Args const&) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x1862fe6)
#8 0x7f3884523306 in tensorflow::FunctionLibraryRuntimeImpl::RunSync(tensorflow::FunctionLibraryRuntime::Options, unsigned long, absl::lts_20230802::Span<tensorflow::Tensor const>, std::vector<tensorflow::Tensor, std::allocator<tensorflow::Tensor> >*) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x173f306)
#9 0x7f3884580f74 in tensorflow::ProcessFunctionLibraryRuntime::RunMultiDeviceSync(tensorflow::FunctionLibraryRuntime::Options const&, unsigned long, std::vector<std::variant<tensorflow::Tensor, tensorflow::TensorShape>, std::allocator<std::variant<tensorflow::Tensor, tensorflow::TensorShape> > >*, std::function<absl::lts_20230802::Status (tensorflow::ProcessFunctionLibraryRuntime::ComponentFunctionData const&, tensorflow::ProcessFunctionLibraryRuntime::InternalArgs*)>) const (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x179cf74)
#10 0x7f388458e8b1 in tensorflow::ProcessFunctionLibraryRuntime::RunSync(tensorflow::FunctionLibraryRuntime::Options const&, unsigned long, absl::lts_20230802::Span<tensorflow::Tensor const>, std::vector<tensorflow::Tensor, std::allocator<tensorflow::Tensor> >*) const (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x17aa8b1)
#11 0x7f384c6b02eb in tensorflow::KernelAndDeviceFunc::Run(tensorflow::ScopedStepContainer*, tensorflow::EagerKernelArgs const&, std::vector<std::variant<tensorflow::Tensor, tensorflow::TensorShape>, std::allocator<std::variant<tensorflow::Tensor, tensorflow::TensorShape> > >*, tsl::CancellationManager*, std::optional<tensorflow::EagerFunctionParams> const&, std::optional<tensorflow::ManagedStackTrace> const&, tsl::CoordinationServiceAgent*) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x31c582eb)
#12 0x7f384c545c2c in tensorflow::EagerKernelExecute(tensorflow::EagerContext*, absl::lts_20230802::InlinedVector<tensorflow::TensorHandle*, 4ul, std::allocator<tensorflow::TensorHandle*> > const&, std::optional<tensorflow::EagerFunctionParams> const&, tsl::core::RefCountPtr<tensorflow::KernelAndDevice> const&, tensorflow::GraphCollector*, tsl::CancellationManager*, absl::lts_20230802::Span<tensorflow::TensorHandle*>, std::optional<tensorflow::ManagedStackTrace> const&) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x31aedc2c)
#13 0x7f384c54866a in tensorflow::ExecuteNode::Run() (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x31af066a)
#14 0x7f384c682939 in tensorflow::EagerExecutor::SyncExecute(tensorflow::EagerNode*) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x31c2a939)
#15 0x7f384c535de4 in tensorflow::(anonymous namespace)::EagerLocalExecute(tensorflow::EagerOperation*, tensorflow::TensorHandle**, int*) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x31addde4)
#16 0x7f384c539dd4 in tensorflow::DoEagerExecute(tensorflow::EagerOperation*, tensorflow::TensorHandle**, int*) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x31ae1dd4)
#17 0x7f384c543d26 in tensorflow::EagerExecute(tensorflow::EagerOperation*, tensorflow::TensorHandle**, int*) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x31aebd26)
#18 0x7f383b52cc33 in tensorflow::EagerOperation::Execute(absl::lts_20230802::Span<tensorflow::AbstractTensorHandle*>, int*) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x20ad4c33)
#19 0x7f384c677e5e in tensorflow::CustomDeviceOpHandler::Execute(tensorflow::ImmediateExecutionOperation*, tensorflow::ImmediateExecutionTensorHandle**, int*) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x31c1fe5e)
#20 0x7f382b0aa45b in TFE_Execute (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x1065245b)
#21 0x7f3882a58274 in TFE_Py_FastPathExecute_C(_object*) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../_pywrap_tensorflow_internal.so+0x3c2274)
#22 0x7f3816a21ccb in pybind11::cpp_function::initialize<pybind11_init__pywrap_tfe(pybind11::module_&)::{lambda(pybind11::args)#61}, pybind11::object, pybind11::args, pybind11::name, pybind11::scope, pybind11::sibling>(pybind11_init__pywrap_tfe(pybind11::module_&)::{lambda(pybind11::args)#61}&&, pybind11::object (*)(pybind11::args), pybind11::name const&, pybind11::scope const&, pybind11::sibling const&)::{lambda(pybind11::detail::function_call&)#3}::_FUN(pybind11::detail::function_call&) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/_pywrap_tfe.so+0xb7ccb)
#23 0x7f3816b38899 in pybind11::cpp_function::dispatcher(_object*, _object*, _object*) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/_pywrap_tfe.so+0x1ce899)
#24 0x51ad66 (/usr/bin/python3.11+0x51ad66)
#25 0x4e75db in _PyObject_MakeTpCall (/usr/bin/python3.11+0x4e75db)
#26 0x4fb151 in _PyEval_EvalFrameDefault (/usr/bin/python3.11+0x4fb151)
#27 0x531822 in _PyFunction_Vectorcall (/usr/bin/python3.11+0x531822)
#28 0x541194 in PyObject_Call (/usr/bin/python3.11+0x541194)
#29 0x4fefe0 in _PyEval_EvalFrameDefault (/usr/bin/python3.11+0x4fefe0)
#30 0x62e1b3 (/usr/bin/python3.11+0x62e1b3)
#31 0x4f3a66 in PyEval_EvalCode (/usr/bin/python3.11+0x4f3a66)
#32 0x647c36 (/usr/bin/python3.11+0x647c36)
#33 0x64534f (/usr/bin/python3.11+0x64534f)
#34 0x650d14 (/usr/bin/python3.11+0x650d14)
#35 0x650a63 in _PyRun_SimpleFileObject (/usr/bin/python3.11+0x650a63)
#36 0x650832 in _PyRun_AnyFileObject (/usr/bin/python3.11+0x650832)
#37 0x64f786 in Py_RunMain (/usr/bin/python3.11+0x64f786)
#38 0x61ee0c in Py_BytesMain (/usr/bin/python3.11+0x61ee0c)
#39 0x7f38cc5a7d8f (/lib/x86_64-linux-gnu/libc.so.6+0x29d8f)
#40 0x7f38cc5a7e3f in __libc_start_main (/lib/x86_64-linux-gnu/libc.so.6+0x29e3f)
#41 0x61ec94 in _start (/usr/bin/python3.11+0x61ec94)
0x60a000000080 is located 0 bytes to the right of 64-byte region [0x60a000000040,0x60a000000080)
allocated by thread T0 here:
#0 0x7f38cc99557c in __interceptor_posix_memalign ../../../../src/libsanitizer/asan/asan_malloc_linux.cpp:226
#1 0x7f3887fcb6de in tsl::port::AlignedMalloc(unsigned long, int) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x51e76de)
#2 0x7f38854ed529 in tsl::(anonymous namespace)::CPUAllocator::AllocateRaw(unsigned long, unsigned long) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x2709529)
#3 0x7f38857c49af in tensorflow::Tensor::Tensor(tsl::Allocator*, tensorflow::DataType, tensorflow::TensorShape const&, tsl::AllocationAttributes const&) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x29e09af)
#4 0x7f3884ee7922 in tensorflow::OpKernelContext::allocate_tensor(tensorflow::DataType, tensorflow::TensorShape const&, tensorflow::Tensor*, tsl::AllocatorAttributes, tsl::AllocationAttributes const&) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x2103922)
#5 0x7f3884eeacdb in tensorflow::OpKernelContext::allocate_output(int, tensorflow::TensorShape const&, tensorflow::Tensor**, tsl::AllocatorAttributes) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x2106cdb)
#6 0x7f3884eeca5b in tensorflow::OpKernelContext::allocate_output(int, tensorflow::TensorShape const&, tensorflow::Tensor**) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x2108a5b)
#7 0x7f385bd9f7f2 in tensorflow::AvgPoolingGradOp<Eigen::ThreadPoolDevice, float>::Compute(tensorflow::OpKernelContext*) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x413477f2)
#8 0x7f3884ac014a in tensorflow::ThreadPoolDevice::Compute(tensorflow::OpKernel*, tensorflow::OpKernelContext*) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x1cdc14a)
#9 0x7f3884646fe6 in tensorflow::(anonymous namespace)::SingleThreadedExecutorImpl::Run(tensorflow::Executor::Args const&) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x1862fe6)
#10 0x7f3884523306 in tensorflow::FunctionLibraryRuntimeImpl::RunSync(tensorflow::FunctionLibraryRuntime::Options, unsigned long, absl::lts_20230802::Span<tensorflow::Tensor const>, std::vector<tensorflow::Tensor, std::allocator<tensorflow::Tensor> >*) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x173f306)
#11 0x7f3884580f74 in tensorflow::ProcessFunctionLibraryRuntime::RunMultiDeviceSync(tensorflow::FunctionLibraryRuntime::Options const&, unsigned long, std::vector<std::variant<tensorflow::Tensor, tensorflow::TensorShape>, std::allocator<std::variant<tensorflow::Tensor, tensorflow::TensorShape> > >*, std::function<absl::lts_20230802::Status (tensorflow::ProcessFunctionLibraryRuntime::ComponentFunctionData const&, tensorflow::ProcessFunctionLibraryRuntime::InternalArgs*)>) const (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x179cf74)
#12 0x7f388458e8b1 in tensorflow::ProcessFunctionLibraryRuntime::RunSync(tensorflow::FunctionLibraryRuntime::Options const&, unsigned long, absl::lts_20230802::Span<tensorflow::Tensor const>, std::vector<tensorflow::Tensor, std::allocator<tensorflow::Tensor> >*) const (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_framework.so.2+0x17aa8b1)
#13 0x7f384c6b02eb in tensorflow::KernelAndDeviceFunc::Run(tensorflow::ScopedStepContainer*, tensorflow::EagerKernelArgs const&, std::vector<std::variant<tensorflow::Tensor, tensorflow::TensorShape>, std::allocator<std::variant<tensorflow::Tensor, tensorflow::TensorShape> > >*, tsl::CancellationManager*, std::optional<tensorflow::EagerFunctionParams> const&, std::optional<tensorflow::ManagedStackTrace> const&, tsl::CoordinationServiceAgent*) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x31c582eb)
#14 0x7f384c545c2c in tensorflow::EagerKernelExecute(tensorflow::EagerContext*, absl::lts_20230802::InlinedVector<tensorflow::TensorHandle*, 4ul, std::allocator<tensorflow::TensorHandle*> > const&, std::optional<tensorflow::EagerFunctionParams> const&, tsl::core::RefCountPtr<tensorflow::KernelAndDevice> const&, tensorflow::GraphCollector*, tsl::CancellationManager*, absl::lts_20230802::Span<tensorflow::TensorHandle*>, std::optional<tensorflow::ManagedStackTrace> const&) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x31aedc2c)
#15 0x7f384c54866a in tensorflow::ExecuteNode::Run() (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x31af066a)
#16 0x7f384c682939 in tensorflow::EagerExecutor::SyncExecute(tensorflow::EagerNode*) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x31c2a939)
#17 0x7f384c535de4 in tensorflow::(anonymous namespace)::EagerLocalExecute(tensorflow::EagerOperation*, tensorflow::TensorHandle**, int*) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x31addde4)
#18 0x7f384c539dd4 in tensorflow::DoEagerExecute(tensorflow::EagerOperation*, tensorflow::TensorHandle**, int*) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x31ae1dd4)
#19 0x7f384c543d26 in tensorflow::EagerExecute(tensorflow::EagerOperation*, tensorflow::TensorHandle**, int*) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x31aebd26)
#20 0x7f383b52cc33 in tensorflow::EagerOperation::Execute(absl::lts_20230802::Span<tensorflow::AbstractTensorHandle*>, int*) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x20ad4c33)
#21 0x7f384c677e5e in tensorflow::CustomDeviceOpHandler::Execute(tensorflow::ImmediateExecutionOperation*, tensorflow::ImmediateExecutionTensorHandle**, int*) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x31c1fe5e)
#22 0x7f382b0aa45b in TFE_Execute (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x1065245b)
#23 0x7f3882a58274 in TFE_Py_FastPathExecute_C(_object*) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../_pywrap_tensorflow_internal.so+0x3c2274)
#24 0x7f3816a21ccb in pybind11::cpp_function::initialize<pybind11_init__pywrap_tfe(pybind11::module_&)::{lambda(pybind11::args)#61}, pybind11::object, pybind11::args, pybind11::name, pybind11::scope, pybind11::sibling>(pybind11_init__pywrap_tfe(pybind11::module_&)::{lambda(pybind11::args)#61}&&, pybind11::object (*)(pybind11::args), pybind11::name const&, pybind11::scope const&, pybind11::sibling const&)::{lambda(pybind11::detail::function_call&)#3}::_FUN(pybind11::detail::function_call&) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/_pywrap_tfe.so+0xb7ccb)
#25 0x7f3816b38899 in pybind11::cpp_function::dispatcher(_object*, _object*, _object*) (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/_pywrap_tfe.so+0x1ce899)
#26 0x51ad66 (/usr/bin/python3.11+0x51ad66)
SUMMARY: AddressSanitizer: heap-buffer-overflow (/tensorflow_src/venv/tensorflow-2.16.1-asan/lib/python3.11/site-packages/tensorflow/python/platform/../../libtensorflow_cc.so.2+0x4132d134) in tensorflow::AvgPoolingGradOp<Eigen::ThreadPoolDevice, float>::Compute(tensorflow::OpKernelContext*)::{lambda(long, long)#1}::operator()(long, long) const
Shadow bytes around the buggy address:
0x0c147fff7fc0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
0x0c147fff7fd0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
0x0c147fff7fe0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
0x0c147fff7ff0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
0x0c147fff8000: fa fa fa fa fa fa fa fa 00 00 00 00 00 00 00 00
=>0x0c147fff8010:[fa]fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
0x0c147fff8020: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
0x0c147fff8030: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
0x0c147fff8040: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
0x0c147fff8050: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
0x0c147fff8060: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
Shadow byte legend (one shadow byte represents 8 application bytes):
Addressable: 00
Partially addressable: 01 02 03 04 05 06 07
Heap left redzone: fa
Freed heap region: fd
Stack left redzone: f1
Stack mid redzone: f2
Stack right redzone: f3
Stack after return: f5
Stack use after scope: f8
Global redzone: f9
Global init order: f6
Poisoned by user: f7
Container overflow: fc
Array cookie: ac
Intra object redzone: bb
ASan internal: fe
Left alloca redzone: ca
Right alloca redzone: cb
Shadow gap: cc
==113982==ABORTING
```
```
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[XLA:GPU] Skip matrix-matrix multiplication in GemvRewriter
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[XLA:GPU] Skip matrix-matrix multiplication in GemvRewriter
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Expose QuantizationConfig to TFLite converter
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[xla:ffi] Fix __has_builtin compilation compilation
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[xla:ffi] Fix __has_builtin compilation compilation
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PR #10344: [XLA:CPU] Enable BMM+Mul+Add fusion
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PR #10344: [XLA:CPU] Enable BMM+Mul+Add fusion
Imported from GitHub PR https://github.com/openxla/xla/pull/10344
This PR enables the BatchMatMul + Mul + Add fusion and adds a simple test
Copybara import of the project:
--
2a202c9d171064bb3005e0753af41e26f2e1baf3 by Kanvi Khanna <[email protected]>:
Enable BMM+Mul+Add fusion
--
584e59861f5f91fae8222b9281e501dbeb270b94 by Kanvi Khanna <[email protected]>:
Address review comments
Merging this change closes #10344
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10344 from Intel-tensorflow:kanvi/bmm+mul+add 9c1fee304a6cfed55c0c869d1930641d942cf93f
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[xla:gpu] Sort sliced operand paths on the fly
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[xla:gpu] Sort sliced operand paths on the fly
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[stream_executor:host] Add LLVM kernel support in kernel_spec
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[stream_executor:host] Add LLVM kernel support in kernel_spec
Related to https://github.com/openxla/xla/issues/7234
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PR #10930: [XLA:CPU] Bug fix for enabling F16 support
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PR #10930: [XLA:CPU] Bug fix for enabling F16 support
Imported from GitHub PR https://github.com/openxla/xla/pull/10930
This PR fixes a bug for f16 matmul enabling.
Since dot is supported in f16, there is no need for adding an extra convert instruction after fusion similar to the bf16 case.
Also add missed changes in build file.
Copybara import of the project:
--
45f7fdbac38cf011707c704cf4e75b49261d0339 by Kanvi Khanna <[email protected]>:
Bug-Fix:Since dot has f16 support,remove extra convert after fusion; add missing build file change
--
af9d2c4f7a92e3d647f15c9d25be047461df8dea by Kanvi Khanna <[email protected]>:
fix buildifier
--
3587ef104f9b47c2fd05dda1f0008cd3b53da421 by Kanvi Khanna <[email protected]>:
address comment
Merging this change closes #10930
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10930 from Intel-tensorflow:kanvi/f16-bug-fix 3587ef104f9b47c2fd05dda1f0008cd3b53da421
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Add unbounded dynamism test for OptimizationBarrierOp.
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Add unbounded dynamism test for OptimizationBarrierOp.
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shlo_ref: Move template specializations
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GCC gives compilation errors that these template specializations are in a non-namespace scope. This PR moves the template specializations out of the struct scope and into a namespace scope.
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[xla:gpu][NFC] Create address computation fusion on the fly
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[xla:gpu][NFC] Create address computation fusion on the fly
Doing so removes the need for a hash map that stores the matched instructions and avoids iterating the matched patterns again.
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#shlo_ref Use _Float16 for F16 with GCC
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#shlo_ref Use _Float16 for F16 with GCC
The GCC OSS build was failing to use `_Float16` for the `shlo_ref::F16` type alias despite `_Float16`'s availability with GCC. This was because the `has_keyword` macro was always returning false on GCC, as it was implemented using `__is_identifier`, which is Clang only. _Float16 should be available on both GCC & Clang, so we should just use those unless std::float16_t is available.
This also removes has_keyword.h, as it is no longer used and ensures that `BF16` and `F16` map to `std::bfloat16_t` and `std::float16_t` when those are available.
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Add unbounded dynamism test for SelectAndScatterOp.
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Add unbounded dynamism test for SelectAndScatterOp.
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[xla:gpu][NFC] Refactor address_computation_fusion_rewriter
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[xla:gpu][NFC] Refactor address_computation_fusion_rewriter
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Adding additional variables that could be of used in determining compatibility.
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Adding additional variables that could be of used in determining compatibility.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10503 from pearu:pearu/log1p d35cef4f5fa09482c49edfee709e86c5ca29adde
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Remove check that the first thread pool is the default, as it is no longer needed.
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Remove check that the first thread pool is the default, as it is no longer needed.
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[tensorflow] Mark `gtl::ArraySlice` and `gtl::MutableArraySlice` with `ABSL_DEPRECATE_AND_INLINE`
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[tensorflow] Mark `gtl::ArraySlice` and `gtl::MutableArraySlice` with `ABSL_DEPRECATE_AND_INLINE`
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PR_kwDOArmXAs5q_H3P
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Create and use 2.17 toolchains in Docker build containers.
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Create and use 2.17 toolchains in Docker build containers.
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[xla:gpu][NFC] Add test for nested tuple custom call
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[xla:gpu][NFC] Add test for nested tuple custom call
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Treat entry computation parameter that has host memory space as MoveToHost
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Treat entry computation parameter that has host memory space as MoveToHost
annotation in host_offload_legalize.
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Use TpuTilingAssignment for SparseCore inside TpuLayoutAssignment
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Add a field to StreamZ metric /tensorflow/core/tf_mlir_bridge_first_phase_count
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Add a field to StreamZ metric /tensorflow/core/tf_mlir_bridge_first_phase_count
Add a filed for the type of TF2XLA Phase 1 Bridge , i.e. Replicated Bridge vs. Non-replicated Bridge.
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PR_kwDOArmXAs5q-pnC
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GPU Load tracker in TFRT.
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GPU Load tracker in TFRT.
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shlo_ref: Disable legacy code build
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The code under shlo/legacy is useful for reference, but cannot build with the oss toolchains. This PR disables those targets with the no_oss tag.
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Do not deadlock the GPU if a pure_callback dispatches a GPU kernel
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Do not deadlock the GPU if a pure_callback dispatches a GPU kernel
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Integrate LLVM at llvm/llvm-project@d18bee2313ab
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Integrate LLVM at llvm/llvm-project@d18bee2313ab
Updates LLVM usage to match
[d18bee2313ab](https://github.com/llvm/llvm-project/commit/d18bee2313ab)
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[stream_executor:host] Add initial version of a host kernel API (ABI)
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[stream_executor:host] Add initial version of a host kernel API (ABI)
XLA:CPU compiler will compile fusions into function pointers compatible with an API defined by StreamExecutor
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Disable load reordering by buildifier in mlir BUILD
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Disable load reordering by buildifier in mlir BUILD
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Supports PjRt CPU array converting py array when the CPU PjRt arrays have non-default layouts.
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Supports PjRt CPU array converting py array when the CPU PjRt arrays have non-default layouts.
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[odml] Remove MHLO from CHLO->StableHLO lowering. Migrate JAX random lowering to StableHLO
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[odml] Remove MHLO from CHLO->StableHLO lowering. Migrate JAX random lowering to StableHLO
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[XLA:GPU] Add a Gemv rewriter pass to convert gemv to gemm with a trivial dimension
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[XLA:GPU] Add a Gemv rewriter pass to convert gemv to gemm with a trivial dimension
We need this pass since GemmFusion only accepts gemms. We should run this pass before GemmFusion. After GemmFusion, we should use AlgebraicSimplifier to remove trivial dimension from gemms that are not fused by GemmFusion.
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PR #10875: [XLA:CPU][oneDNN][Bugfix] Fix BF16 oneDNN Layernorm
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PR #10875: [XLA:CPU][oneDNN][Bugfix] Fix BF16 oneDNN Layernorm
Imported from GitHub PR https://github.com/openxla/xla/pull/10875
OneDNN expects scale and bias to be in Float32.
This PR will convert BF16/FP16 scale and weights into Float32 (similar to whats done with mkl layernorm in tensorflow [here ](https://github.com/tensorflow/tensorflow/blob/3a5a9ec33c1194f8fdec2a243714055c0118b44a/tensorflow/core/kernels/mkl/mkl_layer_norm_op.cc#L138)) , without conversion there is accuracy mismatch with oneDNN layernorm.
Copybara import of the project:
--
8563d4e9fbdc6681329960c44193ed05f8a16dfd by Sachin Muradi <[email protected]>:
Fix for bf16/fp16 scale bias
--
e7328a3bbbe3e30caeebc27b7b0175eda9e8b5e9 by Sachin Muradi <[email protected]>:
close review comments
Merging this change closes #10875
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10875 from Intel-tensorflow:sachin/ln-bug-fix-bf16 e7328a3bbbe3e30caeebc27b7b0175eda9e8b5e9
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Support `data.experimental.distribued_save` and add `wait` to `load`.
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Support `data.experimental.distribued_save` and add `wait` to `load`.
`distribued_save` uses tf.data service
(https://www.tensorflow.org/api_docs/python/tf/data/experimental/service)
to write distributed dataset snapshots. The call is non-blocking and
returns without waiting for the snapshot to finish. Setting `wait=True` to
`tf.data.Dataset.load` allows the snapshots to be read while they are
being written. The default is `False` for backward compatibility. It will
raise an error if the requested snapshot does not exist.
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[xla:gpu] Do not deduplicate kernel arguments when compiling Pallas GPU kerneles
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[xla:gpu] Do not deduplicate kernel arguments when compiling Pallas GPU kerneles
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I_kwDOArmXAs6D01Tb
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interpreter->Invoke(); Segmentation fault in c++ inference
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"Hi @sangyo1, can you please share the file or code snippets/text instead, Also please include your steps to compile and execute. Thanks.",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further."
] | 2024-03-27T18:54:07 | 2024-04-19T01:47:44 | 2024-04-19T01:47:44 |
NONE
| null | null | null |


I am trying to deploy own object detection model in c++.
Once I run the code I am kept getting segmentation fault.
seems like `cerr << memcpy(interpreter->typed_input_tensor<uchar>(0), image.data, image.total() * image.elemSize())<< endl;` print out 0x7fff8c2fe580
However it doesn't print 5 which is having problem from interpreter->Invoke()
This is what I am getting on debugger
`#4 0x00007ffff7cda369 in tflite::optimized_ops::Conv(tflite::ConvParams const&, tflite::RuntimeShape const&, unsigned char const*, tflite::RuntimeShape const&, unsigned char const*, tflite::RuntimeShape const&, int const*, tflite::RuntimeShape const&, unsigned char*, tflite::RuntimeShape const&, unsigned char*, tflite::CpuBackendContext*) ()`
`#5 0x00007ffff7cdabc9 in void tflite::ops::builtin::conv::EvalQuantized<(tflite::ops::builtin::conv::KernelType)2>(TfLiteContext*, TfLiteNode*, TfLiteConvParams*, tflite::ops::builtin::conv::OpData*, TfLiteTensor*, TfLiteTensor*, TfLiteTensor*, TfLiteTensor*, TfLiteTensor*, TfLiteTensor*) () `
I am getting stuck and I am not sure how to fix this problem
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[xla:gpu] Support tupled GEMM with DUS
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[xla:gpu] Support tupled GEMM with DUS
Separate sliced operand chains from sliced user chains to support different scenarios of tupled GEMM -> DUS.
Sliced operand chains always contain the original hero ops (GEMM or custom call) as their last elements.
Sliced user chains contain dataflow sequences from different users of the original hero ops down to the DUS ops.
For hero ops returning tuples, we need to support cases where not all elements of the tuples are used, hence separating dataflow sequences for different users (i.e. get-tuple-elements) of the hero ops.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/64619 from rascani:disable_shlo_legacy_build d87b5650899ff9d0f5eb791a7b15f7bfc9f4b4c7
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Fix macos oss build.
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Fix macos oss build.
Explicitly match the inline elements to avoid type mismatch.
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10930 from Intel-tensorflow:kanvi/f16-bug-fix 3587ef104f9b47c2fd05dda1f0008cd3b53da421
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[XLA:Python] Factors the ":logging" library out from ":xla_extension".
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[XLA:Python] Factors the ":logging" library out from ":xla_extension".
This is a prospective change for https://github.com/openxla/xla/pull/10966. In particular, this will help fix an OSS build problem: "tensorflow/xla/linux/cpu/build_cpu" not being able to find the `InitializeAbslLogging` function.
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PR #10759: [XLA:CPU][oneDNN] Enable matrix-vector and vector-vector product
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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/64604/checks?check_run_id=23166609496) 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."
] | 2024-03-27T18:07:44 | 2024-03-28T17:40:41 | 2024-03-28T17:40:38 |
NONE
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PR #10759: [XLA:CPU][oneDNN] Enable matrix-vector and vector-vector product
Imported from GitHub PR https://github.com/openxla/xla/pull/10759
This PR relaxes conditions to rewrite dot operations of the form vector-matrix, matrix-vector, or vector-vector to oneDNN custom calls, provided the original problem meets the empirically determined multiply-accumulate threshold. In particular this PR:
1. Relaxes some constraints on Dot to oneDNN matmul custom call conversion
2. Reconfigures the dimensions of the operands and outputs of convertible dot operations.
3. Adds tests to verify rewrite and execution result
Copybara import of the project:
--
d24e5cd0b77d0734a0f33011ae03127f00d80e7d by Akhil Goel <[email protected]>:
Relax constraints for matmul rewrite
--
9582288fc5d06eb8f4641be32d6c41746f13dbce by Akhil Goel <[email protected]>:
Fix gemv test after merge
--
604d4fbd29a78a7d305092356176e31081ea4ff0 by Akhil Goel <[email protected]>:
Address review comments
--
4534fbd7e96a6e45ccc5a501b38b940aa8bd9d38 by Akhil Goel <[email protected]>:
Optional commit
Merging this change closes #10759
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/openxla/xla/pull/10759 from Intel-tensorflow:akhil/gemv 4534fbd7e96a6e45ccc5a501b38b940aa8bd9d38
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Run buildifier on all files where it sorts loads differently
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Internal cleanup of BUILD/.bzl files
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Internal cleanup of BUILD/.bzl files
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Enable test for CALIBRATION_METHOD_HISTOGRAM_PERCENTILE.
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NONE
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Enable test for CALIBRATION_METHOD_HISTOGRAM_PERCENTILE.
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PR #64619: shlo_ref: Disable legacy code build
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PR #64619: shlo_ref: Disable legacy code build
Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/64619
The code under shlo/legacy is useful for reference, but cannot build with the oss toolchains. This PR disables those targets with the no_oss tag.
Copybara import of the project:
--
d87b5650899ff9d0f5eb791a7b15f7bfc9f4b4c7 by RJ Ascani <[email protected]>:
shlo_ref: Disable legacy code build
The code under shlo/legacy is useful for reference, but cannot build
with the oss toolchains. This PR disables those targets with the no_oss
tag.
Merging this change closes #64619
FUTURE_COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/64619 from rascani:disable_shlo_legacy_build d87b5650899ff9d0f5eb791a7b15f7bfc9f4b4c7
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Run buildifier on all files where it sorts loads differently
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Run buildifier on all files where it sorts loads differently
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Internal cleanup of BUILD/.bzl files
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Remove dead values before shape refinement
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Remove dead values before shape refinement
Some programs have a lot of dead values, causing shape refinement to fail to
converge. Indeed, even if there is no shape refinement to do, greedy pattern
rewrites keep iterating as long as the IR changes, which includes removing dead
values.
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Add macros to disable tests on GRM
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Add macros to disable tests on GRM
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Change to MSA to ignore constants while calculating operand distance.
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Change to MSA to ignore constants while calculating operand distance.
This is required for MSA to identify and optimize compiler-unrolled loops.
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Run buildifier on all files where it sorts loads differently
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Run buildifier on all files where it sorts loads differently
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Change camera lens
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[
"@Yaze104 If the code directly sets the camera zoom or FOV using CameraX's setZoomRatio() or similar methods, you'd need to adjust the value to 1.0 for a 1x zoom.\r\nThank you!",
"This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further."
] | 2024-03-27T16:00:10 | 2024-04-17T01:47:43 | 2024-04-17T01:47:42 |
NONE
| null | null | null |
**System information**
Hello, In this url https://github.com/tensorflow/examples/tree/master/lite/examples/pose_estimation/android project, i can use success run the code, but the cameras lens is x5, I just wanna change camera lens to x1, so how can i modify the code?
Should I change CameraSource.kt or something else?
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