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- **Have I written custom code (as opposed to using a stock example script
provided in TensorFlow)**:No
- **TensorFlow version (use command below)**: 2.12.0
- **Python version**: 3.10.*
### Describe the feature and the current behavior/state.
The current version of TensorFlow's tf.signal module provides extensive support for various Fourier Transform functions such as fft() and rfft(). However, it does not include helper functions like fftfreq() and rfftfreq() available in other libraries, such as NumPy and PyTorch. These functions are used to compute the discrete Fourier Transform sample frequencies for a signal of a given size, which is a common requirement in many signal-processing tasks.
Currently, to achieve similar functionality, users have to define custom functions or import existing functions from other libraries, such as SciPy. This process requires converting TensorFlow tensors to and from the other library's format, which may not always be efficient or convenient.
### Who will benefit from this feature?
Users who are working on signal processing tasks using TensorFlow will benefit from this feature as they won't have to switch to other libraries (like NumPy or PyTorch) to compute the Fourier Transform frequencies. This will make their code more consistent and potentially more efficient.
### Additional Info.
Adding these functions will make the tf.signal module more complete and competitive with other libraries' offerings in terms of signal processing capabilities. It'll also make TensorFlow more user-friendly for those who are accustomed to these functions in other libraries. | {
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"@mihalt,\r\nLooks like this issue is more related to tensorflow-text and not for the tensorflow. Could you please raise the issue on the **tensorflow-text** repo for the quick resolution. Thank you!\r\nhttps://github.com/tensorflow/text/issues",
"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/61775\">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/61775\">No</a>\n"
] | 2023-09-02T18:05:04 | 2023-09-19T01:47:37 | 2023-09-19T01:47:33 | NONE | null | null | null | You referenced in [this ](https://www.tensorflow.org/text/guide/subwords_tokenizer#generate_the_vocabulary)tutorial to [generate_vocab.py](https://github.com/tensorflow/text/blob/master/tensorflow_text/tools/wordpiece_vocab/generate_vocab.py), if I understand correct, as a ready to prod highlevel func that I can use. But I don't have it in downloaded repository of tensorflow-text.
Can you explain me a bit more how should be my attitude to this reference?
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Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.14rc0
### Custom code
No
### OS platform and distribution
macOS
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Test is failing.
This checkin the regression started:
```
commit e1e4de39de51064a359320f9b32936bf1599d4a0Author: Laura Pak <[email protected]>Date: Tue Apr 26 16:51:30 2022 -0700 Update highwayhash from fd3d9af80465e4383162e4a7c5e2f406e82dd968 to c13d28517a4db259d738ea4886b1f00352a3cc33. PiperOrigin-RevId: 444703993
```
Reverting that fixes the issue.
### Standalone code to reproduce the issue
```shell
bazel --bazelrc='./tensorflow/tools/ci_build/osx/arm64/.macos.bazelrc' test //tensorflow/tools/proto_splitter/cc:saved_model_splitter_test
```
### Relevant log output
```shell
The test is failing with:
INFO: From Testing //tensorflow/tools/proto_splitter/cc:saved_model_splitter_test:==================== Test output for //tensorflow/tools/proto_splitter/cc:saved_model_splitter_test:dyld[1906]: symbol not found in flat namespace
'__ZNK11highwayhash11HighwayHashILj16EEclERA4_KyPKcmPy'====================================================================================================
Test output for //tensorflow/tools/proto_splitter/cc:saved_model_splitter_test:dyld[1942]: symbol not found in flat namespace '__ZNK11highwayhash11HighwayHashILj16EEclERA4_KyPKcmPy'==================================================================================================== Test output for //tensorflow/tools/proto_splitter/cc:saved_model_splitter_test:dyld[1976]: symbol not found in flat namespace '__ZNK11highwayhash11HighwayHashILj16EEclERA4_KyPKcmPy'================================================================================
```
```
cc @learning-to-play , @nitins17 and @mihaimaruseac
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"@gbaned Could you find another reviewer, I don't think that I am the correct person to review this. ",
"> @gbaned Could you find another reviewer, I don't think that I am the correct person to review this.\r\n\r\nHi @BrianWieder Sorry for the late reply. Sure thing, Thank you for the update. ",
"Hi @penpornk, Can you please review this PR ? Thank you!",
"Hi @penpornk, Can you please review this PR ? Thank you!",
"Hi @penpornk, Can you please review this PR ? Thank you!",
"Hi @penpornk, Can you please review this PR ? Thank you!",
"Hi @penpornk, Can you please review this PR ? Thank you!",
"@kanvi-nervana can you fix the image link?",
"Hi @kanvi-nervana Can you please check @cantonios's comments and keep us posted ? Thank you!",
"> Update PR description with working image link\r\n\r\n@cantonios Uploaded the image again, please let me know if you still have any issue. Thanks!"
] | 2023-09-01T18:25:48 | 2024-05-02T08:55:29 | 2024-05-02T08:55:29 | CONTRIBUTOR | null | false | {
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} | Following pattern is seen in 3 models. It looks similar to InstanceNorm pattern but it is actually LayerNorm based on the reduction axis. Under right conditions, this pattern will be fused as LayerNorm to improve performance.
StopGradient is removed as part of optimize_for_inference since it is not required and is a training op.
"When used in a graph, it outputs the input as is" https://www.tensorflow.org/api_docs/python/tf/stop_gradient | {
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"@codyfalkosky Thank you for reporting the issue!\r\nThe provided link for the code is not so could you please provide a gist or code snippet to replicate the issue?\r\nThank you! ",
"#!/usr/bin/env python\r\n# coding: utf-8\r\n\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\nimport tensorflow as tf\r\n\r\n#########\r\n# comment and uncomment the following line out after resetting the kernel to see different CPU/GPU behavior\r\n# tf.config.set_visible_devices([], 'GPU')\r\n\r\n\r\ndef run_test():\r\n \r\n # Data\r\n # data for model training\r\n # a simple quadratic equation\r\n x = np.linspace(-10, 10, 10).reshape((-1, 1))\r\n y = 4*x**2 + 7\r\n \r\n # model\r\n model = tf.keras.models.Sequential([\r\n tf.keras.layers.Dense(16, activation='relu', input_shape=(1,)),\r\n tf.keras.layers.Dense(16, activation='relu'),\r\n tf.keras.layers.Dense(16, activation='relu'),\r\n tf.keras.layers.Dense(1) \r\n ])\r\n \r\n # training objects\r\n opt = tf.keras.optimizers.legacy.Adam(0.001)\r\n los = tf.keras.losses.MeanSquaredError()\r\n met = tf.keras.metrics.MeanAbsoluteError()\r\n \r\n # compile\r\n model.compile(optimizer=opt, loss=los, metrics=[met])\r\n \r\n # train\r\n x_len = len(x)\r\n history = model.fit(x, y, epochs=5000, batch_size=x_len, verbose=0)\r\n \r\n # predict\r\n y_pred = model.predict(x, verbose=0)\r\n \r\n # plot\r\n plt.figure(figsize=(10,4))\r\n\r\n # Model Output Graph\r\n plt.subplot(1, 2, 1)\r\n plt.plot(x, y, label='Actual Data')\r\n plt.plot(x, y_pred, label='Model Output')\r\n plt.title('Model Output vs. Actual')\r\n plt.xlabel('x')\r\n plt.ylabel('y')\r\n plt.legend()\r\n\r\n # Loss History Graph\r\n plt.subplot(1, 2, 2)\r\n plt.plot(history.history['loss'])\r\n plt.xlabel('Epochs')\r\n plt.ylabel('MSE Loss')\r\n plt.title('Model MSE Loss History')\r\n \r\n plt.suptitle('Mac M1 CPU Behavior')\r\n for device in tf.config.get_visible_devices():\r\n if device.device_type == 'GPU':\r\n plt.suptitle('Mac M1 GPU Behavior')\r\n break\r\n\r\n plt.subplots_adjust(wspace=.3)\r\n plt.show() \r\n\r\n\r\nrun_test()\r\n",
"@codyfalkosky I tried to replicate the issue so could you please have a look at the [gist](https://colab.research.google.com/gist/sushreebarsa/18ccf79b04db3d8da36ffdb919cd083f/61772.ipynb) and let me know if I am missing something?\r\nThank you!",
"link to corrected code:[https://colab.research.google.com/drive/1gWu5QNv5lLdPRMzry4ISTOh6_TU0uwFW](url)\r\n\r\nThis error is only when processing on a Mac M1 Max, so running in a colab will not duplicate the issue, as it is hardware related and must be run on an M1 Max MacBook Pro to duplicate the issue.",
"here is a repaired link to the original file if that is helpful: [https://storage.googleapis.com/codyfalkosky/TensorFlowIssue/strange%20M1%20GPU%20Behavior.ipynb](url)",
"@codyfalkosky ,\r\n\r\nCould you please submit the exact link. Above links are not working. Thank you!",
"link:\r\nhttps://storage.googleapis.com/codyfalkosky/TensorFlowIssue/strange%20M1%20GPU%20Behavior.ipynb\r\n\r\nThank you,\r\nCody\r\n\r\n\r\nOn Thu, Oct 12, 2023 at 2:04 AM Surya ***@***.***> wrote:\r\n\r\n> @codyfalkosky <https://github.com/codyfalkosky> ,\r\n>\r\n> Could you please submit the exact link. Above links are not working. Thank\r\n> you!\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/tensorflow/tensorflow/issues/61772#issuecomment-1759219981>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/A7EPYT4CV7IIZ3KWLUJSD5DX66XCTANCNFSM6AAAAAA4H4QVUY>\r\n> .\r\n> You are receiving this because you were mentioned.Message ID:\r\n> ***@***.***>\r\n>\r\n",
"Hi @codyfalkosky ,\r\n\r\nApologies for the delay. I have tested the same code with TF2.15v and its working as intended.Please refer to attached snapshot below.\r\n\r\n<img width=\"1031\" alt=\"Screenshot 2023-11-28 at 16 21 44\" src=\"https://github.com/tensorflow/tensorflow/assets/116063290/3ad1cc5c-b27d-4608-8056-4b94970462a6\">\r\n\r\nCould you please verify with TF2.15v and let us know if issue resolved for you also. 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.",
"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/61772\">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/61772\">No</a>\n"
] | 2023-09-01T18:05:27 | 2023-12-13T01:49:53 | 2023-12-13T01:49:50 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.13.0
### Custom code
Yes
### OS platform and distribution
OSX 12.4
### Mobile device
_No response_
### Python version
3.11.4
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
With all other factors the same, GPU does not minimize loss and CPU does.
### Standalone code to reproduce the issue
```shell
link to ipynb: https://storage.googleapis.com/codyfalkosky/TensorFlowIssue/strange%20M1%20GPU%20Behavior.ipynb
```
### Relevant log output
_No response_ | {
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"@HLneoh \r\nIn order to expedite the trouble-shooting process, please provide a code snippet to reproduce the issue reported here. 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/61771\">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/61771\">No</a>\n"
] | 2023-09-01T16:34:57 | 2023-09-07T03:29:37 | 2023-09-07T03:29:35 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.13.0
### Custom code
Yes
### OS platform and distribution
Ubuntu 20.04.5 LTS
### Mobile device
_No response_
### Python version
Python 3.10.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
A6000
### Current behavior?
Enabled float16 training by setting the mixed precision policy, but why I still need to manually cast the y tensors to float16 before calculating the loss?
Error when no manual cast the tensor:
### Standalone code to reproduce the issue
```shell
Confidential.
```
### Relevant log output
_No response_ | {
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"@manjarolinuxuser,\r\nCould you please confirm the sequence of steps. Tf2.13v supports protobuf >=3.20 versions as per [source](https://github.com/tensorflow/tensorflow/blob/r2.13/tensorflow/tools/pip_package/setup.py#L96) where as it seems protobuf 3.19v has been installed. Thank you!",
"@tilakrayal I am also facing this issue. It will arise when you have pydantic 2.3.0 installed and then try to install tensorflow 2.13.0 or vice versa. It is due to conflicting typing-extensions versions - I'm not sure why protobuf is mentioned, it appears to install just fine above. You can duplicate:\r\n```\r\npython3 -m venv venv\r\nsource venv/bin/activate\r\npip install pydantic==2.3.0 tensorflow==2.13.0\r\n```\r\n\r\nYou will see:\r\n```\r\nERROR: Cannot install pydantic==2.3.0 and tensorflow==2.13.0 because these package versions have conflicting dependencies.\r\n\r\nThe conflict is caused by:\r\n pydantic 2.3.0 depends on typing-extensions>=4.6.1\r\n tensorflow 2.13.0 depends on typing-extensions<4.6.0 and >=3.6.6\r\n\r\nTo fix this you could try to:\r\n1. loosen the range of package versions you've specified\r\n2. remove package versions to allow pip attempt to solve the dependency conflict\r\n\r\nERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/user_guide/#fixing-conflicting-dependencies\r\n```",
"From the logs protobuf 4.24.2 seems installed already. Where did protobuf 3.19v came from?",
"> @tilakrayal I am also facing this issue. It will arise when you have pydantic 2.3.0 installed and then try to install tensorflow 2.13.0 or vice versa. It is due to conflicting typing-extensions versions - I'm not sure why protobuf is mentioned, it appears to install just fine above. You can duplicate:\r\n> \r\n> ```\r\n> python3 -m venv venv\r\n> source venv/bin/activate\r\n> pip install pydantic==2.3.0 tensorflow==2.13.0\r\n> ```\r\n> \r\n> You will see:\r\n> \r\n> ```\r\n> ERROR: Cannot install pydantic==2.3.0 and tensorflow==2.13.0 because these package versions have conflicting dependencies.\r\n> \r\n> The conflict is caused by:\r\n> pydantic 2.3.0 depends on typing-extensions>=4.6.1\r\n> tensorflow 2.13.0 depends on typing-extensions<4.6.0 and >=3.6.6\r\n> \r\n> To fix this you could try to:\r\n> 1. loosen the range of package versions you've specified\r\n> 2. remove package versions to allow pip attempt to solve the dependency conflict\r\n> \r\n> ERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/user_guide/#fixing-conflicting-dependencies\r\n> ```\r\n\r\nHi, I am also facing exactly the issue about the version conflicts of pydantic and tensorflow. Do you already have solutions? Or still waiting a fix ",
"@p1k0pan I had to downgrade to tensorflow==2.12.0 for mac and tensorflow==2.11.0 for ubuntu docker.",
"> @p1k0pan I had to downgrade to tensorflow==2.12.0 for mac and tensorflow==2.11.0 for ubuntu docker.\r\n\r\nThanks! ",
"Similar feature is available and assigned to Developer team #https://github.com/tensorflow/tensorflow/issues/61848",
"@manjarolinuxuser,\r\nCould you please install TensorFlow as per the above comment from official Tensorflow PyPi using pip install tensorflow==2.14 and close the issue. Thanks!\r\nhttps://pypi.org/project/tensorflow/#files",
"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/61770\">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/61770\">No</a>\n"
] | 2023-09-01T12:07:01 | 2023-10-28T01:46:48 | 2023-10-28T01:46:46 | NONE | null | null | null | ### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.13.0
### Custom code
Yes
### OS platform and distribution
Windows 10
### Mobile device
_No response_
### Python version
3.11.3
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Their are different support problems when I install tensorflow
I installed it by entering
`pip install tensorflow`
The output was
> Collecting tensorflow
Obtaining dependency information for tensorflow from https://files.pythonhosted.org/packages/9e/b8/ed5f794359d05cd0bffb894c6418da87b93016ee17b669d55c45d1bd5d5b/tensorflow-2.13.0-cp311-cp311-win_amd64.whl.metadata
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Obtaining dependency information for tensorflow-intel==2.13.0 from https://files.pythonhosted.org/packages/2f/2f/3c84f675931ce3bcbc7e23acbba1e5d7f05ce769adab48322de57a9f5928/tensorflow_intel-2.13.0-cp311-cp311-win_amd64.whl.metadata
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Collecting absl-py>=1.0.0 (from tensorflow-intel==2.13.0->tensorflow)
Using cached absl_py-1.4.0-py3-none-any.whl (126 kB)
Collecting astunparse>=1.6.0 (from tensorflow-intel==2.13.0->tensorflow)
Using cached astunparse-1.6.3-py2.py3-none-any.whl (12 kB)
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Obtaining dependency information for flatbuffers>=23.1.21 from https://files.pythonhosted.org/packages/6f/12/d5c79ee252793ffe845d58a913197bfa02ae9a0b5c9bc3dc4b58d477b9e7/flatbuffers-23.5.26-py2.py3-none-any.whl.metadata
Downloading flatbuffers-23.5.26-py2.py3-none-any.whl.metadata (850 bytes)
Collecting gast<=0.4.0,>=0.2.1 (from tensorflow-intel==2.13.0->tensorflow)
Using cached gast-0.4.0-py3-none-any.whl (9.8 kB)
Collecting google-pasta>=0.1.1 (from tensorflow-intel==2.13.0->tensorflow)
Using cached google_pasta-0.2.0-py3-none-any.whl (57 kB)
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Obtaining dependency information for h5py>=2.9.0 from https://files.pythonhosted.org/packages/d1/93/0f4cf5058095d749d464e4f770d2bf339930e5f3374331f0d2fa6ddfbf28/h5py-3.9.0-cp311-cp311-win_amd64.whl.metadata
Downloading h5py-3.9.0-cp311-cp311-win_amd64.whl.metadata (2.5 kB)
Collecting libclang>=13.0.0 (from tensorflow-intel==2.13.0->tensorflow)
Obtaining dependency information for libclang>=13.0.0 from https://files.pythonhosted.org/packages/02/8c/dc970bc00867fe290e8c8a7befa1635af716a9ebdfe3fb9dce0ca4b522ce/libclang-16.0.6-py2.py3-none-win_amd64.whl.metadata
Downloading libclang-16.0.6-py2.py3-none-win_amd64.whl.metadata (5.3 kB)
Collecting numpy<=1.24.3,>=1.22 (from tensorflow-intel==2.13.0->tensorflow)
Using cached numpy-1.24.3-cp311-cp311-win_amd64.whl (14.8 MB)
Collecting opt-einsum>=2.3.2 (from tensorflow-intel==2.13.0->tensorflow)
Using cached opt_einsum-3.3.0-py3-none-any.whl (65 kB)
Requirement already satisfied: packaging in c:\users\wadhw\appdata\local\programs\python\python311\lib\site-packages (from tensorflow-intel==2.13.0->tensorflow) (23.1)
Collecting protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 (from tensorflow-intel==2.13.0->tensorflow)
Obtaining dependency information for protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 from https://files.pythonhosted.org/packages/14/ff/10f746c03212fe48576b2c0f5ada73c3400b6d90f769728c4f07656d8b27/protobuf-4.24.2-cp310-abi3-win_amd64.whl.metadata
Downloading protobuf-4.24.2-cp310-abi3-win_amd64.whl.metadata (540 bytes)
Requirement already satisfied: setuptools in c:\users\wadhw\appdata\local\programs\python\python311\lib\site-packages (from tensorflow-intel==2.13.0->tensorflow) (65.5.0)
Requirement already satisfied: six>=1.12.0 in c:\users\wadhw\appdata\local\programs\python\python311\lib\site-packages (from tensorflow-intel==2.13.0->tensorflow) (1.16.0)
Collecting termcolor>=1.1.0 (from tensorflow-intel==2.13.0->tensorflow)
Using cached termcolor-2.3.0-py3-none-any.whl (6.9 kB)
Collecting typing-extensions<4.6.0,>=3.6.6 (from tensorflow-intel==2.13.0->tensorflow)
Using cached typing_extensions-4.5.0-py3-none-any.whl (27 kB)
Collecting wrapt>=1.11.0 (from tensorflow-intel==2.13.0->tensorflow)
Using cached wrapt-1.15.0-cp311-cp311-win_amd64.whl (36 kB)
Collecting grpcio<2.0,>=1.24.3 (from tensorflow-intel==2.13.0->tensorflow)
Obtaining dependency information for grpcio<2.0,>=1.24.3 from https://files.pythonhosted.org/packages/8d/58/ede228c07bdf3780c5332660c89f3c7a37fe8bfb9bd73a97ad2614420bd4/grpcio-1.57.0-cp311-cp311-win_amd64.whl.metadata
Downloading grpcio-1.57.0-cp311-cp311-win_amd64.whl.metadata (4.1 kB)
Collecting tensorboard<2.14,>=2.13 (from tensorflow-intel==2.13.0->tensorflow)
Using cached tensorboard-2.13.0-py3-none-any.whl (5.6 MB)
Collecting tensorflow-estimator<2.14,>=2.13.0 (from tensorflow-intel==2.13.0->tensorflow)
Obtaining dependency information for tensorflow-estimator<2.14,>=2.13.0 from https://files.pythonhosted.org/packages/72/5c/c318268d96791c6222ad7df1651bbd1b2409139afeb6f468c0f327177016/tensorflow_estimator-2.13.0-py2.py3-none-any.whl.metadata
Downloading tensorflow_estimator-2.13.0-py2.py3-none-any.whl.metadata (1.3 kB)
Collecting keras<2.14,>=2.13.1 (from tensorflow-intel==2.13.0->tensorflow)
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Downloading keras-2.13.1-py3-none-any.whl.metadata (2.4 kB)
Collecting tensorflow-io-gcs-filesystem>=0.23.1 (from tensorflow-intel==2.13.0->tensorflow)
Using cached tensorflow_io_gcs_filesystem-0.31.0-cp311-cp311-win_amd64.whl (1.5 MB)
Collecting wheel<1.0,>=0.23.0 (from astunparse>=1.6.0->tensorflow-intel==2.13.0->tensorflow)
Obtaining dependency information for wheel<1.0,>=0.23.0 from https://files.pythonhosted.org/packages/b8/8b/31273bf66016be6ad22bb7345c37ff350276cfd46e389a0c2ac5da9d9073/wheel-0.41.2-py3-none-any.whl.metadata
Using cached wheel-0.41.2-py3-none-any.whl.metadata (2.2 kB)
Collecting google-auth<3,>=1.6.3 (from tensorboard<2.14,>=2.13->tensorflow-intel==2.13.0->tensorflow)
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Downloading google_auth-2.22.0-py2.py3-none-any.whl.metadata (4.2 kB)
Collecting google-auth-oauthlib<1.1,>=0.5 (from tensorboard<2.14,>=2.13->tensorflow-intel==2.13.0->tensorflow)
Using cached google_auth_oauthlib-1.0.0-py2.py3-none-any.whl (18 kB)
Collecting markdown>=2.6.8 (from tensorboard<2.14,>=2.13->tensorflow-intel==2.13.0->tensorflow)
Obtaining dependency information for markdown>=2.6.8 from https://files.pythonhosted.org/packages/1a/b5/228c1cdcfe138f1a8e01ab1b54284c8b83735476cb22b6ba251656ed13ad/Markdown-3.4.4-py3-none-any.whl.metadata
Downloading Markdown-3.4.4-py3-none-any.whl.metadata (6.9 kB)
Requirement already satisfied: requests<3,>=2.21.0 in c:\users\wadhw\appdata\local\programs\python\python311\lib\site-packages (from tensorboard<2.14,>=2.13->tensorflow-intel==2.13.0->tensorflow) (2.31.0)
Collecting tensorboard-data-server<0.8.0,>=0.7.0 (from tensorboard<2.14,>=2.13->tensorflow-intel==2.13.0->tensorflow)
Obtaining dependency information for tensorboard-data-server<0.8.0,>=0.7.0 from https://files.pythonhosted.org/packages/da/61/6e9ff8258422d287eec718872fb71e05324356722ab658c8afda25f51539/tensorboard_data_server-0.7.1-py3-none-any.whl.metadata
Downloading tensorboard_data_server-0.7.1-py3-none-any.whl.metadata (1.1 kB)
Requirement already satisfied: werkzeug>=1.0.1 in c:\users\wadhw\appdata\local\programs\python\python311\lib\site-packages (from tensorboard<2.14,>=2.13->tensorflow-intel==2.13.0->tensorflow) (2.3.7)
Collecting cachetools<6.0,>=2.0.0 (from google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow-intel==2.13.0->tensorflow)
Obtaining dependency information for cachetools<6.0,>=2.0.0 from https://files.pythonhosted.org/packages/a9/c9/c8a7710f2cedcb1db9224fdd4d8307c9e48cbddc46c18b515fefc0f1abbe/cachetools-5.3.1-py3-none-any.whl.metadata
Downloading cachetools-5.3.1-py3-none-any.whl.metadata (5.2 kB)
Collecting pyasn1-modules>=0.2.1 (from google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow-intel==2.13.0->tensorflow)
Using cached pyasn1_modules-0.3.0-py2.py3-none-any.whl (181 kB)
Collecting rsa<5,>=3.1.4 (from google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow-intel==2.13.0->tensorflow)
Using cached rsa-4.9-py3-none-any.whl (34 kB)
Collecting urllib3<2.0 (from google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow-intel==2.13.0->tensorflow)
Obtaining dependency information for urllib3<2.0 from https://files.pythonhosted.org/packages/c5/05/c214b32d21c0b465506f95c4f28ccbcba15022e000b043b72b3df7728471/urllib3-1.26.16-py2.py3-none-any.whl.metadata
Downloading urllib3-1.26.16-py2.py3-none-any.whl.metadata (48 kB)
---------------------------------------- 48.4/48.4 kB 1.2 MB/s eta 0:00:00
Collecting requests-oauthlib>=0.7.0 (from google-auth-oauthlib<1.1,>=0.5->tensorboard<2.14,>=2.13->tensorflow-intel==2.13.0->tensorflow)
Using cached requests_oauthlib-1.3.1-py2.py3-none-any.whl (23 kB)
Requirement already satisfied: charset-normalizer<4,>=2 in c:\users\wadhw\appdata\local\programs\python\python311\lib\site-packages (from requests<3,>=2.21.0->tensorboard<2.14,>=2.13->tensorflow-intel==2.13.0->tensorflow) (3.2.0)
Requirement already satisfied: idna<4,>=2.5 in c:\users\wadhw\appdata\local\programs\python\python311\lib\site-packages (from requests<3,>=2.21.0->tensorboard<2.14,>=2.13->tensorflow-intel==2.13.0->tensorflow) (3.4)
Requirement already satisfied: certifi>=2017.4.17 in c:\users\wadhw\appdata\local\programs\python\python311\lib\site-packages (from requests<3,>=2.21.0->tensorboard<2.14,>=2.13->tensorflow-intel==2.13.0->tensorflow) (2023.7.22)
Requirement already satisfied: MarkupSafe>=2.1.1 in c:\users\wadhw\appdata\local\programs\python\python311\lib\site-packages (from werkzeug>=1.0.1->tensorboard<2.14,>=2.13->tensorflow-intel==2.13.0->tensorflow) (2.1.3)
Collecting pyasn1<0.6.0,>=0.4.6 (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow-intel==2.13.0->tensorflow)
Using cached pyasn1-0.5.0-py2.py3-none-any.whl (83 kB)
Collecting oauthlib>=3.0.0 (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.14,>=2.13->tensorflow-intel==2.13.0->tensorflow)
Using cached oauthlib-3.2.2-py3-none-any.whl (151 kB)
Using cached tensorflow-2.13.0-cp311-cp311-win_amd64.whl (1.9 kB)
Using cached tensorflow_intel-2.13.0-cp311-cp311-win_amd64.whl (276.6 MB)
Using cached flatbuffers-23.5.26-py2.py3-none-any.whl (26 kB)
Using cached grpcio-1.57.0-cp311-cp311-win_amd64.whl (4.3 MB)
Using cached h5py-3.9.0-cp311-cp311-win_amd64.whl (2.7 MB)
Using cached keras-2.13.1-py3-none-any.whl (1.7 MB)
Using cached libclang-16.0.6-py2.py3-none-win_amd64.whl (24.4 MB)
Using cached protobuf-4.24.2-cp310-abi3-win_amd64.whl (430 kB)
Using cached tensorflow_estimator-2.13.0-py2.py3-none-any.whl (440 kB)
Using cached google_auth-2.22.0-py2.py3-none-any.whl (181 kB)
Using cached Markdown-3.4.4-py3-none-any.whl (94 kB)
Using cached tensorboard_data_server-0.7.1-py3-none-any.whl (2.4 kB)
Using cached wheel-0.41.2-py3-none-any.whl (64 kB)
Using cached cachetools-5.3.1-py3-none-any.whl (9.3 kB)
Using cached urllib3-1.26.16-py2.py3-none-any.whl (143 kB)
Installing collected packages: libclang, flatbuffers, wrapt, wheel, urllib3, typing-extensions, termcolor, tensorflow-io-gcs-filesystem, tensorflow-estimator, tensorboard-data-server, pyasn1, protobuf, oauthlib, numpy, markdown, keras, grpcio, google-pasta, gast, cachetools, absl-py, rsa, pyasn1-modules, opt-einsum, h5py, astunparse, requests-oauthlib, google-auth, google-auth-oauthlib, tensorboard, tensorflow-intel, tensorflow
Attempting uninstall: urllib3
Found existing installation: urllib3 2.0.4
Uninstalling urllib3-2.0.4:
Successfully uninstalled urllib3-2.0.4
Attempting uninstall: typing-extensions
Found existing installation: typing_extensions 4.7.1
Uninstalling typing_extensions-4.7.1:
Successfully uninstalled typing_extensions-4.7.1
Attempting uninstall: numpy
Found existing installation: numpy 1.25.2
Uninstalling numpy-1.25.2:
Successfully uninstalled numpy-1.25.2
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
pydantic 2.3.0 requires typing-extensions>=4.6.1, but you have typing-extensions 4.5.0 which is incompatible.
pydantic-core 2.6.3 requires typing-extensions!=4.7.0,>=4.6.0, but you have typing-extensions 4.5.0 which is incompatible.
Successfully installed absl-py-1.4.0 astunparse-1.6.3 cachetools-5.3.1 flatbuffers-23.5.26 gast-0.4.0 google-auth-2.22.0 google-auth-oauthlib-1.0.0 google-pasta-0.2.0 grpcio-1.57.0 h5py-3.9.0 keras-2.13.1 libclang-16.0.6 markdown-3.4.4 numpy-1.24.3 oauthlib-3.2.2 opt-einsum-3.3.0 protobuf-4.24.2 pyasn1-0.5.0 pyasn1-modules-0.3.0 requests-oauthlib-1.3.1 rsa-4.9 tensorboard-2.13.0 tensorboard-data-server-0.7.1 tensorflow-2.13.0 tensorflow-estimator-2.13.0 tensorflow-intel-2.13.0 tensorflow-io-gcs-filesystem-0.31.0 termcolor-2.3.0 typing-extensions-4.5.0 urllib3-1.26.16 wheel-0.41.2 wrapt-1.15.0
Due to this error, I installed typing-extensions 4.6.1 by
`pip install typing-extensions==4.6.1`
I got the output as
> Collecting typing-extensions==4.6.1
Obtaining dependency information for typing-extensions==4.6.1 from https://files.pythonhosted.org/packages/82/ed/8ccf53a0ed10bf8fc8877b5833b40f5f99093cadfe6632b8892f74aead0f/typing_extensions-4.6.1-py3-none-any.whl.metadata
Downloading typing_extensions-4.6.1-py3-none-any.whl.metadata (2.8 kB)
Downloading typing_extensions-4.6.1-py3-none-any.whl (31 kB)
Installing collected packages: typing-extensions
Attempting uninstall: typing-extensions
Found existing installation: typing_extensions 4.5.0
Uninstalling typing_extensions-4.5.0:
Successfully uninstalled typing_extensions-4.5.0
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
tensorflow-intel 2.13.0 requires typing-extensions<4.6.0,>=3.6.6, but you have typing-extensions 4.6.1 which is incompatible.
Successfully installed typing-extensions-4.6.1
Then, I again reinstalled typing-extensions 4.5.0 by
`pip install typing-extensions==4.5.0`
Then I again received the error
> Collecting typing-extensions==4.5.0
Using cached typing_extensions-4.5.0-py3-none-any.whl (27 kB)
Installing collected packages: typing-extensions
Attempting uninstall: typing-extensions
Found existing installation: typing_extensions 4.6.1
Uninstalling typing_extensions-4.6.1:
Successfully uninstalled typing_extensions-4.6.1
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
pydantic 2.3.0 requires typing-extensions>=4.6.1, but you have typing-extensions 4.5.0 which is incompatible.
pydantic-core 2.6.3 requires typing-extensions!=4.7.0,>=4.6.0, but you have typing-extensions 4.5.0 which is incompatible.
Successfully installed typing-extensions-4.5.0
Please let me know how to fix this error
### Standalone code to reproduce the issue
```shell
Just use
pip install tensorflow
and you may receive this error
```
### Relevant log output
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"Hi there,\r\n\r\nI do believe this will be highly valuable feature to add these dunders for better cross platform dl acceleration \r\n\r\nI'm interested in working on this issue and would appreciate mentorship. Could someone please guide me as I contribute to adding support for `__dlpack__` and `__dlpack_device__` to TensorFlow tensors\r\n\r\nIf so please mail me at [email protected] as it will also boost me to contribute even more on the project!\r\n\r\nThank you,\r\nPrasanna\r\n",
"Hi @abdulasiraj ,\r\n\r\nCould you please check this [RFC](https://github.com/tensorflow/community/blob/master/rfcs/20191016-dlpack-support.md) which may address your requirements. \r\n\r\nAlso please refer to this API [page](https://www.tensorflow.org/api_docs/python/tf/experimental/dlpack) for currently available APIs.\r\n\r\nIf not please let us know. Thanks!",
"Hi @SuryanarayanaY,\r\nThanks for your response. I've gone through RFC and APIs. `to_dlpack` and `from_dlpack` are working very fine with tensorflow but now new array_api_standard is to support `__dlpack__` and `__dlpack_device__` dunder methods for tensors. details can be seen here:\r\nhttps://dmlc.github.io/dlpack/latest/python_spec.html\r\nhttps://data-apis.org/array-api/latest/API_specification/generated/array_api.array.__dlpack__.html\r\n\r\nTorch and jax supports both functions as well as dunders but numpy only supports dunders, which means we can't consume tf capsule in numpy."
] | 2023-09-01T10:43:42 | 2023-10-12T09:21:42 | null | NONE | null | null | null | ### Issue type
Feature Request
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 2.12.0
### Custom code
Yes
### OS platform and distribution
google colab
### Mobile device
_No response_
### Python version
3.10.12
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
it looks like tensorflow tesnors doesn't support `__dlpack__` and `__dlpack_device__` dunders. Not sure, If it's already something down the road map. If not, can we add these dunders to tf tensors to conform array_api_standrads?
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
tensor = tf.constant([1,3,2])
tensor.__dlpack__()
```
### Relevant log output
```shell
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
[<ipython-input-3-016b1ec8a3b2>](https://localhost:8080/#) in <cell line: 1>()
----> 1 tensor.__dlpack__()
[/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py](https://localhost:8080/#) in __getattr__(self, name)
441 np_config.enable_numpy_behavior()
442 """)
--> 443 self.__getattribute__(name)
444
445 @staticmethod
AttributeError: 'tensorflow.python.framework.ops.EagerTensor' object has no attribute '__dlpack__'
```
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"Not sure what the best approach is for grabbing the files. I've used `populate_tflite_source_vars` and manual additions to variables depending upon what was used in the surrounding context as there didn't seem to be any super clear pattern, but I'm very willing to change the approach if something else is preferred.",
"@terryheo Are you able to take a look at this when you get a chance? Thanks!"
] | 2023-09-01T10:37:37 | 2023-09-12T00:50:23 | 2023-09-12T00:50:23 | CONTRIBUTOR | null | false | {
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} | Some of these files are included by some of the main header files such as interpreter.h but aren't included in the install. This causes build failures when using TFLite built in some configurations.
This was also causing build failures when building LLVM with a near tip of tree TFLite.
CC: @mtrofin @petrhosek
Also related to https://github.com/google/ml-compiler-opt/pull/293. | {
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} | Currently gemmlowp is not included in the dependencies for the CMake file that is created by the install target which causes link time failures when linked against TFLite in certain contexts.
This is causing build failures when trying to build LLVM with a near tip of tree TFLite:
```
CMake Error at /tflite/tensorflow/lib/cmake/tensorflow-lite/tensorflow-liteTargets.cmake:89 (set_target_properties):
The link interface of target "tensorflow-lite::tensorflow-lite" contains:
gemmlowp::gemmlowp
```
Related to https://github.com/google/ml-compiler-opt/pull/293
CC: @mtrofin @petrhosek | {
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"Complete "
] | 2023-09-01T06:24:51 | 2023-09-01T06:25:54 | 2023-09-01T06:25:39 | NONE | null | null | null |
- Android Device information (use `adb shell getprop ro.build.
- Google Play Services version
Settings
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"Try with updating CUDA and cuDNN versions to the latest compatible versions for TensorFlow 2.13. Make sure the path to the CUPTI library is correctly set up. If the issue persists, consider downgrading TensorFlow to version 2.12 or earlier, as the error started occurring after the upgrade. Additionally, check if there are any compatibility issues between TensorFlow and the RTX A6000 and Quatro RTX 8000 GPUs.",
"thanks @msf-caesar I forgot about having this issue open. I have somehow resolved the issue after I remade my environment. I also run into some issue with profiler logs being in wrong directory and unavailable in Tensorboard UI, but I've also found solution in github issues",
"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/61765\">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/61765\">No</a>\n"
] | 2023-08-31T18:22:53 | 2024-02-14T13:08:25 | 2024-02-14T13:08:21 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.13
### Custom code
Yes
### OS platform and distribution
Ubuntu 22
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
11.8, 8.6
### GPU model and memory
RTX A6000, quatro RTX 8000
### Current behavior?
I'm trying to setup profiler for my trainer, using TF.data from TFRecords, but otherwise training with standard keras code and MirroredStrategy.
I've noticed some strange errors after adding `profile_batch=5` to my tensorboard callback. I've attached logs below.
It's worth noting that I started getting `Local randezvous recv item cancelled` as well after upgrade to 2.13 from 2.12.
I have conda environment with cudatoolkit==11.8.0, cuda-nvcc, and pip installed nvidia-cudnn-cu11==8.6.0.163, tensorflow==2.13.*, tensorboard_plugin_profile... Path to CUPTI should be setup well, as it's in the same path as cudatoolkit installed libcuda
### Standalone code to reproduce the issue
```shell
log_dir = "logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1, profile_batch=5)
```
### Relevant log output
```shell
2023-08-31 19:57:55.092538: I tensorflow/tsl/profiler/lib/profiler_session.cc:104] Profiler session initializing.
2023-08-31 19:57:55.092572: I tensorflow/tsl/profiler/lib/profiler_session.cc:119] Profiler session started.
2023-08-31 19:57:55.092639: I tensorflow/compiler/xla/backends/profiler/gpu/cupti_tracer.cc:1671] Profiler found 8 GPUs
2023-08-31 19:57:55.093149: E tensorflow/compiler/xla/backends/profiler/gpu/cupti_error_manager.cc:137] cuptiGetTimestamp: error 999:
2023-08-31 19:57:55.093178: E tensorflow/compiler/xla/backends/profiler/gpu/cupti_error_manager.cc:186] cuptiSubscribe: ignored due to a previous error.
2023-08-31 19:57:55.093189: E tensorflow/compiler/xla/backends/profiler/gpu/cupti_error_manager.cc:459] cuptiGetResultString: ignored due to a previous error.
2023-08-31 19:57:55.093200: E tensorflow/compiler/xla/backends/profiler/gpu/cupti_tracer.cc:1723] function cupti_interface_->Subscribe( &subscriber_, (CUpti_CallbackFunc)ApiCallback, this)failed with error
2023-08-31 19:57:55.093257: I tensorflow/tsl/profiler/lib/profiler_session.cc:131] Profiler session tear down.
2023-08-31 19:57:55.093422: E tensorflow/compiler/xla/backends/profiler/gpu/cupti_error_manager.cc:142] cuptiFinalize: ignored due to a previous error.
2023-08-31 19:57:55.093435: E tensorflow/compiler/xla/backends/profiler/gpu/cupti_error_manager.cc:459] cuptiGetResultString: ignored due to a previous error.
2023-08-31 19:57:55.093445: E tensorflow/compiler/xla/backends/profiler/gpu/cupti_tracer.cc:1814] function cupti_interface_->Finalize()failed with error
Epoch 1/100
...
2023-08-31 19:59:33.523444: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.
4/Unknown - 109s 3s/step - loss: 0.7206 - binary_accuracy: 0.5451 - f1_score: 0.1138 - auc: 0.49592023-08-31 19:59:44.532963: I tensorflow/tsl/profiler/lib/profiler_session.cc:10
4] Profiler session initializing.
2023-08-31 19:59:44.533038: I tensorflow/tsl/profiler/lib/profiler_session.cc:119] Profiler session started.
2023-08-31 19:59:44.533099: E tensorflow/compiler/xla/backends/profiler/gpu/cupti_error_manager.cc:135] cuptiGetTimestamp: ignored due to a previous error.
2023-08-31 19:59:44.533216: E tensorflow/compiler/xla/backends/profiler/gpu/cupti_error_manager.cc:186] cuptiSubscribe: ignored due to a previous error.
2023-08-31 19:59:44.533247: E tensorflow/compiler/xla/backends/profiler/gpu/cupti_error_manager.cc:459] cuptiGetResultString: ignored due to a previous error.
2023-08-31 19:59:44.533273: E tensorflow/compiler/xla/backends/profiler/gpu/cupti_tracer.cc:1723] function cupti_interface_->Subscribe( &subscriber_, (CUpti_CallbackFunc)ApiCallback, t
his)failed with error
2023-08-31 19:59:48.299203: I tensorflow/tsl/profiler/lib/profiler_session.cc:70] Profiler session collecting data.
2023-08-31 19:59:48.352778: E tensorflow/compiler/xla/backends/profiler/gpu/cupti_error_manager.cc:142] cuptiFinalize: ignored due to a previous error.
2023-08-31 19:59:48.352844: E tensorflow/compiler/xla/backends/profiler/gpu/cupti_error_manager.cc:459] cuptiGetResultString: ignored due to a previous error.
2023-08-31 19:59:48.352869: E tensorflow/compiler/xla/backends/profiler/gpu/cupti_tracer.cc:1814] function cupti_interface_->Finalize()failed with error
2023-08-31 19:59:49.390774: E tensorflow/compiler/xla/backends/profiler/gpu/cupti_error_manager.cc:135] cuptiGetTimestamp: ignored due to a previous error.
2023-08-31 19:59:49.390830: E tensorflow/compiler/xla/backends/profiler/gpu/cupti_error_manager.cc:135] cuptiGetTimestamp: ignored due to a previous error.
2023-08-31 19:59:49.390843: I tensorflow/compiler/xla/backends/profiler/gpu/cupti_collector.cc:541] GpuTracer has collected 0 callback api events and 0 activity events.
2023-08-31 19:59:49.413800: I tensorflow/tsl/profiler/lib/profiler_session.cc:131] Profiler session tear down.
2023-08-31 19:59:49.415262: I tensorflow/tsl/profiler/rpc/client/save_profile.cc:144] Collecting XSpace to repository: logs/fit/20230831-195755/plugins/profile/2023_08_31_19_59_49/****.net.xplane.pb
```
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"Try with explicitly marking the internal interfaces within the tensorflow package by adding a single leading underscore to their names, as recommended by the PEP 8 style guide. This will indicate that these interfaces are intended to be private and not to be accessed by users, preventing any unintended consequences. Renaming the `python` module to `_python` and following the same pattern for other internal interfaces will provide clarity and align with Python coding standards.",
"@MO-Helena-Reid,\r\nCould you please elaborate about your Feature. Also, please specify the Use Cases for this feature. Thank you!",
"@tilakrayal\r\nUse Cases: users attempting to access internal interfaces will have a clear, immediate indication that this is what they are doing. Users will no longer need to discover this information separately through other channels, as this information being contained in the import statement will make it impossible to import anything internal without seeing that it is marked private.\r\n\r\nUnder the current naming scheme, it is possible for users to import internal interfaces without realising they are doing so, because they are currently marked public, and to only discover their mistake later, such as when encountering unexpected behaviour.\r\n\r\nThe feature is just to mark all internal interfaces (for example, `tensorflow.python`) as private. The manner of doing so is as recommended by PEP 8. Is there something else specific you would like me to elaborate on? "
] | 2023-08-31T17:27:54 | 2023-09-01T18:23:28 | null | NONE | null | null | null | ### Issue type
Feature Request
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.12.0
### Custom code
No
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
There are internal interfaces within the tensorflow package that I think would benefit from being marked more explicitly. According to [the PEP 8 style guide](https://peps.python.org/pep-0008/#public-and-internal-interfaces), *"Even with `__all__` set appropriately, internal interfaces (packages, modules, classes, functions, attributes or other names) should still be prefixed with a single leading underscore."*
While following all of PEP 8 to the letter is perhaps understandably not always adhered to in all projects, this particular section seems very reasonable. You can have no documentation for a module, and exclude it from `__all__`, both of which already indicate the module is not intended to be public, but it is still recommended to explicitly mark that module as private. Because this is commonly adhered to in the python ecosystem, some users may even expect modules not marked private to be intentionally public.
There exist old issues such as [this one](https://github.com/tensorflow/tensorflow/issues/33075) wherein it is stated that the only supported way to import anything is `import tensorflow` or possibly `from tensorflow.keras import ...`; and that `tensorflow.python` for example (or *"any other modules"*) is unsupported. To quote directly from that thread:
> so to confirm, `tf.python.keras` is *private*, intended for *development*, rather than public use? Thanks
> Yes, that's exactly the case. Anything under `tf.python` is private
If this module is intended to be private, then to be in accordance with the above guidelines, the module should be renamed from `python` to `_python`, as should all other internal interfaces. There seem several advantages to doing so: a user knows instantly that importing from `package._module` is dangerous (even if python technically permits it); an IDE may also be configured to treat such modules differently, such as excluding their contents from being displayed to the user in autocompletion, search results, etc. Doing so would also help prevent users such as the one in the issue above wondering why they encountered problems while using what on the surface looks like a public interface.
### Standalone code to reproduce the issue
```shell
Note that `import tensorflow.python` is possible, where we might expect `import tensorflow._python` indicating this is an internal interface not intended for the user to access. Running code with this import as a user would thus have unintended consequences.
```
### Relevant log output
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"Hi @sirakiin Can you please review this PR ? Thank you!",
"Hi @sirakiin Can you please review this PR ? Thank you!",
"Hi @ryan-holt-1 Can you please resolve conflicts? Thank you!",
"Hi @ryan-holt-1 Can you please resolve conflicts? Thank you!",
"This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.",
"This PR was closed because it has been inactive for 14 days since being marked as stale. Please reopen if you'd like to work on this further."
] | 2023-08-31T16:22:55 | 2024-01-28T01:48:28 | 2024-01-28T01:48:17 | NONE | null | false | {
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"Try with implementing a delayed assertion mechanism that allows for a certain amount of time before checking if all snapshot files have been fully written to the filesystem. This can be achieved by introducing a time delay or using a polling mechanism to periodically check for the existence of all the snapshot files."
] | 2023-08-31T14:04:30 | 2023-09-06T05:21:00 | 2023-09-06T05:21:00 | CONTRIBUTOR | null | false | {
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} | Snapshot files may take a little time to all be written out to the filesystem on a heavily loaded system so need to allow for this before asserting that they are all there.
Fixes: https://github.com/tensorflow/tensorflow/issues/61116 | {
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"can i work on this ?",
"Hi @CuiYifeng ,\r\n\r\nI have replicated the behaviour with Tf2.14.rc0. But with Tf2.12v it working fine. Attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/aba44286fab30dae78b84060d463fbae/61761_2-12-vs-2-14.ipynb) for reference.\r\n\r\nIt seems a regression issue for me. Need to check with Dev team.Thanks!",
"> Hi @CuiYifeng ,\r\n> \r\n> I have replicated the behaviour with Tf2.14.rc0. But with Tf2.12v it working fine. Attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/aba44286fab30dae78b84060d463fbae/61761_2-12-vs-2-14.ipynb) for reference.\r\n> \r\n> It seems a regression issue for me. Need to check with Dev team.Thanks!\r\n\r\nHi @SuryanarayanaY , thanks for your reply. Do you plan to fix this issue for TF-2.14 release?",
"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/61761\">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/61761\">No</a>\n",
"@CuiYifeng,\r\nI tried to execute the mentioned code on tf-nightly(2.16.0-dev20240129) and it was executed without any issue and the output was also as expected. Kindly find the [gist](https://colab.research.google.com/gist/tilakrayal/924d0f8fa4254bb6a248f0946c193429/untitled.ipynb) of it [here](https://colab.sandbox.google.com/gist/tilakrayal/122b9a07288f59b25577b6438d64c997/untitled1700.ipynb).\r\n\r\nAlso the related PR https://github.com/tensorflow/tensorflow/pull/62619 is also merged. Thank you!\r\n\r\n\r\n\r\n",
"@tilakrayal I also tried the case with tf-nightly mentioned and the case passed. Thanks for your information!",
"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/61761\">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/61761\">No</a>\n"
] | 2023-08-31T08:49:00 | 2024-01-31T08:30:32 | 2024-01-31T08:30:30 | CONTRIBUTOR | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.14.0rc0
### Custom code
No
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Quantized types such as QINT8/QUINT8 are not registered for Enter/Exit.
Furthermore, why are some Control Flow Ops registered on `CPU Device` (e.g. `Merge/Switch/LoopCond`) but others not (e.g. `Enter/Exit/NextIteration`) ?
Are these expected?
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import numpy as np
x = tf.constant(np.ones((5, 6)), dtype=tf.qint8)
enter = tf.raw_ops.Enter(data=x, frame_name="test", is_constant=True)
```
### Relevant log output
```shell
tensorflow.python.framework.errors_impl.NotFoundError: Could not find device for node: {{node Enter}} = Enter[T=DT_QINT8, frame_name="test", is_constant=true, parallel_iterations=10]
All kernels registered for op Enter:
device='DEFAULT'; T in [DT_RESOURCE]
device='DEFAULT'; T in [DT_STRING]
device='DEFAULT'; T in [DT_INT32]
device='DEFAULT'; T in [DT_VARIANT]
device='DEFAULT'; T in [DT_BOOL]
device='DEFAULT'; T in [DT_COMPLEX128]
device='DEFAULT'; T in [DT_COMPLEX64]
device='DEFAULT'; T in [DT_INT8]
device='DEFAULT'; T in [DT_UINT8]
device='DEFAULT'; T in [DT_INT16]
device='DEFAULT'; T in [DT_UINT16]
device='DEFAULT'; T in [DT_UINT32]
device='DEFAULT'; T in [DT_INT64]
device='DEFAULT'; T in [DT_UINT64]
device='DEFAULT'; T in [DT_DOUBLE]
device='DEFAULT'; T in [DT_FLOAT]
device='DEFAULT'; T in [DT_BFLOAT16]
device='DEFAULT'; T in [DT_HALF]
device='GPU'; T in [DT_RESOURCE]
device='GPU'; T in [DT_STRING]
device='GPU'; T in [DT_INT32]
device='GPU'; T in [DT_VARIANT]
device='GPU'; T in [DT_BOOL]
device='GPU'; T in [DT_COMPLEX128]
device='GPU'; T in [DT_COMPLEX64]
device='GPU'; T in [DT_INT8]
device='GPU'; T in [DT_UINT8]
device='GPU'; T in [DT_INT16]
device='GPU'; T in [DT_UINT16]
device='GPU'; T in [DT_UINT32]
device='GPU'; T in [DT_INT64]
device='GPU'; T in [DT_UINT64]
device='GPU'; T in [DT_DOUBLE]
device='GPU'; T in [DT_FLOAT]
device='GPU'; T in [DT_BFLOAT16]
device='GPU'; T in [DT_HALF]
device='TPU'
device='TPU_SYSTEM'
```
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"@justlike-prog Could you have a look at this [page](https://www.tensorflow.org/lite/performance/post_training_quantization) and let me know if it gives enough information regarding integer quantization?\r\nIn order to expedite the trouble-shooting process, please provide a code snippet to reproduce the issue reported here. Thank you!\r\n",
"@sushreebarsa Code snippet is above. Yes, I check that page already (obviously), but it doesn't help me with my issue. Basically the issue is that the number of input channels is not divisible by number of channels in one group, hence the number of groups cannot be determined. Apparently this is not a problem when doing dynamic range quantization and in this case the tflite model works fine. For Full integer quantiation this doesn't work though.",
"Hi @justlike-prog \r\n\r\nThanks for bringing this issue.\r\n\r\nWill it possible to share the .pb file so that it will help us to reproduce the issue and investigate further?\r\n\r\nThanks.",
"@pjpratik yeah sure. here is a link to the pb [https://drive.google.com/file/d/1jFBdUAbEEZdgImt6DHiXOwNLQx9w2pUv/view?usp=sharing](url)",
"Hi @justlike-prog \r\n\r\nThanks for sharing the model. I have tried to reproduce with different conversions criteria including dynamic range quantization and full integer quantization and I was successfully able to convert it into TFLite model.\r\n\r\nPlease find this [gist](https://colab.research.google.com/gist/pjpratik/3fe6f84376642f50d3fc9fbd6fe870a0/61760.ipynb).\r\n\r\nThe representative dataset generator might be causing the problem. The model expects the input shape of `[1,3,224,224]` and we need to provide the representative data accordingly.\r\n\r\nThanks.",
"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.",
"> Hi @justlike-prog\r\n> \r\n> Thanks for sharing the model. I have tried to reproduce with different conversions criteria including dynamic range quantization and full integer quantization and I was successfully able to convert it into TFLite model.\r\n> \r\n> Please find this [gist](https://colab.research.google.com/gist/pjpratik/3fe6f84376642f50d3fc9fbd6fe870a0/61760.ipynb).\r\n> \r\n> The representative dataset generator might be causing the problem. The model expects the input shape of `[1,3,224,224]` and we need to provide the representative data accordingly.\r\n> \r\n> Thanks.\r\n\r\nThank you, you were right about the input shape. Somehow I missed this. Now I am running into the problem that when I load the model in the interpreter the jupyter notebook kernel crashes. Also if I try to run it on Android I get problems that look like a memory leak. Can you try to reproduce this issue with the model crashing after full integer quantization on your side? This does not happen with dynamic range quantization. Thank you! @pjpratik ",
"Hi @justlike-prog \r\n\r\nThis seems to be an issue with pytorch->tflite conversion. I was able to reproduce this. Please find this [gist](https://colab.research.google.com/gist/pjpratik/c5f0775a22bbb88511359c47e586bafb/61760.ipynb).\r\n\r\n@pkgoogle Could you please look into this issue?\r\n\r\nThanks.",
"Reproducible with Pratik's [gist](https://colab.sandbox.google.com/gist/pjpratik/c5f0775a22bbb88511359c47e586bafb/61760.ipynb), Hi @abattery, can you please take a look? Thanks.\r\n\r\n"
] | 2023-08-31T08:14:44 | 2023-09-13T20:51:42 | null | NONE | null | null | null | ### 1. System information
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): MacOS 12.5
- TensorFlow installation (pip package or built from source): pip
- TensorFlow library (version, if pip package or github SHA, if built from source): 2.13
### 2. Code
Provide code to help us reproduce your issues using one of the following options:
```
train_ds = tf.keras.utils.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="training",
seed=123,
image_size=(img_height, img_width),
batch_size=batch_size)
def representative_data_gen():
for input_value, labels in train_ds:
yield [input_value]
converter = tf.lite.TFLiteConverter.from_saved_model('./model.pb')
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = representative_data_gen
tflite_model = converter.convert()
with open('model.tflite', 'wb') as f:
f.write(tflite_model)
```
The error that I get is
```
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
Cell In[15], line 8
3 #converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8, tf.lite.OpsSet.SELECT_TF_OPS]
4 #converter.inference_input_type = tf.int8
5 #converter.inference_output_type = tf.int8
6 converter.representative_dataset = representative_data_gen
----> 8 tflite_model = converter.convert()
10 with open('model.tflite', 'wb') as f:
11 f.write(tflite_model)
File ~/miniforge3/envs/tf213/lib/python3.10/site-packages/tensorflow/lite/python/lite.py:1065, in _export_metrics.<locals>.wrapper(self, *args, **kwargs)
1062 @functools.wraps(convert_func)
1063 def wrapper(self, *args, **kwargs):
1064 # pylint: disable=protected-access
-> 1065 return self._convert_and_export_metrics(convert_func, *args, **kwargs)
File ~/miniforge3/envs/tf213/lib/python3.10/site-packages/tensorflow/lite/python/lite.py:1042, in TFLiteConverterBase._convert_and_export_metrics(self, convert_func, *args, **kwargs)
1040 self._save_conversion_params_metric()
1041 start_time = time.process_time()
-> 1042 result = convert_func(self, *args, **kwargs)
1043 elapsed_time_ms = (time.process_time() - start_time) * 1000
1044 if result:
File ~/miniforge3/envs/tf213/lib/python3.10/site-packages/tensorflow/lite/python/lite.py:1390, in TFLiteSavedModelConverterV2.convert(self)
1384 else:
1385 self._debug_info = _get_debug_info(
1386 _convert_debug_info_func(self._trackable_obj.graph_debug_info),
1387 graph_def,
1388 )
-> 1390 return self._convert_from_saved_model(graph_def)
File ~/miniforge3/envs/tf213/lib/python3.10/site-packages/tensorflow/lite/python/lite.py:1257, in TFLiteConverterBaseV2._convert_from_saved_model(self, graph_def)
1254 converter_kwargs.update(quant_mode.converter_flags())
1256 result = _convert_saved_model(**converter_kwargs)
-> 1257 return self._optimize_tflite_model(
1258 result, quant_mode, quant_io=self.experimental_new_quantizer
1259 )
File ~/miniforge3/envs/tf213/lib/python3.10/site-packages/tensorflow/lite/python/convert_phase.py:215, in convert_phase.<locals>.actual_decorator.<locals>.wrapper(*args, **kwargs)
213 except Exception as error:
214 report_error_message(str(error))
--> 215 raise error from None
File ~/miniforge3/envs/tf213/lib/python3.10/site-packages/tensorflow/lite/python/convert_phase.py:205, in convert_phase.<locals>.actual_decorator.<locals>.wrapper(*args, **kwargs)
202 @functools.wraps(func)
203 def wrapper(*args, **kwargs):
204 try:
--> 205 return func(*args, **kwargs)
206 except ConverterError as converter_error:
207 if converter_error.errors:
File ~/miniforge3/envs/tf213/lib/python3.10/site-packages/tensorflow/lite/python/lite.py:991, in TFLiteConverterBase._optimize_tflite_model(self, model, quant_mode, quant_io)
989 q_allow_float = quant_mode.is_allow_float()
990 q_variable_quantization = quant_mode.enable_mlir_variable_quantization
--> 991 model = self._quantize(
992 model,
993 q_in_type,
994 q_out_type,
995 q_activations_type,
996 q_bias_type,
997 q_allow_float,
998 q_variable_quantization,
999 )
1001 m_in_type = in_type if in_type else _dtypes.float32
1002 m_out_type = out_type if out_type else _dtypes.float32
File ~/miniforge3/envs/tf213/lib/python3.10/site-packages/tensorflow/lite/python/lite.py:710, in TFLiteConverterBase._quantize(self, result, input_type, output_type, activations_type, bias_type, allow_float, enable_variable_quantization)
706 calibrate_quantize = _calibrator.Calibrator(
707 result, custom_op_registerers_by_name, custom_op_registerers_by_func
708 )
709 if self._experimental_calibrate_only or self.experimental_new_quantizer:
--> 710 calibrated = calibrate_quantize.calibrate(
711 self.representative_dataset.input_gen
712 )
714 if self._experimental_calibrate_only:
715 return calibrated
File ~/miniforge3/envs/tf213/lib/python3.10/site-packages/tensorflow/lite/python/convert_phase.py:215, in convert_phase.<locals>.actual_decorator.<locals>.wrapper(*args, **kwargs)
213 except Exception as error:
214 report_error_message(str(error))
--> 215 raise error from None
File ~/miniforge3/envs/tf213/lib/python3.10/site-packages/tensorflow/lite/python/convert_phase.py:205, in convert_phase.<locals>.actual_decorator.<locals>.wrapper(*args, **kwargs)
202 @functools.wraps(func)
203 def wrapper(*args, **kwargs):
204 try:
--> 205 return func(*args, **kwargs)
206 except ConverterError as converter_error:
207 if converter_error.errors:
File ~/miniforge3/envs/tf213/lib/python3.10/site-packages/tensorflow/lite/python/optimize/calibrator.py:254, in Calibrator.calibrate(self, dataset_gen)
244 @convert_phase(Component.OPTIMIZE_TFLITE_MODEL, SubComponent.CALIBRATE)
245 def calibrate(self, dataset_gen):
246 """Calibrates the model with specified generator.
247
248 Returns:
(...)
252 dataset_gen: A generator that generates calibration samples.
253 """
--> 254 self._feed_tensors(dataset_gen, resize_input=True)
255 return self._calibrator.Calibrate()
File ~/miniforge3/envs/tf213/lib/python3.10/site-packages/tensorflow/lite/python/optimize/calibrator.py:143, in Calibrator._feed_tensors(self, dataset_gen, resize_input)
139 self._calibrator.Prepare(
140 [list(s.shape) for s in input_array], signature_key
141 )
142 else:
--> 143 self._calibrator.Prepare([list(s.shape) for s in input_array])
144 else:
145 if signature_key is not None:
RuntimeError: tensorflow/lite/kernels/conv.cc:352 input_channel % filter_input_channel != 0 (2 != 0)Node number 6 (CONV_2D) failed to prepare.
```
The model itself got converted from pytorch to onnx and then to pb. The issue here is that the model has a grouped convolutional layers. This is fixed for dynamic range quantisation but for full integer quantisation using a representative dataset this still seems to fail. Any quick fix possible?
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"CC @reedwm."
] | 2023-08-30T22:46:44 | 2023-08-31T20:00:32 | 2023-08-31T20:00:31 | CONTRIBUTOR | null | false | {
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"Try with updating the RELEASE.md file by adding a hyperlink to the dtensor fft blog post. Make sure the link is clickable and directs to the correct URL of the blog post."
] | 2023-08-30T17:19:38 | 2023-09-11T18:00:58 | 2023-09-11T18:00:57 | MEMBER | null | false | {
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} | The line
```
segmentation_options = processor.SegmentationOptions(
output_type=processor.SegmentationOptions.OutputType.CATEGORY_MASK)
```
is changed to
```
segmentation_options = processor.SegmentationOptions(
output_type=processor.SegmentationOptions.output_type.CATEGORY_MASK)
```
Please check this working [gist](https://colab.research.google.com/gist/pjpratik/2e15c0e8eeb262e7ce396ac4cbddb4bd/60836.ipynb).
Fixes #60836 | {
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"can i work on this ?",
"@mirjanic Could you please have a look at this [gist](https://colab.research.google.com/gist/sushreebarsa/e3b2bf759b7c10f7ca7ba32fb0bb6abd/untitled0.ipynb#scrollTo=c7xn4WUH5RH4) and confirm this issue?\r\nThe error log is as follows;\r\n```\r\nShape before (unknown): (10, 2)\r\nShape after (known): (10, 2)\r\nShape before (unknown): (None, 2)\r\nShape after (known): (10, 2)\r\nShape before (unknown): (None, 2)\r\nShape after (known): (10, 2)\r\n---------------------------------------------------------------------------\r\nInvalidArgumentError \r\n```\r\n\r\nThank you!",
"Yes, this is the issue I am having.",
"The error message \"InvalidArgumentError: Cannot concatenate arrays that differ in dimensions other than the one being concatenated\" occurs when trying to concatenate two tensors with different shapes\r\n\r\n\r\nIn the given code, the concatenation fails because the shapes of x and y are different. Specifically, x has shape (10, 2) after its shape is set, while y has shape (10, 1) \r\n\r\nTo fix this error, the shapes of x and y need to be made compatible for concatenation. One possible solution is to change the shape of y to (10, 2) by replacing tf.expand_dims(tf.range(x.shape * 2, dtype=tf.int64), axis=-1) with tf.range(x.shape * 2, dtype=tf.int64)[:, tf.newaxis] \r\n\r\nThis will create a tensor with shape (10, 2) that can be concatenated with x.\r\n\r\nHere is the update code . \r\n\r\nhttps://colab.research.google.com/drive/1rS1T-T7yKH6VyF1bp2st5PLT0FQ-L56g?usp=sharing\r\n\r\nIs it okay now ?",
"Unfortunately, I do not follow your suggestion. You mention that shapes `(10,2)` and `(10,1)` are incompatible for concatenation, but they are; with the result having shape `(10,3)`. You propose reshaping `y` to `(10,2)`, but you do not do that: in your code `y` still has shape `(10,1)` and the concatenation works just as well (as it should).\r\n\r\nThe real issue here is that the snippet in OP fails _when compiled with XLA_ because compiler does not understand the true shape of `x` even after `set_shape` is called and correct length is updated from `None` to `10`.\r\n\r\nAccording to the documentation, `set_shape` is supposed to \"provide additional shape information that cannot be inferred from the graph alone\", but this information does not seem to be used correctly by XLA.\r\n\r\nI hope this clarifies the issue!",
"Try with `tf.RaggedTensor` instead of using `tf.concat` for concatenation. `tf.RaggedTensor` can handle variable-length dimensions and accommodate the shape mismatch that occurs when concatenating tensors with different dimensions. Convert the `tf.where` output to `tf.RaggedTensor` and then use the appropriate method provided by `tf.RaggedTensor` to concatenate the tensors.",
"I thought Ragged Tensors were [unsupported in XLA](https://github.com/tensorflow/tensorflow/issues/56595)? Also, these tensors have matching shapes and the code above runs both with eager and with graph execution. It only fails _in XLA_ because it is not using the static shape that I am trying to provide to it.",
"Here is a distilled version of the problem ([Colab URL](https://colab.research.google.com/drive/1nLIPcrT3Zz5MZ78D7Blhcj-PTeqIlZXn?usp=sharing)):\r\n\r\n```python\r\nimport tensorflow as tf\r\nimport numpy as np\r\n\r\n# Arbitrary parameters\r\nn=10\r\nk=4\r\n\r\ndef fun(x):\r\n # print(x.shape)\r\n # x.shape = (n,)\r\n\r\n y = tf.one_hot(x, k)\r\n # print(y.shape)\r\n # y.shape = (n, k)\r\n\r\n z = tf.where(y == 1)\r\n # print(z.shape)\r\n # z.shape = (None, 2)\r\n\r\n # z.shape cannot be determined statically due to\r\n # tf.where call, but we know that it must be\r\n # equal to (n, 2), so we try to set it manually:\r\n\r\n # Variant 1:\r\n z.set_shape(shape=(n,2))\r\n # print(z.shape)\r\n # z.shape = (n, 2)\r\n # We have used set_shape to supply additional info\r\n # about the shape that tf could not deduce, and we see\r\n # that the shape is updated.\r\n\r\n # Variant 2:\r\n # z = tf.ensure_shape(z, shape=(n, 2)) # <-- FAILS!\r\n # Interestingly, using ensure_shape fails immediately,\r\n # with the message that z.shape is (n*k, 2), not (n, 2)\r\n # This is perhaps the core problem because this violates\r\n # tf.where docs which say that \"The result's shape is\r\n # [tf.math.count_nonzero(condition), tf.rank(condition)]\".\r\n\r\n tf.concat([x[:, tf.newaxis], z], axis=1) # Fails only in XLA\r\n\r\nx = np.random.randint(k, size=n)\r\nx = tf.constant(x, dtype=tf.int64)\r\n\r\nfun(x) # Succeeds\r\ntf.function(fun)(x) # Succeeds\r\ntf.function(fun, jit_compile=True)(x) # Fails!\r\n```\r\n\r\nError message produced by `tf.ensure_shape` suggests that output shape of `tf.where` is wrong when using XLA. However, if we define `x` inside `fun` (as opposed to passing an argument), then `fun` compiles and runs successfully, which further muddies the water.",
"Closing because I found a combination of `autoclustering` and `tf.where` replacement that circumvents the issue.",
"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/61755\">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/61755\">No</a>\n"
] | 2023-08-30T10:49:08 | 2023-09-16T20:05:25 | 2023-09-16T20:05:22 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.13
### Custom code
No
### OS platform and distribution
Google Colab
### Mobile device
_No response_
### Python version
3.10.12
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Output of `tf.where`, when used inside `tf.function` with `jit_compile=True`, can sometimes be used correctly (as with sum), and sometimes raises shape mismatch error (as with concatenation). This error is present even if output shape is set manually with `set_shape`.
The code below runs without `jit_compile` or with sum instead of `tf.concat`, and only fails if concatenating inside a compiled function.
Note: `autoclustering` solves the issue on the toy example, but _not_ on the codebase I am working on.
### Standalone code to reproduce the issue
Colab: https://colab.research.google.com/drive/1FuboVMSao8eCZLcZ2F7Fdsa1UvlnHQmG?usp=sharing
```python
import tensorflow as tf
def fun(x, y):
x = tf.where(x == 1)
print(f'Shape before (unknown): {x.shape}')
x.set_shape(shape=[y.shape[0], 2])
print(f'Shape after (known): {x.shape}')
return tf.concat([x,y], axis=1) # Concatentation fails
# return x + y # Sum would succeed
x = tf.constant([[0,0,1,1,0],
[0,1,0,1,0],
[1,0,0,0,1],
[1,0,1,0,0],
[0,1,1,0,0],], dtype=tf.int32)
y = tf.expand_dims(tf.range(x.shape[0] * 2, dtype=tf.int64), axis=-1)
fun(x, y)
tf.function(fun)(x,y)
tf.function(fun, jit_compile=True)(x,y) # Fails as described above
```
### Relevant log output
```shell
Shape before (unknown): (None, 2)
Shape after (known): (10, 2)
InvalidArgumentError: Cannot concatenate arrays that differ in dimensions other than the one being concatenated. Dimension 0 in both shapes must be equal: s64[<=25,2] vs s64[10,1].
```
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"Possibility \nhttps://github.com/keras-team/keras/issues/18419",
"@innat,\r\nCould you please share a reproducible code that supports your statement so that the issue can be easily understood? Thank you!",
"Try with:\r\n1. Building the model and using model.save(my_model) instead of model.save_weights(my_model.h5) to save the entire model.\r\n2. Loading the model using tf.keras.models.load_model(my_model) without passing jit_compile=True.\r\n3. Initializing the model outside of the strategy.scope() block before saving the weights to avoid the OSError.",
"@tilakrayal It might take some time to create reproducibility code as the underlying issue hard to interpret. In the meantime, if there is any info/doc that I could take a look, please share. \r\n\r\n@msf-caesar Thanks for the suggestions.\r\n\r\n1. I could use only `model.save` but it takes lots of memory (as described above) compared to manually load weights. Also, in my pipeline, I like to add both way to make it feature complete. \r\n2. I could do that, but why should I skip passing `jit_compile`? If a model is not compatible and still this params is passed, I think the API should handle it internally, maybe with warning message that the model is not jit compatible and running without it. But the main discrepancy is that, I could pass jit compile (and get the effect) while manually loading the weight to the model. I guess, `model.save` API did something internally which causing this issue. \r\n3. Again, whether initializing the model in scope or out of it, both model should build and can be saved. This matter only if all components of a model is not initialzing the same (in scope or not).",
"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/61753\">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/61753\">No</a>\n"
] | 2023-08-30T05:51:06 | 2024-03-25T19:23:14 | 2024-03-25T19:23:10 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.12 / 2.13
### Custom code
No
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Simply, I did
- Build the model (`model`)
- Save it using (a). `model.save(my_model)` , (b). `model.save_weights(my_model.h5)`.
Next,
## Case 1
1. Using `model.load_weight(my_model.h5)`, and pass `jit_compile=True` followed by `model.fit`. It works, though 11GB consumed out of 16GB.
2. Using `tf.keras.load_model(``my_model`), and pass `jit_compile=True`, followed by `model.fit`. It doesn't work, simply extremly slow and consumed 15.8GB instantly. Sudden jump from 3GB to 15.8GB. But without passing `jit_compile`, it runs normally but memory consumption remains same.
## Case 2
1. If I initialize model within `with strategy.scope():`, and try to save the weight (by `model.save_weight`), it gives `OSError: Unable to create link (name already exists)` but without strategy scope, this error doesn't occur.
### Standalone code to reproduce the issue
```shell
to do.
```
### Relevant log output
_No response_ | {
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"Hi @jpienaar Can you please review this PR ? Thank you!"
] | 2023-08-29T21:11:45 | 2023-09-12T04:27:43 | 2023-09-12T04:27:43 | CONTRIBUTOR | null | false | {
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} | Update `experimental_convert_saved_model_v1` typo to `experimental_convert_saved_model_v1_to_mlir`.
Closes: https://github.com/tensorflow/tensorflow/issues/61598 | {
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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/61747/checks?check_run_id=16321322179) 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."
] | 2023-08-29T19:33:21 | 2023-09-12T15:51:40 | 2023-09-12T15:51:37 | NONE | null | false | {
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"Hi @walkerped ,\r\n\r\nI have replicated the reported error(incompatibility shapes) and attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/3bba9d33740b07a01b53967aa152a306/61746.ipynb#scrollTo=RynDDmpQFZdq) for reference.\r\n\r\nSince the code involves transformer model which is a third party library debugging needs time to confirm the error source.\r\n",
"Hi @walkerped ,\r\n\r\nIt seems the error is generated from transformer model itself as the node that generated error states it. As we can see there is no shape arguments being passed to keras model here the shapes are inferred from the transformer model and the dataset also not matching with the shapes that has been raised in the error.\r\n\r\nCould you please report the issue at Transformer support forum ? Or you can submit a code snippet without external library dependency to replicate the issue ? 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.",
"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/61746\">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/61746\">No</a>\n"
] | 2023-08-29T17:14:09 | 2023-11-03T01:48:00 | 2023-11-03T01:47:57 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.13.0
### Custom code
Yes
### OS platform and distribution
Ubuntu 22.04.2 LTS
### Mobile device
_No response_
### Python version
3.10.12
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I'm trying to train a model to do binary classification. I am getting an error about incompatible shapes, but my data seems to be in the correct shape.
### Standalone code to reproduce the issue
```shell
!pip install transformers
import tensorflow as tf
import tensorflow_hub as hub
from transformers import LongformerTokenizer, TFLongformerForSequenceClassification
import pandas as pd
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from tensorflow.keras.callbacks import LearningRateScheduler
import math
import numpy as np
positive_sentences = [
"The weather is absolutely gorgeous today.",
"I'm so grateful for the support of my friends and family.",
"I achieved my personal best in the race!",
"The new cafe in town serves amazing coffee.",
"I love spending time with my adorable pets.",
"I received a surprise gift from a dear friend.",
"The sunrise this morning was breathtaking.",
"I'm excited about the upcoming vacation.",
"The concert last night was incredibly entertaining.",
"I'm proud of my hard work paying off.",
"The park is a peaceful place to relax.",
"I found a great book that I can't put down.",
"The team's collaboration led to a successful project.",
"I'm enjoying learning a new skill.",
"Spending time with loved ones always brightens my day.",
"I got a promotion at work, and it's a fantastic feeling.",
"The movie I watched last night was heartwarming.",
"I'm making positive changes in my daily routine.",
"The delicious aroma of home-cooked food fills the air.",
"I'm surrounded by inspiring and supportive people."
]
negative_sentences = [
"The constant rain is making me feel gloomy.",
"I'm disappointed that my plans got canceled.",
"The traffic was horrendous this morning.",
"I made a mistake on the important presentation.",
"I'm feeling overwhelmed with work and tasks.",
"The internet connection is frustratingly slow.",
"The food I ordered was cold and tasteless.",
"I'm exhausted after a long and stressful day.",
"My phone battery died at the worst time.",
"The store was out of stock of the item I needed.",
"I lost my wallet and it's been a hassle.",
"The loud construction noise is giving me a headache.",
"I'm struggling to meet my deadlines.",
"The movie I was looking forward to was disappointing.",
"I'm not feeling well and it's affecting my mood.",
"My computer crashed and I lost my unsaved work.",
"The rude customer service ruined my experience.",
"I'm frustrated with the constant delays.",
"The rainy weather is putting me in a bad mood.",
"I'm stressed about the upcoming exams."
]
sentences = positive_sentences + negative_sentences
labels = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
]
df = pd.DataFrame({'sentences':sentences,'labels':labels})
data = df.copy()
display(data.head(5))
tokenizer = LongformerTokenizer.from_pretrained('allenai/longformer-base-4096')
longformer_model = TFLongformerForSequenceClassification.from_pretrained('allenai/longformer-base-4096')
X_train, X_test, y_train, y_test = train_test_split(df['sentences'],df['labels'], stratify=df['labels'])
X_train.head(4)
display(y_train)
x_train_tokens = tokenizer(list(X_train), padding=True, truncation=True, return_tensors="tf")
x_test_tokens = tokenizer(list(X_test), padding=True, truncation=True, return_tensors="tf")
y_train_encoded = np.array(y_train)
y_test_encoded = np.array(y_test)
display(x_train_tokens)
input_ids = tf.keras.layers.Input(shape=(None,), dtype=tf.int32)
attention_mask = tf.keras.layers.Input(shape=(None,), dtype=tf.int32)
outputs = longformer_model(input_ids, attention_mask=attention_mask)[0]
output_layer = tf.keras.layers.Dense(1, activation='sigmoid', name="output")(outputs)
model = tf.keras.Model(inputs=[input_ids, attention_mask], outputs=[output_layer])
def lr_schedule(epoch):
initial_lr = 0.001 # Set your initial learning rate here
drop = 0.75
epochs_drop = 5 # Adjust this value based on your preference
lr = initial_lr * math.pow(drop, math.floor((1 + epoch) / epochs_drop))
return lr
lr_scheduler = LearningRateScheduler(lr_schedule)
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
history = model.fit(
[x_train_tokens['input_ids'], x_train_tokens['attention_mask']]
, y_train
, epochs=5
, batch_size=5
, validation_data=([x_test_tokens['input_ids'], x_test_tokens['attention_mask']], y_test)
, callbacks=[lr_scheduler]
)
```
### Relevant log output
```shell
Epoch 1/5
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-12-24f08ed0005f> in <cell line: 3>()
1 model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
2
----> 3 history = model.fit(
4 [x_train_tokens['input_ids'], x_train_tokens['attention_mask']]
5 , y_train
1 frames
/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
51 try:
52 ctx.ensure_initialized()
---> 53 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
54 inputs, attrs, num_outputs)
55 except core._NotOkStatusException as e:
InvalidArgumentError: Graph execution error:
Detected at node 'gradient_tape/model/tf_longformer_for_sequence_classification/longformer/encoder/layer_._0/attention/self/BroadcastGradientArgs_1' defined at (most recent call last):
File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
exec(code, run_globals)
File "/usr/local/lib/python3.10/dist-packages/ipykernel_launcher.py", line 16, in <module>
app.launch_new_instance()
File "/usr/local/lib/python3.10/dist-packages/traitlets/config/application.py", line 992, in launch_instance
app.start()
File "/usr/local/lib/python3.10/dist-packages/ipykernel/kernelapp.py", line 619, in start
self.io_loop.start()
File "/usr/local/lib/python3.10/dist-packages/tornado/platform/asyncio.py", line 195, in start
self.asyncio_loop.run_forever()
File "/usr/lib/python3.10/asyncio/base_events.py", line 603, in run_forever
self._run_once()
File "/usr/lib/python3.10/asyncio/base_events.py", line 1909, in _run_once
handle._run()
File "/usr/lib/python3.10/asyncio/events.py", line 80, in _run
self._context.run(self._callback, *self._args)
File "/usr/local/lib/python3.10/dist-packages/tornado/ioloop.py", line 685, in <lambda>
lambda f: self._run_callback(functools.partial(callback, future))
File "/usr/local/lib/python3.10/dist-packages/tornado/ioloop.py", line 738, in _run_callback
ret = callback()
File "/usr/local/lib/python3.10/dist-packages/tornado/gen.py", line 825, in inner
self.ctx_run(self.run)
File "/usr/local/lib/python3.10/dist-packages/tornado/gen.py", line 786, in run
yielded = self.gen.send(value)
File "/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py", line 361, in process_one
yield gen.maybe_future(dispatch(*args))
File "/usr/local/lib/python3.10/dist-packages/tornado/gen.py", line 234, in wrapper
yielded = ctx_run(next, result)
File "/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py", line 261, in dispatch_shell
yield gen.maybe_future(handler(stream, idents, msg))
File "/usr/local/lib/python3.10/dist-packages/tornado/gen.py", line 234, in wrapper
yielded = ctx_run(next, result)
File "/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py", line 539, in execute_request
self.do_execute(
File "/usr/local/lib/python3.10/dist-packages/tornado/gen.py", line 234, in wrapper
yielded = ctx_run(next, result)
File "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py", line 302, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "/usr/local/lib/python3.10/dist-packages/ipykernel/zmqshell.py", line 539, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py", line 2975, in run_cell
result = self._run_cell(
File "/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py", line 3030, in _run_cell
return runner(coro)
File "/usr/local/lib/python3.10/dist-packages/IPython/core/async_helpers.py", line 78, in _pseudo_sync_runner
coro.send(None)
File "/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py", line 3257, in run_cell_async
has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
File "/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py", line 3473, in run_ast_nodes
if (await self.run_code(code, result, async_=asy)):
File "/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py", line 3553, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-12-24f08ed0005f>", line 3, in <cell line: 3>
history = model.fit(
File "/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
return fn(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py", line 1742, in fit
tmp_logs = self.train_function(iterator)
File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py", line 1338, in train_function
return step_function(self, iterator)
File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py", line 1322, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py", line 1303, in run_step
outputs = model.train_step(data)
File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py", line 1084, in train_step
self.optimizer.minimize(loss, self.trainable_variables, tape=tape)
File "/usr/local/lib/python3.10/dist-packages/keras/src/optimizers/optimizer.py", line 543, in minimize
grads_and_vars = self.compute_gradients(loss, var_list, tape)
File "/usr/local/lib/python3.10/dist-packages/keras/src/optimizers/optimizer.py", line 276, in compute_gradients
grads = tape.gradient(loss, var_list)
Node: 'gradient_tape/model/tf_longformer_for_sequence_classification/longformer/encoder/layer_._0/attention/self/BroadcastGradientArgs_1'
Detected at node 'gradient_tape/model/tf_longformer_for_sequence_classification/longformer/encoder/layer_._0/attention/self/BroadcastGradientArgs_1' defined at (most recent call last):
File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
exec(code, run_globals)
File "/usr/local/lib/python3.10/dist-packages/ipykernel_launcher.py", line 16, in <module>
app.launch_new_instance()
File "/usr/local/lib/python3.10/dist-packages/traitlets/config/application.py", line 992, in launch_instance
app.start()
File "/usr/local/lib/python3.10/dist-packages/ipykernel/kernelapp.py", line 619, in start
self.io_loop.start()
File "/usr/local/lib/python3.10/dist-packages/tornado/platform/asyncio.py", line 195, in start
self.asyncio_loop.run_forever()
File "/usr/lib/python3.10/asyncio/base_events.py", line 603, in run_forever
self._run_once()
File "/usr/lib/python3.10/asyncio/base_events.py", line 1909, in _run_once
handle._run()
File "/usr/lib/python3.10/asyncio/events.py", line 80, in _run
self._context.run(self._callback, *self._args)
File "/usr/local/lib/python3.10/dist-packages/tornado/ioloop.py", line 685, in <lambda>
lambda f: self._run_callback(functools.partial(callback, future))
File "/usr/local/lib/python3.10/dist-packages/tornado/ioloop.py", line 738, in _run_callback
ret = callback()
File "/usr/local/lib/python3.10/dist-packages/tornado/gen.py", line 825, in inner
self.ctx_run(self.run)
File "/usr/local/lib/python3.10/dist-packages/tornado/gen.py", line 786, in run
yielded = self.gen.send(value)
File "/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py", line 361, in process_one
yield gen.maybe_future(dispatch(*args))
File "/usr/local/lib/python3.10/dist-packages/tornado/gen.py", line 234, in wrapper
yielded = ctx_run(next, result)
File "/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py", line 261, in dispatch_shell
yield gen.maybe_future(handler(stream, idents, msg))
File "/usr/local/lib/python3.10/dist-packages/tornado/gen.py", line 234, in wrapper
yielded = ctx_run(next, result)
File "/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py", line 539, in execute_request
self.do_execute(
File "/usr/local/lib/python3.10/dist-packages/tornado/gen.py", line 234, in wrapper
yielded = ctx_run(next, result)
File "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py", line 302, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "/usr/local/lib/python3.10/dist-packages/ipykernel/zmqshell.py", line 539, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py", line 2975, in run_cell
result = self._run_cell(
File "/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py", line 3030, in _run_cell
return runner(coro)
File "/usr/local/lib/python3.10/dist-packages/IPython/core/async_helpers.py", line 78, in _pseudo_sync_runner
coro.send(None)
File "/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py", line 3257, in run_cell_async
has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
File "/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py", line 3473, in run_ast_nodes
if (await self.run_code(code, result, async_=asy)):
File "/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py", line 3553, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-12-24f08ed0005f>", line 3, in <cell line: 3>
history = model.fit(
File "/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler
return fn(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py", line 1742, in fit
tmp_logs = self.train_function(iterator)
File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py", line 1338, in train_function
return step_function(self, iterator)
File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py", line 1322, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py", line 1303, in run_step
outputs = model.train_step(data)
File "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py", line 1084, in train_step
self.optimizer.minimize(loss, self.trainable_variables, tape=tape)
File "/usr/local/lib/python3.10/dist-packages/keras/src/optimizers/optimizer.py", line 543, in minimize
grads_and_vars = self.compute_gradients(loss, var_list, tape)
File "/usr/local/lib/python3.10/dist-packages/keras/src/optimizers/optimizer.py", line 276, in compute_gradients
grads = tape.gradient(loss, var_list)
Node: 'gradient_tape/model/tf_longformer_for_sequence_classification/longformer/encoder/layer_._0/attention/self/BroadcastGradientArgs_1'
2 root error(s) found.
(0) INVALID_ARGUMENT: Incompatible shapes: [5,512,12,514] vs. [5,512,12,513]
[[{{node gradient_tape/model/tf_longformer_for_sequence_classification/longformer/encoder/layer_._0/attention/self/BroadcastGradientArgs_1}}]]
[[model/tf_longformer_for_sequence_classification/longformer/encoder/layer_._6/attention/self/cond_2/pivot_t/_676/_1027]]
(1) INVALID_ARGUMENT: Incompatible shapes: [5,512,12,514] vs. [5,512,12,513]
[[{{node gradient_tape/model/tf_longformer_for_sequence_classification/longformer/encoder/layer_._0/attention/self/BroadcastGradientArgs_1}}]]
0 successful operations.
0 derived errors ignored. [Op:__inference_train_function_131688]
```
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} | In particular, this typo triggers scancode-toolkit to report this text block as [commons-clause](https://github.com/nexB/scancode-toolkit/tree/develop/src/licensedcode/data/licenses/commons-clause.LICENSE) with 93.33 accuracy. | {
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"Hi @PatriosTheGreat Can you please resolve the conflicts? Thank you!\r\n",
"> Hi @PatriosTheGreat Can you please resolve the conflicts? Thank you!\r\n\r\nClosing this PR since the version is already bumped to 2.18.5"
] | 2023-08-29T09:28:46 | 2023-11-03T08:36:55 | 2023-11-03T08:36:50 | NONE | null | false | {
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"concrete_func = model_beam_search.__call__.get_concrete_function()# Create a TFLite converter and set the delegate to TfLiteGpuDelegateconverter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func], model_beam_search)converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS]converter.optimizations = [tf.lite.Optimize.DEFAULT]converter.target_spec.supported_types = [tf.float16]# Replace TfLiteFlexDelegate with TfLiteGpuDelegategpu_delegate = tf.lite.experimental.load_delegate('libtensorflowlite_gpu_delegate.so')converter.experimental_new_converter = True # This flag is needed for using the experimental converterconverter.experimental_new_quantizer = False # You can enable quantization if neededconverter.experimental_enable_resource_variable = False # You can enable resource variables if neededconverter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS, gpu_delegate]tflite_model = converter.convert()# Save the TFLite model to a filewith open('testing_gpu.tflite', 'wb') as f: f.write(tflite_model)",
"@Alwaysadil Could you please let us know which TF version you are using here and refer to this GPU [delegate](https://www.tensorflow.org/lite/performance/gpu) guide for more information on this. Thank you!",
"@sushreebarsa I am using 2.13.0",
"@Alwaysadil Thank you for your quick response!\r\nCould you please let us know if the GPU delegate guide helped you anyway. Thank you!",
"@sushreebarsa yes it was helpful for me that u shared GPU delegate documentation, thank you for sharing that ,but i didn't understood how to get .so file libtensorflowlite_gpu_delegate.so",
"Hi @Alwaysadil \r\n\r\nTo get `libtensorflowlite_gpu_delegate.so`, you have to build the `.so` file using following steps\r\n\r\n1. Install bazel 5.3.0\r\n2. git clone https://github.com/tensorflow/tensorflow.git\r\n3. cd tensorflow\r\n4. git checkout r2.13\r\n5. ./configure (yes for android build and provide the NDK and SDK versions)\r\n6. Run `bazel build --config android_arm64 tensorflow/lite/delegates/gpu:libtensorflowlite_gpu_delegate.so`\r\n\r\nPlease refer to this [documentation](https://www.tensorflow.org/lite/android/delegates/gpu_native#enable_gpu_acceleration) for reference.\r\n\r\nThanks.\r\n",
"@pjpratik could you please provide me the google colab notebook with code to get .so file please? i'm getting errors in google colab (please check my google colab )\r\n\r\nhttps://colab.research.google.com/drive/1aaX-Dm_TySAWWWyR1S6UEiPc5EB9kPjQ#scrollTo=nhrzFEC7GDXr\r\n\r\nlike this WARNING: Target pattern parsing failed.\r\nLoading: 0 packages loaded\r\n currently loading: tensorflow/lite/delegates/gpu\r\nERROR: no such package '@local_config_tensorrt//': Repository command failed\r\nCould not find any NvInferVersion.h matching version '' in any subdirectory:\r\n ''\r\n 'include'\r\n 'include/cuda'\r\n 'include/*-linux-gnu'\r\n 'extras/CUPTI/include'\r\n 'include/cuda/CUPTI'\r\n 'local/cuda/extras/CUPTI/include'\r\nof:\r\n '/lib'\r\n '/lib/x86_64-linux-gnu'\r\n '/lib32'\r\n '/usr'\r\n '/usr/local/cuda'\r\n '/usr/local/cuda/targets/x86_64-linux/lib'\r\n '/usr/local/lib'\r\nAnalyzing: 0 targets (0 packages loaded)\r\n currently loading: tensorflow/lite/delegates/gpu\r\nINFO: Elapsed time: 83.761s\r\nAnalyzing: 0 targets (0 packages loaded)\r\n currently loading: tensorflow/lite/delegates/gpu\r\nINFO: 0 processes.\r\nAnalyzing: 0 targets (0 packages loaded)\r\n currently loading: tensorflow/lite/delegates/gpu\r\nFAILED: Build did NOT complete successfully (0 packages loaded)\r\n currently loading: tensorflow/lite/delegates/gpu\r\nFAILED: Build did NOT complete successfully (0 packages loaded)\r\n currently loading: tensorflow/lite/delegates/gpu\r\n Fetching @local_config_rocm; fetching\r\n\r\n\r\nplese help me",
"Hi @Alwaysadil \r\n\r\nThe colab shared is currently not accessible. Could you please provide the steps you have followed?\r\n\r\nYou can follow these [instructions](https://www.tensorflow.org/lite/android/lite_build#install_bazel_and_android_prerequisites) along with links to download for setting up the configurations in your local machine. Please let us know if you are facing any issue after the following the steps.\r\n\r\nThanks.",
"Hi @pjpratik thank you for your response.Could you please check my colab notebook it will now accessible,i'm unable to downloading the .so file please kindly go through this colab link https://colab.research.google.com/drive/1aaX-Dm_TySAWWWyR1S6UEiPc5EB9kPjQ#scrollTo=nhrzFEC7GDXr\r\n\r\ni want to load this with tf \r\nimport tensorflow as tf\r\ndelegate = tf.lite.experimental.load_delegate('libtensorflowlite_gpu_delegate.so')#with this we can get faster predictions of tflite model\r\n\r\nplease help me \r\n\r\n ",
"Hi @Alwaysadil \r\n\r\nThanks for sharing the code. I can see that the android ndk and sdk tools have not been configured. \r\n\r\nThe Android NDK is required to build the native (C/C++) TensorFlow Lite code. The current recommended version is 21e, which may be found [here](https://developer.android.com/ndk/downloads/older_releases.html#ndk-21e-downloads).\r\nThe Android SDK and build tools may be obtained [here](https://developer.android.com/tools/revisions/build-tools.html).\r\n\r\nRun the ./configure script in the root TensorFlow checkout directory, and answer \"Yes\" when the script asks to interactively configure the ./WORKSPACE for Android builds. \r\n\r\nAlso, you can this prebuilt `.so` file and see if it works for your case.\r\n[libtensorflowlite_gpu_delegate.so.zip](https://github.com/tensorflow/tensorflow/files/12488492/libtensorflowlite_gpu_delegate.so.zip)\r\n\r\nThanks.",
"Hi @pjpratik \r\nThanks for you response it means alot ,\r\n\r\nould you like to interactively configure ./WORKSPACE for Android builds? [y/N]: y\r\nSearching for NDK and SDK installations.\r\n\r\nPlease specify the home path of the Android NDK to use. [Default is /root/Android/Sdk/ndk-bundle]: \r\n\r\n\r\nThe path /root/Android/Sdk/ndk-bundle or its child file \"source.properties\" does not exist.\r\nPlease specify the home path of the Android NDK to use. [Default is /root/Android/Sdk/ndk-bundle]: /content/android-ndk-r21e\r\n\r\n\r\nPlease specify the (min) Android NDK API level to use. [Available levels: ['16', '17', '18', '19', '21', '22', '23', '24', '26', '27', '28', '29', '30']] [Default is 26]: \r\n\r\n\r\nPlease specify the home path of the Android SDK to use. [Default is /root/Android/Sdk]: /root/android-sdk\r\n\r\n\r\nPlease specify the Android SDK API level to use. [Available levels: ['30']] [Default is 30]: \r\n\r\n\r\nPlease specify an Android build tools version to use. [Available versions: ['30.0.3']] [Default is 30.0.3]: \r\n\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\nhttps://colab.research.google.com/drive/1aaX-Dm_TySAWWWyR1S6UEiPc5EB9kPjQ#scrollTo=nhrzFEC7GDXr\r\n**but the .so file was not visible ,i downloaded it by giving path like this see above colab link the way i did**\r\n#checking the path\r\nimport os\r\n\r\npath_to_check = '/content/tensorflow/bazel-bin/tensorflow/lite/delegates/gpu/libtensorflowlite_gpu_delegate.so'\r\n\r\nif os.path.exists(path_to_check):\r\n print(\"yes\")\r\nelse:\r\n print(\"no\")\r\n**it was printing yes**\r\n\r\n#downloading the .so file \r\nfrom google.colab import files\r\n\r\nsource_path = '/content/tensorflow/bazel-bin/tensorflow/lite/delegates/gpu/libtensorflowlite_gpu_delegate.so'\r\n\r\nif os.path.exists(source_path):\r\n files.download(source_path)\r\nelse:\r\n print(\"Source file does not exist.\")\r\n\r\n\r\n\r\n i have successfully downloaded the libtensorflowlite_gpu_delegate.so see this below link to get newly built .so file https://drive.google.com/file/d/1848HQ4ExO72zkTdQC-yr7rrc7kvVfeeE/view?usp=sharing\r\n\r\n**while loading this .so in new colab notebook file i am getting this below error for both newly built and you shared prebuild .so file**check this https://colab.research.google.com/drive/1jAaFDTwqRWuISD0nA6OF9d0eSq39t6w1?usp=sharing\r\n\r\nimport tensorflow as tf\r\ndelegate = tf.lite.experimental.load_delegate(''/content/drive/MyDrive/delegate/libtensorflowlite_gpu_delegate.so')#with this we ca get faster predictions\r\n---------------------------------------------------------------------------\r\nOSError Traceback (most recent call last)\r\n<ipython-input-6-b8c40a9955bd> in <cell line: 2>()\r\n 1 import tensorflow as tf\r\n----> 2 delegate = tf.lite.experimental.load_delegate(''/content/drive/MyDrive/delegate/libtensorflowlite_gpu_delegate.so')#with this we ca get faster predictions\r\n\r\n3 frames\r\n/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/interpreter.py in load_delegate(library, options)\r\n 164 \"\"\"\r\n 165 try:\r\n--> 166 delegate = Delegate(library, options)\r\n 167 except ValueError as e:\r\n 168 raise ValueError('Failed to load delegate from {}\\n{}'.format(\r\n\r\n/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/interpreter.py in __init__(self, library, options)\r\n 71 'due to missing immediate reference counting.')\r\n 72 \r\n---> 73 self._library = ctypes.pydll.LoadLibrary(library)\r\n 74 self._library.tflite_plugin_create_delegate.argtypes = [\r\n 75 ctypes.POINTER(ctypes.c_char_p),\r\n\r\n/usr/lib/python3.10/ctypes/__init__.py in LoadLibrary(self, name)\r\n 450 \r\n 451 def LoadLibrary(self, name):\r\n--> 452 return self._dlltype(name)\r\n 453 \r\n 454 __class_getitem__ = classmethod(_types.GenericAlias)\r\n\r\n/usr/lib/python3.10/ctypes/__init__.py in __init__(self, name, mode, handle, use_errno, use_last_error, winmode)\r\n 372 \r\n 373 if handle is None:\r\n--> 374 self._handle = _dlopen(self._name, mode)\r\n 375 else:\r\n 376 self._handle = handle\r\n\r\nOSError: libtensorflowlite_gpu_delegate.so: cannot open shared object file: No such file or directory\r\n\r\n\r\n**but if i check the path**\r\nimport os\r\n\r\npath_to_check = '/content/drive/MyDrive/delegate/libtensorflowlite_gpu_delegate.so'\r\n\r\nif os.path.exists(path_to_check):\r\n print(\"yes\")\r\nelse:\r\n print(\"no\")\r\n\r\n#it was printing \"yes\"\r\n\r\n\r\n**please help me to overcome this issue**\r\n",
"Hi @Alwaysadil \r\n\r\nApologies for the confusion. The delegate can be loaded only if it matches the target architecture. The colab ships with `x86_64`(test it out using `!uname -a`), hence we need to build the `bazel build --config android_x86_64 tensorflow/lite/delegates/gpu:libtensorflowlite_gpu_delegate.so` and then try to load the delegate.\r\n\r\nThanks.",
"Hi @pjpratik i didn't get what u said,i tried in local system terminal too still same error occurring, could you please help me to overcome this issue",
"Hi @Alwaysadil \r\n\r\nThe `libtensorflow_gpu_delegate.so` is built with respect to the target configuration specified during the bazel build. Please check you system target architecture. On linux machine simply, run \r\n\r\n```\r\nbazel build -c opt tensorflow/lite/delegates/gpu:libtensorflowlite_gpu_delegate.so --copt -DEGL_NO_X11=1\r\n\r\n```\r\ninstead of \r\n```\r\nbazel build --config android_arm64 tensorflow/lite/delegates/gpu:libtensorflowlite_gpu_delegate.so\r\n```\r\nAdditionally, you may have to install\r\n```\r\nsudo apt-get install mesa-common-dev libegl1-mesa-dev libgles2-mesa-dev\r\nsudo apt-get install mesa-utils\r\n```\r\n\r\nThanks.",
"> Hi @Alwaysadil\r\n> \r\n> Apologies for the confusion. The delegate can be loaded only if it matches the target architecture. The colab ships with `x86_64`(test it out using `!uname -a`), hence we need to build the `bazel build --config android_x86_64 tensorflow/lite/delegates/gpu:libtensorflowlite_gpu_delegate.so` and then try to load the delegate.\r\n> \r\n> Thanks.\r\n\r\nhi @pjpratik, still getting this below error ,the .so file was not visible tf.lite.experimental.load_delegate(\"/content/drive/MyDrive/test_delegate/libtensorflowlite_gpu_delegate.so\")\r\n---------------------------------------------------------------------------\r\nOSError Traceback (most recent call last)\r\n<ipython-input-8-e59bb353aedb> in <cell line: 1>()\r\n----> 1 tf.lite.experimental.load_delegate(\"/content/drive/MyDrive/test_delegate/libtensorflowlite_gpu_delegate.so\")\r\n\r\n3 frames\r\n/usr/lib/python3.10/ctypes/__init__.py in __init__(self, name, mode, handle, use_errno, use_last_error, winmode)\r\n 372 \r\n 373 if handle is None:\r\n--> 374 self._handle = _dlopen(self._name, mode)\r\n 375 else:\r\n 376 self._handle = handle\r\n\r\nOSError: libEGL.so: cannot open shared object file: No such file or directory\r\nhttps://colab.research.google.com/drive/1aaX-Dm_TySAWWWyR1S6UEiPc5EB9kPjQ#scrollTo=nhrzFEC7GDXr\r\n\r\n!uname -a\r\nit prints---> #Linux a8fc57c0d917 5.15.109+ #1 SMP Fri Jun 9 10:57:30 UTC 2023 x86_64 x86_64 x86_64 GNU/Linux\r\n!bazel build --config android_x86_64 tensorflow/lite/delegates/gpu:libtensorflowlite_gpu_delegate.so \r\n\r\npath_to_check = '/content/drive/MyDrive/test_delegate/libtensorflowlite_gpu_delegate.so'\r\n\r\nif os.path.exists(path_to_check):\r\nprint(\"yes\")\r\nelse:\r\nprint(\"no\")\r\n\r\n#it was printing \"yes\"\r\n\r\n**please help me to get the .so file file please** ",
"> Hi @Alwaysadil\r\n> \r\n> The `libtensorflow_gpu_delegate.so` is built with respect to the target configuration specified during the bazel build. Please check you system target architecture. On linux machine simply, run\r\n> \r\n> ```\r\n> bazel build -c opt tensorflow/lite/delegates/gpu:libtensorflowlite_gpu_delegate.so --copt -DEGL_NO_X11=1\r\n> ```\r\n> \r\n> instead of\r\n> \r\n> ```\r\n> bazel build --config android_arm64 tensorflow/lite/delegates/gpu:libtensorflowlite_gpu_delegate.so\r\n> ```\r\n> \r\n> Additionally, you may have to install\r\n> \r\n> ```\r\n> sudo apt-get install mesa-common-dev libegl1-mesa-dev libgles2-mesa-dev\r\n> sudo apt-get install mesa-utils\r\n> ```\r\n> \r\n> Thanks.\r\n\r\nHi @pjpratik in terminal i am getting this error below dated.\r\nERROR: An error occurred during the fetch of repository 'llvm-raw':\r\n Traceback (most recent call last):\r\n\tFile \"/home/sys2022/tensorflow/third_party/repo.bzl\", line 73, column 33, in _tf_http_archive_impl\r\n\t\tctx.download_and_extract(\r\nError in download_and_extract: java.io.IOException: Error extracting /home/sys2022/.cache/bazel/_bazel_sys2022/49a37d0bef434a2dcf9fbacaf7083ab7/external/llvm-raw/temp13989436987824143012/dc275fd03254d67d29cc70a5a0569acf24d2280d.tar.gz to /home/sys2022/.cache/bazel/_bazel_sys2022/49a37d0bef434a2dcf9fbacaf7083ab7/external/llvm-raw/temp13989436987824143012: write (No space left on device)\r\nERROR: /home/sys2022/tensorflow/WORKSPACE:11:14: fetching _tf_http_archive rule //external:llvm-raw: Traceback (most recent call last):\r\n\tFile \"/home/sys2022/tensorflow/third_party/repo.bzl\", line 73, column 33, in _tf_http_archive_impl\r\n\t\tctx.download_and_extract(\r\nError in download_and_extract: java.io.IOException: Error extracting /home/sys2022/.cache/bazel/_bazel_sys2022/49a37d0bef434a2dcf9fbacaf7083ab7/external/llvm-raw/temp13989436987824143012/dc275fd03254d67d29cc70a5a0569acf24d2280d.tar.gz to /home/sys2022/.cache/bazel/_bazel_sys2022/49a37d0bef434a2dcf9fbacaf7083ab7/external/llvm-raw/temp13989436987824143012: write (No space left on device)\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/tensorflow/runtime/archive/7d879c8b161085a4374ea481b93a52adb19c0529.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nERROR: no such package '@llvm-raw//utils/bazel': java.io.IOException: Error extracting /home/sys2022/.cache/bazel/_bazel_sys2022/49a37d0bef434a2dcf9fbacaf7083ab7/external/llvm-raw/temp13989436987824143012/dc275fd03254d67d29cc70a5a0569acf24dINFO: Repository 'llvm-raw' used the following cache hits instead of downloading the corresponding file.\r\n * Hash '3e91127af59a6b07fea7901c80a7b8b9234eced42b0f14abbad5f9f7674dba69' for https://storage.googleapis.com/mirror.tensorflow.org/github.com/llvm/llvm-project/archive/dc275fd03254d67d29cc70a5a0569acf24d2280d.tar.gz\r\nIf the definition of 'llvm-raw' was updated, verify that the hashes were also updated.\r\nERROR: An error occurred during the fetch of repository 'llvm-raw':\r\n Traceback (most recent call last):\r\n\tFile \"/home/sys2022/tensorflow/third_party/repo.bzl\", line 73, column 33, in _tf_http_archive_impl\r\n\t\tctx.download_and_extract(\r\nError in download_and_extract: java.io.IOException: Error extracting /home/sys2022/.cache/bazel/_bazel_sys2022/49a37d0bef434a2dcf9fbacaf7083ab7/external/llvm-raw/temp13989436987824143012/dc275fd03254d67d29cc70a5a0569acf24d2280d.tar.gz to /home/sys2022/.cache/bazel/_bazel_sys2022/49a37d0bef434a2dcf9fbacaf7083ab7/external/llvm-raw/temp13989436987824143012: write (No space left on device)\r\nERROR: /home/sys2022/tensorflow/WORKSPACE:11:14: fetching _tf_http_archive rule //external:llvm-raw: Traceback (most recent call last):\r\n\tFile \"/home/sys2022/tensorflow/third_party/repo.bzl\", line 73, column 33, in _tf_http_archive_impl\r\n\t\tctx.download_and_extract(\r\nError in download_and_extract: java.io.IOException: Error extracting /home/sys2022/.cache/bazel/_bazel_sys2022/49a37d0bef434a2dcf9fbacaf7083ab7/external/llvm-raw/temp13989436987824143012/dc275fd03254d67d29cc70a5a0569acf24d2280d.tar.gz to /home/sys2022/.cache/bazel/_bazel_sys2022/49a37d0bef434a2dcf9fbacaf7083ab7/external/llvm-raw/temp13989436987824143012: write (No space left on device)\r\nWARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/tensorflow/runtime/archive/7d879c8b161085a4374ea481b93a52adb19c0529.tar.gz failed: class java.io.FileNotFoundException GET returned 404 Not Found\r\nERROR: no such package '@llvm-raw//utils/bazel': java.io.IOException: Error extracting /home/sys2022/.cache/bazel/_bazel_sys2022/49a37d0bef434a2dcf9fbacaf7083ab7/external/llvm-raw/temp13989436987824143012/dc275fd03254d67d29cc70a5a0569acf24d2280d.tar.gz to /home/sys2022/.cache/bazel/_bazel_sys2022/49a37d0bef434a2dcf9fbacaf7083ab7/external/llvm-raw/temp13989436987824143012: write (No space left on device)\r\nINFO: Elapsed time: 13.339s\r\nINFO: 0 processes.\r\n ,**but please provide me code or any other way in google colab i'm comfortable with colab please help me to overcome this** **issue**",
"hi @pjpratik \r\n\r\ndelegate = tf.lite.experimental.load_delegate('/content/tensorflow/bazel-bin/tensorflow/lite/delegates/gpu/libtensorflowlite_gpu_delegate.so')#with this we ca get faster predictions\r\nOSError: libEGL.so: cannot open shared object file: No such file or directory\r\n\r\nplease help me to get .so file",
"Ensure that the directory containing libEGL.so is included in your LD_LIBRARY_PATH environment variable. You can check the current value of LD_LIBRARY_PATH by running:\r\n\r\necho $LD_LIBRARY_PATH\r\nIf the directory is not included, you can add it by modifying your shell profile configuration file (e.g., ~/.bashrc or ~/.bash_profile) and adding the following line:\r\n\r\nexport LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/path/to/directory\r\nReplace /path/to/directory with the actual path to the directory containing libEGL.so. Then, reload your shell or run source ~/.bashrc to apply the changes.",
"> Ensure that the directory containing libEGL.so is included in your LD_LIBRARY_PATH environment variable. You can check the current value of LD_LIBRARY_PATH by running:\r\n> \r\n> echo $LD_LIBRARY_PATH If the directory is not included, you can add it by modifying your shell profile configuration file (e.g., ~/.bashrc or ~/.bash_profile) and adding the following line:\r\n> \r\n> export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/path/to/directory Replace /path/to/directory with the actual path to the directory containing libEGL.so. Then, reload your shell or run source ~/.bashrc to apply the changes.\r\n\r\nthanks for your response,could you please tell me how to overcome this issue in google colab ?\r\n",
"Hello @pjpratik , could you please reply to me?",
"Hi @Alwaysadil \r\n\r\nWe are actively working it. \r\n\r\nThe TFlite GPU delegates are primarily build for the GPU backend support for Android and iOS. For ubuntu machines it is not well supported and tested.\r\n\r\nDepending on the platform and version, we may have to define MESA_EGL_NO_X11_HEADERS and/or EGL_NO_X11 which don't include the X11 headers named slightly differently. We need to dig into EGL header files a little bit.\r\n\r\nThanks.",
"Hi @pjpratik thank you for responding \r\ncould you please tell me, \r\nAt android side, for keras_nlp transformer models GPU delegate will apply or not ?\r\ni am working on this gpu delegation applying at android side from past two months to get fatser inference from tflite model,but i am getting this error **(java.lang.IllegalArgumentException: Internal error: Error applying delegate:)** **but,for image related tflite models the GPU Delegate was appying perfectly without any errors, but not for Keras_nlp transformers,**\r\nwhy for keras_nlp transformer tflite models GPU was not applying? This was my main issue **please give me one reply about this please.....**\r\n\r\n\r\n**I read that when performing TFLite conversion in Python programming, including a GPU delegate like the one below can lead to faster output predictions, resulting in lower latency(is it right or wrong?)**\r\n\r\nconcrete_func = model_beam_search.__call__.get_concrete_function()\r\nconverter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func], model_beam_search)\r\nconverter.optimizations = [tf.lite.Optimize.DEFAULT]\r\nconverter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS,tf.lite.OpsSet.SELECT_TF_OPS]\r\nconverter.experimental_new_converter=tf.lite.experimental.load_delegate('libtensorflowlite_gpu_delegate.so')\r\n\r\ndynamic_quant = converter.convert()\r\n\r\nhttps://colab.research.google.com/drive/1GJ8UBa8GQBSKY4NC6_tYB0uxujvUn2kT?usp=sharing#scrollTo=C4iu7JIanl5d\r\n\r\nthat's why i ask you to how to download -->libtensorflowlite_gpu_delegate.so\r\n\r\n**could you please help me to overcome this issue?**",
"Hi @pjpratik please,give me one reply",
"Hi @Alwaysadil \r\n\r\nSorry for the delayed response. \r\n\r\nThere are some limitations to what TensorFlow ML operations, or ops, can be accelerated by the TensorFlow Lite GPU delegate. Not all Ops currently support using GPU delegate. If the model has Ops that does not support the use of GPU delegate, you might encounter this error.\r\n\r\nPlease check this list of Ops supported [here](https://www.tensorflow.org/lite/performance/gpu#supported_ops).\r\n\r\n>I read that when performing TFLite conversion in Python programming, including a GPU delegate like the one below can lead to faster output predictions, resulting in lower latency(is it right or wrong?)\r\n\r\nYes, enabling use of GPUs with your TensorFlow Lite ML applications can provide the benefits like speed and efficiency. But as mentioned earlier, it is platform dependent and we need to build the delegates accordingly, to make the best use of them.\r\n\r\nAlso, the GPU delegate has some restrictions with the batch dimensions and requires that dimension to be consistent throughout the network.\r\n\r\nBTW, you can use Model Analyzer API to check which node has the compatibility issue.\r\n```\r\nimport tensorflow as tf\r\n\r\ntf.lite.experimental.Analyzer.analyze(model_path='model.tflite', gpu_compatibility=True)\r\n\r\n```\r\n\r\nThanks.",
"Hi @pjpratik \r\nThanks for your response\r\ni already check this **tf.lite.experimental.Analyzer.analyze(model_path='model.tflite', gpu_compatibility=True)**\r\ni am getting GPU COMPATABILITY WARNINGS \r\nSubgraph#0 main(T#0) -> [T#31]\r\n Op#0 EXPAND_DIMS(T#0, T#13[1]) -> [T#15]\r\nGPU COMPATIBILITY WARNING: Not supported op EXPAND_DIMS\r\n Op#1 TILE(T#15, T#7[1, 4, 1]) -> [T#16]\r\n Op#2 SHAPE(T#0) -> [T#17]\r\nGPU COMPATIBILITY WARNING: Not supported op SHAPE\r\n Op#3 STRIDED_SLICE(T#17, T#9[1], T#10[0], T#9[1]) -> [T#18]\r\nGPU COMPATIBILITY WARNING: STRIDED_SLICE supports for 3 or 4 dimensional tensors only.\r\n Op#4 CONCATENATION(T#5[4], T#18) -> [T#19]\r\n Op#5 RESHAPE(T#16, T#19) -> [T#20]\r\nGPU COMPATIBILITY WARNING: Expected 1 runtime input tensor(s), but node has 2 runtime input(s).\r\n Op#6 WHILE(T#11[0], T#4[2, 0, 0, 0, 0, ...], T#13[1], T#14, T#20, Cond: Subgraph#1, Body: Subgraph#2) -> [T#21, T#22, T#23, T#24, T#25]\r\nGPU COMPATIBILITY WARNING: Not supported op WHILE\r\n Op#7 RESHAPE(T#22, T#8[1, 4, 15]) -> [T#26]\r\n Op#8 RESHAPE(T#24, T#1[1, 4]) -> [T#27]\r\n Op#9 TOPK_V2(T#27, T#6[4]) -> [T#28, T#29]\r\nGPU COMPATIBILITY WARNING: Not supported op TOPK_V2\r\n Op#10 CAST(T#29) -> [T#30]\r\nGPU COMPATIBILITY WARNING: Not supported Cast case. Input type: INT32 and output type: INT64\r\n Op#11 GATHER(T#26, T#30) -> [T#31]\r\nGPU COMPATIBILITY WARNING: Does not accept INT32 input.\r\n\r\n\r\nGPU COMPATIBILITY WARNING: Subgraph#0 has GPU delegate compatibility issues at nodes 0, 2, 3, 5, 6, 9, 10, 11 with TFLite runtime version 2.13.0\r\n\r\nplease check this colab\r\n\r\nhttps://colab.research.google.com/drive/1GJ8UBa8GQBSKY4NC6_tYB0uxujvUn2kT#scrollTo=vKdwHlNHLI6Y\r\n\r\n**please tell me how to overcome this issue , to apply the GPU Delegate at android side please to get fatser inference**",
"Hi @Alwaysadil \r\n\r\nGiven the GPU compatibility warnings, I'm afraid we cannot leverage the GPU delegate to its extent. \r\n\r\nI see a similar issue #59232.\r\n\r\nOn Android devices, you can also try [NNAPI delegate](https://www.tensorflow.org/lite/android/delegates/nnapi#trying_the_nnapi_delegate_on_your_own_model) which also helps in accelerating the code and let us know if it works for your case?\r\n\r\nThanks.",
"Thank you @pjpratik for responding\r\nWhile applying NNAPI delegate ,getting this below error \r\n\r\n\r\nDeviceManager::DeviceManager2023-09-06 14:17:38.049 findAvailableDevices2023-09-06 14:17:38.104 Found interface xtensa-xtensa (version = Xtensa ANN 1.2)2023-09-06 14:17:38.104 Created TensorFlow Lite delegate for NNAPI.2023-09-06 **14:17:39.685 java.lang.IllegalArgumentException: Internal error: Error applying delegate: 2023-09-06 14:17:39.687** at org.tensorflow.lite.NativeInterpreterWrapper.createInterpreter(Native Method)2023-09-06 14:17:39.688 at org.tensorflow.lite.NativeInterpreterWrapper.init(NativeInterpreterWrapper.java:110)2023-09-06 14:17:39.689 at org.tensorflow.lite.NativeInterpreterWrapper.<init>(NativeInterpreterWrapper.java:73)2023-09-06 14:17:39.690 at org.tensorflow.lite.NativeInterpreterWrapperExperimental.<init>(NativeInterpreterWrapperExperimental.java:36)2023-09-06 14:17:39.691 at org.tensorflow.lite.Interpreter.<init>(Interpreter.java:232)\r\n\r\n**please @pjpratik help me to overcome this speed related issues please........**\r\n",
"Hi @Alwaysadil, I think we need more context.. if you can export the android studio project or a toy version which exhibits the behavior, that'll be the easiest for us to investigate. If not please at least at least post the code around where you are applying and using the NNAPI delegate, so that we may attempt to reproduce your issue. Thanks!",
"Hi @pkgoogle thank you for responding\r\nsee this below code\r\n\r\n#NNAPI delegate\r\nByteBuffer buffer = loadModelFile(mContext.getAssets()); Interpreter.Options options = (new Interpreter.Options()); NnApiDelegate nnApiDelegate = null;// Initialize interpreter with NNAPI delegate for Android Pie or above if(Build.VERSION.SDK_INT >= Build.VERSION_CODES.P) { nnApiDelegate = new NnApiDelegate(); options.addDelegate(nnApiDelegate); }// Initialize TFLite interpreter try { tflite = new Interpreter(buffer, options); } catch (Exception e) { throw new RuntimeException(e); }\r\n\r\n\r\n\r\n#GPU delegate applying\r\n\r\nByteBuffer buffer = loadModelFile(mContext.getAssets()); Interpreter.Options opt = new Interpreter.Options();// Initialize interpreter with GPU delegate CompatibilityList compatList = new CompatibilityList(); if(compatList.isDelegateSupportedOnThisDevice()){ // if the device has a supported GPU, add the GPU delegate GpuDelegateFactory.Options options1 = new GpuDelegateFactory.Options().setQuantizedModelsAllowed(true); GpuDelegate gpuDelegate = new GpuDelegate(options1); opt.addDelegate(gpuDelegate); } else { // if the GPU is not supported, run on 4 threads opt.setNumThreads(4); }// opt.setNumThreads(4); tflite = new Interpreter(buffer, opt);",
"Hi @Alwaysadil, which model file does your \"mContext.getAssets()\" refer to? There's a couple of possible ones in the various colabs you shared, so I want to just focus on the one that is causing the issue."
] | 2023-08-29T06:27:12 | 2023-10-18T15:26:46 | null | NONE | null | null | null | ### 1. System information
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04):
- TensorFlow installation (pip package or built from source):
- TensorFlow library (version, if pip package or github SHA, if built from source):
### 2. Code
Provide code to help us reproduce your issues using one of the following options:
#### Option A: Reference colab notebooks
1) Reference [TensorFlow Model Colab](https://colab.research.google.com/gist/ymodak/e96a4270b953201d5362c61c1e8b78aa/tensorflow-datasets.ipynb?authuser=1): Demonstrate how to build your TF model.
2) Reference [TensorFlow Lite Model Colab](https://colab.research.google.com/gist/ymodak/0dfeb28255e189c5c48d9093f296e9a8/tensorflow-lite-debugger-colab.ipynb): Demonstrate how to convert your TF model to a TF Lite model (with quantization, if used) and run TFLite Inference (if possible).
```
(You can paste links or attach files by dragging & dropping them below)
- Provide links to your updated versions of the above two colab notebooks.
- Provide links to your TensorFlow model and (optionally) TensorFlow Lite Model.
```
#### Option B: Paste your code here or provide a link to a custom end-to-end colab
```
(You can paste links or attach files by dragging & dropping them below)
- Include code to invoke the TFLite Converter Python API and the errors.
- Provide links to your TensorFlow model and (optionally) TensorFlow Lite Model.
```
### 3. Failure after conversion
If the conversion is successful, but the generated model is wrong, then state what is wrong:
- Model produces wrong results and/or has lesser accuracy.
- Model produces correct results, but it is slower than expected.
### 4. (optional) RNN conversion support
If converting TF RNN to TFLite fused RNN ops, please prefix [RNN] in the title.
### 5. (optional) Any other info / logs
Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached.
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"The installation of the tflite-model-maker with the pip command keeps crashing. Either due to requirements not being found, or the disk running out of space. As a result, cannot complete the Flower Classification tutorial.",
"Hi @dsbyprateekg, tflite-model-maker installation is currently broken and it's unlikely to be resolved soon, in the mean time please try using mediapipe-model-maker instead: here is an example [gist](https://colab.sandbox.google.com/gist/pkgoogle/93fb7581fab1ea14728c61adf584ca13/media_pipe_example.ipynb). Let us know if for some reason you can't use mediapipe model maker to accomplish your goals. For now this is considered a duplicate of https://github.com/tensorflow/tensorflow/issues/60431"
] | 2023-08-29T05:34:43 | 2023-10-24T18:12:41 | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.9.3
### Custom code
No
### OS platform and distribution
Kaggle TPU
### Mobile device
_No response_
### Python version
3.8.16
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
TPU VM v3-8
### Current behavior?
1. Installed TFlite-model-maker using command `!pip install -q tflite-model-maker`
2. Tried to print the installed version using command `import tflite_model_maker
print(tflite_model_maker.__version__)`
3. Got error `ImportError: libusb-1.0.so.0: cannot open shared object file: No such file or directory`
I am attached here the error log.
[kaggle_tpu_error.txt](https://github.com/tensorflow/tensorflow/files/12460630/kaggle_tpu_error.txt)
### Standalone code to reproduce the issue
```shell
In Kaggle, enable TPU accelerator.
Install the TFLite Model Maker library.
Print the version of installed library.
```
### Relevant log output
_No response_ | {
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"This seems very similar to https://uwekorn.com/2019/09/15/how-we-build-apache-arrows-manylinux-wheels.html#annoying-end-users-issue-2-segmentation-fault-after-import-tensorflow, so I suspect the issue has something to do with the `_GLIBCXX_USE_CXX11_ABI` flag.",
"@sid-kap,\r\nI tried to execute the mentioned code on tensorflow v2.13, v2.12 and tf-nightly. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/25a6f2b3b81630aec6065a9a68e8ed3a/untitled1350.ipynb). Also I tried with tensorflow-macos M1 aswell and was able to execute the code without any crash/error.\r\n<img width=\"497\" alt=\"image (16)\" src=\"https://github.com/tensorflow/tensorflow/assets/81610181/dba17d19-090c-4724-8f1e-b0aeed3b8096\">\r\n<img width=\"497\" alt=\"image (17)\" src=\"https://github.com/tensorflow/tensorflow/assets/81610181/3e40f6b8-56a9-490f-bb6f-e395ec24e4a2\">\r\n\r\nAnd also it doesn't looks like an issue from the tensorflow side. Thank you!\r\n",
"I was able to reproduce in a Colab notebook [here](https://colab.research.google.com/drive/11NueSQT-ch8ySQrKXR1tlgjdPTcPYz4E?usp=sharing)",
"The solution for me was to set\r\n```python\r\nLD_PRELOAD=\"/lib/x86_64-linux-gnu/libstdc++.so.6\"\r\n```\r\nbefore starting the python interpreter, as suggested in https://github.com/pytorch/pytorch/issues/102360#issuecomment-1708989096.\r\n\r\nWould still be nice to understand the root cause.",
"Hi, we are looking at some errors that appear whenever we import `tensorflow` first and then another project. We believe we have narrowed it down to `std::random_device` being defined in this library:\r\n\r\n```\r\n$ nm -an libtensorflow_framework.so.2 | grep random_device | c++filt \r\n0000000001cbac50 T std::random_device::_M_init(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)\r\n0000000001cbad10 T std::random_device::_M_fini()\r\n0000000001cbad30 T std::random_device::_M_getval()\r\n0000000001cbadb0 T std::random_device::_M_getentropy() const\r\n0000000001cbae70 T std::random_device::_M_init_pretr1(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)\r\n0000000001cbb040 T std::random_device::_M_getval_pretr1()\r\n```\r\n\r\nThere are a several other symbols that are normally defined in libstdc++ but are instead available in `libtensorflow_framework.so.2`.\r\n\r\nIs there a good reason for these symbols to be exported?",
"@erick-xanadu Can you check if those symbols are still defined in a recent TF nightly (`tf-nightly==2.15.0.dev20230804` or later)?\r\n\r\nIt seems like Google rarely patches old versions of TensorFlow, so even if we identified the fix they are unlikely to patch 2.13 or 2.14. (2.14 is not released yet, but it seems like it doesn't include commits after Aug 2.) It would be good to confirm that this will at least be fixed in 2.15.",
"```bash\r\n(env) erick.ochoalopez@DL7420-GS4N1J3:~/Downloads/CodeTemporary$ pip install tf-nightly==2.15.0.dev20230804\r\nCollecting tf-nightly==2.15.0.dev20230804\r\n Downloading tf_nightly-2.15.0.dev20230804-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (491.8 MB)\r\n \r\n### ---snip---\r\n\r\n(env) erick.ochoalopez@DL7420-GS4N1J3:~/Downloads/CodeTemporary/env/lib64/python3.10/site-packages/tensorflow$ nm -an libtensorflow_framework.so.2 | grep random_device | c++filt \r\n00000000020a6bb0 T std::random_device::_M_init(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)\r\n00000000020a6c70 T std::random_device::_M_fini()\r\n00000000020a6c90 T std::random_device::_M_getval()\r\n00000000020a6d10 T std::random_device::_M_getentropy() const\r\n00000000020a6dd0 T std::random_device::_M_init_pretr1(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)\r\n00000000020a6e40 T std::random_device::_M_getval_pretr1()\r\n\r\n### --- snip ---\r\n\r\n(env) erick.ochoalopez@DL7420-GS4N1J3:~/Downloads/CodeTemporary/env/lib64/python3.10/site-packages/tensorflow$ python -c \"import tensorflow as tf; print(tf.version.GIT_VERSION, tf.version.VERSION)\"\r\n2023-10-05 12:58:49.764634: I tensorflow/core/util/port.cc:111] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n2023-10-05 12:58:49.767231: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\r\n2023-10-05 12:58:49.807953: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9346] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\r\n2023-10-05 12:58:49.808003: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\r\n2023-10-05 12:58:49.808034: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\r\n2023-10-05 12:58:49.814929: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\r\n2023-10-05 12:58:49.815194: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-10-05 12:58:50.790420: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\nWARNING:tensorflow:From /home/erick.ochoalopez/Downloads/CodeTemporary/env/lib/python3.10/site-packages/tensorflow/python/ops/distributions/distribution.py:259: ReparameterizationType.__init__ (from tensorflow.python.ops.distributions.distribution) is deprecated and will be removed after 2019-01-01.\r\nInstructions for updating:\r\nThe TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\r\nWARNING:tensorflow:From /home/erick.ochoalopez/Downloads/CodeTemporary/env/lib/python3.10/site-packages/tensorflow/python/ops/distributions/bernoulli.py:165: RegisterKL.__init__ (from tensorflow.python.ops.distributions.kullback_leibler) is deprecated and will be removed after 2019-01-01.\r\nInstructions for updating:\r\nThe TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\r\nv1.12.1-97980-g93990114b0d 2.15.0-dev20230804\r\n\r\n```",
"@sid-kap,\r\nCould you please check in the latest TensorFlow v2.16, as most of the bugs are resolved in the latest version. As mentioned, I don't find any issue in the Mac-os or the colab. Thank you!",
"Yes, it has been fixed since 2.15 for me",
"@sid-kap,\r\nGlad the issue got fixed. Could you please feel free to move this issue to closed status. 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/61741\">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/61741\">No</a>\n"
] | 2023-08-29T03:13:51 | 2024-06-11T16:40:58 | 2024-06-11T16:40:55 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.13
### Custom code
No
### OS platform and distribution
python:3.10-slim-bookworm
### Mobile device
_No response_
### Python version
3.10
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
In a linux x86_64 machine in a python3.10 venv that contains `tensorflow==2.13.0` and `duckdb==0.8.1`, running this python code:
```python
import tensorflow
import duckdb
```
either hangs indefinitely or crashes with
```
ImportError: random_device could not be read
```
This happens with tensorflow 2.12 and tensorflow 2.13, but not tensorflow 2.11 or 2.11.1.
It happens with both duckdb 0.7.1 and duckdb 0.8.1.
It also happens with all nightlies from July up to `tf-nightly==2.15.0.dev20230803`, but the problem is fixed starting on `tf-nightly==2.15.0.dev20230804` and all nightlies after that.
### Standalone code to reproduce the issue
```shell
python3.10 -m venv ./venv
source ./venv/bin/activate
pip install tensorflow==2.13.0 duckdb==0.8.1
python -c 'import tensorflow; print("done importing tensorflow"); import duckdb'
```
### Relevant log output
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Related PR - https://github.com/tensorflow/tensorflow/pull/61690 | {
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"Hi @jpienaar Can you please review this PR ? Thank you!"
] | 2023-08-28T23:25:12 | 2023-10-09T04:09:35 | 2023-10-09T04:09:35 | CONTRIBUTOR | null | false | {
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} | fix legalization of BroadcastTo by:
- for floats: add with -0.0 (instead of 0.0)
- for integer types other than I32, insert cast to I32, before adding zero, then cast back to output type
(because tosa add only supports I32 for integer types)
- fixed lit tests for above changes as well
Change-Id: I93351250b18e4a318aa67c7029948bf3ab022533 | {
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"Hi @jpienaar Can you please review this PR ? Thank you!"
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"Hi @nmatare ,\r\n\r\nThanks for your time in reporting this. I have replicated the issue with tf-nightly as well. I observed dead lock and proposed work around with `dataset = dataset.ignore_errors()` works here. Attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/e5101988b8f68a39f907faaf7643c012/61736.ipynb) here for reference.\r\n\r\nSince other methods like `dataset.as_numpy_iterator()` works fine by raising the intended exception even without `dataset = dataset.ignore_errors()` , I believe this might be a bug that can be resolvable. Escalating to Dev team for their review.\r\n\r\nThanks!\r\n"
] | 2023-08-28T22:43:16 | 2023-08-29T06:52:49 | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
v2.13.0-rc2-7-g1cb1a030a62 2.13.0
### Custom code
Yes
### OS platform and distribution
Linux
### Mobile device
_No response_
### Python version
3.8.17
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
When saving a dataset to disk, TF will silently brick if there's an error in the data input pipeline. Methinks it should rather raise on the save instead of deadlocking.
Here's a reproducible example:
```python
import tensorflow as tf
# '2.13.0'
dataset = tf.data.Dataset.from_tensor_slices([1., 2., 0., 4.])
dataset = dataset.map(lambda x: tf.debugging.check_numerics(1. / x, "error"))
dataset.save('/tmp/hello')
# => deadlock
```
You can add an `ignore_errors` to the pipeline and it'll rightfully ignore, but it's nonintuitive to track down why the input bricks.
```python
dataset = dataset.ignore_errors()
# => OK save
```
### Standalone code to reproduce the issue
```shell
See above.
```
### Relevant log output
_No response_ | {
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"@xing-w I was able to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/df202f7ea6ad3c85bdf4184cc8e1c9a1/rnn_save_model.ipynb#scrollTo=qHLU9PbnkU4g). \r\nThis issue seems to be Keras issue. Please post this issue on [keras-team/keras repo.](https://github.com/keras-team/keras/issues) as Keras development is fully moving to [github.com/keras-team/keras](http://github.com/keras-team/keras). All issues and PRs related to keras will be addressed in that repo.\r\nTo know more see this TF forum discussion ; \r\n[https://discuss.tensorflow.org/t/keras-project-moved-to-new-repository-in-https-github-com-keras-team-keras/1999](https://discuss.tensorflow.org/t/keras-project-moved-to-new-repository-in-https-github-com-keras-team-keras/1999)\r\nThank you!",
"Posted it to keras. Thanks.",
"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/61732\">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/61732\">No</a>\n"
] | 2023-08-28T20:15:53 | 2023-08-31T20:41:23 | 2023-08-31T20:41:21 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.13.0
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
A simple RNN with LSTMcell model.
I want to initialize the states with `initial_state_h` and `initial_state_c`.
```
batch_size= 16
inputs = tf.keras.layers.Input(shape=(20,5),batch_size=batch_size)
units = 8
lstm_cell_fw = tf.keras.layers.LSTMCell(units)
initial_state_h = tf.random.normal(shape = (batch_size,units), mean=0., stddev=10., dtype=tf.dtypes.float32)
initial_state_c = tf.random.normal(shape = (batch_size,units), mean=0., stddev=10., dtype=tf.dtypes.float32)
lstm_layer_fw = tf.keras.layers.RNN(lstm_cell_fw, stateful=True, return_state=True, return_sequences=False)
outputs,states_h_fw, states_c_fw= lstm_layer_fw(inputs,initial_state = [initial_state_h,initial_state_c])
lstm_dense1 = tf.keras.layers.Dense(16, activation = 'relu')
lstm_dense2 = tf.keras.layers.Dense(2, activation = 'softmax')
out=lstm_dense2(lstm_dense1(outputs))
model = tf.keras.models.Model(inputs, out)
```
After compile and train, the model is saved with `model.save('my_model_test.keras')`.
```
model.compile(optimizer='adam', loss='categorical_crossentropy',metrics=['accuracy'])
model.summary()
xTrain = np.random.rand(96,20,5)
yTrain = np.random.rand(96,2)
for i in range(10):
model.fit(xTrain, yTrain,batch_size=batch_size)
model.save('my_model_test.keras')
```
But when I try to load it with `load_model = tf.keras.models.load_model('my_model_test.keras')`, it gives error:
```
13 frames
[/usr/local/lib/python3.10/dist-packages/keras/src/backend.py](https://localhost:8080/#) in int_shape(x)
1530 """
1531 try:
-> 1532 shape = x.shape
1533 if not isinstance(shape, tuple):
1534 shape = tuple(shape.as_list())
AttributeError: 'float' object has no attribute 'shape'
```
I tried to save in other format, `.h5`, `.json`, etc. All give the same error.
But, if I don't use `initial_state` in `outputs,states_h_fw, states_c_fw= lstm_layer_fw(inputs)`, everything goes well. No problem with `load_model`.
### Standalone code to reproduce the issue
```shell
https://colab.research.google.com/drive/1uKEpnddzeYSRG_1vKjtcQ4OLNwLuQeqy?usp=sharing
```
### Relevant log output
```shell
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-7-8e0130abf25e> in <cell line: 1>()
----> 1 load_model = tf.keras.models.load_model('my_model_test.keras')
13 frames
/usr/local/lib/python3.10/dist-packages/keras/src/backend.py in int_shape(x)
1530 """
1531 try:
-> 1532 shape = x.shape
1533 if not isinstance(shape, tuple):
1534 shape = tuple(shape.as_list())
AttributeError: 'float' object has no attribute 'shape'
```
```
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"@penpornk This PR looks good to us. Thanks @Srini511.",
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"Hi @penpornk, Can you please review this PR ? Thank you!",
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"Hi @Hrayo712 \r\n\r\nMixed precision selective post training quantization is currently not supported in TFLite. However, the selective quantization is currently supported using [Quantization Debugger](https://www.tensorflow.org/lite/performance/quantization_debugger#selective_quantization), which accepts `denylisted_nodes` and `denylisted_op` options for skipping quantization for specific layers, or all instances of specific ops.\r\n\r\nThanks.",
"Hi @pjpratik , thanks for the quick response!\r\n\r\nI understand. However, when using selective quantization via the Quantization Debugger, denylisted_nodes are set to FP32 right ? or is there a way to set the denylisted nodes/ops to INT16 ?",
"Hi @Hrayo712 \r\n\r\n>when using selective quantization via the Quantization Debugger, denylisted_nodes are set to FP32 right ?\r\n\r\nYes, the quantization is skipped for those nodes keeping them in their default FP32.\r\n\r\n>is there a way to set the denylisted nodes/ops to INT16 ?\r\n\r\nThose are just denied ops for quantization. As per the documentation, we don't have an option to set it to INT16.\r\n\r\nCan we consider this as feature request?\r\n\r\nThanks.",
"Hi @pjpratik,\r\n\r\nYes. I think it would be a super valuable feature, to have mixed-precision TFLite models. Perhaps the easy way would be indeed to allow for denylisted ops to be able to be quantized to INT16.\r\n\r\nThanks team!\r\n",
"Hi @abattery, can you please take a look? Thanks."
] | 2023-08-28T14:19:16 | 2023-08-31T19:06:44 | null | NONE | null | null | null | **System information**
TensorFlow version (you are using): TF 2.13.0
Are you willing to contribute it (Yes/No): No
Describe the feature and the current behavior/state.
Dear TF developers, I'm currently experimenting with PTQ using 8 bit weights and 16 bit activations (W8A16), and I've gotten great results. However, after some experimentation I have identified that only a certain part of my network requires the 16 bit activations. In other word, using 16 bit activations for the entire model is sub-optimal for my use-case.
Hence, I'm looking for a way to selectively quantize a part of my model to 8 bit weights and activations (W8A8), and the other part to W8A16.
In the current state, would this be possible somehow ?
**Who will benefit with this feature?**
Platforms that support mixed-precision execution of activations.
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"Hi @JohnFarl, thanks for reporting the issue, it is actively being worked on, please follow the progress on https://github.com/tensorflow/tensorflow/issues/60431 It seems some users have had success with downgrading to python 3.9 for now. Can you try to see if those workarounds will help unblock you for now?",
"I can confirm that installing either the TfLite Model Maker or the MediaPipe one fails using Python v3.11.5 on a Mac M1 for multiple users in our company. @pkgoogle you guys keep saying in multiple GitHub issues that this is actively worked upon, but:\r\n\r\n1. There are at least 5 active issues in this repository apart from this one, with even **twice** as many closed ones. At this point you should probably ask yourselves, if so many people report issues with the installation process, maybe there actually *is* an issue and thus the process should be improved and/or better documented?\r\n<img width=\"1225\" alt=\"image\" src=\"https://github.com/tensorflow/tensorflow/assets/3236878/fc961a99-bd11-409b-a9d5-ac92701cc401\">\r\n2. Not even the \"new kid on the block\" MediaPipe works. If you guys keep releasing new software, even though the old one isn't working correctly, how will that help the community?\r\n\r\nI'm sorry, but at this point, it's a very valid question if the project development is still active. Do you guys even test out if the tools work? Like, seriously. Get a fresh new laptop and try to install either of the 2 model makers following the official tutorial.\r\n",
"I fully empathise with @bogdanzurac's frustration! Arm64-based Macs are not bleeding-edge hardware anymore, and the fact that this issue still persist is IMO pretty embarrassing for the team responsible for these tools.\r\n\r\nI'll try to summarise the approaches I have tried and my findings, in the hope that someone (@pkgoogle perhaps?) might take appropriate actions or provide constructive hints for how to move this forward.\r\n\r\nInstalling with Python 3.11 on MacOs with Arm64 (native environment) fails on [`tflite-support`](https://pypi.org/project/tflite-support/0.4.4/#files)\r\n```text\r\nThe conflict is caused by:\r\n tflite-model-maker 0.4.2 depends on tflite-support>=0.4.2\r\n tflite-model-maker 0.4.1 depends on tflite-support-nightly\r\n :\r\n : \r\n```\r\n\r\nLatest version of `tflite-support` at time of writing (linked above) provides wheels for Linux/Arm64 and MacOs/x86_64, but not the combo MacOs/Arm64. Python 3.11 is not the issue in this case, ref list of wheels in link above.\r\n\r\n- `tflite-support` is built using Bazel, with which I'm not familiar. I might spend some time later to understand Bazel and try to build `tflite-support` for MacOs/Arm64. Any constructive hints are greatly appreciated (not links to the manual - I'm able to find that myself 😉 )\r\n\r\nGiven that `tflite-support` is provided for MacOs/x86_64, it's tempting to lean on [Rosetta](https://developer.apple.com/documentation/apple-silicon/about-the-rosetta-translation-environment) and install x86_64 versions of everything. However, scrolling a little down on the linked page reveals that AVX-instructions are not translated by Rosetta. Furthermore, [this issue](https://github.com/docker/for-mac/issues/6620) indicates that QEMU/Docker may not come to the rescue here either. Since Tensorflow [seems to rely heavily on AVX](https://saturncloud.io/blog/is-there-a-version-of-tensorflow-not-compiled-for-avx-instructions/) the approach of using x86_64 binaries seems to be unfruitful.\r\n\r\nLast possibility I see on my M1 is to try wheels for Linux/Arm64 with some Arm64-based Linux like e.g. python:3.9-bullseye running in Docker:\r\n\r\n```text\r\nMacBook-Air:bjarne$ docker run -it --rm python:3.9-bullseye bash\r\nroot@9656157ee22e:/# arch\r\naarch64\r\nroot@9656157ee22e:/#\r\n```\r\n\r\n_(NOTE: The option `--use-deprecated=legacy-resolver` to pip is crucial in next step - wish the docs were clearer on this. See e.g. [this issue](https://github.com/tensorflow/tensorflow/issues/60431).)_\r\n\r\n```text\r\nroot@ 9656157ee22e:/# pip install --use-deprecated=legacy-resolver tflite-model-maker\r\nCollecting tflite-model-maker\r\n Downloading tflite_model_maker-0.4.2-py3-none-any.whl (577 kB) \r\n : \r\n : \r\nERROR: Could not find a version that satisfies the requirement tensorflow-addons>=0.11.2 (from tflite-model-maker) (from versions: none)\r\nERROR: No matching distribution found for tensorflow-addons>=0.11.2 (from tflite-model-maker)\r\n```\r\n<br/>\r\n\r\nLooking up [wheels for tensorflow-addons on pypi](https://pypi.org/project/tensorflow-addons/0.21.0/#files) we see that there is indeed no wheel for Linux/Arm64. Ironically, there **are** wheels for MacOs/Arm64... 🤦 Moreover, adding insult to injury, [tensorflow-addons will be discontinued](https://github.com/tensorflow/addons/issues/2807) in a year or so.\r\n\r\n*sigh*\r\n\r\n\r\nInstalling [mediapipe-model-maker](https://pypi.org/project/mediapipe-model-maker/0.2.1.3/) (_aka \"new kid on the block\"_) on MacOs/Arm64 fails because [tensorflow-text==2.13.0](https://pypi.org/project/tensorflow-text/2.13.0/#files) has no wheel for the platform\r\n<br/>\r\n\r\n```text\r\nMacBook-Air:bjarne$ pip install mediapipe-model-maker\r\n : \r\n : \r\nERROR: Cannot install mediapipe-model-maker because these package versions have conflicting dependencies.\r\n\r\nThe conflict is caused by:\r\n tf-models-official 2.13.2 depends on tensorflow-text~=2.13.0\r\n : \r\n : \r\n```\r\n<br/>\r\n\r\nInstalling the `mediapipe-model-maker` in Linux/Arm64 under Docker bumps into the problem with `tensorflow-addons`\r\n<br/>\r\n\r\n```text\r\nroot@9656157ee22e:/# pip install mediapipe-model-maker\r\nCollecting mediapipe-model-maker\r\n Downloading mediapipe_model_maker-0.2.1.3-py3-none-any.whl (127 kB)\r\n : \r\n : \r\nERROR: Cannot install mediapipe-model-maker==0.1.0.2, mediapipe-model-maker==0.1.1.0, mediapipe-model-maker==0.1.1.1, mediapipe-model-maker==0.2.1, mediapipe-model-maker==0.2.1.1, mediapipe-model-maker==0.2.1.2 and mediapipe-model-maker==0.2.1.3 because these package versions have conflicting dependencies.\r\n\r\nThe conflict is caused by:\r\n mediapipe-model-maker 0.2.1.3 depends on tensorflow-addons\r\n mediapipe-model-maker 0.2.1.2 depends on tensorflow-addons\r\n : \r\n : \r\n```\r\n<br/>\r\n\r\nHence, on an Arm-based Mac trying to use `tflite-model-maker` I'm stuck until either\r\n\r\n1. `tflite-support` provides wheel for MacOs/Arm64\r\n2. `tensorflow-addons` provides wheel for Linux/Arm64 (requires QEMU/Docker)\r\n\r\nand trying to use `mediapipe-model-maker` I'm stuck until either\r\n\r\n1. `tensorflow-text==2.13.0` provides wheel for MacOs/Arm64\r\n2. `tensorflow-addons` provides wheel for Linux/Arm64 (requires QEMU/Docker)\r\n\r\n",
"The installation of the tflite-model-maker with the pip command keeps crashing on Google Colab. Either due to requirements not being found, or the disk running out of space. As a result, cannot complete the Flower Classification tutorial.",
"Hi @BjarneHerland, it is probably best to post those issues on the mediapipe github issues page: https://github.com/google/mediapipe/issues, I tried a couple of things it seems python=3.10 works best. I was able to install with python=3.10 successfully on linux x86_64. It might be worth it to try on linux ARM. Using colab is probably the most stable currently. I am running into a different issue for my mac M1. :\r\n\r\n```sh\r\nCollecting pyyaml<6.0,>=5.1 (from tf-models-official==2.11.6->mediapipe-model-maker)\r\n Using cached PyYAML-5.4.1.tar.gz (175 kB)\r\n Installing build dependencies ... done\r\n Getting requirements to build wheel ... error\r\n error: subprocess-exited-with-error\r\n \r\n × Getting requirements to build wheel did not run successfully.\r\n │ exit code: 1\r\n ╰─> [62 lines of output]\r\n /private/var/folders/q_/v3c1qs3n1z56cfpxr_5v3p1w0148zx/T/pip-build-env-ye44j30y/overlay/lib/python3.10/site-packages/setuptools/config/setupcfg.py:293: _DeprecatedConfig: Deprecated config in `setup.cfg`\r\n !!\r\n \r\n ********************************************************************************\r\n The license_file parameter is deprecated, use license_files instead.\r\n \r\n By 2023-Oct-30, you need to update your project and remove deprecated calls\r\n or your builds will no longer be supported.\r\n \r\n See https://setuptools.pypa.io/en/latest/userguide/declarative_config.html for details.\r\n ********************************************************************************\r\n \r\n !!\r\n parsed = self.parsers.get(option_name, lambda x: x)(value)\r\n running egg_info\r\n writing lib3/PyYAML.egg-info/PKG-INFO\r\n writing dependency_links to lib3/PyYAML.egg-info/dependency_links.txt\r\n writing top-level names to lib3/PyYAML.egg-info/top_level.txt\r\n Traceback (most recent call last):\r\n File \"/Users/xxxxxx/miniforge3/envs/mediapipe_model_maker/lib/python3.10/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py\", line 353, in <module>\r\n main()\r\n File \"/Users/xxxxxx/miniforge3/envs/mediapipe_model_maker/lib/python3.10/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py\", line 335, in main\r\n json_out['return_val'] = hook(**hook_input['kwargs'])\r\n File \"/Users/xxxxxxx/miniforge3/envs/mediapipe_model_maker/lib/python3.10/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py\", line 118, in get_requires_for_build_wheel\r\n return hook(config_settings)\r\n File \"/private/var/folders/q_/v3c1qs3n1z56cfpxr_5v3p1w0148zx/T/pip-build-env-ye44j30y/overlay/lib/python3.10/site-packages/setuptools/build_meta.py\", line 355, in get_requires_for_build_wheel\r\n return self._get_build_requires(config_settings, requirements=['wheel'])\r\n File \"/private/var/folders/q_/v3c1qs3n1z56cfpxr_5v3p1w0148zx/T/pip-build-env-ye44j30y/overlay/lib/python3.10/site-packages/setuptools/build_meta.py\", line 325, in _get_build_requires\r\n self.run_setup()\r\n File \"/private/var/folders/q_/v3c1qs3n1z56cfpxr_5v3p1w0148zx/T/pip-build-env-ye44j30y/overlay/lib/python3.10/site-packages/setuptools/build_meta.py\", line 341, in run_setup\r\n exec(code, locals())\r\n File \"<string>\", line 271, in <module>\r\n File 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\"/private/var/folders/q_/v3c1qs3n1z56cfpxr_5v3p1w0148zx/T/pip-build-env-ye44j30y/overlay/lib/python3.10/site-packages/setuptools/command/egg_info.py\", line 586, in add_defaults\r\n sdist.add_defaults(self)\r\n File \"/private/var/folders/q_/v3c1qs3n1z56cfpxr_5v3p1w0148zx/T/pip-build-env-ye44j30y/overlay/lib/python3.10/site-packages/setuptools/command/sdist.py\", line 113, in add_defaults\r\n super().add_defaults()\r\n File \"/private/var/folders/q_/v3c1qs3n1z56cfpxr_5v3p1w0148zx/T/pip-build-env-ye44j30y/overlay/lib/python3.10/site-packages/setuptools/_distutils/command/sdist.py\", line 251, in add_defaults\r\n self._add_defaults_ext()\r\n File \"/private/var/folders/q_/v3c1qs3n1z56cfpxr_5v3p1w0148zx/T/pip-build-env-ye44j30y/overlay/lib/python3.10/site-packages/setuptools/_distutils/command/sdist.py\", line 336, in _add_defaults_ext\r\n self.filelist.extend(build_ext.get_source_files())\r\n File \"<string>\", line 201, in get_source_files\r\n File \"/private/var/folders/q_/v3c1qs3n1z56cfpxr_5v3p1w0148zx/T/pip-build-env-ye44j30y/overlay/lib/python3.10/site-packages/setuptools/_distutils/cmd.py\", line 107, in __getattr__\r\n raise AttributeError(attr)\r\n AttributeError: cython_sources\r\n [end of output]\r\n \r\n note: This error originates from a subprocess, and is likely not a problem with pip.\r\nerror: subprocess-exited-with-error\r\n\r\n× Getting requirements to build wheel did not run successfully.\r\n│ exit code: 1\r\n╰─> See above for output.\r\n\r\nnote: This error originates from a subprocess, and is likely not a problem with pip.\r\n```",
"Thanks again for your involvement @pkgoogle! I'll consider a separate post in the right repo later.\r\n\r\nIIRC, this pyyaml-issue you see can be resolved by installing the `pyyaml` binary package via e.g. `conda` prior to running pip (looks like compiling pyyaml on Osx/Arm fails for some reason). Personally I use `miniforge` but other pkg-managers like `anaconda`, `mamba`, `poetry` etc might also work (just make sure to install an acceptable version)\r\n\r\n> Hi @BjarneHerland, it is probably best to post those issues on the mediapipe github issues page: https://github.com/google/mediapipe/issues, I tried a couple of things it seems python=3.10 works best. I was able to install with python=3.10 successfully on linux x86_64. It might be worth it to try on linux ARM. Using colab is probably the most stable currently. I am running into a different issue for my mac M1. :\r\n> \r\n> ```shell\r\n> Collecting pyyaml<6.0,>=5.1 (from tf-models-official==2.11.6->mediapipe-model-maker)\r\n> Using cached PyYAML-5.4.1.tar.gz (175 kB)\r\n> Installing build dependencies ... done\r\n> Getting requirements to build wheel ... error\r\n> error: subprocess-exited-with-error\r\n>\r\n> [snip]\r\n>\r\n> ```\r\n\r\n",
"No worries, correct if I install pyyaml=5.4.1 manually, it reaches the same error you got on Mac M1:\r\n\r\n```\r\n Using cached mediapipe_model_maker-0.1.0.1-py3-none-any.whl (7.2 MB)\r\nCollecting tf-models-official>=2.10.1 (from mediapipe-model-maker)\r\n Downloading tf_models_official-2.13.0-py2.py3-none-any.whl.metadata (1.4 kB)\r\n Downloading tf_models_official-2.12.1-py2.py3-none-any.whl.metadata (1.5 kB)\r\n Downloading tf_models_official-2.12.0-py2.py3-none-any.whl (2.6 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.6/2.6 MB 27.0 MB/s eta 0:00:00\r\n Downloading tf_models_official-2.11.5-py2.py3-none-any.whl (2.4 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.4/2.4 MB 29.8 MB/s eta 0:00:00\r\n Downloading tf_models_official-2.11.4-py2.py3-none-any.whl (2.4 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.4/2.4 MB 19.9 MB/s eta 0:00:00\r\n Downloading tf_models_official-2.11.3-py2.py3-none-any.whl (2.3 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.3/2.3 MB 21.8 MB/s eta 0:00:00\r\n Downloading tf_models_official-2.11.2-py2.py3-none-any.whl (2.3 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.3/2.3 MB 36.4 MB/s eta 0:00:00\r\n Downloading tf_models_official-2.11.0-py2.py3-none-any.whl (2.3 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.3/2.3 MB 32.7 MB/s eta 0:00:00\r\n Downloading tf_models_official-2.10.1-py2.py3-none-any.whl (2.2 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.2/2.2 MB 39.1 MB/s eta 0:00:00\r\nCollecting sacrebleu==2.2.0 (from tf-models-official>=2.10.1->mediapipe-model-maker)\r\n Downloading sacrebleu-2.2.0-py3-none-any.whl (116 kB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 116.6/116.6 kB 19.6 MB/s eta 0:00:00\r\nCollecting mediapipe-model-maker\r\n Using cached mediapipe_model_maker-0.1.0-py3-none-any.whl (7.0 MB)\r\nCollecting tf-models-official>=2.10 (from mediapipe-model-maker)\r\n Downloading tf_models_official-2.10.0-py2.py3-none-any.whl (2.2 MB)\r\n ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.2/2.2 MB 40.4 MB/s eta 0:00:00\r\nINFO: pip is still looking at multiple versions of mediapipe-model-maker to determine which version is compatible with other requirements. This could take a while.\r\nERROR: Cannot install mediapipe-model-maker==0.1.0.2, mediapipe-model-maker==0.1.1.0 and mediapipe-model-maker==0.1.1.1 because these package versions have conflicting dependencies.\r\n\r\nThe conflict is caused by:\r\n mediapipe-model-maker 0.1.1.1 depends on mediapipe==0.9.2.1\r\n mediapipe-model-maker 0.1.1.0 depends on mediapipe==0.9.2.1\r\n mediapipe-model-maker 0.1.0.2 depends on mediapipe==0.9.0.1\r\n\r\nTo fix this you could try to:\r\n1. loosen the range of package versions you've specified\r\n2. remove package versions to allow pip attempt to solve the dependency conflict\r\n\r\nERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/topics/dependency-resolution/#dealing-with-dependency-conflicts\r\n```",
"On the subject of `mediapipe-model-maker` [this reply](https://github.com/google/mediapipe/issues/4088#issuecomment-1832754598) points to an announcement from the Tensorflow Text team stating that wheels will be provided only for Linux x86_64 and Intel-based Macs (`mediapipe-model-maker` depends on `tensorflow-text` at present).\r\n\r\nThe recommended solution is to build wheels for the desired platform from source yourself.\r\n\r\nMoreover, quoting from the [announcement](https://github.com/tensorflow/text/tree/b32645fbf1e4fd7e81d8d03fa2d2b4872e3a270d#a-note-about-different-operating-system-packages)\r\n\r\n> Note that TF Text needs to be built in the same environment as TensorFlow. Thus, if you manually build TF Text, it is highly recommended that you also build TensorFlow.\r\n\r\n",
"> On the subject of `mediapipe-model-maker` [this reply](https://github.com/google/mediapipe/issues/4088#issuecomment-1832754598) points to an announcement from the Tensorflow Text team stating that wheels will be provided only for Linux x86_64 and Intel-based Macs (`mediapipe-model-maker` depends on `tensorflow-text` at present).\r\n> \r\n> The recommended solution is to build wheels for the desired platform from source yourself.\r\n> \r\n> Moreover, quoting from the [announcement](https://github.com/tensorflow/text/tree/b32645fbf1e4fd7e81d8d03fa2d2b4872e3a270d#a-note-about-different-operating-system-packages)\r\n> \r\n> > Note that TF Text needs to be built in the same environment as TensorFlow. Thus, if you manually build TF Text, it is highly recommended that you also build TensorFlow.\r\n\r\nThis is a ridiculous decision. Windows is the desktop OS with most market share and Mac is moving to arm. So the decision to stop support to anything other than Linux x86_64 and Intel-based Macs makes no sense.",
"> On the subject of `mediapipe-model-maker` [this reply](https://github.com/google/mediapipe/issues/4088#issuecomment-1832754598) points to an announcement from the Tensorflow Text team stating that wheels will be provided only for Linux x86_64 and Intel-based Macs (`mediapipe-model-maker` depends on `tensorflow-text` at present).\r\n> \r\n> The recommended solution is to build wheels for the desired platform from source yourself.\r\n\r\nThis opens up a can of worms for those with Apple M1 chips. \r\n\r\nIs there a proper Docker image or anything out there?",
"I'm also trying to figure out how to install model-maker on m1 and it looks like docker is the only way..\r\n@lucksp did u figure out the docker file?",
"> I'm also trying to figure out how to install model-maker on m1 and it looks like docker is the only way.. @lucksp did u figure out the docker file?\r\n\r\nI ended up using Google's Vertex AI to create my exportable TFLite models",
"@lucksp is your use case an image classification? i believe its the only one you can export from Vertex tools. text classification is not supported, there is an old feature request: https://issuetracker.google.com/issues/168860629",
"yes, images."
] | 2023-08-28T14:09:40 | 2024-05-30T21:24:36 | null | NONE | null | null | null | If you try to install tflite-model-maker-nightly basically it starts to download **all nightly build wheels** since the first release rather than latest one as supposed.
This seems caused by a bad configuration. Many people have reported this issue [many months ago](https://github.com/tensorflow/tensorflow/issues/60431), it remained unsolved as this [other issue](https://github.com/tensorflow/tensorflow/issues/61337).
But they aren't the only setup issues that affects tflite-model-maker.
If you try to install the latest build with `pip install tflite-model-maker` it raises various errors
e.g.
tflite-support dependency that hasn't any wheel available in repository (for windows since the 0.4.0 released in May 2022 there is no wheel at all and the latest is 0.3.1 that has no wheel for latest Python versions).
Tried on Linux arm64 and needed wheels are missing too.
How is decided to upgrade version dependencies requirements without release updated dependencies coherently, considering these are developed by same company?
If it isn't followed a coherent path, it is obvious that troubles arise and you have to waste time trying to find if there is a combination of OS type, architecture, Python version, package version, dependency version, that may work, digging in the repository release history and requirements.
tflite-model-maker has been released a couple of years ago to simplify model training and it was very good at this, before the project maintenance started to become erratic and lackluster creating a lot of compatibility issues and conflicts.
Is this project development still active? | {
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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/61718/checks?check_run_id=16272518486) 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.",
"Hi @turgut-baba Can you please sign CLA. Thank you!",
"@turgut-baba Can you please resolve conflicts? Thank you!",
"@turgut-baba Can you please resolve conflicts? Thank you!",
"This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.",
"You definitely need to rebase this, it has too many commits",
"I think you only want [ded506a](https://github.com/tensorflow/tensorflow/pull/61718/commits/ded506a043abab7a5c37c8cf1636eab2eb25d517), but you started this on the `r2.14` branch while targeting master. You need to start from `master` branch",
"Hi @turgut-baba Any update of this PR? Please. Thank you!",
"Hi @turgut-baba Any update of this PR? Please. Thank you!",
"We should close this PR and reopen only with the commit that is needed",
"@mihaimaruseac @gbaned , Sorry for the delay, as of now I'm using tensorflow with the changes I've made at the beginning of this thread, no updates on my part. I tried to resolve conflicts however I do not have the right permissions to do so. Thank you for your time on this.",
"In this case, closing the PR.\r\n\r\nPlease open it again with the conflicts resolved if you want to contribute the changes upstream."
] | 2023-08-28T13:24:07 | 2023-12-23T00:17:55 | 2023-12-23T00:17:54 | NONE | null | false | {
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} | As the commit message mentions, when trying to build tensorflow lite with `-DTFLITE_ENABLE_GPU=ON` on windows to get the dll/lib files the `cmake --build . -j ` causes the following error: 'any_cast is not a member of std' on r2.14 branch. After making sure my c++ version is indeed above 17, I figured that `operation_selector.cc` and `conv_pointwise.cc` does not include `#include <any>` to use those functions. So I just added them next to other includes. | {
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"HI @Aismart20170718 ,\r\n\r\nCould you please confirm the build command used along with bazel and GCC versions? Also 2.6 version is quiet older and not actively supported. Could you please try the build with latest versions? 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.",
"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/61717\">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/61717\">No</a>\n"
] | 2023-08-28T11:51:40 | 2023-09-15T01:47:35 | 2023-09-15T01:47:33 | NONE | null | null | null | ### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf2.6.2
### Custom code
Yes
### OS platform and distribution
ubuntu20.04
### Mobile device
rk3588
### Python version
python3.8
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.8/dist-packages/tensorflow/__init__.py", line 41, in <module>
from tensorflow.python.tools import module_util as _module_util
File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/__init__.py", line 40, in <module>
from tensorflow.python.eager import context
File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/context.py", line 35, in <module>
from tensorflow.python import pywrap_tfe
File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/pywrap_tfe.py", line 28, in <module>
from tensorflow.python import pywrap_tensorflow
File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/pywrap_tensorflow.py", line 83, in <module>
raise ImportError(msg)
ImportError: Traceback (most recent call last):
File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/pywrap_tensorflow.py", line 64, in <module>
from tensorflow.python._pywrap_tensorflow_internal import *
ImportError: /usr/local/lib/python3.8/dist-packages/tensorflow/python/_pywrap_tensorflow_internal.so: cannot open shared object file: No such file or directory
### Standalone code to reproduce the issue
```shell
import tensorflow
Traceback (most recent call last):
File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/pywrap_tensorflow.py", line 64, in <module>
from tensorflow.python._pywrap_tensorflow_internal import *
ImportError: /usr/local/lib/python3.8/dist-packages/tensorflow/python/_pywrap_tensorflow_internal.so: cannot open shared object file: No such file or directory
```
### Relevant log output
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"@ThomasRichtsfeld Could you please let us know the TF version you are using?\r\nPlease refer to [Build from Source ](https://www.tensorflow.org/install/source) and [GPU acceleration delegate ](https://www.tensorflow.org/lite/android/delegates/gpu). Could you please create the delegate with TfLiteGpuDelegateV2Create() in the latest version.\r\n\r\n Thank 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.",
"> Could you please let us know the TF version you are using?\r\n\r\nCan you elaborate on this? I am not quite sure which exact library you are referring to. I listed the dependencies in the post above. \r\n\r\n> Could you please create the delegate with TfLiteGpuDelegateV2Create() in the latest version.\r\n\r\nWhich delegate do you mean?\r\n\r\nAll the code that is causing the problem comes with the `com.google.android.gms:play-services-tflite` dependencies.\r\n\r\nOr are you talking about the tf lite model itself? I am just the stakeholder using that model\r\n",
"Hi @ThomasRichtsfeld, Can you please make a PR with your suggested changes? We will review it. Thanks!",
"@pkgoogle Can you point me to the source of `org.tensorflow.lite.support.model.GpuDelegateProxy`? I can't find it easily in this repo. \r\nIs this also the one that is shipped via the **Play Services**? I am a bit confused about the old tf lite stuff and the new one that is shipped through the play services. I am not sure if the sources are the same",
"No worries, @ThomasRichtsfeld, is this what you are looking for? https://github.com/tensorflow/tflite-support/blob/master/tensorflow_lite_support/java/src/java/org/tensorflow/lite/support/model/GpuDelegateProxy.java",
"Yep, that is what I was looking for. Thanks.\r\nOne question, if we update this class now and you release a new version, where is this reflected? In the stand-alone TensorFlow Lite or in the Play services API?",
"Np, It should be reflected in both, assuming we keep supporting both workflows.",
"@pkgoogle do you know how I can get the permission for pushing branches? I get back a 403",
"Hi @ThomasRichtsfeld,\r\n\r\nPlease ensure you are following this link: https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md\r\n\r\nGenerally you can't push branches directly, you need to make a PR, let me know if you run into any trouble following that.",
"> Generally you can't push branches directly, you need to make a PR, let me know if you run into any trouble following that.\r\n\r\nYes, but how can I open a PR without the changes being pushed to a branch?",
"Hi @ThomasRichtsfeld, I don't think I'm understanding your issue currently. Can you show me the exact git steps you use and the error you get. Please include context such as if you forked the repo/what branches you are in. Thanks!",
"- I cloned the repo you sent me\r\n- Created a branch \r\n- Fixed the issue\r\n- Tried to push the branch (This is where the error happened)\r\n\r\nI didn't fork the repo",
"Hi @ThomasRichtsfeld, Thanks for the info, can you:\r\n\r\n1. Fork the repo\r\n2. Update your fork with your fix\r\n3. Merge your fork back in by making a PR\r\n\r\nHere's an example PR: https://github.com/tensorflow/tensorflow/pull/61877\r\n\r\nLet me know if that works for you.",
"Hi @ThomasRichtsfeld ,\r\n\r\nFirstly, thank you for reporting this issue!\r\n\r\nSecondly, if you're having trouble creating a PR, feel free to just attach a patch file here.",
"I have made a fix for this.",
"Hi @fergushenderson, wondering if you have a link to the PR or if it has already merged? Thanks for any update.",
"The PR is this one:\r\nhttps://github.com/tensorflow/tflite-support/commit/e4bc12500c1ae3110c11345c8d93599515013b55\r\n\r\nIt was merged on November 21st.",
"Hi @ThomasRichtsfeld, can you please verify that your issue is now resolved? 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.",
"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/61716\">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/61716\">No</a>\n"
] | 2023-08-28T09:47:07 | 2024-02-21T01:47:12 | 2024-02-21T01:47:10 | NONE | null | null | null | We get a class not found exception when calling
```
Model.createModel(
FileUtil.loadMappedFile(
TfLiteFileProvider(
context!!
).getModelFile(modelFileName)
),
modelFileName,
runningOptions!!
)
```
The problem is that inside `GpuDelegateProxy` you are using reflection that creates `org.tensorflow.lite.gpu.GpuDelegate`. This dependency is not part of the play services dependencies anymore. It should rather point to `com.google.android.gms.tflite.gpu.GpuDelegate`.
#### Dependencies
```
tflite_vision = { module = "org.tensorflow:tensorflow-lite-task-vision-play-services", version= "0.4.4" }
tflite_gpu = { module = "com.google.android.gms:play-services-tflite-gpu", version = "16.1.0" }
tflite_java = { module = "com.google.android.gms:play-services-tflite-java", version = "16.0.1" }
tflite_support = { module = "com.google.android.gms:play-services-tflite-support", version = "16.0.1" }
tflite_metadata = { module = "org.tensorflow:tensorflow-lite-metadata", version = "0.4.0" }
```
#### Crash Log
```
Failed to create the GpuDelegate dynamically.
java.lang.ClassNotFoundException: org.tensorflow.lite.gpu.GpuDelegate
at java.lang.Class.classForName(Native Method)
at java.lang.Class.forName(Class.java:454)
at java.lang.Class.forName(Class.java:379)
at org.tensorflow.lite.support.model.GpuDelegateProxy.maybeNewInstance(GpuDelegateProxy.java:38)
at org.tensorflow.lite.support.model.Model.createModel(Model.java:204)
at com..kcc.domain.usecase.poseestimation.Model2Stack257.<init>(Model2Stack257.kt:170)
at com..kcc.domain.usecase.InferPoseModelTFLiteUseCase.tfModel$lambda$0(InferPoseModelTFLiteUseCase.kt:27)
at com..kcc.domain.usecase.InferPoseModelTFLiteUseCase.$r8$lambda$xjFI4TuW8y9D4mlU8833KNnigbg(Unknown Source:0)
at com..kcc.domain.usecase.InferPoseModelTFLiteUseCase$$ExternalSyntheticLambda0.then(Unknown Source:2)
at com.google.android.gms.tasks.zzc.run(com.google.android.gms:play-services-tasks@@18.0.2:3)
at android.os.Handler.handleCallback(Handler.java:942)
at android.os.Handler.dispatchMessage(Handler.java:99)
at android.os.Looper.loopOnce(Looper.java:226)
at android.os.Looper.loop(Looper.java:313)
at android.app.ActivityThread.main(ActivityThread.java:8757)
at java.lang.reflect.Method.invoke(Native Method)
at com.android.internal.os.RuntimeInit$MethodAndArgsCaller.run(RuntimeInit.java:571)
at com.android.internal.os.ZygoteInit.main(ZygoteInit.java:1067)
Caused by: java.lang.ClassNotFoundException: Didn't find class "org.tensorflow.lite.gpu.GpuDelegate" on path: DexPathList[[zip file "/data/app/~~7PczWMC5IEgE71QUXcKhDQ==/com..mt-cQ_JVkbM4HelJiyQApEttQ==/base.apk"],nativeLibraryDirectories=[/data/app/~~7PczWMC5IEgE71QUXcKhDQ==/com..mt-cQ_JVkbM4HelJiyQApEttQ==/lib/arm64, /data/app/~~7PczWMC5IEgE71QUXcKhDQ==/com..mt-cQ_JVkbM4HelJiyQApEttQ==/base.apk!/lib/arm64-v8a, /system/lib64, /system/s
at dalvik.system.BaseDexClassLoader.findClass(BaseDexClassLoader.java:259)
at java.lang.ClassLoader.loadClass(ClassLoader.java:379)
at java.lang.ClassLoader.loadClass(ClassLoader.java:312)
at java.lang.Class.classForName(Native Method)
at java.lang.Class.forName(Class.java:454)
at java.lang.Class.forName(Class.java:379)
at org.tensorflow.lite.support.model.GpuDelegateProxy.maybeNewInstance(GpuDelegateProxy.java:38)
at org.tensorflow.lite.support.model.Model.createModel(Model.java:204)
at com..kcc.domain.usecase.poseestimation.Model2Stack257.<init>(Model2Stack257.kt:170)
at com..kcc.domain.usecase.InferPoseModelTFLiteUseCase.tfModel$lambda$0(InferPoseModelTFLiteUseCase.kt:27)
at com..kcc.domain.usecase.InferPoseModelTFLiteUseCase.$r8$lambda$xjFI4TuW8y9D4mlU8833KNnigbg(Unknown Source:0)
at com..kcc.domain.usecase.InferPoseModelTFLiteUseCase$$ExternalSyntheticLambda0.then(Unknown Source:2)
at com.google.android.gms.tasks.zzc.run(com.google.android.gms:play-services-tasks@@18.0.2:3)
at android.os.Handler.handleCallback(Handler.java:942)
at android.os.Handler.dispatchMessage(Handler.java:99)
at android.os.Looper.loopOnce(Looper.java:226)
at android.os.Looper.loop(Looper.java:313)
at android.app.ActivityThread.main(ActivityThread.java:8757)
```
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"I've tried adding SET(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS} /std:c++17\")\r\n\r\nto cmake_install.cmake under tflite-opencl but it still generates the same error.",
"Update: \r\nApparently `operation_selector.cc` does not include `#include <any>` as it should, causing the aforementioned ` 'any_cast': is not a member of 'std' ` error. After including and building it, I get a .lib file under `tflite-opencl/Debug` (no .dll's found). \r\n\r\nHowever, when I include it and try to use it on a project, I get many unresolved external symbol errors. Am I doing something wrong? Were there supposed to be a .dll file?",
"Hi @TurgutBababalim, this is effectively a duplicate of https://github.com/tensorflow/tensorflow/issues/61269",
"Yeah, I mentioned issue #61269 several months ago, and now more people bump into it...\r\nTwo possible solutions:\r\n(1) Change `std::any_cast` to `absl::any_cast`\r\n(2) Add `#include <any>` in operation_selector.cc and conv_pointwise.cc\r\nYou could propose a pull request with one of the above solutions. Though I believe such tiny matters can be solved far quicker by a Tensorflow member, than by an external person proposing a pull request.\r\n\r\nYour other errors (as detailed in https://stackoverflow.com/q/76998448/7268445) are not related to the `any_cast` problem.\r\n"
] | 2023-08-28T08:14:09 | 2023-11-07T11:24:01 | null | NONE | null | null | null | ### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.14
### Custom code
Yes
### OS platform and distribution
Widnows 10
### Mobile device
Asus pc
### Python version
3.11
### Bazel version
6.1.4
### GCC/compiler version
gcc version 6.3.0 (MinGW.org GCC-6.3.0-1)
### CUDA/cuDNN version
-
### GPU model and memory
NVIDIA GeForce GTX 960m
### Current behavior?
Relevant stackoverflow question: https://stackoverflow.com/questions/76990961/tensorflow-c-error-while-building-with-cmake-on-windows-with-gpu-support
I'm trying to get a tensorflow C++ build (or tensorflow lite) for Windows that runs on GPU (WITHOUT using CUDA, it should work on AMD). I decided to opt in for tensorflow lite with the -DTFLITE_ENABLE_GPU=ON flag to enable OpenCL.
### Standalone code to reproduce the issue
```shell
The steps I followed:
- Clone tensorflow github repo to tensorflow_src and checout to r2.14
- Create a folder called tflite-opencl next to the cloned repo
- Go to tflite-opencl and run cmake C:/Users/Asus/Desktop/tensorflow/tensorflow/lite -DTFLITE_ENABLE_GPU=ON
- Run cmake --build . -j
```
### Relevant log output
```shell
When I try to run `cmake --build . -j` I get the following error:
C:\Users\Asus\Desktop\tensorflow\tensorflow\lite\delegates\gpu\common\selectors\operation_selector.cc(313,20): error
C2039: 'any_cast': is not a member of 'std' [C:\Users\Asus\Desktop\tflite-opencl\tensorflow-lite.vcxproj]
...
C:\Users\Asus\Desktop\tensorflow\tensorflow\lite\delegates\gpu\common\selectors\operation_selector.cc(313,1): error
C2065: 'any_cast': undeclared identifier [C:\Users\Asus\Desktop\tflite-opencl\tensorflow-lite.vcxproj]
C:\Users\Asus\Desktop\tensorflow\tensorflow\lite\delegates\gpu\common\selectors\operation_selector.cc(313,29): error
C2275: 'tflite::gpu::ElementwiseAttributesBase<tflite::gpu::DataType::BOOL,T>': expected an expression instead of a
type [C:\Users\Asus\Desktop\tflite-opencl\tensorflow-lite.vcxproj]
with
[
T=bool
]
C:\Users\Asus\Desktop\tensorflow\tensorflow\lite\delegates\gpu\common\selectors\operation_selector.cc(317,1): error
C3536: 'attr': cannot be used before it is initialized [C:\Users\Asus\Desktop\tflite-opencl\tensorflow-lite.vcxproj]
C:\Users\Asus\Desktop\tensorflow\tensorflow\lite\delegates\gpu\common\selectors\operation_selector.cc(317,17): error
C2672: 'CreateElementwiseWithBroadcast': no matching overloaded function found [C:\Users\Asus\Desktop\tflite-opencl
\tensorflow-lite.vcxproj]
...
C:\Users\Asus\Desktop\tensorflow\tensorflow\lite\delegates\gpu\common\selectors\operation_selector.cc(321,17): error
C2672: 'CreateElementwise': no matching overloaded function found [C:\Users\Asus\Desktop\tflite-opencl\tensorflow-l
ite.vcxproj]
...
C:\Users\Asus\Desktop\tensorflow\tensorflow\lite\delegates\gpu\common\selectors\operation_selector.cc(313,29): error
C2275: 'tflite::gpu::ElementwiseAttributesBase<tflite::gpu::DataType::INT32,T>': expected an expression instead of
a type [C:\Users\Asus\Desktop\tflite-opencl\tensorflow-lite.vcxproj]
with
[
T=int32_t
]
C:\Users\Asus\Desktop\tensorflow\tensorflow\lite\delegates\gpu\common\selectors\operation_selector.cc(313,29): error
C2275: 'tflite::gpu::ElementwiseAttributesBase<tflite::gpu::DataType::FLOAT32,float>': expected an expression inste
ad of a type [C:\Users\Asus\Desktop\tflite-opencl\tensorflow-lite.vcxproj]
...
C:\Users\Asus\Desktop\tensorflow\tensorflow\lite\delegates\gpu\common\tasks\special\conv_pointwise.cc(129,12): error
C2039: 'any_cast': is not a member of 'std' [C:\Users\Asus\Desktop\tflite-opencl\tensorflow-lite.vcxproj]
C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Tools\MSVC\14.36.32532\include\variant(30,1): message : s
ee declaration of 'std' [C:\Users\Asus\Desktop\tflite-opencl\tensorflow-lite.vcxproj]
C:\Users\Asus\Desktop\tensorflow\tensorflow\lite\delegates\gpu\common\tasks\special\conv_pointwise.cc(129,20): error
C2065: 'any_cast': undeclared identifier [C:\Users\Asus\Desktop\tflite-opencl\tensorflow-lite.vcxproj]
C:\Users\Asus\Desktop\tensorflow\tensorflow\lite\delegates\gpu\common\tasks\special\conv_pointwise.cc(129,21): error
C2275: 'tflite::gpu::ReduceAttributes': expected an expression instead of a type [C:\Users\Asus\Desktop\tflite-open
cl\tensorflow-lite.vcxproj]
C:\Users\Asus\Desktop\tensorflow\tensorflow\lite\delegates\gpu\common\tasks\special\conv_pointwise.cc(130): error C3
536: 'reduce_attr': cannot be used before it is initialized [C:\Users\Asus\Desktop\tflite-opencl\tensorflow-lite.vcx
proj]
```
But despite these errors it kept on compiling and eventually ended on:
```
...
tensorflow_profiler_logger_shim.cc
tflite_with_xnnpack_optional.cc
minimal_logging_default.cc
C:\Users\Asus\Desktop\tensorflow\tensorflow\lite\minimal_logging_default.cc(37,9): warning C4068: unknown pragma 'cl
ang' [C:\Users\Asus\Desktop\tflite-opencl\tensorflow-lite.vcxproj]
C:\Users\Asus\Desktop\tensorflow\tensorflow\lite\minimal_logging_default.cc(38,9): warning C4068: unknown pragma 'cl
ang' [C:\Users\Asus\Desktop\tflite-opencl\tensorflow-lite.vcxproj]
C:\Users\Asus\Desktop\tensorflow\tensorflow\lite\minimal_logging_default.cc(40,9): warning C4068: unknown pragma 'cl
ang' [C:\Users\Asus\Desktop\tflite-opencl\tensorflow-lite.vcxproj]
platform_profiler.cc
root_profiler.cc
profiler.cc
sparsity_format_converter.cc
schema_utils.cc
Generating Code...
C:\Users\Asus\Desktop\tflite-opencl>
```
I think I'm getting the `any_cast is not a member of std` because my C++ standard version is below 14. But I've been coding in windows for a while and I'm pretty sure I'm above 17 as I use many modern features. I've updated g++ from Visual Studio Installer but I'm not sure how to properly update my C++ version on Windows.
I'm sure this build has failed, but regardless I searched for the dll.
This failed build gave me a Visual Studio solution inside tflite-opencl. I need to import tflite into a huge project so I need either the dll files or the lib files. I tried looking under `tflite-opencl/Release`, `tflite-opencl/Debug` and `tflite-opencl/x64` but found nothing. I'm also adviced to look under `\bazel-bin\tensorflow\lite\kernels` on [my previous question][1], which I can find under `tensorflow_src` and I can't find any dll's in it.
How can I fix that error and change my C++ standard on Windows? How can I get these dll or lib files to use under my project? If I should build `INSTALL` how am I supposed to do it? I developed mainly on Linux environments so I don't know how to use Visual Studio propperly.
```
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"@powermew,\r\nCould you please take a look at this official code link which helps to limit the gpu on C++.\r\nhttps://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/protobuf/config.proto\r\n\r\nThe _allocated part of set has to do with protobuf's memory management. If you set the options, it will expect an object that was dynamically allocated and it will take ownership over it (and delete it when appropriate). Your code won't work, because you're passing an address to a local variable that will get destroyed when you exit the code block.\r\n\r\nInstead, if you access the existing instance of GpuOptions via mutable_gpu_options() you get a pointer to the object that you can use to edit directly that instance.\r\n\r\n```\r\nauto options = tensorflow::SessionOptions();\r\noptions.config.mutable_gpu_options()->set_per_process_gpu_memory_fraction(0.2);\r\noptions.config.mutable_gpu_options()->set_allow_growth(true);\r\ntensorflow::Status status = tensorflow::NewSession(options, &session);\r\n```\r\nhttps://github.com/serizba/cppflow/issues/102\r\n\r\nThank you!",
"> @powermew, Could you please take a look at this official code link which helps to limit the gpu on C++. https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/protobuf/config.proto\r\n> \r\n> The _allocated part of set has to do with protobuf's memory management. If you set the options, it will expect an object that was dynamically allocated and it will take ownership over it (and delete it when appropriate). Your code won't work, because you're passing an address to a local variable that will get destroyed when you exit the code block.\r\n> \r\n> Instead, if you access the existing instance of GpuOptions via mutable_gpu_options() you get a pointer to the object that you can use to edit directly that instance.\r\n> \r\n> ```\r\n> auto options = tensorflow::SessionOptions();\r\n> options.config.mutable_gpu_options()->set_per_process_gpu_memory_fraction(0.2);\r\n> options.config.mutable_gpu_options()->set_allow_growth(true);\r\n> tensorflow::Status status = tensorflow::NewSession(options, &session);\r\n> ```\r\n> \r\n> [serizba/cppflow#102](https://github.com/serizba/cppflow/issues/102)\r\n> \r\n> Thank you!\r\n\r\nThank you for reply\r\nI solved this problem with adding code from below comment.\r\nhttps://github.com/Neargye/hello_tf_c_api/issues/21#issuecomment-505055121",
"@powermew,\r\nGlad the issue was resolved. If there are no more open items for this issue, could you please feel free to move this issue to closed status. Thank you!"
] | 2023-08-28T07:20:19 | 2023-08-30T06:52:57 | 2023-08-30T06:52:57 | NONE | null | null | null | ### Issue type
Feature Request
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 2.4
### Custom code
Yes
### OS platform and distribution
Windows 10
### Mobile device
_No response_
### Python version
3.7.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
11.0 / 8.1.0
### GPU model and memory
RTX 3090 / 24GB
### Current behavior?
I loaded the saved model using the already compiled tensorflow-gpu 2.4.0.
When this model was used for prediction, it was confirmed that all available memory of the gpu was used.
I've seen limiting using the growing method in python, but I don't know how to use it in c++. could you please tell me how?
### Standalone code to reproduce the issue
```shell
#include <stdlib.h>
#include <stdio.h>
#include <tensorflow/c/c_api.h>
#include <iostream>
#include <fstream>
#include <sstream>
#include <string>
#include <vector>
void NoOpDeallocator(void* data, size_t a, void* b) {}
int main() {
TF_Graph* Graph = TF_NewGraph();
TF_Status* Status = TF_NewStatus();
TF_SessionOptions* SessionOpts = TF_NewSessionOptions();
TF_Buffer* RunOpts = NULL;
const char* saved_model_dir = "H:\\my_model\\"; // Path of the model
const char* tags = "serve"; // default model serving tag; can change in future
int ntags = 1;
TF_Session* Session = TF_LoadSessionFromSavedModel(SessionOpts, RunOpts, saved_model_dir, &tags, ntags, Graph, NULL, Status);
if (TF_GetCode(Status) == TF_OK)
{
printf("TF_LoadSessionFromSavedModel OK\n");
}
else
{
printf("%s", TF_Message(Status));
}
}
```
### Relevant log output
_No response_ | {
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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/61713/checks?check_run_id=16249250619) 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."
] | 2023-08-27T13:15:58 | 2023-08-30T07:16:49 | 2023-08-27T13:27:49 | NONE | spam | false | {
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"Hi @aegkmq ,\r\n\r\nI have replicated the reported behaviour with Colab. On TF2.12v got `ValueError: Structure of Python function inputs does not match input_signature:` and with tf-nightly got `KeyError: 'y1'`. Attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/89454be89d29e04c290bb02cc244be9a/61712_-extensiontype.ipynb) for reference.\r\n\r\nIt needs to dig more for the root cause. Thanks!\r\n"
] | 2023-08-27T12:47:18 | 2023-10-19T18:45:47 | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
tf 2.13.0, tf 2.12.0
### Custom code
Yes
### OS platform and distribution
Colab, Mac
### Mobile device
_No response_
### Python version
3.11.3
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
When calling a method on ExtensionType, it seems that `self` is flattened, making it incompatible with `tf.function` and `input_signature`. Is this the intended behavior?
### Standalone code to reproduce the issue
Colab: https://colab.research.google.com/drive/19b65SIbsUgV4CKGX0DGgS0IcbXeOsOw9?usp=sharing
```shell
import tensorflow as tf
print("tf.__version__:", tf.__version__)
class A(tf.experimental.ExtensionType):
x1: tf.Tensor
x2: tf.Tensor
_a = A.Spec(tf.TensorSpec((5, 6)), tf.TensorSpec((5, 6)))
class B(tf.experimental.ExtensionType):
y1: tf.Tensor
y2: tf.Tensor
y3: int
_b = B.Spec(tf.TensorSpec((7, 8)), tf.TensorSpec((7, 8)), 9)
class C(tf.experimental.ExtensionType):
a1: A
a2: A
a3: A
a4: A
@tf.function(input_signature=[_b])
def f(self, b: B):
return b
x = tf.zeros((5, 6))
y = tf.zeros((7, 8))
a = A(x, x)
b = B(y, y, 9)
c = C(a, a, a, a)
c.f(b)
```
### Relevant log output
Colab:
```
tf.__version__: 2.12.0
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-1-6a3d1772c2f1> in <cell line: 33>()
31 b = B(y, y, 9)
32 c = C(a, a, a, a)
---> 33 c.f(b)
1 frames
/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/polymorphic_function/function_spec.py in cast_inputs_to_signature(inputs, input_signature)
538 check_types=False) # lists are convert to tuples for `tf.data`.
539 except ValueError:
--> 540 raise ValueError("Structure of Python function inputs does not match "
541 "input_signature:\n"
542 f"{format_error_message(inputs, input_signature)}.")
ValueError: Structure of Python function inputs does not match input_signature:
inputs: (
C(a1=A(x1=<tf.Tensor: shape=(5, 6), dtype=float32, numpy=
array([[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.]], dtype=float32)>, x2=<tf.Tensor: shape=(5, 6), dtype=float32, numpy=
array([[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.]], dtype=float32)>), a2=A(x1=<tf.Tensor: shape=(5, 6), dtype=float32, numpy=
array([[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.]], dtype=float32)>, x2=<tf.Tensor: shape=(5, 6), dtype=float32, numpy=
array([[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.]], dtype=float32)>), a3=A(x1=<tf.Tensor: shape=(5, 6), dtype=float32, numpy=
array([[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.]], dtype=float32)>, x2=<tf.Tensor: shape=(5, 6), dtype=float32, numpy=
array([[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.]], dtype=float32)>), a4=A(x1=<tf.Tensor: shape=(5, 6), dtype=float32, numpy=
array([[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.]], dtype=float32)>, x2=<tf.Tensor: shape=(5, 6), dtype=float32, numpy=
array([[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.]], dtype=float32)>)),
B(y1=<tf.Tensor: shape=(7, 8), dtype=float32, numpy=
array([[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.]], dtype=float32)>, y2=<tf.Tensor: shape=(7, 8), dtype=float32, numpy=
array([[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.]], dtype=float32)>, y3=9))
input_signature: (
B.Spec(y1=TensorSpec(shape=(7, 8), dtype=tf.float32, name=None), y2=TensorSpec(shape=(7, 8), dtype=tf.float32, name=None), y3=9)).
```
Mac:
```shell
tf.__version__: 2.13.0
Traceback (most recent call last):
File "/tmp/a.py", line 31, in <module>
c.f(b)
File "/opt/homebrew/anaconda3/envs/ml/lib/python3.11/site-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/opt/homebrew/anaconda3/envs/ml/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py", line 181, in __call__
raise ValueError(
ValueError: Signature specifies 4 arguments, got: 10.
```
Mac (tf_nightly-2.15.0.dev20230827-cp311-cp311-macosx_12_0_arm64):
```shell
tf.__version__: 2.15.0-dev20230827
Traceback (most recent call last):
File "/tmp/a.py", line 33, in <module>
c.f(b)
File "/opt/homebrew/anaconda3/envs/ml/lib/python3.11/site-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/opt/homebrew/anaconda3/envs/ml/lib/python3.11/site-packages/tensorflow/core/function/polymorphism/function_type.py", line 391, in unpack_inputs
p.type_constraint._to_tensors(bound_parameters.arguments[p.name]) # pylint: disable=protected-access
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
KeyError: 'y1'
```
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"Hi @david20571015 ,\r\n\r\nThanks for the writeup about this feature request.\r\n\r\nWe do not have plans to add explicit support for MLPs at this time for the following reasons:\r\n* The implementation of the example MLP is 10 lines of code in Keras, which you can easily reuse:\r\n```python\r\nclass MLP(tf.keras.Sequential):\r\n def __init__(self, hidden_channels, norm_layer, activation_layer, dropout, **kwargs):\r\n super().__init__(**kwargs)\r\n for units in hidden_channels[:-1]:\r\n self.add(tf.keras.layers.Dense(units))\r\n self.add(norm_layer())\r\n self.add(activation_layer())\r\n self.add(tf.keras.layers.Dropout(dropout))\r\n self.add(tf.keras.layers.Dense(hidden_channels[-1]))\r\n self.add(tf.keras.layers.Dropout(dropout))\r\n```\r\n* Most people will want to customize this. It will be easier to take the code above and modify it than try to create an MLP class that has many extension points\r\n* When we consider adding architectures to Tensorflow and Keras, we focus on state of the art models\r\n\r\nThanks,\r\nFabien\r\n"
] | 2023-08-26T19:26:42 | 2023-08-31T20:32:31 | 2023-08-31T20:32:30 | NONE | null | null | null | ### Issue type
Feature Request
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.13.0
### Custom code
No
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
When building deep learning models like Multi-Layer Perceptrons (MLPs), code reusability and conciseness are crucial factors. Currently, using `tf.keras.Sequential` in TensorFlow allows for convenient creation of sequential models. However, manually adding common operations such as Batch Normalization or Dropout to each layer can lead to code redundancy and an increased burden in terms of coding and maintenance. Therefore, proposing the addition of a feature in TensorFlow to directly create MLPs with Batch Normalization and Dropout is highly beneficial.
Here are several reasons why this feature would be advantageous for TensorFlow users:
1. **Simplified Code**: Users won't need to manually add Batch Normalization and Dropout operations to each layer, resulting in cleaner, more readable, and maintainable code.
2. **Reduced Error Rate**: Manual copy-pasting of code is error-prone, especially as model complexity increases. Automating the integration of Batch Normalization and Dropout operations can reduce issues arising from code errors.
3. **Increased Productivity**: Developers can build and iterate on models more swiftly, focusing on design and parameter tuning rather than rewriting the same code segments for every new model.
4. **Education and Learning**: For newcomers to TensorFlow, this feature can provide a quicker onboarding process, lowering the learning curve and enabling them to grasp and apply deep learning concepts faster.
Certainly, here's the additional information:
I also believe that PyTorch has implemented MLP functionality quite effectively. An example of this can be found in the following URL: [PyTorch MLP](https://pytorch.org/vision/main/generated/torchvision.ops.MLP.html). PyTorch's approach to creating MLPs provides a good reference for how TensorFlow could potentially integrate similar features.
### Standalone code to reproduce the issue
origin
```python
model = tf.keras.Sequential([
tf.keras.layers.Dense(128),
tf.keras.layers.BatchNormalization(),
tf.keras.layers.ReLU(),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(64),
tf.keras.layers.BatchNormalization(),
tf.keras.layers.ReLU(),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(32),
tf.keras.layers.BatchNormalization(),
tf.keras.layers.ReLU(),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10),
])
```
with MLP model
```python
model = tf.keras.MLP(
hidden_channels=[128, 64, 32, 10],
norm_layer=tf.keras.layers.BatchNormalization,
activation_layer=tf.keras.layers.ReLU,
dropout=0.2,
)
```
### Relevant log output
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"Are you satisfied with the resolution of your issue?\n<a href=\"https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/61710\">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/61710\">No</a>\n"
] | 2023-08-26T19:24:11 | 2023-08-26T19:25:26 | 2023-08-26T19:25:24 | NONE | null | null | null | ### Issue type
Feature Request
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.13.0
### Custom code
No
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
When building deep learning models like Multi-Layer Perceptrons (MLPs), code reusability and conciseness are crucial factors. Currently, using `tf.keras.Sequential` in TensorFlow allows for convenient creation of sequential models. However, manually adding common operations such as Batch Normalization or Dropout to each layer can lead to code redundancy and an increased burden in terms of coding and maintenance. Therefore, proposing the addition of a feature in TensorFlow to directly create MLPs with Batch Normalization and Dropout is highly beneficial.
Here are several reasons why this feature would be advantageous for TensorFlow users:
1. **Simplified Code**: Users won't need to manually add Batch Normalization and Dropout operations to each layer, resulting in cleaner, more readable, and maintainable code.
2. **Reduced Error Rate**: Manual copy-pasting of code is error-prone, especially as model complexity increases. Automating the integration of Batch Normalization and Dropout operations can reduce issues arising from code errors.
3. **Increased Productivity**: Developers can build and iterate on models more swiftly, focusing on design and parameter tuning rather than rewriting the same code segments for every new model.
4. **Education and Learning**: For newcomers to TensorFlow, this feature can provide a quicker onboarding process, lowering the learning curve and enabling them to grasp and apply deep learning concepts faster.
Certainly, here's the additional information:
I also believe that PyTorch has implemented MLP functionality quite effectively. An example of this can be found in the following URL: [PyTorch MLP](https://pytorch.org/vision/main/generated/torchvision.ops.MLP.html). PyTorch's approach to creating MLPs provides a good reference for how TensorFlow could potentially integrate similar features.
### Standalone code to reproduce the issue
```shell
origin
model = tf.keras.Sequential([
tf.keras.layers.Dense(128),
tf.keras.layers.BatchNormalization(),
tf.keras.layers.ReLU(),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(64),
tf.keras.layers.BatchNormalization(),
tf.keras.layers.ReLU(),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(32),
tf.keras.layers.BatchNormalization(),
tf.keras.layers.ReLU(),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10),
])
with MLP model
```python
model = tf.keras.MLP(
hidden_channels=[128, 64, 32, 10],
norm_layer=tf.keras.layers.BatchNormalization,
activation_layer=tf.keras.layers.ReLU,
dropout=0.2,
)
```
```
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"Hi @Pavelikgr, I'm having trouble understanding your project structure/compiling... specifically \"ui_mainwindow.h\" is included but the file included is mainwindow.ui. It also seems you have some dependencies. Can you please list your dependencies and the commands you used to compile? Are you using CMake or Bazel? If so, please include relevant files as well (CMakeLists.txt / BUILD).",
"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/61709\">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/61709\">No</a>\n"
] | 2023-08-26T15:17:24 | 2023-09-12T01:46:48 | 2023-09-12T01:46:46 | NONE | null | null | null | ### 1. System information
- Platform and Linux distribution kubuntu 22.04:
- TensorFlow is built from C++ source code:
- Tensorflow 2.11:
### 2. Code
- Link to models that I trained and tried but they don't work in C++ - https://github.com/asuemg1/models_hub/tree/main/Tensorflow%20Lite/Object%20Detection/my_ssd_mobnet/Optimized%20Models
- Link to the model that works in C++ - https://github.com/ankdesh/tflite/blob/master/Android-TensorFlow-Lite-Example/app/src/main/assets/mobilenet_quant_v1_224.tflite
- Link to C++ code (mainwindow.cpp file):
https://drive.google.com/file/d/1u87yK-1qqKeHBjUKq-Lkxi0LMQKkdrPg/view?usp=sharing
### 3. Crash after conversion
- The model does not work in C++.
Please tell me how you can run the Tensorflow Lite model (tflite format) for object detection or image classification in C ++.
My steps:
- Trained the model for object detection using Tensorflow 2 API object detection.
- After training, I converted the model to the savedmodel format, and then to tflite.
- Next, I needed to embed this model into a C++ project. In order to use it in the future on low-power devices such as rasberry pi
My actions:
- Compiled the Tensorflow Lite library for C++.
- Found a test case using the mobilenet_quant_v1_224.tflite model. In this test case, the model runs successfully. However, when trying to use my own model, it does not work, although it has been tested and works in Python.
What was found out:
- The mobilenet_quant_v1_224.tflite model was quantized and had no metadata and no internal labelmap.txt file.
- TensorFlow Lite API 2 for C++ does not currently support metadata.
If you have any information on how to get my tflite model to work in C++ please share. | {
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Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.13.0
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Segmentation fault.
### Standalone code to reproduce the issue
[NPE.zip](https://github.com/tensorflow/tensorflow/files/12444694/NPE.zip)
Run the poc.py in the zip.
In constant folding, after `FoldNode`, fold nodes will be updated with the new name at 【1】. But later, the old node name will still be referenced in `MergeConcat` (【2】) with `GetNode`(【3】), leading to an NPE.
```C++
//tensorflow/core/grappler/optimizers/constant_folding.cc
Status ConstantFolding::FoldNode(NodeDef* node, GraphDef* output_graph,
bool* result_too_large) {
...
else if (port < static_cast<int>(const_nodes.size()) &&
!const_nodes[port].name().empty()) {
//【1】
node_map_->UpdateInput(output->name(), NodeName(output->input(i)),
const_nodes[port].name());
*output->mutable_input(i) = const_nodes[port].name();
}
bool ConstantFolding::MergeConcat(bool use_shape_info,
GraphProperties* properties,
GraphDef* optimized_graph, NodeDef* node) {
……
for (int i = 0; i < num_regular_inputs - 1; ++i) {
//【2】
const NodeDef* input_node = node_map_->GetNode(node->input(i));
if (!IsReallyConstant(*input_node)) {……}
}
//tensorflow/core/grappler/utils.h
NodeDefT* GetNode(const string& name) const {
const string node_name = NodeName(name);
auto it = nodes_.find(node_name);
if (it == nodes_.end()) {
VLOG(1) << "Node could not be found: " << name;
return nullptr; //【3】
}
return it->second;
}
```
### Relevant log output
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"```bash\r\n....\r\n./bazel-bin/tensorflow/tools/pip_package/build_pip_package: line 255: patchelf: command not found\r\n...\r\n```\r\nyou need patchelf as shown in the message\r\n\r\ntry\r\n```bash\r\npip install patchelf\r\n```\r\nbefore running `build_pip_package`",
"Thanks. I got it working",
"> ```shell\r\n> ....\r\n> ./bazel-bin/tensorflow/tools/pip_package/build_pip_package: line 255: patchelf: command not found\r\n> ...\r\n> ```\r\n> \r\n> you need patchelf as shown in the message\r\n> \r\n> try\r\n> \r\n> ```shell\r\n> pip install patchelf\r\n> ```\r\n> \r\n> before running `build_pip_package`\r\n\r\nI built tensorflow from source but still get this notification. Can u help:\r\nThis TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. \r\nTo enable the following instructions: SSE3 SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.",
"> > ```shell\r\n> > ....\r\n> > ./bazel-bin/tensorflow/tools/pip_package/build_pip_package: line 255: patchelf: command not found\r\n> > ...\r\n> > ```\r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > you need patchelf as shown in the message\r\n> > try\r\n> > ```shell\r\n> > pip install patchelf\r\n> > ```\r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > \r\n> > before running `build_pip_package`\r\n> \r\n> I built tensorflow from source but still get this notification. Can u help: 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.\r\n\r\nas suggested by the message: \"rebuild TensorFlow with the appropriate compiler flags\"\r\neither\r\n1. add `-march=native` or any other compiler flags for your CPUs\r\n - when `./configure` asks you what to add, or \r\n - edit `build:opt --copt=\"...\" entry in `.tf_configure.bazelrc` \r\n2. `./configure --config=mkl` to enable using Intel MKL DNN",
"> > > ```shell\r\n> > > ....\r\n> > > ./bazel-bin/tensorflow/tools/pip_package/build_pip_package: line 255: patchelf: command not found\r\n> > > ...\r\n> > > ```\r\n> > > \r\n> > > \r\n> > > \r\n> > > \r\n> > > \r\n> > > \r\n> > > \r\n> > > \r\n> > > \r\n> > > \r\n> > > \r\n> > > you need patchelf as shown in the message\r\n> > > try\r\n> > > ```shell\r\n> > > pip install patchelf\r\n> > > ```\r\n> > > \r\n> > > \r\n> > > \r\n> > > \r\n> > > \r\n> > > \r\n> > > \r\n> > > \r\n> > > \r\n> > > \r\n> > > \r\n> > > before running `build_pip_package`\r\n> > \r\n> > \r\n> > I built tensorflow from source but still get this notification. Can u help: 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.\r\n> \r\n> as suggested by the message: \"rebuild TensorFlow with the appropriate compiler flags\" either\r\n> \r\n> 1. add `-march=native` or any other compiler flags for your CPUs\r\n> \r\n> * when `./configure` asks you what to add, or\r\n> * edit `build:opt --copt=\"...\" entry in `.tf_configure.bazelrc`\r\n> 2. `./configure --config=mkl` to enable using Intel MKL DNN\r\n\r\nexternal/eigen_archive/Eigen/Core:71:10: fatal error: 'omp.h' file not found\r\n#include <omp.h>\r\n ^~~~~~~\r\n1 error generated.\r\nTarget //tensorflow/tools/pip_package:build_pip_package failed to build\r\nUse --verbose_failures to see the command lines of failed build steps.\r\nadded --config=mkl but i got this error. I search somewhere told that i need -fopenmp=libomp. Where to add it\r\n",
"@thinhdinh25 Could you please have a look at this build from [source](https://www.tensorflow.org/install/source#build_the_package) and check the tested build configurations? FYKI, the latest stable TF version is 2.13 so please try to use that.\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.",
"@sushreebarsa , Can't we build with tf-nightly? Is the issue is due to nightly version build ? What configuration of nightly build might be causing the issue ?",
"@JyotiPDLr It is always recommended to use the stable version as the nightly builds would be broken sometimes. \r\nThe tested build configuration is also stable for the latest stable release TF v2.13 . Thank you!",
"@sushreebarsa , Thanks for reply. That means users don't have option to build nightly wheels ? If so why there is option for it in first place? Can it be documented like nightly builds are not recommended as it may broken sometimes?",
"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/61707\">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/61707\">No</a>\n"
] | 2023-08-26T04:30:23 | 2023-10-01T01:49:43 | 2023-10-01T01:49:41 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
newest
### Custom code
Yes
### OS platform and distribution
Debian 12
### Mobile device
_No response_
### Python version
Python 3.9
### Bazel version
newest
### GCC/compiler version
Clang 16
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I have searched everywhere but cannot find anything about this error
### Standalone code to reproduce the issue
```shell
./bazel-bin/tensorflow/tools/pip_package/build_pip_package /home/drowsiness/
TensorFlow Version: 2.15.0
TensorFlow Major Version: 2
TMPDIR: /tmp/tmp.BINOHRyJHF
Fri Aug 25 09:26:15 PM PDT 2023 : === Preparing sources in dir: /tmp/tmp.BINOHRyJHF
Symlink already exists: bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/tensorflow/libtensorflow_cc.so.2
~/tensorflow ~/tensorflow
~/tensorflow
~/tensorflow ~/tensorflow
~/tensorflow
~/tensorflow/bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow ~/tensorflow
~/tensorflow
./bazel-bin/tensorflow/tools/pip_package/build_pip_package: line 255: patchelf: command not found
```
### Relevant log output
_No response_ | {
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"CC @reedwm."
] | 2023-08-25T22:46:59 | 2023-08-28T07:48:21 | 2023-08-28T07:48:20 | CONTRIBUTOR | null | false | {
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"It looks like you're facing a build/installation issue related to TensorFlow. The problem you're describing is about the size of the statically linked binary when compiling TensorFlow Lite for Windows.\r\n\r\nThe issue seems to be regarding the size of the compiled `tensorflowlite.dll` binary, which is significantly larger on Windows compared to other platforms. This is causing a 5x increase in size, which is unexpected and might be impacting the overall application size.\r\n\r\nHere are a few steps you could consider to investigate and potentially address the issue:\r\n\r\n1. **Debug Symbols**: Check if the compiled `tensorflowlite.dll` binary includes debug symbols. Debug symbols can significantly increase the size of the binary. Ensure that you're building with optimizations (`-c opt`) and without debug symbols if size is a concern.\r\n\r\n2. **Linker Flags**: Review the linker flags and options used during the build process. Sometimes, specific linker flags can lead to larger binary sizes. Ensure that you're using appropriate linker flags for size optimization.\r\n\r\n3. **Compiler Flags**: Review the compiler flags used during the build process. Certain compiler flags might result in larger code size. Make sure you're using flags that encourage size optimization.\r\n\r\n4. **Unused Code Elimination**: Ensure that unused code elimination is enabled during the compilation process. Unused code can contribute to the binary size. You can explore options like \"tree shaking\" to remove unused code.\r\n\r\n5. **Platform Differences**: Windows might have some inherent differences in how it compiles and links binaries compared to other platforms. Check if there are specific platform-related factors that might be contributing to the larger binary size.\r\n\r\n6. **Build Configuration**: Make sure you're building the TensorFlow Lite library using the appropriate configuration for Windows. Different build configurations can affect the size and behavior of the generated binary.\r\n\r\n7. **Dependency Management**: Check if there are any unnecessary or redundant dependencies being linked into the binary. Removing unused dependencies can help reduce the size.\r\n\r\n8. **Compiler Version**: Sometimes, using different compiler versions can result in different binary sizes. You might want to try different versions of MSVC and see if it affects the size.\r\n\r\n9. **TensorFlow Versions**: The issue might be specific to the version of TensorFlow you're using. Consider trying different TensorFlow versions to see if the problem persists or if it's resolved in a newer version.\r\n\r\nSince this issue seems to be related to the internal build process of TensorFlow Lite and the specific platform (Windows), it might be beneficial to engage with the TensorFlow community directly. You can post your issue on the TensorFlow GitHub repository, following their guidelines for reporting build/installation issues. This way, the developers and maintainers of TensorFlow will be able to provide you with more targeted assistance and solutions.",
"@nnolas27 it looks like you copied the issue into chatgpt and posted the answer. Why?"
] | 2023-08-25T22:32:05 | 2023-08-29T04:21:09 | null | NONE | null | null | null | Opened on behalf of @sztomi, Issue is current as of this writing. Previous Issue: https://github.com/tensorflow/tensorflow/issues/48118
*Please make sure that this is a build/installation issue. As per our [GitHub Policy](https://github.com/tensorflow/tensorflow/blob/master/ISSUES.md), we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:build_template*
#### System information
* OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Windows 10
* TensorFlow installed from (source or binary): Source
* TensorFlow version: https://github.com/tensorflow/tensorflow/commit/5d6cc7bf97a226c1e6a73ad4fc391c154dd622ac
* Python version: n/a
* Installed using virtualenv? pip? conda?: n/a
* Bazel version (if compiling from source): 3.7.2
* GCC/Compiler version (if compiling from source): MSVC 2019 (cl: 19.28.29334)
* CUDA/cuDNN version: n/a
* GPU model and memory: n/a
#### Describe the problem
#### Provide the exact sequence of commands / steps that you executed before running into the problem
```
bazel build -c opt //tensorflow/lite/tensorflowlite.dll
```
This yields a 15MB statically linked binary when compiled for 32-bit windows (i.e. the 64-bit version will be even larger). Compared to other platforms, this is a 5x increase that is difficult to explain, especially in lieu of tools like bloaty on Windows. Unfortunately I don't know where to start poking this at all - but I do believe that even accounting for platform differences, a 5x size difference is unexpected. The same issue was noted here: https://github.com/tensorflow/tensorflow/issues/33634#issuecomment-620645664 (with a suggestion to use the C API instead, but the C API is a binding that cannot be used independent from the C++ binary).
#### Any other info / logs
Let me know if I can include any more info. | {
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"Thanks for the PR, @justkw! The Windows presubmits are failing with:\r\n```\r\n C:\\Program Files (x86)\\Microsoft Visual Studio\\2019\\Community\\VC\\Tools\\MSVC\\14.29.30133\\bin\\HostX64\\x64\\cl.exe @bazel-out/x64_windows-opt-exec-A82A024B/bin/external/curl/_objs/curl/schannel_verify.obj.params\r\n# Configuration: 952e1b9eadc76b69a54e3392b667aac8bc8f29e7deb6150a9a8d85df99154be6\r\n# Execution platform: //tensorflow/tools/toolchains/win:rbe_windows_ltsc2019\r\nexternal/curl/lib/vtls/schannel_verify.c(40): fatal error C1083: Cannot open include file: 'schannel_int.h': No such file or directory\r\n```\r\n\r\nCan you take a look?",
"@mraunak Can you please help to take a look?",
"Updated the PR, added the missing file."
] | 2023-08-25T22:22:09 | 2023-09-01T15:27:40 | 2023-08-30T01:22:22 | CONTRIBUTOR | null | false | {
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} | Before merging this PR, please double check that it has correctly updated
`core/public/version.h`, `tools/pip_package/setup.py`, and
`tensorflow/tensorflow.bzl`. Also review the execution notes below:
```
Major: 2 -> 2
Minor: 14 -> 14
Patch: 0 -> 0
No lingering old version strings "2.14.0-rc0" found in source directory
"tensorflow/". Good.
WARNING: Below are potentially instances of lingering old version string
"2.14.0rc0" in source directory "tensorflow/" that are not updated by this
script. Please check them manually!
tensorflow/tools/pip_package/setup.py:125:2.14.0rc0
tensorflow/tools/pip_package/setup.py:129:2.14.0rc0
``` | {
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"@sachinprasadhs,\r\nI was able to reproduce the issue on tensorflow v2.13, v2.12 and tf-nightly. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/50f0256d5649e35801e57272f337b38c/untitled1345.ipynb).",
"hey @sachinprasadhs and @tilakrayal i try to run the tf.math.top_k on a 1D array and its return a error with the index_type has a unexpected argument. is this same problem with you guys.\r\n\r\n\r\n\r\nbut in docs they have given this parameter. 🤔 \r\n\r\nthank you\r\n"
] | 2023-08-25T18:38:43 | 2023-09-07T13:21:31 | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.13.0
### Custom code
Yes
### OS platform and distribution
Ubuntu 22.04
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
cuda_11.8.r11.8/compiler.31833905_0 / cuDNN version 8700
### GPU model and memory
NVIDIA GeForce RTX 2080 Ti
### Current behavior?
top_k gradient taping fails when `index_type=tf.int64`, unfortunately switching to `int32` leads to this other issue https://github.com/tensorflow/tensorflow/issues/61692.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
import numpy as np
x = tf.Variable(np.ones((3, 3)))
with tf.GradientTape() as tape:
y = tf.math.top_k(x, k=1, index_type=tf.int64)
dy_dx = tape.gradient(y, x)
```
### Relevant log output
```shell
tensorflow.python.framework.errors_impl.InvalidArgumentError: cannot compute AddV2 as input #1(zero-based) was expected to be a int64 tensor but is a int32 tensor [Op:AddV2] name:
```
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"# Update on Issue with TensorFlow GPU Detection\r\n\r\nAfter my initial post, I took the following steps to further investigate the issue:\r\n\r\n1. ** Replicated the problem with Different cuDNN Version:** Even after pip installing the cuDNN version `8.6.0.163` recommended in TensorFlow's documentation, the problem persisted. TensorFlow still could not detect the GPU.\r\n2. **Module Deactivation Check:** To determine if there was a conflict between the cluster's module system and my conda/pip installations, I deactivated the modules and relied solely on the versions installed via conda and pip.\r\n3. **Custom Environment Variables for Modules:** After confirming the issue persisted without the modules, I then tried setting up the environment using the modules exclusively configuring the paths using those of the module system rather than those provided by the installation instructions:\r\n - For CUDA: `/home/software/spack/opt/spack/linux-centos7-x86_64/gcc-8.2.0/cuda-11.8.0-rqftjjg3pwtogsetgcrrytjcqutxgtaj/bin`\r\n - For cuDNN: `/home/software/spack/opt/spack/linux-centos7-x86_64/gcc-8.2.0/cudnn-8.7.0.84-11.8-qibz3uecpmz5hiosbohqaniedyu6m6r5/lib`\r\n\r\nI adjusted the PATH and LD_LIBRARY_PATH environment variables to prioritize the module system's CUDA and cuDNN installations whenever my specific Conda environment was activated.\r\n\r\nDespite these troubleshooting steps, TensorFlow still does not recognize the GPU on the system. I'm continuing to seek insights or suggestions to resolve this issue.\r\n\r\n",
"Check this [Tensorflow doesn't seem to see my gpu](https://stackoverflow.com/questions/41402409/tensorflow-doesnt-seem-to-see-my-gpu)",
"To my understanding tensorflow-gpu has been deprecated since 2022.\r\n\r\nTried downgrading to TensorFlow 2.10 as suggested for windows users even though those problems don't seem super relevant to this case as I am running on Linux. After downgrading my CUDA and cuDNN to 11.2 and 8.1 respectively to no avail.",
"Hi @masonhargrave ,\r\n\r\nI think you are missing these 2 steps as mentioned in documentation [instructions](https://www.tensorflow.org/install/pip#step-by-step_instructions) for GPU setup. Please refer the attached source and follow the instructions sequentially.\r\n\r\n```\r\nCUDNN_PATH=$(dirname $(python -c \"import nvidia.cudnn;print(nvidia.cudnn.__file__)\"))\r\nexport LD_LIBRARY_PATH=$CUDNN_PATH/lib:$CONDA_PREFIX/lib/:$LD_LIBRARY_PATH\r\n```\r\n\r\nI have followed the same instructions and able to detect GPU.Please refer attached logs.\r\n```\r\n(tf2.13) suryanarayanay@surya-ubuntu20:~$ python3 -c \"import tensorflow as tf; print(tf.__version__)\"\r\n2023-08-31 06:52:35.206843: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9498] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\r\n2023-08-31 06:52:35.206913: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\r\n2023-08-31 06:52:35.211084: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\r\n2023-08-31 06:52:35.524830: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-08-31 06:52:37.148708: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\nWARNING:tensorflow:From /home/suryanarayanay/miniconda3/envs/tf2.13/lib/python3.11/site-packages/tensorflow/python/ops/distributions/distribution.py:259: ReparameterizationType.__init__ (from tensorflow.python.ops.distributions.distribution) is deprecated and will be removed after 2019-01-01.\r\nInstructions for updating:\r\nThe TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\r\nWARNING:tensorflow:From /home/suryanarayanay/miniconda3/envs/tf2.13/lib/python3.11/site-packages/tensorflow/python/ops/distributions/bernoulli.py:165: RegisterKL.__init__ (from tensorflow.python.ops.distributions.kullback_leibler) is deprecated and will be removed after 2019-01-01.\r\nInstructions for updating:\r\nThe TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\r\n2.15.0-dev20230813\r\n\r\n(tf2.13) suryanarayanay@surya-ubuntu20:~$ nvidia-smi\r\nThu Aug 31 06:56:15 2023 \r\n+-----------------------------------------------------------------------------+\r\n| NVIDIA-SMI 525.85.12 Driver Version: 525.85.12 CUDA Version: 12.0 |\r\n|-------------------------------+----------------------+----------------------+\r\n| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\r\n| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\r\n| | | MIG M. |\r\n|===============================+======================+======================|\r\n| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\r\n| N/A 71C P0 31W / 70W | 2MiB / 15360MiB | 0% Default |\r\n| | | N/A |\r\n+-------------------------------+----------------------+----------------------+\r\n| 1 Tesla T4 Off | 00000000:00:05.0 Off | 0 |\r\n| N/A 67C P0 28W / 70W | 2MiB / 15360MiB | 0% Default |\r\n| | | N/A |\r\n+-------------------------------+----------------------+----------------------+\r\n| 2 Tesla T4 Off | 00000000:00:06.0 Off | 0 |\r\n| N/A 69C P0 30W / 70W | 2MiB / 15360MiB | 0% Default |\r\n| | | N/A |\r\n+-------------------------------+----------------------+----------------------+\r\n| 3 Tesla T4 Off | 00000000:00:07.0 Off | 0 |\r\n| N/A 72C P0 31W / 70W | 2MiB / 15360MiB | 7% Default |\r\n| | | N/A |\r\n+-------------------------------+----------------------+----------------------+\r\n \r\n+-----------------------------------------------------------------------------+\r\n| Processes: |\r\n| GPU GI CI PID Type Process name GPU Memory |\r\n| ID ID Usage |\r\n|=============================================================================|\r\n| No running processes found |\r\n+-----------------------------------------------------------------------------+\r\n(tf2.13) suryanarayanay@surya-ubuntu20:~$ \r\n\r\n(tf2.13) suryanarayanay@surya-ubuntu20:~$ python3 -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"\r\n2023-08-31 06:54:14.005166: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9498] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\r\n2023-08-31 06:54:14.005226: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\r\n2023-08-31 06:54:14.005283: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\r\n2023-08-31 06:54:14.014421: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-08-31 06:54:14.824009: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\nWARNING:tensorflow:From /home/suryanarayanay/miniconda3/envs/tf2.13/lib/python3.11/site-packages/tensorflow/python/ops/distributions/distribution.py:259: ReparameterizationType.__init__ (from tensorflow.python.ops.distributions.distribution) is deprecated and will be removed after 2019-01-01.\r\nInstructions for updating:\r\nThe TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\r\nWARNING:tensorflow:From /home/suryanarayanay/miniconda3/envs/tf2.13/lib/python3.11/site-packages/tensorflow/python/ops/distributions/bernoulli.py:165: RegisterKL.__init__ (from tensorflow.python.ops.distributions.kullback_leibler) is deprecated and will be removed after 2019-01-01.\r\nInstructions for updating:\r\nThe TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\r\n[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU'), PhysicalDevice(name='/physical_device:GPU:1', device_type='GPU'), PhysicalDevice(name='/physical_device:GPU:2', device_type='GPU'), PhysicalDevice(name='/physical_device:GPU:3', device_type='GPU')]\r\n```",
"Hi @SuryanarayanaY, thank you for your reply!\r\n\r\nAs you can see in the *Configuring System Path* section of the *Standalone code to reproduce the issue* I do in fact run those commands from the instructions, except in the more automated way suggested below the commands you are referencing. \r\n\r\n```\r\n# Configuring system paths\r\nmkdir -p $CONDA_PREFIX/etc/conda/activate.d\r\necho 'CUDNN_PATH=$(dirname $(python -c \"import nvidia.cudnn;print(nvidia.cudnn.__file__)\"))' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh\r\necho 'export LD_LIBRARY_PATH=$CUDNN_PATH/lib:$CONDA_PREFIX/lib/:$LD_LIBRARY_PATH' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh\r\n```\r\n\r\nI reran my code using the non-automated commands you suggested with no change to the problem. This is expected as the commands appear to be redundant and no different than the commands included in my minimal example.\r\n\r\n```\r\n(tf-test) [mah4021@scu-node018 mah4021]$ python3 -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"\r\n2023-09-02 21:53:49.520842: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n2023-09-02 21:53:49.566393: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-09-02 21:53:50.515064: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2023-09-02 21:53:51.443662: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:268] failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error\r\n2023-09-02 21:53:51.443727: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:168] retrieving CUDA diagnostic information for host: scu-node018.scu\r\n2023-09-02 21:53:51.443767: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:175] hostname: scu-node018.scu\r\n2023-09-02 21:53:51.443861: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:199] libcuda reported version is: 535.104.5\r\n2023-09-02 21:53:51.443921: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:203] kernel reported version is: 535.104.5\r\n2023-09-02 21:53:51.443946: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:309] kernel version seems to match DSO: 535.104.5\r\n[]\r\n```\r\n\r\nAny other thoughts here?",
"Hi @masonhargrave ,\r\n\r\nCould you please confirm the command you used for tensorflow installation?\r\n\r\nPlease note that from Tf2.14V onwards GPU package available as `tensorflow[and-cuda]`. Please try using the command \r\n`pip install tensorflow[and-cuda] `. You can refer the documentation [here](https://www.tensorflow.org/install/pip).",
"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/61700\">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/61700\">No</a>\n"
] | 2023-08-25T18:22:00 | 2023-11-03T01:48:03 | 2023-11-03T01:48:00 | NONE | null | null | null | ### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
v1.12.1-99044-gc6ecfeac886 2.15.0-dev20230825
### Custom code
No
### OS platform and distribution
CentOS Linux release 7.9.2009 (Core)
### Mobile device
_No response_
### Python version
3.9.13
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
11.8.0 / 8.7.0.84
### GPU model and memory
_No response_
### Current behavior?
## Behavior summary 🖥️
I'm trying to set up TensorFlow on a compute node within our scientific computing cluster and am running into a problem. After setting up a new environment and installing TensorFlow, running TensorFlow's `tf.config.list_physical_devices('GPU')` method returns an empty list, indicating no GPU devices are detected. However, `nvidia-smi` shows a Quadro RTX 6000 GPU present on the system. The TensorFlow CPU validation works without issues, but the GPU validation does not.
## Possible Areas of Concern 🚩
1. **Modules vs. Conda/Pip Installations:** I've loaded specific versions of CUDA and cuDNN using the cluster's module system. However, I also used conda and pip to install these within my environment. Could this mixed approach cause any conflicts for TensorFlow? I'm unsure which version TensorFlow might prioritize or if it would create any confusion.
2. **CUDA Version Mismatch?:** When I run `nvidia-smi`, it indicates a CUDA version of 12.2. Yet, I've loaded and installed a CUDA version of 11.8 using both modules and conda. I'm wondering if this difference could lead to any issues. Does TensorFlow need a strict match with the CUDA version?
3. **Different cuDNN Version:** The TensorFlow installation guide mentioned cuDNN version 8.6.0.163, but I installed 8.7.0.84 for what I thought might be better consistency with the modules I loaded. Could this version difference be problematic? Is TensorFlow particular about the cuDNN version it works with?
I'm looking for insights from anyone who might have navigated similar issues or can provide clarity on these points. Also of course if anyone knows how I might go about further troubleshooting/diagnosing this that'd be great. Thanks for the help!
### Standalone code to reproduce the issue
```shell
# Setting up a new conda environment with python 3.9
conda create --name myenv python=3.9
conda activate myenv
# Running NVIDIA's System Management Interface to check GPU
nvidia-smi
# Loading necessary modules for CUDA/cuDNN
module load cuda-11.8.0-gcc-8.2.0-rqftjjg
module load cudnn-8.7.0.84-11.8-gcc-8.2.0-qibz3ue
# Installing CUDA Toolkit and cuDNN using Conda and Pip
conda install -c conda-forge cudatoolkit=11.8.0
pip install nvidia-cudnn-cu11==8.7.0.84
# Configuring system paths
mkdir -p $CONDA_PREFIX/etc/conda/activate.d
echo 'CUDNN_PATH=$(dirname $(python -c "import nvidia.cudnn;print(nvidia.cudnn.__file__)"))' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
echo 'export LD_LIBRARY_PATH=$CUDNN_PATH/lib:$CONDA_PREFIX/lib/:$LD_LIBRARY_PATH' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
# Upgrading pip and installing TensorFlow
pip install --upgrade pip
pip install tensorflow==2.13.*
# Running CPU validation
python3 -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
# Running GPU validation
python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
```
### Relevant log output
```shell
# CPU validation
2023-08-25 14:11:07.056941: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2023-08-25 14:11:07.105836: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-25 14:11:08.620525: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2023-08-25 14:11:10.459520: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:268] failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error
2023-08-25 14:11:10.459591: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:168] retrieving CUDA diagnostic information for host: scu-node018.scu
2023-08-25 14:11:10.459608: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:175] hostname: scu-node018.scu
2023-08-25 14:11:10.459712: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:199] libcuda reported version is: 535.104.5
2023-08-25 14:11:10.459783: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:203] kernel reported version is: 535.104.5
2023-08-25 14:11:10.459811: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:309] kernel version seems to match DSO: 535.104.5
tf.Tensor(-964.9978, shape=(), dtype=float32)
# GPU validation
2023-08-25 14:11:35.537281: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2023-08-25 14:11:35.582391: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-25 14:11:36.286683: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2023-08-25 14:11:36.956393: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:268] failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error
2023-08-25 14:11:36.956458: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:168] retrieving CUDA diagnostic information for host: scu-node018.scu
2023-08-25 14:11:36.956495: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:175] hostname: scu-node018.scu
2023-08-25 14:11:36.956591: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:199] libcuda reported version is: 535.104.5
2023-08-25 14:11:36.956655: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:203] kernel reported version is: 535.104.5
2023-08-25 14:11:36.956683: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:309] kernel version seems to match DSO: 535.104.5
[]
# nvidia-smi Outpu
Fri Aug 25 13:59:33 2023
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.104.05 Driver Version: 535.104.05 CUDA Version: 12.2 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 Quadro RTX 6000 Off | 00000000:AF:00.0 Off | 0 |
| N/A 31C P0 54W / 250W | 0MiB / 23040MiB | 5% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| No running processes found |
+---------------------------------------------------------------------------------------+
```
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"I don't think I'm the right reviewer for this.",
"PR has been updated. PTAL."
] | 2023-08-25T17:41:41 | 2023-09-08T05:14:01 | 2023-09-08T05:14:01 | MEMBER | null | false | {
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"Hi @akrapukhin Can you please resolve conflicts? Thank you!",
"I resolved the conflicts",
"Hi @LukeBoyer Any update on this PR? Please. Thank you!",
"Hi @akrapukhin Can you please resolve conflicts? Thank you!",
"ack, I will take a look. Need to understand whats happening a bit better.",
"Hi @akrapukhin Can you please resolve conflicts? Thank you!",
"This PR is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.",
"resolved",
"Hi @LukeBoyer Can you please review this PR ? Thank you!",
"Hi @LukeBoyer Can you please review this PR ? Thank you!",
"Hi @LukeBoyer Can you please review this PR ? Thank you!",
"Hi @LukeBoyer Can you please review this PR ? Thank you!"
] | 2023-08-25T16:34:05 | 2024-06-07T16:11:49 | null | NONE | null | false | {
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} | This PR fixes #60815
Notes:
1) the problem and the fix are similar to https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2022-081.md where lite/kernels/comparisons.cc is fixed.
2) It's not clear what to do when a multiplier is equal to one. I think that this case should be covered by QuantizeMultiplierGreaterThanOne, because in this case it works as a multiplication by 0.5 followed by a left shift by 1. But to enable it I had to change TFLITE_CHECK_GT to TFLITE_CHECK_GE in tensorflow/lite/kernels/internal/quantization_util.cc, and now this check doesn't match the name of the function (QuantizeMultiplierGreaterThanOne). Ideally there should be no multiplication at all of course, but I don't know how to implement it correctly. Maybe it's just not worth the effort considering that the probability of a multiplier == 1 seems to be very low.
3) I think that the same problem might exist in two other kernels:
lite/kernels/squared_difference.cc
lite/kernels/sub.cc
4) I changed x * (1 << left_shift) to SaturatingRoundingMultiplyByPOTParam(x, left_shift) because I ran into overflow issues when testing real_output_multiplier > 1 with int16 numbers.
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"Hi @zzq96 ,\r\n\r\nThanks for reporting the issue. I have tested the code with **TF2.10v, TF2.12v and tf-nightly** versions and I found the execution times as **29.5 , 81.8 and 137** seconds respectively.Attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/b3de65d4b863cfa61e83ff3089adb25d/61697.ipynb) for reference.\r\n\r\nOur Dev team will have a look into it. Thanks!"
] | 2023-08-25T12:10:11 | 2023-08-31T05:25:24 | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.14.0
### Custom code
Yes
### OS platform and distribution
_No response_
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
i test autograph speed in different version of tf from 2.5.0 to 2.14.0 with different gpu card num(1 card and 4 cards).
i found multi card and newer tf both will slow down autograph speed.
autograph speed in 4 card gpu is 4x slower than 1 card.
and autograph seed in tf 2.14.0 is 10x slower than tf 2.5.0.
Any suggestion?
<img width="649" alt="image" src="https://github.com/tensorflow/tensorflow/assets/39215341/08580964-f401-4ccf-883a-cc71873b1304">
### Standalone code to reproduce the issue
```shell
import os
import sys
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import tensorflow as tf
import time
print("TensorFlow version:", tf.__version__)
physical_devices = tf.config.list_physical_devices('GPU')
for physical_device in physical_devices:
tf.config.experimental.set_memory_growth(physical_device, True)
gpu_names = [device.name.replace("/physical_device:", "") for device in physical_devices]
strategy = tf.distribute.MirroredStrategy(gpu_names)
from tensorflow.keras.layers import Dense, Flatten, Conv2D
from tensorflow.keras import Model
with strategy.scope():
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
x_test = x_test[..., tf.newaxis].astype("float32")
train_ds = tf.data.Dataset.from_tensor_slices(
(x_train, y_train)).shuffle(10000).batch(32)
test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)
class MyModel(Model):
def __init__(self):
super(MyModel, self).__init__()
self.conv1 = Conv2D(32, 3, activation='relu')
self.flatten = Flatten()
self.layer = dict()
for i in range(500):
self.layer[f"d{i}"]= Dense(128, activation='relu')
self.d = Dense(10)
def call(self, x):
x = tf.expand_dims(x, -1)
x = self.conv1(x)
x = self.flatten(x)
for i in range(500):
x = self.layer[f"d{i}"](x)
return self.d(x)
model = MyModel()
"""Choose an optimizer and loss function for training:"""
loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
optimizer = tf.keras.optimizers.Adam()
"""Select metrics to measure the loss and the accuracy of the model. These metrics accumulate the values over epochs and then print the overall result."""
train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')
test_loss = tf.keras.metrics.Mean(name='test_loss')
test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')
"""Use `tf.GradientTape` to train the model:"""
def train_step(images, labels):
print(images)
with tf.GradientTape() as tape:
predictions = model(images, training=True)
loss = tf.reduce_mean(predictions)
loss = tf.distribute.get_replica_context().all_reduce(tf.distribute.ReduceOp.SUM, loss)
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
@tf.function
def train(images, labels):
print(images)
strategy.run(train_step, args=(images, labels,))
"""Test the model:"""
for epoch in range(1):
time_s = time.time()
time_e = time.time()
for images, labels in train_ds:
time_s = time.time()
train.get_concrete_function(images, labels)
time_e = time.time()
break;
times = time_e - time_s
print(
f'tf version:{tf.__version__}; times: {times}; '
)
```
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] | null | [] | 2023-08-25T12:00:28 | 2023-09-01T18:48:34 | null | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.13.0
### Custom code
Yes
### OS platform and distribution
Debian Bullseye 11.7
### Mobile device
_No response_
### Python version
_No response_
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
Tensorflow 2.13.0 binary causes some conflict with libOSMesa which uses LLVM-11. Tensorflow 2.7.1 didn't have this problem.
libosmesa6 (20.3.5-1) is installed through apt-get
https://packages.debian.org/bullseye/libosmesa6
### Standalone code to reproduce the issue
```shell
#include <stdio.h>
#include <tensorflow/c/c_api.h>
#include <dlfcn.h>
int main() {
printf("Hello from TensorFlow C library version %s\n", TF_Version());
dlopen("libOSMesa.so.6", RTLD_NOW);
return 0;
}
```
### Relevant log output
```shell
gcc hello_tf.c -ltensorflow -ldl -o hello_tf
./hello_tf
2023-08-25 13:50:27.935004: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
Hello from TensorFlow C library version 2.13.0
: CommandLine Error: Option 'debug-counter' registered more than once!
LLVM ERROR: inconsistency in registered CommandLine options
Aborted
```
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"Hi @SantiagoMoreno-UdeA \r\n\r\nI was able to reproduce this issue in TF Nightly as well. Please find the gist [here](https://colab.research.google.com/gist/pjpratik/446755d8eff42ea5815ed19fa9257418/61695.ipynb).\r\n\r\nA similar issue is being tracked in #59716 \r\n\r\nDoes dynamic range quantization works for your case?\r\n\r\nThanks.",
"Hi @pjpratik! \r\n\r\nThanks for answering \r\n\r\nI need all model in Int8 'cause I'm attempting to run whisper inference in a NPU and this only support int8 data type. \r\nSo Dynamic quantization is not an option for me :/. \r\n\r\nLooking in advance for your answer. \r\n\r\nCheers! ",
"@SantiagoMoreno-UdeA Thanks for the information.\r\n\r\n@pkgoogle Could you please look into this issue?\r\n\r\nThanks.",
"I was able to reproduce from @pjpratik's gist.\r\n\r\n@abattery can you please take a look",
"Hello @abattery have you had time to take a look on this? ",
"@SantiagoMoreno-UdeA I guess MUL op used in this model requires 16bit activation in order to preserve its accuracy. I am still not sure what is going on with TFLiteconverter \r\nHere is the same issue I raised so long time back and no one addresses it\r\n https://github.com/tensorflow/tensorflow/issues/58451",
"When I have analyzed, observed seg fault here ...\r\n#0 0x00007f623573d7d3 in mlir::quant::QuantizedType::getExpressedType() const () from /usr/local/lib/python3.9/dist-packages/tensorflow/python/_pywrap_tensorflow_internal.so\r\n#1 0x00007f623573e1ac in mlir::quant::QuantizedType::castFromExpressedType(mlir::Type) ()\r\n from /usr/local/lib/python3.9/dist-packages/tensorflow/python/_pywrap_tensorflow_internal.so",
"@nyadla-sys It seems that so far quantize Whisper it's very tricky. Thank you for your information I'll take a look. ",
"Related to https://github.com/tensorflow/tensorflow/issues/29829",
"Hi there, I am facing the same issue when trying to convert whisper into int8 for running on TPU, is there any update please? Thank you.",
"Hi @James-Shared-Studios, No the error remains. It seems that it's very low level error. ",
"I found that tflite versions of Whisper generate NaN values when processing the -float(\"inf\") values that are used in one part of the transformer codebase (specifically, the logits processor that kicks in when you call generate with forced tokens). Perhaps those NaNs make the int8 quantization crash too. I made a crude patch here, which has worked for me to stop the NaNs happening: https://github.com/nyadla-sys/whisper.tflite/discussions/15"
] | 2023-08-25T11:32:23 | 2024-03-08T08:51:43 | null | NONE | null | null | null | System information
Linux 20.04
pip Tensorflow==2.12.0
using tranformers WhisperForConditionalgeneration
I'm trying to convert from TF to tflite and quantized to int8 Whisper, using the whisper model from tranformers WhisperForConditionalGeneration. At some point the conversion crash.
Here is the colab for more details:
Colab: https://colab.research.google.com/drive/1oAVoUxRFZLkS1uqqFN8HdgRVk0IWAlsN?usp=sharing
Also I attach the Error Trace from my server running in CPU and also running in GPU (TITAN RTX 24GB).
CPU: [TraceTflite.txt](https://github.com/tensorflow/tensorflow/files/12438668/TraceTflite.txt)
GPU: [TraceTflite_GPU.txt](https://github.com/tensorflow/tensorflow/files/12452007/TraceTflite_GPU.txt)
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"Hi @MilesTheProwler,\r\n\r\nI am sorry to say that 1.x versions no more supported. Could you please test with latest versions without tf.Session and let us know if any problem arises. 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.",
"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/61694\">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/61694\">No</a>\n"
] | 2023-08-25T08:27:34 | 2023-09-13T01:47:27 | 2023-09-13T01:47:25 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
1.13.1
### Custom code
Yes
### OS platform and distribution
Unbuntu 22
### Mobile device
_No response_
### Python version
Python 2.7
### Bazel version
_No response_
### GCC/compiler version
9.4.0
### CUDA/cuDNN version
10
### GPU model and memory
GTX 1060
### Current behavior?
Hi, I got this error when I run Dirt model with my tensorflow-gpu 1.13.1 .
`2023-08-25 15:23:37.711796: F /home/engineer1/dirt/csrc/gl_common.h:46] extensions eglQueryDevicesEXT, eglQueryDeviceAttribEXT and eglGetPlatformDisplayEXT not available`
### Standalone code to reproduce the issue
```shell
import numpy as np
import tensorflow as tf
import dirt
canvas_width, canvas_height = 128, 128
centre_x, centre_y = 32, 64
square_size = 16
def get_non_dirt_pixels():
xs, ys = tf.meshgrid(tf.range(canvas_width), tf.range(canvas_height))
xs = tf.cast(xs, tf.float32) + 0.5
ys = tf.cast(ys, tf.float32) + 0.5
x_in_range = tf.less_equal(tf.abs(xs - centre_x), square_size / 2)
y_in_range = tf.less_equal(tf.abs(ys - centre_y), square_size / 2)
return tf.cast(tf.logical_and(x_in_range, y_in_range), tf.float32)
def get_dirt_pixels():
# Build square in screen space
square_vertices = tf.constant([[0, 0], [0, 1], [1, 1], [1, 0]], dtype=tf.float32) * square_size - square_size / 2.
square_vertices += [centre_x, centre_y]
# Transform to homogeneous coordinates in clip space
square_vertices = square_vertices * 2. / [canvas_width, canvas_height] - 1.
square_vertices = tf.concat([square_vertices, tf.zeros([4, 1]), tf.ones([4, 1])], axis=1)
return dirt.rasterise(
vertices=square_vertices,
faces=[[0, 1, 2], [0, 2, 3]],
vertex_colors=tf.ones([4, 1]),
background=tf.zeros([canvas_height, canvas_width, 1]),
height=canvas_height, width=canvas_width, channels=1
)[:, :, 0]
def main():
if '.' in tf.__version__ and int(tf.__version__.split('.')[0]) < 2:
session = tf.Session()
with session.as_default():
non_dirt_pixels = get_non_dirt_pixels().eval()
dirt_pixels = get_dirt_pixels().eval()
else:
non_dirt_pixels = get_non_dirt_pixels().numpy()
dirt_pixels = get_dirt_pixels().numpy()
if np.all(non_dirt_pixels == dirt_pixels):
print('successful: all pixels agree')
else:
print('failed: {} pixels disagree'.format(np.sum(non_dirt_pixels != dirt_pixels)))
if __name__ == '__main__':
main()
```
### Relevant log output
```shell
2023-08-25 15:23:37.181664: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2023-08-25 15:23:37.186756: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2496000000 Hz
2023-08-25 15:23:37.189184: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x24d6bf0 executing computations on platform Host. Devices:2023-08-25 15:23:37.189205: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): <undefined>, <undefined>
2023-08-25 15:23:37.478413: E tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2023-08-25 15:23:37.478518: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x233ae60 executing computations on platform CUDA. Devices:2023-08-25 15:23:37.478540: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): NVIDIA GeForce GTX 1060 6GB, Compute Capability 6.1
2023-08-25 15:23:37.478666: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties:
name: NVIDIA GeForce GTX 1060 6GB major: 6 minor: 1 memoryClockRate(GHz): 1.7085
pciBusID: 0000:01:00.0
totalMemory: 6.00GiB freeMemory: 5.09GiB
2023-08-25 15:23:37.478681: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2023-08-25 15:23:37.478801: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2023-08-25 15:23:37.478813: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0
2023-08-25 15:23:37.478817: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N
2023-08-25 15:23:37.478968: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1194] Could not identify NUMA node of platform GPU id 0, defaulting to 0. Your kernel may not have been built with NUMA support.
2023-08-25 15:23:37.479033: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4913 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0, compute capability: 6.1)
2023-08-25 15:23:37.711796: F /home/engineer1/dirt/csrc/gl_common.h:46] extensions eglQueryDevicesEXT, eglQueryDeviceAttribEXT and eglGetPlatformDisplayEXT not available
Aborted
```
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"Check out this pull request on <a href=\"https://app.reviewnb.com/tensorflow/tensorflow/pull/61693\"><img align=\"absmiddle\" alt=\"ReviewNB\" height=\"28\" class=\"BotMessageButtonImage\" src=\"https://raw.githubusercontent.com/ReviewNB/support/master/images/button_reviewnb.png\"/></a> \n\n See visual diffs & provide feedback on Jupyter Notebooks. \n\n---\n\n <i>Powered by <a href='https://www.reviewnb.com/?utm_source=gh'>ReviewNB</a></i>",
"Hi @LukeBoyer Can you please review this PR ? Thank you!",
"Hi @LukeBoyer Can you please review this PR ? Thank you!",
"Hi @LukeBoyer Can you please review this PR ? Thank you!",
"Hi @LukeBoyer Can you please review this PR ? Thank you!",
"Hi @LukeBoyer Can you please review this PR ? Thank you!",
"Hi @LukeBoyer Can you please review this PR ? Thank you!"
] | 2023-08-25T07:47:11 | 2024-03-05T17:26:53 | 2024-03-05T17:26:52 | CONTRIBUTOR | null | false | {
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} | The TFLite `SignatureRunner` is supported in C API with commit https://github.com/tensorflow/tensorflow/commit/3a6a6465c45797bd573c118a535405da43ab03fa. | {
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"@nicolaspi Could you please have a look at the colab gist of TF [v2.13](https://colab.research.google.com/gist/sushreebarsa/eec8d03e8c0fd64ca06dd633ffa31057/untitled840.ipynb#scrollTo=GE1XkYdiU6ie) and tf-[nightly](https://colab.research.google.com/gist/sushreebarsa/aec2f99123ca0e0b9b4268a14995cc12/untitled840.ipynb#scrollTo=OvarIK-iTOX2). I couldn't reproduce the error reported here. \r\nThe output is as follows;\r\n```\r\nshould_be_all_zero shape (1024, 3):\r\n[[0.]\r\n [0.]\r\n [0.]]\r\nshould_be_all_zero shape (1023, 3):\r\n[[0.]\r\n [0.]\r\n [0.]]\r\nshould_be_all_zero shape (1024, 2):\r\n[[0.]\r\n [0.]]\r\n```\r\nThank you! ",
"Updating the GPU drivers fixed the issue.\r\nThanks",
"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/61692\">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/61692\">No</a>\n",
"@nicolaspi Glad your issue has been fixed. \r\nThank you!"
] | 2023-08-25T07:37:33 | 2023-08-31T06:48:04 | 2023-08-30T09:28:11 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.15.0-dev20230824
### Custom code
Yes
### OS platform and distribution
Ubuntu 22.04
### Mobile device
_No response_
### Python version
3.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
cuda_11.8.r11.8/compiler.31833905_0 / cuDNN version 8700
### GPU model and memory
NVIDIA GeForce RTX 2080 Ti
### Current behavior?
JIT yields inconsistent results using `tf.math.top_k` when `index_type=tf.int32` (no issue with `index_type=tf.int64`).
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
@tf.function(jit_compile=True)
def tf_func(shape):
x = tf.random.stateless_normal(shape, seed=(1, 2))
x = tf.transpose(x, perm=[1, 0])
topk_max, indices = tf.math.top_k(x, 1, sorted=False, index_type=tf.int32)
reduce_max = tf.reduce_max(x, axis=1, keepdims=True)
return topk_max - reduce_max
def check(shape):
should_be_all_zero = tf_func(shape)
print(f"should_be_all_zero shape {shape}:\n{should_be_all_zero}")
check((1024, 3))
check((1023, 3))
check((1024, 2))
```
### Relevant log output
```shell
should_be_all_zero shape (1024, 3):
[[-0.7787831 ]
[ 0.47324872]
[ 0.30553436]]
should_be_all_zero shape (1023, 3):
[[0.]
[0.]
[0.]]
should_be_all_zero shape (1024, 2):
[[0.]
[0.]]
```
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"I think it is not issue with `tf` but `windows 10`. Please check:\r\n\r\n1. if your system is detecting GPU or not ([How to check GPU in Windows](https://www.microsoft.com/en-us/windows/learning-center/how-to-check-gpu)) if not check this [My RTX 3050 ti is gone and not working](https://answers.microsoft.com/en-us/windows/forum/all/my-rtx-3050-ti-is-gone-and-not-working/3f672229-77c8-4083-b921-03e2e891ca07). \r\n2. Is `tensorflow` or `jupyter notebook` GPU enabled ([how-to-install-tensorflow-gpu-version-with-jupyter-windows-10-in-8-easy-steps](https://vivek-singh.medium.com/how-to-install-tensorflow-gpu-version-with-jupyter-windows-10-in-8-easy-steps-8797547028a4)) (one change after `tf v2.12` `tensorflow-gpu` & `tensorflow `are same)\r\n\r\nEdit:\r\nSimiliar issue also reported with linux in #61700, #61673 , might be the case `tf` broken for gpu or check [Tensorflow doesn't seem to see my gpu](https://stackoverflow.com/questions/41402409/tensorflow-doesnt-seem-to-see-my-gpu).\r\n",
"I did the system check and RTX 3500 can be detected. I found the specifications of the RTX Ada Generation card and the CUDA version seems to be 8.9 (perhaps this is the cuDNN version)? The link is [https://www.techpowerup.com/gpu-specs/rtx-3500-mobile-ada-generation.c4098](url)\r\nSo I tried to install CUDA 8.9 and associated cuDNN. The version of python is also downgraded and the GPU card can be detected with the old tensorflow Session function. But because the version of tensorflow is old and I have to debug my current code to get it working. This doesn't make any sense as this graphic card should be used with the latest library. Does anyone have any idea how should the environment be correctly configured?",
"Hi @YiDuan-useismic ,\r\n\r\nFor native Windows TF2.10v is the last supported version for GPU support.Please refer the attached [source](https://www.tensorflow.org/install/pip#windows-native:~:text=Caution%3A%20TensorFlow%202.10%20was%20the%20last%20TensorFlow%20release%20that%20supported%20GPU%20on%20native%2DWindows.%20Starting%20with%20TensorFlow%202.11%2C%20you%20will%20need%20to%20install%20TensorFlow%20in%20WSL2%2C%20or%20install%20tensorflow%20or%20tensorflow%2Dcpu%20and%2C%20optionally%2C%20try%20the%20TensorFlow%2DDirectML%2DPlugin) here.\r\n\r\nFor TF versions >=2.11 v you need to install WSL2 on windows. For enabling GPU support with WSL2 please refer the instructions [here](https://www.tensorflow.org/install/pip#step-by-step_instructions).\r\n\r\nThanks!",
"> Hi @YiDuan-useismic ,\r\n> \r\n> For native Windows TF2.10v is the last supported version for GPU support.Please refer the attached [source](https://www.tensorflow.org/install/pip#windows-native:~:text=Caution%3A%20TensorFlow%202.10%20was%20the%20last%20TensorFlow%20release%20that%20supported%20GPU%20on%20native%2DWindows.%20Starting%20with%20TensorFlow%202.11%2C%20you%20will%20need%20to%20install%20TensorFlow%20in%20WSL2%2C%20or%20install%20tensorflow%20or%20tensorflow%2Dcpu%20and%2C%20optionally%2C%20try%20the%20TensorFlow%2DDirectML%2DPlugin) here.\r\n> \r\n> For TF versions >=2.11 v you need to install WSL2 on windows. For enabling GPU support with WSL2 please refer the instructions [here](https://www.tensorflow.org/install/pip#step-by-step_instructions).\r\n> \r\n> Thanks!\r\n\r\nThanks @SuryanarayanaY I will follow the instruction to install the Tensorflow with WSL2.",
"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/61691\">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/61691\">No</a>\n"
] | 2023-08-25T06:32:02 | 2023-08-29T09:39:06 | 2023-08-29T09:39:04 | NONE | null | null | null | ### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
tf 2.13.0
### Custom code
Yes
### OS platform and distribution
Windows 10
### Mobile device
_No response_
### Python version
3.11.4
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
v11
### GPU model and memory
RTX 3500 Ada generation mobile 12GB
### Current behavior?
The RTX 3500 card cannot be recognised by tensorflow. I ran tf.config.list_physical_devices('GPU') and tf.test.is_gpu_available(). The results returned "0 GPUs available" and "False".
The microsoft visual studio 2017 is also installed.
### Standalone code to reproduce the issue
```shell
import tensorflow as tf
print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
tf.test.is_gpu_available()
```
### Relevant log output
_No response_ | {
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} | Add missing elem-wize functions to xla operation semantics doc:
- Atan2
- Clz
- Log1n
- Xor
Related issue: https://github.com/openxla/xla/issues/5203 | {
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"@Boban223,\r\n>2023-08-25 11:11:47.521702: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\r\n2023-08-25 11:11:47.546940: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\r\n2023-08-25 11:11:47.547250: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n\r\nThe provided above details contain only the information(I) while installing the tensorflow. It doesn't provide any errors. Also the tensorflow v2.13 is compatible with compiler-Clang 16.0.0, Bazel - 5.3.0, CUDA - 8.6, cuDNN - 11.8.\r\nhttps://www.tensorflow.org/install/source#gpu\r\n\r\nThank you!\r\n\r\n\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.",
"@tilakrayal , I think the question asked by @Boban223 is why the logs stating no drivers detected though nvidia-smi shows driver version 525.125.06 installed.\r\n\r\n@Boban223 , I think you need to visit step no.4 for GPU setup in pip instruction guide [here](https://www.tensorflow.org/install/pip). Not sure may be CUDA toolkit seems missing?",
"@Boban223,\r\nThe tensorflow v2.13 is compatible with compiler-Clang 16.0.0, Bazel - 5.3.0, CUDA - 8.6, cuDNN - 11.8.\r\nhttps://www.tensorflow.org/install/source#gpu\r\n\r\nAlso please follow the steps provided in this official docuement for the GPU setup.\r\nhttps://www.tensorflow.org/install/pip#step-by-step_instructions\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.",
"@Boban223 Did you solve it? I have the same issue.",
"@Boban223, @Crispy13,\r\nI guess you missed following the steps which are mentioned in official documentation https://www.tensorflow.org/install/pip#step-by-step_instructions for setting up the **GPU**. \r\n\r\n\r\n```\r\nCUDNN_PATH=$(dirname $(python -c \"import nvidia.cudnn;print(nvidia.cudnn.__file__)\"))\r\nexport LD_LIBRARY_PATH=$CUDNN_PATH/lib:$CONDA_PREFIX/lib/:$LD_LIBRARY_PATH\r\n```\r\nI tried to follow the documentation instructions and was able to detect the GPU.\r\n\r\n```\r\n(tf2.13) tilak-gpu@tilak-ubuntu20:~$ nvidia-smi\r\nThu Nov 09 04:56:15 2023 \r\n+-----------------------------------------------------------------------------+\r\n| NVIDIA-SMI 525.85.12 Driver Version: 525.85.12 CUDA Version: 12.0 |\r\n|-------------------------------+----------------------+----------------------+\r\n| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\r\n| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\r\n| | | MIG M. |\r\n|===============================+======================+======================|\r\n| 0 Tesla T4 Off | 00000000:00:05.0 Off | 0 |\r\n| N/A 71C P0 31W / 70W | 2MiB / 15360MiB | 0% Default |\r\n| | | N/A |\r\n+-------------------------------+----------------------+----------------------+\r\n| 1 Tesla T4 Off | 00000000:00:06.0 Off | 0 |\r\n| N/A 67C P0 28W / 70W | 2MiB / 15360MiB | 0% Default |\r\n| | | N/A |\r\n+-------------------------------+----------------------+----------------------+\r\n| 2 Tesla T4 Off | 00000000:00:07.0 Off | 0 |\r\n| N/A 69C P0 30W / 70W | 2MiB / 15360MiB | 0% Default |\r\n| | | N/A |\r\n+-------------------------------+----------------------+----------------------+\r\n| 3 Tesla T4 Off | 00000000:00:08.0 Off | 0 |\r\n| N/A 72C P0 31W / 70W | 2MiB / 15360MiB | 7% Default |\r\n| | | N/A |\r\n+-------------------------------+----------------------+----------------------+\r\n \r\n+-----------------------------------------------------------------------------+\r\n| Processes: |\r\n| GPU GI CI PID Type Process name GPU Memory |\r\n| ID ID Usage |\r\n|=============================================================================|\r\n| No running processes found |\r\n+-----------------------------------------------------------------------------+\r\n\r\n\r\n(tf2.13) tilak_gpu@tilak-ubuntu20:~$ python3 -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"\r\n2023-09-04 04:54:14.005166: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9498] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\r\n2023-09-04 04:54:14.005226: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\r\n2023-09-04 04:54:14.005283: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\r\n2023-09-04 04:54:14.014421: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-09-04 04:54:14.824009: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\nThe TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\r\n[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU'), PhysicalDevice(name='/physical_device:GPU:1', device_type='GPU'), PhysicalDevice(name='/physical_device:GPU:2', device_type='GPU'), PhysicalDevice(name='/physical_device:GPU:3', device_type='GPU')]\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.",
"@tilakrayal, where did you find that step? https://www.tensorflow.org/install/pip#step-by-step_instructions does not currently show that. ",
"The GPU setup steps are mentioned in official documentation \r\n\r\nhttps://www.tensorflow.org/install/pip#step-by-step_instructions for setting up the GPU.\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.",
"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/61689\">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/61689\">No</a>\n"
] | 2023-08-25T02:25:49 | 2023-12-13T01:49:56 | 2023-12-13T01:49:53 | NONE | null | null | null | ### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.13.0
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.8.10
### Bazel version
-
### GCC/compiler version
9.4.0
### CUDA/cuDNN version
CUDA 12.0 / cuDNN 8.9.4
### GPU model and memory
GEFORCE RTX 2080 SUPER
### Current behavior?
I don't understand why the error occurs..please help me...
### Standalone code to reproduce the issue
```shell
$ python3
Python 3.8.10 (default, May 26 2023, 14:05:08)
[GCC 9.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorrt
>>> import tensorflow
2023-08-25 11:11:47.521702: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
2023-08-25 11:11:47.546940: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
2023-08-25 11:11:47.547250: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
```
### Relevant log output
```shell
$ nvidia-smi
Fri Aug 25 11:15:19 2023
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 525.125.06 Driver Version: 525.125.06 CUDA Version: 12.0 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA GeForce ... Off | 00000000:01:00.0 On | N/A |
| 0% 46C P8 15W / 250W | 366MiB / 8192MiB | 25% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 924 G /usr/lib/xorg/Xorg 35MiB |
| 0 N/A N/A 1649 G /usr/lib/xorg/Xorg 130MiB |
| 0 N/A N/A 1790 G /usr/bin/gnome-shell 36MiB |
| 0 N/A N/A 2098 G /usr/lib/firefox/firefox 152MiB |
+-----------------------------------------------------------------------------+
$ nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Sun_Jul_28_19:07:16_PDT_2019
Cuda compilation tools, release 10.1, V10.1.243
$ pip show tensorflow
Name: tensorflow
Version: 2.13.0
Summary: TensorFlow is an open source machine learning framework for everyone.
Home-page: https://www.tensorflow.org/
Author: Google Inc.
Author-email: [email protected]
License: Apache 2.0
Location: /home/chemistry/.local/lib/python3.8/site-packages
Requires: libclang, six, absl-py, opt-einsum, typing-extensions, astunparse, tensorboard, packaging, tensorflow-estimator, keras, protobuf, h5py, termcolor, numpy, gast, google-pasta, tensorflow-io-gcs-filesystem, setuptools, grpcio, flatbuffers, wrapt
Required-by:
$ pip show tensorrt
Name: tensorrt
Version: 8.6.1
Summary: A high performance deep learning inference library
Home-page: https://developer.nvidia.com/tensorrt
Author: NVIDIA Corporation
Author-email: None
License: Proprietary
Location: /home/chemistry/.local/lib/python3.8/site-packages
Requires:
Required-by:
$ cat /usr/include/cudnn_version.h | grep CUDNN_MAJOR -A 2
#define CUDNN_MAJOR 8
#define CUDNN_MINOR 9
#define CUDNN_PATCHLEVEL 4
--
#define CUDNN_VERSION (CUDNN_MAJOR * 1000 + CUDNN_MINOR * 100 + CUDNN_PATCHLEVEL)
/* cannot use constexpr here since this is a C-only file */
```
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"@nnagarajan ,\r\n\r\nFor enabling GPU you need to follow additional instructions mentioned in documentation [here](https://www.tensorflow.org/install/pip#linux).\r\n\r\nPlease check and if you still have issue the let us know.Thanks!",
"Thank you for the instructions @SuryanarayanaY , missed to install cudatookit. After installing the toolkit TF, can now recognize the GPU but there are couple of warnings, \r\n\r\n1. TF using CPU optimization\r\n```\r\nThis TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\n```\r\n2. NUMA not available\r\n```\r\nsuccessful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n```\r\n\r\nLooking for suggestions to resolve these warnings as well. \r\n\r\n\r\n\r\n```\r\npython3 -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"\r\n2023-08-28 12:46:34.327130: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n2023-08-28 12:46:34.351325: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\nTo enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\r\n2023-08-28 12:46:34.723738: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\r\n2023-08-28 12:46:35.245761: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-28 12:46:35.260189: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n2023-08-28 12:46:35.260380: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\r\n[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\r\n```",
"Hi @nnagarajan ,\r\n\r\n> ```\r\n> This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\r\n> ```\r\n\r\nThis is not warning. This is just information giving to user that the binaries are optimized for CPU to give better performance.\r\n\r\n> ```\r\n> successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\r\n> ```\r\n\r\nThis is just a warning not an error and should not affect TF. To get rid of this warning you can refer the attached [source](https://github.com/tensorflow/tensorflow/issues/42738#issuecomment-922422874).\r\n\r\nAlternatively you can use the below command to suppress the warnings.\r\n\r\n```\r\nimport os\r\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\r\n```\r\n\r\nThanks!",
"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.",
"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/61688\">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/61688\">No</a>\n",
"\r\nI had a similar problem, but installing cudatoolkit = 11.8 in the environment didn't solve it. \r\n",
">  I had a similar problem, but installing cudatoolkit = 11.8 in the environment didn't solve it.\r\n\r\nmy env: python 3.10 cuda 12.2 cudatoolkit 11.8.0",
"I Am Running On Cpu And getting The Following Issue:\r\n\r\n\r\n2023-12-30 12:06:56.760763: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n\r\nwith lots of other warnings \r\n\r\ni have tried env method but didn't work for me \r\n",
"> I Am Running On Cpu And getting The Following Issue:\r\n> \r\n> 2023-12-30 12:06:56.760763: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n> \r\n> with lots of other warnings\r\n> \r\n> i have tried env method but didn't work for me\r\n\r\nTo turn off this warning you can set the environment variable `export TF_ENABLE_ONEDNN_OPTS=0`.\r\n> 2023-12-30 12:06:56.760763: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.",
"> > I Am Running On Cpu And getting The Following Issue:\r\n> > 2023-12-30 12:06:56.760763: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n> > with lots of other warnings\r\n> > i have tried env method but didn't work for me\r\n> \r\n> To turn off this warning you can set the environment variable `export TF_ENABLE_ONEDNN_OPTS=0`.\r\n> \r\n> > 2023-12-30 12:06:56.760763: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\r\n\r\nHow to set the environment variable?\r\n"
] | 2023-08-24T19:52:10 | 2024-03-04T07:50:47 | 2023-09-14T04:15:51 | NONE | null | null | null | ### Issue type
Others
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
binary
### TensorFlow version
2.13.0
### Custom code
Yes
### OS platform and distribution
Rocky Linux 8.8
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
12
### GPU model and memory
24 GB
### Current behavior?
Unable to load tensorflow in python with CUDA 12.2. Looking for some pointers
### Standalone code to reproduce the issue
```shell
pip install tensorflow
python
>>> import tensorflow as tf
python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
2023-08-24 15:39:47.107770: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2023-08-24 15:39:47.108805: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
2023-08-24 15:39:47.130307: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.
2023-08-24 15:39:47.130612: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-08-24 15:39:47.496076: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2023-08-24 15:39:48.008342: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:995] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2023-08-24 15:39:48.023762: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1960] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would
like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
[]
```
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"Hi @meekus-fischer ,\r\n\r\nApologies for the delay. This issue somehow skipped from my attention. I have tried replicating the issue but its executed without any problem.Please refer to attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/b45a66a7d19a7e01d5b61572fd5eb588/61687.ipynb#scrollTo=XBLXOPF3xdKV). Could you please cross check once and confirm.\r\n\r\nThank you!",
"I just ran your gist on my system, and received the same error as before. \r\n\r\n```\r\nEpoch 1/50\r\n2023-10-03 07:23:36.765732: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] layout failed: INVALID_ARGUMENT: Size of values 0 does not match size of permutation 4 @ fanin shape inCustom_MC_Dropout_CNN/monte_carlo_dropout_22/dropout/SelectV2-2-TransposeNHWCToNCHW-LayoutOptimizer\r\n935/938 [============================>.] - ETA: 0s - Accuracy: 0.9011 - Loss: 0.3918 - Precision: 0.3404 - Recall: 0.0217 - F1: 0.0408\r\n2023-10-03 07:23:47.185562: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] layout failed: INVALID_ARGUMENT: Size of values 0 does not match size of permutation 4 @ fanin shape inCustom_MC_Dropout_CNN/monte_carlo_dropout_22/dropout/SelectV2-2-TransposeNHWCToNCHW-LayoutOptimizer\r\n```\r\n\r\nOne thing I noticed in your gist is that you were running with python 3.10 and I am running with 3.9; however, I am not sure the issue is 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conda-forge\r\nrpds-py 0.9.2 py39h9fdd4d6_0 conda-forge\r\nrsa 4.9 pypi_0 pypi\r\nscikit-learn 1.3.0 py39hc236052_0 conda-forge\r\nscipy 1.11.2 py39h6183b62_0 conda-forge\r\nseaborn 0.12.2 hd8ed1ab_0 conda-forge\r\nseaborn-base 0.12.2 pyhd8ed1ab_0 conda-forge\r\nsend2trash 1.8.2 pyh41d4057_0 conda-forge\r\nsetuptools 68.1.2 pyhd8ed1ab_0 conda-forge\r\nsip 6.6.2 py39h6a678d5_0 \r\nsix 1.16.0 pyh6c4a22f_0 conda-forge\r\nsniffio 1.3.0 pyhd8ed1ab_0 conda-forge\r\nsoupsieve 2.3.2.post1 pyhd8ed1ab_0 conda-forge\r\nsoxr 0.1.3 h0b41bf4_3 conda-forge\r\nsoxr-python 0.3.5 py39h0f8d45d_0 conda-forge\r\nsqlite 3.42.0 h2c6b66d_0 conda-forge\r\nstack_data 0.6.2 pyhd8ed1ab_0 conda-forge\r\nstatsmodels 0.14.0 py39h0f8d45d_1 conda-forge\r\ntensorboard 2.13.0 pypi_0 pypi\r\ntensorboard-data-server 0.7.1 pypi_0 pypi\r\ntensorflow 2.13.0 pypi_0 pypi\r\ntensorflow-estimator 2.13.0 pypi_0 pypi\r\ntensorflow-io-gcs-filesystem 0.33.0 pypi_0 pypi\r\ntensorflow-probability 0.21.0 pypi_0 pypi\r\ntermcolor 2.3.0 pypi_0 pypi\r\nterminado 0.17.1 pyh41d4057_0 conda-forge\r\nthreadpoolctl 3.2.0 pyha21a80b_0 conda-forge\r\ntinycss2 1.2.1 pyhd8ed1ab_0 conda-forge\r\ntk 8.6.12 h27826a3_0 conda-forge\r\ntoml 0.10.2 pyhd8ed1ab_0 conda-forge\r\ntomli 2.0.1 pyhd8ed1ab_0 conda-forge\r\ntornado 6.3.3 py39hd1e30aa_0 conda-forge\r\ntraitlets 5.9.0 pyhd8ed1ab_0 conda-forge\r\ntyping-extensions 4.5.0 pypi_0 pypi\r\ntyping_extensions 4.7.1 pyha770c72_0 conda-forge\r\ntyping_utils 0.1.0 pyhd8ed1ab_0 conda-forge\r\ntzdata 2023c h71feb2d_0 conda-forge\r\nunicodedata2 15.0.0 py39hb9d737c_0 conda-forge\r\nuri-template 1.3.0 pyhd8ed1ab_0 conda-forge\r\nurllib3 1.26.16 pypi_0 pypi\r\nwcwidth 0.2.6 pyhd8ed1ab_0 conda-forge\r\nwebcolors 1.13 pyhd8ed1ab_0 conda-forge\r\nwebencodings 0.5.1 py_1 conda-forge\r\nwebsocket-client 1.6.2 pyhd8ed1ab_0 conda-forge\r\nwerkzeug 2.3.7 pypi_0 pypi\r\nwheel 0.41.2 pyhd8ed1ab_0 conda-forge\r\nwidgetsnbextension 4.0.8 pyhd8ed1ab_0 conda-forge\r\nwrapt 1.15.0 pypi_0 pypi\r\nx264 1!157.20191217 h7b6447c_0 \r\nxorg-libxau 1.0.11 hd590300_0 conda-forge\r\nxorg-libxdmcp 1.1.3 h7f98852_0 conda-forge\r\nxz 5.2.6 h166bdaf_0 conda-forge\r\nyaml 0.2.5 h7f98852_2 conda-forge\r\nzeromq 4.3.4 h9c3ff4c_1 conda-forge\r\nzipp 3.16.2 pyhd8ed1ab_0 conda-forge\r\nzlib 1.2.13 hd590300_5 conda-forge\r\nzstd 1.5.2 hfc55251_7 conda-forge\r\n```",
"@SuryanarayanaY any update on this?",
"I'm getting some similar warnings/errors. TF 2.14 with a small custom model, on a machine with a single GPU.\r\n\r\n```\r\n2023-11-02 16:48:26.266488: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:961] layout failed: INVALID_ARGUMENT: Size of values 0 does not match size of permutation 4 @ fanin shape insequential/dropout/dropout/SelectV2-2-TransposeNHWCToNCHW-LayoutOptimizer\r\n\r\n...\r\n\r\n2023-11-02 16:48:27.584269: I tensorflow/core/framework/local_rendezvous.cc:421] Local rendezvous recv item cancelled. Key hash: 12961131670770205513\r\n2023-11-02 16:48:27.584277: I tensorflow/core/framework/local_rendezvous.cc:421] Local rendezvous recv item cancelled. Key hash: 5985745776991298471\r\n2023-11-02 16:48:27.676188: I tensorflow/core/framework/local_rendezvous.cc:421] Local rendezvous recv item cancelled. Key hash: 17452564472235251298\r\n```\r\n\r\nOnly a couple of the \"Local rendezvous recv item cancelled\" messages before the model starts training, at which point it seems to do okay. I got rid of the first error/warning by turning off the layout optimizer, as mentioned at the bottom of [this thread](https://github.com/tensorflow/tensorflow/issues/34499).",
"I also got the same error, Can anyone help me?\r\n\r\n```python\r\n2023-11-08 17:48:04.891105: W tensorflow/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 8295284736 exceeds 10% of free system memory.\r\n2023-11-08 17:48:08.059367: W tensorflow/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 8295284736 exceeds 10% of free system memory.\r\nEpoch 1/100\r\n2023-11-08 17:48:11.436880: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:961] layout failed: INVALID_ARGUMENT: Size of values 0 does not match size of permutation 4 @ fanin shape inmodel/dropout/dropout/SelectV2-2-TransposeNHWCToNCHW-LayoutOptimizer\r\n2023-11-08 17:48:11.812426: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\r\n```\r\n\r\n**`The Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click [here](https://aka.ms/vscodeJupyterKernelCrash) for more info. View Jupyter [log](command:jupyter.viewOutput) for further details.`**",
"Error also persist in tensorflow `2.14.0` version!",
"Hi @meekus-fischer ,\r\n\r\nI have tested with TF2.14V on colab GPU(single GPU) runtime and it works fine. Attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/fcf32c75cbf79e132ef9a5be004766de/61687-tf2-14v.ipynb) for reference.\r\n\r\nPlease make sure to install `tensorflow[and-cuda]` package for GPU support. Please cross check the gist and confirm.\r\n\r\nPlease also check whether the issue is on multi GPU environment. ",
"Hi @SuryanarayanaY, I have tested on Windows WSL2 on version 2.14.0 and the error persists. I also have the single GPU support.",
"@iamtekson ,\r\n\r\nCould you please provide a gist to have a look into it.Please confirm whether you are using single GPU setup or Multi GPU ?\r\n\r\nThanks!",
"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/61687\">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/61687\">No</a>\n"
] | 2023-08-24T18:29:11 | 2023-11-30T01:49:45 | 2023-11-30T01:49:28 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
binary
### TensorFlow version
2.13.0
### Custom code
Yes
### OS platform and distribution
RHELS 7.9
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
11.8 / 8.8
### GPU model and memory
NVIDIA A100-SXM4-40GB, Compute Capability 8.0
### Current behavior?
```
2023-08-24 11:01:15.073207: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] layout failed: INVALID_ARGUMENT: Size of values 0 does not match size of permutation 4 @ fanin shape inCustom_MC_Dropout_CNN/monte_carlo_dropout/dropout/SelectV2-2-TransposeNHWCToNCHW-LayoutOptimizer
```
This arises from keras Dropout layer with training set to True for MC Dropout implementation. Unclear why this happens in 2.13 and does not happen in previous versions of Tensorflow.
gist can be found [here](https://gist.github.com/meekus-fischer/46d92232d9c503be3bfa76f225315b72)
### Standalone code to reproduce the issue
```shell
Custom model doing binary classification.
gist can be found [here](https://gist.github.com/meekus-fischer/46d92232d9c503be3bfa76f225315b72)
```
### Relevant log output
```shell
Epoch 1/2000
2023-08-24 11:01:15.073207: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] layout failed: INVALID_ARGUMENT: Size of values 0 does not match size of permutation 4 @ fanin shape inCustom_MC_Dropout_CNN/monte_carlo_dropout/dropout/SelectV2-2-TransposeNHWCToNCHW-LayoutOptimizer
2023-08-24 11:01:18.489988: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:432] Loaded cuDNN version 8800
2023-08-24 11:01:19.296731: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:432] Loaded cuDNN version 8800
2023-08-24 11:01:20.504944: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:432] Loaded cuDNN version 8800
2023-08-24 11:01:21.410170: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:432] Loaded cuDNN version 8800
2023-08-24 11:01:22.693953: I tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:606] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.
NCCL version 2.13.4+cudaCUDA_MAJOR.CUDA_MINOR
2023-08-24 11:01:25.775749: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f0008dbb5b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2023-08-24 11:01:25.775881: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA A100-SXM4-40GB, Compute Capability 8.0
2023-08-24 11:01:25.775907: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (1): NVIDIA A100-SXM4-40GB, Compute Capability 8.0
2023-08-24 11:01:25.775920: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (2): NVIDIA A100-SXM4-40GB, Compute Capability 8.0
2023-08-24 11:01:25.775931: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (3): NVIDIA A100-SXM4-40GB, Compute Capability 8.0
2023-08-24 11:01:25.786715: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:255] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
2023-08-24 11:01:25.786798: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:255] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
2023-08-24 11:01:26.258524: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.
10622/Unknown - 346s 30ms/step - Accuracy: 0.8316 - Loss: 0.2066 - Precision: 0.0749 - Recall: 0.5010 - F1: 0.13022023-08-24 11:06:50.967839: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 10230612448716145652
2023-08-24 11:06:50.968030: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 15407084872456274683
2023-08-24 11:06:50.968252: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 17592376860366209384
2023-08-24 11:06:50.968315: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 59586944474759984
2023-08-24 11:06:50.968338: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 4843790225391952165
2023-08-24 11:06:50.968526: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 5723252417712899607
2023-08-24 11:06:50.968555: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 7116492272812476647
2023-08-24 11:06:50.968575: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 6169583800350491318
2023-08-24 11:06:50.968603: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 14279008608205112804
2023-08-24 11:06:50.968624: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 2035256671229577509
2023-08-24 11:06:50.968637: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 16934632576416694905
2023-08-24 11:06:50.968693: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 3559188353333742787
2023-08-24 11:06:50.968719: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 15823058833201008187
2023-08-24 11:06:50.968737: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 14146042709357596467
2023-08-24 11:06:50.968754: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 7736629887233675932
2023-08-24 11:06:50.968772: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 8429871751161250943
2023-08-24 11:06:50.968797: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 10517342425574574684
2023-08-24 11:06:50.968818: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 6970197396290489839
2023-08-24 11:06:50.968831: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 4931681574040413300
2023-08-24 11:06:50.968875: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 626925344230971222
2023-08-24 11:06:50.968901: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 7470810518621668803
2023-08-24 11:06:50.968925: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 12385602897828204054
2023-08-24 11:06:50.968946: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 18188314560958681369
2023-08-24 11:06:50.968958: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 13624849890299536610
2023-08-24 11:06:50.969001: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 17807137559239481777
2023-08-24 11:06:50.969025: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 2139352706329042097
2023-08-24 11:06:50.969044: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 1640340635315586973
2023-08-24 11:06:50.969069: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 6223702484981372229
2023-08-24 11:06:50.969089: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 10172410999163213764
2023-08-24 11:06:50.969122: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 6497789849068078695
2023-08-24 11:06:50.969166: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 14547651345659747702
2023-08-24 11:06:50.969193: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 17623891083240824387
2023-08-24 11:06:50.969211: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 13064636275713549317
2023-08-24 11:06:50.969228: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 12396583141502166397
2023-08-24 11:06:50.969246: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 5913395196567159933
2023-08-24 11:06:50.969271: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 13627801007941572809
2023-08-24 11:06:50.969293: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 14421550386438117999
2023-08-24 11:06:50.969307: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 17275079556186662745
2023-08-24 11:06:50.969351: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 17233527917734094487
2023-08-24 11:06:50.969377: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 1559338696184663628
2023-08-24 11:06:50.969400: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 5466597647379635179
2023-08-24 11:06:50.969419: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 3733731331292716199
2023-08-24 11:06:50.969433: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 1612774677619861335
2023-08-24 11:06:50.969477: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 8582450577167281147
2023-08-24 11:06:50.969501: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 2782820814590454977
2023-08-24 11:06:50.969519: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 3393985160674389282
2023-08-24 11:06:50.969537: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 10933067372207001568
2023-08-24 11:06:50.969561: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 12568695295317122124
2023-08-24 11:06:50.969581: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 9541194832674758364
2023-08-24 11:06:50.969594: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 8581202827114056180
2023-08-24 11:06:50.969638: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 6233296683631670242
2023-08-24 11:06:50.969672: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 9668322976265270062
2023-08-24 11:06:50.969689: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 14184838215839790752
2023-08-24 11:06:50.969706: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 7224663333712770602
2023-08-24 11:06:50.969723: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 7397044618240997942
2023-08-24 11:06:50.969740: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 8130222007033543507
2023-08-24 11:06:50.969784: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 9705492144130363178
2023-08-24 11:06:50.969807: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 16679483023940330274
2023-08-24 11:06:50.969828: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 3293157344159655099
2023-08-24 11:06:50.969841: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 16377046709051984690
2023-08-24 11:06:50.969887: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 15402017112026381377
2023-08-24 11:06:50.969912: I tensorflow/core/framework/local_rendezvous.cc:409] Local rendezvous send item cancelled. Key hash: 9526661262069731973
2023-08-24 11:06:50.969934: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 13997955057405387944
2023-08-24 11:06:50.969954: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 7535581894821128379
2023-08-24 11:06:50.969967: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 8329440098288561250
2023-08-24 11:06:50.970010: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 9064305880271633177
2023-08-24 11:06:50.970033: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 10920339178713879663
2023-08-24 11:06:50.970054: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 17331780224083848384
2023-08-24 11:06:50.970073: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 4387767839775239829
2023-08-24 11:06:50.970087: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 14235745259444133525
2023-08-24 11:06:50.970129: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 15121875451797684147
2023-08-24 11:06:50.970156: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 2086183684301986528
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2023-08-24 11:07:29.291886: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 7157660774755047909
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2023-08-24 11:07:29.292027: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 16940283489605771424
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2023-08-24 11:07:29.292074: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 10793570788563462816
2023-08-24 11:07:29.292096: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 16389789610319880674
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2023-08-24 11:07:29.292162: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 282440926482311630
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2023-08-24 11:07:29.292231: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 13471908878414400865
2023-08-24 11:07:29.292252: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 5920473717615552089
2023-08-24 11:07:29.292276: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 17321862789316030721
2023-08-24 11:07:29.292297: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 230923248894930545
2023-08-24 11:07:29.292319: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 9790266787664553869
2023-08-24 11:07:29.292341: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 8329428023832785896
2023-08-24 11:07:29.292364: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 7908458004814008345
2023-08-24 11:07:29.292385: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 4993197094509241984
2023-08-24 11:07:29.292407: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 4091926352704099496
2023-08-24 11:07:29.292429: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 8630806193082773190
2023-08-24 11:07:29.292451: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 1510013706380373244
2023-08-24 11:07:29.292473: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous recv item cancelled. Key hash: 7061673450255603616
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```
```
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"Closing this as I've reverted the rollback of #61237 from internally with commit https://github.com/tensorflow/tensorflow/commit/765314dd0b5e3fae242c9b7441ce1bda3daa8070. (Incorporating the fix from commit https://github.com/tensorflow/tensorflow/commit/2abff97921a566d143be67451aad85e735e9f1ab). Thank you again for the fix!"
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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/61685/checks?check_run_id=16167035791) 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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"@hunse Could you please share all the dependencies to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/925ea8b334191ed3da1aa6b81d488275/61683.ipynb). \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.",
"@sushreebarsa ,\r\n\r\nWhat dependencies you need still? Seems your colab file executed fine. Why do you think some dependencies missing?\r\n\r\n@hunse ,\r\nIt seems in colab environment `assert_allclose()` is success for given tolerance. Are you sure the attached code snippet is the one raising error for you? Could you please execute the attached code snippet in colab and confirm the behaviour with gist?\r\n\r\n",
"I looked into this a bit more, and it seems to be a CUDA/driver/GPU issue. We have two machines that show the error, and a few other machines that don't, despite trying to make all CUDA/driver/etc. versions equivalent among all machines.\r\n\r\nFurthermore, on the new machines/environments that show the issue, it also appears in earlier TF versions.\r\n\r\nI'll close this, since it does not seem to be TF, but rather some lower-level issue.",
"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/61683\">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/61683\">No</a>\n"
] | 2023-08-23T19:29:13 | 2023-09-11T19:25:05 | 2023-09-11T19:25:02 | NONE | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
2.13 / 2.14.0-dev20230706
### Custom code
Yes
### OS platform and distribution
Linux Ubuntu 20.04
### Mobile device
_No response_
### Python version
3.8
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
```
$ mamba list | grep cu
cuda-nvcc 12.2.128 0 nvidia
cudatoolkit 11.8.0 h4ba93d1_12 conda-forge
nvidia-cublas-cu11 2022.4.8 pypi_0 pypi
nvidia-cublas-cu117 11.10.1.25 pypi_0 pypi
nvidia-cuda-nvrtc-cu11 2022.4.8 pypi_0 pypi
nvidia-cuda-nvrtc-cu117 11.7.50 pypi_0 pypi
nvidia-cudnn-cu11 8.9.4.25 pypi_0 pypi
```
### GPU model and memory
NVIDIA GeForce RTX 3060 - 12 GB
Driver Version: 520.61.05
### Current behavior?
I'm trying to do a batch matrix multiply (i.e. I've got a bunch of m x n matrices, all in one tensor, and I want to do a matrix multiply of each of them with some other matrix). However, I'm getting slightly different results than I get from Numpy (and previous TensorFlow versions, this script passed for me in 2.9).
One interesting thing is that the value 25 (the second dimension of `x`) is significant. If I reduce this to 16 or below, it passes. Also, if I change the second dimension of `c` from 2 to 1, it also passes.
### Standalone code to reproduce the issue
```python
import numpy as np
import tensorflow as tf
print(f"file: {tf.__file__}")
print(f"git version: {tf.version.GIT_VERSION}")
print(f"version: {tf.__version__}")
rng = np.random.RandomState(0)
x = rng.uniform(-1, 1, size=(1, 25, 5)).astype(np.float32)
c = rng.uniform(-1, 1, size=(5, 2)).astype(np.float32)
y = x @ c
x2 = np.tile(x, (2, 1, 1))
y2 = x2 @ c
for y2i in y2:
np.testing.assert_allclose(y2i, y.squeeze(0))
tols = dict(atol=1e-7, rtol=1e-5)
z = tf.matmul(x, c).numpy()
# z = tf.einsum("...tq,...qr->...tr", x, c)
np.testing.assert_allclose(z, y, **tols)
z2 = tf.matmul(x2, c).numpy()
# z2 = tf.einsum("...tq,...qr->...tr", x2, c)
np.testing.assert_allclose(z2, y2, **tols)
```
### Relevant log output
```shell
file: /home/ehunsber/mambaforge/envs/tf213/lib/python3.8/site-packages/tensorflow/__init__.py
git version: v1.12.1-96406-gfa4d29bfef8
version: 2.14.0-dev20230706
Traceback (most recent call last):
File "test_batch.py", line 26, in <module>
np.testing.assert_allclose(z2, y2)
File "/home/ehunsber/mambaforge/envs/tf213/lib/python3.8/site-packages/numpy/testing/_private/utils.py", line 1592, in assert_allclose
assert_array_compare(compare, actual, desired, err_msg=str(err_msg),
File "/home/ehunsber/mambaforge/envs/tf213/lib/python3.8/contextlib.py", line 75, in inner
return func(*args, **kwds)
File "/home/ehunsber/mambaforge/envs/tf213/lib/python3.8/site-packages/numpy/testing/_private/utils.py", line 862, in assert_array_compare
raise AssertionError(msg)
AssertionError:
Not equal to tolerance rtol=1e-07, atol=0
Mismatched elements: 100 / 100 (100%)
Max absolute difference: 0.00046098
Max relative difference: 0.01047752
x: array([[[-0.187503, 0.005696],
[-0.255552, -0.84404 ],
[ 0.268836, -1.250793],...
y: array([[[-0.187499, 0.005691],
[-0.255404, -0.844322],
[ 0.268935, -1.251033],...
```
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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/61682/checks?check_run_id=16153669853) 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."
] | 2023-08-23T18:52:16 | 2023-08-27T15:17:56 | 2023-08-27T15:17:53 | NONE | null | false | {
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} | Passing `[4, 2]` as `shape` to `array_ops.reshape` always produces an error (in my case with a silent exit and no clue about what happened). Seems to happen because `[4,2]` is of type `List` and when `array_ops.reshape` calls `pywrap_tfe.TFE_Py_FastPathExecute` it expects Eager tensors.
I have tested this in two different scenarios:
* The first, was working without issues before, in this case because when invoking `pywrap_tfe.TFE_Py_FastPathExecute` it produced a `_core._FallbacException`.
* On the second one, which had more complexity, I am not fully sure why (I guess because the context is different), `pywrap_tfe.TFE_Py_FastPathExecute` simply fails, and doesn't return or throw any error.
After this change, both scenarios work fine.
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Feature Request
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
2.10.0
### Custom code
No
### OS platform and distribution
Linux Ubuntu 18.04
### Mobile device
_No response_
### Python version
3.10.11
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
11.2
### GPU model and memory
_No response_
### Current behavior?
Hey guys, I have a question regarding sharing layer weights between processes of a cluster.
I am in the flied of Deep Reinforcement Learning, where often there are multiple actors, generating experience (samples) and a central learner, who updates the network weights based on the gathered experience. For this to work efficient actors and learner are running in different processes on a single machine or possible on multiple machines. `tf.distribute` contains a lot functionalities useful for distributed Supervised Learning, but due to the different nature of DRL, `tf.distribute` can hardly be applied for this field.
Is there currently a way, or possibly a planned feature, to share trainable variables between TensorFlow instances running on different processes of a cluster? This is already done under the hood in `tf.distribute.experimental.MultiWorkerMirroredStrategy` and `tf.distribute.experimental.ParameterServerStrategy`, but as I mentioned above, are these distribution strategies not really suitable for DRL.
In the code below I create a cluster of processes using `tf.config.experimental_connect_to_cluster`, since running eager code and/or `tf.function` directly on a `tf.distribute.Server` is not possible. Experience is send to the learner via a shared `tf.queue.FIFOQueue` and now I would like to share the layer weights between the actor and learner.
### Standalone code to reproduce the issue
```shell
from absl import app
import multiprocessing as mp
import tensorflow as tf
from tensorflow.keras.layers import Dense
def run_actor_process(cluster_spec : tf.train.ClusterSpec) -> None:
"""
Runs an actor which gathers experience and enqueues it to the learner.
Args:
cluster_spec (tf.distribute.Server): The cluster specification the actor belongs to.
"""
# only use first GPU
physical_devices = tf.config.list_physical_devices('GPU')
tf.config.set_visible_devices([physical_devices[0]], 'GPU')
tf.config.experimental.set_memory_growth(physical_devices[0], True)
# connect process to cluster
tf.config.experimental_connect_to_cluster(cluster_spec, job_name='actor', task_index=0, protocol='grpc')
# use shared queue on actor
# NOTE: This needs to be done before any real TensorFlow operation is executed, otherwise the shared queue will not work.
# Not sure why this happens.
with tf.device('/job:learner/replica:0/task:0/CPU:0'):
queue = tf.queue.FIFOQueue(1, [tf.float32, tf.float32], shapes=[(5, ), (2, )], shared_name='trajectory_buffer')
# NOTE: Ideally this layer's weights and biases would be shared between the processes.
dense_layer = Dense(units=2,
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
name='model_dense')
@tf.function
def run_actor(input_tensor : tf.Tensor):
output_tensor = dense_layer(input_tensor)
_ = queue.enqueue([tf.squeeze(input_tensor), tf.squeeze(output_tensor)])
return output_tensor
index = 1
while True:
observation = tf.range(index, index + 5, delta=1, dtype=tf.float32, name='range')
observation = tf.expand_dims(observation, 0)
output = run_actor(observation)
print(f'Actor enqueued [{observation[0].numpy()}, {output[0].numpy()}].')
index += 1
def run_server_process(cluster_spec : tf.distribute.Server) -> None:
"""
Runs a TensorFlow distributed server.
Args:
cluster_spec (tf.distribute.Server): The cluster specification the server belongs to.
"""
# disable GPUs for TensorFlow session and server
tf.config.set_visible_devices([], 'GPU')
server_config = tf.compat.v1.ConfigProto(device_count={'GPU': 0})
unused_server = tf.distribute.Server(cluster_spec,
job_name='ps',
task_index=0,
protocol='grpc',
config=server_config)
unused_server.join()
def main(unused_argv):
mp.set_start_method('spawn')
# only use first GPU
physical_devices = tf.config.list_physical_devices('GPU')
tf.config.set_visible_devices([physical_devices[0]], 'GPU')
tf.config.experimental.set_memory_growth(physical_devices[0], True)
# define training cluster
cluster_spec = tf.train.ClusterSpec({'ps' : ['localhost:7000'],
'learner': ['localhost:7001'],
'actor' : ['localhost:7002']})
# run the actor in a separate process
actor_process = mp.Process(target=run_actor_process, args=(cluster_spec, ), daemon=True)
actor_process.start()
# start parameter sever to initialize cluster
# NOTE: This running server is solely needed to mimic a running cluster which the actor and learner can
# connect to using 'tf.config.experimental_connect_to_cluster'.
server_process = mp.Process(target=run_server_process, args=(cluster_spec, ), daemon=True)
server_process.start()
# NOTE: The learner should not see the actor, otherwise the learner will wait until the actor is connected
# and the actor will wait until the leaner is connected, resulting in a deadlock.
learner_cluster_spec = tf.train.ClusterSpec({'ps' : ['localhost:7000'],
'learner': ['localhost:7001']})
# connect process to cluster
tf.config.experimental_connect_to_cluster(learner_cluster_spec,
job_name='learner',
task_index=0,
protocol='grpc')
# use shared queue on learner
# NOTE: This needs to be done before any real TensorFlow operation is executed, otherwise the shared queue will not work.
# Not sure why this happens.
with tf.device('/job:learner/replica:0/task:0/CPU:0'):
queue = tf.queue.FIFOQueue(1, [tf.float32, tf.float32], shapes=[(5, ), (2, )], shared_name='trajectory_buffer')
# NOTE: Ideally this layer's weights and biases would be shared between the processes.
dense_layer = Dense(units=2,
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
name='model_dense')
@tf.function
def run_learner():
dequeued = queue.dequeue_many(1)
return dequeued, dense_layer(dequeued[0])
while True:
dequeued, learner_result = run_learner()
# NOTE: If layer weights would be shared between learner and actor 'dequeued[1][0]' and 'learner_result[0]' would be equal.
print(f'Learner dequeued [{dequeued[0][0].numpy()}, {dequeued[1][0].numpy()}] with result {learner_result[0].numpy()}.')
if __name__ == "__main__":
try:
app.run(main)
except SystemExit:
pass
```
### Relevant log output
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} | Ensure that the vector just created has valid data as this is passed to memcpy as the destination. Passing a null pointer here will result in a segfault.
Fixes: https://github.com/tensorflow/tensorflow/issues/61677 | {
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"Hi @Teledhil \r\n\r\nCan you please try with the latest master pull and see if the issue still persists?\r\n\r\nI see commit https://github.com/google/XNNPACK/commit/ae2875cb2538b00f11564c5cbf649249caf510f1 in GEMM microkernels which might be a related to the issue.\r\n\r\nThanks. ",
"Hi @pjpratik \r\n\r\nI tried again to do the build, this time with 5d18993f81c9990a35dce7c34ad233ad14c29591 and the same happens.\r\n\r\nManually updating the version of **cpuinfo** downloaded by `tensorflow/lite/tools/cmake/modules/cpuinfo.cmake` from `3dc310302210c1891ffcfb12ae67b11a3ad3a150` to a newer version that knows about `cpuinfo_uarch_cortex_a715` and `cpuinfo_uarch_cortex_x3`, like `959002f82d7962a473d8bf301845f2af720e0aa4` fixes the issue I have.",
"@Teledhil Thanks for the information.\r\n\r\n@pkgoogle Could you please look into this issue?\r\n\r\nThanks.",
"Hi @Teledhil, I was able to build at HEAD on master doing:\r\n\r\n```\r\nPYTHON=python3 tensorflow/lite/tools/pip_package/build_pip_package_with_cmake.sh native\r\n```\r\n\r\ncan you try on master or nightly, update and try again and let me know how it goes?",
"Hi @pkgoogle @pjpratik \r\n\r\nI tried again with HEAD (9af1d271c240e22d20196cbc3a0c993450b35d4e) and still the same error. I followed this steps:\r\n```bash\r\ngit clone https://github.com/tensorflow/tensorflow.git tensorflow_src\r\ncd tensorflow_src\r\nPYTHON=python3 BUILD_NUM_JOBS=4 tensorflow/lite/tools/pip_package/build_pip_package_with_cmake.sh native 2>&1 | tee build_9af1d271c240e22d20196cbc3a0c993450b35d4e.log\r\n```\r\n\r\nI added `BUILD_NUM_JOBS=4` because I'm building tensorflow with a Raspberry Pi 4 with 8 GB of ram and otherwise it will run out of memory.\r\n\r\nI'm attaching the build log: [build_9af1d271c240e22d20196cbc3a0c993450b35d4e.log](https://github.com/tensorflow/tensorflow/files/12437977/build_9af1d271c240e22d20196cbc3a0c993450b35d4e.log)\r\n\r\nI also tried to build HEAD, updating the version of **cpuinfo**. I replaced the version downloaded by `tensorflow/lite/tools/cmake/modules/cpuinfo.cmake` from `3dc310302210c1891ffcfb12ae67b11a3ad3a150` to a newer version (`959002f82d7962a473d8bf301845f2af720e0aa4`) that knows about `cpuinfo_uarch_cortex_a715` and `cpuinfo_uarch_cortex_x3`, and the build was successful.\r\n\r\nI followed this steps:\r\n```bash\r\ngit clone https://github.com/tensorflow/tensorflow.git tensorflow_src\r\ncd tensorflow_src\r\nsed -i -e 's/3dc310302210c1891ffcfb12ae67b11a3ad3a150/959002f82d7962a473d8bf301845f2af720e0aa4/g' tensorflow/lite/tools/cmake/modules/cpuinfo.cmake\r\nPYTHON=python3 BUILD_NUM_JOBS=4 tensorflow/lite/tools/pip_package/build_pip_package_with_cmake.sh native 2>&1 | tee build_9af1d271c240e22d20196cbc3a0c993450b35d4e_cpuinfo_updated.log\r\n```\r\n\r\nI'm attaching the build log: [build_9af1d271c240e22d20196cbc3a0c993450b35d4e_cpuinfo_updated.log](https://github.com/tensorflow/tensorflow/files/12438303/build_9af1d271c240e22d20196cbc3a0c993450b35d4e_cpuinfo_updated.log)\r\n\r\n\r\n",
"@Teledhil Thank you for investigating! The issue should be fixed as of f4b266951aec468c4ee0a3ffe6d4e65742699261",
"@Maratyszcza Thanks!, @Teledhil please let us know if the issue is fixed, thanks.",
"It works!! 🎉\r\n\r\nThank you for your help!",
"Hi @Teledhil, awesome, if there are no more open items for this issue, please close as completed. Thanks!",
"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/61678\">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/61678\">No</a>\n"
] | 2023-08-23T15:58:22 | 2023-08-29T18:11:28 | 2023-08-29T17:43:10 | NONE | null | null | null | ### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
Didn't tried
### Source
source
### TensorFlow version
7701a45e3893bbf8c451f6437d039172db89d548
### Custom code
No
### OS platform and distribution
Raspberry Pi 4, Raspbian
### Mobile device
_No response_
### Python version
3.9
### Bazel version
_No response_
### GCC/compiler version
_No response_
### CUDA/cuDNN version
_No response_
### GPU model and memory
Raspberry Pi 4
### Current behavior?
Updating **xnnpack** in https://github.com/tensorflow/tensorflow/commit/7701a45e3893bbf8c451f6437d039172db89d548 broke the compilation with this error:
```
/home/pi/src/tensorflow_src/tensorflow/lite/tools/pip_package/gen/tflite_pip/python3/cmake_build/xnnpack/src/configs/gemm-config.c: In function ‘init_f16_gemm_config’:
gmake[3]: Entering directory '/home/pi/src/tensorflow_src/tensorflow/lite/tools/pip_package/gen/tflite_pip/python3/cmake_build'
cd /home/pi/src/tensorflow_src/tensorflow/lite/tools/pip_package/gen/tflite_pip/python3/cmake_build && /home/pi/.local/pipx/venvs/cmake/lib/python3.9/site-packages/cmake/data/bin/cmake -E cmake_depends "Unix Makefiles" /home/pi/src/tensorflow_src/tensorflow/lite /home/pi/src/tensorflow_src/tensorflow/lite/tools/pip_package/gen/tflite_pip/python3/cmake_build/abseil-cpp/absl/strings /home/pi/src/tensorflow_src/tensorflow/lite/tools/pip_package/gen/tflite_pip/python3/cmake_build /home/pi/src/tensorflow_src/tensorflow/lite/tools/pip_package/gen/tflite_pip/python3/cmake_build/_deps/abseil-cpp-build/absl/strings /home/pi/src/tensorflow_src/tensorflow/lite/tools/pip_package/gen/tflite_pip/python3/cmake_build/_deps/abseil-cpp-build/absl/strings/CMakeFiles/absl_cordz_handle.dir/DependInfo.cmake --color=
/home/pi/src/tensorflow_src/tensorflow/lite/tools/pip_package/gen/tflite_pip/python3/cmake_build/xnnpack/src/configs/gemm-config.c:159:18: error: ‘cpuinfo_uarch_cortex_a715’ undeclared (first use in this function); did you mean ‘cpuinfo_uarch_cortex_a710’?
159 | case cpuinfo_uarch_cortex_a715:
| ^~~~~~~~~~~~~~~~~~~~~~~~~
| cpuinfo_uarch_cortex_a710
/home/pi/src/tensorflow_src/tensorflow/lite/tools/pip_package/gen/tflite_pip/python3/cmake_build/xnnpack/src/configs/gemm-config.c:159:18: note: each undeclared identifier is reported only once for each function it appears in
gmake[3]: Entering directory '/home/pi/src/tensorflow_src/tensorflow/lite/tools/pip_package/gen/tflite_pip/python3/cmake_build'
/home/pi/src/tensorflow_src/tensorflow/lite/tools/pip_package/gen/tflite_pip/python3/cmake_build/xnnpack/src/configs/gemm-config.c:162:18: error: ‘cpuinfo_uarch_cortex_x3’ undeclared (first use in this function); did you mean ‘cpuinfo_uarch_cortex_x2’?
162 | case cpuinfo_uarch_cortex_x3:
| ^~~~~~~~~~~~~~~~~~~~~~~
| cpuinfo_uarch_cortex_x2
gmake[3]: Leaving directory '/home/pi/src/tensorflow_src/tensorflow/lite/tools/pip_package/gen/tflite_pip/python3/cmake_build'
/usr/bin/gmake -f _deps/abseil-cpp-build/absl/flags/CMakeFiles/absl_flags_program_name.dir/build.make _deps/abseil-cpp-build/absl/flags/CMakeFiles/absl_flags_program_name.dir/build
```
The version of **cpuinfo** downloaded in `tensorflow/lite/tools/cmake/modules/cpuinfo.cmake` doesn't include `cpuinfo_uarch_cortex_a715` or `cpuinfo_uarch_cortex_x3`
### Standalone code to reproduce the issue
```shell
Follow steps from https://www.tensorflow.org/lite/guide/build_cmake_pip
```
### Relevant log output
```shell
/home/pi/src/tensorflow_src/tensorflow/lite/tools/pip_package/gen/tflite_pip/python3/cmake_build/xnnpack/src/configs/gemm-config.c: In function ‘init_f16_gemm_config’:
gmake[3]: Entering directory '/home/pi/src/tensorflow_src/tensorflow/lite/tools/pip_package/gen/tflite_pip/python3/cmake_build'
cd /home/pi/src/tensorflow_src/tensorflow/lite/tools/pip_package/gen/tflite_pip/python3/cmake_build && /home/pi/.local/pipx/venvs/cmake/lib/python3.9/site-packages/cmake/data/bin/cmake -E cmake_depends "Unix Makefiles" /home/pi/src/tensorflow_src/tensorflow/lite /home/pi/src/tensorflow_src/tensorflow/lite/tools/pip_package/gen/tflite_pip/python3/cmake_build/abseil-cpp/absl/strings /home/pi/src/tensorflow_src/tensorflow/lite/tools/pip_package/gen/tflite_pip/python3/cmake_build /home/pi/src/tensorflow_src/tensorflow/lite/tools/pip_package/gen/tflite_pip/python3/cmake_build/_deps/abseil-cpp-build/absl/strings /home/pi/src/tensorflow_src/tensorflow/lite/tools/pip_package/gen/tflite_pip/python3/cmake_build/_deps/abseil-cpp-build/absl/strings/CMakeFiles/absl_cordz_handle.dir/DependInfo.cmake --color=
/home/pi/src/tensorflow_src/tensorflow/lite/tools/pip_package/gen/tflite_pip/python3/cmake_build/xnnpack/src/configs/gemm-config.c:159:18: error: ‘cpuinfo_uarch_cortex_a715’ undeclared (first use in this function); did you mean ‘cpuinfo_uarch_cortex_a710’?
159 | case cpuinfo_uarch_cortex_a715:
| ^~~~~~~~~~~~~~~~~~~~~~~~~
| cpuinfo_uarch_cortex_a710
/home/pi/src/tensorflow_src/tensorflow/lite/tools/pip_package/gen/tflite_pip/python3/cmake_build/xnnpack/src/configs/gemm-config.c:159:18: note: each undeclared identifier is reported only once for each function it appears in
gmake[3]: Entering directory '/home/pi/src/tensorflow_src/tensorflow/lite/tools/pip_package/gen/tflite_pip/python3/cmake_build'
/home/pi/src/tensorflow_src/tensorflow/lite/tools/pip_package/gen/tflite_pip/python3/cmake_build/xnnpack/src/configs/gemm-config.c:162:18: error: ‘cpuinfo_uarch_cortex_x3’ undeclared (first use in this function); did you mean ‘cpuinfo_uarch_cortex_x2’?
162 | case cpuinfo_uarch_cortex_x3:
| ^~~~~~~~~~~~~~~~~~~~~~~
| cpuinfo_uarch_cortex_x2
gmake[3]: Leaving directory '/home/pi/src/tensorflow_src/tensorflow/lite/tools/pip_package/gen/tflite_pip/python3/cmake_build'
/usr/bin/gmake -f _deps/abseil-cpp-build/absl/flags/CMakeFiles/absl_flags_program_name.dir/build.make _deps/abseil-cpp-build/absl/flags/CMakeFiles/absl_flags_program_name.dir/build
```
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"@nSircombe ",
"Same fault for //tensorflow/compiler/mlir/lite/quantization/lite:quantize_model_test and //tensorflow/compiler/mlir/lite/quantization/lite:quantize_weights_test",
"Building for UBSAN gives the following error.\r\n\r\n```\r\n$ bazel-bin/tensorflow/compiler/mlir/lite/sparsity/sparsify_model_test\r\n2023-08-23 14:08:48.156178: W tensorflow/tsl/lib/monitoring/collection_registry.cc:81] Trying to register 2 metrics with the same name: /tensorflow/core/bfc_allocator_delay. The old value will be erased in order to register a new one. Please check if you link the metric more than once, or if the name is already used by other metrics.\r\nRunning main() from gmock_main.cc\r\n[==========] Running 1 test from 1 test suite.\r\n[----------] Global test environment set-up.\r\n[----------] 1 test from SparsifyModelTest\r\n[ RUN ] SparsifyModelTest.MetadataIsAddedToOutputModel\r\ntensorflow/compiler/mlir/lite/flatbuffer_export.cc:1249:10: runtime error: null pointer passed as argument 1, which is declared to never be null\r\n/dt10/usr/include/string.h:43:28: note: nonnull attribute specified here\r\n2023-08-23 14:08:48.191418: I tensorflow/compiler/mlir/lite/flatbuffer_export.cc:2523] Estimated count of arithmetic ops: 0 ops, equivalently 0 MACs\r\n[ OK ] SparsifyModelTest.MetadataIsAddedToOutputModel (35 ms)\r\n[----------] 1 test from SparsifyModelTest (35 ms total)\r\n\r\n[----------] Global test environment tear-down\r\n[==========] 1 test from 1 test suite ran. (36 ms total)\r\n[ PASSED ] 1 test.\r\n```",
"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/61677\">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/61677\">No</a>\n"
] | 2023-08-23T15:18:03 | 2023-09-06T14:40:50 | 2023-09-06T14:40:47 | CONTRIBUTOR | null | null | null | ### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
Yes
### Source
source
### TensorFlow version
git HEAD
### Custom code
No
### OS platform and distribution
Ubuntu 20.04
### Mobile device
n/a
### Python version
3.9.17
### Bazel version
6.1.0
### GCC/compiler version
16.0.6
### CUDA/cuDNN version
n/a
### GPU model and memory
n/a
### Current behavior?
//tensorflow/compiler/mlir/lite/sparsity:sparsify_model_test throws a segfault
### Standalone code to reproduce the issue
```shell
bazel test --cache_test_results=no --config=mkl_aarch64_threadpool --copt=-flax-vector-conversions --test_env=TF_ENABLE_ONEDNN_OPTS=1 --test_env=TF2_BEHAVIOR=1 --define=tf_api_version=2 --test_output=errors --verbose_failures=true --test_keep_going --notest_verbose_timeout_warnings --build_tests_only -- //tensorflow/compiler/mlir/lite/sparsity:sparsify_model_test
```
### Relevant log output
```shell
==================== Test output for //tensorflow/compiler/mlir/lite/sparsity:sparsify_model_test:
2023-08-23 14:23:54.976080: I tensorflow/core/util/port.cc:111] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2023-08-23 14:23:54.977641: W tensorflow/tsl/lib/monitoring/collection_registry.cc:81] Trying to register 2 metrics with the same name: /tensorflow/core/bfc_allocator_delay. The old value will be erased in order to register a new one. Please check if you link the metric more than once, or if the name is already used by other metrics.
Running main() from gmock_main.cc
[==========] Running 1 test from 1 test suite.
[----------] Global test environment set-up.
[----------] 1 test from SparsifyModelTest
[ RUN ] SparsifyModelTest.MetadataIsAddedToOutputModel
================================================================================
Target //tensorflow/compiler/mlir/lite/sparsity:sparsify_model_test up-to-date:
bazel-bin/tensorflow/compiler/mlir/lite/sparsity/sparsify_model_test
INFO: Elapsed time: 557.635s, Critical Path: 355.66s
INFO: 4267 processes: 707 internal, 3560 local.
INFO: Build completed, 1 test FAILED, 4267 total actions
//tensorflow/compiler/mlir/lite/sparsity:sparsify_model_test FAILED in 1.0s
/home/andrew/src/tf_test/tensorflow-git/bazel-ci_build-cache/.cache/bazel/_bazel_andrew/eab0d61a99b6696edb3d2aff87b585e8/execroot/org_tensorflow/bazel-out/aarch64-opt/testlogs/tensorflow/compiler/mlir/lite/sparsity/sparsify_model_test/test.log
Executed 1 out of 1 test: 1 fails locally.
andrew@8bde10e59b61:/workspace$ gdb bazel-bin/tensorflow/compiler/mlir/lite/sparsity/sparsify_model_test
GNU gdb (Ubuntu 9.2-0ubuntu1~20.04.1) 9.2
Copyright (C) 2020 Free Software Foundation, Inc.
License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>
This is free software: you are free to change and redistribute it.
There is NO WARRANTY, to the extent permitted by law.
Type "show copying" and "show warranty" for details.
This GDB was configured as "aarch64-linux-gnu".
Type "show configuration" for configuration details.
For bug reporting instructions, please see:
<http://www.gnu.org/software/gdb/bugs/>.
Find the GDB manual and other documentation resources online at:
<http://www.gnu.org/software/gdb/documentation/>.
For help, type "help".
Type "apropos word" to search for commands related to "word"...
Reading symbols from bazel-bin/tensorflow/compiler/mlir/lite/sparsity/sparsify_model_test...
(gdb) run
Starting program: /home/andrew/src/tf_test/tensorflow-git/bazel-ci_build-cache/.cache/bazel/_bazel_andrew/eab0d61a99b6696edb3d2aff87b585e8/execroot/org_tensorflow/bazel-out/aarch64-opt/bin/tensorflow/compiler/mlir/lite/sparsity/sparsify_model_test
warning: Error disabling address space randomization: Operation not permitted
[Thread debugging using libthread_db enabled]
Using host libthread_db library "/lib/aarch64-linux-gnu/libthread_db.so.1".
2023-08-23 14:26:40.332993: W tensorflow/tsl/lib/monitoring/collection_registry.cc:81] Trying to register 2 metrics with the same name: /tensorflow/core/bfc_allocator_delay. The old value will be erased in order to register a new one. Please check if you link the metric more than once, or if the name is already used by other metrics.
Running main() from gmock_main.cc
[==========] Running 1 test from 1 test suite.
[----------] Global test environment set-up.
[----------] 1 test from SparsifyModelTest
[ RUN ] SparsifyModelTest.MetadataIsAddedToOutputModel
Program received signal SIGSEGV, Segmentation fault.
0x0000ffff80a09040 in ?? () from /lib/aarch64-linux-gnu/libc.so.6
(gdb) bt
#0 0x0000ffff80a09040 in ?? () from /lib/aarch64-linux-gnu/libc.so.6
#1 0x0000ffff8b3df258 in std::__fill_a1<unsigned char> (__first=0xaaaaf5cbc9c1 "", __last=0xaaaaf5cbc9c0 "", __c=@0xaaaaf5cbc9c0: 0 '\000')
at /dt10/usr/lib/gcc/aarch64-unknown-linux-gnu/10/../../../../include/c++/10/bits/stl_algobase.h:893
#2 std::__fill_a<unsigned char*, unsigned char> (__first=0xaaaaf5cbc9c1 "", __last=0xaaaaf5cbc9c0 "", __value=@0xaaaaf5cbc9c0: 0 '\000')
at /dt10/usr/lib/gcc/aarch64-unknown-linux-gnu/10/../../../../include/c++/10/bits/stl_algobase.h:914
#3 std::__fill_n_a<unsigned char*, unsigned long, unsigned char> (__n=18446744073708562752, __value=@0xaaaaf5cbc9c0: 0 '\000',
__first=<optimized out>) at /dt10/usr/lib/gcc/aarch64-unknown-linux-gnu/10/../../../../include/c++/10/bits/stl_algobase.h:1065
#4 std::fill_n<unsigned char*, unsigned long, unsigned char> (__n=18446744073708562752, __value=@0xaaaaf5cbc9c0: 0 '\000',
__first=<optimized out>) at /dt10/usr/lib/gcc/aarch64-unknown-linux-gnu/10/../../../../include/c++/10/bits/stl_algobase.h:1094
#5 std::__uninitialized_default_n_1<true>::__uninit_default_n<unsigned char*, unsigned long> (__first=0xaaaaf5cbc9c0 "", __n=<optimized out>)
at /dt10/usr/lib/gcc/aarch64-unknown-linux-gnu/10/../../../../include/c++/10/bits/stl_uninitialized.h:598
#6 std::__uninitialized_default_n<unsigned char*, unsigned long> (__first=0xaaaaf5cbc9c0 "", __n=<optimized out>)
at /dt10/usr/lib/gcc/aarch64-unknown-linux-gnu/10/../../../../include/c++/10/bits/stl_uninitialized.h:632
#7 std::__uninitialized_default_n_a<unsigned char*, unsigned long, unsigned char> (__first=0xaaaaf5cbc9c0 "", __n=<optimized out>)
at /dt10/usr/lib/gcc/aarch64-unknown-linux-gnu/10/../../../../include/c++/10/bits/stl_uninitialized.h:698
#8 std::vector<unsigned char, std::allocator<unsigned char> >::_M_default_initialize (this=0xffffe8e7e728, __n=<optimized out>)
at /dt10/usr/lib/gcc/aarch64-unknown-linux-gnu/10/../../../../include/c++/10/bits/stl_vector.h:1606
#9 std::vector<unsigned char, std::allocator<unsigned char> >::vector (this=0xffffe8e7e728, __n=0, __a=...)
at /dt10/usr/lib/gcc/aarch64-unknown-linux-gnu/10/../../../../include/c++/10/bits/stl_vector.h:512
#10 (anonymous namespace)::Translator::BuildCustomOperator (this=this@entry=0xffffe8e801b8, inst=<optimized out>, op=...,
operands=std::vector of length 1, capacity 1 = {...}, results=std::vector of length 1, capacity 1 = {...})
at tensorflow/compiler/mlir/lite/flatbuffer_export.cc:1248
#11 0x0000ffff8b3d7ca0 in (anonymous namespace)::Translator::BuildOperator (this=this@entry=0xffffe8e801b8, inst=inst@entry=0xaaaaf5d48ec0,
operands=std::vector of length 1, capacity 1 = {...}, results=std::vector of length 1, capacity 1 = {...},
intermediates=std::vector of length 0, capacity 0) at tensorflow/compiler/mlir/lite/flatbuffer_export.cc:1434
#12 0x0000ffff8b3d0774 in (anonymous namespace)::Translator::BuildSubGraph (this=this@entry=0xffffe8e801b8, name="main", region=0x0,
index=index@entry=0) at tensorflow/compiler/mlir/lite/flatbuffer_export.cc:2036
#13 0x0000ffff8b3c7934 in (anonymous namespace)::Translator::TranslateInternal[abi:cxx11]() (this=<optimized out>, this@entry=0xffffe8e801b8)
at tensorflow/compiler/mlir/lite/flatbuffer_export.cc:2429
#14 0x0000ffff8b3c5bb8 in (anonymous namespace)::Translator::Translate (module=..., toco_flags=..., Python Exception <class 'gdb.error'> No type named std::__detail::_Hash_node<class std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, true>.:
tags=std::unordered_set with 0 elements,
op_or_arg_name_mapper=0xffffe8e7fbc0, metadata=Python Exception <class 'AttributeError'> 'NoneType' object has no attribute 'pointer':
std::map with 1 element) at tensorflow/compiler/mlir/lite/flatbuffer_export.cc:2331
#15 tflite::MlirToFlatBufferTranslateFunction (module=..., options=..., serialized_flatbuffer=serialized_flatbuffer@entry=0xffffe8e80a00)
at tensorflow/compiler/mlir/lite/flatbuffer_export.cc:2838
#16 0x0000ffff8b443498 in mlir::lite::SparsifyModel (input_model=..., builder=builder@entry=0xffffe8e80c08,
error_reporter=error_reporter@entry=0xffffe8e80c00) at tensorflow/compiler/mlir/lite/sparsity/sparsify_model.cc:91
#17 0x0000aaaae03023c0 in mlir::lite::(anonymous namespace)::SparsifyModelTest_MetadataIsAddedToOutputModel_Test::TestBody (
this=<optimized out>) at tensorflow/compiler/mlir/lite/sparsity/sparsify_model_test.cc:67
#18 0x0000ffff81043fd4 in testing::internal::HandleSehExceptionsInMethodIfSupported<testing::Test, void> (
method=&virtual testing::Test::TestBody(), location=0xffff80fef955 "the test body", object=<optimized out>)
at external/com_google_googletest/googletest/src/gtest.cc:2599
#19 testing::internal::HandleExceptionsInMethodIfSupported<testing::Test, void> (object=0xaaaaf5c74320,
method=(void (testing::Test::*)(class testing::Test * const)) 0x20, location=0xffff80fef955 "the test body")
at external/com_google_googletest/googletest/src/gtest.cc:2635
#20 0x0000ffff81043e6c in testing::Test::Run (this=0xaaaaf5c74320) at external/com_google_googletest/googletest/src/gtest.cc:2674
#21 0x0000ffff810454f8 in testing::TestInfo::Run (this=0xaaaaf5c67960) at external/com_google_googletest/googletest/src/gtest.cc:2853
#22 0x0000ffff81046480 in testing::TestSuite::Run (this=0xaaaaf5c740b0) at external/com_google_googletest/googletest/src/gtest.cc:3012
#23 0x0000ffff81057db4 in testing::internal::UnitTestImpl::RunAllTests (this=0xaaaaf5c73d30)
at external/com_google_googletest/googletest/src/gtest.cc:5870
#24 0x0000ffff81057844 in testing::internal::HandleSehExceptionsInMethodIfSupported<testing::internal::UnitTestImpl, bool> (
method=(bool (testing::internal::UnitTestImpl::*)(class testing::internal::UnitTestImpl * const)) 0xffff810579e8 <testing::internal::UnitTestImpl::RunAllTests()>, location=0xffff80fee5f4 "auxiliary test code (environments or event listeners)", object=<optimized out>)
--Type <RET> for more, q to quit, c to continue without paging--q
```
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"@B-JackMao Sorry for the late response!\r\nCould you please follow this [page](https://www.tensorflow.org/lite/inference_with_metadata/lite_support) which explains about processing input and output data into tflite model?\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.",
"@sushreebarsa ,\r\n\r\n> When TFLite processes image data, which variable will the read in image data be stored in? Is it TfLiteTensor.data?\r\n\r\nCould you please confirm your reply to this? I am unable to find whether attached resource has the information required.\r\n",
"Hello, @JyotiPDLr! That above page is referring to the process of input and output data in TFlite. To process an image data we have to set the tensor, invoke it and lastly we can get the tensor.\r\n@B-JackMao We can store any image data in the form of a tensor while processing it. We have to consider only the size and type of the image. Please have a look at this [example](https://www.tensorflow.org/lite/performance/post_training_quant) where you can see how a model can be tested on a single image. Sample code is as below;\r\n```\r\ntest_image = np.expand_dims(test_images[0], axis=0).astype(np.float32)\r\n\r\ninput_index = interpreter.get_input_details()[0][\"index\"]\r\noutput_index = interpreter.get_output_details()[0][\"index\"]\r\n\r\ninterpreter.set_tensor(input_index, test_image)\r\ninterpreter.invoke()\r\npredictions = interpreter.get_tensor(output_index)\r\n```\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.",
"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/61676\">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/61676\">No</a>\n"
] | 2023-08-23T15:09:24 | 2023-10-07T01:47:38 | 2023-10-07T01:47:35 | NONE | null | null | null | When TFLite processes image data, which variable will the read in image data be stored in? Is it TfLiteTensor.data?
How can I directly process a single image using TensorFlow Lite source code without relying on an Android app? | {
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